Thanks for the help of QC members. I could now do some meaningful tests. 
However, I still meet some problems. Could someone help, please? Thank you

1. How to update the ADX/ATR in the Fine Universe? I have searched some posts and found that Jared said universe data does not have OHLC and cannot update it. 

2. Runtime Error: Trying to retrieve an element from a collection using a key that does not exist in that collection throws a KeyError exception. To prevent the exception, ensure that the LEVI X305F7R1QVS5 key exist in the collection and/or that collection is not empty.
at FineSelectionFunction in 61
KeyError : 'the label [LEVI X305F7R1QVS5] is not in the [index]'


from clr import AddReference

from System import *
from System.Collections.Generic import List
from QuantConnect import *
from QuantConnect.Algorithm import QCAlgorithm
from QuantConnect.Data.UniverseSelection import *
import numpy as np
class VerticalCalibratedRegulators(QCAlgorithm):

def Initialize(self):
self.SetStartDate(2019, 3, 1) # Set Start Date
self.SetEndDate(2019, 4, 1)
self.SetCash(100000) # Set Strategy Cash
# self.AddEquity("SPY", Resolution.Minute)
self.__numberOfSymbols = 100
self.__numberOfSymbolsFine = 5
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)

self.smav = {}
self.rsi = {}
#self.adx = {}
#self.atr = {}
self.sma = {}
self.selectedSymbols = []
self._changes = None

def OnData(self, data):

# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
def CoarseSelectionFunction(self, coarse):

filtered = [ x for x in coarse if x.HasFundamentalData and x.Price > 1 and x.DollarVolume > 2500000 ]
sortedByDollarVolume = sorted(filtered, 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] ]

# 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)

## Retrieve 20 days of historical data for each symbol
symbols = [x.Symbol for x in sortedByPeRatio]
history = self.History(symbols, 150, Resolution.Daily)
## Iterate through symbols
for symbol in symbols:
## Find hsitory for specific symbol
symbolVolumeHistory = history.loc[str(symbol)]
if symbolVolumeHistory.empty:
self.Log("EMPTY dataframe!")

## Create SMA for symbol and register it with algorithm
symbolSMAv = SimpleMovingAverage(50)
symbolRSI = RelativeStrengthIndex(3)
#symbolADX = AverageDirectionalIndex(7)
#symbolATR = AverageTrueRange(10)
symbolSMA = SimpleMovingAverage(150)

## Iterate through historical data
for tuple in symbolVolumeHistory.itertuples():
## Update SMA with data time and volume
symbolSMAv.Update(tuple.Index, tuple.volume)
#self.Debug(f'Updating {symbol.Value} SMA...')
symbolRSI.Update(tuple.Index, tuple.close)
symbolSMA.Update(tuple.Index, tuple.close)

## Add SMA to dictionary so you can access it later
self.smav[symbol] = symbolSMAv
self.rsi[symbol] = symbolRSI
#self.adx[symbol] = symbolADX
#self.atr[symbol] = symbolATR
self.sma[symbol] = symbolSMA
#self.Debug(f' {symbol.Value} Price ' + str(symbolVolumeHistory['close'][-1]) + 'SMA' + str(self.sma[symbol]))

## Perform SMA filtering conditions and select/return the symbols you want to add to your universe
## Fine Selection will use only these ones

self.selectedSymbols = [ x for x in symbols if self.smav[x].Current.Value > 500000 and self.rsi[x].Current.Value < 30 and history.loc[str(x)]['close'][-1] > self.sma[x].Current.Value ]

return self.selectedSymbols

def OnData(self, data):
# if we have no changes, do nothing
if self._changes is None: return

# liquidate removed securities
for security in self._changes.RemovedSecurities:
if security.Invested:

# we want 20% allocation in each security in our universe
for security in self._changes.AddedSecurities:
self.SetHoldings(security.Symbol, 0.1)

self._changes = None

# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
self._changes = changes