Proxies for Price Monitoring

Competitor price monitoring is an important part of e-commerce strategy. Proxies help collect pricing data without blocks.

Why Price Monitoring is Needed

• Competitive pricing — keep prices at market level
• Detecting sales — react to competitor discounts
• Trend analysis — understand market dynamics
• MAP compliance — monitor minimum price adherence

Why Proxies are Needed

E-commerce sites actively protect against parsing:
- Rate limiting
- CAPTCHA
- IP blocking
- Behavior analysis

Solution Architecture

┌─────────────────┐     ┌──────────────┐     ┌─────────────┐
│ Your Server     │────▶│ Proxy Pool   │────▶│ Competitor  │
│ (script/crawler)│     │ (Residential)│     │ Sites       │
└─────────────────┘     └──────────────┘     └─────────────┘
         │                                          │
         └──────────── Price Data ◀─────────────────┘

Data Collection Strategy

1. Choosing Proxies

• Residential proxies for protected sites (Amazon, eBay)
• Datacenter proxies for simple sites
• Geo-targeting for regional prices

2. IP Rotation

# New IP for each product
for product_url in products:
    proxy = get_rotating_proxy()
    price = scrape_price(product_url, proxy)
    save_price(product_url, price)

3. Behavior Imitation

import random
import time

# Random delays
time.sleep(random.uniform(2, 5))

# Different User-Agents
headers = {'User-Agent': random.choice(user_agents)}

Code Example

import requests
from bs4 import BeautifulSoup

def get_price(url, proxy):
    proxies = {
        'http': f'http://user:pass@{proxy}',
        'https': f'http://user:pass@{proxy}'
    }

    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) ...'
    }

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

    # Amazon example
    price = soup.select_one('.a-price-whole')
    return price.text if price else None

# Usage with rotation
from gproxy import ProxyPool

pool = ProxyPool(api_key='your_key')

for product in products:
    proxy = pool.get_proxy(country='US')
    price = get_price(product['url'], proxy)
    print(f"{product['name']}: ${price}")

Recommendations

1. Distribute load — no more than 1 request per second per domain
2. Use caching — don't parse the same URL often
3. Monitor quality — check data freshness
4. Respect robots.txt — or be prepared for consequences
5. Store history — for trend analysis

Tools

• Scrapy + proxy middleware
• Selenium for JS rendering
• Playwright as alternative
• Ready solutions: Price2Spy, Prisync