wehub-resource-sync
120 lines
4.8 KiB
C#
120 lines
4.8 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 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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/// Show cases how to use the <see cref="CompositeAlphaModel"/> to define
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/// </summary>
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public class CompositeAlphaModelFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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SetStartDate(2013, 10, 07);
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SetEndDate(2013, 10, 11);
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// even though we're using a framework algorithm, we can still add our securities
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// using the AddEquity/Forex/Crypto/ect methods and then pass them into a manual
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// universe selection model using Securities.Keys
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AddEquity("SPY");
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AddEquity("IBM");
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AddEquity("BAC");
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AddEquity("AIG");
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// define a manual universe of all the securities we manually registered
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SetUniverseSelection(new ManualUniverseSelectionModel());
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// define alpha model as a composite of the rsi and ema cross models
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SetAlpha(new CompositeAlphaModel(
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new RsiAlphaModel(),
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new EmaCrossAlphaModel()
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));
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// default models for the rest
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
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SetExecution(new ImmediateExecutionModel());
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SetRiskManagement(new NullRiskManagementModel());
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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 List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
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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 long DataPoints => 15643;
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/// <summary>
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/// Data Points count of the algorithm history
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/// </summary>
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public int AlgorithmHistoryDataPoints => 208;
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/// <summary>
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/// Final status of the algorithm
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/// </summary>
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public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
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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", "16"},
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{"Average Win", "0.01%"},
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{"Average Loss", "-0.18%"},
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{"Compounding Annual Return", "-35.728%"},
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{"Drawdown", "1.700%"},
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{"Expectancy", "-0.690"},
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{"Start Equity", "100000"},
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{"End Equity", "99436.42"},
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{"Net Profit", "-0.564%"},
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{"Sharpe Ratio", "-2.767"},
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{"Sortino Ratio", "-3.388"},
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{"Probabilistic Sharpe Ratio", "32.075%"},
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{"Loss Rate", "70%"},
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{"Win Rate", "30%"},
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{"Profit-Loss Ratio", "0.03"},
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{"Alpha", "-0.771"},
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{"Beta", "0.296"},
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{"Annual Standard Deviation", "0.068"},
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{"Annual Variance", "0.005"},
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{"Information Ratio", "-13.734"},
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{"Tracking Error", "0.157"},
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{"Treynor Ratio", "-0.632"},
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{"Total Fees", "$39.85"},
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{"Estimated Strategy Capacity", "$4700000.00"},
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{"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
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{"Portfolio Turnover", "60.79%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "7a65de0f613e5c6161e410d499f45445"}
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};
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}
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}
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