Stock Volatility Analyzer

A Python-based tool for analyzing stock market data, tracking volatility, and visualizing market trends in real-time. This application provides comprehensive stock analysis tools including historical data analysis, price tracking, and graphical visualizations.

Table of Contents

• Features
• Prerequisites
• Installation
• Usage
• Code Examples
• Output Examples
• Troubleshooting
• Development
• Contributing
• License

Features

Core Functionality

• Real-time stock data analysis
• Historical price tracking
• Top gainers and losers identification
• Custom date range analysis
• Stock price visualization with charts

Time Period Options

Intraday Analysis:

• 1 minute intervals
• 5 minute intervals
• 15 minute intervals
• 30 minute intervals
• 1 hour intervals
• 12 hour intervals

Regular Periods:

• Daily (1d, 5d)
• Monthly (1mo, 3mo, 6mo)
• Yearly (1y, 2y, 5y, 10y)
• Year to date (ytd)
• Maximum available data

Data Visualization

• Price charts with moving averages
• Volume analysis
• Comparative analysis tools
• Gainers/Losers visualization

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)
• Virtual environment (recommended)
• Internet connection for real-time data

Installation

Clone the Repository:

git clone https://github.com/yourusername/Stock-Volatility-Analyzer.git
cd Stock-Volatility-Analyzer

Set Up Virtual Environment:

For Windows:

python -m venv venv
venv\Scripts\activate

For macOS/Linux:

python3 -m venv venv
source venv/bin/activate

Install Dependencies:

pip install -r requirements.txt

Dependencies

• yfinance
• pandas>=2.0.0
• matplotlib>=3.7.0
• numpy>=1.24.0

Usage

Starting the Program

python main.py

Main Menu Options

1. Get Gainers and Losers (Option 1)

   • View top performing and underperforming stocks
   • Choose from various time periods
   • See percentage changes and volume data

2. Custom Period Analysis (Option 2)

   • Enter custom date ranges
   • Get detailed historical data
   • Format: YYYY-MM-DD

3. General Stock Info (Option 3)

   • Basic stock information
   • Current market data
   • Historical performance

4. Options Trading Features (Option 4)

   • Coming Soon

5. Graph Display (Option 5)

   • Interactive price charts
   • Volume visualization
   • Technical indicators

Code Examples

Fetching Stock Data

from src.stock_analysis import StockAnalysis

analyzer = StockAnalysis()
data = analyzer.get_stock_info("AAPL", "1d")

Creating Visualizations

from src.visualization import StockVisualizer

visualizer = StockVisualizer()
visualizer.plot_stock_data(data, "AAPL", "1d")

Output Examples

Stock Information Display

Current Information for AAPL
--------------------------------------------------------------------------------
Current Price: $234.93
Market Cap: $3,551,155,000,000.00
52 Week Range: $189.99 - $234.93
P/E Ratio: 28.5
Dividend Yield: 0.54%

Gainers/Losers Analysis

Top 20 Gainers
--------------------------------------------------------------------------------
Symbol Change%    Start Price    End Price       Volume
TSLA   +88.34%   $176.75       $332.89         91,534,656
ORCL   +46.76%   $124.49       $182.70         8,466,684

Troubleshooting

Common Issues

1. No Data Available

   • Check internet connection
   • Verify stock ticker exists
   • Ensure market hours for intraday data

2. API Rate Limits

   • Wait a few minutes between requests
   • Use longer time intervals
   • Reduce batch size for multiple stocks

3. Installation Issues

   • Update pip: pip install --upgrade pip
   • Check Python version compatibility
   • Verify all dependencies are installed

Development

Setting Up Development Environment

1. Clone and Install:

git clone https://github.com/yourusername/Stock-Volatility-Analyzer.git
cd Stock-Volatility-Analyzer
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt

2. Docker Support:

docker build -t stock-analyzer .
docker run -it stock-analyzer

Project Structure

Stock-Volatility-Analyzer/
├── src/
│   ├── __init__.py
│   ├── stock_analysis.py
│   ├── stock_data.py
│   ├── stock_info.py
│   ├── menu.py
│   └── visualization.py
├── utils/
│   ├── __init__.py
│   ├── constants.py
│   └── validators.py
├── main.py
├── requirements.txt
└── README.md

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Coding Standards

• Follow PEP 8 guidelines
• Include docstrings for all functions
• Add type hints
• Write unit tests for new features

License

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

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For additional support or questions, please open an issue on the GitHub repository.