Ricardo Andrés Marino Rojas
cce8945fe8
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* Add improvements * Add improvments and unit tests * Add XML comments * Nit changes * Add unit tests for OrderJsonConverter * Improve unit tests * Address requested changes * Fix bugs * Fix bugs * Fix bugs and self-review * Fix bugs * Address requested changes * Fix unit test bug * Fix bugs * Improve unit tests * Solve bugs
79 lines
3.3 KiB
C#
79 lines
3.3 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.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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/// Regression algorithm to assert the behavior of <see cref="MaximumDrawdownPercentPortfolio"/> Risk Management Model
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/// </summary>
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public class MaximumDrawdownPercentPortfolioFrameworkRegressionAlgorithm : BaseFrameworkRegressionAlgorithm
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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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SetUniverseSelection(new ManualUniverseSelectionModel(QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA)));
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// define risk management model as a composite of several risk management models
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SetRiskManagement(new CompositeRiskManagementModel(
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new MaximumDrawdownPercentPortfolio(0.01m), // Avoid loss of initial capital
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new MaximumDrawdownPercentPortfolio(0.015m, true) // Avoid profit losses
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));
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}
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/// <summary>
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/// Data Points count of all timeslices of algorithm
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/// </summary>
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public override long DataPoints => 304;
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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 override Dictionary<string, string> ExpectedStatistics => new()
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{
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{"Total Orders", "2"},
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{"Average Win", "2.43%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "34.465%"},
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{"Drawdown", "2.900%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "102436.11"},
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{"Net Profit", "2.436%"},
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{"Sharpe Ratio", "2.474"},
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{"Sortino Ratio", "2.224"},
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{"Probabilistic Sharpe Ratio", "66.764%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "100%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0.124"},
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{"Beta", "0.558"},
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{"Annual Standard Deviation", "0.093"},
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{"Annual Variance", "0.009"},
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{"Information Ratio", "0.429"},
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{"Tracking Error", "0.092"},
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{"Treynor Ratio", "0.413"},
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{"Total Fees", "$6.56"},
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{"Estimated Strategy Capacity", "$57000000.00"},
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{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
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{"Portfolio Turnover", "6.63%"},
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{"OrderListHash", "63a7d75043456cdd8c518f77df8f024f"}
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};
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}
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}
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