Cryptocurrency Price Prediction

A deep learning project for cryptocurrency price prediction using LSTM networks.

Features

• Real-time data collection from multiple sources (CoinGecko, Binance)
• Comprehensive technical indicator calculation
• Deep learning model with LSTM architecture
• Interactive visualizations using Plotly
• Progress tracking and logging
• Configurable parameters via YAML
• Real-time monitoring dashboard
• Automated development environment setup

Project Structure

Crypto_Prediction/
├── setup_dev.sh          # Development environment setup script
├── crypto_prediction_app # Interactive application script
├── data/                 # Data storage
├── models/               # Saved models
├── src/
│   ├── data_collection/  # Data collection scripts
│   ├── preprocessing/    # Data preprocessing
│   ├── visualization/    # Visualization utilities
│   ├── training/         # Model training code
│   ├── monitoring/       # Monitoring dashboard
│   └── utils/            # Utility functions
├── tests/                # Test files
├── Final_Project_Report.ipynb # Findings and Evaluations
├── configs/              # Configuration files
├── logs/                 # Log files
├── results/              # Training & Prediction results
└── visualizations/       # Generated plots

Quick Start

1. Set up the development environment:

   chmod +x setup_dev.sh
   ./setup_dev.sh

2. Launch the interactive application:

   chmod +x crypto_prediction_app
   ./crypto_prediction_app

3. Start the monitoring dashboard:

   streamlit run src/monitoring/dashboard.py

Detailed Installation

1. Create a virtual environment:

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

2. Install dependencies:

   pip install -r requirements.txt

Interactive Application

The crypto_prediction_app script provides an interactive way to execute the pipeline, allowing users to:

1. View the default configuration.
2. Choose to run specific modes (collect-data, train, predict, or full-pipeline).
3. Optionally specify a custom configuration file.

Run the script:

./crypto_prediction_app

This interactive application will guide you through:

1. Updating and collecting the latest data.
2. Preprocessing the collected data.
3. Training a new LSTM model.
4. Generating predictions using the trained model.

Advanced Usage

Training a New Model

Train the model independently:

python main.py --config configs/config.yaml --mode train

Making Predictions Only

Use a pre-trained model to generate predictions:

python main.py --config configs/config.yaml --mode predict

Updating Data Only

Update data without proceeding to model training:

python main.py --config configs/config.yaml --mode collect-data

Monitoring Dashboard

The monitoring dashboard provides real-time insights into:

• Model performance metrics
• System resource utilization
• Data pipeline health
• Live predictions
• Training history

Start the Dashboard

1. Ensure Streamlit is installed:

   pip install streamlit

2. Run the dashboard:

   streamlit run src/monitoring/dashboard.py

3. Open your browser to http://localhost:8501.

Development Environment

The setup_dev.sh script automates setting up your development environment:

• Creates a virtual environment.
• Installs dependencies.
• Sets up the project structure.
• Initializes git hooks.
• Configures Jupyter kernels.
• Sets up environment variables.

Configuration

The project is configured using:

• configs/config.yaml: Main configuration file for paths, model settings, and training options.
• Environment variables: For dynamic configurations.
• Command-line arguments: For runtime flexibility.

Testing

Run all tests using pytest:

pytest tests/

Contributing

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

License

MIT License

Support

For assistance:

1. Check the documentation.
2. Search existing issues.
3. Create a new issue providing:
   • A clear problem description.
   • Steps to reproduce the issue.
   • Error messages and logs.
   • Your system information.