Changelog#

All notable changes to this project are documented here.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning. Versions are tag-driven (hatch-vcs).

Unreleased#

[0.2.1] - 2026-07-06#

Ecosystem release: the plotting and data companion packages are now on PyPI, and this release adds convenience extras to pull them in. Also carries the post-0.2.0 API work (all backward-compatible — old names keep working as deprecated aliases for one release).

Added#

  • Ecosystem extras — numeraire[graphics] (pulls numeraire-graphics), numeraire[data] (pulls numeraire-dataset), and numeraire[all]. pip install numeraire stays the minimal spine; opt into the companions here.

  • backtest(estimator, view, splitter, *, method, in_sample=False) — a discoverable dispatching entry point that routes by the model’s capability and the view type to the typed drivers backtest_weights / backtest_forecast / backtest_panel / backtest_pricing / backtest_pricing_in_sample.

  • Risk-adjusted evaluators — TreynorEvaluator, InformationRatioEvaluator, M2Evaluator, SortinoEvaluator; ICEvaluator (rank IC); ExposureEvaluator (per-date leverage / net / turnover / concentration); and fama_macbeth (two-pass cross-sectional regression with Shanken + Newey-West).

Changed#

  • Renamed for a clearer register (old names remain as deprecated aliases emitting DeprecationWarning): walk_forward* → backtest_*, adjust_tests → adjust_pvalues, clark_west → clark_west_test, make_sorts → sort_portfolios, OOSR2Evaluator → OutOfSampleR2Evaluator. WalkForwardSplitter is unchanged.

Fixed#

  • Weights/forecast backtests now align the model’s output to the view’s asset order by label before scoring (previously positional), so a method returning permuted/subset columns is scored correctly rather than silently mis-scored. Clear errors on a missing or misused splitter.

0.2.0 - 2026-07-05#

First tagged release. The spine is capability-complete: to_weights, to_forecast, and to_pricing are all crystallized protocols with walk-forward drivers, native evaluators, and a conformance suite.

Added#

  • Pricing capability — SupportsPricing.expected_returns, walk_forward_pricing / pricing_in_sample, cross-sectional R² and average-|α| evaluators, and numeraire.comparison.compare to score competing pricing models (factor models, SDFs, risk-premium estimators) on one common set of test assets. Every result row carries an explicit protocol label (in_sample / walk_forward), so explanatory numbers are never confusable with out-of-sample ones.

  • Conformance suite (numeraire.testing.check_estimator) — capabilities, output shapes, determinism, a no-look-ahead property test, and an engine round-trip: the self-certification any extension runs before its numbers are trusted.

  • Reference registry (numeraire.reference.ReferenceResult) — pinned published results with tolerance bands and data-access tiers (public / credentialed / restricted); CI stays green on public data while the same case runs verbatim wherever licensed data is present.

  • Bundled baselines (numeraire.baselines) — equal weight (1/N), minimum variance, mean-variance, and historical mean, registered through the same entry-point mechanism as any external method.

  • Weight-stream simulator — simulate_weights + RebalanceSchedule with explicit drift, turnover, and cost conventions.

  • Inference toolkit (core.stats) — GRS, Clark-West, paired Sharpe (Jobson-Korkie–Memmel), HAC alpha regression, Bonferroni/Holm/BHY adjustments, and certainty-equivalent / return-loss / performance-fee measures.

  • Cross-sectional data layer — CrossSectionView with zero-copy point-in-time windows, a ragged-panel walk-forward engine, parallel fold execution, refit-cadence control, and a validation-split helper.

  • Interop — polars/arrow ingestion at the view boundary (narwhals-optional, zero new hard dependencies) and a skfolio adapter ([skfolio] extra) that wraps portfolio optimizers as to_weights estimators.

Python ≥ 3.11, pandas ≥ 2.2.