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 MultidimensionalHorizontalCompensator(QCAlgorithm): def Initialize(self): self.SetStartDate(2018, 12, 3) # Set Start Date self.SetEndDate(2019, 1, 2) self.SetCash(100000) # Set Strategy Cash # self.AddEquity("SPY", Resolution.Minute) self.UniverseSettings.Resolution = Resolution.Daily self.__numberOfSymbols = 100 self.__numberOfSymbolsFine = 5 self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None)) self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday, DayOfWeek.Tuesday, DayOfWeek.Wednesday, DayOfWeek.Thursday, DayOfWeek.Friday), \ self.TimeRules.At(12, 0), \ self.Reselect) self.Select = False self.symbols = [] 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): if not self.Select: return self.symbols # sort descending by daily dollar volume sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True) # return the symbol objects of the top entries from our sorted collection return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ] # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine' def FineSelectionFunction(self, fine): if not self.Select: return self.symbols # sort descending by P/E ratio sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True) # take the top entries from our sorted collection self.symbols = [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ] self.Select = False self.Log(f"selection complete. Time:{self.Time}. Symbol selected:{[x.Value for x in self.symbols]}") return self.symbols def Reselect(self): self.Select = True