API reference

The API is organised in three layers: the top-level numeraire namespace (the common surface, re-exported for convenience), the numeraire.core spine, and the core-adjacent infrastructure (testing, reference, comparison, baselines, adapters).

• numeraire
• numeraire.core.data
• numeraire.core.engine
• numeraire.core.evaluators
• numeraire.core.protocols
• numeraire.core.capabilities
• numeraire.core.schema
• numeraire.core.registry
• numeraire.core.simulate
• numeraire.core.splitter
• numeraire.core.stats
• numeraire.core.sorts
• numeraire.testing
• numeraire.reference
• numeraire.comparison
• numeraire.baselines
• numeraire.adapters.skfolio_adapter

Top-level namespace

The most common classes and functions are re-exported at the top level, so from numeraire import TimeSeriesView, backtest, SharpeEvaluator works directly.

TimeSeriesView	A point-in-time view: a returns (decision) calendar + one or more aligned feature blocks.
CrossSectionView	A cross-sectional (panel) view: many assets with per-asset characteristics, ragged over time.
WalkForwardSplitter	Expanding- or rolling-window walk-forward splitter.
validation_split	Split a (train) view into PIT (fit, valid): valid = the last valid_size dates.
backtest	Backtest estimator over view, dispatching to the right typed driver by capability.
backtest_weights	Run a walk-forward OOS backtest of a to_weights estimator over view.
backtest_panel	Walk-forward OOS backtest of a cross-sectional to_weights estimator over a ragged panel.
backtest_forecast	Walk-forward pseudo-OOS forecast (forecast-origin convention; GW2008 / 1-A / VoC).
backtest_pricing	Walk-forward OOS pricing of a to_pricing estimator: pooled predicted vs realized panels.
backtest_pricing_in_sample	In-sample pricing: one full-sample fit, expected returns over the whole view (in_sample).
config_hash	Stable short hash of a JSON-serializable config dict (preprocessing provenance).
WeightsOutput	OOS output for a to_weights method: realized weights aligned with realized returns.
PanelWeightsOutput	OOS output for a cross-sectional to_weights method over a ragged panel.
ForecastOutput	OOS output for a to_forecast method: per-origin forecast, realized return, benchmark.
PricingOutput	Output for a to_pricing method: predicted expected returns vs realized, on test assets.
SharpeEvaluator	Annualized Sharpe ratio of the realized strategy returns (the timing headline).
MeanReturnEvaluator	Annualized mean of the realized strategy returns.
CEQEvaluator	DGU (2009) certainty-equivalent return of the realized strategy returns (economic value).
AlphaEvaluator	Time-series alpha of the strategy vs a factor benchmark (HAC t-stat).
StrategyReturnEvaluator	Per-period (time-indexed) realized strategy return — one result row per date.
OutOfSampleR2Evaluator	Out-of-sample R^2 of a forecast vs a benchmark, 1 - SSE_model / SSE_benchmark (percent).
SquaredErrorDiffEvaluator	Per-origin squared-error difference (benchmark minus model), one row per date.
ClarkWestEvaluator	Clark-West (2007) MSPE-adjusted test of the forecast against its nested benchmark.
CrossSectionalR2Evaluator	Cross-sectional R^2 of mean realized returns on mean predicted expected returns (OLS).
AverageAbsAlphaEvaluator	Average absolute pricing error (mean over assets of |mean realized - mean predicted|).
DataView	A point-in-time aligned view of the data.
Estimator	scikit-learn-compatible.
Model	A fitted model.
Splitter	Yields (train, test) views — purge/embargo/PIT aware.
Evaluator	Scores OOS output, emitting rows of the standard tidy result schema.
SupportsWeights	Capability protocol (v0): a model that emits portfolio/timing weights (to_weights).
SupportsForecast	Capability protocol (v0): a model that emits a next-horizon return forecast.
SupportsPricing	Capability protocol: a model that prices a cross-section of test assets (to_pricing).
validate_result	Raise ValueError if df is missing any required result-schema column.
register_evaluator	Register evaluator under name.
get_evaluator	Return the evaluator registered under name.
available_evaluators	Return the names of all registered evaluators, sorted.
simulate_weights	Simulate a target-weight stream over data-frequency returns (conventions in module doc).
RebalanceSchedule	Decision (signal) dates mapped to the half-open data-row spans they govern.
SimulationResult	Realized simulation output plus the accounting provenance every result row needs.
sort_portfolios	Cross-sectional n_bins sort of signal with per-period weighted portfolio returns.
SortResult	Per-period sorted-portfolio returns plus the long-short spread.
grs_test	Gibbons-Ross-Shanken (1989) test that all time-series alphas are jointly zero.
sharpe_diff_test	Jobson-Korkie (1981) z-test of equal Sharpe ratios with the Memmel (2003) correction.
clark_west_test	Clark-West (2007) MSPE-adjusted test for nested models.
alpha_regression	OLS of portfolio (excess) returns on factor returns; HAC (Bartlett) coefficient errors.
adjust_pvalues	Multiple-testing adjustment for a family of tests (Harvey-Liu-Zhu 2016 §4.4 toolbox).
newey_west_lrv	Bartlett-kernel long-run variance of a 1-D series (lags=0 = plain variance, MLE).
certainty_equivalent	DGU (2009) eq.
return_loss	DGU (2009) eq.
performance_fee	Quadratic-utility performance fee (Fleming-Kirby-Ostdiek; Kirby-Ostdiek 2012 eq.