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 |
import math class ModulatedUncoupledGearbox(QCAlgorithm): def Initialize(self): self.SetStartDate(2018, 11, 29) # Set Start Date self.SetCash(100000) # Set Strategy Cash # self.AddEquity("SPY", Resolution.Minute) self.symbols = [] self.__numberOfSymbols = 700 self.__numberOfSymbolsFine = 5 self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None)) 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) self.Log("got here") for symbol in self.symbols: self.Log(symbol.Value) self.Quit() # sort the data by daily dollar volume and take the top 'NumberOfSymbols' def CoarseSelectionFunction(self, coarse): # 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] ] # get stocks with earnings reports due tomorrow def FineSelectionFunction(self, fine): earningsTomorrow = [x for x in \ filter(lambda x: (self.Time \ - x.EarningReports.FileDate).days < 3, fine)] self.symbols = [ x.Symbol for x in earningsTomorrow[:self.__numberOfSymbolsFine] ] return self.symbols