/* * 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.Collections.Generic; using System.Linq; using QuantConnect.Data; using QuantConnect.Interfaces; using QuantConnect.Data.UniverseSelection; using QuantConnect.Securities; using System; namespace QuantConnect.Algorithm.CSharp { /// /// Regression algorithm asserting that options from universe are added with the same resolution, fill forward and extended market hours settings as the universe settings. /// public class EquityOptionsUniverseSettingsRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition { private SecurityType[] _securityTypes; private HashSet _checkedSecurityTypes = new(); protected virtual DateTime TestStartDate => new DateTime(2015, 12, 24); public override void Initialize() { SetStartDate(TestStartDate); SetEndDate(TestStartDate.AddDays(1)); SetCash(100000); UniverseSettings.Resolution = Resolution.Daily; UniverseSettings.FillForward = false; UniverseSettings.ExtendedMarketHours = true; _securityTypes = AddSecurity(); } protected virtual SecurityType[] AddSecurity() { var equity = AddEquity("GOOG"); var option = AddOption(equity.Symbol); option.SetFilter(u => u.StandardsOnly().Strikes(-2, +2).Expiration(0, 180)); return [option.Symbol.SecurityType]; } public override void OnSecuritiesChanged(SecurityChanges changes) { var securities = changes.AddedSecurities.Where(x => _securityTypes.Contains(x.Type) && !x.Symbol.IsCanonical()).Select(x => x.Symbol).ToList(); var configs = SubscriptionManager.Subscriptions.Where(x => securities.Contains(x.Symbol)); foreach (var config in configs) { if (config.Resolution != UniverseSettings.Resolution) { throw new RegressionTestException($"Config '{config}' resolution {config.Resolution} does not match universe settings resolution {UniverseSettings.Resolution}"); } if (config.FillDataForward != UniverseSettings.FillForward) { throw new RegressionTestException($"Config '{config}' fill forward {config.FillDataForward} does not match universe settings fill forward {UniverseSettings.FillForward}"); } if (config.ExtendedMarketHours != UniverseSettings.ExtendedMarketHours) { throw new RegressionTestException($"Config '{config}' extended market hours {config.ExtendedMarketHours} does not match universe settings extended market hours {UniverseSettings.ExtendedMarketHours}"); } _checkedSecurityTypes.Add(config.SecurityType); } } public override void OnEndOfAlgorithm() { if (_checkedSecurityTypes.Count != _securityTypes.Length || !_securityTypes.All(_checkedSecurityTypes.Contains)) { throw new RegressionTestException($"Not all security types were checked. Expected: {string.Join(", ", _securityTypes)}. Checked: {string.Join(", ", _checkedSecurityTypes)}"); } } /// /// 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 virtual long DataPoints => 4275; /// /// 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 virtual 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"}, {"Tracking Error", "0"}, {"Treynor Ratio", "0"}, {"Total Fees", "$0.00"}, {"Estimated Strategy Capacity", "$0"}, {"Lowest Capacity Asset", ""}, {"Portfolio Turnover", "0%"}, {"Drawdown Recovery", "0"}, {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"} }; } }