Martin-Molinero
bbbab6d9a8
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* Refactor alpha statistics - Refactor alpha statistics, cleaning up and simplifying no longer required calculations and scoring - Adding new InsightEvaluator abstraction, adding C# & PY regression algorithms * Optimization backtest result json converter update * Address reviews - Remove IAlphaHandler, move insight storage responsability to IResultHandler and centralizing insight collection on the QCAlgorithm.Insights to be reused by the framework models - Fix portfolio turnover single day backtests and duplicate time sampling handling. Updating regression algorithms * Add InsightCollection tests and minor fixes * Adding more & improved tests
97 lines
3.6 KiB
C#
97 lines
3.6 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.Data;
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using QuantConnect.Interfaces;
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using QuantConnect.Algorithm.Framework.Risk;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm which tests that a trailing stop liquidates and restarts correctly
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/// </summary>
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public class TrailingStopRiskFrameworkRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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SetStartDate(2014, 6, 5);
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SetEndDate(2014, 6, 9);
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SetCash(100000);
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AddEquity("AAPL");
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AddRiskManagement(new TrailingStopRiskManagementModel(0.01m));
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}
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public override void OnData(Slice slice)
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{
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if (!Portfolio.Invested)
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{
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SetHoldings("AAPL", 1);
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}
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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, 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 => 2371;
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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 => 0;
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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", "3"},
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{"Average Win", "0%"},
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{"Average Loss", "-0.31%"},
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{"Compounding Annual Return", "202.556%"},
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{"Drawdown", "1.400%"},
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{"Expectancy", "-1"},
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{"Net Profit", "1.426%"},
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{"Sharpe Ratio", "9.374"},
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{"Probabilistic Sharpe Ratio", "81.575%"},
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{"Loss Rate", "100%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "1.741"},
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{"Beta", "-1.155"},
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{"Annual Standard Deviation", "0.133"},
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{"Annual Variance", "0.018"},
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{"Information Ratio", "5.447"},
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{"Tracking Error", "0.149"},
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{"Treynor Ratio", "-1.075"},
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{"Total Fees", "$71.90"},
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{"Estimated Strategy Capacity", "$20000000.00"},
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{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
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{"Portfolio Turnover", "59.13%"},
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{"OrderListHash", "87da67837d4a2c4c4a419f01b467d9c6"}
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};
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}
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}
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