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.Linq;
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using QuantConnect.Data;
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using QuantConnect.Indicators;
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using QuantConnect.Interfaces;
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using QuantConnect.Securities;
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using System.Collections.Generic;
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using QuantConnect.Data.UniverseSelection;
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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.Securities.Future;
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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 using universe selection with futures
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/// </summary>
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public class FuturesFrameworkRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private static Symbol _es = QuantConnect.Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.CME);
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private static Symbol _gold = QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX);
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private readonly Dictionary<Symbol, bool> _addedCanonical = new() { { _gold, false }, { _es, false } };
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private readonly Dictionary<Symbol, bool> _removedCanonical = new() { { _gold, false }, { _es, false } };
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private readonly Dictionary<Symbol, SimpleMovingAverage> _canonicalData = new() { { _gold, null }, { _es, null } };
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public override void Initialize()
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{
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UniverseSettings.Resolution = Resolution.Minute;
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SetStartDate(2013, 10, 07);
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SetEndDate(2013, 10, 11);
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SetUniverseSelection(new FutureUniverseSelectionModel(QuantConnect.Time.OneDay, SelectFutureChainSymbols));
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SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
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SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
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SetExecution(new ImmediateExecutionModel());
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}
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private static IEnumerable<Symbol> SelectFutureChainSymbols(DateTime utcTime)
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{
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var newYorkTime = utcTime.ConvertFromUtc(TimeZones.NewYork);
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if (newYorkTime.Date < new DateTime(2013, 10, 09))
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{
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yield return _es;
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}
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if (newYorkTime.Date >= new DateTime(2013, 10, 09))
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{
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yield return _gold;
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}
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}
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public override void OnData(Slice slice)
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{
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var future = _es;
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if (Time.Date >= new DateTime(2013, 10, 09))
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{
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future = _gold;
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}
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var continuous = Securities[future];
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if (continuous.Price == Securities[(continuous as Future).Mapped].Price)
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{
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// prices should never match because we are using the default backwards adjusted mode, they would match if we used raw mode
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throw new RegressionTestException($"Unexpected continuous future price {continuous.Price}");
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}
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}
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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foreach (var added in changes.AddedSecurities)
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{
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if (added.Symbol.IsCanonical())
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{
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_addedCanonical[added.Symbol] = true;
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_canonicalData[added.Symbol] = SMA(added.Symbol, 10);
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}
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}
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foreach (var removed in changes.RemovedSecurities)
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{
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if (removed.Symbol.IsCanonical())
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{
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_removedCanonical[removed.Symbol] = true;
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}
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}
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var canonicals = changes.AddedSecurities.Select(x => x.Symbol.Canonical).ToHashSet();
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var nonCanonicals = changes.AddedSecurities.Where(x => !x.Symbol.IsCanonical()).ToList();
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foreach (var subscriptions in SubscriptionManager.Subscriptions.Where(x => canonicals.Contains(x.Symbol.Canonical)).GroupBy(x => x.Symbol.Canonical))
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{
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// trade & quote for canonical + contract chain (universe data)
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if (subscriptions.Count(x => x.Symbol.IsCanonical()) != canonicals.Count * 3)
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{
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throw new RegressionTestException($"Unexpected canonical subscription count {subscriptions.Count(x => x.Symbol.IsCanonical())}");
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}
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// trade and quote for non canonicals
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if (subscriptions.Count(x => !x.Symbol.IsCanonical()) != nonCanonicals.Count * 2)
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{
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throw new RegressionTestException($"Unexpected non canonical subscription count {subscriptions.Count(x => !x.Symbol.IsCanonical())}");
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}
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}
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var internalSubscriptions = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(includeInternalConfigs: true)
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.Where(x => x.SecurityType == SecurityType.Future && x.IsInternalFeed && canonicals.Contains(x.Symbol.Canonical)).ToList();
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// an open interest subscription for each + trade and quote for the currently mapped continuous future
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if (internalSubscriptions.Count != (nonCanonicals.Count + canonicals.Count + canonicals.Count * 2))
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{
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throw new RegressionTestException($"Unexpected internal subscription count {internalSubscriptions.Count}");
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}
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// we expect a single continuous universe at the time
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var universeSubscriptions = SubscriptionManager.Subscriptions.Count(x => x.Symbol.ID.Symbol.Contains("QC-UNIVERSE-CONTINUOUS"));
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if (universeSubscriptions != 1)
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{
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throw new RegressionTestException($"Unexpected universe subscription count {universeSubscriptions}");
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}
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// we expect a single canonical at the time
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var canonicalSubscriptions = SubscriptionManager.Subscriptions.Where(x => !x.Symbol.ID.Symbol.Contains("QC-UNIVERSE-CONTINUOUS") && x.Symbol.IsCanonical())
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.Select(x => x.Symbol.Canonical).ToHashSet();
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if (canonicalSubscriptions.Count != 1)
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{
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throw new RegressionTestException($"Unexpected universe subscription count {universeSubscriptions}");
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}
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}
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public override void OnEndOfAlgorithm()
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{
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foreach (var canonical in _addedCanonical)
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{
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if (!canonical.Value)
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{
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throw new RegressionTestException($"Canonical {canonical} was not added!");
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}
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}
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foreach (var canonical in _removedCanonical)
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{
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if (canonical.Key.ID.Symbol == "ES" && !canonical.Value || canonical.Key.ID.Symbol == "GC" && canonical.Value)
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{
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throw new RegressionTestException($"Canonical {canonical} was not removed!");
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}
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}
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foreach (var canonical in _canonicalData)
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{
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if (canonical.Value == null || !canonical.Value.IsReady)
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{
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throw new RegressionTestException($"Canonical {canonical} emitted no data!");
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}
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}
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if (SubscriptionManager.Subscriptions.Any(x => x.Symbol.ID.Symbol == "ES"))
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{
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throw new RegressionTestException($"There should be no ES subscription!");
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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 virtual 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 virtual List<Language> Languages { get; } = new() { 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 virtual long DataPoints => 101119;
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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 virtual 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 virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "10"},
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{"Average Win", "0%"},
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{"Average Loss", "-4.51%"},
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{"Compounding Annual Return", "-100.000%"},
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{"Drawdown", "33.200%"},
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{"Expectancy", "-1"},
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{"Start Equity", "100000"},
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{"End Equity", "79364"},
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{"Net Profit", "-20.636%"},
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{"Sharpe Ratio", "-0.531"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "20.042%"},
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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", "-11.342"},
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{"Beta", "5.231"},
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{"Annual Standard Deviation", "1.896"},
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{"Annual Variance", "3.593"},
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{"Information Ratio", "-1.687"},
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{"Tracking Error", "1.768"},
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{"Treynor Ratio", "-0.193"},
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{"Total Fees", "$86.00"},
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{"Estimated Strategy Capacity", "$410000.00"},
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{"Lowest Capacity Asset", "ES VRJST036ZY0X"},
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{"Portfolio Turnover", "764.63%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "3d14700af93152895c4bceae6ad37620"}
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};
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}
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}
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