/*

* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.

* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.

*

* Licensed under the Apache License, Version 2.0 (the "License");

* you may not use this file except in compliance with the License.

* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

*

* Unless required by applicable law or agreed to in writing, software

* distributed under the License is distributed on an "AS IS" BASIS,

* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

* See the License for the specific language governing permissions and

* limitations under the License.

*

*/

using System;

using System.Collections.Generic;

using System.Linq;

using QuantConnect.Data;

using QuantConnect.Data.Market;

using QuantConnect.Interfaces;

using QuantConnect.Orders;

using QuantConnect.Securities.Option;

namespace QuantConnect.Algorithm.CSharp

{

/// <summary>

/// This algorithm demonstrate how to use Option Strategies (e.g. OptionStrategies.Straddle) helper classes to batch send orders for common strategies.

/// It also shows how you can prefilter contracts easily based on strikes and expirations, and how you can inspect the

/// option chain to pick a specific option contract to trade.

/// </summary>

/// <meta name="tag" content="using data" />

/// <meta name="tag" content="options" />

/// <meta name="tag" content="option strategies" />

/// <meta name="tag" content="filter selection" />

public class BasicTemplateOptionStrategyAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition

{

private Symbol _optionSymbol;

public override void Initialize()

{

SetStartDate(2015, 12, 24);

SetEndDate(2015, 12, 24);

SetCash(1000000);

var option = AddOption("GOOG");

_optionSymbol = option.Symbol;

// set our strike/expiry filter for this option chain

option.SetFilter(-2, +2, TimeSpan.Zero, TimeSpan.FromDays(180));

// Adding this to reproduce GH issue #2314

SetWarmup(TimeSpan.FromMinutes(1));

// use the underlying equity as the benchmark

SetBenchmark("GOOG");

}

public override void OnData(Slice slice)

{

if (!Portfolio.Invested)

{

OptionChain chain;

if (slice.OptionChains.TryGetValue(_optionSymbol, out chain))

{

var atmStraddle = chain

.OrderBy(x => Math.Abs(chain.Underlying.Price - x.Strike))

.ThenByDescending(x => x.Expiry)

.FirstOrDefault();

if (atmStraddle != null)

{

Sell(OptionStrategies.Straddle(_optionSymbol, atmStraddle.Strike, atmStraddle.Expiry), 2);

}

}

}

else

{

Liquidate();

}

foreach(var kpv in slice.Bars)

{

Log($"---> OnData: {Time}, {kpv.Key.Value}, {kpv.Value.Close:0.00}");

}

}

/// <summary>

/// Order fill event handler. On an order fill update the resulting information is passed to this method.

/// </summary>

/// <param name="orderEvent">Order event details containing details of the evemts</param>

/// <remarks>This method can be called asynchronously and so should only be used by seasoned C# experts. Ensure you use proper locks on thread-unsafe objects</remarks>

public override void OnOrderEvent(OrderEvent orderEvent)

{

Log(orderEvent.ToString());

}

/// <summary>

/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.

/// </summary>

public bool CanRunLocally { get; } = true;

/// <summary>

/// This is used by the regression test system to indicate which languages this algorithm is written in.

/// </summary>

public Language[] Languages { get; } = { Language.CSharp };

/// <summary>

/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm

/// </summary>

public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>

{

{"Total Trades", "778"},

{"Average Win", "0%"},

{"Average Loss", "-0.02%"},

{"Compounding Annual Return", "-100%"},

{"Drawdown", "6.900%"},

{"Expectancy", "-1"},

{"Net Profit", "-6.860%"},

{"Sharpe Ratio", "0"},

{"Loss Rate", "100%"},

{"Win Rate", "0%"},

{"Profit-Loss Ratio", "0"},

{"Alpha", "0"},

{"Beta", "0"},

{"Annual Standard Deviation", "0"},

{"Annual Variance", "0"},

{"Information Ratio", "0"},

{"Tracking Error", "0"},

{"Treynor Ratio", "0"},

{"Total Fees", "$778.00"}

};

}

}