Overall Statistics |
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees $0.00 |
from Alphas.EmaCrossAlphaModel import EmaCrossAlphaModel class ResistanceNadionThrustAssembly(QCAlgorithm): def Initialize(self): self.SetStartDate(2018, 10, 9) # Set Start Date self.SetEndDate(2019,1,31) self.SetCash(100000) # Set Strategy Cash self.AddAlpha(EmaCrossAlphaModel(50, 200, Resolution.Minute)) self.__numberOfSymbols = 100 self.__numberOfSymbolsFine = 5 self.lastMonth = -1 self.symbols = [] self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None)) def OnData(self, data): pass def OnSecuritiesChanged(self, changes): self.Log([security.Symbol.Value for security in changes.AddedSecurities]) def CoarseSelectionFunction(self, coarse): if self.Time.month == self.lastMonth: return self.symbols self.lastMonth = self.Time.month # sort descending by daily dollar volume self.Log('Refreshing Universe >> ' + str(self.Time)) sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True) self.symbols = [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ] # return the symbol objects of the top entries from our sorted collection return self.symbols # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine' def FineSelectionFunction(self, fine): if self.Time.month == self.lastMonth: return self.symbols self.Log('Refreshing Universe >> ' + str(self.Time)) # sort descending by P/E ratio sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True) self.symbols = [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ] # take the top entries from our sorted collection return self.symbols