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.