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