E-commerce Backend System

A robust Django-based e-commerce backend system designed for scalability and financial accuracy.

Core Features

• Product Management

  • Inventory tracking
  • Price and cost management
  • Multi-currency support

• Order Processing

  • Cart management
  • Order status tracking
  • Payment integration

• Financial Management

  • Transaction recording
  • Multi-currency support
  • Historical price tracking
  • Tax handling

• Business Analytics

  • Sales and returns tracking

System Architecture

Product Module

• Product catalog with inventory management
• Historical price tracking for analytics
• Cost tracking for profit calculations

Order Module

• Cart management system
• Order status workflow
• Integration with payment processing

Financial Module

• Transaction processing
• Multi-currency support with exchange rates
• Tax calculation and tracking
• Historical price records for auditing

Reporting Module

• Business metrics aggregation
• Period-based reporting
• Sales and returns tracking

Data Models

Core Models

• Product: Central product catalog with inventory tracking
• Order & OrderCartItem: Order management system
• Transaction: Financial transaction records
• Currency: Exchange rate management
• ProductPriceRecord: Historical price tracking
• Report: Business metrics and analytics

Future Implementation Models

• Customer: Customer profile management
• CustomerAddress: Multiple address support
• CustomerPhone: Contact information management
• Coupon: Promotional discount system

Technical Details

Base Features

• Soft delete support across all models
• Timestamp tracking (created, updated, deleted)
• Note-taking capability on all records

Data Integrity

• Protected financial records
• Transaction history preservation
• Currency conversion tracking

Getting Started

1. Clone the repository
2. docker compose up -d --build
3. Go to http://localhost:8000/swagger/
4. Create product
5. Create order
6. Create successful transaction
7. Generate report:

docker compose exec web python manage.py shell -c "from reports.tasks import generate_report; generate_report.delay(start_date='2025-02-15', end_date='2025-02-18')"

TODO:

1. Create model test factories and write property-based tests using hypothesis.

2. Fix linter warnings

3. Check bottlenecks and optimise (add indexes, use caching, etc.)