Hi i have a zipline pipline algo(2017-2019) that was long only with binance's data and works only during bull market. 

But i am trying to re-implement with Quantconnect bitfinex's *BTC paris so i can build long/shorts portfolio and hedge during bears market.

  1. .Univers should changes over time with new listing and delisting.
  2. Factor:Momentum/Returns over D days.
  3. Rebalance every R day at the close with Market order at the open: 
  • -longs=Top quantile Momentum with 50% of BTC Equity
  • shorts=Bottom Quantile of Momentum with 50% of BTC Equity

so i far i am stuck with the universe filtering giving me error:

from Alphas.HistoricalReturnsAlphaModel import HistoricalReturnsAlphaModel
from Execution.ImmediateExecutionModel import ImmediateExecutionModel
from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel

class ResistanceNadionGearbox(QCAlgorithm):

def Initialize(self):
self.stateData = { };
self.SetStartDate(2019, 1, 19) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
# self.AddEquity("SPY", Resolution.Minute)
self.AddAlpha(HistoricalReturnsAlphaModel(7, Resolution.Daily))

self.SetExecution(ImmediateExecutionModel())

self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())

self.__numberOfSymbols = 100
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)

# 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] ]
for cf in coarse:
if cf.Symbol.SecurityType == SecurityType.Crypto:
#self.Debug(type(cf))
#self.Debug(type(cf.Symbol))
self.Debug(cf.Symbol.SecurityType)
self.Debug("SecurityType.Crypto={}".format(SecurityType.Crypto))
self.stateData[cf.Symbol] = SelectionData(cf.Symbol, 200)




# sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
def FineSelectionFunction(self, fine):
# sort descending by P/E ratio
sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)

# take the top entries from our sorted collection
return [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]