/* * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ using System.Collections.Generic; using QuantConnect.Algorithm.Framework; using QuantConnect.Algorithm.Framework.Alphas; using QuantConnect.Algorithm.Framework.Execution; using QuantConnect.Algorithm.Framework.Portfolio; using QuantConnect.Algorithm.Framework.Risk; using QuantConnect.Algorithm.Framework.Selection; using QuantConnect.Interfaces; namespace QuantConnect.Algorithm.CSharp { /// /// Show example of how to use the Risk Management Model /// public class MaximumPortfolioDrawdownFrameworkAlgorithm : QCAlgorithmFramework, IRegressionAlgorithmDefinition { public override void Initialize() { // Set requested data resolution UniverseSettings.Resolution = Resolution.Minute; SetStartDate(2013, 10, 07); //Set Start Date SetEndDate(2013, 10, 11); //Set End Date SetCash(100000); //Set Strategy Cash // set algorithm framework models SetUniverseSelection(new ManualUniverseSelectionModel(QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA))); SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, System.TimeSpan.FromMinutes(20), 0.025, null)); SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel()); SetExecution(new ImmediateExecutionModel()); // define risk management model as a composite of several risk management models SetRiskManagement(new CompositeRiskManagementModel( new MaximumDrawdownPercentPortfolio(0.01m), // Avoid loss of initial capital new MaximumDrawdownPercentPortfolio(0.015m, true) // Avoid profit losses )); } /// /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm. /// public bool CanRunLocally { get; } = true; /// /// This is used by the regression test system to indicate which languages this algorithm is written in. /// public Language[] Languages { get; } = { Language.CSharp, Language.Python }; /// /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm /// public Dictionary ExpectedStatistics => new Dictionary { {"Total Trades", "2"}, {"Average Win", "0%"}, {"Average Loss", "-0.97%"}, {"Compounding Annual Return", "-53.355%"}, {"Drawdown", "1.500%"}, // Should be less than or equal to max trailing drawdown {"Expectancy", "-1"}, {"Net Profit", "-0.970%"}, // Should be less than or equal to max absolute drawdown {"Loss Rate", "100%"}, {"Win Rate", "0%"}, {"Profit-Loss Ratio", "0"}, {"Alpha", "0.007"}, {"Beta", "-44.915"}, {"Annual Standard Deviation", "0.069"}, {"Annual Variance", "0.005"}, {"Information Ratio", "-7.224"}, {"Tracking Error", "0.069"}, {"Treynor Ratio", "0.011"}, {"Total Fees", "$6.51"} }; } }