Source code for skfolio.descriptor._value._sales_to_price

"""Sales-to-price ratio descriptor."""

# Copyright (c) 2023-2026
# Author: Hugo Delatte <hugo.delatte@skfoliolabs.com>
# SPDX-License-Identifier: BSD-3-Clause

from __future__ import annotations

import numpy as np

from skfolio.containers import AssetPanel
from skfolio.descriptor._base import BaseDescriptor
from skfolio.typing import FloatArray
from skfolio.utils.stats import safe_divide
from skfolio.utils.validation import validate_asset_panel


[docs] class SalesToPrice(BaseDescriptor, stateless=True): r"""Sales-to-price ratio descriptor. Computes the ratio of trailing twelve-month sales to market capitalization: .. math:: \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 :class:`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. 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: .. math:: \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. See Also -------- BookToPrice : Common equity normalized by market capitalization. CashFlowToPrice : Operating cash flow normalized by market capitalization. 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) """
[docs] def fit_transform(self, X: AssetPanel, y=None, **fit_params) -> FloatArray: """Compute sales to price. Parameters ---------- X : AssetPanel Input panel containing `sales_ttm` and `market_cap`. y : None Ignored. Present for compatibility with scikit-learn's API. **fit_params : dict Additional fit parameters. Ignored. Returns ------- sales_to_price : ndarray of shape (n_observations, n_assets) Sales-to-price ratio for each observation and asset. """ validate_asset_panel( self, X, required_fields=["sales_ttm", "market_cap"], finite_or_nan=["sales_ttm"], strictly_positive_or_nan=["market_cap"], ) return safe_divide(X["sales_ttm"], X["market_cap"], fill_value=np.nan)