/* * 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.Linq; using QuantConnect.Data; using QuantConnect.Interfaces; using System.Collections.Generic; namespace QuantConnect.Algorithm.CSharp { /// /// We add an option contract using and place a trade and wait till it expires /// later will liquidate the resulting equity position and assert both option and underlying get removed /// public class AddOptionContractExpiresRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition { private DateTime _expiration = new DateTime(2014, 06, 21); private Symbol _option; private Symbol _twx; private bool _traded; public override void Initialize() { SetStartDate(2014, 06, 05); SetEndDate(2014, 06, 30); _twx = QuantConnect.Symbol.Create("TWX", SecurityType.Equity, Market.USA); AddUniverse("my-daily-universe-name", time => new List { "AAPL" }); } public override void OnData(Slice slice) { if (_option == null) { var option = OptionChain(_twx) .OrderBy(x => x.ID.Symbol) .FirstOrDefault(optionContract => optionContract.ID.Date == _expiration && optionContract.ID.OptionRight == OptionRight.Call && optionContract.ID.OptionStyle == OptionStyle.American); if (option != null) { _option = AddOptionContract(option).Symbol; } } if (_option != null && Securities[_option].Price != 0 && !_traded) { _traded = true; Buy(_option, 1); foreach (var symbol in new [] { _option, _option.Underlying }) { var config = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(symbol).ToList(); if (!config.Any()) { throw new RegressionTestException($"Was expecting configurations for {symbol}"); } if (config.Any(dataConfig => dataConfig.DataNormalizationMode != DataNormalizationMode.Raw)) { throw new RegressionTestException($"Was expecting DataNormalizationMode.Raw configurations for {symbol}"); } } } if (Time.Date > _expiration) { if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_option).Any()) { throw new RegressionTestException($"Unexpected configurations for {_option} after it has been delisted"); } if (Securities[_twx].Invested) { if (!SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_twx).Any()) { throw new RegressionTestException($"Was expecting configurations for {_twx}"); } // first we liquidate the option exercised position Liquidate(_twx); } } else if (Time.Date > _expiration && !Securities[_twx].Invested) { if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_twx).Any()) { throw new RegressionTestException($"Unexpected configurations for {_twx} after it has been liquidated"); } } } /// /// 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, Language.Python }; /// /// Data Points count of all timeslices of algorithm /// public long DataPoints => 37597; /// /// Data Points count of the algorithm history /// public int AlgorithmHistoryDataPoints => 1; /// /// 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", "3"}, {"Average Win", "2.73%"}, {"Average Loss", "-2.98%"}, {"Compounding Annual Return", "-4.619%"}, {"Drawdown", "0.300%"}, {"Expectancy", "-0.042"}, {"Start Equity", "100000"}, {"End Equity", "99668"}, {"Net Profit", "-0.332%"}, {"Sharpe Ratio", "-4.614"}, {"Sortino Ratio", "0"}, {"Probabilistic Sharpe Ratio", "0.427%"}, {"Loss Rate", "50%"}, {"Win Rate", "50%"}, {"Profit-Loss Ratio", "0.92"}, {"Alpha", "-0.022"}, {"Beta", "-0.012"}, {"Annual Standard Deviation", "0.005"}, {"Annual Variance", "0"}, {"Information Ratio", "-2.823"}, {"Tracking Error", "0.049"}, {"Treynor Ratio", "2.01"}, {"Total Fees", "$2.00"}, {"Estimated Strategy Capacity", "$5700000.00"}, {"Lowest Capacity Asset", "AOL VRKS95ENLBYE|AOL R735QTJ8XC9X"}, {"Portfolio Turnover", "0.55%"}, {"Drawdown Recovery", "0"}, {"OrderListHash", "fc5ab25181a01ca5ce39212f60eb0ecd"} }; } }