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.
93 lines
3.9 KiB
C#
93 lines
3.9 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.Collections.Generic;
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using QuantConnect.Algorithm.Framework;
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using QuantConnect.Algorithm.Framework.Alphas;
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using QuantConnect.Algorithm.Framework.Execution;
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using QuantConnect.Algorithm.Framework.Portfolio;
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using QuantConnect.Algorithm.Framework.Risk;
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using QuantConnect.Algorithm.Framework.Selection;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Framework algorithm that uses the <see cref="PairsTradingAlphaModel"/> to detect
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/// divergences between correllated assets. Detection of asset correlation is not
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/// performed and is expected to be handled outside of the alpha model.
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/// </summary>
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public class PairsTradingAlphaModelFrameworkAlgorithm : QCAlgorithmFramework, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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SetStartDate(2013, 10, 07);
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SetEndDate(2013, 10, 11);
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var bac = AddEquity("BAC");
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var aig = AddEquity("AIG");
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SetUniverseSelection(new ManualUniverseSelectionModel(Securities.Keys));
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SetAlpha(new PairsTradingAlphaModel(bac.Symbol, aig.Symbol));
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
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SetExecution(new ImmediateExecutionModel());
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SetRiskManagement(new NullRiskManagementModel());
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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", "4"},
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{"Average Win", "2.18%"},
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{"Average Loss", "-1.38%"},
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{"Compounding Annual Return", "75.075%"},
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{"Drawdown", "0.600%"},
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{"Expectancy", "0.288"},
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{"Net Profit", "0.719%"},
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{"Sharpe Ratio", "6.982"},
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{"Loss Rate", "50%"},
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{"Win Rate", "50%"},
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{"Profit-Loss Ratio", "1.58"},
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{"Alpha", "0"},
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{"Beta", "32.812"},
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{"Annual Standard Deviation", "0.052"},
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{"Annual Variance", "0.003"},
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{"Information Ratio", "6.782"},
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{"Tracking Error", "0.052"},
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{"Treynor Ratio", "0.011"},
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{"Total Fees", "$74.09"},
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{"Total Insights Generated", "4"},
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{"Total Insights Closed", "4"},
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{"Total Insights Analysis Completed", "4"},
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{"Long Insight Count", "2"},
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{"Short Insight Count", "2"},
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{"Long/Short Ratio", "100%"},
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{"Estimated Monthly Alpha Value", "$-1148.429"},
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{"Total Accumulated Estimated Alpha Value", "$-185.0247"},
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{"Mean Population Estimated Insight Value", "$-46.25617"},
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{"Mean Population Direction", "50%"},
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{"Mean Population Magnitude", "0%"},
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{"Rolling Averaged Population Direction", "3.8827%"},
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{"Rolling Averaged Population Magnitude", "0%"}
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};
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}
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}
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