The last part of this code does not run and I got errors like timeout, could you help me?

#Purpose: Download and install packages required for downloading data

import eikon as ek

import pandas as pd

import sklearn as sk

import numpy as np

import math

from datetime import datetime

from functools import reduce

#Setting Up the user key to acces Eikon data

ek.set_app_key('YOURAPIKEYGOESHERE')

#Purpose: Declare parameters for downloding price index data

#The indexes array contains the RIC of all of the equity indexes we wish to analyze

indexes = ['.SPX', '.STOXX','.SSMI','.FTSE','.SZSC','.TOPX','.HSI','.GSPTSE','.TRXFLDAUP','.MXX','.TRXFLDGEPU','.TRXFLDGEPU'] # United States, Eurozone, Switzerland, China, Japan, Hong Kong, Canada, Australia, Mexico, Emerging Markets

start_date = '20150206' #start_date parameter is a string in that encodes the first date that we wish to download data in the format YYYYMMDD

end_date = '20210216' #end_date parameter is a string in that encodes the last date that we wish to download data in the format YYYYMMDD

concepts = ['TR.CLOSEPRICE.date', 'TR.CLOSEPRICE'] #Concepts is an array that contains the fields that we wish to download

parametros = {'SDate':start_date, 'EDate': end_date, 'CH':'Fd', 'RH':'date', 'Frq':'D'} #Other function parameters

#Purpose: With this chunk of code, we create a pandas dataframe that cointains the following columns:

#Instrument: whose values are one of the elements in the indexes array.

#Date: That cointains the year, month, day and time that we extract the observation

#Close price: The closing price of the instrument.

#Return: This column contains the percentage rate of change between adjacent observations of the closing price.

index_price, err = ek.get_data(

instruments = indexes,

fields = concepts,

parameters = parametros)

index_price["Return"] = index_price.groupby("Instrument")["Close Price"].pct_change()

#Purpose: Create a new variable with index names in new format to get financial data of constituents

added_string = "0#"

indexes_priceindex = [added_string + s for s in indexes] #indexes_priceindex is an array that cointains the newly formated equity index names

#Purpose: Create a list of data frames that contains a pandas dataframe the financial data of index constituents at every point of in the time period established

empresas_indice = [] #empresas_indice is an array,in which every element is a pandas dataframe that cointains df

#df is a pandas nxm data frame organized in a tidy format, in which every row is an observation and every column is a feacture

#In this particular case,every row is an index constituent at a given point in time (within the period of time stablished in the parameters) and every column is the value of the financial feature. You can see descriptions on the right side of each feature downloaded or calculated

for i in indexes_priceindex:

df_tmp, err = ek.get_data(i,"TR.RIC")

instruments = df_tmp['RIC'].tolist()

concepts = ['TR.CLOSEPRICE.date', #Date

'TR.CLOSEPRICE', #Price

'TR.CompanyMarketCap', #Market Capitalization

'TR.FwdPE', #Forward PE

'TR.DividendYield', #Dividend Yield

'TR.EVToEBITDA', #EV to EBITDA

'TR.OrganicSalesGrowthActValue', #sales growth

'TR.F.DebtTot', #Total Debt

'TR.F.ComStockBuybackNet', #Buybacks

'TR.ShortInterest', #Short interest

'TR.Volume', # Volume

'TR.GICSSector',#Sector (for market concentration score)

"TR.PriceTargetMedian", #Median Target Price of consulted analysts

'TR.WACCBeta', #Beta (for ciclicity score)

'TR.ROEActValue', #Return on equity

'TR.RepEPSAvgRevisionPct',#Average revision on Earnings per share monthly (%)

'TR.EpsRepSmartEstF24MtoF12MGrowth' #EPS Growth (forward) (%)

]

df,err = ek.get_data(instruments,concepts, parametros)

df["Weight"] = df["Company Market Cap"]/df.groupby('Date')['Company Market Cap'].transform('sum') #Market cap weight of the index constituent

df["Daily Return"] = df.groupby("Instrument")["Close Price"].pct_change() #Percentage rate of change between adjacent observations of the closing price.

df["Price Momentum 1M"] = df.groupby("Instrument")["Close Price"].pct_change(periods = 21) # Price momentum 1m; Percentage rate of change between 1 month apart observations of the closing price.

df["Price Momentum 3M"] = df.groupby("Instrument")["Close Price"].pct_change(periods = 63) # Price momentum 3m; Percentage rate of change between 3 month apart observations of the closing price.

df["Price Momentum 6M"] = df.groupby("Instrument")["Close Price"].pct_change(periods = 126) #Price momentum 6m; Percentage rate of change between 6 month apart observations of the closing price.

df["Market Concentration"] = df.groupby(["Date","GICS Sector Name"])['Company Market Cap'].transform(lambda x: (x - x.mean()) / x.std()) #Market concentration score

df["Earnings Yield"] = 1/df["Forward P/E (Daily Time Series Ratio)"] #Earnings yield

df["Sigma 1M"] = df.groupby("Instrument")["Daily Return"].rolling(window = 21).std().apply(lambda x: x*math.sqrt(252)).reset_index(level=0, drop=True) #Rolling 1 month volatility

df["Sigma 3M"] = df.groupby("Instrument")["Daily Return"].rolling(window = 63).std().apply(lambda x: x*math.sqrt(252)).reset_index(level=0, drop=True) #Rolling 3 month volatility

df["Sigma 6M"] = df.groupby("Instrument")["Daily Return"].rolling(window = 126).std().apply(lambda x: x*math.sqrt(252)).reset_index(level=0, drop=True) #Rolling 6 month volatility

empresas_indice.append(df)