skfolio.descriptor.ChangeToScale#

class skfolio.descriptor.ChangeToScale(field, scale_field, lag)[source]#

Lagged change normalized by a positive scale.

Computes the change in field over a fixed lag, divided by the current value of scale_field:

\[\text{ChangeToScale}_\ell(t) = \frac{A(t) - A(t - \ell)}{S(t)}\]

where \(A\) is the field value and \(S\) is the scale_field value.

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

This descriptor is appropriate when the numerator field can be negative or cross zero, such as earnings, capex or cash flows, and the change should be scaled by the firm’s current size or valuation. NaNs are allowed as missing observations and propagate when the current, lagged or scale value is missing.

Non-missing numerator values must be finite. Non-missing scale values must be finite and strictly positive. A ValueError is raised otherwise.

Parameters:

fieldstr: Field name in the AssetPanel to compute the change for. Non-missing values must be finite.
scale_fieldstr: Field name in the AssetPanel used as the current positive denominator. Non-missing values must be finite and strictly positive.
lagint: 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.
change_to_scale_ndarray of shape (n_assets,): Last change-to-scale value for each asset.

Methods

`fit_transform`(X[, y])	Compute changes in level normalized by current scale.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit_transform`(X[, y])	Compute changes in level normalized by current scale.
`set_params`(**params)	Set the parameters of this estimator.

See also

ChangeInIntensity: Change in the ratio \(A/S\) (intensity change).
GrowthRate: Simple growth rate for positive-definite characteristics.

Examples

>>> from skfolio.descriptor import ChangeToScale
>>>
>>> # Earnings change to price (equivalent to EarningsChangeToPrice)
>>> earnings_chg = ChangeToScale("net_income_ttm", "market_cap", lag=12)
>>>
>>> # Capex change to total assets
>>> capex_chg = ChangeToScale("capex_ttm", "total_assets", lag=12)

fit_transform(X, y=None, **fit_params)[source]#

Compute changes in level normalized by current scale.

Parameters:

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

Returns:

change_to_scalendarray of shape (n_observations, n_assets): Change in field over the lag window, divided by current scale_field 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)[source]#

Compute changes in level normalized by current scale.

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 and scale_field fields configured at construction.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. Ignored.

Returns:

change_to_scalendarray of shape (n_observations, n_assets): Change in field over the lag window, divided by current scale_field 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.