A FastAPI-based market sentiment analysis tool that provides real-time stock momentum analysis and news sentiment evaluation using AI.

• Real-time Stock Analysis: Get price momentum scores based on recent trading data
• News Sentiment Analysis: AI-powered sentiment classification (bullish, neutral, bearish)
• Caching System: Efficient TTL-based caching for improved performance
• RESTful API: Clean and well-documented API endpoints
• CORS Support: Ready for frontend integration

• Python 3.8+
• Node.js 16+
• API Keys for:
  • Alpha Vantage (stock data)
  • MediaStack (news data)
  • Google Gemini AI (sentiment analysis)

1. Clone the repository

   git clone https://github.com/rockingyash1717/TICKER.git
   cd TICKER

2. Create virtual environment

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

3. Install dependencies

   pip install -r requirements.txt

4. Environment Configuration Create a .env file in the root directory:

   ALPHA_KEY=your_alpha_vantage_api_key
   MEDIASTACK_KEY=your_mediastack_api_key
   GEMINI_API_KEY=your_gemini_api_key

5. Run the backend server

   uvicorn main:app --reload --host 0.0.0.0 --port 8000

1. Navigate to frontend directory

   cd frontend

2. Install dependencies

   npm install

3. Start development server

   npm start

The frontend will be available at http://localhost:3000 and will automatically proxy API requests to the backend at http://localhost:8000.

fastapi
uvicorn
pandas
yfinance
httpx
cachetools
google-generativeai
python-dotenv

{
  "dependencies": {
    "react": "^18.2.0",
    "react-dom": "^18.2.0",
    "axios": "^1.4.0"
  },
  "devDependencies": {
    "@vitejs/plugin-react": "^4.0.3",
    "vite": "^4.4.5"
  }
}

Get market sentiment analysis for a stock ticker.

Parameters:

• ticker (required): Stock ticker symbol (e.g., "AAPL", "GOOGL")

Response:

{
  "ticker": "AAPL",
  "as_of": "2024-01-15",
  "momentum": {
    "returns": [0.0123, -0.0045, 0.0067, 0.0089, -0.0012],
    "score": 0.0222
  },
  "news": [
    {
      "title": "Apple Reports Strong Q4 Earnings",
      "description": "Apple Inc. reported better-than-expected earnings...",
      "url": "https://example.com/news/apple-earnings"
    }
  ],
  "pulse": "bullish",
  "llm_explanation": "Strong momentum score of 0.0222 combined with positive earnings news suggests bullish sentiment."
}

curl "http://localhost:8000/api/v1/market-pulse?ticker=AAPL"

import requests

response = requests.get("http://localhost:8000/api/v1/market-pulse?ticker=AAPL")
data = response.json()
print(f"Sentiment: {data['pulse']}")

import axios from 'axios';

const fetchMarketPulse = async (ticker) => {
  try {
    const response = await axios.get(`/api/v1/market-pulse?ticker=${ticker}`);
    return response.data;
  } catch (error) {
    console.error('Error fetching market pulse:', error);
  }
};

1. Price Momentum: Calculates 5-day return momentum using Alpha Vantage daily price data
2. News Collection: Fetches recent news headlines using MediaStack API
3. Sentiment Analysis: Uses Google Gemini AI to analyze combined momentum and news data
4. Caching: Results are cached for 10 minutes to reduce API calls and improve performance

• Responsive Design: Mobile-friendly interface built with modern React
• Real-time Updates: Live market sentiment tracking
• Interactive Charts: Visual representation of momentum and sentiment
• Search Functionality: Easy ticker symbol lookup
• Dark/Light Mode: User preference theme switching

TICKER/
├── main.py              # FastAPI backend application
├── getname.py           # Company name extraction utility
├── requirements.txt     # Python dependencies
├── .env                 # Environment variables
├── README.md           # This file
└── frontend/
    ├── src/
    │   ├── components/
    │   ├── pages/
    │   ├── utils/
    │   └── App.jsx
    ├── package.json
    ├── vite.config.js
    └── index.html

1. Visit Alpha Vantage
2. Sign up for a free API key
3. Add to .env file as ALPHA_KEY

1. Visit MediaStack
2. Create account and get API key
3. Add to .env file as MEDIASTACK_KEY

1. Visit Google AI Studio
2. Generate API key
3. Add to .env file as GEMINI_API_KEY

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

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

• API rate limits may apply based on your subscription tier
• News data availability depends on MediaStack coverage
• Sentiment analysis accuracy may vary based on news quality

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

• Initial release with basic sentiment analysis
• FastAPI backend with caching
• Vite React frontend integration
• Support for major stock tickers

Made with ❤️ by rockingyash1717