Overall Statistics |
Total Trades 39 Average Win 19.59% Average Loss 0% Compounding Annual Return 28.621% Drawdown 32.600% Expectancy 0 Net Profit 141.393% Sharpe Ratio 0.882 Loss Rate 0% Win Rate 100% Profit-Loss Ratio 0 Alpha 0.197 Beta 5.71 Annual Standard Deviation 0.352 Annual Variance 0.124 Information Ratio 0.826 Tracking Error 0.352 Treynor Ratio 0.054 Total Fees $316.78 |
# https://quantpedia.com/Screener/Details/25 class SmallCapInvestmentAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2016, 1, 1) self.SetEndDate(2019, 7, 1) self.SetCash(100000) self.UniverseSettings.Resolution = Resolution.Daily self.count = 10 self.year = -1 self.symbols = [] self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) def CoarseSelectionFunction(self, coarse): ''' Drop stocks which have no fundamental data or have low price ''' if self.year == self.Time.year: return self.symbols return [x.Symbol for x in coarse if x.HasFundamentalData and x.Price > 5] def FineSelectionFunction(self, fine): ''' Selects the stocks by lowest market cap ''' if self.year == self.Time.year: return self.symbols # Calculate the market cap and add the "MarketCap" property to fine universe object for i in fine: i.MarketCap = (i.EarningReports.BasicAverageShares.ThreeMonths * i.EarningReports.BasicEPS.TwelveMonths * i.ValuationRatios.PERatio) sorted_market_cap = sorted([x for x in fine if x.MarketCap > 0], key=lambda x: x.MarketCap) self.symbols = [i.Symbol for i in sorted_market_cap[:self.count]] return self.symbols def OnData(self, data): if self.year == self.Time.year: return self.year = self.Time.year for symbol in self.symbols: self.SetHoldings(symbol, 1.0/self.count) def OnSecuritiesChanged(self, changes): ''' Liquidate the securities that were removed from the universe ''' for security in changes.RemovedSecurities: symbol = security.Symbol if self.Portfolio[symbol].Invested: self.Liquidate(symbol)