Stefano Raggi
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", "2.15%"},
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{"Average Loss", "-1.35%"},
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{"Compounding Annual Return", "75.075%"},
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{"Drawdown", "0.600%"},
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{"Expectancy", "0.293"},
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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", "1.59"},
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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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