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Stock Market Analysis Tool

A comprehensive tool for analyzing stock market data using various technical indicators, including RSI analysis, confidence intervals, and day trading capabilities.

Features

  • 95% Confidence Interval Analysis: Track and analyze stock performance using statistical confidence intervals
  • RSI Analysis: Calculate and monitor Relative Strength Index with accuracy metrics
  • Database Management: Create and maintain stock databases with flexible ticker management
  • Day Trading Module: Paper trading simulation capabilities
  • Stock Index Scraper: Automated scraping of major market indices (Dow Jones, S&P 500, NASDAQ 100)
  • Moving Average Analysis: Track market trends using various moving average calculations
  • Smart Caching: Efficient data management with automatic cache optimization

Prerequisites

  • Python 3.8 or higher
  • Node.js 14 or higher
  • Chrome/Chromium browser (for web scraping functionality)

Dependencies

Python Packages

All Python dependencies are specified in requirements.txt and include:

  • alpaca-trade-api (for market data)
  • Flask & Flask-CORS (backend server)
  • pandas, numpy, scipy (data analysis)
  • matplotlib (visualization)
  • selenium (web scraping)
  • scikit-learn (machine learning components)
  • Additional supporting packages

Node.js Packages

Frontend dependencies are managed through package.json and include:

  • React (UI framework)
  • Tailwind CSS (styling)
  • Additional development dependencies

Installation

  1. Clone the repository:
git clone [repository-url]
cd Stock_Analysis
  1. Configure Alpaca API credentials: Create or modify config.py:
import os
os.environ['ALPACA_KEY'] = "your_api_key"
os.environ['ALPACA_SECRET'] = "your_api_secret"

Starting the Application

Windows Users (start.bat)

Simply run start.bat in the root directory. The script will:

  1. Check for Python and Node.js installations
  2. Install/update Python requirements automatically
  3. Install frontend dependencies if needed
  4. Start both backend and frontend servers
  5. Open the application in your default browser

To stop the application:

  • Press 'Q' in the command window
  • All processes will be automatically cleaned up
  • Virtual environment will be deactivated

Unix/Linux/Mac Users (start.sh)

Run start.sh in the root directory. The script will:

  1. Verify Python and Node.js installations
  2. Install/update Python requirements automatically
  3. Install frontend dependencies if needed
  4. Start both backend and frontend servers
  5. Open the application in your default browser

To stop the application:

  • Press Ctrl+C in the terminal
  • All processes will be automatically cleaned up
  • Virtual environment will be deactivated

Project Structure

Backend Components

  • app.py: Flask server implementation with API endpoints
  • analysis.py: Core analysis functions including RSI and confidence intervals
  • database.py: Database management and operations
  • day_trade.py: Day trading simulation logic
  • scraper.py: Web scraping functionality for stock indices

Frontend Components

  • App.js: Main React application component
  • ConfidenceModule.js: 95% confidence interval analysis interface
  • DatabaseModule.js: Database management interface
  • DayTradeModule.js: Day trading interface
  • SettingsModule.js: Application settings and cache management
  • package.json: Node.js dependency specifications

API Endpoints

Database Operations

  • GET /api/databases: List all available databases
  • POST /api/database/<dbname>/create: Create a new database
  • POST /api/database/<dbname>/update: Update existing database
  • POST /api/database/<dbname>/add: Add ticker to database
  • POST /api/database/<dbname>/remove: Remove ticker from database
  • POST /api/database/<dbname>/reset: Reset database
  • GET /api/database/<dbname>/estimate: Get update time estimate

Analysis Operations

  • GET /api/rsi/<ticker>: Get RSI calculations
  • GET /api/rsi/accuracy/<ticker>: Get RSI accuracy metrics
  • GET /api/rsi/turnover/<ticker>: Get RSI turnover analysis
  • GET /api/rsi/ma/<ticker>: Get moving average analysis

Scraping Operations

  • POST /api/scrape: Scrape stock symbols from major indices

Cache Management

  • GET /api/cache/info: Get cache status
  • POST /api/cache/clear: Clear cache

Performance Considerations

Rate Limits

  • Alpaca API calls are limited to 150 requests per minute
  • The application implements smart throttling to prevent rate limit issues

Caching

  • Automatic caching of frequently accessed data
  • Cache management through Settings module
  • Configurable cache duration and size limits

Virtual Environment

  • Isolated Python environment for consistent dependencies
  • Automatic creation and management through startup scripts
  • Prevents conflicts with system Python packages

Security Notes

  • API keys should be stored securely and never committed to version control
  • The application runs in paper trading mode by default for safety
  • Virtual environment ensures dependency security
  • Always verify API endpoint access and permissions

Error Handling

The application implements comprehensive error handling for:

  • API rate limiting
  • Network connectivity issues
  • Invalid ticker symbols
  • Database operations
  • File I/O operations
  • Dependency management
  • Virtual environment issues

Troubleshooting

Common Issues

  1. Virtual Environment Problems

    • Delete the venv directory and let the startup script recreate it
    • Ensure Python version compatibility

  2. Node.js Dependencies

    • Clear node_modules and let the startup script reinstall
    • Check for Node.js version compatibility

  3. API Rate Limits

    • Monitor the Settings module for cache status
    • Adjust batch operations timing

Logs

  • Backend logs are available in the Flask server output
  • Frontend development server provides React-related logs
  • Check startup script output for dependency issues

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

Please ensure:

  • Virtual environment is not committed
  • Dependencies are properly documented
  • API keys and secrets are not included

License

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