chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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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 QuantConnect.Data;
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using QuantConnect.Interfaces;
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using System.Collections.Generic;
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using QuantConnect.Data.Fundamental;
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using QuantConnect.Data.UniverseSelection;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression test algorithm for scheduled universe selection GH 3890
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/// </summary>
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public class FundamentalCustomSelectionTimeRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private int _specificDateSelection;
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private int _monthStartSelection;
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private int _monthEndSelection;
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private readonly Symbol _symbol = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
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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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SetStartDate(2014, 03, 25);
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SetEndDate(2014, 05, 10);
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UniverseSettings.Resolution = Resolution.Daily;
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// Test use case A
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AddUniverse(DateRules.MonthStart(), SelectionFunction_MonthStart);
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// Test use case B
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var otherSettings = new UniverseSettings(UniverseSettings);
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otherSettings.Schedule.On(DateRules.MonthEnd());
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AddUniverse(FundamentalUniverse.USA(SelectionFunction_MonthEnd, otherSettings));
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// Test use case C
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UniverseSettings.Schedule.On(DateRules.On(new DateTime(2014, 05, 9)));
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AddUniverse(FundamentalUniverse.USA(SelectionFunction_SpecificDate));
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}
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public IEnumerable<Symbol> SelectionFunction_SpecificDate(IEnumerable<Fundamental> coarse)
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{
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_specificDateSelection++;
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if (Time != new DateTime(2014, 05, 9))
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{
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throw new RegressionTestException($"SelectionFunction_SpecificDate unexpected selection: {Time}");
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}
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return new[] { _symbol };
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}
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public IEnumerable<Symbol> SelectionFunction_MonthStart(IEnumerable<Fundamental> coarse)
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{
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if (_monthStartSelection++ == 0)
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{
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if (Time != StartDate)
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{
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throw new RegressionTestException($"Month Start unexpected initial selection: {Time}");
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}
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}
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else if (Time != new DateTime(2014, 4, 1)
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&& Time != new DateTime(2014, 5, 1))
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{
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throw new RegressionTestException($"Month Start unexpected selection: {Time}");
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}
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return new[] { _symbol };
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}
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public IEnumerable<Symbol> SelectionFunction_MonthEnd(IEnumerable<CoarseFundamental> coarse)
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{
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if (_monthEndSelection++ == 0)
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{
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if (Time != StartDate)
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{
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throw new RegressionTestException($"Month End unexpected initial selection: {Time}");
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}
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}
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else if (Time != new DateTime(2014, 3, 31)
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&& Time != new DateTime(2014, 4, 30))
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{
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throw new RegressionTestException($"Month End unexpected selection: {Time}");
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}
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return new[] { _symbol };
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}
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/// <summary>
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/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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/// </summary>
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/// <param name="slice">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice slice)
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{
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if (!Portfolio.Invested)
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{
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SetHoldings(_symbol, 1);
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Debug($"Purchased Stock {_symbol}");
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}
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}
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public override void OnEndOfAlgorithm()
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{
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if (_monthEndSelection != 3)
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{
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throw new RegressionTestException($"Month End unexpected selection count: {_monthEndSelection}");
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}
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if (_monthStartSelection != 3)
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{
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throw new RegressionTestException($"Month start unexpected selection count: {_monthStartSelection}");
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}
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if (_specificDateSelection != 1)
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{
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throw new RegressionTestException($"Specific date unexpected selection count: {_specificDateSelection}");
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}
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}
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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 => 14466;
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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 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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/// 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", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "4.334%"},
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{"Drawdown", "3.900%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "100532.22"},
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{"Net Profit", "0.532%"},
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{"Sharpe Ratio", "0.28"},
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{"Sortino Ratio", "0.283"},
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{"Probabilistic Sharpe Ratio", "38.189%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "-0.022"},
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{"Beta", "1.018"},
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{"Annual Standard Deviation", "0.099"},
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{"Annual Variance", "0.01"},
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{"Information Ratio", "-2.462"},
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{"Tracking Error", "0.009"},
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{"Treynor Ratio", "0.027"},
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{"Total Fees", "$3.07"},
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{"Estimated Strategy Capacity", "$920000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "2.20%"},
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{"Drawdown Recovery", "5"},
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{"OrderListHash", "87438e51988f37757a2d7f97389483ea"}
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};
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}
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}
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