/* * 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 NUnit.Framework; using Python.Runtime; using QuantConnect.Algorithm; using QuantConnect.Data.Market; using QuantConnect.Data.UniverseSelection; using QuantConnect.Securities; using QuantConnect.Statistics; using QuantConnect.Tests.Engine.DataFeeds; namespace QuantConnect.Tests.Python { [TestFixture] public class PythonOptionTests { [Test] public void PythonFilterFunctionReturnsList() { var algorithm = new QCAlgorithm(); algorithm.SubscriptionManager.SetDataManager(new DataManagerStub(algorithm)); var spyOption = algorithm.AddOption("SPY"); using (Py.GIL()) { //Filter function that returns a list of symbols var module = PyModule.FromString(Guid.NewGuid().ToString(), "def filter(universe):\n" + " universe = universe.WeeklysOnly().Expiration(0, 10)\n" + " return [symbol for symbol in universe\n"+ " if symbol.ID.OptionRight != OptionRight.Put\n" + " and universe.Underlying.Price - symbol.ID.StrikePrice < 10]\n" ); var filterFunction = module.GetAttr("filter"); Assert.DoesNotThrow(() => spyOption.SetFilter(filterFunction)); } } [Test] public void PythonFilterFunctionReturnsUniverse() { var algorithm = new QCAlgorithm(); algorithm.SubscriptionManager.SetDataManager(new DataManagerStub(algorithm)); var spyOption = algorithm.AddOption("SPY"); using (Py.GIL()) { //Filter function that returns a OptionFilterUniverse var module = PyModule.FromString(Guid.NewGuid().ToString(), "def filter(universe):\n" + " universe = universe.WeeklysOnly().Expiration(0, 5)\n" + " return universe" ); var filterFunction = module.GetAttr("filter"); Assert.DoesNotThrow(() => spyOption.SetFilter(filterFunction)); } } [Test] public void PythonFilterFunctionReturnsNone() { var algorithm = new QCAlgorithm(); algorithm.SubscriptionManager.SetDataManager(new DataManagerStub(algorithm)); var spyOption = algorithm.AddOption("SPY"); using (Py.GIL()) { //Filter function that modifies the universe in place and returns None: //the return value is only necessary for chaining var module = PyModule.FromString(Guid.NewGuid().ToString(), "def filter(universe):\n" + " universe.strikes(-20, 20).expiration(0, 10)\n" ); var filterFunction = module.GetAttr("filter"); spyOption.SetFilter(filterFunction); } var underlying = new Tick { Value = 10m, Time = new DateTime(2016, 12, 29) }; var symbols = new[] { // within the 0-10 days expiration window Symbol.CreateOption("SPY", Market.USA, OptionStyle.American, OptionRight.Call, 10, new DateTime(2017, 01, 04)), Symbol.CreateOption("SPY", Market.USA, OptionStyle.American, OptionRight.Put, 10, new DateTime(2017, 01, 06)), // beyond the 0-10 days expiration window Symbol.CreateOption("SPY", Market.USA, OptionStyle.American, OptionRight.Call, 10, new DateTime(2017, 01, 20)), }; var data = symbols.Select(x => new OptionUniverse() { Symbol = x }).ToList(); var filtered = spyOption.ContractFilter.Filter(new OptionFilterUniverse(spyOption, data, underlying)).ToList(); Assert.AreEqual(2, filtered.Count); Assert.AreEqual(symbols[0], filtered[0].Symbol); Assert.AreEqual(symbols[1], filtered[1].Symbol); } [Test] public void FilterReturnsUniverseRegression() { var parameter = new RegressionTests.AlgorithmStatisticsTestParameters("FilterUniverseRegressionAlgorithm", new Dictionary { {PerformanceMetrics.TotalOrders, "2"}, {"Average Win", "0%"}, {"Average Loss", "-0.02%"}, {"Compounding Annual Return", "-1.521%"}, {"Drawdown", "0.000%"}, {"Expectancy", "-1"}, {"End Equity", "99979"}, {"Net Profit", "-0.021%"}, {"Sharpe Ratio", "0"}, {"Probabilistic Sharpe Ratio", "0%"}, {"Loss Rate", "100%"}, {"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", "$1.00"}, {"OrderListHash", "22f0bc8a92f13dfa5d16c507824e2b68"} }, Language.Python, AlgorithmStatus.Completed); AlgorithmRunner.RunLocalBacktest(parameter.Algorithm, parameter.Statistics, parameter.Language, parameter.ExpectedFinalStatus); } } }