text: 
  import requests
  import io
  import dask
  #from bs4 import BeautifulSoup as BS
  # import nltk
  import pandas
  import numpy as np
  
  # def News(ticker):
  #     B = BS(requests.get(f"https://www.wsj.com/market-data/quotes/{ticker}", headers={'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36'}).content, features="html.parser")
  #     News = B.find('ul', {'id': "newsSummary_c"})
  #     News = [a.getText() for a in News.find_all('a')]
  #     News = [nltk.word_tokenize(h) for h in News]
  #     return dask.dataframe.from_array(np.asarray(News))
                    
  
  api_key = 'MZE3U0MSR1DCE53Z'
  
  
  def daily(ticker, outputsize = 'compact'):
      csv = pandas.read_csv(io.StringIO(requests.get(f'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&&symbol={ticker}&apikey={api_key}&outputsize={outputsize}&datatype=csv').content.decode('utf-8')))
      return csv
      
  
  
  def intraday_data(ticker, time='1min', outputsize = 'compact'):
      return pandas.read_csv(io.StringIO(requests.get(f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={ticker}&interval={time}&apikey={api_key}&outputsize={outputsize}&datatype=csv').content.decode('utf-8')))
  
  
  def tickers():
      return pandas.read_csv("NYSE_TICKERS.csv").iloc[:,0]
      