I've done multiple changes to this algorithm in order to fix it but the error code still occurs.

from System.Collections.Generic import List 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 def Initialize(self): self.SetCash(100000) self.SetStartDate(2015,1,1) # if not specified, the Backtesting EndDate would be today # self.SetEndDate(2017,1,1) 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 (List[Symbol]()) # 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] list = List[Symbol]() for x in top: list.Add(x.Symbol) return list def FineSelectionFunction(self, fine): # return null list if it's not time to rebalance if self.reb != 1: return (List[Symbol]()) 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.Symbol for x in sorted_symbol[:self.num_fine]] self.short = [x.Symbol for x in sorted_symbol[-self.num_fine:]] topFine = self.long+self.short list = List[Symbol]() for x in topFine: list.Add(x) return list def OnData(self, data): pass def rebalance(self): if self.first_month == 0: self.first_month += 1 return # if this month the stock are not going to be long/short, liquidate it. long_short_list = self.long + self.short for i in self.Portfolio.Values: if (i.Invested) and (i.Symbol not in long_short_list): self.Liquidate(i.Symbol) # Alternatively, you can liquidate all the stocks at the end of each month. # Which method to choose depends on your investment philosiphy # if you prefer to realized the gain/loss each month, you can choose this method. #self.Liquidate() # Assign each stock equally. Alternatively you can design your own portfolio construction method for i in self.long: self.SetHoldings(i,0.9/self.num_fine) for i in self.short: self.SetHoldings(i,-0.9/self.num_fine) self.reb = 1