SignalBench

ML-powered stock signal backtesting platform.

Project Structure

SignalBench/
├── backend/                # Backend service
│   ├── app/               # FastAPI application
│   │   ├── main.py       # Main application entry point
│   │   ├── ml/           # ML models and data fetching
│   │   │   └── data.py
│   │   ├── models.py     # Database models
│   │   └── config.py     # Configuration
│   ├── requirements.txt   # Python dependencies
│   └── Dockerfile        # Backend container
├── frontend/              # Frontend application
├── scripts/               # Utility scripts
├── docker-compose.yml     # Full stack orchestration
└── README.md

Setup

Local Development

1. Install dependencies:

cd backend
pip install -r requirements.txt

2. Run the backend:

cd backend
YF_USE_CURL_CFFI=1 uvicorn app.main:app --reload --port 8000

Docker

1. Build and run all services:

docker-compose up --build

2. Access the application:
   • Backend API: http://localhost:8000
   • Frontend: http://localhost:5173
   • PostgreSQL: localhost:5432
   • Redis: localhost:6379

Database

Connect to PostgreSQL:

docker-compose exec postgres psql -U app -d signalbench

View tables:

\dt

API Documentation

Once running, visit:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Environment Variables

• YF_USE_CURL_CFFI=1 - Required for yfinance to work properly
• DATABASE_URL - PostgreSQL connection string
• REDIS_URL - Redis connection string