Cryptocurrency Price Prediction System

Overview

A comprehensive cryptocurrency price prediction system that uses multiple machine learning models and technical analysis to predict price movements. The system includes data collection, preprocessing, model training, backtesting, and a web interface for visualization.

Features

• Multiple ML model implementations:
  • LSTM (Long Short-Term Memory)
  • Random Forest
  • Gradient Boosting Machine (GBM)
  • XGBoost
  • LightGBM
• Technical analysis indicators
• Advanced backtesting framework
• Web-based dashboard
• Real-time price updates
• Performance metrics and visualization

Prerequisites

• Python 3.8+
• Binance API credentials
• TA-Lib installation

Installation

1. Clone the repository:

git clone https://github.com/yourusername/crypto-predictor.git
cd crypto-predictor

2. Create and activate virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

4. Create .env file:

touch .env

5. Add your Binance API credentials to .env:

BINANCE_API_KEY=your_api_key
BINANCE_SECRET=your_api_secret

Usage

1. Run the application:

python main.py

2. Access the web interface:

• Main interface: http://localhost:5000
• Dashboard: http://localhost:5000/dash

Model Details

Random Forest Model

• Ensemble learning method
• Features:
  • 100 estimators
  • Max depth of 10
  • Parallel processing enabled

Gradient Boosting Machine (GBM)

• Boosting algorithm
• Features:
  • 100 estimators
  • Learning rate of 0.1
  • Max depth of 5

XGBoost Model

• Advanced implementation of gradient boosting
• Features:
  • Early stopping
  • Subsample ratio of 0.8
  • Column sampling of 0.8

LightGBM Model

• Light Gradient Boosting Machine
• Features:
  • Leaf-wise growth
  • 31 leaves per tree
  • Parallel processing enabled

Project Structure

crypto_predictor/
├── config/          # Configuration settings
├── data/            # Data collection and preprocessing
├── models/          # ML model implementations
├── backtesting/     # Backtesting framework
├── web/             # Web interface
└── utils/           # Utility functions

Configuration

• Adjust model parameters in config.py
• Modify technical indicators in technical_indicators.py
• Configure backtesting parameters in advanced_backtester.py

Testing

Run tests:

python -m pytest tests/

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

• Binance API for cryptocurrency data
• CCXT library for exchange integration
• TA-Lib for technical analysis indicators
• Various ML libraries contributors

Disclaimer

This software is for educational purposes only. Do not use it for trading without proper validation and risk management.