# skfolio.alpha.alpha_forecast_evaluation ### skfolio.alpha.alpha_forecast_evaluation(estimator, X, \*, target='idio_returns', holding_period=1, signal_lag=1, evaluation_step=None, n_forward_periods=10, cs_weighting=IDENTITY, factor_exposures='exposures', factor_correlation_method=PEARSON, quantiles=(0.1,), annualization_factor=252.0, min_count=3, params=None, name=None) Evaluate alpha forecast quality. The function fits `estimator` with `fit_transform`, obtains historical alpha forecasts, and compares them with a forward mean target built from an `AssetPanel` field. The default target is `idio_returns`, which evaluates the idiosyncratic component forecast by the alpha estimators used by [`CharacteristicsFactorModel`](https://skfolio.org/generated/skfolio.prior.CharacteristicsFactorModel.html.md#skfolio.prior.CharacteristicsFactorModel). The diagnostics evaluate alpha forecasts before the alpha is passed to an optimizer. IC measures cross-sectional ordering and Pearson correlation. Simple rank-weighted and z-score-weighted portfolios measure the realized target return of 200% gross alpha-only long-short portfolios. The calibration slope estimates the scale multiplier needed to map forecast values to realized target units. Holding-period diagnostics evaluate the same forecasts against cumulative target windows. Decay diagnostics evaluate the same forecasts against disjoint future target windows. Diagnostics are computed on the final alpha forecast returned by the estimator. For rank-transformed forecasts, `pearson_ic` and `zscore_weighted_portfolio` evaluate the transformed rank scores, not raw descriptor magnitudes. If `factor_exposures` is available and `factor_correlation_method` is not `None`, the evaluation also measures contemporaneous correlation between the alpha forecast and factor exposures. Holding-period and decay diagnostics use the same evaluation dates as the main evaluation. For example, with `holding_period=5`, `signal_lag=1` and `n_forward_periods=3`, `decay_summary` computes IC on disjoint windows: $corr(\alpha_t, \bar{y}_{t+1:t+5})$, $corr(\alpha_t, \bar{y}_{t+6:t+10})$ and $corr(\alpha_t, \bar{y}_{t+11:t+15})$. `holding_period_summary` computes IC on cumulative windows: $corr(\alpha_t, \bar{y}_{t+1:t+5})$, $corr(\alpha_t, \bar{y}_{t+1:t+10})$ and $corr(\alpha_t, \bar{y}_{t+1:t+15})$. With `holding_period=5` and `n_forward_periods=79`, the last cumulative window is $corr(\alpha_t, \bar{y}_{t+1:t+395})$. * **Parameters:** **estimator** *BaseEstimator or Pipeline* : Alpha estimator exposing `fit_transform` and returning historical alpha forecasts with shape `(n_observations, n_assets)`. **X** *AssetPanel or AssetPanelView* : Point-in-time asset panel containing `target` and all fields required by `estimator`. **target** *str, default=”idio_returns”* : Name of the 2D target field in `X`. **holding_period** *int, default=1* : Number of observations in the forward target window used for the main evaluation. For a forecast at date $t$, the target is the mean value over $[t + \ell, t + \ell + h)$, where $\ell$ is `signal_lag` and $h$ is `holding_period`. **signal_lag** *int, default=1* : Number of observations between the forecast date and the first target observation. `signal_lag=1` evaluates next-period targets and avoids same-period look-ahead when forecasts are observed after the current target is known. `signal_lag=0` evaluates same-period targets. **evaluation_step** *int, optional* : Spacing between evaluated forecast dates. The default `None` uses `holding_period`, which produces mostly non-overlapping target windows for the main evaluation. `evaluation_step=1` evaluates every valid forecast date, which is common for signal research and creates overlapping forward targets when `holding_period > 1`. Values greater than `holding_period` produce a sparse evaluation. When `evaluation_step < holding_period`, summary means remain descriptive diagnostics, but IC t-statistics and IR should be interpreted with the serial dependence from overlapping targets in mind. **n_forward_periods** *int, default=10* : Number of consecutive forward periods used for holding-period and decay diagnostics. `holding_period_summary` evaluates cumulative windows from $1 \times h$ to $n \times h$. `decay_summary` evaluates $n$ disjoint forward windows of length $h$, where $h$ is `holding_period` and $n$ is `n_forward_periods`. **cs_weighting** *CSWeighting or str, default=CSWeighting.IDENTITY* : Cross-sectional weighting for Pearson IC and the calibration scale multiplier. A string is interpreted as a 2D field name in `X`. Descriptive forecast, target and calibration-curve statistics are unweighted. **factor_exposures** *str, optional, default=”exposures”* : Name of a 3D field in `AssetPanel` `X` containing factor exposures used to compute alpha-factor correlation diagnostics. If the default field is not present, factor correlation diagnostics are skipped. Passing `None` skips them explicitly. **factor_correlation_method** *CorrelationMethod, optional, default=CorrelationMethod.PEARSON* : Factor correlation method to compute. `PEARSON` measures linear tilt of forecast values to factor exposures and is weighted by `cs_weighting`. `SPEARMAN` measures monotonic alignment of forecast ordering with exposure ordering and is more expensive for large exposure tensors. Passing `None` skips factor correlation diagnostics. **quantiles** *tuple of float, default=(0.1,)* : Forecast quantiles for top-minus-bottom spread diagnostics. Each value must be in `(0, 0.5]`. **annualization_factor** *float, default=252.0* : Number of observations per year used to annualize return statistics in `portfolio_summary` and `quantile_summary`. **min_count** *int, default=3* : Minimum number of valid assets required for each cross-sectional diagnostic. **params** *dict, optional* : Parameters routed to `estimator.fit_transform`. **name** *str, optional* : Display name for the evaluation. Defaults to `str(estimator)`. * **Returns:** **evaluation** *AlphaForecastEvaluation* : Frozen dataclass with diagnostic series, summary statistics and plots.