chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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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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*/
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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 QuantConnect.Data;
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using QuantConnect.Util;
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using QuantConnect.Interfaces;
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using QuantConnect.Securities.Option;
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using QuantConnect.Securities.Positions;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm asserting that we can open a position on two option strategies for the same underlying and then liquidate both of them.
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/// This reproduces GH issue #7205.
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///
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/// The algorithm works in two steps:
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/// 1. Buy a bull call and a bear put spread.
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/// 2. Liquidate both spreads bough in step 1.
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/// - The issue was on this step, the algorithm failed with the following error when attempting to liquidate the first spread:
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/// Unable to create group for orders: [5,6]
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/// </summary>
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public class LiquidatingMultipleOptionStrategiesRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _symbol;
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OptionStrategy _bullCallSpread;
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OptionStrategy _bearPutSpread;
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private bool _done;
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public override void Initialize()
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{
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SetStartDate(2015, 12, 23);
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SetEndDate(2015, 12, 25);
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SetCash(500000);
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var option = AddOption("GOOG");
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option.SetFilter(universe => universe.StandardsOnly().Strikes(-3, 3).Expiration(0, 180));
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_symbol = option.Symbol;
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}
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public override void OnData(Slice slice)
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{
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if (_done || !slice.OptionChains.TryGetValue(_symbol, out var chain))
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{
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return;
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}
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var calls = chain
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.Where(x => x.Right == OptionRight.Call)
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.GroupBy(x => x.Expiry)
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.FirstOrDefault(x => x.Count() > 2)
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?.OrderBy(x => x.Strike)
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?.ToList();
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var puts = chain
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.Where(x => x.Right == OptionRight.Put)
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.GroupBy(x => x.Expiry)
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.FirstOrDefault(x => x.Count() > 2)
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?.OrderByDescending(x => x.Strike)
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?.ToList();
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if (calls == null || puts == null)
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{
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return;
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}
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if (!Portfolio.Invested)
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{
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// Step 1: buy spreads
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_bullCallSpread = OptionStrategies.BullCallSpread(_symbol, calls[0].Strike, calls[1].Strike, calls[0].Expiry);
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Buy(_bullCallSpread, 1);
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_bearPutSpread = OptionStrategies.BearPutSpread(_symbol, puts[0].Strike, puts[1].Strike, puts[0].Expiry);
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Buy(_bearPutSpread, 1);
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}
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else
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{
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// Let's check that we have the right position groups, just to make sure we are good.
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var positionGroups = Portfolio.Positions.Groups;
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if (positionGroups.Count != 2)
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{
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throw new RegressionTestException($"Expected 2 position groups, one for each spread, but found {positionGroups.Count}");
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}
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var positionGroupMatchesSpreadStrategy = (IPositionGroup positionGroup, OptionStrategy strategy) =>
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{
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return strategy.OptionLegs.All(leg =>
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{
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var legSymbol = QuantConnect.Symbol.CreateOption(strategy.Underlying, strategy.CanonicalOption?.ID?.Symbol,
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strategy.Underlying.ID.Market, _symbol.ID.OptionStyle, leg.Right, leg.Strike, leg.Expiration);
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return positionGroup.Positions.Any(position => position.Symbol == legSymbol);
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});
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};
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if (!positionGroups.All(group =>
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positionGroupMatchesSpreadStrategy(group, _bullCallSpread) || positionGroupMatchesSpreadStrategy(group, _bearPutSpread)))
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{
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throw new RegressionTestException("Expected both spreads to have a matching position group in the portfolio.");
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}
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// Step 2: liquidate spreads
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Sell(_bullCallSpread, 1);
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Sell(_bearPutSpread, 1);
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_done = true;
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}
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}
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public override void OnEndOfAlgorithm()
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{
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if (!_done)
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{
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throw new RegressionTestException("Expected the algorithm to have bought and sold a Bull Call Spread and a Bear Put Spread.");
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}
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if (Portfolio.Invested)
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{
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throw new RegressionTestException("The spreads should have been liquidated by the end of the algorithm");
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}
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}
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/// <summary>
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/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
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/// </summary>
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public bool CanRunLocally { get; } = true;
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/// <summary>
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/// This is used by the regression test system to indicate which languages this algorithm is written in.
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/// </summary>
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public List<Language> Languages { get; } = new() { Language.CSharp };
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/// <summary>
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/// Data Points count of all timeslices of algorithm
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/// </summary>
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public long DataPoints => 20263;
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/// <summary>
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/// Data Points count of the algorithm history
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/// </summary>
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public int AlgorithmHistoryDataPoints => 0;
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/// <summary>
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/// Final status of the algorithm
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/// </summary>
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public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
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/// <summary>
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/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
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/// </summary>
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public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "8"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "0%"},
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{"Drawdown", "0%"},
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{"Expectancy", "0"},
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{"Start Equity", "500000"},
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{"End Equity", "499592"},
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{"Net Profit", "0%"},
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{"Sharpe Ratio", "0"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "0%"},
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{"Loss Rate", "0%"},
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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", "$8.00"},
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{"Estimated Strategy Capacity", "$13000.00"},
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{"Lowest Capacity Asset", "GOOCV W78ZERHAT67A|GOOCV VP83T1ZUHROL"},
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{"Portfolio Turnover", "1.31%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "7e6fb74d29704118659d2fcc59b6cd78"}
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};
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}
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}
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