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 System.Collections.Generic;
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using System.Linq;
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using QuantConnect.Data;
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using QuantConnect.Data.Market;
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using QuantConnect.Interfaces;
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using QuantConnect.Securities;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm asserting the behavior of auxiliary data history requests
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/// </summary>
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public class HistoryAuxiliaryDataRegressionAlgorithm : 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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SetStartDate(2021, 1, 1);
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SetEndDate(2021, 1, 5);
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var aapl = AddEquity("AAPL", Resolution.Daily).Symbol;
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// multi symbol request
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var spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
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var multiSymbolRequest = History<Dividend>(new[] { aapl, spy }, 360, Resolution.Daily).ToList();
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if (multiSymbolRequest.Count != 12)
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{
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throw new RegressionTestException($"Unexpected multi symbol dividend count: {multiSymbolRequest.Count}");
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}
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// continuous future mapping requests
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var sp500 = QuantConnect.Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.CME);
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var continuousFutureOpenInterestMapping = History<SymbolChangedEvent>(sp500, new DateTime(2007, 1, 1), new DateTime(2012, 1, 1),
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dataMappingMode: DataMappingMode.OpenInterest).ToList();
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if (continuousFutureOpenInterestMapping.Count != 9)
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{
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throw new RegressionTestException($"Unexpected continuous future mapping event count: {continuousFutureOpenInterestMapping.Count}");
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}
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var continuousFutureLastTradingDayMapping = History<SymbolChangedEvent>(sp500, new DateTime(2007, 1, 1),new DateTime(2012, 1, 1),
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dataMappingMode: DataMappingMode.LastTradingDay).ToList();
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if (continuousFutureLastTradingDayMapping.Count != 9)
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{
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throw new RegressionTestException($"Unexpected continuous future mapping event count: {continuousFutureLastTradingDayMapping.Count}");
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}
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// mapping dates should be different
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if (Enumerable.SequenceEqual(continuousFutureOpenInterestMapping.Select(x => x.EndTime), continuousFutureLastTradingDayMapping.Select(x => x.EndTime)))
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{
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throw new RegressionTestException($"Unexpected continuous future mapping times");
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}
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var dividends = History<Dividend>(aapl, 360).ToList();
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if (dividends.Count != 6)
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{
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throw new RegressionTestException($"Unexpected dividend count: {dividends.Count}");
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}
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foreach (var dividend in dividends)
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{
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if (dividend.Distribution == 0)
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{
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throw new RegressionTestException($"Unexpected Distribution: {dividend.Distribution}");
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}
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}
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var splits = History<Split>(aapl, 360).ToList();
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if (splits.Count != 2)
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{
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throw new RegressionTestException($"Unexpected split count: {splits.Count}");
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}
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foreach (var split in splits)
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{
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if (split.SplitFactor == 0)
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{
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throw new RegressionTestException($"Unexpected SplitFactor: {split.SplitFactor}");
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}
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}
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var cryptoFuture = QuantConnect.Symbol.Create("BTCUSD", SecurityType.CryptoFuture, Market.Binance);
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var marginInterests = History<MarginInterestRate>(cryptoFuture, 24 * 3, Resolution.Hour).ToList();
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if (marginInterests.Count != 8)
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{
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throw new RegressionTestException($"Unexpected margin interest count: {marginInterests.Count}");
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}
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foreach (var marginInterest in marginInterests)
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{
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if (marginInterest.InterestRate == 0)
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{
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throw new RegressionTestException($"Unexpected InterestRate: {marginInterest.InterestRate}");
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}
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}
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// last trading date on 2007-05-18
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var delistedSymbol = QuantConnect.Symbol.Create("AAA.1", SecurityType.Equity, Market.USA);
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var delistings = History<Delisting>(delistedSymbol, new DateTime(2007, 5, 15), new DateTime(2007, 5, 21)).ToList();
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if (delistings.Count != 2)
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{
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throw new RegressionTestException($"Unexpected delistings count: {delistings.Count}");
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}
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if (delistings[0].Type != DelistingType.Warning)
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{
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throw new RegressionTestException($"Unexpected delisting: {delistings[0]}");
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}
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if (delistings[1].Type != DelistingType.Delisted)
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{
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throw new RegressionTestException($"Unexpected delisting: {delistings[1]}");
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}
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// get's remapped:
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// 2008-09-30 spwr -> spwra
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// 2011-11-17 spwra -> spwr
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var remappedSymbol = QuantConnect.Symbol.Create("SPWR", SecurityType.Equity, Market.USA);
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var symbolChangedEvents = History<SymbolChangedEvent>(remappedSymbol, new DateTime(2007, 1, 1), new DateTime(2012, 1, 1)).ToList();
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if (symbolChangedEvents.Count != 2)
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{
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throw new RegressionTestException($"Unexpected SymbolChangedEvents count: {symbolChangedEvents.Count}");
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}
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if (symbolChangedEvents[0].OldSymbol != "SPWR" || symbolChangedEvents[0].NewSymbol != "SPWRA" || symbolChangedEvents[0].EndTime != new DateTime(2008, 9, 30))
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{
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throw new RegressionTestException($"Unexpected SymbolChangedEvents: {symbolChangedEvents[0]}");
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}
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if (symbolChangedEvents[1].NewSymbol != "SPWR" || symbolChangedEvents[1].OldSymbol != "SPWRA" || symbolChangedEvents[1].EndTime != new DateTime(2011, 11, 17))
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{
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throw new RegressionTestException($"Unexpected SymbolChangedEvents: {symbolChangedEvents[1]}");
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}
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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("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 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 => 24;
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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 => 50;
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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", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "235.317%"},
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{"Drawdown", "0%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "101558.94"},
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{"Net Profit", "1.559%"},
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{"Sharpe Ratio", "13.436"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "95.868%"},
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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", "1.873"},
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{"Beta", "0.603"},
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{"Annual Standard Deviation", "0.124"},
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{"Annual Variance", "0.015"},
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{"Information Ratio", "18.773"},
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{"Tracking Error", "0.107"},
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{"Treynor Ratio", "2.756"},
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{"Total Fees", "$3.86"},
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{"Estimated Strategy Capacity", "$1100000000.00"},
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{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
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{"Portfolio Turnover", "19.55%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "7b1f04613621baa2333be0876f106ad3"}
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};
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}
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}
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