Louis Szeto
b655bd776e
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* / * Models * Models * Partial test * Complete test and fix bug * Address peer review * Address review and fix bugs * Fix regression result * Fix regression result
116 lines
4.7 KiB
C#
116 lines
4.7 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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*/
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using System;
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using System.Collections.Generic;
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using QuantConnect.Algorithm;
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using QuantConnect.Algorithm.Framework.Alphas;
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using QuantConnect.Algorithm.Framework.Portfolio;
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using QuantConnect.Interfaces;
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namespace QuantConnect.DataLibrary.Tests
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{
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/// <summary>
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/// Example algorithm of using RiskParityPortfolioConstructionModel
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/// </summary>
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public class RiskParityPortfolioAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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SetStartDate(2021, 2, 21);
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SetEndDate(2021, 3, 30);
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SetCash(100000);
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SetSecurityInitializer(security => security.SetMarketPrice(GetLastKnownPrice(security)));
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AddEquity("SPY", Resolution.Daily);
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AddEquity("AAPL", Resolution.Daily);
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AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
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SetPortfolioConstruction(new RiskParityPortfolioConstructionModel());
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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 };
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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 => 252;
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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 => 509;
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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", "42"},
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{"Average Win", "0.01%"},
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{"Average Loss", "0.00%"},
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{"Compounding Annual Return", "3.593%"},
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{"Drawdown", "4.900%"},
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{"Expectancy", "0.304"},
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{"Net Profit", "0.368%"},
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{"Sharpe Ratio", "0.227"},
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{"Probabilistic Sharpe Ratio", "38.412%"},
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{"Loss Rate", "47%"},
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{"Win Rate", "53%"},
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{"Profit-Loss Ratio", "1.48"},
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{"Alpha", "-0.103"},
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{"Beta", "1.222"},
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{"Annual Standard Deviation", "0.201"},
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{"Annual Variance", "0.04"},
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{"Information Ratio", "-0.845"},
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{"Tracking Error", "0.09"},
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{"Treynor Ratio", "0.037"},
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{"Total Fees", "$42.65"},
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{"Estimated Strategy Capacity", "$720000000.00"},
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{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
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{"Fitness Score", "0.019"},
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{"Kelly Criterion Estimate", "-12.671"},
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{"Kelly Criterion Probability Value", "0.88"},
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{"Sortino Ratio", "0.252"},
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{"Return Over Maximum Drawdown", "0.73"},
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{"Portfolio Turnover", "0.034"},
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{"Total Insights Generated", "54"},
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{"Total Insights Closed", "52"},
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{"Total Insights Analysis Completed", "52"},
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{"Long Insight Count", "54"},
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{"Short Insight Count", "0"},
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{"Long/Short Ratio", "100%"},
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{"Estimated Monthly Alpha Value", "$-3742968"},
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{"Total Accumulated Estimated Alpha Value", "$-4761887"},
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{"Mean Population Estimated Insight Value", "$-91574.75"},
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{"Mean Population Direction", "32.6923%"},
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{"Mean Population Magnitude", "0%"},
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{"Rolling Averaged Population Direction", "38.5572%"},
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{"Rolling Averaged Population Magnitude", "0%"},
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{"OrderListHash", "f0d4972dbf679730bf8c8de2674d4975"}
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};
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}
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}
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