Ricardo Andrés Marino Rojas
b4bad69772
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* First attempt to solve the bug * Enhance implementation * Enhance implementation * Simplify implementation * Rebase regression stats * Solve unit test bugs * Review * Update Rolling.Sharpe() method * Update regression stats * Update unit tests * Update missing regression algos * Update Rolling.cs --------- Co-authored-by: Martin Molinero <martin.molinero1@gmail.com> Co-authored-by: Martin-Molinero <martin@quantconnect.com>
76 lines
3.1 KiB
C#
76 lines
3.1 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 QuantConnect.Algorithm.Framework.Portfolio;
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using QuantConnect.Interfaces;
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using System;
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using System.Collections.Generic;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm asserting we can specify a custom portfolio optimizer with a MeanVarianceOptimizationPortfolioConstructionModel
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/// </summary>
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public class CustomPortfolioOptimizerRegressionAlgorithm : MeanVarianceOptimizationFrameworkAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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base.Initialize();
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SetPortfolioConstruction(new MeanVarianceOptimizationPortfolioConstructionModel(optimizer: new CustomPortfolioOptimizer()));
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}
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private class CustomPortfolioOptimizer : IPortfolioOptimizer
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{
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public double[] Optimize(double[,] historicalReturns, double[] expectedReturns = null, double[,] covariance = null)
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{
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var result = new double[historicalReturns.GetLength(0)];
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Array.Fill(result, 0.5);
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return result;
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}
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}
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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", "13"},
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{"Average Win", "0%"},
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{"Average Loss", "-0.14%"},
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{"Compounding Annual Return", "773.203%"},
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{"Drawdown", "3.300%"},
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{"Expectancy", "-1"},
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{"Net Profit", "3.013%"},
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{"Sharpe Ratio", "12.422"},
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{"Probabilistic Sharpe Ratio", "62.198%"},
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{"Loss Rate", "100%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "1.949"},
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{"Beta", "2.094"},
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{"Annual Standard Deviation", "0.49"},
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{"Annual Variance", "0.24"},
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{"Information Ratio", "14.343"},
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{"Tracking Error", "0.287"},
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{"Treynor Ratio", "2.906"},
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{"Total Fees", "$39.73"},
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{"Estimated Strategy Capacity", "$3100000.00"},
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{"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
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{"Portfolio Turnover", "52.21%"},
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{"OrderListHash", "12425242f98844dc855c5c5e1dff9a6a"}
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};
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}
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}
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