Bitcoin Price Monitor Pro

A professional-grade Bitcoin price monitoring application with real-time price tracking, trend analysis, and price prediction capabilities. Built with Python and modern data science libraries.

Features

• Real-time Price Tracking: Monitors Bitcoin price in real-time using multiple reliable cryptocurrency APIs
• Price Prediction: Implements polynomial regression to predict short-term price trends
• Interactive Visualization: Dynamic charts with real-time updates and trend analysis
• Multi-source Data: Fetches data from multiple sources (Binance, CryptoCompare, CoinGecko) for reliability
• Professional UI: Clean and intuitive user interface with key information display
• Robust Error Handling: Comprehensive error handling and logging system
• Automatic Recovery: Auto-retry mechanism for API failures and network issues

Requirements

• Python 3.7 or higher
• Required Python packages:

  requests>=2.26.0
  numpy>=1.19.2
  matplotlib>=3.3.4
  scikit-learn>=0.24.2
  

Installation

1. Clone the repository:

git clone https://github.com/shendengtech/bitcoin-price-monitor.git
cd bitcoin-price-monitor

2. Create and activate a virtual environment (optional but recommended):

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

3. Install required packages:

pip install -r requirements.txt

Usage

1. Start the application:

python bitcoin_monitor_pro.py

2. The application will:
   • Launch a graphical interface showing real-time Bitcoin price
   • Display price predictions and trend analysis
   • Update automatically every 60 seconds
   • Log all activities to bitcoin_monitor.log

Configuration

Key parameters can be modified in the code:

• max_data_points: Maximum number of data points to display (default: 90)
• Update interval: Can be adjusted in the data_loop method (default: 60 seconds)
• Polynomial degree for prediction: Set in update_chart method (default: 3)

Technical Details

Data Sources

• Primary: Binance API
• Secondary: CryptoCompare API
• Fallback: CoinGecko API

Prediction Model

• Uses Polynomial Regression (degree 3)
• Implemented using scikit-learn
• Updates in real-time with new data points

Error Handling

• Multiple API endpoints with failover
• Automatic retry mechanism
• Comprehensive logging system
• Graceful error recovery

Contributing

1. Fork the repository
2. Create your feature branch:

git checkout -b feature/new-feature

3. Commit your changes:

git commit -am 'Add new feature'

4. Push to the branch:

git push origin feature/new-feature

5. Submit a pull request

License

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

Acknowledgments

• Data provided by Binance, CryptoCompare, and CoinGecko APIs
• Built with Python's scientific computing stack
• UI implemented using Tkinter and Matplotlib

Author

shendengtech

Support

For support, please:

1. Check the Issues page
2. Create a new issue if needed
3. Include relevant logs and error messages when reporting problems

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Last updated: 2025-03-03