Overall Statistics |
Total Trades 10001 Average Win 0.05% Average Loss -0.05% Compounding Annual Return 7.162% Drawdown 9.500% Expectancy 0.040 Net Profit 17.310% Sharpe Ratio 0.666 Probabilistic Sharpe Ratio 27.778% Loss Rate 49% Win Rate 51% Profit-Loss Ratio 1.06 Alpha 0.039 Beta 0.124 Annual Standard Deviation 0.088 Annual Variance 0.008 Information Ratio -0.607 Tracking Error 0.153 Treynor Ratio 0.469 Total Fees $12092.74 |
namespace QuantConnect { public partial class BootCampTask : QCAlgorithm { public override void Initialize() { SetStartDate(2002, 10, 1); SetEndDate(2020, 05, 25); SetCash(100000); UniverseSettings.Resolution = Resolution.Hour; AddUniverseSelection(new LiquidValueUniverseSelectionModel()); //1. Create an instance of the LongShortEYAlphaModelLongShortEYAlphaModel() SetAlpha(new LongShortEYAlphaModel()); SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel()); SetExecution(new ImmediateExecutionModel()); SetRiskManagement(new MaximumDrawdownPercentPerSecurity(0.02m)); } } public class LiquidValueUniverseSelectionModel : FundamentalUniverseSelectionModel { private int _lastMonth = -1; public LiquidValueUniverseSelectionModel() : base(true, null, null) { } public override IEnumerable<Symbol> SelectCoarse(QCAlgorithm algorithm, IEnumerable<CoarseFundamental> coarse) { if (_lastMonth == algorithm.Time.Month) { return Universe.Unchanged; } _lastMonth = algorithm.Time.Month; var sortedByDollarVolume = coarse .Where(x => x.HasFundamentalData) .OrderByDescending(x => x.DollarVolume); return sortedByDollarVolume .Take(100) .Select(x => x.Symbol); } public override IEnumerable<Symbol> SelectFine(QCAlgorithm algorithm, IEnumerable<FineFundamental> fine) { var sortedByYields = fine.OrderByDescending(x => x.ValuationRatios.EarningYield); var universe = sortedByYields.Take(10).Concat(sortedByYields.TakeLast(10)); return universe.Select(x => x.Symbol); } } // Define the LongShortEYAlphaModel class public class LongShortEYAlphaModel : AlphaModel { private int _lastMonth = -1; public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data) { var insights = new List<Insight>(); //2. If statement to emit signals once a month if(_lastMonth == algorithm.Time.Month){ return insights; } _lastMonth = algorithm.Time.Month; //3. Use foreach to emit insights with insight directions foreach (var security in algorithm.ActiveSecurities.Values){ var yield = security.Fundamentals.ValuationRatios.EarningYield; var direction = (InsightDirection) Math.Sign(yield); insights.Add(Insight.Price(security.Symbol, TimeSpan.FromDays(28), direction)); } // based on whether earnings yield is greater or less than zero once a month return insights; } } }