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.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.Data;
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using QuantConnect.Data.UniverseSelection;
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using QuantConnect.Interfaces;
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using QuantConnect.Orders;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Tests ETF constituents universe selection with the algorithm framework models (Alpha, PortfolioConstruction, Execution)
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/// </summary>
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public class ETFConstituentUniverseFrameworkRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private List<ETFConstituentUniverse> ConstituentData = new List<ETFConstituentUniverse>();
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/// <summary>
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/// Initializes the algorithm, setting up the framework classes and ETF constituent universe settings
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2020, 12, 1);
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SetEndDate(2021, 1, 31);
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SetCash(100000);
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SetAlpha(new ETFConstituentAlphaModel());
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SetPortfolioConstruction(new ETFConstituentPortfolioModel());
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SetExecution(new ETFConstituentExecutionModel());
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var spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
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UniverseSettings.Resolution = Resolution.Hour;
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AddUniverseWrapper(spy);
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}
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protected virtual void AddUniverseWrapper(Symbol symbol)
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{
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var universe = AddUniverse(Universe.ETF(symbol, UniverseSettings, FilterETFConstituents));
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var historicalData = History(universe, 1).ToList();
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if (historicalData.Count != 1)
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{
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throw new RegressionTestException($"Unexpected history count {historicalData.Count}! Expected 1");
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}
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foreach (var universeDataCollection in historicalData)
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{
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if (universeDataCollection.Data.Count < 200)
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{
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throw new RegressionTestException($"Unexpected universe DataCollection count {universeDataCollection.Data.Count}! Expected > 200");
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}
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}
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}
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/// <summary>
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/// Filters ETF constituents
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/// </summary>
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/// <param name="constituents">ETF constituents</param>
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/// <returns>ETF constituent Symbols that we want to include in the algorithm</returns>
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public IEnumerable<Symbol> FilterETFConstituents(IEnumerable<ETFConstituentUniverse> constituents)
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{
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var constituentData = constituents
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.Where(x => (x.Weight ?? 0m) >= 0.001m)
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.ToList();
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ConstituentData = constituentData;
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return constituentData
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.Select(x => x.Symbol)
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.ToList();
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}
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/// <summary>
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/// no-op for performance
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/// </summary>
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public override void OnData(Slice data)
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{
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}
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/// <summary>
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/// Alpha model for ETF constituents, where we generate insights based on the weighting
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/// of the ETF constituent
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/// </summary>
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private class ETFConstituentAlphaModel : IAlphaModel
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{
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public void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
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{
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}
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/// <summary>
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/// Creates new insights based on constituent data and their weighting
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/// in their respective ETF
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/// </summary>
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public IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data)
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{
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var algo = (ETFConstituentUniverseFrameworkRegressionAlgorithm) algorithm;
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foreach (var constituent in algo.ConstituentData)
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{
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if (!data.Bars.ContainsKey(constituent.Symbol) &&
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!data.QuoteBars.ContainsKey(constituent.Symbol))
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{
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continue;
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}
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var insightDirection = constituent.Weight != null && constituent.Weight >= 0.01m
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? InsightDirection.Up
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: InsightDirection.Down;
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yield return new Insight(
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algorithm.UtcTime,
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constituent.Symbol,
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TimeSpan.FromDays(1),
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InsightType.Price,
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insightDirection,
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1 * (double)insightDirection,
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1.0,
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weight: (double)(constituent.Weight ?? 0));
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}
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}
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}
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/// <summary>
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/// Generates targets for ETF constituents, which will be set to the weighting
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/// of the constituent in their respective ETF
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/// </summary>
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private class ETFConstituentPortfolioModel : IPortfolioConstructionModel
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{
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private bool _hasAdded;
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/// <summary>
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/// Securities changed, detects if we've got new additions to the universe
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/// so that we don't try to trade every loop
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/// </summary>
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public void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
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{
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_hasAdded = changes.AddedSecurities.Count != 0;
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}
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/// <summary>
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/// Creates portfolio targets based on the insights provided to us by the alpha model.
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/// Emits portfolio targets setting the quantity to the weight of the constituent
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/// in its respective ETF.
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/// </summary>
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public IEnumerable<IPortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
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{
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if (!_hasAdded)
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{
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yield break;
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}
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foreach (var insight in insights)
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{
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yield return new PortfolioTarget(insight.Symbol, (decimal) (insight.Weight ?? 0));
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_hasAdded = false;
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}
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}
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}
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/// <summary>
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/// Executes based on ETF constituent weighting
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/// </summary>
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private class ETFConstituentExecutionModel : IExecutionModel
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{
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/// <summary>
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/// Liquidates if constituents have been removed from the universe
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/// </summary>
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public void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
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{
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foreach (var change in changes.RemovedSecurities)
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{
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algorithm.Liquidate(change.Symbol);
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}
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}
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/// <summary>
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/// Creates orders for constituents that attempts to add
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/// the weighting of the constituent in our portfolio. The
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/// resulting algorithm portfolio weight might not be equal
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/// to the leverage of the ETF (1x, 2x, 3x, etc.)
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/// </summary>
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public void Execute(QCAlgorithm algorithm, IPortfolioTarget[] targets)
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{
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foreach (var target in targets)
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{
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algorithm.SetHoldings(target.Symbol, target.Quantity);
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}
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}
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public void OnOrderEvent(QCAlgorithm algorithm, OrderEvent orderEvent)
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{
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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 virtual 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 => 2436;
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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 => 1;
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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", "3"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "3.006%"},
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{"Drawdown", "0.700%"},
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{"Expectancy", "0"},
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{"Start Equity", "100000"},
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{"End Equity", "100485.34"},
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{"Net Profit", "0.485%"},
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{"Sharpe Ratio", "1.055"},
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{"Sortino Ratio", "1.53"},
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{"Probabilistic Sharpe Ratio", "50.834%"},
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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", "0.012"},
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{"Beta", "0.096"},
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{"Annual Standard Deviation", "0.017"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-0.544"},
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{"Tracking Error", "0.096"},
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{"Treynor Ratio", "0.191"},
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{"Total Fees", "$3.00"},
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{"Estimated Strategy Capacity", "$1400000000.00"},
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{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
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{"Portfolio Turnover", "0.12%"},
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{"Drawdown Recovery", "22"},
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{"OrderListHash", "5d1e80a607d65ba4c7329f6f0b86999f"}
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};
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}
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}
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