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 |
class CoarseFineFundamentalRegressionAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2019,1,3) #Set Start Date self.SetEndDate(2019,1,4) #Set End Date self.SetCash(50000) #Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Minute # this add universe method accepts two parameters: # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol> # - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol> self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) # return a list of three fixed symbol objects def CoarseSelectionFunction(self, coarse): Coarse_filtered_stocks = [x for x in coarse if x.DollarVolume > 0 and x.Price > 0 and x.HasFundamentalData] self.Debug(' coarse # of stocks {}'.format(len(Coarse_filtered_stocks))) return [stock.Symbol for stock in Coarse_filtered_stocks] # sort the data based on what trades on NYSE def FineSelectionFunction(self, fine): filtered_fine = [x for x in fine if x.CompanyReference.PrimaryExchangeID == "NYS"] self.Debug(' fine # of stocks {}'.format(len(filtered_fine))) return [ x.Symbol for x in filtered_fine]