Overall Statistics |
Total Trades 9907 Average Win 0.01% Average Loss -0.01% Compounding Annual Return -14.168% Drawdown 8.800% Expectancy -0.219 Net Profit -7.608% Sharpe Ratio -2.683 Probabilistic Sharpe Ratio 0.089% Loss Rate 63% Win Rate 37% Profit-Loss Ratio 1.13 Alpha -0.114 Beta -0.062 Annual Standard Deviation 0.046 Annual Variance 0.002 Information Ratio -2.607 Tracking Error 0.11 Treynor Ratio 2.017 Total Fees $9907.76 |
from datetime import timedelta from QuantConnect.Data.UniverseSelection import * from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel class LiquidValueStocks(QCAlgorithm): def Initialize(self): self.SetStartDate(2014, 5, 15) self.SetEndDate(2018, 5, 15) self.SetCash(100000) self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverseSelection(LiquidValueUniverseSelectionModel()) #1. Create and instance of the LongShortEYAlphaModel self.AddAlpha(LongShortEYAlphaModel()) self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) self.SetExecution(ImmediateExecutionModel()) class LiquidValueUniverseSelectionModel(FundamentalUniverseSelectionModel): def __init__(self): super().__init__(True, None, None) self.lastMonth = -1 def SelectCoarse(self, algorithm, coarse): if not algorithm.Time.weekday() == 1: return Universe.Unchanged sortedByDollarVolume = sorted([x for x in coarse if x.HasFundamentalData], key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in sortedByDollarVolume[:500]] def SelectFine(self, algorithm, fine): sortedByYields = sorted(fine, key=lambda f: f.ValuationRatios.EarningYield, reverse=True) universe = sortedByYields[:100] + sortedByYields[-100:] return [f.Symbol for f in universe] # Define the LongShortAlphaModel class class LongShortEYAlphaModel(AlphaModel): def __init__(self): self.lastMonth = -1 def Update(self, algorithm, data): insights = [] #2. If else statement to emit signals once a month if not algorithm.Time.weekday() == 1: return insights #self.lastMonth = algorithm.Time.month #3. For loop to emit insights with insight directions # based on whether earnings yield is greater or less than zero once a month for security in algorithm.ActiveSecurities.Values: direction = 1 if security.Fundamentals.ValuationRatios.EarningYield > 0 else -1 insights.append(Insight.Price(security.Symbol, timedelta(7), direction)) return insights