# AlphaPy Pro
[](https://scottfreellc.github.io/alphapy-pro/)
[](https://pypi.org/project/alphapy-pro/)
[](https://www.python.org/downloads/)
[](https://github.com/ScottFreeLLC/alphapy-pro/actions)
[](LICENSE)
**AlphaPy Pro** is an advanced machine learning framework designed for speculators and data scientists. Building on the foundation of the original AlphaPy, this professional edition offers enhanced features, improved performance, and enterprise-grade capabilities for financial modeling and prediction.
Written in Python with `scikit-learn`, `pandas`, and many other powerful libraries, AlphaPy Pro provides a comprehensive toolkit for feature engineering, model development, and portfolio analysis.
## 🚀 Project Status
- 📦 **Package**: Ready for PyPI publication (alphapy-pro)
- 📚 **Documentation**: Live on [GitHub Pages](https://scottfreellc.github.io/alphapy-pro/)
- 🐍 **Python**: Requires 3.12+ (modern Python features)
- 🔄 **Development**: Actively maintained and enhanced
## ✨ What's New in AlphaPy Pro
- **MetaLabeling Support**: Advanced financial ML labeling techniques
- **NLP Features**: Natural language processing for sentiment analysis
- **Enhanced MarketFlow**: Improved financial data pipeline
- **Modern Packaging**: Built with `uv` for faster dependency management
- **Python 3.12+**: Leverages latest Python performance improvements
## Key Features
* **Advanced ML Pipeline**: Run machine learning models using `scikit-learn`, `XGBoost`, `LightGBM`, and `CatBoost`
* **Ensemble Methods**: Generate sophisticated blended and stacked ensembles
* **MarketFlow**: Specialized pipeline for financial market analysis and trading system development
* **Portfolio Analysis**: Comprehensive trading system backtesting and portfolio optimization
* **Configuration-Driven**: Flexible YAML-based configuration system
* **Feature Engineering**: Advanced feature creation, selection, and transformation tools
* **Time Series Support**: Built-in support for time series forecasting and analysis
## Architecture
### Core Components
- **alphapy/**: Main package with core ML functionality
- **config/**: YAML configuration files for algorithms, features, and systems
- **projects/**: Individual project workspaces with isolated configurations
- **docs/**: Comprehensive documentation and tutorials
### Pipeline Flow
1. **Data Ingestion**: Load and preprocess data from various sources
2. **Feature Engineering**: Create, transform, and select features
3. **Model Training**: Train and optimize multiple ML algorithms
4. **Ensemble Creation**: Combine models for improved performance
5. **Evaluation**: Generate comprehensive performance metrics and visualizations
6. **Deployment**: Export models and predictions for production use
## Quick Start
### Installation
#### From PyPI (Coming Soon)
```bash
# Install the latest stable version
pip install alphapy-pro
# Or using uv (faster)
uv pip install alphapy-pro
# Install with optional dependencies
pip install alphapy-pro[dev,docs]
```
> 📆 **Note**: Package will be available on PyPI shortly. For now, use development installation below.
#### Development Installation (Current)
```bash
# Clone the repository
git clone https://github.com/ScottFreeLLC/alphapy-pro.git
cd alphapy-pro
# Using uv (recommended - faster)
uv pip install -e .[dev]
# Or using pip
pip install -e .[dev]
```
#### From Source
```bash
# Build and install using modern packaging
python -m build
pip install dist/alphapy_pro-*.whl
```
### Configuration Setup
1. **Copy configuration templates**:
```bash
cd config
cp alphapy.yml.template alphapy.yml
cp sources.yml.template sources.yml
```
2. **Edit configurations**:
- Update `alphapy.yml` with your local directory paths
- Add your API keys to `sources.yml` (keep this file secure!)
3. **See `config/README.md` for detailed setup instructions**
### Running AlphaPy Pro
```bash
# Main pipeline
alphapy
# Market analysis pipeline
mflow
```
## Project Structure
```
alphapy-pro/
├── alphapy/ # Core package
│ ├── alphapy_main.py # Main pipeline entry point
│ ├── mflow_main.py # Market flow pipeline
│ ├── model.py # Model management
│ ├── features.py # Feature engineering
│ └── ...
├── config/ # Configuration files
│ ├── alphapy.yml # Main configuration (user-specific)
│ ├── sources.yml # API keys (user-specific, gitignored)
│ ├── algos.yml # ML algorithm definitions
│ ├── variables.yml # Feature definitions
│ └── ...
├── projects/ # Project workspaces
│ ├── kaggle/ # Kaggle competition example
│ ├── time-series/ # Time series analysis
│ └── ...
└── docs/ # Documentation
```
## Configuration System
AlphaPy Pro uses a comprehensive YAML-based configuration system:
- **`config/alphapy.yml`**: Main configuration with project paths
- **`config/sources.yml`**: API keys for data sources ⚠️ **Keep Secret!**
- **`config/algos.yml`**: ML algorithm definitions and hyperparameters
- **`config/variables.yml`**: Feature variable definitions
- **`config/groups.yml`**: Variable groupings for feature engineering
- **`config/systems.yml`**: Trading system definitions
- **`projects/*/config/model.yml`**: Project-specific model configurations
## MarketFlow
Specialized pipeline for financial market analysis featuring:
- **Multi-source data integration** (EOD Historical Data, Finnhub, IEX, Polygon, etc.)
- **Advanced technical indicators** and market features
- **Trading system development** and backtesting
- **Portfolio optimization** and risk analysis
- **Real-time prediction** capabilities
## Data Sources
AlphaPy Pro supports multiple data providers:
- **EOD Historical Data**: Historical market data
- **Finnhub**: Real-time market data and news
- **IEX Cloud**: Financial data platform
- **Polygon**: Stock market data API
- **Yahoo Finance**: Free market data
- **Custom sources**: Easily integrate your own data
## Examples
### Kaggle Competition
```bash
cd projects/kaggle
alphapy
```
### Time Series Analysis
```bash
cd projects/time-series
alphapy
```
### Market Analysis
```bash
cd projects/your-market-project
mflow
```
## Documentation
📚 **Live Documentation**: [https://scottfreellc.github.io/alphapy-pro/](https://scottfreellc.github.io/alphapy-pro/)
Documentation is automatically built and deployed via GitHub Actions. It covers:
- **Installation and setup**
- **Configuration guide**
- **Feature engineering**
- **Model development**
- **Trading systems**
- **API reference**
### Building Documentation Locally
```bash
# Using uv
uv pip install sphinx sphinx-rtd-theme
cd docs
make html
# Or using pip
pip install sphinx sphinx-rtd-theme
cd docs
make html
```
## Development
### Build Documentation
```bash
# Install documentation dependencies
uv pip install sphinx sphinx-rtd-theme sphinx-autodoc-typehints
# Build documentation
cd docs
make html
```
### Clean Up Old Runs
```bash
./utils/cleanup_runs.sh
```
### Testing
```bash
# Run tests with pytest
pytest
# Run tests with coverage
pytest --cov=alphapy --cov-report=html
# Run specific test files
pytest tests/test_version.py -v
```
## Requirements
- **Python 3.12+** (Latest Python features and performance)
- **pandas**: Data manipulation and analysis
- **scikit-learn**: Machine learning algorithms
- **NumPy**: Numerical computing
- **PyYAML**: Configuration file parsing
- **matplotlib**: Plotting and visualization
- **seaborn**: Statistical data visualization
### Optional Dependencies
- **pytest**: For running tests (install with `uv pip install alphapy-pro[test]`)
- **black, isort, flake8**: Code quality tools (install with `uv pip install alphapy-pro[dev]`)
- **sphinx**: Documentation building (install with `uv pip install alphapy-pro[docs]`)
## Contributing
We welcome contributions to AlphaPy Pro! Here's how you can help:
1. **Fork the repository** and create your feature branch from `main`
2. **Make your changes** following the existing code style
3. **Add tests** for any new functionality
4. **Update documentation** if needed
5. **Submit a pull request** with a clear description of your changes
### Development Setup
```bash
# Clone your fork
git clone https://github.com/ScottFreeLLC/alphapy-pro.git
cd alphapy-pro
# Install in development mode with all dev dependencies
uv pip install -e .[dev]
# Install pre-commit hooks
pre-commit install
# Set up configuration
cd config
cp alphapy.yml.template alphapy.yml
cp sources.yml.template sources.yml
# Edit with your settings
```
### Code Quality
This project uses several tools to maintain code quality:
```bash
# Format code with black
black .
# Sort imports with isort
isort .
# Check code style with flake8
flake8 .
# Run type checking with mypy
mypy alphapy/
# Run all pre-commit hooks
pre-commit run --all-files
```
### Code Guidelines
- Follow PEP 8 style guidelines (enforced by flake8)
- Use black for code formatting
- Add docstrings to new functions and classes
- Include type hints where appropriate
- Write tests for new functionality
- Update CLAUDE.md if adding new development commands
## License
AlphaPy Pro is licensed under the Apache License 2.0. See `LICENSE` for details.
## Support
- **Issues**: Open an issue on GitHub for bug reports and feature requests
- **Documentation**: Check the `docs/` directory for comprehensive guides
- **Configuration**: See `config/README.md` for setup help
## Donations
If you find AlphaPy Pro valuable for your work, please consider supporting its development:
- **GitHub Sponsors**: [Sponsor this project](https://github.com/sponsors/your-username)
- **Buy Me a Coffee**: Support ongoing development
- **PayPal**: Direct donations welcome
Your support helps maintain and improve AlphaPy Pro for the entire community.
## Acknowledgments
AlphaPy Pro builds upon the foundation of the original AlphaPy framework, incorporating lessons learned and feature requests from the community. Special thanks to all contributors and users who have helped shape this professional edition.
---
*AlphaPy Pro - Professional Machine Learning for Financial Markets and Beyond*
Raw data
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"description": "# AlphaPy Pro\n\n[](https://scottfreellc.github.io/alphapy-pro/)\n[](https://pypi.org/project/alphapy-pro/)\n[](https://www.python.org/downloads/)\n[](https://github.com/ScottFreeLLC/alphapy-pro/actions)\n[](LICENSE)\n\n**AlphaPy Pro** is an advanced machine learning framework designed for speculators and data scientists. Building on the foundation of the original AlphaPy, this professional edition offers enhanced features, improved performance, and enterprise-grade capabilities for financial modeling and prediction.\n\nWritten in Python with `scikit-learn`, `pandas`, and many other powerful libraries, AlphaPy Pro provides a comprehensive toolkit for feature engineering, model development, and portfolio analysis.\n\n## \ud83d\ude80 Project Status\n\n- \ud83d\udce6 **Package**: Ready for PyPI publication (alphapy-pro)\n- \ud83d\udcda **Documentation**: Live on [GitHub Pages](https://scottfreellc.github.io/alphapy-pro/)\n- \ud83d\udc0d **Python**: Requires 3.12+ (modern Python features)\n- \ud83d\udd04 **Development**: Actively maintained and enhanced\n\n## \u2728 What's New in AlphaPy Pro\n\n- **MetaLabeling Support**: Advanced financial ML labeling techniques\n- **NLP Features**: Natural language processing for sentiment analysis\n- **Enhanced MarketFlow**: Improved financial data pipeline\n- **Modern Packaging**: Built with `uv` for faster dependency management\n- **Python 3.12+**: Leverages latest Python performance improvements\n\n## Key Features\n\n* **Advanced ML Pipeline**: Run machine learning models using `scikit-learn`, `XGBoost`, `LightGBM`, and `CatBoost`\n* **Ensemble Methods**: Generate sophisticated blended and stacked ensembles\n* **MarketFlow**: Specialized pipeline for financial market analysis and trading system development\n* **Portfolio Analysis**: Comprehensive trading system backtesting and portfolio optimization\n* **Configuration-Driven**: Flexible YAML-based configuration system\n* **Feature Engineering**: Advanced feature creation, selection, and transformation tools\n* **Time Series Support**: Built-in support for time series forecasting and analysis\n\n## Architecture\n\n### Core Components\n\n- **alphapy/**: Main package with core ML functionality\n- **config/**: YAML configuration files for algorithms, features, and systems\n- **projects/**: Individual project workspaces with isolated configurations\n- **docs/**: Comprehensive documentation and tutorials\n\n### Pipeline Flow\n\n1. **Data Ingestion**: Load and preprocess data from various sources\n2. **Feature Engineering**: Create, transform, and select features\n3. **Model Training**: Train and optimize multiple ML algorithms\n4. **Ensemble Creation**: Combine models for improved performance\n5. **Evaluation**: Generate comprehensive performance metrics and visualizations\n6. **Deployment**: Export models and predictions for production use\n\n## Quick Start\n\n### Installation\n\n#### From PyPI (Coming Soon)\n```bash\n# Install the latest stable version\npip install alphapy-pro\n\n# Or using uv (faster)\nuv pip install alphapy-pro\n\n# Install with optional dependencies\npip install alphapy-pro[dev,docs]\n```\n\n> \ud83d\udcc6 **Note**: Package will be available on PyPI shortly. For now, use development installation below.\n\n#### Development Installation (Current)\n```bash\n# Clone the repository\ngit clone https://github.com/ScottFreeLLC/alphapy-pro.git\ncd alphapy-pro\n\n# Using uv (recommended - faster)\nuv pip install -e .[dev]\n\n# Or using pip\npip install -e .[dev]\n```\n\n#### From Source\n```bash\n# Build and install using modern packaging\npython -m build\npip install dist/alphapy_pro-*.whl\n```\n\n### Configuration Setup\n\n1. **Copy configuration templates**:\n ```bash\n cd config\n cp alphapy.yml.template alphapy.yml\n cp sources.yml.template sources.yml\n ```\n\n2. **Edit configurations**:\n - Update `alphapy.yml` with your local directory paths\n - Add your API keys to `sources.yml` (keep this file secure!)\n\n3. **See `config/README.md` for detailed setup instructions**\n\n### Running AlphaPy Pro\n\n```bash\n# Main pipeline\nalphapy\n\n# Market analysis pipeline\nmflow\n```\n\n## Project Structure\n\n```\nalphapy-pro/\n\u251c\u2500\u2500 alphapy/ # Core package\n\u2502 \u251c\u2500\u2500 alphapy_main.py # Main pipeline entry point\n\u2502 \u251c\u2500\u2500 mflow_main.py # Market flow pipeline\n\u2502 \u251c\u2500\u2500 model.py # Model management\n\u2502 \u251c\u2500\u2500 features.py # Feature engineering\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 config/ # Configuration files\n\u2502 \u251c\u2500\u2500 alphapy.yml # Main configuration (user-specific)\n\u2502 \u251c\u2500\u2500 sources.yml # API keys (user-specific, gitignored)\n\u2502 \u251c\u2500\u2500 algos.yml # ML algorithm definitions\n\u2502 \u251c\u2500\u2500 variables.yml # Feature definitions\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 projects/ # Project workspaces\n\u2502 \u251c\u2500\u2500 kaggle/ # Kaggle competition example\n\u2502 \u251c\u2500\u2500 time-series/ # Time series analysis\n\u2502 \u2514\u2500\u2500 ...\n\u2514\u2500\u2500 docs/ # Documentation\n```\n\n## Configuration System\n\nAlphaPy Pro uses a comprehensive YAML-based configuration system:\n\n- **`config/alphapy.yml`**: Main configuration with project paths\n- **`config/sources.yml`**: API keys for data sources \u26a0\ufe0f **Keep Secret!**\n- **`config/algos.yml`**: ML algorithm definitions and hyperparameters\n- **`config/variables.yml`**: Feature variable definitions\n- **`config/groups.yml`**: Variable groupings for feature engineering\n- **`config/systems.yml`**: Trading system definitions\n- **`projects/*/config/model.yml`**: Project-specific model configurations\n\n## MarketFlow\n\nSpecialized pipeline for financial market analysis featuring:\n\n- **Multi-source data integration** (EOD Historical Data, Finnhub, IEX, Polygon, etc.)\n- **Advanced technical indicators** and market features\n- **Trading system development** and backtesting\n- **Portfolio optimization** and risk analysis\n- **Real-time prediction** capabilities\n\n\n## Data Sources\n\nAlphaPy Pro supports multiple data providers:\n\n- **EOD Historical Data**: Historical market data\n- **Finnhub**: Real-time market data and news\n- **IEX Cloud**: Financial data platform\n- **Polygon**: Stock market data API\n- **Yahoo Finance**: Free market data\n- **Custom sources**: Easily integrate your own data\n\n## Examples\n\n### Kaggle Competition\n```bash\ncd projects/kaggle\nalphapy\n```\n\n### Time Series Analysis\n```bash\ncd projects/time-series\nalphapy\n```\n\n### Market Analysis\n```bash\ncd projects/your-market-project\nmflow\n```\n\n## Documentation\n\n\ud83d\udcda **Live Documentation**: [https://scottfreellc.github.io/alphapy-pro/](https://scottfreellc.github.io/alphapy-pro/)\n\nDocumentation is automatically built and deployed via GitHub Actions. It covers:\n- **Installation and setup**\n- **Configuration guide**\n- **Feature engineering**\n- **Model development**\n- **Trading systems**\n- **API reference**\n\n### Building Documentation Locally\n```bash\n# Using uv\nuv pip install sphinx sphinx-rtd-theme\ncd docs\nmake html\n\n# Or using pip\npip install sphinx sphinx-rtd-theme\ncd docs\nmake html\n```\n\n## Development\n\n### Build Documentation\n```bash\n# Install documentation dependencies\nuv pip install sphinx sphinx-rtd-theme sphinx-autodoc-typehints\n\n# Build documentation\ncd docs\nmake html\n```\n\n### Clean Up Old Runs\n```bash\n./utils/cleanup_runs.sh\n```\n\n### Testing\n```bash\n# Run tests with pytest\npytest\n\n# Run tests with coverage\npytest --cov=alphapy --cov-report=html\n\n# Run specific test files\npytest tests/test_version.py -v\n```\n\n## Requirements\n\n- **Python 3.12+** (Latest Python features and performance)\n- **pandas**: Data manipulation and analysis\n- **scikit-learn**: Machine learning algorithms\n- **NumPy**: Numerical computing\n- **PyYAML**: Configuration file parsing\n- **matplotlib**: Plotting and visualization\n- **seaborn**: Statistical data visualization\n\n### Optional Dependencies\n- **pytest**: For running tests (install with `uv pip install alphapy-pro[test]`)\n- **black, isort, flake8**: Code quality tools (install with `uv pip install alphapy-pro[dev]`)\n- **sphinx**: Documentation building (install with `uv pip install alphapy-pro[docs]`)\n\n## Contributing\n\nWe welcome contributions to AlphaPy Pro! Here's how you can help:\n\n1. **Fork the repository** and create your feature branch from `main`\n2. **Make your changes** following the existing code style\n3. **Add tests** for any new functionality\n4. **Update documentation** if needed\n5. **Submit a pull request** with a clear description of your changes\n\n### Development Setup\n\n```bash\n# Clone your fork\ngit clone https://github.com/ScottFreeLLC/alphapy-pro.git\ncd alphapy-pro\n\n# Install in development mode with all dev dependencies\nuv pip install -e .[dev]\n\n# Install pre-commit hooks\npre-commit install\n\n# Set up configuration\ncd config\ncp alphapy.yml.template alphapy.yml\ncp sources.yml.template sources.yml\n# Edit with your settings\n```\n\n### Code Quality\n\nThis project uses several tools to maintain code quality:\n\n```bash\n# Format code with black\nblack .\n\n# Sort imports with isort\nisort .\n\n# Check code style with flake8\nflake8 .\n\n# Run type checking with mypy\nmypy alphapy/\n\n# Run all pre-commit hooks\npre-commit run --all-files\n```\n\n### Code Guidelines\n\n- Follow PEP 8 style guidelines (enforced by flake8)\n- Use black for code formatting\n- Add docstrings to new functions and classes\n- Include type hints where appropriate\n- Write tests for new functionality\n- Update CLAUDE.md if adding new development commands\n\n## License\n\nAlphaPy Pro is licensed under the Apache License 2.0. See `LICENSE` for details.\n\n## Support\n\n- **Issues**: Open an issue on GitHub for bug reports and feature requests\n- **Documentation**: Check the `docs/` directory for comprehensive guides\n- **Configuration**: See `config/README.md` for setup help\n\n## Donations\n\nIf you find AlphaPy Pro valuable for your work, please consider supporting its development:\n\n- **GitHub Sponsors**: [Sponsor this project](https://github.com/sponsors/your-username)\n- **Buy Me a Coffee**: Support ongoing development\n- **PayPal**: Direct donations welcome\n\nYour support helps maintain and improve AlphaPy Pro for the entire community.\n\n## Acknowledgments\n\nAlphaPy Pro builds upon the foundation of the original AlphaPy framework, incorporating lessons learned and feature requests from the community. Special thanks to all contributors and users who have helped shape this professional edition.\n\n---\n\n*AlphaPy Pro - Professional Machine Learning for Financial Markets and Beyond*\n\n",
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