Overall Statistics |
Total Trades 1630 Average Win 0.32% Average Loss -0.36% Compounding Annual Return 3.700% Drawdown 17.300% Expectancy 0.057 Net Profit 19.575% Sharpe Ratio 0.342 Probabilistic Sharpe Ratio 5.236% Loss Rate 44% Win Rate 56% Profit-Loss Ratio 0.89 Alpha 0.037 Beta -0.011 Annual Standard Deviation 0.105 Annual Variance 0.011 Information Ratio -0.47 Tracking Error 0.176 Treynor Ratio -3.428 Total Fees $2069.83 Estimated Strategy Capacity $830000.00 |
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(2015, 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(lambda time: None)) 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) return [f.Symbol for f in sortedByYields[:10] + sortedByYields[-10:]] # Define the LongShortAlphaModel class class LongShortEYAlphaModel(AlphaModel): def __init__(self): self.lastMonth = None self.symbols = [] 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 symbol in self.symbols: security = algorithm.Securities[symbol] direction = 1 if security.Fundamentals.ValuationRatios.EarningYield > 0 else -1 insights.append(Insight.Price(symbol, Expiry.EndOfMonth(data.Time) - timedelta(seconds=1), direction)) return insights def OnSecuritiesChanged(self, algorithm, changes): for security in changes.AddedSecurities: self.symbols.append(security.Symbol) for security in changes.RemovedSecurities: if security.Symbol in self.symbols: self.symbols.remove(security.Symbol)