quantconnect--lean/Algorithm.CSharp/CompositeAlphaModelFrameworkAlgorithm.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.Algorithm.Framework.Alphas;
using QuantConnect.Algorithm.Framework.Execution;
using QuantConnect.Algorithm.Framework.Portfolio;
using QuantConnect.Algorithm.Framework.Risk;
using QuantConnect.Algorithm.Framework.Selection;
using QuantConnect.Interfaces;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Show cases how to use the <see cref="CompositeAlphaModel"/> to define
    /// </summary>
    public class CompositeAlphaModelFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        public override void Initialize()
        {
            SetStartDate(2013, 10, 07);
            SetEndDate(2013, 10, 11);

            // even though we're using a framework algorithm, we can still add our securities
            // using the AddEquity/Forex/Crypto/ect methods and then pass them into a manual
            // universe selection model using Securities.Keys
            AddEquity("SPY");
            AddEquity("IBM");
            AddEquity("BAC");
            AddEquity("AIG");

            // define a manual universe of all the securities we manually registered
            SetUniverseSelection(new ManualUniverseSelectionModel());

            // define alpha model as a composite of the rsi and ema cross models
            SetAlpha(new CompositeAlphaModel(
                new RsiAlphaModel(),
                new EmaCrossAlphaModel()
            ));

            // default models for the rest
            SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
            SetExecution(new ImmediateExecutionModel());
            SetRiskManagement(new NullRiskManagementModel());
        }

        /// <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 Language[] Languages { get; } = {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 Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
        {
            {"Total Trades", "7"},
            {"Average Win", "0.01%"},
            {"Average Loss", "-0.40%"},
            {"Compounding Annual Return", "1114.772%"},
            {"Drawdown", "1.800%"},
            {"Expectancy", "-0.319"},
            {"Net Profit", "3.244%"},
            {"Sharpe Ratio", "23.478"},
            {"Probabilistic Sharpe Ratio", "80.383%"},
            {"Loss Rate", "33%"},
            {"Win Rate", "67%"},
            {"Profit-Loss Ratio", "0.02"},
            {"Alpha", "4.314"},
            {"Beta", "1.239"},
            {"Annual Standard Deviation", "0.285"},
            {"Annual Variance", "0.081"},
            {"Information Ratio", "47.452"},
            {"Tracking Error", "0.101"},
            {"Treynor Ratio", "5.409"},
            {"Total Fees", "$67.00"},
            {"Fitness Score", "0.501"},
            {"Kelly Criterion Estimate", "0"},
            {"Kelly Criterion Probability Value", "0"},
            {"Sortino Ratio", "148.636"},
            {"Return Over Maximum Drawdown", "1502.912"},
            {"Portfolio Turnover", "0.501"},
            {"Total Insights Generated", "2"},
            {"Total Insights Closed", "0"},
            {"Total Insights Analysis Completed", "0"},
            {"Long Insight Count", "2"},
            {"Short Insight Count", "0"},
            {"Long/Short Ratio", "100%"},
            {"Estimated Monthly Alpha Value", "$0"},
            {"Total Accumulated Estimated Alpha Value", "$0"},
            {"Mean Population Estimated Insight Value", "$0"},
            {"Mean Population Direction", "0%"},
            {"Mean Population Magnitude", "0%"},
            {"Rolling Averaged Population Direction", "0%"},
            {"Rolling Averaged Population Magnitude", "0%"},
            {"OrderListHash", "-28636839"}
        };
    }
}