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.Interfaces;
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using QuantConnect.Orders;
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using QuantConnect.Securities;
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using QuantConnect.Statistics;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm asserting final trade statistics for options assignment
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///
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/// Expected win/loss rate statistics for the regression algorithm:
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/// Loss Rate 25%
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/// Win Rate 75%
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/// </summary>
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public class OptionAssignmentStatisticsRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Security _goog;
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private Security _googCall600;
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private Symbol _googCall600Symbol;
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private Security _googCall650;
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private Symbol _googCall650Symbol;
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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, 28);
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SetCash(100000);
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_goog = AddEquity("GOOG", Resolution.Minute);
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var contracts = OptionChain(_goog.Symbol).ToList();
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_googCall600Symbol = contracts
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.Where(c => c.ID.OptionRight == OptionRight.Call)
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.OrderBy(c => c.ID.Date)
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.First(c => c.ID.StrikePrice == 600m);
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_googCall600 = AddOptionContract(_googCall600Symbol);
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_googCall600["closed"] = false;
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_googCall650Symbol = contracts
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.Where(c => c.ID.OptionRight == OptionRight.Call)
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.OrderBy(c => c.ID.Date)
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.First(c => c.ID.StrikePrice == 650m);
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_googCall650 = AddOptionContract(_googCall650Symbol);
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_googCall650["closed"] = false;
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_googCall650["bought"] = false;
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}
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public override void OnData(Slice slice)
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{
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if (!Portfolio.Invested)
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{
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if (Time < _googCall600Symbol.ID.Date)
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{
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// This option assignment is expected to be a losing trade. The option is ITM but the premium paid is higher than the pay off
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MarketOrder(_googCall600Symbol, 1);
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}
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if (Time < _googCall650Symbol.ID.Date && !(bool)_googCall650["bought"])
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{
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// This option assignment is expected to be a winning trade
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LimitOrder(_googCall650Symbol, 1, 0.95m * _googCall650.Price);
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// This is to avoid placing another order for this option
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_googCall650["bought"] = true;
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}
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}
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else if (_goog.Invested && (bool)_googCall600["closed"] && (bool)_googCall650["closed"])
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{
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Liquidate(_goog.Symbol);
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}
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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if (orderEvent.Status == OrderStatus.Filled && orderEvent.Symbol.SecurityType.IsOption())
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{
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Securities[orderEvent.Symbol]["closed"] = 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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AssertTradeStatistics();
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AssertPortfolioStatistics();
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}
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private void AssertTradeStatistics()
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{
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var trades = TradeBuilder.ClosedTrades;
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if (trades.Count != 4)
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{
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throw new RegressionTestException($@"AssertTradeStatistics(): Expected 4 closed trades: 2 for the options, 2 for the underlying. Actual: {
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trades.Count}");
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}
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var statistics = new TradeStatistics(trades);
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if (statistics.TotalNumberOfTrades != 4)
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{
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throw new RegressionTestException($@"AssertTradeStatistics(): Expected 4 total trades: 2 for the options, 2 for the underlying. Actual: {
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statistics.TotalNumberOfTrades}");
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}
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if (statistics.NumberOfWinningTrades != 3)
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{
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throw new RegressionTestException($@"AssertTradeStatistics(): Expected 3 winning trades (the ITM 650 strike option and the underlying trades). Actual {
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statistics.NumberOfWinningTrades}");
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}
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if (statistics.NumberOfLosingTrades != 1)
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{
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throw new RegressionTestException($@"AssertTradeStatistics(): Expected 1 losing trade (the 600 strike option). Actual {
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statistics.NumberOfLosingTrades}");
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}
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if (statistics.WinRate != 0.75m)
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{
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throw new RegressionTestException($"AssertTradeStatistics(): Expected win rate to be 0.75. Actual {statistics.WinRate}");
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}
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if (statistics.LossRate != 0.25m)
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{
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throw new RegressionTestException($"AssertTradeStatistics(): Expected loss rate to be 0.25. Actual {statistics.LossRate}");
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}
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if (statistics.WinLossRatio != 3)
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{
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throw new RegressionTestException($"AssertTradeStatistics(): Expected win-loss ratio to be 3. Actual {statistics.WinLossRatio}");
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}
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// Let's assert the trades per symbol just to be sure
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// We expect the first option (600 strike) to be a losing trade
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var googCall600Trade = trades.Where(t => t.Symbol == _googCall600Symbol).FirstOrDefault();
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if (googCall600Trade == null)
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{
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throw new RegressionTestException("AssertTradeStatistics(): Expected a closed trade for the 600 strike option");
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}
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if (googCall600Trade.IsWin)
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{
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throw new RegressionTestException("AssertTradeStatistics(): Expected the 600 strike option to be a losing trade");
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}
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// We expect the second option (650 strike) to be a winning trade
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var googCall650Trade = trades.Where(t => t.Symbol == _googCall650Symbol).FirstOrDefault();
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if (googCall650Trade == null)
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{
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throw new RegressionTestException("AssertTradeStatistics(): Expected a closed trade for the 650 strike option");
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}
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if (!googCall650Trade.IsWin)
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{
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throw new RegressionTestException("AssertTradeStatistics(): Expected the 650 strike option to be a winning trade");
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}
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// We expect the both underlying trades to be winning trades
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var googTrades = trades.Where(t => t.Symbol == _goog.Symbol).ToList();
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if (googTrades.Count != 2)
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{
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throw new RegressionTestException(
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$@"AssertTradeStatistics(): Expected 2 closed trades for the underlying, one for each option assignment. Actual: {
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googTrades.Count}");
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}
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if (googTrades.Any(x => !x.IsWin || x.ProfitLoss < 0))
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{
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throw new RegressionTestException("AssertTradeStatistics(): Expected both underlying trades to be winning trades");
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}
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}
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private void AssertPortfolioStatistics()
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{
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// First, let's check the transactions, which are used to build the portfolio statistics
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// We expected 2 winning transactions (one of the options assignment and the underlying liquidation)
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// and 1 losing transaction (the other option assignment)
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if (Transactions.WinCount != 2)
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{
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throw new RegressionTestException($"AssertPortfolioStatistics(): Expected 2 winning transactions. Actual {Transactions.WinCount}");
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}
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if (Transactions.LossCount != 1)
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{
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throw new RegressionTestException($"AssertPortfolioStatistics(): Expected 1 losing transaction. Actual {Transactions.LossCount}");
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}
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var portfolioStatistics = Statistics.TotalPerformance.PortfolioStatistics;
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if (portfolioStatistics.WinRate != 2m / 3m)
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{
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throw new RegressionTestException($"AssertPortfolioStatistics(): Expected win rate to be 2/3. Actual {portfolioStatistics.WinRate}");
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}
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if (portfolioStatistics.LossRate != 1m / 3m)
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{
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throw new RegressionTestException($"AssertPortfolioStatistics(): Expected loss rate to be 1/3. Actual {portfolioStatistics.LossRate}");
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}
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var expectedAverageWinRate = 0.3425273813030554588544037705m;
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if (!AreEqual(expectedAverageWinRate, portfolioStatistics.AverageWinRate))
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{
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throw new RegressionTestException($@"AssertPortfolioStatistics(): Expected average win rate to be {expectedAverageWinRate}. Actual {
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portfolioStatistics.AverageWinRate}");
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}
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var expectedAverageLossRate = -0.13556638257576m;
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if (!AreEqual(expectedAverageLossRate, portfolioStatistics.AverageLossRate))
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{
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throw new RegressionTestException($@"AssertPortfolioStatistics(): Expected average loss rate to be {expectedAverageLossRate}. Actual {
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portfolioStatistics.AverageLossRate}");
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}
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var expectedProfitLossRatio = 2.5266395310920343630590960734m;
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if (!AreEqual(expectedProfitLossRatio, portfolioStatistics.ProfitLossRatio))
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{
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throw new RegressionTestException($@"AssertPortfolioStatistics(): Expected profit loss ratio to be {expectedProfitLossRatio}. Actual {
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portfolioStatistics.ProfitLossRatio}");
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}
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var totalNetProfit = 0.00267m;
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if (!AreEqual(totalNetProfit, portfolioStatistics.TotalNetProfit))
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{
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throw new RegressionTestException($@"AssertPortfolioStatistics(): Expected total net profit to be {totalNetProfit}. Actual {
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portfolioStatistics.TotalNetProfit}");
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}
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}
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private static bool AreEqual(decimal expected, decimal actual)
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{
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return Math.Abs(expected - actual) < 1e-12m;
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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 virtual 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 => 4359;
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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 => 1;
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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 virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "5"},
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{"Average Win", "34.25%"},
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{"Average Loss", "-13.56%"},
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{"Compounding Annual Return", "18.738%"},
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{"Drawdown", "1.400%"},
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{"Expectancy", "1.351"},
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{"Start Equity", "100000"},
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{"End Equity", "100267"},
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{"Net Profit", "0.267%"},
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{"Sharpe Ratio", "4.957"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "70.276%"},
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{"Loss Rate", "33%"},
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{"Win Rate", "67%"},
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{"Profit-Loss Ratio", "2.53"},
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{"Alpha", "0.024"},
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{"Beta", "-1.724"},
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{"Annual Standard Deviation", "0.072"},
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{"Annual Variance", "0.005"},
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{"Information Ratio", "6.8"},
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{"Tracking Error", "0.081"},
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{"Treynor Ratio", "-0.208"},
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{"Total Fees", "$3.00"},
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{"Estimated Strategy Capacity", "$10000000.00"},
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{"Lowest Capacity Asset", "GOOCV W6NBKMB4N492|GOOCV VP83T1ZUHROL"},
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{"Portfolio Turnover", "50.23%"},
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{"Drawdown Recovery", "5"},
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{"OrderListHash", "9d48744bc10d9c00aeba0f7a11dbbee0"}
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};
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}
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}
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