Michael Handschuh
9ee61f425c
A mechanical refactoring was performed to make algorithms currently used in regression algorithms to implement IRegressionAlgorithmDefinition, which allows algorithms to define their own expected statistics and what languages should be run as part of regression. The type name of the C# type is used to determine the file/model name for python. This was for simplicity, but if needed, could later be refactored to expose more information, but for now the convention of keeping names the same makes sense and just works easily.
104 lines
4.3 KiB
C#
104 lines
4.3 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;
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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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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 : QCAlgorithmFramework, 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(Securities.Keys));
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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 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", "468"},
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{"Average Win", "0.06%"},
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{"Average Loss", "-0.04%"},
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{"Compounding Annual Return", "-96.930%"},
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{"Drawdown", "4.500%"},
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{"Expectancy", "-0.490"},
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{"Net Profit", "-4.356%"},
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{"Sharpe Ratio", "-26.255"},
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{"Loss Rate", "81%"},
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{"Win Rate", "19%"},
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{"Profit-Loss Ratio", "1.75"},
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{"Alpha", "-1.975"},
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{"Beta", "-23.261"},
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{"Annual Standard Deviation", "0.085"},
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{"Annual Variance", "0.007"},
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{"Information Ratio", "-26.372"},
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{"Tracking Error", "0.085"},
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{"Treynor Ratio", "0.096"},
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{"Total Fees", "$2215.48"},
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{"Total Insights Generated", "289"},
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{"Total Insights Closed", "0"},
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{"Total Insights Analysis Completed", "0"},
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{"Long Insight Count", "146"},
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{"Short Insight Count", "143"},
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{"Long/Short Ratio", "102.10%"},
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{"Estimated Monthly Alpha Value", "$0"},
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{"Total Accumulated Estimated Alpha Value", "$0"},
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{"Mean Population Estimated Insight Value", "$0"},
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{"Mean Population Direction", "0%"},
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{"Mean Population Magnitude", "0%"},
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{"Rolling Averaged Population Direction", "0%"},
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{"Rolling Averaged Population Magnitude", "0%"},
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};
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}
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}
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