StockSenseAI

An AI-powered stock price prediction application that combines neural networks and sentiment analysis to provide comprehensive stock movement predictions.

Features

• Neural Network Analysis: Uses MLPRegressor with technical indicators for price prediction
• Sentiment Analysis: Analyzes news sentiment using VADER sentiment analysis
• Technical Indicators: Includes moving averages, volume analysis, and price changes
• Real-time Data: Fetches live stock data from Yahoo Finance
• News Integration: Incorporates recent news sentiment for comprehensive analysis
• Beautiful UI: Modern, responsive React frontend with TypeScript
• RESTful API: Flask backend with CORS support

Technology Stack

Backend

• Python 3.8+
• Flask: Web framework
• scikit-learn: Machine learning library
• yfinance: Stock data fetching
• NLTK: Natural language processing
• VADER Sentiment: Sentiment analysis
• pandas & numpy: Data manipulation

Frontend

• React 18: UI framework
• TypeScript: Type safety
• CSS3: Modern styling with gradients and animations
• Axios: HTTP client

Project Structure

stock-price-predictor/
├── stock_predictor.py          # Main Flask backend with ML models
├── requirements.txt            # Python dependencies
├── package.json               # Node.js dependencies and scripts
├── package-lock.json          # Locked dependency versions
├── tsconfig.json              # TypeScript configuration
├── README.md                  # Project documentation
├── public/
│   └── index.html            # Main HTML template
└── src/
    ├── App.tsx               # Main React application
    ├── App.css               # App-level styles
    ├── index.tsx             # React entry point
    ├── index.css             # Global styles
    └── components/
        ├── Header.tsx        # Header component
        ├── Header.css        # Header styles
        ├── StockPredictor.tsx # Main stock prediction component
        └── StockPredictor.css # Stock predictor styles

Installation & Setup

Prerequisites

• Python 3.8 or higher
• Node.js 16 or higher
• npm or yarn

Quick Start

1. Clone the repository:

   git clone <your-repo-url>
   cd stock-price-predictor

2. Install Python dependencies:

   pip install -r requirements.txt

3. Install Node.js dependencies:

   npm install

4. Start the backend server:

   python stock_predictor.py

   The server will start on http://localhost:5000

5. Start the frontend (in a new terminal):

   npm start

   The frontend will open on http://localhost:3000

Usage

1. Open the application in your browser
2. Enter a stock ticker symbol (e.g., AAPL, MSFT, NVDA, TSLA)
3. Click "Analyze Stock" to analyze the stock
4. View comprehensive results including:
   • Current vs. predicted price
   • Buy/Sell/Hold recommendation
   • Confidence level
   • News sentiment analysis
   • Technical analysis reasoning

API Endpoints

POST /predict

Predicts stock movement for a given ticker.

Request:

{
  "ticker": "AAPL"
}

Response:

{
  "ticker": "AAPL",
  "current_price": 150.25,
  "predicted_price": 152.80,
  "predicted_change_percent": 1.70,
  "movement": "BUY",
  "confidence": "Medium",
  "reasoning": "Technical analysis and sentiment...",
  "sentiment_score": 0.15,
  "sentiment_analysis": "Positive - News sentiment is generally favorable",
  "analysis_date": "2025-01-15 14:30:00",
  "features_used": ["Open", "High", "Low", "Close", "Volume", "sentiment_score"],
  "recent_articles": [...]
}

GET /health

Health check endpoint.

Technical Details

Neural Network Architecture

• Type: Multi-layer Perceptron Regressor
• Layers: 100 → 50 → 25 neurons
• Activation: ReLU
• Optimizer: Adam
• Max Iterations: 1000

Features Used

1. Open, High, Low, Close prices
2. Volume data
3. Moving averages (20-day)
4. News sentiment scores

Sentiment Analysis

• Uses VADER (Valence Aware Dictionary and sEntiment Reasoner)
• Analyzes news headlines
• Aggregates daily sentiment scores
• Preprocesses text (removes stopwords, tokenizes)

Data Sources

• Stock Data: Yahoo Finance API
• News Data: NewsAPI (requires API key)
• Technical Indicators: Calculated from price data

Disclaimer

⚠️ Important: This application is for educational and research purposes only. Stock predictions are based on AI analysis and should not be considered as financial advice. Always conduct your own research and consult with financial professionals before making investment decisions.

Troubleshooting

Common Issues

1. Python dependencies missing

Symptoms: ModuleNotFoundError or import errors Solutions:

pip install -r requirements.txt

2. Node.js dependencies missing

Symptoms: Cannot find module errors Solutions:

npm install

3. Port already in use

Symptoms: Address already in use errors Solutions:

• Close other applications using ports 3000 or 5000
• Kill processes: netstat -ano | findstr :5000 (Windows) or lsof -i :5000 (Mac/Linux)

4. News API errors

Symptoms: News sentiment analysis fails Solutions:

• Get a free API key from https://newsapi.org/
• Update the API key in stock_predictor.py

Performance Tips

• The neural network trains on each prediction for accuracy
• First prediction may take longer due to model initialization
• Subsequent predictions for the same stock are faster

Contributing

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

License

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

Support

For issues and questions:

1. Check the troubleshooting section above
2. Review the API documentation
3. Open an issue on GitHub

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