Coarse selection to History function?

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):
# if not specified, the Backtesting EndDate would be today

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

self.UniverseSettings.Resolution = Resolution.Daily


# Schedule the rebalance function to execute at the begining of each month
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.

Update Backtest

Hi all, Please disregard my question. I found a good way to do it in Jing Wu's another post. See below.

# Add all the list of symbols from Coarse Selection and Fine Selection to History function.

symbols = [ x.Symbol.Value for x in self.topFine]

for i in symbols:
self.AddEquity(i, Resolution.Daily)

history = self.History(symbols, 100, Resolution.Daily)

if history is None: return

data = {}
for i in symbols:
if i in history.index.levels[0]:
data[i] = history.loc[i]['close']

df_price = pd.DataFrame(data,columns=data.keys())

Hi all, I got an error message as below. Can anyone explain about this and show me how to fix this? Thank you.

Runtime Error: In Scheduled Event 'SPY: MonthStart: SPY: 30 min after MarketOpen-4a940735306b4176a8b5ba937e68d8ff', Exception : This asset symbol ( ) was not found in your security list. Please add this security or check it exists before using it with 'Securities.ContainsKey(" ")'
at QuantConnect.Securities.SecurityManager.get_Item (QuantConnect.Symbol symbol) [0x00026] in <e6e93ccda46b4bdfabcdb401f035e462>:0
at QuantConnect.Securities.SecurityPortfolioManager.get_Item (QuantConnect.Symbol symbol) [0x00001] in <e6e93ccda46b4bdfabcdb401f035e462>:0 (Open Stacktrace)


Update Backtest


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