Michael Handschuh
8402b6f01e
It's important that we keep the factor files consistent with respect to the date that they were generated. This enables us to run the regression algorithms in the cloud and get the same results by using the factor files from the correct date.
117 lines
4.4 KiB
C#
117 lines
4.4 KiB
C#
/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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using System;
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using System.Collections.Generic;
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using QuantConnect.Data.Market;
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using QuantConnect.Indicators;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Simple indicator demonstration algorithm of MACD
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/// </summary>
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/// <meta name="tag" content="indicators" />
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/// <meta name="tag" content="indicator classes" />
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/// <meta name="tag" content="plotting indicators" />
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public class MACDTrendAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private DateTime _previous;
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private MovingAverageConvergenceDivergence _macd;
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private readonly string _symbol = "SPY";
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2004, 01, 01);
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SetEndDate(2015, 01, 01);
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AddSecurity(SecurityType.Equity, _symbol, Resolution.Daily);
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// define our daily macd(12,26) with a 9 day signal
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_macd = MACD(_symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily);
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}
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/// <summary>
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/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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/// </summary>
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/// <param name="data">TradeBars IDictionary object with your stock data</param>
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public void OnData(TradeBars data)
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{
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// only once per day
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if (_previous.Date == Time.Date) return;
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if (!_macd.IsReady) return;
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var holding = Portfolio[_symbol];
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var signalDeltaPercent = (_macd - _macd.Signal)/_macd.Fast;
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var tolerance = 0.0025m;
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// if our macd is greater than our signal, then let's go long
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if (holding.Quantity <= 0 && signalDeltaPercent > tolerance) // 0.01%
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{
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// longterm says buy as well
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SetHoldings(_symbol, 1.0);
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}
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// of our macd is less than our signal, then let's go short
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else if (holding.Quantity >= 0 && signalDeltaPercent < -tolerance)
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{
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Liquidate(_symbol);
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}
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// plot both lines
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Plot("MACD", _macd, _macd.Signal);
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Plot(_symbol, "Open", data[_symbol].Open);
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Plot(_symbol, _macd.Fast, _macd.Slow);
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_previous = Time;
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}
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/// <summary>
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/// This is used by the regression test system to indicate which languages this algorithm is written in.
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/// </summary>
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public Language[] Languages { get; } = { Language.CSharp, Language.Python };
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/// <summary>
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/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
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/// </summary>
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public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Trades", "84"},
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{"Average Win", "4.79%"},
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{"Average Loss", "-4.17%"},
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{"Compounding Annual Return", "2.965%"},
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{"Drawdown", "34.800%"},
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{"Expectancy", "0.228"},
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{"Net Profit", "37.946%"},
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{"Sharpe Ratio", "0.298"},
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{"Loss Rate", "43%"},
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{"Win Rate", "57%"},
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{"Profit-Loss Ratio", "1.15"},
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{"Alpha", "0.111"},
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{"Beta", "-3.722"},
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{"Annual Standard Deviation", "0.124"},
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{"Annual Variance", "0.015"},
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{"Information Ratio", "0.137"},
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{"Tracking Error", "0.124"},
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{"Treynor Ratio", "-0.01"},
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{"Total Fees", "$444.64"}
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};
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}
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}
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