factorlasso 0.7.1

Sparse multi-asset factor model estimation with cluster-pooled sign derivation, hierarchical group LASSO (HCGL), adaptive penalty reweighting, and consistent factor covariance assembly

factorlasso

Sparse multi-output regression with sign constraints, prior-centered regularisation, and a family of grouped and cooperative penalties — Hierarchical Clustering Group LASSO (HCGL), Factor-Clustering Group LASSO (FCGL), UniLasso, and cooperative LASSO — via CVXPY.

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/.

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, and a known group structure or inferred cluster structure. On a multi-asset benchmark it matches scikit-learn, skglm, and asgl on generic accuracy while recovering a collinear credit factor that every one of them shrinks to zero (see Empirical illustration).

factorlasso is useful when four things matter:

• Some coefficients must be zero, non-negative, or non-positive, possibly by asset, by factor, or both.
• You have a prior β₀ and want to penalise ‖β − β₀‖, not ‖β‖.
• You want structured sparsity — groups of responses entering or leaving the model together — where the groups are either user-supplied or discovered by hierarchical clustering of the response correlation matrix. Two grouping geometries are offered: HCGL (Hierarchical Clustering Group LASSO), which groups the penalty over each asset's factor loadings, and FCGL (Factor-Clustering Group LASSO), which groups it over a cluster's assets on each factor.
• You want to combine group-level selection with within-group elementwise sparsity via a tunable mix of group L2 and L1 penalties (Sparse Group LASSO).
• You want the sign coherence within a group to be soft, so the data can overrule it, through the cooperative LASSO, or a per-response univariate-guided estimator that needs no grouping, through UniLasso.

It is written in pure numpy/pandas/scipy/cvxpy. No numba, no custom coordinate descent. The solver is CVXPY (default CLARABEL), so problem formulation is explicit and auditable.

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Installation

pip install factorlasso

Requires Python ≥ 3.9, CVXPY ≥ 1.3, and numpy / pandas / scipy / openpyxl.

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Quickstart

import numpy as np
import pandas as pd
from factorlasso import LassoModel, LassoModelType

rng = np.random.default_rng(0)
T, M, N = 200, 4, 10
X = pd.DataFrame(rng.standard_normal((T, M)), columns=[f"f{i}" for i in range(M)])
Y = pd.DataFrame(rng.standard_normal((T, N)), columns=[f"y{i}" for i in range(N)])

model = LassoModel(model_type=LassoModelType.LASSO, reg_lambda=1e-5).fit(x=X, y=Y)

model.coef_         # (N, M) estimated β
model.intercept_    # (N,) estimated α
model.predict(X)    # Ŷ
model.score(X, Y)   # mean R²

The API mirrors scikit-learn: fit(x, y), predict(x), score(x, y), get_params(), set_params(). Fitted attributes carry a trailing underscore. fit/predict/score accept NumPy arrays as well as pandas objects, and the estimator declares __sklearn_tags__, so it composes directly with sklearn.pipeline.Pipeline, GridSearchCV, and cross_val_score. A fitted model also exposes summary() (a text fit report) and plot_signs() (a heatmap of the derived sign matrix).

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Motivation

Factor models are central to quantitative finance, underpinning the commercial risk systems in industry use (among others, MSCI Barra, Axioma, and Bloomberg's multi-asset-class models). These systems decompose asset returns into a few common factors and an idiosyncratic residual (Rosenberg and McKibben 1973; Connor 1995). Practitioners who build factor-risk and capital-market-assumption systems must estimate the loadings of N asset returns on M tradable factors over a sample of length T, and they need software that produces loadings stable enough to feed a portfolio optimiser. In financial applications the estimation regime is adverse on four counts. First, the sample is short by statistical standards, because most core cross-asset indices and funds launched in the late 2000s and 2010s, giving 5 to 25 years of data. Second, the factors are often strongly correlated, so the design carries severe multicollinearity. Third, the cross-section is moderate (N ∈ [50, 200] at the multi-asset-class level). Fourth, the true coefficient matrix β ∈ ℝ^(N×M) exhibits a cluster structure that the practitioner does not know a priori, and traditional provider-based asset classifications are a weak proxy for the interdependence of assets. The cluster structure matters: assets in the same economic group (regional equity, investment-grade credit, hedge funds, and so on) typically share signs on the dominant factors, and an estimator that exploits this structure can outperform one that treats each asset independently.

factorlasso is the estimation engine behind a multi-asset capital market assumptions and allocation framework. The loadings it produces feed the ROSAA framework for robust strategic and tactical asset allocation (Sepp, Ossa and Kastenholz 2026) and the MATF-CMA framework for capital market assumptions (Sepp, Hansen and Kastenholz 2026). Both papers are listed under Citation.

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What makes it different

1. Per-element sign constraints

A (N × M) matrix drives the constraints. Each entry is one of {0, 1, -1, NaN}: equality-to-zero, non-negative, non-positive, or free. This lets a single fit encode structural knowledge that spans multiple responses.

signs = pd.DataFrame(np.nan, index=Y.columns, columns=X.columns)
signs.loc["y0", "f0"] = 1      # β[y0, f0] ≥ 0
signs.loc["y0", "f1"] = 0      # β[y0, f1] == 0
signs.loc["y1", "f0"] = -1     # β[y1, f0] ≤ 0

model = LassoModel(
    reg_lambda=1e-5,
    factors_beta_loading_signs=signs,
).fit(x=X, y=Y)

Scikit-learn's Lasso supports only a single positive flag across the whole coefficient matrix. Arbitrary per-element sign constraints are not expressible without a custom CVXPY problem; this is that custom problem, packaged.

2. Data-driven sign constraints with a noise-floor gate

Hand-coding an (N × M) sign matrix scales poorly. Setting auto_sign_constraints=True derives signs inside fit() from pooled univariate slopes computed on the same EWMA-demeaned arrays the CVXPY solver consumes (no train/test inconsistency, automatic per-fold derivation under LassoModelCV).

model = LassoModel(
    reg_lambda=1e-5,
    auto_sign_constraints=True,    # derive signs from univariate slopes
    auto_sign_threshold_t=0.75,    # noise floor (default 0.75)
).fit(x=X, y=Y)

# Inspect the matrix the solver actually saw
model.derived_signs_

How the pooling is dispatched depends on model_type:

model_type	Sign-derivation pooling
LASSO (or single-column y)	Per-y-column independent univariate fit. Rows of derived_signs_ may differ across responses.
GROUP_LASSO	Pool y within each group_data group. All members of a group share their derived_signs_ row.
HIERARCHICAL_CLUSTER_GROUP_LASSO	Pool y within each HCGL asset cluster (the same clustering the solver uses).
FACTOR_CLUSTER_GROUP_LASSO	Pool y within each FCGL asset cluster — sign derivation is identical to HCGL; the modes differ only in the group norm of the penalty.
UNILASSO	Per-y-column, like LASSO, with no grouping. The sign is handled by the stage-2 non-negativity, not by gated derivation.
COOPERATIVE_GROUP_LASSO	No hard sign is derived or imposed. The cooperative penalty couples the positive and negative parts of each group_data group, so members tend to share a sign while the data can overrule it.
COOPERATIVE_CLUSTER_GROUP_LASSO	As above, on the discovered clusters rather than group_data.

The threshold gate. auto_sign_threshold_t (default 0.75) is a noise floor on the per-column univariate t-statistic. Factors with |t| < threshold have their sign pinned to 0, forcing β = 0 in the fit. Rationale: under weak L1 (typical of factor models with reg_lambda ≪ 1 and l1_weight = 0), an unfiltered slope sign drawn from sampling noise becomes a hard constraint that the solver can exploit to fit residual variance via offsetting loading pairs (e.g. +Credit ↔ −Inflation on a factor whose true effect is zero). The gate is not a significance test — |t| = 0.75 corresponds to two-sided p ≈ 0.45. It is a defensive filter that removes only the worst noise-driven sign constraints.

Set auto_sign_threshold_t=None to disable the gate entirely (reproduces v0.3.6 behaviour, every univariate sign is enforced regardless of evidence strength).

Explicit overrides still work. Setting factors_beta_loading_signs alongside auto_sign_constraints=True overlays the user's matrix on top of the auto-derived signs per-cell — non-NaN entries win, NaN cells inherit the auto value. Use this for asset-specific constraints that no amount of marginal-correlation data could surface (e.g. forcing a mandate-restricted bond fund to zero equity loading regardless of spurious sample correlations).

Adaptive L1 penalty weights (Zou 2006). Set auto_sign_adaptive_weights=True (default False) alongside auto_sign_constraints=True to reweight the L1 penalty elementwise by the inverse univariate-slope magnitude:

model = LassoModel(
    reg_lambda=1e-5,
    auto_sign_constraints=True,
    auto_sign_adaptive_weights=True,   # opt in to magnitude-aware L1
    auto_sign_adaptive_gamma=1.0,      # Zou (2006) exponent γ
    auto_sign_adaptive_floor=1e-3,     # stabiliser on tiny slopes
).fit(x=X, y=Y)

The L1 penalty becomes

λ · |β_kj − β⁰_kj| / max(|β̂_uni_kj|, floor)^γ

where β̂_uni_kj is the same pooled univariate slope used to derive the sign matrix. Strong-evidence factors (large |β̂_uni|) get a lighter L1 penalty and can take larger multivariate coefficients; weak-evidence factors get a heavier penalty and are pushed harder toward the prior. This is the Zou (2006) adaptive Lasso oracle property: the penalty becomes magnitude-aware without being a thresholding operator. The univariate-guided sign constraint of Richland et al. (2025) eq. (3.3) is a separate mechanism, applied via the sign matrix above; here the univariate slope supplies only the magnitude-aware penalty weight.

The adaptive layer is independent of the threshold gate: cells pinned to β = 0 by the gate continue to be forced to zero by the hard sign constraint, with the adaptive weight acting only on the non-pinned cells. Default behaviour (auto_sign_adaptive_weights=False) reproduces v0.3.8 fits bit-for-bit on fully observed panels. (On panels with leading-NaN inception prefixes, v0.4.1 corrects the univariate slope and gate t-statistic to accumulate only over valid observations; see the CHANGELOG. Fully observed panels are unaffected.)

Group LASSO mode (l1_weight=0). In pure group-LASSO configurations where the L1 term is inactive, the adaptive reweighting is routed through the group L2 norms following Wang & Leng (2008)'s adaptive group lasso. Per-cell weights are aggregated per-asset by root-mean-square over the non-pinned factors:

W_k  =  sqrt( mean_{j: s_kj ≠ 0} W_kj² )

and each asset's contribution ‖β_k − β⁰_k‖₂ to the group penalty is scaled by W_k. This is what gives the adaptive flag actual impact in the production HIERARCHICAL_CLUSTER_GROUP_LASSO configuration where the L1 term is zero-weighted. Assets with uniformly strong univariate evidence across factors get W_k → 1 (preserved); assets with uniformly weak evidence get W_k > 1 (shrunk harder toward the prior). Assets with all cells pinned by the gate fall back to W_k = 1 (no-op).

Related work and intellectual lineage. The univariate-slope-as- sign-constraint mechanism is adapted from the uniLasso framework of Chatterjee, Hastie & Tibshirani (2025) and its biobank-scale follow-up by Richland et al. (2025). Specifically, Richland et al. (2025) eq. (3.3) imposes sign(γ_j) = sign(β̃_j) as a hard constraint on the original variables — structurally identical to what factors_beta_loading_signs encodes here. The broader idea of using univariate marginal evidence to guide a multivariate fit goes back to Zou (2006)'s adaptive Lasso, which uses univariate magnitudes as adaptive penalty weights.

Two things factorlasso does not inherit from uniLasso, worth flagging to avoid overclaiming:

• uniLasso's stage-2 architecture (Chatterjee et al. 2025 §2.1) fits a non-negative Lasso on leave-one-out fitted values used as new features. factorlasso instead constrains coefficients directly on the original variables via the CVXPY sign-constraint set — simpler in financial-panel sizes where n is typically in the hundreds, not the hundreds of thousands.
• The hard t-statistic noise floor (auto_sign_threshold_t) is not in uniLasso. uniLasso's LOO machinery achieves a smoother form of noise downweighting via stage-2 regularization on out-of-sample predictions. The threshold gate here is conceptually closer to Sure Independence Screening (Fan & Lv 2008): screen marginal evidence first, then regularize.

References:

• Chatterjee, S., Hastie, T., & Tibshirani, R. (2025). Univariate- guided sparse regression. Harvard Data Science Review 7(3).
• Fan, J., & Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space. J. R. Stat. Soc. B 70(5), 849–911.
• Richland, J., Kiiskinen, T., Wang, W., Lu, S., Narasimhan, B., Hastie, T., Rivas, M., & Tibshirani, R. (2025). Univariate-guided sparse regression for biobank-scale high-dimensional -omics data. arXiv:2511.22049.
• Wang, H., & Leng, C. (2008). A note on adaptive group lasso. Comput. Stat. Data Anal. 52(12), 5277–5286.
• Zou, H. (2006). The adaptive Lasso and its oracle properties. J. Amer. Stat. Assoc. 101(476), 1418–1429.

3. Prior-centered regularisation

Pass a (N × M) DataFrame factors_beta_prior to penalise ‖β − β₀‖ instead of ‖β‖. The prior is a soft target, not a hard constraint — the penalty tension between data fit and prior is still controlled by reg_lambda.

prior = 0.5 * np.sign(X.corrwith(Y["y0"]).to_numpy())
# ... build an (N, M) DataFrame `prior_df` with that structure ...

model = LassoModel(
    reg_lambda=1e-5,
    factors_beta_prior=prior_df,
).fit(x=X, y=Y)

4. Hierarchical Clustering Group LASSO (HCGL)

The groups in classical group LASSO are user-specified. HCGL discovers them from the data: EWMA correlation of the response matrix → Ward's linkage → dendrogram cut at cutoff_fraction × max(pdist) → block-sparse penalty on the resulting clusters.

model = LassoModel(
    model_type=LassoModelType.HIERARCHICAL_CLUSTER_GROUP_LASSO,
    reg_lambda=1e-5,
    cutoff_fraction=0.5,   # tune granularity; smaller → tighter clusters
    span=60,               # EWMA span for correlation estimate
).fit(x=X, y=Y)

model.coef_        # (N, M)
model.clusters_    # pd.Series of cluster labels per response
model.linkage_     # scipy linkage matrix

Useful when you suspect group structure in the responses but don't know the partition — or when the correct partition drifts over time, so any manual grouping would need to be refit anyway.

5. Sparse Group LASSO

Group LASSO selects whole groups in or out — every response inside an "active" group gets a non-zero loading. When the discovered groups are slightly heterogeneous (and HCGL clusters often are, especially at coarser cutoff_fraction), this admits noisy within-group loadings on responses that don't actually load on the factor.

The l1_weight mixing parameter α ∈ [0, 1] adds an elementwise L1 penalty on top of the group L2 (Simon, Friedman, Hastie & Tibshirani 2013):

$$ \mathcal{P}(\beta) = (1 - \alpha),\lambda \sum_g w_g , |\beta_g - \beta_0|_{2,1} ;+; \alpha,\lambda , |\beta - \beta_0|_1 $$

model = LassoModel(
    model_type=LassoModelType.HIERARCHICAL_CLUSTER_GROUP_LASSO,
    reg_lambda=1e-5,
    cutoff_fraction=0.65,   # coarser clusters
    l1_weight=0.10,         # α — group L2 still primary, L1 corrects within-group
).fit(x=X, y=Y)

The interpretation is "group-then-prune": the group L2 term still drives group-level selection, while the L1 term zeros individual asset-factor coefficients within active groups whose contribution is noise. Setting l1_weight=0.0 (the default) reduces exactly to pure group LASSO and is backward-compatible — the L1 term is dropped from the CVX problem entirely when α = 0, with zero runtime cost.

Typical research range: α ∈ [0.05, 0.20]. Above ~0.30 the group structure stops driving the model and the result reverts toward plain LASSO. The penalty is centered on the same prior β₀ as the group term, so the two shrinkage mechanisms compose consistently.

The L1 term respects the same per-element sign constraints and the same prior as the group term, so all four features in this section compose: a single fit can simultaneously enforce sign constraints, shrink toward a prior, group-select via HCGL clusters, and apply within-group elementwise sparsity.

6. Factor-Clustering Group LASSO (FCGL)

HCGL groups the penalty along the rows of the loading matrix: the L2 norm runs over each asset's factor loadings, and the discovered cluster enters only through the per-cluster weight. factorlasso also offers the complementary grouping, FCGL (FACTOR_CLUSTER_GROUP_LASSO), in which the L2 norm runs over the assets of a cluster on each factor:

$$ \mathcal{P}(\beta) = (1 - \alpha),\lambda \sum_g w_g \sum_{j} ,|\beta_{g, j} - \beta^0_{g, j}|_2 ;+; \alpha,\lambda , |\beta - \beta^0|_1 $$

where $\beta_{g, j}$ collects the loadings of cluster $g$'s assets on factor $j$. In FCGL the cluster is the group of the norm itself, so a whole cluster-by-factor block enters or leaves the model together.

model = LassoModel(
    model_type=LassoModelType.FACTOR_CLUSTER_GROUP_LASSO,
    reg_lambda=1e-5,
    cutoff_fraction=0.5,
    auto_sign_constraints=True,   # sign derivation is identical to HCGL
).fit(x=X, y=Y)

The two modes encode different beliefs about where loadings are sparse. HCGL treats each asset as the unit and lets loadings vary freely within a cluster, which fits a heterogeneous cluster. FCGL shrinks a cluster's loadings on a factor jointly toward the prior through the per-block norm, which fits a homogeneous cluster: when the shrinkage binds fully the block collapses to the prior, and when the cluster carries a strong shared signal the block retains loadings away from the prior, shrunk as a group rather than equalised. The sign derivation and adaptive reweighting are shared; the modes differ only in the group norm. FCGL is not block-separable across assets (it couples a cluster's assets through the per-block norm) and is solved as one coupled cone programme. In practice this coupling does not add a measurable runtime cost at production scale — FCGL matches HCGL in wall-clock to within a couple of percent at N = 500 — because the extra cone constraints are of the same order as the row-grouped norm. Neither mode dominates in general — the appropriate choice depends on the within-cluster homogeneity of the application.

7. UniLasso (per-response univariate-guided)

UniLasso fits each response in two stages and uses no grouping. Stage one runs a univariate regression of the response on each factor separately. Stage two combines those univariate fits with a non-negative coefficient on each, so the final loading keeps the sign of its univariate slope. The estimator follows Chatterjee, Hastie, and Tibshirani (2025).

model = LassoModel(
    model_type=LassoModelType.UNILASSO,
    reg_lambda=1e-3,
    unilasso_loo=True,            # leave-one-out (prevalidated) stage-1 fits
    unilasso_non_negative=True,   # theta >= 0 in stage 2, so signs follow the univariate slope
).fit(x=X, y=Y)

UniLasso ignores group_data and cutoff_fraction, since it neither groups nor clusters. It suits the case where a univariate sign is trustworthy per response and no cluster structure is assumed. The trade-off is that it cannot borrow strength across related responses, so a group method recovers more when the responses share structure and the per-response sample is small.

8. Cooperative LASSO (soft within-group sign coherence)

The cluster methods above impose a hard pooled sign through the gate. The cooperative LASSO of Chiquet, Grandvalet, and Charbonnier (2012) instead encourages the members of a group to share a sign without forcing it. It splits each coefficient into a positive and a negative part and penalises the two parts as separate groups, so a group tends to load on a factor with one sign while the data can still overrule it.

# external groups
model = LassoModel(
    model_type=LassoModelType.COOPERATIVE_GROUP_LASSO,
    reg_lambda=1e-3,
    group_data=group_data,
).fit(x=X, y=Y)

# discovered clusters
model = LassoModel(
    model_type=LassoModelType.COOPERATIVE_CLUSTER_GROUP_LASSO,
    reg_lambda=1e-3,
    cutoff_fraction=0.5,
).fit(x=X, y=Y)

The cooperative modes never gate and never set a hard sign constraint, so auto_sign_constraints does not apply. They suit the case where a group's sign is a soft prior rather than a known fact. On correlated factors a soft penalty recovers more of the signs at lower coefficient error than a hard constraint, at the cost of a higher sign-flip rate when the leakage is strong.

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When to use it — and when not

Use it when:

• Multi-output LASSO with heterogeneous sign constraints across the coefficient matrix.
• You have a prior β₀ that should shrink the fit instead of zero.
• You need discovered-group structured sparsity (HCGL).
• You need group-level selection with within-group elementwise sparsity (sparse group LASSO at small-to-moderate α).
• You want a small, auditable CVXPY-based tool rather than a coordinate-descent library with opaque internals.

Reach for something else when:

• You need non-linear models, random effects, or GLM link functions.

Feature comparison

The table below compares factorlasso against six sparse-regression packages in the Python and R ecosystems. A checkmark indicates that the feature is provided natively; a dash indicates that it is absent or must be implemented externally by the practitioner. sklearn refers specifically to the Lasso class.

Feature	sklearn	gglasso	sparsegl	asgl	celer	adelie	factorlasso
Multi-response interface (N × M in one fit)	–	–	–	–	–	✓	✓
ℓ₁ penalty (LASSO)	✓	–	✓	✓	✓	–	✓
Group ℓ₂ penalty (group LASSO)	–	✓	✓	✓	–	✓	✓
Sparse-group mixing (α ∈ [0, 1])	–	–	✓	✓	–	–	✓
HCGL cluster discovery (clusters found internally)	–	–	–	–	–	–	✓
Cluster-by-factor block penalty (FCGL)	–	–	–	–	–	–	✓
Sign constraints at cell level	–	–	–	–	–	–	✓
Noise-floor t-statistic gate	–	–	–	–	–	–	✓
Adaptive penalty reweighting	–	–	–	✓	–	–	✓
Prior-centred shrinkage (β⁰ ≠ 0)	–	–	–	–	–	–	✓
EWMA observation weighting	–	–	–	–	–	–	✓
NaN-tolerant API	–	–	–	–	–	–	✓
Factor covariance assembly	–	–	–	–	–	–	✓
Implementation language	Python	R	R	Python	Python	Python	Python

A more detailed feature-by-feature comparison is in COMPARISON.md.

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Empirical illustration

The figure below is from the accompanying paper. It shows the mean Credit loading of seventeen investment-grade, high-yield, and emerging-market bond ETFs as a function of regularisation strength, on a 2017–2026 excess-return panel where the Credit and Equity factors are 0.84 correlated.

These instruments are, by construction, credit exposures. Under that collinearity an unconstrained sparse or group penalty (red) shrinks the weakly identified Credit loading to zero and books the exposure as Equity instead — a misattribution that no choice of penalty strength repairs. The prior-centred HCGL estimator (green) holds the loading at its economic prior. The prior-centred FCGL estimator (blue) retains more credit attribution under shrinkage, because its cluster-by-factor penalty keeps the high-signal high-yield and emerging-market sleeves above the prior rather than pulling every sleeve to it.

The benefit for this class of factor model is concrete. A capital market assumptions engine, or any system that decomposes portfolio risk by factor, needs the credit risk to stay on the credit factor. factorlasso provides the three mechanisms that hold the attribution in place where an off-the-shelf LASSO cannot: cell-level sign constraints that forbid the sign flip into Equity, prior-centred shrinkage that pulls a weakly identified loading toward an economic target rather than toward zero, and the cluster-grouped penalties that share signal across economically similar assets. The loadings stay interpretable and stable enough to feed a portfolio optimiser, which is the property a deployed factor-risk system requires and an unconstrained penalty does not deliver.

The paper quantifies this on a calibrated 102-asset universe with known true loadings. On generic metrics — support recovery, out-of-sample R², systematic covariance error — factorlasso is statistically indistinguishable from scikit-learn, skglm, and asgl, so the structural machinery costs nothing in ordinary accuracy. But under the 0.84 credit–equity collinearity the universe carries, every competing package and every unconstrained configuration shrinks the weakly identified credit loading toward zero and books it as equity. The sign- and prior-constrained configuration alone recovers it: a mean Credit loading of 0.32 against a true 0.36, versus at most 0.08 for the competing packages. The combination is the point — a prior alone can be re-centred around any package, but no surveyed package expresses a prior jointly with cell-level sign constraints and internally discovered clusters.

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Examples

Three runnable examples in examples/:

• genomics_factor_model.py — QTL-style multi-response LASSO: genotype matrix → expression panel, with sign constraints derived from biological priors.
• finance_factor_model.py — Multi-asset factor decomposition with sign constraints and HCGL clustering.
• cv_lambda_selection.py — Time-series cross-validated reg_lambda selection via LassoModelCV with expanding-window splits.

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Testing

pip install -e ".[dev]"
pytest

The suite currently has 273 tests at 98%+ coverage, including numerical parity tests against qis for the EWMA primitives and against scikit-learn for the LASSO path.

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Citation

If you use factorlasso in academic work, please cite the software paper describing the package (submitted to the Journal of Statistical Software), the methodology paper documenting the framework in which it was developed, and the software itself:

@article{SeppKastenholz2026factorlasso,
  author  = {Sepp, Artur and Kastenholz, Mika},
  title   = {{factorlasso}: Hierarchical Clustering Group {LASSO} ({HCGL})
             with Cluster-Pooled Sign Derivation for Multi-Asset Factor
             Models in {Python}},
  journal = {Journal of Statistical Software},
  year    = {2026},
  note    = {Submitted.}
}

@article{SeppHansenKastenholz2026MATF,
  author  = {Sepp, Artur and Hansen, Emilie and Kastenholz, Mika},
  title   = {Capital Market Assumptions Using Multi-Asset Tradable Factors:
             The {MATF-CMA} Framework},
  journal = {Journal of Portfolio Management},
  year    = {2026},
  note    = {Forthcoming.}
}

@article{SeppOssaKastenholz2026,
  author  = {Sepp, Artur and Ossa, Ivan and Kastenholz, Mika},
  title   = {Robust Optimization of Strategic and Tactical Asset Allocation
             for Multi-Asset Portfolios},
  journal = {The Journal of Portfolio Management},
  year    = {2026},
  volume  = {52},
  number  = {4},
  pages   = {86--120},
}

@software{factorlasso,
  author  = {Sepp, Artur and Kastenholz, Mika},
  title   = {factorlasso: Sparse Multi-Asset Factor Model Estimation with
             Cluster-Pooled Sign Derivation and Hierarchical Group {LASSO}
             in {Python}},
  year    = {2026},
  version = {0.5.5},
  url     = {https://github.com/ArturSepp/factorlasso},
}

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Contributing & feedback

Issues and pull requests welcome at https://github.com/ArturSepp/factorlasso.

See CHANGELOG.md for release history and COMPATIBILITY.md for the API stability policy covering the v0.4.x line.

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License

GPL-3.0-or-later — see LICENSE.