AI-Powered Stock Analysis API 📈

A sophisticated FastAPI-based REST API that combines traditional stock analysis with AI-powered insights using AutoGen agents. This system provides comprehensive stock analysis, technical indicators, and market sentiment analysis.

🌟 Features

• Advanced Stock Analysis

  • Year-to-date (YTD) performance tracking
  • Technical indicators (RSI, MACD, Moving Averages)
  • Interactive price visualizations
  • Historical data analysis

• AI-Powered Insights

  • Market sentiment analysis
  • Automated technical analysis
  • Trend predictions
  • News sentiment integration

• Modern Architecture

  • FastAPI for high-performance async operations
  • AutoGen agents for intelligent analysis
  • Pydantic for robust data validation
  • Swagger/OpenAPI documentation
  • Type hints and modern Python practices

🚀 Quick Start

Prerequisites

• Python 3.9+
• OpenAI API key
• Git

Installation

1. Clone the repository:

git clone https://github.com/yourusername/stock-analysis-api.git
cd stock-analysis-api

2. Create and activate virtual environment:

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

3. Install dependencies:

pip install -r backend/requirements.txt

4. Set up environment variables:

cp backend/.env.example backend/.env
# Edit .env and add your OpenAI API key

5. Run the application:

cd backend
uvicorn app.main:app --reload

The API will be available at:

• API Endpoint: http://localhost:8000
• Interactive Docs: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

📚 API Documentation

Endpoints

Stock Analysis

POST /api/v1/stocks/analysis

Performs comprehensive stock analysis with AI insights.

Request body:

{
    "symbols": ["AAPL", "MSFT"]
}

Market Sentiment

POST /api/v1/stocks/sentiment

Analyzes market sentiment using AI agents.

Request body:

{
    "symbols": ["AAPL", "TSLA"]
}

Get Plot

GET /api/v1/stocks/plot/{filename}

Retrieves generated stock visualizations.

🛠️ Development

Project Structure

backend/
├── app/
│   ├── agents/          # AutoGen agents
│   ├── api/             # API routes
│   ├── models/          # Pydantic models
│   ├── services/        # Business logic
│   ├── utils/           # Utility functions
│   ├── config.py        # Configuration
│   └── main.py         # Application entry
├── static/             # Static files
├── tests/             # Test suite
├── .env               # Environment variables
└── requirements.txt   # Dependencies

Running Tests

pytest

Code Quality

# Format code
black .

# Check types
mypy .

# Lint code
flake8

📊 Example Usage

import httpx
import asyncio

async def analyze_stocks():
    async with httpx.AsyncClient() as client:
        response = await client.post(
            'http://localhost:8000/api/v1/stocks/analysis',
            json={'symbols': ['AAPL', 'MSFT']}
        )
        return response.json()

# Run analysis
result = asyncio.run(analyze_stocks())

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

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

🙏 Acknowledgments

• FastAPI for the amazing web framework
• AutoGen for AI agent capabilities
• yfinance for stock data
• OpenAI for AI capabilities

📧 Contact

Aman - @LinkedIN - aman1024soni@gmail.com