cla

Portfolio Construction and Risk

Critical Line Algorithm implementation for constrained mean-variance optimization.

Why This Module Exists

CLA solves constrained Markowitz problems efficiently with active-set style line updates.

Key Public APIs

CLA
covariance
ReturnsEstimation

Core Math

MVO Objective

\[\min_w\;\frac{1}{2}w^T\Sigma w-\lambda\mu^T w\]

Budget Constraint

\[\mathbf{1}^T w=1\]

Code Examples

Prepare covariance for CLA

use nalgebra::DMatrix;
use openquant::cla::covariance;

let returns = DMatrix::from_row_slice(3, 2, &[0.01, 0.02, -0.01, 0.01, 0.015, 0.03]);
let sigma = covariance(&returns);

Implementation Notes

CLA behavior depends on weight bounds and return estimates.
Use robust covariance estimators when sample size is small.