Monitor Competitor Prices

Set up automated price monitoring across competitor websites using the FourA API.

What You'll Build

A Python script that:

1. Fetches product pages from a list of competitor URLs
2. Extracts price data from the HTML
3. Logs results to a CSV file
4. Runs on a schedule

Prerequisites

• A FourA API key (get one here)
• Python 3.8+
• requests and beautifulsoup4 packages

pip install requests beautifulsoup4

Step 1: Define Your Targets

Create a list of product URLs to monitor:

targets = [
    {"name": "Competitor A - Widget", "url": "https://competitor-a.com/widget", "selector": ".price"},
    {"name": "Competitor B - Widget", "url": "https://competitor-b.com/products/widget", "selector": "[data-price]"},
    {"name": "Competitor C - Widget", "url": "https://competitor-c.com/item/123", "selector": ".product-price span"},
]

Step 2: Fetch Pages via FourA

import requests
import time

API_URL = "https://eu.api.foura.ai/api/v1/tasks"
API_KEY = "YOUR_API_KEY"

def fetch_page(url, task_type="single"):
    resp = requests.post(API_URL, headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }, json={"url": url, "type": task_type})

    if resp.status_code == 429:
        time.sleep(5)
        return fetch_page(url, task_type)

    data = resp.json()
    return data.get("content", "")

Step 3: Extract Prices

from bs4 import BeautifulSoup
import re

def extract_price(html, selector):
    soup = BeautifulSoup(html, "html.parser")
    element = soup.select_one(selector)
    if not element:
        return None
    # Extract numeric price from text like "$49.99" or "49,99 EUR"
    text = element.get_text(strip=True)
    match = re.search(r'[\d,.]+', text)
    return float(match.group().replace(',', '.')) if match else None

Step 4: Run and Log Results

import csv
from datetime import datetime

def monitor_prices():
    timestamp = datetime.now().isoformat()
    results = []

    for target in targets:
        html = fetch_page(target["url"])
        price = extract_price(html, target["selector"])
        results.append({
            "timestamp": timestamp,
            "name": target["name"],
            "url": target["url"],
            "price": price
        })
        print(f"{target['name']}: {price}")
        time.sleep(1)  # Be polite

    # Append to CSV
    with open("prices.csv", "a", newline="") as f:
        writer = csv.DictWriter(f, fieldnames=["timestamp", "name", "url", "price"])
        if f.tell() == 0:
            writer.writeheader()
        writer.writerows(results)

if __name__ == "__main__":
    monitor_prices()

Step 5: Schedule It

Run the script hourly with cron:

crontab -e
# Add this line:
0 * * * * cd /path/to/project && python3 monitor.py >> monitor.log 2>&1

Tips

• Start with single, switch to browser if pages use JavaScript rendering
• Add error handling: sites change layouts. Log failures separately.
• Keep selectors updated: when a competitor redesigns, update the CSS selector
• Respect the sites: space requests, avoid peak hours, follow robots.txt

Next Steps

• Choosing the Right Task Type: Pick the best approach
• Error Handling: Handle failures gracefully
• Scrape a Dynamic Website: Handle JavaScript-rendered pages