skfolio.descriptor.EWMomentum#

class skfolio.descriptor.EWMomentum(half_life=87.0, skip=21, min_periods=None, exponentiate=False)[source]#

Exponentially weighted momentum descriptor.

Computes an EWMA of log returns with an optional skip period to exclude the most recent observations:

The skip period separates medium-term momentum from short-term reversal. The classic “12-1” momentum signal uses a skip of approximately one month [1].

\[ \begin{aligned} x(t) &= \log(1 + r(t)) \\[0.75em] S(t) &= \lambda \cdot S(t-1) + (1 - \lambda) \cdot x(t - \text{skip}) \\[0.75em] \text{momentum}(t) &= \begin{cases} \exp(S(t)) - 1 & \text{if } \texttt{exponentiate=True} \\ S(t) & \text{otherwise} \end{cases} \end{aligned} \]

where \(\lambda = \exp(-\ln(2) / \text{half\_life})\) is the EWMA decay factor. At observation \(t\), the EWMA input is the log return from \(t - \text{skip}\). Therefore, \(\text{half\_life}\) is measured on the delayed input series. An EWMA with this half-life is comparable to a fixed window of about \(2 \cdot \text{half\_life} / \ln 2\) delayed observations, with the most recent \(\text{skip}\) observations excluded.

Parameters:

half_lifefloat, default=87.0: Controls how fast old returns decay in the EWMA of \(\log(1 + r(t - \text{skip}))\). The default of 87 approximately matches a RollingMomentum window of 252 observations (~1 year of daily data). To match a different window \(W\), use \(\text{half\_life} \approx W \cdot \ln(2) / 2 \approx 0.35 \, W\). Adjust for other frequencies (e.g., half_life=6 for monthly data).
skipint, default=21: Number of most recent observations to exclude before the EWMA window starts. Used to separate medium-term momentum from short-term reversal. The default assumes daily data and skips approximately one month. Set to 0 for short-term momentum.
min_periodsint, optional: Minimum number of valid delayed returns required for each asset. Until an asset reaches this count, its output is NaN. This warm-up period avoids exposing early EWMA values before the estimate has sufficiently converged from its zero initialization. If None, defaults to \(\lceil\text{half\_life}\rceil\), with a minimum of 1.
exponentiatebool, default=False: If True, output is \(\exp(S(t)) - 1\) (return units). If False, output is \(S(t)\) (EWMA of log returns; log space). Cross-sectional ranking is unchanged.

Attributes:

n_assets_int: Number of assets seen during fitting.
asset_names_ndarray of shape (n_assets,): Asset names seen during fitting.
momentum_ndarray of shape (n_assets,): Last exponentially weighted momentum value for each asset.

Methods

`fit_transform`(X[, y])	Compute exponentially weighted momentum from returns.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit_transform`(X[, y])	Compute exponentially weighted momentum from returns.
`set_params`(**params)	Set the parameters of this estimator.

See also

RollingMomentum: Fixed-window (equal-weighted) momentum.

Notes

The EWMA is initialized to zero (no bias correction).

NaNs are allowed as missing observations. Non-missing returns values must be finite and greater than -1, so \(\log(1 + r)\) is finite. The EWMA state is updated only for finite delayed log returns, and each asset’s valid-observation count controls when its output starts. The active_mask property of the input AssetPanel distinguishes holidays from delistings.

References

[1]

“Returns to buying winners and selling losers: Implications for stock market efficiency” The Journal of Finance. Jegadeesh, N., & Titman, S. (1993).

Examples

>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import EWMomentum
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> # 12-1 momentum with daily data (default)
>>> descriptor = EWMomentum()
>>> momentum = descriptor.fit_transform(X)
>>>
>>> # Short-term momentum (no skip)
>>> descriptor = EWMomentum(half_life=10, skip=0)
>>> short_term_momentum = descriptor.fit_transform(X)
>>>
>>> # Log-space output
>>> descriptor = EWMomentum(exponentiate=False)
>>> momentum_log = descriptor.fit_transform(X)

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

Compute exponentially weighted momentum from returns.

Parameters:

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

Returns:

momentumndarray of shape (n_observations, n_assets): EWMA momentum signal 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 exponentially weighted momentum from returns.

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 returns.
yNone: Ignored. Present for compatibility with scikit-learn’s API.
**fit_paramsdict: Additional fit parameters. Ignored.

Returns:

momentumndarray of shape (n_observations, n_assets): EWMA momentum signal 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.