Financial Stock Screener

A powerful financial stock screening application that allows users to create custom screens based on various financial metrics and criteria. The application integrates with Yahoo Finance to provide real-time stock data and historical price information.

Features

🔐 User Authentication: Secure user registration and login system
📊 Stock Data: Real-time stock information from Yahoo Finance
🔍 Custom Screens: Create and save custom stock screening criteria
📈 Historical Data: Access historical price data for stocks
👥 Public/Private Screens: Share your screens with other users or keep them private
📱 RESTful API: Well-documented API for easy integration\

Tech Stack

Backend: FastAPI (Python)
Database: SQLite
Authentication: JWT
Stock Data: Yahoo Finance API
Documentation: OpenAPI/Swagger

Prerequisites

Python 3.8+
pip (Python package manager)
SQLite3

Installation

Clone the repository:

git clone https://github.com/garbhitsh/Financialbackend.git
cd financial-screener

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Set up environment variables:

cp .env.example .env

Edit .env file with your configuration.

Initialize the database:

python app/scripts/init_db.py

Populate initial stock data:

python app/scripts/populate_stocks.py

Running the Application

Start the development server:

uvicorn app.main:app --reload

Access the API documentation:

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

API Documentation

The API documentation is available in two formats:

API Documentation: Detailed API endpoint documentation
Architecture Documentation: System architecture and design

Project Structure

financial-screener/
├── app/
│   ├── api/
│   │   └── v1/
│   │       ├── endpoints/
│   │       └── api.py
│   ├── core/
│   │   ├── config.py
│   │   └── security.py
│   ├── db/
│   │   └── base.py
│   ├── models/
│   │   ├── stock.py
│   │   ├── screen.py
│   │   └── user.py
│   ├── schemas/
│   │   ├── stock.py
│   │   ├── screen.py
│   │   └── user.py
│   ├── services/
│   │   ├── stock_service.py
│   │   ├── screen_service.py
│   │   └── yfinance_service.py
│   └── scripts/
│       ├── init_db.py
│       └── populate_stocks.py
├── tests/
├── .env.example
├── requirements.txt
└── README.md

Available Screen Criteria

The application supports screening based on the following metrics:

Market Capitalization
P/E Ratio
Price
Price to Book Ratio
Dividend Yield
EPS (Earnings per Share)
Beta
52-Week High/Low
Average Volume

Example Usage

curl -X POST http://localhost:8000/api/v1/auth/register \
  -H "Content-Type: application/json" \
  -d '{"email": "user@example.com", "password": "securepassword", "full_name": "John Doe"}'

Create a screen:

curl -X POST http://localhost:8000/api/v1/screens \
  -H "Authorization: Bearer <your_token>" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Value Stocks",
    "description": "Stocks with low P/E ratio and high dividend yield",
    "is_public": false,
    "criteria": [
      {
        "field": "pe_ratio",
        "operator": "<",
        "value": 15
      },
      {
        "field": "dividend_yield",
        "operator": ">",
        "value": 3.0
      }
    ]
  }'

Run a screen:

curl -X POST http://localhost:8000/api/v1/screens/1/run \
  -H "Authorization: Bearer <your_token>"

Contributing

Fork the repository
Create a feature branch: git checkout -b feature-name
Commit your changes: git commit -am 'Add feature'
Push to the branch: git push origin feature-name
Submit a pull request

Testing

Run the test suite:

pytest

Security

All passwords are hashed using bcrypt
JWT tokens are used for authentication
Rate limiting is implemented to prevent abuse
Input validation is performed on all endpoints
SQL injection prevention through SQLAlchemy

Performance

Database queries are optimized with proper indexing
Response caching for frequently accessed data
Pagination for large datasets
Efficient data serialization

License

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

Support

For support, please open an issue in the GitHub repository or contact the maintainers.

Acknowledgments

Yahoo Finance for providing stock data
FastAPI for the excellent web framework
SQLAlchemy for the ORM
All contributors who have helped with the project

GarbhitSh/Financialbackend