CustomOptionAssignmentRegressionAlgorithm.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.Collections.Generic;
using QuantConnect.Securities.Option;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Regression algorithm asserting we can specify a custom option assignment
    /// </summary>
    public class CustomOptionAssignmentRegressionAlgorithm : OptionAssignmentRegressionAlgorithm
    {
        public override void Initialize()
        {
            SetSecurityInitializer((security) =>
            {
                var option = security as Option;
                // we have to be 10% in the money to get assigned
                option?.SetOptionAssignmentModel(new CustomOptionAssignmentModel(0.1m));
            });

            base.Initialize();
        }

        private class CustomOptionAssignmentModel : DefaultOptionAssignmentModel
        {
            public CustomOptionAssignmentModel(decimal requiredInTheMoneyPercent) : base (requiredInTheMoneyPercent)
            {
            }
            public override OptionAssignmentResult GetAssignment(OptionAssignmentParameters parameters)
            {
                var result = base.GetAssignment(parameters);
                result.Tag = "Custom Option Assignment";
                return result;
            }
        }

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

        /// <summary>
        /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
        /// </summary>
        public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
        {
            {"Total Orders", "4"},
            {"Average Win", "9.48%"},
            {"Average Loss", "-16.73%"},
            {"Compounding Annual Return", "-25.790%"},
            {"Drawdown", "0.500%"},
            {"Expectancy", "-0.478"},
            {"Start Equity", "100000"},
            {"End Equity", "99538"},
            {"Net Profit", "-0.462%"},
            {"Sharpe Ratio", "3.755"},
            {"Sortino Ratio", "0"},
            {"Probabilistic Sharpe Ratio", "95.713%"},
            {"Loss Rate", "67%"},
            {"Win Rate", "33%"},
            {"Profit-Loss Ratio", "0.57"},
            {"Alpha", "-0.008"},
            {"Beta", "-0.096"},
            {"Annual Standard Deviation", "0.003"},
            {"Annual Variance", "0"},
            {"Information Ratio", "10.577"},
            {"Tracking Error", "0.019"},
            {"Treynor Ratio", "-0.115"},
            {"Total Fees", "$2.00"},
            {"Estimated Strategy Capacity", "$4800000.00"},
            {"Lowest Capacity Asset", "GOOCV 305RBQ20WLZZA|GOOCV VP83T1ZUHROL"},
            {"Portfolio Turnover", "26.72%"},
            {"Drawdown Recovery", "0"},
            {"OrderListHash", "20f33e143b62ee896aa56f85dd2aa2e8"}
        };
    }
}