/* * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ using System; using System.Collections.Generic; using System.Linq; using QuantConnect.Data; using QuantConnect.Data.UniverseSelection; using QuantConnect.Interfaces; namespace QuantConnect.Algorithm.CSharp { /// /// Assert that ETF universe selection happens right away after algorithm starts /// public class ETFConstituentUniverseImmediateSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition { private List _constituents = new(); private Symbol _spy; private bool _filtered; private bool _securitiesChanged; private bool _firstOnData = true; /// /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized. /// public override void Initialize() { SetStartDate(2020, 12, 1); SetEndDate(2021, 1, 31); SetCash(100000); UniverseSettings.Resolution = Resolution.Hour; _spy = AddEquity("SPY", Resolution.Hour).Symbol; AddUniverse(Universe.ETF(_spy, universeFilterFunc: FilterETFs)); } /// /// Filters ETFs, performing some sanity checks /// /// Constituents of the ETF universe added above /// Constituent Symbols to add to algorithm /// Constituents collection was not structured as expected private IEnumerable FilterETFs(IEnumerable constituents) { _filtered = true; _constituents = constituents.Select(x => x.Symbol).Distinct().ToList(); return _constituents; } /// /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. /// /// Slice object keyed by symbol containing the stock data public override void OnData(Slice slice) { if (_firstOnData) { if (!_filtered) { throw new RegressionTestException("Universe selection should have been triggered right away. " + "The first OnData call should have had happened after the universe selection"); } _firstOnData = false; } } /// /// Checks if new securities have been added to the algorithm after universe selection has occurred /// /// Security changes /// Expected number of stocks were not added to the algorithm public override void OnSecuritiesChanged(SecurityChanges changes) { if (!_filtered) { throw new RegressionTestException("Universe selection should have been triggered right away"); } if (!_securitiesChanged) { // Selection should be happening right on algorithm start if (Time != StartDate) { throw new RegressionTestException("Universe selection should have been triggered right away"); } // All constituents should have been added to the algorithm. // Plus the ETF itself. if (changes.AddedSecurities.Count != _constituents.Count + 1) { throw new RegressionTestException($"Expected {_constituents.Count + 1} stocks to be added to the algorithm, " + $"instead added: {changes.AddedSecurities.Count}"); } if (!_constituents.All(constituent => changes.AddedSecurities.Any(security => security.Symbol == constituent))) { throw new RegressionTestException("Not all constituents were added to the algorithm"); } _securitiesChanged = true; } } /// /// Ensures that all expected events were triggered by the end of the algorithm /// /// An expected event didn't happen public override void OnEndOfAlgorithm() { if (_firstOnData || !_filtered || !_securitiesChanged) { throw new RegressionTestException("Expected events didn't happen"); } } /// /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm. /// public bool CanRunLocally { get; } = true; /// /// This is used by the regression test system to indicate which languages this algorithm is written in. /// public List Languages { get; } = new() { Language.CSharp }; /// /// Data Points count of all timeslices of algorithm /// public long DataPoints => 2722; /// /// Data Points count of the algorithm history /// public int AlgorithmHistoryDataPoints => 0; /// /// Final status of the algorithm /// public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed; /// /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm /// public Dictionary ExpectedStatistics => new Dictionary { {"Total Orders", "0"}, {"Average Win", "0%"}, {"Average Loss", "0%"}, {"Compounding Annual Return", "0%"}, {"Drawdown", "0%"}, {"Expectancy", "0"}, {"Start Equity", "100000"}, {"End Equity", "100000"}, {"Net Profit", "0%"}, {"Sharpe Ratio", "0"}, {"Sortino Ratio", "0"}, {"Probabilistic Sharpe Ratio", "0%"}, {"Loss Rate", "0%"}, {"Win Rate", "0%"}, {"Profit-Loss Ratio", "0"}, {"Alpha", "0"}, {"Beta", "0"}, {"Annual Standard Deviation", "0"}, {"Annual Variance", "0"}, {"Information Ratio", "-0.695"}, {"Tracking Error", "0.105"}, {"Treynor Ratio", "0"}, {"Total Fees", "$0.00"}, {"Estimated Strategy Capacity", "$0"}, {"Lowest Capacity Asset", ""}, {"Portfolio Turnover", "0%"}, {"Drawdown Recovery", "0"}, {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"} }; } }