skfolio.optimization.InverseVolatility#

class skfolio.optimization.InverseVolatility(prior_estimator=None, portfolio_params=None, fallback=None, previous_weights=None, raise_on_failure=True)[source]#

Inverse Volatility estimator.

Each asset weight is computed using the inverse of its volatility and rescaled to have a sum of weights equal to one. The assets volatilities are derived from the prior estimator’s covariance matrix.

Parameters:

prior_estimatorBasePrior, optional: Prior estimator. The prior estimator is used to estimate the ReturnDistribution containing the estimation of assets expected returns, covariance matrix, returns and Cholesky decomposition of the covariance. The default (None) is to use EmpiricalPrior.
portfolio_paramsdict, optional: Portfolio parameters forwarded to the resulting Portfolio in predict. If not provided and if available on the estimator, the following attributes are propagated to the portfolio by default: name, and previous_weights.
fallbackBaseOptimization | “previous_weights” | list[BaseOptimization | “previous_weights”], optional: Fallback estimator or a list of estimators to try, in order, when the primary optimization raises during fit. Alternatively, use "previous_weights" (alone or in a list) to fall back to the estimator’s previous_weights. When a fallback succeeds, its fitted weights_ are copied back to the primary estimator so that fit still returns the original instance. For traceability, fallback_ stores the successful estimator (or the string "previous_weights")

and fallback_chain_ stores each attempt with the associated outcome.
previous_weightsfloat | dict[str, float] | array-like of shape (n_assets,), optional: When fallback="previous_weights", failures will fall back to these weights if provided.
raise_on_failurebool, default=True: Controls error handling when fitting fails. If True, any failure during fit is raised immediately, no weights_ are set and subsequent calls to predict will raise a NotFittedError. If False, errors are not raised; instead, a warning is emitted, weights_ is set to None and subsequent calls to predict will return a FailedPortfolio. When fallbacks are specified, this behavior applies only after all fallbacks have been exhausted.

Attributes:

weights_ndarray of shape (n_assets,) or (n_optimizations, n_assets): Weights of the assets.
prior_estimator_BasePrior: Fitted prior_estimator.
fallback_BaseOptimization | “previous_weights” | None: The fallback estimator instance, or the string "previous_weights", that produced the final result. None if no fallback was used.
fallback_chain_list[tuple[str, str]] | None: Sequence describing the optimization fallback attempts. Each element is a pair (estimator_repr, outcome) where estimator_repr is the string representation of the primary estimator or a fallback (e.g. "EqualWeighted()", "previous_weights"), and outcome is "success" if that step produced a valid solution, otherwise the stringified error message. For successful fits without any fallback, this is None.
error_str | list[str] | None: Captured error message(s) when fit fails. For multi-portfolio outputs (weights_ is 2D), this is a list aligned with portfolios.

Methods

`fit`(X[, y])	Fit the Inverse Volatility estimator.
`fit_predict`(X)	Perform `fit` on `X` and returns the predicted `Portfolio` or `Population` of `Portfolio` on `X` based on the fitted `weights`.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict the `Portfolio` or a `Population` of portfolios on `X`.
`score`(X[, y])	Prediction score using the Sharpe Ratio.
`set_params`(**params)	Set the parameters of this estimator.

Notes

All estimators should specify all parameters as explicit keyword arguments in __init__ (no *args or **kwargs), following scikit-learn conventions.

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

Fit the Inverse Volatility estimator.

Parameters:

Xarray-like of shape (n_observations, n_assets): Price returns of the assets.
yarray-like of shape (n_observations, n_targets), optional: Price returns of factors or a target benchmark. The default is None.
**fit_paramsdict: Parameters to pass to the underlying estimators. Only available if enable_metadata_routing=True, which can be set by using sklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.

Returns:

selfInverseVolatility: Fitted estimator.

fit_predict(X)#

Perform fit on X and returns the predicted Portfolio or Population of Portfolio on X based on the fitted weights. For factor models, use fit(X, y) then predict(X) separately.

If fitting fails and raise_on_failure=False, this returns a FailedPortfolio.

Parameters:

Xarray-like of shape (n_observations, n_assets): Price returns of the assets.

Returns:

Portfolio | Population: The predicted Portfolio or Population based on the fitted weights.

get_metadata_routing()[source]#

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.

property needs_previous_weights#

Whether previous_weights must be propagated between folds/rebalances.

Used by cross_val_predict to decide whether to run sequentially and pass the weights from the previous rebalancing to the next. This is True when transaction costs, a maximum turnover, or a fallback depending on previous_weights are present.

predict(X)#

Predict the Portfolio or a Population of portfolios on X.

Optimization estimators can return a 1D or a 2D array of weights. For a 1D array, the prediction is a single Portfolio. For a 2D array, the prediction is a Population of Portfolio.

If name is not provided in the portfolio parameters, the estimator class name is used.

Parameters:

Xarray-like of shape (n_observations, n_assets) | ReturnDistribution: Asset returns or a ReturnDistribution carrying returns and optional sample weights.

Returns:

Portfolio | Population: The predicted Portfolio or Population based on the fitted weights.

score(X, y=None)#

Prediction score using the Sharpe Ratio. If the prediction is a single Portfolio, the score is its Sharpe Ratio. If the prediction is a Population, the score is the mean Sharpe Ratio across portfolios.

Parameters:

Xarray-like of shape (n_observations, n_assets): Price returns of the assets.
yIgnored: Not used, present here for API consistency by convention.

Returns:

scorefloat: The Sharpe Ratio of the portfolio if the prediction is a single Portfolio or the mean of all the portfolios Sharpe Ratios if the prediction is a Population of Portfolio.

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.