Python WebScrape With BeautifulSoup - Proxy Error Handler

Question

I am trying to webscrape ETFs daily information with Python and BeautifulSoup. My code extracts info from Wall Street Journal Page. But I get a max number of retries. I succesfully scraped 10+ ETFs in one run but now I am trying to scrape new ETFs but I keep getting this proxy error:

ProxyError: HTTPSConnectionPool(host='quotes.wsj.com', port=443): Max retries exceeded with url: /etf/ACWI (Caused by ProxyError('Cannot connect to proxy.', error('Tunnel connection failed: 407 Proxy Authorization Required',)))

I was wondering if there is a way to handle this error. My code is the following:

import requests
from bs4 import BeautifulSoup
import pandas as pd

ticker_list = ["ACWI", "AGG", "EMB", "VTI", "GOVT", "IEMB", "IEMG", "EEM", "PCY", "CWI", "SPY", "EMLC"]
x = len(ticker_list)

date, open_list, previous_list, assets_list, nav_list, shares_list = ([] for a in range(6))

for i in range(0,x):
    ticker = ticker_list[i]
    date.append("20181107")
    link = "https://quotes.wsj.com/etf/" + ticker
    proxies = {"http":"http://username:password@proxy_ip:proxy_port"}
    r = requests.get(link, proxies=proxies)
    #print (r.content)
    html = r.text
    soup = BeautifulSoup(html, "html.parser")

    aux_list, aux_list_2 = ([] for b in range(2))

    for data in soup.find_all("ul", attrs={"class":"cr_data_collection"}):
        for d in data: 
            if d.name == "li":
                aux_list.append(d.text)
                print(d.text)        
    print ("Start List Construction!")
    k = len(aux_list)
    for j in range(0,k):
        auxiliar = []
        if "Volume" in aux_list[j]:
            auxiliar = aux_list[j].split()
            volume = auxiliar[1]
        if "Open" in aux_list[j]:
            auxiliar = aux_list[j].split()
            open_price = auxiliar[1]
            open_list.append(auxiliar[1])
        if "Prior Close" in aux_list[j]:
            auxiliar = aux_list[j].split()
            previous_price = auxiliar[2]
            previous_list.append(auxiliar[2])
        if "Net Assets" in aux_list[j]:
            auxiliar = aux_list[j].split()
            net_assets = auxiliar[2] # In Billions
            assets_list.append(auxiliar[2])
        if "NAV" in aux_list[j]:
            auxiliar = aux_list[j].split()
            nav = auxiliar[1]
            nav_list.append(auxiliar[1])
        if "Shares Outstanding" in aux_list[j]:
            auxiliar = aux_list[j].split()
            shares = auxiliar[2] # In Millions
            shares_list.append(auxiliar[2])
            
    print ("Open Price: ", open_price, "Previous Price: ", previous_price)
    print ("Net Assets: ", net_assets, "NAV: ", nav, "Shares Outstanding: ", shares)

print nav_list, len(nav_list)
print open_list, len(open_list)
print previous_list, len(previous_list)
print assets_list, len(assets_list)
print shares_list, len(shares_list)
                    
data = {"Fecha": date, "Ticker": ticker_list, "NAV": nav_list, "Previous Close": previous_list, "Open Price": open_list, "Net Assets (Bn)": assets_list, "Shares (Mill)": shares_list}
df = pd.DataFrame(data, columns = ["Fecha", "Ticker", "Net Assets", "Previous Close", "Open Price", "NAV", "Shares"])
df

df.to_excel("C:\\Users\\labnrodriguez\\Documents\\out_WSJ.xlsx", sheet_name="ETFs", header = True, index = False) #, startrow = rows)

The output is the following table in a Excel file:

Answer 1

You don't need to scrape their data in the first place. The etfdb-api Node.js package provides you with ETF data:

• Ticker
• Assets under Management
• Open Price
• Avg. Volumne
• etc.

See my post here: https://stackoverflow.com/a/53859924/9986657