py-vallocation 0.4.0

Portfolio allocation tools with views, robust optimization, and ensembling

Py-vAllocation

Py-vAllocation is a research-to-production toolkit for scenario-based portfolio optimisation. Build mean-variance, CVaR, and relaxed risk parity frontiers; incorporate Black–Litterman and entropy pooling views; apply shrinkage-heavy statistics (NIW, Ledoit–Wolf, nonlinear shrinkage, Tyler, Huber, POET); ensemble strategies; and convert weights to discrete trades. Pandas labels are preserved throughout the workflow.

Highlights

• Consistent optimisation surface - switch between mean-variance, CVaR, relaxed risk parity, and robust formulations without rewriting constraints.
• View integration - Black–Litterman mean views plus entropy pooling constraints keep discretionary inputs consistent with posterior moments.
• Robust models - relaxed risk parity, Bayesian NIW updates, and Meucci-style probability tilts.
• Moment estimation - Ledoit–Wolf, James–Stein, nonlinear shrinkage, Tyler, Huber, POET, graphical lasso, and more via estimate_moments.
• Production plumbing - ensemble builders, discrete allocation, plotting, and reporting helpers reduce friction between research and delivery.
• Stress testing & PnL - one-line helpers for probability tilts, linear shocks, and compact risk reports.
• Optional extras - install the robust extra only when heavy dependencies are needed.

Design principles

• Pandas-first inputs/outputs with consistent labels when they are supplied.
• Scenario-based risk by default, with clear risk labels across frontiers.
• Minimal reformatting: utility helpers standardise shapes, weights, and probabilities.

Installation

pip install py-vallocation

For nonlinear shrinkage and POET estimators:

pip install py-vallocation[robust]

Requires cvxopt>=1.2.0. If you don't have it, see the installation guide.

Quickstart

Run the end-to-end ETF example (writes plots and CSVs to output/):

python examples/quickstart_etf_allocation.py

Key artefacts:

• output/frontiers.png - in-sample vs out-of-sample efficient frontiers with robust overlay.
• output/robust_uncertainty.png, robust_param_impact.png, robust_assumptions_3d.png - robust diagnostics.
• output/stacked_weights.csv, selected_weights.csv, average_weights.csv - ensemble summaries.
• Terminal output covering discrete trade sizing and stress results.

Or use the API directly:

import pandas as pd
from pyvallocation.portfolioapi import AssetsDistribution, PortfolioWrapper

scenarios = pd.DataFrame({
    "Stock_A": [0.01, -0.02, 0.015],
    "Stock_B": [0.007, 0.003, 0.004]
})

port = PortfolioWrapper(AssetsDistribution(scenarios=scenarios))
port.set_constraints({"long_only": True, "total_weight": 1.0})

frontier = port.variance_frontier(num_portfolios=20)
weights, ret, risk = frontier.get_tangency_portfolio(risk_free_rate=0.01)
print(weights)

# Pick a portfolio by CVaR even on a variance frontier (scenarios required)
weights_cvar, ret_cvar, cvar_val = frontier.portfolio_at_risk_target(
    max_risk=0.02, risk_label="CVaR (alpha=0.05)"
)
print(weights_cvar)

Examples

The examples/ directory contains runnable scripts (see examples/README.md):

• quickstart_etf_allocation.py - moments → frontiers → ensemble → trades
• mean_variance_frontier.py, cvar_allocation.py, robust_frontier.py (use variance_frontier / cvar_frontier)
• relaxed_risk_parity_frontier.py, portfolio_ensembles.py, discrete_allocation.py
• stress_and_pnl.py - probability tilts + linear shocks + performance reports

Notebooks (examples/*.ipynb) mirror the tutorials.

Documentation

• Full documentation: https://py-vallocation.readthedocs.io
• Tutorials live under docs/tutorials/ and mirror the runnable scripts.
• API reference is generated from docstrings (docs/pyvallocation*.rst).
• Build locally:

pip install -e .[robust]
sphinx-build -b html docs docs/_build/html

Repository layout

• pyvallocation/ - library source code.
• examples/ - runnable workflows (ETF quickstart, CVaR frontier, ensembles, stress testing, discrete allocation).
• docs/ - Sphinx site (tutorials, API reference, bibliography).
• tests/ - pytest suite covering numerical routines, ensembles, plotting, and discrete allocation.
• output/ - artefacts written by example scripts.

Requirements

• Python 3.8+
• numpy, pandas, scipy, cvxopt

References

• Markowitz (1952) - Portfolio Selection
• Black & Litterman (1992) - Global Portfolio Optimization
• Ledoit & Wolf (2004, 2020) - Covariance shrinkage
• Meucci (2008) - Fully Flexible Views (entropy pooling)
• Rockafellar & Uryasev (2000) - CVaR optimization

See the bibliography for the complete list.

Contributing

Issues and PRs welcome. See CONTRIBUTING.md.

License

GPL-3.0-or-later — see LICENSE for the full text. Portions of the optimisation routines are adapted (with attribution) from fortitudo-tech.