quantconnect--lean/Algorithm.CSharp/PearsonCorrelationPairsTradingAlphaModelFrameworkAlgorithm.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 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;
using System.Collections.Generic;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Framework algorithm that uses the <see cref="PearsonCorrelationPairsTradingAlphaModel"/>.
    /// This model extendes <see cref="BasePairsTradingAlphaModel"/> and uses Pearson correlation
    /// to rank the pairs trading candidates and use the best candidate to trade.
    /// </summary>
    public class PearsonCorrelationPairsTradingAlphaModelFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        public override void Initialize()
        {
            SetStartDate(2013, 10, 07);
            SetEndDate(2013, 10, 11);

            SetUniverseSelection(new ManualUniverseSelectionModel(
                QuantConnect.Symbol.Create("AIG", SecurityType.Equity, Market.USA),
                QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA),
                QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA),
                QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)));

            SetAlpha(new PearsonCorrelationPairsTradingAlphaModel(252, Resolution.Daily));
            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", "6"},
            {"Average Win", "0.83%"},
            {"Average Loss", "-0.40%"},
            {"Compounding Annual Return", "47.354%"},
            {"Drawdown", "0.700%"},
            {"Expectancy", "0.548"},
            {"Net Profit", "0.497%"},
            {"Sharpe Ratio", "8.022"},
            {"Loss Rate", "50%"},
            {"Win Rate", "50%"},
            {"Profit-Loss Ratio", "2.10"},
            {"Alpha", "0"},
            {"Beta", "22.675"},
            {"Annual Standard Deviation", "0.031"},
            {"Annual Variance", "0.001"},
            {"Information Ratio", "7.695"},
            {"Tracking Error", "0.031"},
            {"Treynor Ratio", "0.011"},
            {"Total Fees", "$23.83"},
            {"Total Insights Generated", "4"},
            {"Total Insights Closed", "0"},
            {"Total Insights Analysis Completed", "0"},
            {"Long Insight Count", "2"},
            {"Short Insight Count", "2"},
            {"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%"}
        };
    }
}