quantconnect--lean/Algorithm.CSharp/SectorExposureRiskFrameworkAlgorithm.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.Portfolio;
using QuantConnect.Algorithm.Framework.Risk;
using QuantConnect.Algorithm.Framework.Selection;
using QuantConnect.Data.Fundamental;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Orders;
using QuantConnect.Interfaces;
using System;
using System.Collections.Generic;
using System.Linq;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// This example algorithm defines its own custom coarse/fine fundamental selection model
    /// with equally weighted portfolio and a maximum sector exposure
    /// </summary>
    public class SectorExposureRiskFrameworkAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
    {
        public override void Initialize()
        {
            // Set requested data resolution
            UniverseSettings.Resolution = Resolution.Daily;

            SetStartDate(2014, 03, 24);
            SetEndDate(2014, 04, 07);
            SetCash(100000);

            SetUniverseSelection(new FineFundamentalUniverseSelectionModel(SelectCoarse, SelectFine));
            SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, QuantConnect.Time.OneDay));
            SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
            SetRiskManagement(new MaximumSectorExposureRiskManagementModel());
        }

        public override void OnOrderEvent(OrderEvent orderEvent)
        {
            if (orderEvent.Status.IsFill())
            {
                Debug($"Order event: {orderEvent}. Holding value: {Securities[orderEvent.Symbol].Holdings.AbsoluteHoldingsValue}");
            }
        }

        private IEnumerable<Symbol> SelectCoarse(IEnumerable<CoarseFundamental> coarse)
        {
            var tickers = Time.Date < new DateTime(2014, 4, 1)
                ? new[] { "AAPL", "AIG", "IBM" }
                : new[] { "GOOG", "BAC", "SPY" };

            return tickers.Select(x => QuantConnect.Symbol.Create(x, SecurityType.Equity, Market.USA));
        }

        private IEnumerable<Symbol> SelectFine(IEnumerable<FineFundamental> fine) => fine.Select(f => f.Symbol);

        /// <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", "19"},
            {"Average Win", "0.12%"},
            {"Average Loss", "-0.03%"},
            {"Compounding Annual Return", "-47.750%"},
            {"Drawdown", "3.000%"},
            {"Expectancy", "1.860"},
            {"Net Profit", "-2.632%"},
            {"Sharpe Ratio", "-6.155"},
            {"Loss Rate", "43%"},
            {"Win Rate", "57%"},
            {"Profit-Loss Ratio", "4.01"},
            {"Alpha", "-0.209"},
            {"Beta", "-20.117"},
            {"Annual Standard Deviation", "0.09"},
            {"Annual Variance", "0.008"},
            {"Information Ratio", "-6.337"},
            {"Tracking Error", "0.09"},
            {"Treynor Ratio", "0.028"},
            {"Total Fees", "$26.42"},
            {"Total Insights Generated", "33"},
            {"Total Insights Closed", "30"},
            {"Total Insights Analysis Completed", "30"},
            {"Long Insight Count", "33"},
            {"Short Insight Count", "0"},
            {"Long/Short Ratio", "100%"},
            {"Estimated Monthly Alpha Value", "$-10605760"},
            {"Total Accumulated Estimated Alpha Value", "$-5361800"},
            {"Mean Population Estimated Insight Value", "$-178726.7"},
            {"Mean Population Direction", "36.6667%"},
            {"Mean Population Magnitude", "0%"},
            {"Rolling Averaged Population Direction", "56.482%"},
            {"Rolling Averaged Population Magnitude", "0%"}
        };
    }
}