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Use Case 3 min read

Building a Real-Time Price Tracker With FourA

Competitor prices shift hourly. Here's how to build an automated price tracker across hundreds of product pages with a single API call and basic Python.

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Pricing decisions in e-commerce happen fast. A competitor drops their price by 5% and within hours, customers notice. The companies that win are the ones who notice first.

Building a price monitoring system used to require maintaining proxy infrastructure, dealing with anti-bot systems, and writing custom scrapers for every target site. FourA collapses that complexity into a single API call.

The Problem

Manual price checking doesn't scale. Even a small e-commerce business with 50 competitors needs to track hundreds of product pages. At three checks per day, that's over 1,000 requests daily, each potentially blocked, rate-limited, or broken by a site redesign.

The Approach

Here's what a production-ready price monitoring pipeline looks like with FourA:

1. Define Your Product Catalog

Start with a structured list of competitor URLs and the CSS selectors where prices appear:

products = [
    {"sku": "WDG-001", "competitor": "Store A", "url": "https://store-a.com/widget", "selector": ".price-current"},
    {"sku": "WDG-001", "competitor": "Store B", "url": "https://store-b.com/products/widget", "selector": "[data-price]"},
]

2. Fetch and Parse

FourA handles the hard parts: TLS fingerprinting, proxy rotation, and JavaScript rendering. Your code just sends a URL and gets HTML back:

import requests
from bs4 import BeautifulSoup

def get_price(product):
    resp = requests.post("https://eu.api.foura.ai/api/v1/tasks", headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json"
    }, json={"url": product["url"], "type": "proxy"})
    
    html = resp.json()["content"]
    soup = BeautifulSoup(html, "html.parser")
    el = soup.select_one(product["selector"])
    return float(el.text.strip().replace("$", "").replace(",", "")) if el else None

3. Track Changes Over Time

Store results in a database or CSV. Flag significant changes for alerts:

if abs(new_price - last_price) / last_price > 0.03:  # 3% threshold
    send_alert(f"{product['competitor']} changed {product['sku']} from ${last_price} to ${new_price}")

4. Schedule with Cron

Run the tracker every hour or every few hours depending on your market's price volatility.

Why This Works

  • No infrastructure to maintain. No proxy servers, no browser farms, no IP rotation logic.
  • Adapts to protection changes. FourA's proxy type automatically retries through different routes when one gets blocked.
  • Scales linearly. Adding 100 more products is just 100 more API calls with no architectural changes needed.

Getting Started

The whole pipeline above (minus the alert logic) runs in under 50 lines of Python. And once it's working for 50 products, scaling to 500 is just a longer list. The API call stays the same, the parsing stays the same, and the scheduling stays the same. That's the point.

The full example code is available in the How-To guide, and the API documentation covers every parameter you'll need to customize it.