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.__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, security in data:
self.Log(str(symbol) + ": " + str(security))
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.PeriodEndingDate).days == 1, fine)]

return [ x.Symbol for x in earningsTomorrow[:self.__numberOfSymbolsFine] ]```