Overall Statistics |
Total Trades 582 Average Win 0.16% Average Loss -0.16% Compounding Annual Return -9.880% Drawdown 11.900% Expectancy -0.229 Net Profit -9.106% Sharpe Ratio -1.601 Probabilistic Sharpe Ratio 0.039% Loss Rate 62% Win Rate 38% Profit-Loss Ratio 1.01 Alpha -0.08 Beta -0.02 Annual Standard Deviation 0.051 Annual Variance 0.003 Information Ratio -0.501 Tracking Error 0.222 Treynor Ratio 4.027 Total Fees $802.92 |
from datetime import timedelta from QuantConnect.Data.UniverseSelection import * from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel class LiquidValueStocks(QCAlgorithm): def Initialize(self): self.SetStartDate(2011, 1, 1) self.SetEndDate(2011, 12, 1) 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()) self.Settings.RebalancePortfolioOnInsightChanges = False self.Settings.RebalancePortfolioOnSecurityChanges = False def OnData(self, data): self.Plot("Positions", "Number of open positions", len(self.Portfolio)) class LiquidValueUniverseSelectionModel(FundamentalUniverseSelectionModel): def __init__(self): super().__init__(True, None, None) self.lastMonth = -1 def SelectCoarse(self, algorithm, coarse): if self.lastMonth == algorithm.Time.month: return Universe.Unchanged self.lastMonth = algorithm.Time.month sortedByDollarVolume = sorted([x for x in coarse if x.HasFundamentalData], key=lambda x: x.DollarVolume, reverse=True) return [x.Symbol for x in sortedByDollarVolume[:100]] def SelectFine(self, algorithm, fine): sortedByYields = sorted(fine, key=lambda f: f.ValuationRatios.EarningYield, reverse=True) universe = sortedByYields[:10] + sortedByYields[-10:] return [f.Symbol for f in universe] # Define the LongShortAlphaModel class class LongShortEYAlphaModel(AlphaModel): def __init__(self): self.lastMonth = None def Update(self, algorithm, data): insights = [] #2. If else statement to emit signals once a month if self.lastMonth == algorithm.Time.month: 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(1), direction)) return insights