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.Indicators;
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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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/// Example algorithm demonstrating the usage of the RSI indicator
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/// in combination with ETF constituents data to replicate the weighting
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/// of the ETF's assets in our own account.
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/// </summary>
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public class ETFConstituentUniverseRSIAlphaModelAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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/// <summary>
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/// Initialize 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(2020, 12, 1);
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SetEndDate(2021, 1, 31);
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SetCash(100000);
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SetAlpha(new ConstituentWeightedRsiAlphaModel());
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SetPortfolioConstruction(new InsightWeightingPortfolioConstructionModel());
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SetExecution(new ImmediateExecutionModel());
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var spy = AddEquity("SPY", Resolution.Hour).Symbol;
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// We load hourly data for ETF constituents in this algorithm
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UniverseSettings.Resolution = Resolution.Hour;
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Settings.MinimumOrderMarginPortfolioPercentage = 0.01m;
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AddUniverse(Universe.ETF(spy, UniverseSettings, FilterETFConstituents));
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}
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/// <summary>
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/// Filters ETF constituents and adds the resulting Symbols to the ETF constituent universe
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/// </summary>
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/// <param name="constituents">ETF constituents, i.e. the components of the ETF and their weighting</param>
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/// <returns>Symbols to add to universe</returns>
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public IEnumerable<Symbol> FilterETFConstituents(IEnumerable<ETFConstituentUniverse> constituents)
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{
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return constituents
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.Where(x => x.Weight != null && x.Weight >= 0.001m)
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.Select(x => x.Symbol);
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}
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/// <summary>
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/// Alpha model making use of the RSI indicator and ETF constituent weighting to determine
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/// which assets we should invest in and the direction of investment
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/// </summary>
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private class ConstituentWeightedRsiAlphaModel : AlphaModel
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{
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private Dictionary<Symbol, SymbolData> _rsiSymbolData = new Dictionary<Symbol, SymbolData>();
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/// <summary>
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/// Receives new data and emits new <see cref="Insight"/> instances
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/// </summary>
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/// <param name="algorithm">Algorithm</param>
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/// <param name="data">Current data</param>
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/// <returns>Enumerable of insights for assets to invest with a specific weight</returns>
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public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data)
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{
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// Cast first, and then access the constituents collection defined in our algorithm.
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var algoConstituents = data.Bars.Keys
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.Where(x => algorithm.Securities[x].Cache.HasData(typeof(ETFConstituentUniverse)))
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.Select(x => algorithm.Securities[x].Cache.GetData<ETFConstituentUniverse>())
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.ToList();
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if (algoConstituents.Count == 0 || data.Bars.Count == 0)
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{
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// Don't do anything if we have no data we can work with
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yield break;
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}
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var constituents = algoConstituents
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.ToDictionary(x => x.Symbol, x => x);
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foreach (var bar in data.Bars.Values)
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{
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if (!constituents.ContainsKey(bar.Symbol))
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{
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// Dealing with a manually added equity, which in this case is SPY
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continue;
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}
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if (!_rsiSymbolData.ContainsKey(bar.Symbol))
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{
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// First time we're initializing the RSI.
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// It won't be ready now, but it will be
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// after 7 data points.
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var constituent = constituents[bar.Symbol];
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_rsiSymbolData[bar.Symbol] = new SymbolData(bar.Symbol, algorithm, constituent, 7);
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}
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}
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// Let's make sure all RSI indicators are ready before we emit any insights.
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var allReady = _rsiSymbolData.All(kvp => kvp.Value.Rsi.IsReady);
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if (!allReady)
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{
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// We're still warming up the RSI indicators.
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yield break;
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}
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foreach (var kvp in _rsiSymbolData)
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{
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var symbol = kvp.Key;
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var symbolData = kvp.Value;
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var averageLoss = symbolData.Rsi.AverageLoss.Current.Value;
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var averageGain = symbolData.Rsi.AverageGain.Current.Value;
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// If we've lost more than gained, then we think it's going to go down more
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var direction = averageLoss > averageGain
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? InsightDirection.Down
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: InsightDirection.Up;
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// Set the weight of the insight as the weight of the ETF's
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// holding. The InsightWeightingPortfolioConstructionModel
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// will rebalance our portfolio to have the same percentage
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// of holdings in our algorithm that the ETF has.
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yield return Insight.Price(
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symbol,
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TimeSpan.FromDays(1),
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direction,
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(double)(direction == InsightDirection.Down
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? averageLoss
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: averageGain),
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weight: (double?) symbolData.Constituent.Weight);
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}
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}
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}
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/// <summary>
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/// Helper class to access ETF constituent data and RSI indicators
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/// for a single Symbol
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/// </summary>
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private class SymbolData
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{
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/// <summary>
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/// Symbol this data belongs to
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/// </summary>
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public Symbol Symbol { get; }
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/// <summary>
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/// Symbol's constituent data for the ETF it belongs to
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/// </summary>
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public ETFConstituentUniverse Constituent { get; }
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/// <summary>
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/// RSI indicator for the Symbol's price data
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/// </summary>
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public RelativeStrengthIndex Rsi { get; }
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/// <summary>
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/// Creates a new instance of SymbolData
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/// </summary>
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/// <param name="symbol">The symbol to add data for</param>
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/// <param name="constituent">ETF constituent data</param>
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/// <param name="period">RSI period</param>
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public SymbolData(Symbol symbol, QCAlgorithm algorithm, ETFConstituentUniverse constituent, int period)
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{
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Symbol = symbol;
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Constituent = constituent;
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Rsi = algorithm.RSI(symbol, period, MovingAverageType.Exponential, Resolution.Hour);
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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 => 2722;
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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", "55"},
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{"Average Win", "0.09%"},
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{"Average Loss", "-0.05%"},
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{"Compounding Annual Return", "3.321%"},
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{"Drawdown", "0.500%"},
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{"Expectancy", "0.047"},
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{"Start Equity", "100000"},
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{"End Equity", "100535.45"},
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{"Net Profit", "0.535%"},
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{"Sharpe Ratio", "1.377"},
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{"Sortino Ratio", "1.963"},
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{"Probabilistic Sharpe Ratio", "56.920%"},
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{"Loss Rate", "63%"},
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{"Win Rate", "37%"},
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{"Profit-Loss Ratio", "1.83"},
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{"Alpha", "0.022"},
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{"Beta", "-0.024"},
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{"Annual Standard Deviation", "0.015"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-0.46"},
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{"Tracking Error", "0.109"},
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{"Treynor Ratio", "-0.878"},
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{"Total Fees", "$55.00"},
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{"Estimated Strategy Capacity", "$440000000.00"},
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{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
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{"Portfolio Turnover", "11.16%"},
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{"Drawdown Recovery", "19"},
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{"OrderListHash", "8a25d215ea8cd5781953695e8ae93e56"}
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};
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}
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}
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