In this blog post, we will learn how to scrape stock prices from a financial website using Python. We will be using the requests and BeautifulSoup libraries to fetch the HTML content of a webpage and then parse it to extract the desired data.

1. Import necessary libraries

Python

import requests
from bs4 import BeautifulSoup
import json

• requests: This library is used to make HTTP requests to websites and retrieve their content.
• BeautifulSoup: This library is used to parse HTML content and extract data from it.
• json: This library is used to work with JSON data.

2. Obtain a Quick Scraper Access Token

To use the Quick Scraper API, you will need to obtain an access token. You can get a free token by signing up for an account on [invalid URL removed].

3. Set up the API request

Python

access_token = 'YOUR_ACCESS_TOKEN'  # Replace with your actual access token
url = f"<https://api.quickscraper.co/parse?access_token={access_token}&url=https://seekingalpha.com/symbol/AAPL>"

We are using the Quick Scraper API to simplify the process of fetching the HTML content of the target webpage. The API takes two arguments:

• access_token: Your Quick Scraper access token.
• url: The URL of the webpage you want to scrape.

4. Make the API request and parse the HTML content

Python

response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

• The requests.get(url) method sends a GET request to the specified URL and retrieves the HTML content of the webpage.
• The BeautifulSoup library is used to parse the HTML content into a tree-like structure that can be easily navigated.

5. Extract the stock data

Python

symbol_name = soup.select_one('div[data-test-id="symbol-name"] h1').text.strip()
symbol_price = soup.select_one('span[data-test-id="symbol-price"]').text.strip()

• We are using CSS selectors to target specific elements on the webpage.
• soup.select_one('div[data-test-id="symbol-name"] h1') selects the first <h1> element within a div element that has the attribute data-test-id set to "symbol-name". This element most likely contains the name of the stock.
• .text.strip() extracts the text content from the selected element and removes any leading or trailing whitespace characters.
• Similarly, we can select the element containing the stock price using soup.select_one('span[data-test-id="symbol-price"]').text.strip().

6. Save the data to a JSON file

Python

data = {
    "name": symbol_name,
    "price": symbol_price
}

with open('symbol_data.json', 'w') as f:
    json.dump(data, f)

print("Data saved to symbol_data.json")

• We create a dictionary named data to store the scraped stock data.
• The json.dump function is used to convert the Python dictionary to a JSON string and write it to a file named symbol_data.json.

7. Scheduling the script to run daily

To scrape stock prices every day, you can use a task scheduler like cron on Linux/macOS or Task Scheduler on Windows. You can set the scheduler to run the Python script at a specific time each day.

Additional Considerations

• This code example scrapes data from a specific website. The HTML structure of the website can change, so you may need to modify the CSS selectors if the website structure changes.
• It is important to be respectful of the website’s robots.txt file and avoid overwhelming the server with too many requests.
• Consider using a more robust scraping library like Scrapy for more complex scraping tasks.

I hope this blog post helps you get started with scraping stock prices using Python. By following these steps, you can create a Python script that automatically scrapes stock prices from a financial website and saves the data to a JSON file every day.