Overall Statistics |
Total Trades 6052 Average Win 0.67% Average Loss -0.61% Compounding Annual Return 1.878% Drawdown 67.500% Expectancy 0.034 Net Profit 22.737% Sharpe Ratio 0.216 Loss Rate 51% Win Rate 49% Profit-Loss Ratio 1.11 Alpha 0.619 Beta -33.484 Annual Standard Deviation 0.344 Annual Variance 0.119 Information Ratio 0.169 Tracking Error 0.344 Treynor Ratio -0.002 Total Fees $61254.40 |
from dateutil.relativedelta import relativedelta class PuppiesOfTheDow(QCAlgorithm): def Initialize(self): self.SetStartDate(2008, 1, 1) # Set Start Date self.SetEndDate(2019, 1, 1) # Set Start Date self.SetCash(100000) # Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Daily self.coarseUniverseSize = 500 self.portfolioSize = 5 self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.selectUniverse = True self.monthCounter = 0 self.lastRebalancing = self.Time - relativedelta(years=1) def CoarseSelectionFunction(self, coarse): if self.lastRebalancing.year != self.Time.year: selected = [x for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)] filtered = sorted(selected, key=lambda x: x.DollarVolume, reverse=True) self.filteredCoarse = [ x.Symbol for x in filtered[:self.coarseUniverseSize]] return self.filteredCoarse else: return self.filteredCoarse def FineSelectionFunction(self, fine): if self.lastRebalancing.year != self.Time.year: dogsOfTheDow = sorted(fine, key = lambda x: x.ValuationRatios.TotalYield, reverse=True) puppiesOfTheDow = sorted(dogsOfTheDow, key = lambda x: x.ValuationRatios.BuyBackYield)[:self.portfolioSize] self.filteredFine = [x.Symbol for x in puppiesOfTheDow] return self.filteredFine else: return self.filteredFine def OnData(self, data): pass def OnSecuritiesChanged(self, changes): for shortStock in changes.RemovedSecurities: self.Liquidate(shortStock.Symbol) for longStock in changes.AddedSecurities: self.SetHoldings(longStock.Symbol,1/self.portfolioSize)