<a id="skfolio-descriptor-ewshareturnover"></a>

# skfolio.descriptor.EWShareTurnover

<a id="skfolio.descriptor.EWShareTurnover"></a>

### *class* skfolio.descriptor.EWShareTurnover(half_life=21.0, min_periods=None)

Exponentially weighted share turnover descriptor.

Computes an EWMA of per-observation share turnover:

$$
\[
\begin{aligned}
\text{turnover\_raw}(t)
    &= \frac{\text{adj\_volume}(t)}
            {\text{adj\_shares\_outstanding}(t)} \\[0.75em]
\text{turnover}(t)
    &= \lambda \cdot \text{turnover}(t-1)
       + (1 - \lambda) \cdot \text{turnover\_raw}(t)
\end{aligned}
\]
$$

where $\lambda = \exp(-\ln(2) / \text{half\_life})$ is the EWMA decay factor.

Share turnover measures trading intensity as the fraction of shares outstanding that
changes hands over each observation period. Lower turnover indicates weaker trading
activity and lower liquidity, making trades more likely to incur price impact.
Low-turnover stocks are often associated with higher expected returns, commonly
interpreted as an illiquidity premium [[1]](#r890ece2b5cbc-1).

EWMA smoothing is preferred over a fixed rolling average because turnover can spike
around earnings, index rebalances or news events. EWMA dampens these spikes
gradually, producing more stable factor exposures.

* **Parameters:**
  **half_life** *float, default=21.0*
  : EWMA half-life in observations. Controls how fast old turnover values decay.
    With daily data, common choices are:
    - `half_life=21`: ~1 month
    - `half_life=63`: ~3 months
    - `half_life=252`: ~1 year

  **min_periods** *int, optional*
  : Minimum number of valid turnover observations 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 turnover estimate has sufficiently
    converged from its zero initialization. If `None`, defaults to
    $\lceil\text{half\_life}\rceil$, with a minimum of 1.
* **Attributes:**
  **n_assets_** *int*
  : Number of assets seen during fitting.

  **asset_names_** *ndarray of shape (n_assets,)*
  : Asset names seen during fitting.

  **turnover_** *ndarray of shape (n_assets,)*
  : Last EWMA-smoothed share turnover value for each asset.

### Methods

| [`fit_transform`](#skfolio.descriptor.EWShareTurnover.fit_transform)(X[, y])         | Compute exponentially weighted share turnover.            |
|--------------------------------------------------------------------------------|-----------------------------------------------------------|
| [`get_metadata_routing`](#skfolio.descriptor.EWShareTurnover.get_metadata_routing)()        | Get metadata routing of this object.                      |
| [`get_params`](#skfolio.descriptor.EWShareTurnover.get_params)([deep])            | Get parameters for this estimator.                        |
| [`partial_fit_transform`](#skfolio.descriptor.EWShareTurnover.partial_fit_transform)(X[, y]) | Update state and return smoothed turnover for this batch. |
| [`set_params`](#skfolio.descriptor.EWShareTurnover.set_params)(\*\*params)        | Set the parameters of this estimator.                     |

#### SEE ALSO
[`EWAmihudIlliquidity`](https://skfolio.org/generated/skfolio.descriptor.EWAmihudIlliquidity.html.md#skfolio.descriptor.EWAmihudIlliquidity)
: EWMA price-impact illiquidity measure.

### Notes

`adj_shares_outstanding` is common shares outstanding. Both `adj_volume` and
`adj_shares_outstanding` must use the same split-adjustment basis.

NaNs are allowed as missing observations. Non-missing `adj_volume` values must be
finite and non-negative. Non-missing `adj_shares_outstanding` values must be finite
and strictly positive.

The EWMA state is updated only for valid observations. NaN in `adj_volume` or
`adj_shares_outstanding` holds the EWMA state and does not increment the
valid-observation count. Zero `adj_volume` is valid and produces zero turnover.

The `active_mask` property of the [`AssetPanel`](https://skfolio.org/generated/skfolio.containers.AssetPanel.html.md#skfolio.containers.AssetPanel)
distinguishes holidays from delistings.

### References

* <a id='r890ece2b5cbc-1'>**[1]**</a> “Liquidity and stock returns: an alternative test” Journal of Financial Markets. Datar, V. T., Naik, N. Y., & Radcliffe, R. (1998).

### Examples

```pycon
>>> from skfolio.datasets import make_synthetic_characteristics
>>> from skfolio.descriptor import EWShareTurnover
>>>
>>> X = make_synthetic_characteristics()
>>>
>>> # 1-month effective window (default)
>>> descriptor = EWShareTurnover()
>>> turnover = descriptor.fit_transform(X)
>>>
>>> # 3-month effective window
>>> descriptor = EWShareTurnover(half_life=63)
>>> turnover_3m = descriptor.fit_transform(X)
```

<a id="skfolio.descriptor.EWShareTurnover.fit_transform"></a>

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

Compute exponentially weighted share turnover.

* **Parameters:**
  **X** *AssetPanel*
  : Input panel containing `adj_volume` and `adj_shares_outstanding`.

  **y** *None*
  : Ignored. Present for compatibility with scikit-learn’s API.

  **\*\*fit_params** *dict*
  : Additional fit parameters. Ignored.
* **Returns:**
  **turnover** *ndarray of shape (n_observations, n_assets)*
  : EWMA-smoothed share turnover for each observation and asset.

<a id="skfolio.descriptor.EWShareTurnover.get_metadata_routing"></a>

#### get_metadata_routing()

Get metadata routing of this object.

Please check [User Guide](https://skfolio.org/user_guide/metadata_routing.html.md#metadata-routing) on how the routing
mechanism works.

* **Returns:**
  **routing** *MetadataRequest*
  : A `MetadataRequest` encapsulating
    routing information.

<a id="skfolio.descriptor.EWShareTurnover.get_params"></a>

#### get_params(deep=True)

Get parameters for this estimator.

* **Parameters:**
  **deep** *bool, default=True*
  : If True, will return the parameters for this estimator and
    contained subobjects that are estimators.
* **Returns:**
  **params** *dict*
  : Parameter names mapped to their values.

<a id="skfolio.descriptor.EWShareTurnover.partial_fit_transform"></a>

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

Update state and return smoothed turnover for this batch.

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

* **Parameters:**
  **X** *AssetPanel*
  : Input panel containing `"adj_volume"` and
    `"adj_shares_outstanding"`.

  **y** *None*
  : Ignored. Present for compatibility with scikit-learn’s API.

  **\*\*fit_params** *dict*
  : Additional fit parameters. Ignored.
* **Returns:**
  **turnover** *ndarray of shape (n_observations, n_assets)*
  : EWMA-smoothed share turnover for each observation and asset.

<a id="skfolio.descriptor.EWShareTurnover.set_params"></a>

#### 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:**
  **\*\*params** *dict*
  : Estimator parameters.
* **Returns:**
  **self** *estimator instance*
  : Estimator instance.