/* * 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.Market; using QuantConnect.Orders; using QuantConnect.Interfaces; namespace QuantConnect.Algorithm.CSharp { /// /// This regression algorithm tests option exercise and assignment functionality /// We open two positions and go with them into expiration. We expect to see our long position exercised and short position assigned. /// /// /// public class OptionExerciseAssignRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition { private const string UnderlyingTicker = "GOOG"; private readonly Symbol _optionSymbol = QuantConnect.Symbol.Create(UnderlyingTicker, SecurityType.Option, Market.USA); private bool _assignedOption = false; public override void Initialize() { SetStartDate(2015, 12, 24); SetEndDate(2015, 12, 28); SetCash(100000); var equity = AddEquity(UnderlyingTicker); var option = AddOption(UnderlyingTicker); // set our strike/expiry filter for this option chain option.SetFilter(u => u.IncludeWeeklys() .Strikes(-2, +2) .Expiration(TimeSpan.Zero, TimeSpan.FromDays(10))); // use the underlying equity as the benchmark SetBenchmark(equity.Symbol); } public override void OnEndOfAlgorithm() { if (!_assignedOption) { throw new RegressionTestException("In the end, short ITM option position was not assigned."); } } /// /// Event - v3.0 DATA EVENT HANDLER: (Pattern) Basic template for user to override for receiving all subscription data in a single event /// /// The current slice of data keyed by symbol string public override void OnData(Slice slice) { if (!Portfolio.Invested) { OptionChain chain; if (slice.OptionChains.TryGetValue(_optionSymbol, out chain)) { // find the second call strike under market price expiring today var contracts = ( from optionContract in chain.OrderByDescending(x => x.Strike) where optionContract.Right == OptionRight.Call where optionContract.Expiry == Time.Date where optionContract.Strike < chain.Underlying.Price select optionContract ).Take(2); if (contracts.Any()) { MarketOrder(contracts.FirstOrDefault().Symbol, 1); MarketOrder(contracts.Skip(1).FirstOrDefault().Symbol, -1); } } } } /// /// Order fill event handler. On an order fill update the resulting information is passed to this method. /// /// Order event details containing details of the events /// This method can be called asynchronously and so should only be used by seasoned C# experts. Ensure you use proper locks on thread-unsafe objects public override void OnOrderEvent(OrderEvent orderEvent) { Log(orderEvent.ToString()); } public override void OnAssignmentOrderEvent(OrderEvent assignmentEvent) { Log(assignmentEvent.ToString()); _assignedOption = true; } /// /// 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 => 26483; /// /// 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", "4"}, {"Average Win", "0.30%"}, {"Average Loss", "-0.32%"}, {"Compounding Annual Return", "-22.695%"}, {"Drawdown", "0.400%"}, {"Expectancy", "-1"}, {"Start Equity", "100000"}, {"End Equity", "99648"}, {"Net Profit", "-0.352%"}, {"Sharpe Ratio", "0"}, {"Sortino Ratio", "0"}, {"Probabilistic Sharpe Ratio", "0%"}, {"Loss Rate", "100%"}, {"Win Rate", "0%"}, {"Profit-Loss Ratio", "0.92"}, {"Alpha", "0"}, {"Beta", "0"}, {"Annual Standard Deviation", "0"}, {"Annual Variance", "0"}, {"Information Ratio", "0"}, {"Tracking Error", "0"}, {"Treynor Ratio", "0"}, {"Total Fees", "$2.00"}, {"Estimated Strategy Capacity", "$0"}, {"Lowest Capacity Asset", "GOOCV VP83T1ZUHROL"}, {"Portfolio Turnover", "30.10%"}, {"Drawdown Recovery", "0"}, {"OrderListHash", "c32a840b8e572bce151e319354df0723"} }; } }