uses multithreading to get a list of tickers

get_list_of_tickers.py

from alpha_vantage.timeseries import TimeSeries
from concurrent.futures import ThreadPoolExecutor
import os

KEY = os.path.expandvars("$ALPHAVANTAGE_API_KEY")
ts = TimeSeries(key=KEY, output_format='pandas')

def get_daily_adjusted_ignore_failure(ticker):
    try:
        return ts.get_daily_adjusted(symbol=ticker,outputsize='full')
    except Exception:
        print("An error occured with ticker {}\n".format(ticker))
            

# If you don't have a key that can do 3,500 tickers
# feel free to use a subset
# tickers_subset = tickers[:5]
tickers_subset = tickers
def get_data(tickers_subset):
    with ThreadPoolExecutor(max_workers=20) as executor:
        generator = executor.map(lambda ticker:get_daily_adjusted_ignore_failure(ticker), tickers_subset)
        return [data_pair for data_pair in list(generator) if data_pair] 

# This is a list of tuples of data, and metadata with all the tickers from the list
dataset = get_data(tickers_subset)