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%
ProfitLoss 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(2018, 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.__numberOfSymbols = 1000 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) # 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] ] # return symbols that has had positive EPS for previous 8 quarters def FineSelectionFunction(self, fine): selected = [ x.Symbol for x in fine if x.EarningReports.BasicEPS.TwelveMonths > 0 ] self.Log([x.Value for x in selected]) return selected