numeraire.backtest_pricing_in_sample#

numeraire.backtest_pricing_in_sample(estimator: Estimator, view: Any, *, method: str, config: dict[str, Any] | None = None, data_vintage: str = 'unknown', run_id: str | None = None) PricingOutput[source]#

In-sample pricing: one full-sample fit, expected returns over the whole view (in_sample).

The paper cross-sectional-pricing tradition — a single fit on all of view (no walk-forward discipline) whose expected returns are scored against the same sample’s realized returns, tagged protocol="in_sample" so the explanatory nature of the number is explicit in each result row. Use backtest_pricing() for the out-of-sample counterpart.