Module portfolio 

Portfolio optimization engine (Rust/nalgebra port, quantwave-a465).

Rust port of the Python-first v1 shipped at quantwave-py/python/quantwave/portfolio.py (p2k0.5, PR #21). That module is the numerical SPEC: sample/EWMA/Ledoit-Wolf covariance estimators; min-variance / max-Sharpe (closed-form) / risk-parity (Spinu fixed-point) / HRP (hand-rolled single-linkage) optimizers; capped-simplex projection for box+budget constraints. This module mirrors that algorithmically, operating on nalgebra DMatrix<f64> / DVector<f64> instead of numpy arrays, so it can be embedded in the core engine and driven from Rust callers (backtests, live sizing) without a Python round-trip. PyO3 bindings in quantwave-py expose it to Python as quantwave._portfolio (see that crate for the numpy <-> nalgebra bridge).

Conventions

• Returns matrices are (rows = time, cols = assets).
• Every optimizer returns a weights vector that sums to 1.0.
• Degenerate inputs (singular covariance, a single asset, NaN returns) return PortfolioError rather than panicking or returning NaN weights (workspace lint: unwrap_used/expect_used = warn, panic = deny).

Enums

PortfolioError

Errors from the portfolio-optimization engine.

Functions

apply_bounds_and_budget

Project raw weights onto the box-constrained, budget=1 feasible set.

ewma_cov

Exponentially-weighted covariance matrix (RiskMetrics-style).

hrp

Hierarchical Risk Parity (Lopez de Prado) portfolio weights.

ledoit_wolf_cov

Ledoit-Wolf shrinkage covariance estimator (shrinkage target: identity-scaled).

max_sharpe

Maximum-Sharpe (tangency) portfolio weights.

min_variance

Minimum-variance portfolio weights.

risk_parity

Equal risk-contribution (“risk parity”) portfolio weights.

sample_cov

Sample covariance matrix of asset returns: np.cov(returns, rowvar=False, ddof=1).

Type Aliases

Bounds

Per-asset (low, high) weight bounds.