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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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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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Regression algorithm asserting that a short option position is auto exercised even when there is insufficient margin,
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/// but triggering a margin call for the underlying stock to cover the assignment.
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/// </summary>
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public class InsufficientBuyingPowerForAutomaticExerciseRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _stock;
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private Symbol _option;
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private bool _stockBought;
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private bool _optionSold;
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private bool _optionAssigned;
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private bool _marginCallReceived;
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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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_stock = AddEquity("GOOG").Symbol;
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var contracts = OptionChain(_stock).ToList();
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_option = contracts
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.Where(c => c.ID.OptionRight == OptionRight.Put)
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.OrderBy(c => c.ID.Date)
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.First(c => c.ID.StrikePrice == 800m);
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AddOptionContract(_option);
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}
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public override void OnData(Slice slice)
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{
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// We are done with buying
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if (_stockBought && _optionSold)
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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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// We'll use all our buying power to buy the stock, so when we then open a short put position,
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// the margin will not be enough to cover the automatic exercise
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SetHoldings(_stock, 1);
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}
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if (_stockBought && Securities[_option].Price != 0)
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{
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MarketOrder(_option, -2);
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}
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}
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public override void OnMarginCall(List<SubmitOrderRequest> requests)
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{
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if (!_optionAssigned)
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{
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throw new RegressionTestException("Expected option to have been assigned before the margin call " +
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"(which should have been triggered by the auto-exercise of the option with inssuficient margin).");
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}
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if (_marginCallReceived)
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{
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throw new RegressionTestException("Received multiple margin calls. Expected just one.");
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}
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var request = requests.Single();
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if (request.Symbol != _stock)
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{
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throw new RegressionTestException("Expected margin call for the stock, but got margin call for: " + request.Symbol);
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}
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_marginCallReceived = true;
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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var order = Transactions.GetOrderById(orderEvent.OrderId);
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Debug($"{Time} :: {order.Id} - {order.Type} - {orderEvent.Symbol}: {orderEvent.Status} - {orderEvent.Quantity} shares at {orderEvent.FillPrice}");
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if (orderEvent.Status == OrderStatus.Filled)
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{
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if (orderEvent.Symbol == _stock)
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{
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_stockBought = true;
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}
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else if (orderEvent.Symbol == _option)
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{
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if (order.Type == OrderType.Market)
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{
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if (!_stockBought)
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{
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throw new RegressionTestException("Stock should have been bought first");
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}
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_optionSold = true;
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}
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else if (order.Type == OrderType.OptionExercise && orderEvent.IsAssignment)
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{
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if (!_optionSold)
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{
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throw new RegressionTestException("Option should have been sold first");
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}
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_optionAssigned = true;
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}
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}
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else
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{
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throw new RegressionTestException("Unexpected symbol: " + orderEvent.Symbol);
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}
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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 (!_stockBought)
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{
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throw new RegressionTestException("Stock was not bought");
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}
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if (!_optionSold)
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{
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throw new RegressionTestException("Option was not sold");
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}
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if (!_optionAssigned)
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{
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throw new RegressionTestException("Option was not assigned");
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}
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if (!_marginCallReceived)
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{
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throw new RegressionTestException("Margin call was not received");
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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 => 2822;
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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 Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Orders", "4"},
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{"Average Win", "8.96%"},
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{"Average Loss", "-1.95%"},
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{"Compounding Annual Return", "248.965%"},
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{"Drawdown", "2.900%"},
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{"Expectancy", "-1"},
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{"Start Equity", "100000"},
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{"End Equity", "101959.28"},
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{"Net Profit", "1.959%"},
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{"Sharpe Ratio", "14.873"},
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{"Sortino Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "85.516%"},
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{"Loss Rate", "100%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "4.60"},
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{"Alpha", "2.459"},
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{"Beta", "-8.745"},
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{"Annual Standard Deviation", "0.279"},
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{"Annual Variance", "0.078"},
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{"Information Ratio", "15.029"},
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{"Tracking Error", "0.289"},
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{"Treynor Ratio", "-0.475"},
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{"Total Fees", "$3.30"},
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{"Estimated Strategy Capacity", "$2400000.00"},
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{"Lowest Capacity Asset", "GOOCV 305RBQ20WLZZA|GOOCV VP83T1ZUHROL"},
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{"Portfolio Turnover", "54.01%"},
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{"Drawdown Recovery", "5"},
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{"OrderListHash", "2f22fc5e9584c2d201b9e0b767a7160d"}
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};
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}
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}
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