This is a multiprocessing enabled batch Yahoo Stock Price downloader.

yahoo_batch_stock_price_downloader(multi-core).py

# Choose Ticker Universe (example: Run russell3000.py)
exec(open("russell3000(v.1.0).py").read())

# Import Libraries
import datetime as dt
import pandas_datareader as web
import multiprocessing as multi
import numpy as np
import math

# Build Yahoo fetch data function
def fetch_data(symbol):
    df = web.DataReader(symbol,  'yahoo', start, end)
    f_path = 'EOD_data/'
    df.to_csv(f_path + symbol + '.csv')

# Build looping function
def loop(subset):
    for ticker in subset:
        try:
            fetch_data(ticker)
            print(ticker + '(%d): success!' % subset.index(ticker) )
        except:
            print(ticker + '(%d): failed!' % subset.index(ticker) )
        if subset.index(ticker) == 5:
            break    # break here:

# Set default start date and end date
start = dt.datetime(1980,1,1)
end = dt.datetime.today()

# Split work among CPUs workers
num_workers = mp.cpu_count()-1
par_start_idx = list(np.arange(0, len(Russell3000), round(len(Russell3000)/num_workers)))
par_end_idx = par_start_idx[1:]
par_end_idx.append(len(Russell3000))

# Bring it all together           
if __name__ == '__main__':
    for i in range(num_workers):
        chunk = Russell3000[par_start_idx[i]:par_end_idx[i]]   
        p = multi.Process(target = loop, args = (chunk,))
        p.start()
    
del i, chunk, start, end, par_end_idx, par_start_idx