wehub-resource-sync
96 lines
3.8 KiB
C#
96 lines
3.8 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 System.Linq;
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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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/// Show example of how to use the <see cref="MaximumSectorExposureRiskManagementModel"/> Risk Management Model
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/// </summary>
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public class MaximumSectorExposureRiskManagementModelFrameworkRegressionAlgorithm : 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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// Set requested data resolution
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UniverseSettings.Resolution = Resolution.Daily;
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SetStartDate(2014, 2, 1); //Set Start Date
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SetEndDate(2014, 5, 1); //Set End Date
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// set algorithm framework models
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var tickers = new string[] { "AAPL", "MSFT", "GOOG", "AIG", "BAC" };
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SetUniverseSelection(new FineFundamentalUniverseSelectionModel(
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coarse => coarse.Where(x => tickers.Contains(x.Symbol.Value)).Select(x => x.Symbol),
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fine => fine.Select(x => x.Symbol)
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));
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// define risk management model such that maximum weight of a single sector be 10%
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// Number of of trades changed from 34 to 30 when using the MaximumSectorExposureRiskManagementModel
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SetRiskManagement(new MaximumSectorExposureRiskManagementModel(0.1m));
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}
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public override void OnEndOfAlgorithm()
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{
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// The MaximumSectorExposureRiskManagementModel does not expire insights
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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 => 555;
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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", "15"},
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{"Average Win", "0.09%"},
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{"Average Loss", "-0.16%"},
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{"Compounding Annual Return", "-2.427%"},
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{"Drawdown", "1.400%"},
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{"Expectancy", "-0.544"},
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{"Start Equity", "100000"},
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{"End Equity", "99396.26"},
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{"Net Profit", "-0.604%"},
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{"Sharpe Ratio", "-1.264"},
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{"Sortino Ratio", "-0.962"},
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{"Probabilistic Sharpe Ratio", "7.240%"},
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{"Loss Rate", "71%"},
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{"Win Rate", "29%"},
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{"Profit-Loss Ratio", "0.60"},
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{"Alpha", "-0.038"},
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{"Beta", "0.078"},
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{"Annual Standard Deviation", "0.019"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-2.24"},
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{"Tracking Error", "0.092"},
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{"Treynor Ratio", "-0.312"},
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{"Total Fees", "$18.92"},
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{"Estimated Strategy Capacity", "$96000000.00"},
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{"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
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{"Portfolio Turnover", "0.80%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "34eff097cfac686aedf205bc2eaab4d4"}
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};
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}
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}
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