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 Estimated Strategy Capacity $0 Lowest Capacity Asset |
from AlgorithmImports import * from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel from itertools import groupby #functools class UpgradedFluorescentPinkZebra(QCAlgorithm): def Initialize(self): self.SetStartDate(2021, 11, 4) # Set Start Date self.SetCash(2000000) # Set Strategy Cash self.AddUniverse(self.Coarse, self.Fine) self.longTotal = 40 self.shortTotal = 40 self.longStocks = [] self.shortStocks = [] def OnData(self, data): pass # portfolioTargets = [] # for stock in self.longStocks: # portfolioTargets.append(PortfolioTarget(stock, )) def OnSecuritiesChanged(self, changes): pass def Coarse(self, coarse): filtered = [x for x in coarse if x.HasFundamentalData and x.Price > 0 and x.Volume > 0] self.dollarVolumeBySymbol = {} return [x.Symbol for x in filtered] def Fine(self, fine): self.metrics = {} rankinglist = [] allFine = [x for x in fine] for security in fine: symbol = security.Symbol roic = security.OperationRatios.ROIC.ThreeMonths earning_yield = security.ValuationRatios.EarningYield pe = security.ValuationRatios.NormalizedPERatio ps = security.ValuationRatios.PSRatio pb = security.ValuationRatios.PBRatio self.metrics[security] = [roic, earning_yield, pe, ps, pb] rankinglist.append(security) '''Rank Securities''' self.rankings = {} ranking1 = sorted(rankinglist, key = lambda x: self.metrics[x][0], reverse=False) ranking2 = sorted(rankinglist, key = lambda x: self.metrics[x][1], reverse=False) ranking3 = sorted(rankinglist, key = lambda x: self.metrics[x][2], reverse=False) ranking4 = sorted(rankinglist, key = lambda x: self.metrics[x][3], reverse=False) ranking5 = sorted(rankinglist, key = lambda x: self.metrics[x][4], reverse=False) '''Convert their rankings to numerical values and sum a stocks rankings''' for stock in rankinglist: rank1 = ranking1.index(stock) rank2 = ranking2.index(stock) rank3 = ranking3.index(stock) rank4 = ranking4.index(stock) rank5 = ranking5.index(stock) total = (rank1 + rank2 + rank3 + rank4 + rank5) self.rankings[stock] = total finalSecurities = [] sectorCodes = [MorningstarSectorCode.Technology, MorningstarSectorCode.FinancialServices, MorningstarSectorCode.RealEstate, MorningstarSectorCode.Healthcare] sector1 = [x for x in allFine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology] sector2 = [x for x in allFine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.FinancialServices] sector3 = [x for x in allFine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.RealEstate] sector4 = [x for x in allFine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Healthcare] sectors = [sector1,sector2,sector3,sector4] for sector in sectors: y = sorted(sector, key = lambda x: self.rankings[x], reverse = True) finalSecurities.extend(y[0:10]) #Top 10 finalSecurities.extend(y[-10:]) #Bottom 1010 self.longStocks.extend([x.Symbol.Value for x in y[0:10]]) self.shortStocks.extend([x.Symbol.Value for x in y[-10:]]) # for code, g in groupby(allFine, lambda x: x.AssetClassification.MorningstarSectorCode): # if code not in sectorCodes: continue # y = sorted(g, key = lambda x: self.rankings[x], reverse = True) # finalSecurities.extend(y[0:10]) #Top 10 # finalSecurities.extend(y[-10:]) #Bottom 1010 finalSymbols = [x.Symbol for x in finalSecurities] finalTickers = [x.Value for x in finalSymbols] self.Debug("LongStocks") self.Debug(self.longStocks) self.Debug("ShortStocks") self.Debug(self.shortStocks) return finalSymbols