skfolio.descriptor.SalesGrowthRate#

class skfolio.descriptor.SalesGrowthRate(lag=252)[source]#

Sales growth rate descriptor.

Computes period-over-period growth in trailing twelve-month sales:

\[\text{sales\_growth}(t) = \frac{\text{sales\_ttm}(t)}{\text{sales\_ttm}(t - \text{lag})} - 1\]

The first lag observations are NaN because no lagged history is available.

sales_ttm must contain non-missing finite non-negative values. NaNs are allowed as missing observations and propagate when either the current or lagged value is missing. Zero values are allowed and a zero lagged value makes the growth rate undefined and produces NaN.

Sales growth measures top-line expansion over the lag window. For TTM sales with a one-year lag, the two observations cover non-overlapping fiscal content.

This is a convenience subclass of GrowthRate with field="sales_ttm".

Parameters:

lagint, default=252: Number of observations to look back. The interpretation depends on the data frequency: lag=12 means 1 year for monthly data, lag=252 for daily data, lag=4 for quarterly data.

Attributes:

n_assets_int: Number of assets seen during fitting.
asset_names_ndarray of shape (n_assets,): Asset names seen during fitting.
growth_rate_ndarray of shape (n_assets,): Last sales growth value for each asset.

Methods

`fit_transform`(X[, y])	Compute simple growth rates of the configured field.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit_transform`(X[, y])	Compute simple growth rates of the configured field.
`set_params`(**params)	Set the parameters of this estimator.

See also

GrowthRate: Generic period-over-period growth rate descriptor.

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import SalesGrowthRate
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = SalesGrowthRate(lag=252)
>>> sales_growth_rate = descriptor.fit_transform(X)

fit_transform(X, y=None, **fit_params)#

Compute simple growth rates of the configured field.

Parameters:

XAssetPanel: Input panel containing the field characteristic configured at construction.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. Ignored.

Returns:

growth_ratendarray of shape (n_observations, n_assets): Period-over-period growth rate for each observation and asset.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

partial_fit_transform(X, y=None, **fit_params)#

Compute simple growth rates of the configured field.

This method supports online updates by continuing from the current fitted state. Use fit_transform to start from a clean state.

Parameters:

XAssetPanel: Input panel containing the field characteristic configured at construction.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. Ignored.

Returns:

growth_ratendarray of shape (n_observations, n_assets): Period-over-period growth rate for each observation and asset.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.