{"repo":"ArturSepp/factorlasso","free":true,"listed":false,"github":"https://github.com/ArturSepp/factorlasso","clone":"git clone https://github.com/ArturSepp/factorlasso.git","description":"factorlasso is a Python library for sparse multi-output factor-model estimation with sign constraints, prior-centred shrinkage, data-driven grouped penalties, and consistent factor covariance assembly.","language":"Python","stars":23,"topics":["covariance-estimation","cvxpy","factor-models","group-lasso","hierarchical-clustering","lasso","portfolio-construction","python","quantitative-finance","scikit-learn-compatible"],"license":"GPL-3.0","category":"trading","readme_excerpt":"factorlasso factorlasso is a Python library for sparse multi-output factor-model estimation with sign constraints, prior-centred shrinkage, data-driven grouped penalties, and consistent factor covariance assembly. It provides LASSO, Hierarchical Clustering Group LASSO (HCGL), Factor-Clustering Group LASSO (FCGL), sparse-group, UniLasso, and cooperative penalties through auditable CVXPY formulations. Paper: Sepp, A. and Kastenholz, M. (2026), factorlasso: Hierarchical Clustering Group LASSO (HCGL) with Cluster-Pooled Sign Derivation for Multi-Asset Factor Models in Python , submitted to the Journal of Statistical Software . The manuscript is available here. See Citation for the BibTeX entry. The replication material for the paper is in papers/jss 2026/ . Methodology: The cluster-pooled sign derivation and the noise-floor gate are developed in Sepp, A. and Kastenholz, M. (2026), Gated Cluster-Pooled Sign Constraints for Multi-Output Sparse Regression , submitted to Computational Statistics and Data Analysis . The replication material for that paper is in papers/sign pooling 2026/ . factorlasso is a small, dependency-light Python package for fitting sparse multi-output linear models $$ Y = X\\beta^\\top + \\varepsilon, \\qquad \\beta \\in \\mathbb{R}^{N \\times M} $$ with L1 and L2 penalties along with sign constraints on factor loadings. It targets the financial data and regime where standard sparse-regression tools quietly misattribute risk: short samples, strongly correlated factors,","default_branch":null,"files":null,"tree":[],"storefront":"/r/ArturSepp","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ArturSepp/factorlasso/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}