/*
* 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.Linq;
using QuantConnect.Data;
using QuantConnect.Interfaces;
using System.Collections.Generic;
namespace QuantConnect.Algorithm.CSharp
{
///
/// We add an option contract using and place a trade and wait till it expires
/// later will liquidate the resulting equity position and assert both option and underlying get removed
///
public class AddOptionContractExpiresRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private DateTime _expiration = new DateTime(2014, 06, 21);
private Symbol _option;
private Symbol _twx;
private bool _traded;
public override void Initialize()
{
SetStartDate(2014, 06, 05);
SetEndDate(2014, 06, 30);
_twx = QuantConnect.Symbol.Create("TWX", SecurityType.Equity, Market.USA);
AddUniverse("my-daily-universe-name", time => new List { "AAPL" });
}
public override void OnData(Slice slice)
{
if (_option == null)
{
var option = OptionChain(_twx)
.OrderBy(x => x.ID.Symbol)
.FirstOrDefault(optionContract => optionContract.ID.Date == _expiration
&& optionContract.ID.OptionRight == OptionRight.Call
&& optionContract.ID.OptionStyle == OptionStyle.American);
if (option != null)
{
_option = AddOptionContract(option).Symbol;
}
}
if (_option != null && Securities[_option].Price != 0 && !_traded)
{
_traded = true;
Buy(_option, 1);
foreach (var symbol in new [] { _option, _option.Underlying })
{
var config = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(symbol).ToList();
if (!config.Any())
{
throw new RegressionTestException($"Was expecting configurations for {symbol}");
}
if (config.Any(dataConfig => dataConfig.DataNormalizationMode != DataNormalizationMode.Raw))
{
throw new RegressionTestException($"Was expecting DataNormalizationMode.Raw configurations for {symbol}");
}
}
}
if (Time.Date > _expiration)
{
if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_option).Any())
{
throw new RegressionTestException($"Unexpected configurations for {_option} after it has been delisted");
}
if (Securities[_twx].Invested)
{
if (!SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_twx).Any())
{
throw new RegressionTestException($"Was expecting configurations for {_twx}");
}
// first we liquidate the option exercised position
Liquidate(_twx);
}
}
else if (Time.Date > _expiration && !Securities[_twx].Invested)
{
if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(_twx).Any())
{
throw new RegressionTestException($"Unexpected configurations for {_twx} after it has been liquidated");
}
}
}
///
/// 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 => 37597;
///
/// Data Points count of the algorithm history
///
public int AlgorithmHistoryDataPoints => 1;
///
/// 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", "3"},
{"Average Win", "2.73%"},
{"Average Loss", "-2.98%"},
{"Compounding Annual Return", "-4.619%"},
{"Drawdown", "0.300%"},
{"Expectancy", "-0.042"},
{"Start Equity", "100000"},
{"End Equity", "99668"},
{"Net Profit", "-0.332%"},
{"Sharpe Ratio", "-4.614"},
{"Sortino Ratio", "0"},
{"Probabilistic Sharpe Ratio", "0.427%"},
{"Loss Rate", "50%"},
{"Win Rate", "50%"},
{"Profit-Loss Ratio", "0.92"},
{"Alpha", "-0.022"},
{"Beta", "-0.012"},
{"Annual Standard Deviation", "0.005"},
{"Annual Variance", "0"},
{"Information Ratio", "-2.823"},
{"Tracking Error", "0.049"},
{"Treynor Ratio", "2.01"},
{"Total Fees", "$2.00"},
{"Estimated Strategy Capacity", "$5700000.00"},
{"Lowest Capacity Asset", "AOL VRKS95ENLBYE|AOL R735QTJ8XC9X"},
{"Portfolio Turnover", "0.55%"},
{"Drawdown Recovery", "0"},
{"OrderListHash", "fc5ab25181a01ca5ce39212f60eb0ecd"}
};
}
}