Stock Trade Backtesting Platform (Demo Design)

A web-based platform for backtesting stock trading strategies using historical and real-time market data. Built with Django, Django REST Framework, and Tailwind CSS.

Features

• Upload datasets (CSV, JSON, XLSX) or use sample datasets (S&P 500, NASDAQ, etc.).
• Support for ML trading models (joblib, keras, pickle).
• Real-time data feed simlation and interactive performance charts with TradingView (previously Chart.js).
• Detailed trade history and key metrics.

Example

Prerequisites

• Python 3.9+
• Docker and Docker Compose
• Node.js (optional for frontend development)

Setup

1. Clone the repository:

   git clone https://github.com/maxhe5919/backtesting.git
   cd backtesting

2. Set up environment variables: Create .env in the project root:

   echo "DJANGO_SECRET_KEY=$(python -c 'import secrets; print(secrets.token_urlsafe(50))')" > .env
   echo "DEBUG=True" >> .env
   echo "DB_NAME=your_db_name" >> .env
   echo "DB_USER==your_user_name" >> .env
   echo "DB_PASSWORD=your_secure_password" >> .env
   echo "DB_HOST=db" >> .env
   echo "DB_PORT=5432" >> .env

3. Install Python dependencies (optional, if not using Docker):

   python -m venv .venv
   source .venv/bin/activate
   pip install -r backend/requirements.txt

4. Build and run with Docker:

   docker-compose build --no-cache
   docker-compose up

5. Run migrations:

   docker-compose exec web python backend/manage.py makemigrations
   docker-compose exec web python backend/manage.py migrate

6. Create a superuser:

   docker-compose exec web python backend/manage.py createsuperuser

7. Access the application: Open http://localhost:8000 in your browser. The admin interface is at http://localhost:8000/admin.

Development

• Backend: Located in backend/. Uses Django and Django REST Framework.
• Frontend: Located in frontend/. Uses Django templates, Tailwind CSS, and vanilla JavaScript with TradingView charts.
• Static files: Run python backend/manage.py collectstatic to collect static files.
• Database: PostgreSQL runs in a Docker container. Data is persisted in the postgres_data volume.

Security

• CSRF protection enabled.
• API authentication with token-based authentication.
• Sensitive data stored in .env.

Contributions

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License

This project is licensed under the MIT License. See the LICENSE file for details.