Stock Market Analysis Tool

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

Last year's realized return: 29%

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
• recharts

Installation

1. Clone the repository:

git clone [repository-url]
cd Stock_Analysis

2. 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

TODO

The application currently has no settings. Would love this to implement:

• Setting API/Creating config
• Autorun in background once per day, quit on update. Send email on new "sell" signal.
• News trading functionality (paper)
• News integration to "show database" with google news api
• Paper trading bot (long term)
• Dividend section

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.