Martin Molinero
102 lines
4.5 KiB
C#
102 lines
4.5 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;
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using System.Collections.Generic;
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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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using QuantConnect.Interfaces;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Test algorithm using <see cref="QCAlgorithm.AddRiskManagement(IRiskManagementModel)"/>
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/// </summary>
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public class AddRiskManagementAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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UniverseSettings.Resolution = Resolution.Minute;
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SetStartDate(2013, 10, 07); //Set Start Date
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SetEndDate(2013, 10, 11); //Set End Date
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SetCash(100000); //Set Strategy Cash
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SetUniverseSelection(new ManualUniverseSelectionModel(QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)));
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SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null));
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
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SetExecution(new ImmediateExecutionModel());
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AddRiskManagement(new MaximumDrawdownPercentPortfolio(0.02m));
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AddRiskManagement(new MaximumUnrealizedProfitPercentPerSecurity(0.01m));
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}
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/// <summary>
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/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
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/// </summary>
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public bool CanRunLocally { get; } = true;
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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", "3"},
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{"Average Win", "1.02%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "255.419%"},
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{"Drawdown", "2.200%"},
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{"Expectancy", "0"},
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{"Net Profit", "1.752%"},
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{"Sharpe Ratio", "4.176"},
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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", "0.713"},
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{"Beta", "0.349"},
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{"Annual Standard Deviation", "0.178"},
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{"Annual Variance", "0.032"},
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{"Information Ratio", "3.197"},
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{"Tracking Error", "0.205"},
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{"Treynor Ratio", "2.131"},
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{"Total Fees", "$9.77"},
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{"Total Insights Generated", "100"},
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{"Total Insights Closed", "99"},
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{"Total Insights Analysis Completed", "99"},
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{"Long Insight Count", "100"},
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{"Short Insight Count", "0"},
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{"Long/Short Ratio", "100%"},
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{"Estimated Monthly Alpha Value", "$148197.8440"},
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{"Total Accumulated Estimated Alpha Value", "$25522.9620"},
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{"Mean Population Estimated Insight Value", "$257.8077"},
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{"Mean Population Direction", "54.5455%"},
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{"Mean Population Magnitude", "54.5455%"},
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{"Rolling Averaged Population Direction", "59.8056%"},
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{"Rolling Averaged Population Magnitude", "59.8056%"}
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};
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}
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}
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