Stock and Options Trading Prediction System

This project implements a stock and options trading prediction system using the Black-Scholes model for options pricing and RANSAC & Linear Regression for stock predictions. It leverages the Polygon.io API for market data, integrates with a News API for sentiment analysis, and uses LLM for processing information.

Features

• Stock Price Prediction: Uses RANSAC and Linear Regression models to predict future stock prices
• Options Pricing: Implements the Black-Scholes model for theoretical options pricing
• Options Analysis: Analyzes options contracts to identify potentially undervalued options
• Combined Analysis: Integrates stock predictions with options analysis for comprehensive trading insights
• News Analysis: Categorizes news articles by topic and analyzes their potential impact on stocks
• LLM Integration: Uses Large Language Models to process news and generate trading insights
• Real-Time Data: Provides real-time stock price updates via WebSocket connection
• Interactive Dashboard: Visualizes predictions and analysis through an intuitive web interface

Tech Stack

Backend

• Python
• Flask
• pandas, numpy, scikit-learn
• Polygon.io API for market data
• News API for financial news
• OpenAI API for LLM processing
• WebSockets for real-time data streaming

Frontend

• React.js
• Tailwind CSS
• Recharts for data visualization
• WebSocket client for real-time updates

Setup Instructions

Prerequisites

• Python 3.8+
• Node.js 14+
• Polygon.io API key

Backend Setup

1. Clone the repository:

git clone <repository-url>
cd StockAndOption_Prediction

2. Create a virtual environment and activate it:

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

3. Install dependencies:

pip install -r requirements.txt

4. Set up your API keys:

   • Create a .env file in the root directory
   • Add your API keys:

     POLYGON_API_KEY=your_polygon_api_key_here
     NEWS_API_KEY=your_newsapi_key_here
     OPENAI_API_KEY=your_openai_api_key_here
     

5. Start the Flask server:

cd backend
python app.py

Frontend Setup

1. Install dependencies:

cd frontend
npm install

2. Start the development server:

npm start

3. Access the application at http://localhost:3000

Usage

1. Select a stock from the dropdown menu
2. View stock price predictions using RANSAC and Linear Regression models
3. Explore options analysis to identify potentially profitable options contracts
4. Use the Black-Scholes calculator to price options with custom parameters
5. Check the combined analysis for comprehensive trading insights

Project Structure

StockAndOption_Prediction/
├── backend/                   # Backend Flask application
│   ├── blueprints/            # API route blueprints
│   ├── models/                # Machine learning models
│   ├── app.py                 # Main Flask application
│   ├── black_scholes.py       # Black-Scholes model implementation
│   ├── data_fetcher.py        # API integration with Polygon.io
│   ├── stock_predictior.py    # Stock prediction models
│   └── README.md              # Backend documentation
├── frontend/                  # Frontend React application
│   ├── public/                # Static files
│   ├── src/                   # Source code
│   │   ├── components/        # React components
│   │   │   ├── analysis/      # Analysis components
│   │   │   ├── common/        # Common UI components
│   │   │   └── tools/         # Tool components
│   │   ├── App.js             # Main application component
│   │   └── index.js           # Entry point
│   └── README.md              # Frontend documentation
├── requirements.txt           # Python dependencies
└── README.md                  # Project documentation

For more detailed information about the project structure, see the README files in the backend and frontend directories.

Future Enhancements

• Implement more advanced prediction models (LSTM, ARIMA)
• Add portfolio management features
• Integrate with trading platforms via APIs
• Implement backtesting functionality
• Add user authentication and personalized watchlists

License

MIT License