Looping API Request in Python to retrieve data in bulk

Looping API Request in Python to retrieve data in bulk

Python stock price analysis 02: Extract stock prices with a list of ticker symbols

Introduction

My previous?article?talked about the basic idea of extracting stock price data with Python code. However, I only talked about extracting stock prices for 1 stock.

What if I want to analyze the relationship between all FAANG stocks? What if I want to extract stock prices for all the stocks in SP500?

In today’s article, let’s talk about extracting stock prices in bulk.

Revision — How to extract stock prices

Recall that this is the code to extract stock prices for the stock that we specify. In this case, I chose MSFT.

import requests

url = "https://alpha-vantage.p.rapidapi.com/query"

querystring = {"function":"TIME_SERIES_DAILY_ADJUSTED","symbol":"MSFT","outputsize":"full","datatype":"json"}

headers = {
 "X-RapidAPI-Key": 'your-api-key',
 "X-RapidAPI-Host": "alpha-vantage.p.rapidapi.com"
}

response = requests.request("GET", url, headers=headers, params=querystring)

print(response.text)        

If you’re running this for the first time, refer to my?previous article?to set up the API and understand more about extracting stock prices.

Multiple stocks

We can’t just pass a list of ticker symbols into the query string. Passing this list [‘MSFT’, ‘AAPL’] into the symbol parameter will return only AAPL (the last ticker) price data.

querystring = {"function":"TIME_SERIES_DAILY_ADJUSTED","symbol":["MSFT","AAPL"],"outputsize":"full","datatype":"json"}        

We need to write a list of ticker symbols and loop through the list to request stock price data for each of the symbols. Say, we want to retrieve the stock prices for FAANG-MT stocks. We will create a list that stores the ticker symbols for these stocks.

stocks = ['META','AAPL','AMZN','NFLX','GOOG','MSFT','TSLA']        

Then we’ll loop through these ticker symbols and save those data into a JSON file. Before we get to the code, remember to create a JSON folder in the same directory as your python code.

Then we can write a loop to request data with the API and write to a JSON file.

import requests
import json
import os

json_path = f"{os.getcwd()}\\JSON"
url = "https://alpha-vantage.p.rapidapi.com/query"
headers = {
    "X-RapidAPI-Key": "your-api-key",
    "X-RapidAPI-Host": "alpha-vantage.p.rapidapi.com"
}

for symbol in stocks:
    # Change ticker symbol in the query string in each loop
    querystring = {"function":"TIME_SERIES_DAILY_ADJUSTED","symbol":symbol,"outputsize":"full","datatype":"json"}
    print(f"Symbol = {symbol}")
    
    # Get a new request in every loop
    response = requests.request("GET", url, headers=headers, params=querystring)
    print(f"Done request {symbol} data")
    
    # Write the response into a JSON file in the JSON folder
    with open(f"{json_path}/{symbol} stock prices.json", "w") as outfile:
        json.dump(response.json(), outfile)
    
    # Output message to indicate a successful loop
    print(f"Wrote {symbol} to JSON file")
    print("")
    print('-'*30)
    print("")
    
else:
    # Output message to indicate the loop is complete
    print(f"Wrote all symbols to JSON file")        

Output

Then you can convert it into a pandas data frame using the method we discussed in the previous article.

Conclusion

That’s all for today’s article. In my next article, I will talk about how we can store these JSON files into one CSV file and the preparation needed to do that.

Thank you for reading today’s article. See you again in my next article.

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