📈 AI Stock Market Sentiment Analyzer

A powerful AI-driven application that analyzes stock market sentiment using Google's Gemini AI. Get real-time sentiment analysis, technical indicators, news feeds, and investment recommendations for any stock across multiple exchanges.

✨ Features

• 🔍 Multi-Exchange Support: NSE, BSE, NASDAQ, NYSE, LSE, TSE
• 🤖 AI-Powered Analysis: Uses Google Gemini 2.5 Flash for sentiment analysis
• 📊 Technical Indicators: RSI, Moving Averages, Volume analysis
• 📰 News Integration: Latest news articles and market updates
• 📈 Interactive Charts: Price charts with technical overlays
• 🎯 Investment Recommendations: Buy/Hold/Sell suggestions with reasoning
• 📱 Responsive Design: Works on desktop, tablet, and mobile
• 🔄 Real-time Data: Up-to-date market information
• 📤 Export & Share: CSV export and social media sharing

🚀 Quick Start

Prerequisites

• Node.js (v16 or higher)
• npm or yarn
• Google Gemini API Key

Installation

1. Clone the repository

   git clone <your-repo-url>
   cd Stock-Market-Sentiment

2. Install dependencies

   npm install

3. Set up environment variables

   # Copy the template file
   cp env.template .env
   
   # Edit .env file and add your API key
   GEMINI_API_KEY=your_gemini_api_key_here

4. Get your Gemini API Key

   • Visit: https://makersuite.google.com/app/apikey
   • Sign in with your Google account
   • Create a new API key
   • Copy the key to your .env file

5. Run the development server

   npm run dev

6. Open your browser

   • Navigate to http://localhost:3000
   • Start analyzing stocks! 🎉

🛠️ Available Scripts

# Development
npm run dev          # Start development server
npm run build        # Build for production
npm run preview      # Preview production build

# Docker
docker build -t stock-sentiment-analyzer .                    # Build Docker image
docker run -p 3000:3000 -e GEMINI_API_KEY=xxx stock-sentiment-analyzer  # Run container
docker-compose up -d                                          # Run with Docker Compose
docker-compose --profile dev up                              # Run development mode

# Linting
npm run lint         # Run ESLint
npm run lint:fix     # Fix ESLint issues

📁 Project Structure

Stock-Market-Sentiment/
├── src/
│   ├── components/          # React components
│   │   ├── icons/          # SVG icon components
│   │   ├── AspectRadarChart.tsx
│   │   ├── LoadingSpinner.tsx
│   │   ├── NewsFeed.tsx
│   │   ├── PriceChart.tsx
│   │   ├── SentimentChart.tsx
│   │   ├── SentimentResult.tsx
│   │   └── StockInputForm.tsx
│   ├── services/           # API services
│   │   └── geminiService.ts
│   ├── types.ts           # TypeScript type definitions
│   ├── utils.ts           # Utility functions
│   └── App.tsx            # Main application component
├── public/                # Static assets
├── .env                   # Environment variables
├── env.template          # Environment template
├── package.json          # Dependencies and scripts
├── vite.config.ts        # Vite configuration
└── README.md            # This file

🔧 Configuration

Environment Variables

Create a .env file in the root directory:

# Required: Gemini API Configuration
GEMINI_API_KEY=your_gemini_api_key_here

# Optional: Server Configuration
PORT=3000

Supported Exchanges

• NSE (India) - National Stock Exchange
• BSE (India) - Bombay Stock Exchange
• NASDAQ (USA) - National Association of Securities Dealers
• NYSE (USA) - New York Stock Exchange
• LSE (UK) - London Stock Exchange
• TSE (Japan) - Tokyo Stock Exchange

📊 How It Works

1. Input: Enter a stock symbol and select an exchange
2. AI Analysis: Gemini AI analyzes market sentiment using:
   • Recent news articles
   • Social media trends
   • Financial reports
   • Technical indicators
3. Results: Get comprehensive analysis including:
   • Overall sentiment score (-1 to +1)
   • Investment recommendation
   • Positive and negative points
   • Technical indicators (RSI, Moving Averages)
   • Historical price data
   • Recent news articles

🎨 Technologies Used

• Frontend: React 18, TypeScript, Vite
• Styling: Tailwind CSS
• Charts: Recharts
• AI: Google Gemini 2.5 Flash
• Icons: Custom SVG components
• Build Tool: Vite

🚀 Deployment

Docker (Recommended)

Using Docker Compose

1. Set up environment variables

   cp env.template .env
   # Edit .env with your API key

2. Build and run with Docker Compose

   # Production
   docker-compose up -d
   
   # Development with hot reload
   docker-compose --profile dev up

3. Access the application

   • Production: http://localhost:3000
   • Development: http://localhost:3001

Using Docker directly

1. Build the image

   docker build -t stock-sentiment-analyzer .

2. Run the container

   docker run -p 3000:3000 \
     -e GEMINI_API_KEY=your_api_key_here \
     stock-sentiment-analyzer

Vercel (Cloud Platform)

1. Push your code to GitHub
2. Connect your repository to Vercel
3. Add environment variables in Vercel dashboard
4. Deploy!

Netlify

1. Build the project: npm run build
2. Deploy the dist folder to Netlify
3. Add environment variables in Netlify dashboard

Manual Deployment

1. Build the project:

   npm run build

2. Upload the dist folder to your web server
3. Configure your server to serve the built files

🔒 Security Notes

• Never commit your .env file to version control
• Keep your API keys secure
• Use environment variables for all sensitive data
• Consider using a proxy server for production API calls

🐛 Troubleshooting

Common Issues

"GEMINI_API_KEY environment variable is not set"

• Ensure your .env file exists in the root directory
• Check that the API key is correctly formatted
• Restart the development server after adding the key

"Network error"

• Check your internet connection
• Verify your API key is valid
• Ensure the Gemini API is accessible from your location

"Model not found"

• The app uses gemini-2.5-flash model
• Ensure your API key has access to this model
• Check Google's model availability

Getting Help

1. Check the browser console for detailed error messages
2. Verify your API key is working: https://makersuite.google.com/app/apikey
3. Ensure all dependencies are installed: npm install
4. Try restarting the development server

📝 License

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

🤝 Contributing

1. Fork the repository
2. Create a 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

📞 Support

If you encounter any issues or have questions:

1. Check the troubleshooting section above
2. Search existing issues in the repository
3. Create a new issue with detailed information
4. Include error messages and steps to reproduce

🙏 Acknowledgments

• Google Gemini AI for powerful language processing
• Recharts for beautiful chart components
• Tailwind CSS for utility-first styling
• React team for the amazing framework

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Disclaimer: This application is for educational and informational purposes only. It is not intended as financial advice. Always consult with a qualified financial advisor before making investment decisions.