wehub-resource-sync
123 lines
5.2 KiB
C#
123 lines
5.2 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;
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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.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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/// Test algorithm using <see cref="AccumulativeInsightPortfolioConstructionModel"/> and <see cref="ConstantAlphaModel"/>
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/// generating a constant <see cref="Insight"/> with a 0.25 confidence
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/// </summary>
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public class AccumulativeInsightPortfolioRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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// Set requested data resolution
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UniverseSettings.Resolution = Resolution.Minute;
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SetStartDate(2013, 10, 07); //Set Start Date
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SetEndDate(2013, 10, 11); //Set End Date
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SetCash(100000); //Set Strategy Cash
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// set algorithm framework models
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SetUniverseSelection(new ManualUniverseSelectionModel(QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)));
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SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, 0.25));
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SetPortfolioConstruction(new AccumulativeInsightPortfolioConstructionModel());
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SetExecution(new ImmediateExecutionModel());
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}
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public override void OnEndOfAlgorithm()
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{
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if (// holdings value should be 0.03 - to avoid price fluctuation issue we compare with 0.06 and 0.01
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Portfolio.TotalHoldingsValue > Portfolio.TotalPortfolioValue * 0.06m
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||
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Portfolio.TotalHoldingsValue < Portfolio.TotalPortfolioValue * 0.01m)
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{
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throw new RegressionTestException($"Unexpected Total Holdings Value: {Portfolio.TotalHoldingsValue}");
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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 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 => 3943;
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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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/// 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", "199"},
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{"Average Win", "0.00%"},
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{"Average Loss", "0.00%"},
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{"Compounding Annual Return", "-12.611%"},
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{"Drawdown", "0.200%"},
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{"Expectancy", "-0.585"},
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{"Start Equity", "100000"},
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{"End Equity", "99827.80"},
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{"Net Profit", "-0.172%"},
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{"Sharpe Ratio", "-11.13"},
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{"Sortino Ratio", "-16.704"},
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{"Probabilistic Sharpe Ratio", "10.330%"},
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{"Loss Rate", "78%"},
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{"Win Rate", "22%"},
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{"Profit-Loss Ratio", "0.87"},
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{"Alpha", "-0.156"},
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{"Beta", "0.035"},
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{"Annual Standard Deviation", "0.008"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-9.603"},
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{"Tracking Error", "0.215"},
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{"Treynor Ratio", "-2.478"},
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{"Total Fees", "$199.00"},
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{"Estimated Strategy Capacity", "$26000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "119.89%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "d06c26f557b83d8d42ac808fe2815a1e"}
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};
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}
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}
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