FutureOptionCallITMGreeksExpiryRegressionAlgorithm.cs

/*
 * 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 QuantConnect.Securities;
using System.Collections.Generic;
using QuantConnect.Securities.Option;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// This regression algorithm tests In The Money (ITM) future option expiry for calls.
    /// We test to make sure that FOPs have greeks enabled, same as equity options.
    /// </summary>
    public class FutureOptionCallITMGreeksExpiryRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        private bool _invested;
        private int _onDataCalls;
        private Security _es19m20;
        private Option _esOption;
        private Symbol _expectedOptionContract;

        public override void Initialize()
        {
            SetStartDate(2020, 1, 5);
            SetEndDate(2020, 6, 30);

            _es19m20 = AddFutureContract(
                QuantConnect.Symbol.CreateFuture(
                    Futures.Indices.SP500EMini,
                    Market.CME,
                    new DateTime(2020, 6, 19)),
                Resolution.Minute);

            // We must set the volatility model on the underlying, since the defaults are
            // too strict to calculate greeks with when we only have data for a single day
            _es19m20.VolatilityModel = new StandardDeviationOfReturnsVolatilityModel(
                60,
                Resolution.Minute,
                TimeSpan.FromMinutes(1));

            // Select a future option expiring ITM, and adds it to the algorithm.
            _esOption = AddFutureOptionContract(OptionChain(_es19m20.Symbol)
                .Where(x => x.ID.StrikePrice <= 3200m && x.ID.OptionRight == OptionRight.Call)
                .OrderByDescending(x => x.ID.StrikePrice)
                .Take(1)
                .Single(), Resolution.Minute);

            _esOption.PriceModel = OptionPriceModels.QuantLib.BjerksundStensland();

            _expectedOptionContract = QuantConnect.Symbol.CreateOption(_es19m20.Symbol, Market.CME, OptionStyle.American, OptionRight.Call, 3200m, new DateTime(2020, 6, 19));
            if (_esOption.Symbol != _expectedOptionContract)
            {
                throw new RegressionTestException($"Contract {_expectedOptionContract} was not found in the chain");
            }
        }

        public override void OnData(Slice slice)
        {
            // Let the algo warmup, but without using SetWarmup. Otherwise, we get
            // no contracts in the option chain
            if (_invested || _onDataCalls++ < 40)
            {
                return;
            }

            if (slice.OptionChains.Count == 0)
            {
                return;
            }
            if (slice.OptionChains.Values.All(o => o.Contracts.Values.Any(c => !slice.ContainsKey(c.Symbol))))
            {
                return;
            }
            if (slice.OptionChains.Values.First().Contracts.Count == 0)
            {
                throw new RegressionTestException($"No contracts found in the option {slice.OptionChains.Keys.First()}");
            }

            var deltas = slice.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Delta).ToList();
            var gammas = slice.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Gamma).ToList();
            var lambda = slice.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Lambda).ToList();
            var rho = slice.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Rho).ToList();
            var theta = slice.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Theta).ToList();
            var vega = slice.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Vega).ToList();

            // The commented out test cases all return zero.
            // This is because of failure to evaluate the greeks in the option pricing model.
            // For now, let's skip those.
            if (deltas.Any(d => d == 0))
            {
                throw new AggregateException("Option contract Delta was equal to zero");
            }
            if (gammas.Any(g => g == 0))
            {
                throw new AggregateException("Option contract Gamma was equal to zero");
            }
            if (lambda.Any(l => l == 0))
            {
                throw new AggregateException("Option contract Lambda was equal to zero");
            }
            if (rho.Any(r => r == 0))
            {
                throw new AggregateException("Option contract Rho was equal to zero");
            }
            if (theta.Any(t => t == 0))
            {
                throw new AggregateException("Option contract Theta was equal to zero");
            }
            if (vega.Any(v => v == 0))
            {
                throw new AggregateException("Option contract Vega was equal to zero");
            }

            if (!_invested)
            {
                // the margin requirement for the FOPs is less than the one of the underlying so we can't allocate all our buying power
                // into FOPs else we won't be able to exercise
                SetHoldings(slice.OptionChains.Values.First().Contracts.Values.First().Symbol, 0.25);
                _invested = true;
            }
        }

        /// <summary>
        /// Ran at the end of the algorithm to ensure the algorithm has no holdings
        /// </summary>
        /// <exception cref="RegressionTestException">The algorithm has holdings</exception>
        public override void OnEndOfAlgorithm()
        {
            if (Portfolio.Invested)
            {
                throw new RegressionTestException($"Expected no holdings at end of algorithm, but are invested in: {string.Join(", ", Portfolio.Keys)}");
            }
            if (!_invested)
            {
                throw new RegressionTestException($"Never checked greeks, maybe we have no option data?");
            }
        }

        /// <summary>
        /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
        /// </summary>
        public bool CanRunLocally { get; } = true;

        /// <summary>
        /// This is used by the regression test system to indicate which languages this algorithm is written in.
        /// </summary>
        public List<Language> Languages { get; } = new() { Language.CSharp };

        /// <summary>
        /// Data Points count of all timeslices of algorithm
        /// </summary>
        public long DataPoints => 212198;

        /// <summary>
        /// Data Points count of the algorithm history
        /// </summary>
        public int AlgorithmHistoryDataPoints => 1;

        /// <summary>
        /// Final status of the algorithm
        /// </summary>
        public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;

        /// <summary>
        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
        /// </summary>
        public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
        {
            {"Total Orders", "3"},
            {"Average Win", "16.44%"},
            {"Average Loss", "-35.38%"},
            {"Compounding Annual Return", "-44.262%"},
            {"Drawdown", "26.800%"},
            {"Expectancy", "-0.268"},
            {"Start Equity", "100000"},
            {"End Equity", "75242.9"},
            {"Net Profit", "-24.757%"},
            {"Sharpe Ratio", "-0.965"},
            {"Sortino Ratio", "0"},
            {"Probabilistic Sharpe Ratio", "0.060%"},
            {"Loss Rate", "50%"},
            {"Win Rate", "50%"},
            {"Profit-Loss Ratio", "0.46"},
            {"Alpha", "-0.303"},
            {"Beta", "0.016"},
            {"Annual Standard Deviation", "0.313"},
            {"Annual Variance", "0.098"},
            {"Information Ratio", "-0.649"},
            {"Tracking Error", "0.483"},
            {"Treynor Ratio", "-18.59"},
            {"Total Fees", "$7.10"},
            {"Estimated Strategy Capacity", "$24000000.00"},
            {"Lowest Capacity Asset", "ES XFH59UPBMTJ8|ES XFH59UK0MYO1"},
            {"Portfolio Turnover", "12.22%"},
            {"Drawdown Recovery", "0"},
            {"OrderListHash", "1d4a9403cd69b8510f15d100acdffa26"}
        };
    }
}