Overall Statistics |
Total Trades 324 Average Win 0.52% Average Loss -0.45% Compounding Annual Return 24.213% Drawdown 20.600% Expectancy 0.265 Net Profit 91.762% Sharpe Ratio 1.199 Probabilistic Sharpe Ratio 56.461% Loss Rate 41% Win Rate 59% Profit-Loss Ratio 1.16 Alpha 0.227 Beta -0.083 Annual Standard Deviation 0.179 Annual Variance 0.032 Information Ratio 0.297 Tracking Error 0.241 Treynor Ratio -2.587 Total Fees $459.33 |
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel class NadionUncoupledPrism(QCAlgorithm): def Initialize(self): self.SetStartDate(2011, 1, 1) self.SetEndDate(2014, 1, 1) self.SetCash(100000) self.AddEquity("SPY", Resolution.Daily) self.AddEquity("TLT", Resolution.Daily) self.AddEquity("QQQ", Resolution.Daily) self.symbols = [Symbol.Create("SPY", SecurityType.Equity, Market.USA), \ Symbol.Create("TLT", SecurityType.Equity, Market.USA), Symbol.Create("QQQ", SecurityType.Equity, Market.USA)] self.averages = {} self.AddUniverseSelection(TechnologyUniverseModule()) self.AddRiskManagement(NullRiskManagementModel()) self.SetPortfolioConstruction(NullPortfolioConstructionModel()) self.SetExecution(ImmediateExecutionModel()) self.UniverseSettings.Resolution = Resolution.Daily self.SetWarmUp(200) def OnData(self, data): if self.IsWarmingUp: return for security in self.changes.RemovedSecurities: if security.Invested: self.Liquidate(security.Symbol) for security in self.changes.AddedSecurities: if not security.Invested and security.Symbol not in self.symbols: self.SetHoldings(security.Symbol, 0.05) else: return def OnSecuritiesChanged(self, changes): self.changes = changes class TechnologyUniverseModule(FundamentalUniverseSelectionModel): #This module selects the most liquid stocks listed on the Nasdaq Stock Exchange. def __init__(self, filterFineData = True, universeSettings = None, securityInitializer = None): #Initializes a new default instance of the TechnologyUniverseModule super().__init__(filterFineData, universeSettings, securityInitializer) self.numberOfSymbolsCoarse = 1000 self.numberOfSymbolsFine = 100 self.dollarVolumeBySymbol = {} self.lastMonth = -1 def SelectCoarse(self, algorithm, coarse): if algorithm.Time.month == self.lastMonth: return Universe.Unchanged coarse = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 10], key = lambda x: x.DollarVolume, reverse=True)[:self.numberOfSymbolsCoarse] for security in coarse: symbol = security.Symbol self.dollarVolumeBySymbol[symbol] = security.DollarVolume #self.dollarVolumeBySymbol = {x.Symbol:x.DollarVolume for x in coarse} # If no security has met the QC500 criteria, the universe is unchanged. if len(self.dollarVolumeBySymbol) == 0: return Universe.Unchanged return list(self.dollarVolumeBySymbol.keys()) def SelectFine(self, algorithm, fine): sortedByDollarVolume = sorted([x for x in fine if x.CompanyReference.CountryId == "USA" \ and x.CompanyReference.PrimaryExchangeID == "NAS" \ and x.CompanyReference.IndustryTemplateCode == "N" \ and (algorithm.Time - x.SecurityReference.IPODate).days > 180], \ key = lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse=True) #Industry Template Codes: N=Normal (Manufacturing), M=Mining, U=Utility, T=Transportation, B=Bank, I=Insurance if len(sortedByDollarVolume) == 0: return Universe.Unchanged self.lastMonth = algorithm.Time.month return [x.Symbol for x in sortedByDollarVolume[:20]] class NullPortfolioConstructionModel(PortfolioConstructionModel): def CreateTargets(self, algorithm, insights): return [] class ImmediateExecutionModel(ExecutionModel): def __init__(self): self.targetsCollection = PortfolioTargetCollection() def Execute(self, algorithm, targets): # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call self.targetsCollection.AddRange(targets) if self.targetsCollection.Count > 0: for target in self.targetsCollection.OrderByMarginImpact(algorithm): # calculate remaining quantity to be ordered quantity = OrderSizing.GetUnorderedQuantity(algorithm, target) if quantity != 0: algorithm.MarketOrder(target.Symbol, quantity) self.targetsCollection.ClearFulfilled(algorithm)
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel class TechnologyUniverseModule(FundamentalUniverseSelectionModel): ''' This module selects the most liquid stocks listed on the Nasdaq Stock Exchange. ''' def __init__(self, filterFineData = True, universeSettings = None, securityInitializer = None): '''Initializes a new default instance of the TechnologyUniverseModule''' super().__init__(filterFineData, universeSettings, securityInitializer) self.numberOfSymbolsCoarse = 1000 self.numberOfSymbolsFine = 100 self.dollarVolumeBySymbol = {} self.lastMonth = -1 def SelectCoarse(self, algorithm, coarse): ''' Performs a coarse selection: -The stock must have fundamental data -The stock must have positive previous-day close price -The stock must have positive volume on the previous trading day ''' if algorithm.Time.month == self.lastMonth: return Universe.Unchanged sortedByDollarVolume = sorted([x for x in coarse if x.HasFundamentalData and x.Volume > 0 and x.Price > 0], key = lambda x: x.DollarVolume, reverse=True)[:self.numberOfSymbolsCoarse] self.dollarVolumeBySymbol = {x.Symbol:x.DollarVolume for x in sortedByDollarVolume} # If no security has met the QC500 criteria, the universe is unchanged. if len(self.dollarVolumeBySymbol) == 0: return Universe.Unchanged return list(self.dollarVolumeBySymbol.keys()) def SelectFine(self, algorithm, fine): ''' Performs a fine selection: -The company's headquarter must in the U.S. -The stock must be traded on the NASDAQ stock exchange -The stock must be in the Industry Template Code catagory N -At least half a year since its initial public offering ''' # Filter stocks and sort on dollar volume sortedByDollarVolume = sorted([x for x in fine if x.CompanyReference.CountryId == "USA" and x.CompanyReference.PrimaryExchangeID == "NAS" and x.CompanyReference.IndustryTemplateCode == "N" and (algorithm.Time - x.SecurityReference.IPODate).days > 180], key = lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse=True) if len(sortedByDollarVolume) == 0: return Universe.Unchanged self.lastMonth = algorithm.Time.month return [x.Symbol for x in sortedByDollarVolume[:self.numberOfSymbolsFine]]