Stock Notification Application

A comprehensive stock market analysis and notification system that provides real-time stock data, technical analysis, sentiment analysis, and trading recommendations.

🚀 Features

• Real-time Stock Data: Fetch live stock data from Yahoo Finance
• Technical Analysis: Calculate RSI, MACD, moving averages, and other technical indicators
• Sentiment Analysis: Analyze news sentiment for stocks
• ML Predictions: Generate price predictions using multiple models (SARIMA, Prophet, GARCH)
• Trading Recommendations: AI-powered buy/sell/hold recommendations
• Web Dashboard: Beautiful Flask web interface
• Risk Metrics: Value at Risk (VaR), volatility analysis
• Market Regime Detection: Identify market conditions using clustering

📋 Prerequisites

• Python 3.8 or higher
• pip package manager
• Internet connection for stock data

🛠️ Installation

1. Clone or download the project

   cd "stock notify"

2. Install dependencies

   pip install -r requirements.txt

3. Optional: Install additional packages for enhanced functionality

   # For advanced ML features
   pip install prophet arch ruptures lightgbm xgboost shap
   
   # For additional data sources
   pip install alpha-vantage fredapi
   
   # For news sentiment
   pip install newsapi-python feedparser

🔧 Configuration

Environment Variables (Optional)

Create a .env file in the project root for API keys:

# News API for sentiment analysis
NEWS_API_KEY=your_news_api_key

# Alpha Vantage for additional market data
ALPHA_VANTAGE_API_KEY=your_alpha_vantage_key

# FRED API for macroeconomic data
FRED_API_KEY=your_fred_api_key

# Flask secret key
SECRET_KEY=your_secret_key

API Keys (Optional)

• News API: Get free API key from newsapi.org
• Alpha Vantage: Get free API key from alphavantage.co
• FRED API: Get free API key from fred.stlouisfed.org

🚀 Usage

Quick Start

1. Run the application

   python app.py

2. Open your browser Navigate to http://localhost:5000

3. View the dashboard

   • See stock opportunities
   • View technical analysis
   • Check sentiment scores
   • Get trading recommendations

Testing the Application

Run the test suite to verify everything works:

python test_app.py

Command Line Usage

# Test specific functionality
python -c "from app import app; print('App loaded successfully')"

# Run with debug mode
python app.py

📊 Dashboard Features

Main Dashboard (/)

• Buy Opportunities: Stocks with strong buy signals
• Sell Signals: Stocks to consider selling
• Hold Recommendations: Stocks to maintain positions
• Top Gainers/Losers: Best and worst performing stocks
• Market Sentiment: Overall market sentiment score
• Prediction Accuracy: ML model performance metrics

API Endpoints

• GET /: Main dashboard
• GET /api/stock/<symbol>: Detailed analysis for specific stock

🏗️ Project Structure

stock notify/
├── app.py                 # Main Flask application
├── requirements.txt       # Python dependencies
├── test_app.py           # Test suite
├── README.md             # This file
├── data_ingestion/       # Data fetching modules
│   ├── market_data.py    # Stock data fetcher
│   └── news_sentiment.py # News and sentiment analysis
├── analysis/             # Analysis modules
│   ├── market_analysis.py    # Technical analysis
│   └── recommendation.py     # ML recommendations
├── templates/            # HTML templates
│   ├── index.html        # Main dashboard
│   ├── opportunities.html # Opportunities page
│   └── ...               # Other pages
└── static/               # Static files
    └── style.css         # CSS styles

🔍 How It Works

1. Data Ingestion

• Fetches stock data from Yahoo Finance
• Collects news articles and sentiment
• Handles multiple data sources with fallbacks

2. Technical Analysis

• Calculates technical indicators (RSI, MACD, SMA)
• Generates price predictions using ML models
• Computes risk metrics (VaR, volatility)

3. Sentiment Analysis

• Analyzes news sentiment using TextBlob
• Processes RSS feeds for additional news
• Calculates overall sentiment scores

4. Recommendation Engine

• Combines technical, sentiment, and ML signals
• Generates buy/sell/hold recommendations
• Provides confidence scores and reasoning

🛡️ Error Handling

The application is designed to be robust:

• Graceful degradation: Works even if some dependencies are missing
• Fallback mechanisms: Uses alternative data sources when primary fails
• Comprehensive logging: Detailed error messages for debugging
• Data validation: Checks for empty or invalid data

🔧 Troubleshooting

Common Issues

1. Import Errors

   pip install -r requirements.txt

2. No Stock Data

   • Check internet connection
   • Verify stock symbols are correct
   • Try different stock symbols

3. Missing Dependencies

   • Install optional packages for enhanced features
   • Application works with basic dependencies

4. API Rate Limits

   • Some APIs have rate limits
   • Application uses caching to minimize API calls

Debug Mode

Run with debug mode for detailed error messages:

python app.py

📈 Stock List

The application tracks these Indian stocks by default:

• RELIANCE.NS, TCS.NS, HDFCBANK.NS, INFY.NS, ICICIBANK.NS
• HINDUNILVR.NS, HDFC.NS, SBIN.NS, BAJFINANCE.NS, BHARTIARTL.NS

You can modify the STOCK_LIST in app.py to track different stocks.

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License

This project is open source and available under the MIT License.

⚠️ Disclaimer

This application is for educational and informational purposes only. It does not constitute financial advice. Always do your own research and consult with financial professionals before making investment decisions.

🆘 Support

If you encounter any issues:

1. Check the troubleshooting section above
2. Run the test suite: python test_app.py
3. Check the logs for error messages
4. Verify all dependencies are installed

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Happy Trading! 📈