Hi all, I want to add all the list of symbols to a 'History' function to play with. Can you help me on this? My coarse selection example is below (This is the same one that Jing Wu posted before). 

from QuantConnect.Data.UniverseSelection import *

class BasicTemplateAlgorithm(QCAlgorithm):

def __init__(self):
# set the flag for rebalance
self.reb = 1
# Number of stocks to pass CoarseSelection process
self.num_coarse = 250
# Number of stocks to long/short
self.num_fine = 20
self.symbols = None
self.first_month = 0
self.topFine = None


def Initialize(self):
self.SetCash(100000)
self.SetStartDate(2017,1,1)
# if not specified, the Backtesting EndDate would be today
self.SetEndDate(2018,1,7)


self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol

self.UniverseSettings.Resolution = Resolution.Daily

self.AddUniverse(self.CoarseSelectionFunction,self.FineSelectionFunction)

# Schedule the rebalance function to execute at the begining of each month
self.Schedule.On(self.DateRules.MonthStart(self.spy),
self.TimeRules.AfterMarketOpen(self.spy,5), Action(self.rebalance))


def CoarseSelectionFunction(self, coarse):
# if the rebalance flag is not 1, return null list to save time.
if self.reb != 1:
return self.topFine if self.topFine is not None else []


# make universe selection once a month
# drop stocks which have no fundamental data or have too low prices
selected = [x for x in coarse if (x.HasFundamentalData)
and (float(x.Price) > 5)]

sortedByDollarVolume = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)
top = sortedByDollarVolume[:self.num_coarse]
return [i.Symbol for i in top]

def FineSelectionFunction(self, fine):
# return null list if it's not time to rebalance
if self.reb != 1:
return self.topFine if self.topFine is not None else []

self.reb = 0

# drop stocks which don't have the information we need.
# you can try replacing those factor with your own factors here

filtered_fine = [x for x in fine if x.OperationRatios.OperationMargin.Value
and x.ValuationRatios.PriceChange1M
and x.ValuationRatios.BookValuePerShare]

self.Log('remained to select %d'%(len(filtered_fine)))

# rank stocks by three factor.
sortedByfactor1 = sorted(filtered_fine, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
sortedByfactor2 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PriceChange1M, reverse=True)
sortedByfactor3 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)

stock_dict = {}

# assign a score to each stock, you can also change the rule of scoring here.
for i,ele in enumerate(sortedByfactor1):
rank1 = i
rank2 = sortedByfactor2.index(ele)
rank3 = sortedByfactor3.index(ele)
score = sum([rank1*0.2,rank2*0.4,rank3*0.4])
stock_dict[ele] = score

# sort the stocks by their scores
self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=False)
sorted_symbol = [x[0] for x in self.sorted_stock]

# sotre the top stocks into the long_list and the bottom ones into the short_list
self.long = [x for x in sorted_symbol[:self.num_fine]]
self.short = [x for x in sorted_symbol[-self.num_fine:]]



self.topFine = [i.Symbol for i in self.long + self.short]

return self.topFine

 As you can see above, we get 'self.topFine' for the candidates. Now I want to convert all of these 'self.topFine' list to dataframe by using 'History' function. 

# Call the 'History' function for the list of self.topFine !

hist = self.History(self.topFine, 60, Resolution.Daily)

The above does not work. Can you help me? Thank you.