155 lines
6.3 KiB
C#
155 lines
6.3 KiB
C#
/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using NUnit.Framework;
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using Python.Runtime;
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using QuantConnect.Algorithm;
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using QuantConnect.Data.Market;
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using QuantConnect.Data.UniverseSelection;
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using QuantConnect.Securities;
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using QuantConnect.Statistics;
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using QuantConnect.Tests.Engine.DataFeeds;
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namespace QuantConnect.Tests.Python
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{
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[TestFixture]
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public class PythonOptionTests
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{
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[Test]
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public void PythonFilterFunctionReturnsList()
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{
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var algorithm = new QCAlgorithm();
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algorithm.SubscriptionManager.SetDataManager(new DataManagerStub(algorithm));
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var spyOption = algorithm.AddOption("SPY");
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using (Py.GIL())
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{
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//Filter function that returns a list of symbols
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var module = PyModule.FromString(Guid.NewGuid().ToString(),
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"def filter(universe):\n" +
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" universe = universe.WeeklysOnly().Expiration(0, 10)\n" +
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" return [symbol for symbol in universe\n"+
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" if symbol.ID.OptionRight != OptionRight.Put\n" +
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" and universe.Underlying.Price - symbol.ID.StrikePrice < 10]\n"
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);
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var filterFunction = module.GetAttr("filter");
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Assert.DoesNotThrow(() => spyOption.SetFilter(filterFunction));
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}
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}
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[Test]
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public void PythonFilterFunctionReturnsUniverse()
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{
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var algorithm = new QCAlgorithm();
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algorithm.SubscriptionManager.SetDataManager(new DataManagerStub(algorithm));
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var spyOption = algorithm.AddOption("SPY");
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using (Py.GIL())
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{
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//Filter function that returns a OptionFilterUniverse
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var module = PyModule.FromString(Guid.NewGuid().ToString(),
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"def filter(universe):\n" +
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" universe = universe.WeeklysOnly().Expiration(0, 5)\n" +
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" return universe"
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);
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var filterFunction = module.GetAttr("filter");
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Assert.DoesNotThrow(() => spyOption.SetFilter(filterFunction));
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}
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}
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[Test]
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public void PythonFilterFunctionReturnsNone()
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{
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var algorithm = new QCAlgorithm();
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algorithm.SubscriptionManager.SetDataManager(new DataManagerStub(algorithm));
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var spyOption = algorithm.AddOption("SPY");
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using (Py.GIL())
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{
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//Filter function that modifies the universe in place and returns None:
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//the return value is only necessary for chaining
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var module = PyModule.FromString(Guid.NewGuid().ToString(),
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"def filter(universe):\n" +
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" universe.strikes(-20, 20).expiration(0, 10)\n"
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);
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var filterFunction = module.GetAttr("filter");
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spyOption.SetFilter(filterFunction);
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}
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var underlying = new Tick { Value = 10m, Time = new DateTime(2016, 12, 29) };
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var symbols = new[]
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{
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// within the 0-10 days expiration window
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Symbol.CreateOption("SPY", Market.USA, OptionStyle.American, OptionRight.Call, 10, new DateTime(2017, 01, 04)),
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Symbol.CreateOption("SPY", Market.USA, OptionStyle.American, OptionRight.Put, 10, new DateTime(2017, 01, 06)),
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// beyond the 0-10 days expiration window
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Symbol.CreateOption("SPY", Market.USA, OptionStyle.American, OptionRight.Call, 10, new DateTime(2017, 01, 20)),
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};
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var data = symbols.Select(x => new OptionUniverse() { Symbol = x }).ToList();
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var filtered = spyOption.ContractFilter.Filter(new OptionFilterUniverse(spyOption, data, underlying)).ToList();
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Assert.AreEqual(2, filtered.Count);
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Assert.AreEqual(symbols[0], filtered[0].Symbol);
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Assert.AreEqual(symbols[1], filtered[1].Symbol);
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}
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[Test]
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public void FilterReturnsUniverseRegression()
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{
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var parameter = new RegressionTests.AlgorithmStatisticsTestParameters("FilterUniverseRegressionAlgorithm",
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new Dictionary<string, string> {
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{PerformanceMetrics.TotalOrders, "2"},
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{"Average Win", "0%"},
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{"Average Loss", "-0.02%"},
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{"Compounding Annual Return", "-1.521%"},
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{"Drawdown", "0.000%"},
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{"Expectancy", "-1"},
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{"End Equity", "99979"},
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{"Net Profit", "-0.021%"},
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{"Sharpe Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "0%"},
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{"Loss Rate", "100%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0"},
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{"Beta", "0"},
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{"Annual Standard Deviation", "0"},
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{"Annual Variance", "0"},
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{"Information Ratio", "0"},
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{"Tracking Error", "0"},
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{"Treynor Ratio", "0"},
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{"Total Fees", "$1.00"},
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{"OrderListHash", "22f0bc8a92f13dfa5d16c507824e2b68"}
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},
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Language.Python,
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AlgorithmStatus.Completed);
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AlgorithmRunner.RunLocalBacktest(parameter.Algorithm,
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parameter.Statistics,
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parameter.Language,
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parameter.ExpectedFinalStatus);
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}
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}
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}
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