skfolio.optimization.BaseOptimization#

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

Base class for all portfolio optimizations in skfolio.

Parameters:

portfolio_paramsdict, optional: Portfolio parameters forwarded to the resulting Portfolio in predict. Unless set in this dictionary, transaction_costs, management_fees, previous_weights and risk_free_rate are forwarded from the optimizer when available, and name defaults to the optimizer class name. For example, portfolio_params={"weight_drift": True} evaluates the predicted portfolios with drifted weights instead of the target weights on every observation.
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. With partial_fit, only None or "previous_weights" is supported because fallback estimators have not accumulated the primary model’s online history. See Fallbacks.
previous_weightsfloat | dict[str, float] | array-like of shape (n_assets,), optional: Previous asset weights. Some portfolio optimizers use this to compute costs or turnover. Additionally, when fallback="previous_weights", failures will fall back to these weights if provided.
raise_on_failurebool, default=True: Controls error handling when fitting fails and no fallback succeeds. If True, the estimator raises the final error. If False, the estimator emits a warning and sets weights_ to None, so subsequent calls to predict return a FailedPortfolio. During fit, raise_on_failure applies to any fitting error, including errors raised by the prior estimator. During partial_fit, raise_on_failure applies only to optimization failures after learning completes. Input validation failures and errors from the prior or other learning estimators are always raised. See Failure Handling for batch recovery and Updates and Failure Handling for online continuation and restart rules.

Attributes:

weights_ndarray of shape (n_assets,) or (n_optimizations, n_assets): Weights of the assets.
n_features_in_int: Number of assets seen during fit.
feature_names_in_ndarray of shape (n_features_in_,): Names of assets seen during fit. Defined only when X has assets names that are all strings.
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] | None: For a single portfolio, this is the recorded error message, or None after a successful allocation or fallback. For multiple portfolios, it is a list with one entry per row of weights_, containing an error message for each failed portfolio and None for each successful portfolio.

Methods

`fit`(X[, y])	Fit the optimization 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.

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

Fit the optimization 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.

Returns:

selfBaseOptimization: Fitted estimator.

fit_predict(X)[source]#

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, factors=...) 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()#

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 and online_predict to decide whether to run sequentially and pass the weights from the previous rebalancing to the next. This is True when portfolio_params sets weight_drift=True, or when transaction costs, a maximum turnover, or a fallback depending on previous_weights are present.

predict(X)[source]#

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)[source]#

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 portfolio 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.