I want to create a stock selection model base on: 

1) market cap > $n Million, 

2) average turnover($dollar) > $n Million

3) in two industry 

4) select above list of stock that match: n period EMA < today EMA (e.g two months 60 days EMA < today 60 days EMA) 

 

So how to add average turnover to the model and return a list that can screen out all the stocks that meet 4) EMA criteria ?? Thanks

Here is my work for now, please feel free to comment any improvement:

main.py: 

from universe_selection import UniverseSelectionModel

class UncoupledNadionFlange(QCAlgorithm):

def Initialize(self):
self.SetStartDate(2020, 6, 13) # Set Start Date
self.SetCash(10000) # Set Strategy Cash

self.UniverseSettings.Resolution = Resolution.Daily

self.CustomUniverseSelectionModel = UniverseSelectionModel(self)
self.AddUniverse(self.UniverseSelectionModel.SelectCoarse, self.UniverseSelectionModel.SelectFine)

def OnData(self, data):
pass

universe_selection.py:

class UniverseSelectionModel():

def __init__(self, algorithm):
self.algorithm = algorithm

def SelectCoarse(self, coarse):

universe = self.FilterMarket(coarse)
return [c.Symbol for c in universe]

def SelectFine(self, fine):
universe = self.FilterIndustry(fine)

self.algorithm.securities = universe
return [f.Symbol for f in universe]

def FilterMarket(self, coarse):
marketcap = [c for c in coarse if c.Fundamentals.MarketCap > 10000000000]

return marketcap

def FilterIndustry(self, fine):
filter_industry = [f for f in fine if f.AssetClassification.MorningstarIndustryCode.ShippingAndPorts and MorningstarIndustryCode.Trucking]

return filter_industry