JosueNina
7008d17714
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* Implement a prototype of the maximum recovery time function. * Add unit test skeletons. * Add failing test * Issue #4581: Implement MaxDrawdownRecoveryTime. * Issue 4581: Add DTO for Drawdown Percentage, Drawdown Enddate, and High Value * Issue 4581: Fix bgu for when lDrawdowns list is empty. * Issue 4581: Change names of tests. Change name of file. * Issue 4581: Make adjustements to flow of adding drawdowns to lDrawdowns. * Issue 4581: Add multiple unit tests. * Issue #4581: Change name of unit test * Issue #4581: Add to PerformanceMetrics * Issue #4581: Add Maximum Drawdown Recovery to PortolioStatistics class. * Issue #4581: Add to portolfio statistics class. * Issue #4581: Add to statistics builder. * Issue #4581: Add report key. * Case #4581: Convert to decimal. * Issue #4581: Correct comment. * Issue #4581: Correct performance metrics view model string. * Case #4581: Correct statistics builder view model string..again. * Issue #4581: Placed DradownDradownDateHighValueDTO at the end of the file for simpler diff. * Issue #4581: Add 2 new tests. * Issue #4581: Change algorithm so that when multiple maximum drawdowns occur, the longest of all recoveries is reported. * Issue #4581: Add unit test. * Issue #4581: Remove reportkey. Change dto name. * Issue #4581: Change summary. * Issue #4581: Change comment. * Add max drawdown recovery calculation with unit tests * Update regression algorithms with the new metric * Solve review comments * Update regression algorithms * Add TryGet to safely get the key: MaximumDrawdownRecovery * Ignore MaximumDrawdownRecovery metric in OptimizationBacktest Json * Revert changes in Messaging * Update regression algorithms * Add test case: TakesLongestRecoveryAmongMultipleDrawdowns * Use integer days for MaximumDrawdownRecovery * Add MaximumDrawdownRecoveryReportElement * Use more explicit names * Rename files and variables for consistency * Update regression algorithms --------- Co-authored-by: Alain Schaerer <aschaerer@pcatg.com>
80 lines
3.2 KiB
C#
80 lines
3.2 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 Orders", "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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{"Start Equity", "100000"},
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{"End Equity", "103012.99"},
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{"Net Profit", "3.013%"},
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{"Sharpe Ratio", "12.422"},
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{"Sortino Ratio", "0"},
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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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{"Drawdown Recovery", "2"},
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{"OrderListHash", "a18ad75219f800ac4435bfa4f750a67d"}
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};
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}
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}
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