Build a price change alert system

Creating a price change alert system involves several key components: tracking product prices from various sources, detecting changes, and notifying users promptly. Below is a comprehensive guide on how to build such a system, covering architecture, technologies, and implementation details.

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Key Components of a Price Change Alert System

1. Data Collection (Price Scraping or API Integration)

2. Price Monitoring and Change Detection

3. User Management and Preferences

4. Notification System

5. Storage and Database

6. Frontend Interface (optional) for user interaction

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1. Data Collection: Tracking Product Prices

To monitor prices, you need to collect current price data for products.

• Web Scraping: For sites without APIs, use web scraping libraries such as:

  • Python: BeautifulSoup, Scrapy, Selenium (for dynamic pages)

  • Node.js: Puppeteer, Cheerio

• APIs: If the vendor provides an API (e.g., Amazon Product Advertising API), use that for accurate data.

Example (Python with BeautifulSoup):

python
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import requests
from bs4 import BeautifulSoup

def get_price(url):
    headers = {'User-Agent': 'Mozilla/5.0'}
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.content, 'html.parser')
    
    # Find price element based on site structure (example)
    price = soup.find('span', {'class': 'price'}).text.strip()
    return float(price.replace('$', ''))

# Example usage
url = 'https://example.com/product123'
current_price = get_price(url)

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2. Price Monitoring and Change Detection

• Store the previous price for each product.

• Regularly (e.g., hourly, daily) fetch the current price.

• Compare current price to the stored price.

• If price differs, trigger an alert and update stored price.

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3. User Management and Preferences

• Users should be able to register and specify which products they want to monitor.

• Options for alert thresholds (e.g., notify only if price drops by 10%).

• Store user subscriptions linked to product IDs.

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4. Notification System

• Send alerts via email, SMS, or push notifications.

• Email services: SMTP, SendGrid, Amazon SES.

• SMS: Twilio, Nexmo.

• Push: Firebase Cloud Messaging (FCM).

Example Email Notification (Python using SMTP):

python
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import smtplib
from email.mime.text import MIMEText

def send_email(to_email, subject, body):
    msg = MIMEText(body)
    msg['Subject'] = subject
    msg['From'] = 'alerts@example.com'
    msg['To'] = to_email
    
    with smtplib.SMTP('smtp.example.com', 587) as server:
        server.starttls()
        server.login('username', 'password')
        server.send_message(msg)

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5. Storage and Database

• Use a database to store product info, prices, user data, and subscriptions.

• Relational DB (MySQL, PostgreSQL) or NoSQL (MongoDB) based on your preference.

Schema Example:

• Products: id, url, last_price, last_checked

• Users: id, email, preferences

• Subscriptions: id, user_id, product_id, price_threshold

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6. Frontend Interface

• Optional web dashboard for users to add products and manage alerts.

• Technologies: React, Vue.js, Angular, or simple server-rendered pages with Flask/Django.

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Basic Workflow Summary

1. User subscribes to a product URL and sets alert criteria.

2. Scheduler triggers price scraping script periodically.

3. Script fetches the current price and compares it with the stored price.

4. If a price change meets user’s criteria, system sends notification.

5. Database is updated with the new price and alert timestamp.

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Example High-Level Python Scheduler Logic

python
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import time

def monitor_prices():
    products = get_all_products()
    for product in products:
        current_price = get_price(product.url)
        if price_changed(product.last_price, current_price):
            notify_users(product.id, current_price)
            update_price(product.id, current_price)

def price_changed(old_price, new_price, threshold=0):
    # Change detected if difference exceeds threshold (e.g., 0 or %)
    return abs(old_price - new_price) >= threshold

while True:
    monitor_prices()
    time.sleep(3600)  # Check every hour

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This system can be extended with more advanced features such as:

• Historical price tracking and analytics

• Multi-source price comparisons

• Machine learning to predict price drops

• Browser extensions or mobile apps for instant alerts

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If you want, I can help you build specific parts like the scraper, database schema, or notification setup. Just let me know!