How to scrape real-time stock prices from a financial website?

Dennis Yelysaveta · 2024-12-18T06:00:51+00:00

Scraping real-time stock prices requires understanding the structure of the website and how it updates its data. Many financial websites use JavaScript to refresh stock prices dynamically, making it necessary to use tools like Selenium, Puppeteer, or Playwright to capture these updates. Some websites expose stock price data through APIs, which is a more reliable and faster option. For sites without APIs, you can monitor network traffic in the browser developer tools to identify AJAX calls that provide stock data in JSON format. This approach allows you to bypass rendering the entire webpage and directly fetch the data.Here’s an example using Selenium to scrape dynamically loaded stock prices:from selenium import webdriver from selenium.webdriver.common.by import Byimport timedriver webdriver.Chrome()driver.get("https://example.com/stocks")time.sleep(5) # Wait for the page to load completelystocks driver.find_elements(By.CLASS_NAME, "stock-item")for stock in stocks: name stock.find_element(By.CLASS_NAME, "stock-name").text price stock.find_element(By.CLASS_NAME, "stock-price").text print(f"Stock: {name}, Price: {price}")driver.quit()Efficiently scraping stock prices also involves adding error handling to deal with dynamic changes on the website. Additionally, it’s important to respect the website’s terms of service to avoid being blocked. Have you ever faced challenges when scraping real-time data, and how did you address them?

General Web Scraping

How to scrape real-time stock prices from a financial website?

Posted by Dennis Yelysaveta on 12/18/2024 at 6:00 am
Scraping real-time stock prices requires understanding the structure of the website and how it updates its data. Many financial websites use JavaScript to refresh stock prices dynamically, making it necessary to use tools like Selenium, Puppeteer, or Playwright to capture these updates. Some websites expose stock price data through APIs, which is a more reliable and faster option. For sites without APIs, you can monitor network traffic in the browser developer tools to identify AJAX calls that provide stock data in JSON format. This approach allows you to bypass rendering the entire webpage and directly fetch the data.
Here’s an example using Selenium to scrape dynamically loaded stock prices:
```
from selenium import webdriver
from selenium.webdriver.common.by import By
import time
driver = webdriver.Chrome()
driver.get("https://example.com/stocks")
time.sleep(5)  # Wait for the page to load completely
stocks = driver.find_elements(By.CLASS_NAME, "stock-item")
for stock in stocks:
    name = stock.find_element(By.CLASS_NAME, "stock-name").text
    price = stock.find_element(By.CLASS_NAME, "stock-price").text
    print(f"Stock: {name}, Price: {price}")
driver.quit()
```
Efficiently scraping stock prices also involves adding error handling to deal with dynamic changes on the website. Additionally, it’s important to respect the website’s terms of service to avoid being blocked. Have you ever faced challenges when scraping real-time data, and how did you address them?
Dennis Yelysaveta replied 4 days, 13 hours ago 1 Member · 0 Replies
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