MarketAnalytic

Docker Compose Dev Setup

• Prereqs: Docker and Docker Compose installed; optional: .env with DB_PASSWORD.
• Services: PostgreSQL (db), Redis (redis), Django (backend), Celery (celery_worker, celery_beat), Vite React (frontend).

Quick Start

docker compose up --build backend redis db frontend

• Backend: http://localhost:8000
• Frontend (Vite dev): http://localhost:3000

Notes

• Frontend uses Vite dev server bound to 0.0.0.0 on port 3000 (see frontend/Dockerfile).
• Nginx is for production and expects a build at frontend/build; skip it in dev.
• Backend uses config.settings.development and connects to db and redis containers.

Common Commands

# Start specific services
docker compose up --build backend frontend

# Rebuild frontend only
docker compose build frontend && docker compose up frontend

# Tear down
docker compose down -v

Algorithmic trading platform scaffold combining Django backend, React frontend, and a trading engine.

Overview

This repository provides a 12-week curriculum scaffold to build a production-ready trading platform. It includes:

• Django backend with REST API, Celery tasks, Channels (WebSockets), and admin UI.
• React frontend with MUI-based layout and Redux state management.
• Trading engine modules: data loader, signals, backtester, risk manager, and broker client.
• Docker and docker-compose setup for local development.
• GitHub Actions CI pipeline for tests, linting, and builds.

Quickstart (development)

1. Copy .env.example to .env and fill in the secrets.

2. Build and start containers:

make build
make up

3. Run migrations and create superuser:

make migrate
make createsuperuser

4. Frontend: http://localhost:3000 Backend: http://localhost:8000 Admin: http://localhost:8000/admin

Project Structure

• backend/ - Django project and apps
• frontend/ - React app
• trading_engine/ - Core trading modules (data loader, signals, backtester, risk manager, broker client)
• data/ - Local storage for market data
• logs/ - Logs for services
• .github/workflows - CI/CD configuration

Week-by-week roadmap

See the prompt for the detailed 12-week curriculum. Implementations in trading_engine/ include placeholders and examples to be expanded during each week.

Next steps

• Implement API clients for real market data providers (AlphaVantage, Tiingo, Polygon)
• Complete Celery scheduled tasks for data ingestion
• Implement backtester engine tie-ins to Django models
• Add authentication flows and secure storage for API keys
• Add unit tests and integration tests

License

MIT