I am trying to create a universe that filters equities by the average dollar volume. I know the CoarseSelectionFunction has the built in DollarVolume object, however, I believe that only provides dollar volume for a single period and I want to calculate it with a trailing window such as 63, 126, or 252 days. Here is what I have come up with so far:

#I only want to look at equities that are fairly liquid and have fundamentals.

def CoarseSelectionFunction(self, coarse):

sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
filtered = [ x.Symbol for x in sortedByDollarVolume if x.HasFundamentalData ]

return filtered[:500]

#After the initial filter I want to try to sort equities by their 63 day average dollar volume (adv63).

def FineSelectionFunction(self, fine):

symbols = [x.Symbol for x in fine]
history = self.History(symbols, 63, Resolution.Daily)
adv63 = []

for s in symbols:
adv = history.loc[s]['volume'].mean() * history.loc[s]['close'].mean()

adv63 = pd.DataFrame(adv63,columns=['symbol','adv63']).set_index('symbol')

self.symbols = list(adv63.sort_values('adv63',ascending=False).iloc[:5].index.values)

return self.symbols

I believe this achieves the outcome that I want in terms of filtering, however, doing a history call for 500 equities everyday takes entirely too long to in backtesting. Any ideas on how I could optimize this process?