py-vallocation 0.2.0

Python package for asset allocation with a primary focus on integrating investor views.

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 with a simple API, while remaining flexible and customizable for advanced users
• Avoid hidden assumptions or black-box components, every modeling choice is explicitly stated
• Incorporate investor views via fully flexible probabilities using entropy pooling and the Black-Litterman methodology
• Support shrinkage and other Bayesian estimation methods
• Support variance-based, scenario-based CVaR, and robust optimization models
• Combine portfolios using ensemble averaging and exposure stacking to build diversified allocations across models

Installation

You can install Py-vAllocation from PyPI using:

pip install py-vallocation

Quick Start

See examples here

Requirements

• Python 3.8+
• numpy >= 1.20.0
• cvxopt >= 1.2.0
• pandas >=1.0.0
• scipy >= 1.10.0

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.

Underlying literature

• Meucci, A. (2008). Fully Flexible Views: Theory and Practice. https://ssrn.com/abstract=1213325
• Black, F., & Litterman, R. (1992). Global Portfolio Optimization. https://doi.org/10.2469/faj.v48.n5.28
• Ledoit, O., & Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. https://doi.org/10.1016/S0047-259X(03)00096-4
• Jorion, P. (1986). Bayes-Stein Estimation for Portfolio Analysis. https://doi.org/10.2307/2331042
• Rockafellar, R. T., & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. 10.21314/JOR.2000.038
• Markowitz, H. (1952). Portfolio Selection. https://doi.org/10.2307/2975974
• Idzorek, T. (2005). A Step-by-Step Guide to the Black-Litterman Model. https://people.duke.edu/~charvey/Teaching/BA453_2006/Idzorek_onBL.pdf
• Meucci, A. (2005). Robust Bayesian Allocation, https://dx.doi.org/10.2139/ssrn.681553
• Vorobets, A. (2021). Sequential Entropy Pooling Heuristics, http://dx.doi.org/10.2139/ssrn.3936392

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 code, where explicitly stated, is adapted from fortitudo-tech

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