skfolio.descriptor.SalesToPrice#

class skfolio.descriptor.SalesToPrice[source]#

Sales-to-price ratio descriptor.

Computes the ratio of trailing twelve-month sales to market capitalization:

\[\text{sales\_to\_price}(t) = \frac{\text{sales\_ttm}(t)}{\text{market\_cap}(t)}\]

Sales are less directly affected by accounting choices than earnings, providing a stable value signal. Firms with high sales relative to market capitalization are cheap on a sales basis [1]. This ratio remains available for firms with negative earnings or book equity, complementing BookToPrice.

Parameters:

None

Attributes:

n_assets_int: Number of assets seen during fitting.
asset_names_ndarray of shape (n_assets,): Asset names seen during fitting.

Methods

`fit_transform`(X[, y])	Compute sales to price.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit_transform`(X[, y])	Stateless class delegation to `fit_transform`.
`set_params`(**params)	Set the parameters of this estimator.

See also

BookToPrice: Common equity normalized by market capitalization.
CashFlowToPrice: Operating cash flow normalized by market capitalization.

Notes

Non-missing market_cap values must be finite and strictly positive.

This descriptor uses aggregate quantities (total sales divided by total market capitalization) rather than per-share quantities (sales per share divided by price). The two are mathematically equivalent:

\[\frac{\text{sales\_ttm}}{\text{market\_cap}} = \frac{\text{sales\_per\_share}}{\text{price}}\]

The aggregate form is preferred because it avoids subtle split-adjustment mismatches between numerator and denominator. Aggregate fundamentals are the primary form from data providers; per-share quantities are derived from them.

References

[1]

“Do sales-price and debt-equity explain stock returns better than book-market and firm size?” Financial Analysts Journal. Barbee, Mukherji & Raines (1996).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import SalesToPrice
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> descriptor = SalesToPrice()
>>> sales_to_price = descriptor.fit_transform(X)

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

Compute sales to price.

Parameters:

XAssetPanel: Input panel containing sales_ttm and market_cap.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. Ignored.

Returns:

sales_to_pricendarray of shape (n_observations, n_assets): Sales-to-price ratio 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)#: Stateless class delegation to fit_transform.

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.