py-vallocation 0.1.0

Python package for asset allocation integrating investor views via entropy pooling and Black–Litterman methodology

Py-vAllocation

Py-vAllocation is a Python package for asset allocation with a primary focus on integrating investor views.

Features

It's yet another portfolio optimization library, but unlike many others, Py-vAllocation aims to:

• Be modular and beginner-friendly, while remaining flexible and customizable for advanced users
• Support Variance, CVaR and Robust Bayesian optimisation, using either mean/variance distributions or scenario probabilities
• Avoid hidden assumptions or black-box components — every modeling choice is explicitly stated
• Incorporate investor views via historical flexible probabilities using entropy pooling and the Black-Litterman methodology
• Support shrinkage estimators and other Bayesian estimation methods

Installation

Since Py-vAllocation is under active development and not yet published on PyPI, install from source:

git clone https://github.com/enexqnt/py-vallocation.git
cd py-vallocation
pip install .

Quick Start

See examples here

Requirements

• Python 3.8+
• numpy >= 1.20.0
• cvxopt >= 1.2.0
• pandas (optional, recommended for enhanced functionality)
  • check availability via pyvallocation.optional.HAS_PANDAS

Development Status

Alpha release: Under active development. Many features are not yet implemented or fully tested. Breaking changes may occur without notice. Use at your own risk.

Contributing

Contributions and feedback are welcome! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the GNU General Public License v3.0 License. See LICENSE for details.

Credits

Some part of the code, where explictly stated, is adapted from fortitudo-tech