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 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 |
class UncoupledResistanceEngine(QCAlgorithm): def Initialize(self): self.SetStartDate(2014, 11, 27) # Set Start Date self.SetEndDate(2018, 12, 20) self.SetCash(100000) # Set Strategy Cash # self.AddEquity("SPY", Resolution.Minute) self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None)) self.update = False # flag self.rw = {} # stores a dictionary mapping from symbol to its past 8 years EPS record # warm up for 2 years self.SetWarmUp(timedelta(days=730)) def OnData(self, data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. Arguments: data: Slice object keyed by symbol containing the stock data ''' # if not self.Portfolio.Invested: # self.SetHoldings("SPY", 1) # sort the data by daily dollar volume and take the top 'NumberOfSymbols' def CoarseSelectionFunction(self, coarse): # enable update, wait for the start of next quarter if not self.Time.month%3 == 0: self.update = True if not (self.update and self.Time.month%3==0): return [] # pass all tickers with fundamental data to fine selection self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)] return self.filtered_coarse # return symbols that has had positive EPS for previous 8 quarters def FineSelectionFunction(self, fine): if not (self.update and self.Time.month%3==0): return [] selected = [] for f in fine: if not f.Symbol in self.rw.keys(): self.rw[f.Symbol] = RollingWindow[int](8) if f.EarningReports.BasicEPS.ThreeMonths > 0: self.rw[f.Symbol].Add(1) else: self.rw[f.Symbol].Add(0) if self.rw[f.Symbol].IsReady and sum(self.rw[f.Symbol]) == 8: selected.append(f.Symbol) self.Log([x.Value for x in selected]) # disable update until next month self.update = False return selected