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Constrained and Unconstrained Risk Budgeting Allocation in Python

Actions Status Code style: ruff License: Apache 2.0

This repository contains the code for solving constrained risk budgeting with generalized standard deviation-based risk measure:

This formulation encompasses Gaussian value-at-risk and Gaussian expected shortfall and the volatility. The algorithm supports bounds constraints and inequality constraints. It is is efficient for large dimension and suitable for backtesting.

A description can be found in Constrained Risk Budgeting Portfolios: Theory, Algorithms, Applications & Puzzles by Jean-Charles Richard and Thierry Roncalli.

You can solve

  • Equally risk contribution
  • Risk budgeting
  • Risk parity with expected return
  • Constrained Risk parity

Installation

Can be done using pip:

pip install git+https://github.com/jcrichard/pyrb

Or using uv (recommended for Python 3.12+):

uv add git+https://github.com/jcrichard/pyrb

Usage

from pyrb import EqualRiskContribution

ERC = EqualRiskContribution(cov)
ERC.solve()
ERC.get_risk_contributions()
ERC.get_volatility()

Development

This project uses modern Python development tools:

Code Quality

  • ruff: For linting and formatting
  • pytest: For testing

Installation for Development

# Clone the repository
git clone https://github.com/fchareyr/pyrb.git
cd pyrb

# Install in development mode
pip install -e .[dev]

Running Tests

pytest

Code Formatting and Linting

# Format code
ruff format

# Lint code
ruff check

# Fix linting issues automatically
ruff check --fix

CI/CD

  • GitHub Actions workflow runs on every push and pull request
  • Tests are run on Python 3.12 and 3.13
  • Code must pass linting and formatting checks
  • Dependabot automatically updates dependencies

References

Griveau-Billion, T., Richard, J-C., and Roncalli, T. (2013), A Fast Algorithm for Computing High-dimensional Risk Parity Portfolios, SSRN.

Maillard, S., Roncalli, T. and Teiletche, J. (2010), The Properties of Equally Weighted Risk Contribution Portfolios, Journal of Portfolio Management, 36(4), pp. 60-70.

Richard, J-C., and Roncalli, T. (2015), Smart Beta: Managing Diversification of Minimum Variance Portfolios, in Jurczenko, E. (Ed.), Risk-based and Factor Investing, ISTE Press -- Elsevier.

Richard, J-C., and Roncalli, T. (2019), Constrained Risk Budgeting Portfolios: Theory, Algorithms, Applications & Puzzles, SSRN.

Roncalli, T. (2015), Introducing Expected Returns into Risk Parity Portfolios: A New Framework for Asset Allocation, Bankers, Markets & Investors, 138, pp. 18-28.