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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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm asserting that we can liquidate an existing option position with an option strategy.
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///
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/// This specific case rolls out a front month put to a back month put using a calendar spread, working in two steps:
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/// 1. Short front month put
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/// 2. Roll out front month put to back month put using a calendar spread.
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/// </summary>
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public class RollOutFrontMonthToBackMonthOptionUsingCalendarSpreadRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _symbol;
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private Symbol _frontMonthPutSymbol;
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private Symbol _backMonthPutSymbol;
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private decimal _atmStrike;
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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, 24);
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SetEndDate(2015, 12, 24);
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SetCash(500000);
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var option = AddOption("GOOG", Resolution.Minute);
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option.SetFilter(universe => universe.StandardsOnly().Strikes(-1, 1).Expiration(0, 62));
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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) || !chain.Any())
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{
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return;
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}
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var isFirstStep = !Portfolio.Invested;
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if (isFirstStep)
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{
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_atmStrike = chain.MinBy(x => Math.Abs(x.Strike - chain.Underlying.Price)).Strike;
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}
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var puts = chain.Where(x => x.Strike == _atmStrike && x.Right == OptionRight.Put).ToList();
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if (isFirstStep)
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{
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if (puts.Count == 0)
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{
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return;
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}
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// Step 1: short front month put
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_frontMonthPutSymbol = puts.MinBy(x => x.Expiry).Symbol;
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Sell(_frontMonthPutSymbol, 1);
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}
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else if (puts.Count > 1)
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{
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// Step 2: roll out front month put to back month put using a calendar spread.
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// Near expiry contract would be the same we shorted in step 1 (closets expiry, same strike),
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// which we want to roll out to the farther expiry
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var frontMonthExpiry = puts[0].Expiry;
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var backMonthExpiry = puts[puts.Count - 1].Expiry;
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var optionStrategy = OptionStrategies.PutCalendarSpread(_symbol, _atmStrike, frontMonthExpiry, backMonthExpiry);
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var tickets = Sell(optionStrategy, 1);
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if (!tickets.Any(ticket => ticket.Symbol == _frontMonthPutSymbol && ticket.Quantity == 1))
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{
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throw new RegressionTestException($"Expected to find a ticket for {_frontMonthPutSymbol} with quantity {-Securities[_frontMonthPutSymbol].Holdings.Quantity}");
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}
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_backMonthPutSymbol = tickets.First(ticket => ticket.Symbol != _frontMonthPutSymbol).Symbol;
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_done = true;
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}
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}
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public override void OnEndOfAlgorithm()
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{
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if (!_done)
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{
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throw new RegressionTestException("Expected the algorithm to have bought and sold a Bull Call Spread and a Bear Put Spread.");
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}
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if (Portfolio.Positions.Groups.Count != 1)
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{
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throw new RegressionTestException($"Expected 1 position group, found {Portfolio.Positions.Groups.Count}");
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}
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var positions = Portfolio.Positions.Groups.Single().Positions.ToList();
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if (positions.Count != 1)
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{
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throw new RegressionTestException($"Expected 1 position in the position group, found {positions.Count}");
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}
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// The position should correspond to the far expiry contract
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var position = positions[0];
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if (position.Symbol != _backMonthPutSymbol)
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{
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throw new RegressionTestException($"Expected final portfolio position to be {_backMonthPutSymbol}, found {position.Symbol}");
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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 => 8151;
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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", "3"},
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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", "499792"},
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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", "$3.00"},
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{"Estimated Strategy Capacity", "$190000.00"},
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{"Lowest Capacity Asset", "GOOCV 306CZK4DP4VNQ|GOOCV VP83T1ZUHROL"},
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{"Portfolio Turnover", "1.19%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "860bacced1208f152cfc0aad369a111e"}
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};
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}
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}
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