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Copy pathMACDTrendAlgorithm.cs
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/*
* 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 QuantConnect.Data.Market;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Simple indicator demonstration algorithm of MACD
/// </summary>
/// <meta name="tag" content="indicators" />
/// <meta name="tag" content="indicator classes" />
/// <meta name="tag" content="plotting indicators" />
public class MACDTrendAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private DateTime _previous;
private MovingAverageConvergenceDivergence _macd;
private readonly string _symbol = "SPY";
/// <summary>
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
/// </summary>
public override void Initialize()
{
SetStartDate(2004, 01, 01);
SetEndDate(2015, 01, 01);
AddSecurity(SecurityType.Equity, _symbol, Resolution.Daily);
// define our daily macd(12,26) with a 9 day signal
_macd = MACD(_symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily);
}
/// <summary>
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
/// </summary>
/// <param name="data">TradeBars IDictionary object with your stock data</param>
public void OnData(TradeBars data)
{
// only once per day
if (_previous.Date == Time.Date) return;
if (!_macd.IsReady) return;
var holding = Portfolio[_symbol];
var signalDeltaPercent = (_macd - _macd.Signal)/_macd.Fast;
var tolerance = 0.0025m;
// if our macd is greater than our signal, then let's go long
if (holding.Quantity <= 0 && signalDeltaPercent > tolerance) // 0.01%
{
// longterm says buy as well
SetHoldings(_symbol, 1.0);
}
// of our macd is less than our signal, then let's go short
else if (holding.Quantity >= 0 && signalDeltaPercent < -tolerance)
{
Liquidate(_symbol);
}
// plot both lines
Plot("MACD", _macd, _macd.Signal);
Plot(_symbol, "Open", data[_symbol].Open);
Plot(_symbol, _macd.Fast, _macd.Slow);
_previous = Time;
}
/// <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, Language.Python };
/// <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", "84"},
{"Average Win", "4.78%"},
{"Average Loss", "-4.16%"},
{"Compounding Annual Return", "2.958%"},
{"Drawdown", "34.800%"},
{"Expectancy", "0.228"},
{"Net Profit", "37.837%"},
{"Sharpe Ratio", "0.297"},
{"Loss Rate", "43%"},
{"Win Rate", "57%"},
{"Profit-Loss Ratio", "1.15"},
{"Alpha", "0.107"},
{"Beta", "-3.51"},
{"Annual Standard Deviation", "0.124"},
{"Annual Variance", "0.015"},
{"Information Ratio", "0.136"},
{"Tracking Error", "0.125"},
{"Treynor Ratio", "-0.011"},
{"Total Fees", "$443.50"}
};
}
}