Overall Statistics |
Total Trades 10001 Average Win 0.04% Average Loss -0.03% Compounding Annual Return 2.659% Drawdown 12.900% Expectancy 0.120 Net Profit 18.544% Sharpe Ratio 0.568 Loss Rate 52% Win Rate 48% Profit-Loss Ratio 1.32 Alpha -0.006 Beta 1.695 Annual Standard Deviation 0.04 Annual Variance 0.002 Information Ratio 0.15 Tracking Error 0.04 Treynor Ratio 0.013 Total Fees $10683.04 |
import operator from math import ceil,floor class CoarseFineFundamentalComboAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2000,9,9) # Set Start Date self.SetEndDate(2018,9,9) # Set End Date self.SetCash(100000) # Set Strategy Cash self.flag1 = 1 self.flag2 = 0 self.flag3 = 0 self.UniverseSettings.Resolution = Resolution.Minute self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.AddEquity("SPY") self.numberOfSymbols = 300 self.numberOfSymbolsFine = 10 self.num_portfolios = 6 self._changes = None self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing)) self.window = RollingWindow[TradeBar](2) self.SetWarmUp(20) def CoarseSelectionFunction(self, coarse): if self.flag1: CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData] sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True) top = sortedByDollarVolume[:self.numberOfSymbols] return [i.Symbol for i in top] else: return [] def FineSelectionFunction(self, fine): if self.flag1: self.flag1 = 0 self.flag2 = 1 filtered_fine = [x for x in fine if x.FinancialStatements.IncomeStatement.EBIT.ThreeMonths and x.FinancialStatements.BalanceSheet.InvestedCapital.ThreeMonths and x.FinancialStatements.BalanceSheet.PreferredStock.ThreeMonths] sortedByROIC = sorted(filtered_fine, key=lambda x: (x.FinancialStatements.IncomeStatement.EBIT.ThreeMonths)/((x.FinancialStatements.BalanceSheet.InvestedCapital.ThreeMonths)+(x.FinancialStatements.BalanceSheet.PreferredStock.ThreeMonths)), reverse=True) # The FinancialStatements is the property of elements in filtered_fine, so you need to basically say "from the data in filtered_fine, take x, y and z and do this function, and return the answer as sortedbyROIC. #financial data comes in quaters i.e Three Month periods, therefore make sure it is three months so that there is no 0 on denominator. return [i.Symbol for i in sortedByROIC] num_stocks = floor(len(filtered_fine)/self.num_portfolios) stock_dict = {} for i,ele in enumerate(ROIC): rank1 = i score = [ceil(rank1/num_stocks)] score = sum(score) stock_dict[ele] = score self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=True) sorted_symbol = [self.sorted_stock[i][0] for i in range(len(self.sorted_stock))] topFine = sorted_symbol[:self.numberOfSymbolsFine] self.flag3 = self.flag3 + 1 return [i.Symbol for i in topFine] else: return [] def OnData(self, data): if self.flag3 > 0: if self.flag2 == 1: self.flag2 = 0 if self._changes == None: return for security in self._changes.RemovedSecurities: if security.Invested: self.Liquidate(security.Symbol) for security in self._changes.AddedSecurities: self.SetHoldings(security.Symbol, 0.8/float(len(self._changes.AddedSecurities))) self._changes = None def OnSecuritiesChanged(self, changes): self._changes = changes def Rebalancing(self): self.flag1 = 1