/*
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*/
using System;
using System.Collections.Generic;
using System.Linq;
using QuantConnect.Data;
using QuantConnect.Data.Market;
using QuantConnect.Orders;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
///
/// This regression algorithm tests option exercise and assignment functionality
/// We open two positions and go with them into expiration. We expect to see our long position exercised and short position assigned.
///
///
///
public class OptionExerciseAssignRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private const string UnderlyingTicker = "GOOG";
private readonly Symbol _optionSymbol = QuantConnect.Symbol.Create(UnderlyingTicker, SecurityType.Option, Market.USA);
private bool _assignedOption = false;
public override void Initialize()
{
SetStartDate(2015, 12, 24);
SetEndDate(2015, 12, 28);
SetCash(100000);
var equity = AddEquity(UnderlyingTicker);
var option = AddOption(UnderlyingTicker);
// set our strike/expiry filter for this option chain
option.SetFilter(u => u.IncludeWeeklys()
.Strikes(-2, +2)
.Expiration(TimeSpan.Zero, TimeSpan.FromDays(10)));
// use the underlying equity as the benchmark
SetBenchmark(equity.Symbol);
}
public override void OnEndOfAlgorithm()
{
if (!_assignedOption)
{
throw new RegressionTestException("In the end, short ITM option position was not assigned.");
}
}
///
/// Event - v3.0 DATA EVENT HANDLER: (Pattern) Basic template for user to override for receiving all subscription data in a single event
///
/// The current slice of data keyed by symbol string
public override void OnData(Slice slice)
{
if (!Portfolio.Invested)
{
OptionChain chain;
if (slice.OptionChains.TryGetValue(_optionSymbol, out chain))
{
// find the second call strike under market price expiring today
var contracts = (
from optionContract in chain.OrderByDescending(x => x.Strike)
where optionContract.Right == OptionRight.Call
where optionContract.Expiry == Time.Date
where optionContract.Strike < chain.Underlying.Price
select optionContract
).Take(2);
if (contracts.Any())
{
MarketOrder(contracts.FirstOrDefault().Symbol, 1);
MarketOrder(contracts.Skip(1).FirstOrDefault().Symbol, -1);
}
}
}
}
///
/// Order fill event handler. On an order fill update the resulting information is passed to this method.
///
/// Order event details containing details of the events
/// This method can be called asynchronously and so should only be used by seasoned C# experts. Ensure you use proper locks on thread-unsafe objects
public override void OnOrderEvent(OrderEvent orderEvent)
{
Log(orderEvent.ToString());
}
public override void OnAssignmentOrderEvent(OrderEvent assignmentEvent)
{
Log(assignmentEvent.ToString());
_assignedOption = true;
}
///
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
///
public bool CanRunLocally { get; } = true;
///
/// This is used by the regression test system to indicate which languages this algorithm is written in.
///
public List Languages { get; } = new() { Language.CSharp, Language.Python };
///
/// Data Points count of all timeslices of algorithm
///
public long DataPoints => 26483;
///
/// Data Points count of the algorithm history
///
public int AlgorithmHistoryDataPoints => 0;
///
/// Final status of the algorithm
///
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
///
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
///
public Dictionary ExpectedStatistics => new Dictionary
{
{"Total Orders", "4"},
{"Average Win", "0.30%"},
{"Average Loss", "-0.32%"},
{"Compounding Annual Return", "-22.695%"},
{"Drawdown", "0.400%"},
{"Expectancy", "-1"},
{"Start Equity", "100000"},
{"End Equity", "99648"},
{"Net Profit", "-0.352%"},
{"Sharpe Ratio", "0"},
{"Sortino Ratio", "0"},
{"Probabilistic Sharpe Ratio", "0%"},
{"Loss Rate", "100%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0.92"},
{"Alpha", "0"},
{"Beta", "0"},
{"Annual Standard Deviation", "0"},
{"Annual Variance", "0"},
{"Information Ratio", "0"},
{"Tracking Error", "0"},
{"Treynor Ratio", "0"},
{"Total Fees", "$2.00"},
{"Estimated Strategy Capacity", "$0"},
{"Lowest Capacity Asset", "GOOCV VP83T1ZUHROL"},
{"Portfolio Turnover", "30.10%"},
{"Drawdown Recovery", "0"},
{"OrderListHash", "c32a840b8e572bce151e319354df0723"}
};
}
}