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 Probabilistic 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 |
#https://www.quantconnect.com/docs/algorithm-reference/universes#Universes-Coarse-Universe-Selection #https://www.quantconnect.com/forum/discussion/6485/onsecuritieschanged-questions/p1 #https://www.quantconnect.com/forum/discussion/9751/create-rating-based-on-coarse-and-fine-selection/p1 class UncoupledResistancePrism(QCAlgorithm): def Initialize(self): self.SetStartDate(2020, 12, 24) # Set Start Date self.SetEndDate(2020, 12, 24) # Set Start Date self.SetCash(100000) # Set Strategy Cash self.AddUniverse(self.Coarse, self.Fine) def Coarse(self, coarse): symbols = ['MDLA', 'CRSP','GAN', 'GNSS','PEP','JNJ', 'OSS', 'ZDGE'] return [Symbol.Create(symbol, SecurityType.Equity, Market.USA) for symbol in symbols] def Fine(self, fine): self.Log("symbol,type,grwgrade,grwscore,valscore,pe,eps,shs,rev,curatio,qratio,debteq,fcf,roe") for f in fine: self.Log(str(f.Symbol.Value) + "," + str(f.AssetClassification.StockType) + "," + str(f.AssetClassification.GrowthGrade) + "," + '{:3.1f}'.format(f.AssetClassification.GrowthScore) + "," + '{:3.1f}'.format(f.AssetClassification.ValueScore) + "," + '{:3.1f}'.format(f.ValuationRatios.PERatio) + "," + '{:3.2f}'.format(f.EarningReports.BasicEPS.TwelveMonths) + "," + '{:3.0f}'.format(f.EarningReports.BasicAverageShares.ThreeMonths / 1000000) + "," + '{:3.1f}'.format((f.OperationRatios.RevenueGrowth.Value) * 100) + "," + '{:3.1f}'.format(f.OperationRatios.QuickRatio.Value) + "," + '{:3.1f}'.format(f.OperationRatios.CurrentRatio.Value) + "," + '{:3.1f}'.format(f.OperationRatios.TotalDebtEquityRatio.Value) + "," + '{:3.1f}'.format(f.ValuationRatios.FCFYield * 100) + "," + '{:3.1f}'.format(f.OperationRatios.ROE.Value * 100)) return []