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How to Build a Flexible E-commerce Scraper for Tracking Collectible Prices

A comprehensive guide to creating a versatile web scraper that monitors and analyzes collectible prices across various e-commerce platforms, with a focus on CGC graded comics. The scraper runs automatically every 6 hours and provides a simple web interface for data visualization and market analysis.

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Simple Summary

This plan outlines the development of a flexible web scraper to track collectible prices across e-commerce platforms, running on a cron job every 6 hours and featuring a simple web interface.

Product Requirements Document (PRD)

Goals:

  • Create a flexible web scraper capable of tracking collectible prices across multiple e-commerce platforms
  • Initially focus on CGC graded comics, with potential to expand to other collectibles
  • Implement automatic scraping every 6 hours via a cron job
  • Develop a simple web interface for data visualization and analysis
  • Enable identification of market trends and price anomalies

Target Audience:

  • Personal use by the project creator, with potential for expansion

Key Features:

  1. Multi-platform scraping (eBay, Shopify stores, etc.)
  2. Automatic data collection every 6 hours
  3. Comprehensive data gathering (price, grade, title, issue number, seller information, etc.)
  4. Local data storage with potential for Cloudflare Worker integration
  5. Simple web interface for data visualization and analysis
  6. Anomaly detection for identifying unusual prices
  7. Scalable design to handle an open-ended number of tracked items

User Requirements:

  • Easy-to-use interface suitable for users with limited technical expertise
  • Ability to view and analyze collected data
  • Flexibility to expand to different types of collectibles in the future

User Flows

  1. Data Collection:

    • Scraper automatically runs every 6 hours
    • Collects data from configured e-commerce platforms
    • Stores data locally or in cloud storage

  2. Data Visualization:

    • User accesses web interface
    • Views collected data in a simple, understandable format
    • Analyzes trends and identifies price anomalies

  3. Configuration:

    • User adds or modifies target e-commerce platforms or specific collectibles to track
    • Updates are reflected in subsequent scraping cycles

Technical Specifications

Recommended Stack:

  • Backend: Python (for scraping and data processing)
  • Web Framework: Flask or FastAPI (for creating a simple web interface)
  • Database: SQLite (for local storage) or PostgreSQL (for scalability)
  • Frontend: HTML, CSS, JavaScript (for basic visualization)
  • Scraping Tools: Beautiful Soup or Scrapy
  • Scheduling: cron (for Linux/macOS) or Windows Task Scheduler
  • Cloud Integration: Cloudflare Workers (optional)

Key Components:

  1. Scraper Module: Flexible design to handle multiple e-commerce platforms
  2. Data Storage Module: Local database with potential for cloud integration
  3. Scheduler: Cron job setup for automatic execution every 6 hours
  4. Web Interface: Simple dashboard for data visualization and analysis
  5. Anomaly Detection: Algorithm to identify unusual prices or trends

API Endpoints

N/A

Database Schema

CREATE TABLE collectibles (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    type TEXT,
    title TEXT,
    issue_number TEXT,
    grade TEXT,
    price DECIMAL,
    seller TEXT,
    platform TEXT,
    timestamp DATETIME
);

File Structure

collectible-price-tracker/ ├── scraper/ │ ├── __init__.py │ ├── ebay_scraper.py │ ├── shopify_scraper.py │ └── base_scraper.py ├── data/ │ └── collectibles.db ├── web/ │ ├── templates/ │ │ └── index.html │ ├── static/ │ │ ├── css/ │ │ └── js/ │ └── app.py ├── utils/ │ ├── __init__.py │ ├── database.py │ └── anomaly_detection.py ├── config.py ├── main.py └── requirements.txt

Implementation Plan

  1. Set up project structure and environment
  2. Develop base scraper class with common functionality
  3. Implement platform-specific scrapers (eBay, Shopify)
  4. Create local database and data storage module
  5. Develop scheduling mechanism for automatic execution
  6. Implement basic web interface for data visualization
  7. Add anomaly detection algorithm
  8. Integrate all components and test thoroughly
  9. Implement error handling and logging
  10. Optimize performance and scalability
  11. Document code and create user guide
  12. Set up deployment environment (local or cloud)

Deployment Strategy

  1. Local Deployment:

    • Set up Python environment on local machine
    • Install required dependencies
    • Configure cron job for automatic execution
    • Run web interface on localhost

  2. Cloud Deployment (optional):

    • Set up Cloudflare Worker for scraping tasks
    • Deploy web interface to a cloud platform (e.g., Heroku, DigitalOcean)
    • Configure cloud-based scheduling for automatic execution

Design Rationale

The design focuses on flexibility and simplicity to meet the user's needs. Python was chosen for its strong scraping libraries and ease of use. A local SQLite database provides simple storage, with the option to scale to PostgreSQL if needed. The modular scraper design allows for easy addition of new platforms. A basic web interface caters to the user's limited technical expertise while providing essential visualization capabilities. The use of a cron job ensures regular data updates without manual intervention. The open-ended approach to tracked items and potential for cloud integration via Cloudflare Workers allows for future scalability.