CODEWITHSUNDEEP
pythonpandasmultithreadingapi
Arpeet
Arpeet

Reputation: 101

How would I apply multithread to this loop and api call to shorten the time it takes to run?

I am trying to leverage multithreading to get this code to run faster and to call the API in a non-sequential way. Does anyone have any ideas on how I can implement it?

# START RSI LOOP
start_time = time.time()
i = 0

for s in Stock:
    
    
    RSI_url = "https://www.alphavantage.co/query?function=RSI&symbol=" + s + "&interval=daily&time_period=14&series_type=close&apikey=" + Token + "&datatype=csv"
    rsi=pd.read_csv(RSI_url,usecols= [1],nrows = 1)
    
    #print(rsi)
    #print(rsi.loc[i,'RSI'])
    
    df.loc[s,'RSI']=rsi.loc[i,'RSI']
    
    i == i + 1
    
    
print(((time.time() - start_time)/60))

Upvotes: 0

Views: 34

Answers (2)

flakes
flakes

Reputation: 23624

You can use a ThreadPoolExecutor:

from concurrent.futures.thread import ThreadPoolExecutor


def get_rsi(stock):
    rsi_url = (
        "https://www.alphavantage.co/query"
        "?function=RSI"
       f"&symbol={stock}"
        "&interval=daily"
        "&time_period=14"
        "&series_type=close"
       f"&apikey={token}"
        "&datatype=csv"
    )
    rsi = pd.read_csv(rsi_url, usecols=[1], nrows=1)
    return stock, rsi


with ThreadPoolExecutor(max_workers=5) as ex:
    for stock, rsi in ex.map(get_id, stocks):
        df.loc[stock, 'RSI'] = rsi.loc[i, 'RSI']
        i = i + 1

Upvotes: 1

Rezart Qelibari
Rezart Qelibari

Reputation: 94

You should use the asyncio library. The documentation has some good examples, e.g:

import asyncio
import time

async def say_after(delay, what):
    await asyncio.sleep(delay)
    print(what)

async def main():
    print(f"started at {time.strftime('%X')}")

    await say_after(1, 'hello')
    await say_after(2, 'world')

    print(f"finished at {time.strftime('%X')}")

asyncio.run(main())

Upvotes: 0

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