skfolio.optimization.MeanRisk#

class skfolio.optimization.MeanRisk(objective_function=MINIMIZE_RISK, risk_measure=Variance, risk_aversion=1.0, efficient_frontier_size=None, prior_estimator=None, min_weights=0.0, max_weights=1.0, budget=1.0, min_budget=None, max_budget=None, max_short=None, max_long=None, cardinality=None, group_cardinalities=None, threshold_long=None, threshold_short=None, transaction_costs=0.0, management_fees=0.0, previous_weights=None, groups=None, linear_constraints=None, left_inequality=None, right_inequality=None, l1_coef=0.0, l2_coef=0.0, mu_uncertainty_set_estimator=None, covariance_uncertainty_set_estimator=None, risk_free_rate=0.0, min_return=None, max_tracking_error=None, max_turnover=None, max_mean_absolute_deviation=None, max_first_lower_partial_moment=None, max_variance=None, max_standard_deviation=None, max_semi_variance=None, max_semi_deviation=None, max_worst_realization=None, max_cvar=None, max_evar=None, max_max_drawdown=None, max_average_drawdown=None, max_cdar=None, max_edar=None, max_ulcer_index=None, max_gini_mean_difference=None, min_acceptable_return=None, cvar_beta=0.95, evar_beta=0.95, cdar_beta=0.95, edar_beta=0.95, solver='CLARABEL', solver_params=None, scale_objective=None, scale_constraints=None, save_problem=False, add_objective=None, add_constraints=None, overwrite_expected_return=None, portfolio_params=None, fallback=None, raise_on_failure=True)[source]#

Mean-Risk Optimization estimator.

The below 4 objective functions can be optimized:

Minimize Risk:

\[\begin{split}\begin{cases} \begin{aligned} &\min_{w} & & risk_{i}(w) \\ &\text{s.t.} & & w^T \cdot \mu \ge min\_return \\ & & & A \cdot w \ge b \\ & & & risk_{j}(w) \le max\_risk_{j} \quad \forall \; j \ne i \end{aligned} \end{cases}\end{split}\]

Maximize Expected Return:

\[\begin{split}\begin{cases} \begin{aligned} &\max_{w} & & w^T \cdot \mu \\ &\text{s.t.} & & risk_{i}(w) \le max\_risk_{i} \\ & & & A \cdot w \ge b \\ & & & risk_{j}(w) \le max\_risk_{j} \quad \forall \; j \ne i \end{aligned} \end{cases}\end{split}\]

Maximize Utility:

\[\begin{split}\begin{cases} \begin{aligned} &\max_{w} & & w^T \cdot \mu - \lambda \times risk_{i}(w)\\ &\text{s.t.} & & risk_{i}(w) \le max\_risk_{i} \\ & & & w^T \cdot \mu \ge min\_return \\ & & & A \cdot w \ge b \\ & & & risk_{j}(w) \le max\_risk_{j} \quad \forall \; j \ne i \end{aligned} \end{cases}\end{split}\]

Maximize Ratio:

\[\begin{split}\begin{cases} \begin{aligned} &\max_{w} & & \frac{w^T \cdot \mu - r_{f}}{risk_{i}(w)}\\ &\text{s.t.} & & risk_{i}(w) \le max\_risk_{i} \\ & & & w^T \cdot \mu \ge min\_return \\ & & & A \cdot w \ge b \\ & & & risk_{j}(w) \le max\_risk_{j} \quad \forall \; j \ne i \end{aligned} \end{cases}\end{split}\]

With \(risk_{i}\) a risk measure among:

Mean Absolute Deviation

First Lower Partial Moment

Variance

Semi-Variance

CVaR (Conditional Value at Risk)

EVaR (Entropic Value at Risk)

Worst Realization (worst return)

CDaR (Conditional Drawdown at Risk)

Maximum Drawdown

Average Drawdown

EDaR (Entropic Drawdown at Risk)

Ulcer Index

Gini Mean Difference

Cost, regularization, uncertainty set, and additional constraints can also be added to the optimization problem (see the parameters description).

The assets expected returns, covariance matrix and returns are estimated from the prior estimator.

Parameters:

objective_functionObjectiveFunction, default=ObjectiveFunction.MINIMIZE_RISK

ObjectiveFunction of the optimization. Can be any of:

MINIMIZE_RISK

MAXIMIZE_RETURN

MAXIMIZE_UTILITY

MAXIMIZE_RATIO

The default is ObjectiveFunction.MINIMIZE_RISK.

risk_measureRiskMeasure, default=RiskMeasure.VARIANCE

RiskMeasure of the optimization. Can be any of:

VARIANCE

SEMI_VARIANCE

STANDARD_DEVIATION

SEMI_DEVIATION

MEAN_ABSOLUTE_DEVIATION

FIRST_LOWER_PARTIAL_MOMENT

CVAR

EVAR

WORST_REALIZATION

CDAR

MAX_DRAWDOWN

AVERAGE_DRAWDOWN

EDAR

ULCER_INDEX

GINI_MEAN_DIFFERENCE_RATIO

The default is RiskMeasure.VARIANCE.

risk_aversionfloat, default=1.0

Risk aversion factor \(\lambda\) of the utility function. Only used for objective_function=ObjectiveFunction.MAXIMIZE_UTILITY. The default value is 1.0.

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.

efficient_frontier_sizeint, optional

If provided, it represents the number of Pareto-optimal portfolios along the efficient frontier to be computed. This parameter can only be used with objective_function = ObjectiveFunction.MINIMIZE_RISK.

min_weightsfloat | dict[str, float] | array-like of shape (n_assets, ) | None, default=0.0

Minimum assets weights (weights lower bounds). If a float is provided, it is applied to each asset. None is equivalent to -np.Inf (no lower bound). If a dictionary is provided, its (key/value) pair must be the (asset name/asset minimum weight) and the input X of the fit method must be a DataFrame with the assets names in columns. When using a dictionary, assets values that are not provided are assigned a minimum weight of 0.0. The default value is 0.0 (no short selling).

Example:

min_weights = 0 –> long only portfolio (no short selling).

min_weights = None –> no lower bound (same as -np.Inf).

min_weights = -2 –> each weight must be above -200%.

min_weights = {"SX5E": 0, "SPX": -2}

min_weights = [0, -2]

max_weightsfloat | dict[str, float] | array-like of shape (n_assets, ) | None, default=1.0

Maximum assets weights (weights upper bounds). If a float is provided, it is applied to each asset. None is equivalent to +np.Inf (no upper bound). If a dictionary is provided, its (key/value) pair must be the (asset name/asset maximum weight) and the input X of the fit method must be a DataFrame with the assets names in columns. When using a dictionary, assets values that are not provided are assigned a minimum weight of 1.0. The default value is 1.0 (each asset is below 100%).

Example:

max_weights = 0 –> no long position (short only portfolio).

max_weights = None –> no upper bound.

max_weights = 2 –> each weight must be below 200%.

max_weights = {"SX5E": 1, "SPX": 2}

max_weights = [1, 2]

budgetfloat | None, default=1.0

Investment budget. It is the sum of long positions and short positions (sum of all weights). None means no budget constraints. The default value is 1.0 (fully invested portfolio).

For example:

budget = 1 –> fully invested portfolio.

budget = 0 –> market neutral portfolio.

budget = None –> no constraints on the sum of weights.

min_budgetfloat, optional

Minimum budget. It is the lower bound of the sum of long and short positions (sum of all weights). If provided, you must set budget=None. The default (None) means no minimum budget constraint.

max_budgetfloat, optional

Maximum budget. It is the upper bound of the sum of long and short positions (sum of all weights). If provided, you must set budget=None. The default (None) means no maximum budget constraint.

max_shortfloat, optional

Maximum short position. The short position is defined as the sum of negative weights (in absolute term). The default (None) means no maximum short position.

max_longfloat, optional

Maximum long position. The long position is defined as the sum of positive weights. The default (None) means no maximum long position.

cardinalityint, optional

Specifies the cardinality constraint to limit the number of invested assets (non-zero weights). This feature requires a mixed-integer solver. For an open-source option, we recommend using SCIP by setting solver="SCIP". To install it, use: pip install cvxpy[SCIP]. For commercial solvers, supported options include MOSEK, GUROBI, or CPLEX.

group_cardinalitiesdict[str, int], optional

A dictionary specifying cardinality constraints for specific groups of assets. The keys represent group names (strings), and the values specify the maximum number of assets allowed in each group. You must provide the groups using the groups parameter. This requires a mixed-integer solver (see cardinality for more details).

threshold_longfloat | dict[str, float] | array-like of shape (n_assets, ), optional

Specifies the minimum weight threshold for assets in the portfolio to be considered as a long position. Assets with weights below this threshold will not be included as part of the portfolio’s long positions. This constraint can help eliminate insignificant allocations. This requires a mixed-integer solver (see cardinality for more details). It follows the same format as min_weights and max_weights.

threshold_shortfloat | dict[str, float] | array-like of shape (n_assets, ), optional

Specifies the maximum weight threshold for assets in the portfolio to be considered as a short position. Assets with weights above this threshold will not be included as part of the portfolio’s short positions. This constraint can help control the magnitude of short positions. This requires a mixed-integer solver (see cardinality for more details). It follows the same format as min_weights and max_weights.

transaction_costsfloat | dict[str, float] | array-like of shape (n_assets, ), default=0.0

Transaction costs of the assets. It is used to add linear transaction costs to the optimization problem:

\[total\_cost = \sum_{i=1}^{N} c_{i} \times |w_{i} - w\_prev_{i}|\]

with \(c_{i}\) the transaction cost of asset i, \(w_{i}\) its weight and \(w\_prev_{i}\) its previous weight (defined in previous_weights). The float \(total\_cost\) is impacting the portfolio expected return in the optimization:

\[expected\_return = \mu^{T} \cdot w - total\_cost\]

with \(\mu\) the vector of assets’ expected returns and \(w\) the vector of assets weights.

If a float is provided, it is applied to each asset. If a dictionary is provided, its (key/value) pair must be the (asset name/asset cost) and the input X of the fit method must be a DataFrame with the assets names in columns. The default value is 0.0.

Warning

Based on the above formula, the periodicity of the transaction costs needs to be homogenous to the periodicity of \(\mu\). For example, if the input X is composed of daily returns, the transaction_costs need to be expressed as daily costs. (See Transaction Costs)

management_feesfloat | dict[str, float] | array-like of shape (n_assets, ), default=0.0

Management fees of the assets. It is used to add linear management fees to the optimization problem:

\[total\_fee = \sum_{i=1}^{N} f_{i} \times w_{i}\]

with \(f_{i}\) the management fee of asset i and \(w_{i}\) its weight. The float \(total\_fee\) is impacting the portfolio expected return in the optimization:

\[expected\_return = \mu^{T} \cdot w - total\_fee\]

with \(\mu\) the vector of assets’ expected returns and \(w\) the vector of assets weights.

If a float is provided, it is applied to each asset. If a dictionary is provided, its (key/value) pair must be the (asset name/asset fee) and the input X of the fit method must be a DataFrame with the assets names in columns. The default value is 0.0.

Warning

Based on the above formula, the periodicity of the management fees needs to be homogenous to the periodicity of \(\mu\). For example, if the input X is composed of daily returns, the management_fees need to be expressed in daily fees.

Note

Another approach is to directly impact the management fees to the input X in order to express the returns net of fees. However, when estimating the \(\mu\) parameter using for example Shrinkage estimators, this approach would mix a deterministic value with an uncertain one leading to unwanted bias in the management fees.

previous_weightsfloat | dict[str, float] | array-like of shape (n_assets, ), optional

Previous weights of the assets. Previous weights are used to compute the portfolio cost and the portfolio turnover. If a float is provided, it is applied to each asset. If a dictionary is provided, its (key/value) pair must be the (asset name/asset previous weight) and the input X of the fit method must be a DataFrame with the assets names in columns. The default (None) means no previous weights. Additionally, when fallback="previous_weights", failures will fall back to these weights if provided.

l1_coeffloat, default=0.0

L1 regularization coefficient. It is used to penalize the objective function by the L1 norm:

\[l1\_coef \times \Vert w \Vert_{1} = l1\_coef \times \sum_{i=1}^{N} |w_{i}|\]

Increasing this coefficient will reduce the number of non-zero weights (cardinality). It tends to increase robustness (out-of-sample stability) but reduces diversification. The default value is 0.0.

l2_coeffloat, default=0.0

L2 regularization coefficient. It is used to penalize the objective function by the L2 norm:

\[l2\_coef \times \Vert w \Vert_{2}^{2} = l2\_coef \times \sum_{i=1}^{N} w_{i}^2\]

It tends to increase robustness (out-of-sample stability). The default value is 0.0.

mu_uncertainty_set_estimatorBaseMuUncertaintySet, optional

Mu Uncertainty set estimator. If provided, the assets expected returns are modelled with an ellipsoidal uncertainty set. It is called worst-case optimization and is a class of robust optimization. It reduces the instability that arises from the estimation errors of the expected returns. The worst case portfolio expect return is:

\[w^T \cdot \hat{\mu} - \kappa_{\mu} \lVert S_{\mu}^\frac{1}{2} \cdot w \rVert_{2}\]

with \(\kappa\) the size of the ellipsoid (confidence region) and \(S\) its shape. The default (None) means that no uncertainty set is used.

covariance_uncertainty_set_estimatorBaseCovarianceUncertaintySet, optional

Covariance Uncertainty set estimator. If provided, the assets covariance matrix is modelled with an ellipsoidal uncertainty set. It is called worst-case optimization and is a class of robust optimization. It reduces the instability that arises from the estimation errors of the covariance matrix. The default (None) means that no uncertainty set is used.

linear_constraintsarray-like of shape (n_constraints,), optional

Linear constraints. The linear constraints must match any of following patterns:

"2.5 * ref1 + 0.10 * ref2 + 0.0013 <= 2.5 * ref3"

"ref1 >= 2.9 * ref2"

"ref1 == ref2"

"ref1 >= ref1"

With "ref1", "ref2" … the assets names or the groups names provided in the parameter groups. Assets names can be referenced without the need of groups if the input X of the fit method is a DataFrame with these assets names in columns.

For example:

"SPX >= 0.10" –> SPX weight must be greater than 10% (note that you can also use min_weights)

"SX5E + TLT >= 0.2" –> the sum of SX5E and TLT weights must be greater than 20%

"US == 0.7" –> the sum of all US weights must be equal to 70%

"Equity == 3 * Bond" –> the sum of all Equity weights must be equal to 3 times the sum of all Bond weights.

"2*SPX + 3*Europe <= Bond + 0.05" –> mixing assets and group constraints

groupsdict[str, list[str]] or array-like of shape (n_groups, n_assets), optional

The assets groups referenced in linear_constraints. If a dictionary is provided, its (key/value) pair must be the (asset name/asset groups) and the input X of the fit method must be a DataFrame with the assets names in columns.

For example:

groups = {"SX5E": ["Equity", "Europe"], "SPX": ["Equity", "US"], "TLT": ["Bond", "US"]}

groups = [["Equity", "Equity", "Bond"], ["Europe", "US", "US"]]

left_inequalityarray-like of shape (n_constraints, n_assets), optional

Left inequality matrix \(A\) of the linear constraint \(A \cdot w \leq b\).

right_inequalityarray-like of shape (n_constraints, ), optional

Right inequality vector \(b\) of the linear constraint \(A \cdot w \leq b\).

risk_free_ratefloat, default=0.0

Risk-free interest rate. The default value is 0.0.

max_tracking_errorfloat, optional

Upper bound constraint on the tracking error. The tracking error is defined as the RMSE (root-mean-square error) of the portfolio returns compared to a target returns. If max_tracking_error is provided, the target returns y must be provided in the fit method.

max_turnoverfloat, optional

Upper bound constraint of the turnover. The turnover is defined as the absolute difference between the portfolio weights and the previous_weights. Note that another way to control for turnover is by using the transaction_costs parameter.

max_mean_absolute_deviationfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the Mean Absolute Deviation.

max_first_lower_partial_momentfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the First Lower Partial Moment.

max_variancefloat | array-like of shape (n_optimization), optional

Upper bound constraint on the Variance.

max_standard_deviationfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the Standard deviation.

max_semi_variancefloat | array-like of shape (n_optimization), optional

Upper bound constraint on the Semi-Variance (Second Lower Partial Moment or Downside Variance).

max_semi_deviationfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the Semi-Standard deviation.

max_worst_realizationfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the Worst Realization (Worst Return).

max_cvarfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the CVaR (Conditional Value-at-Risk or Expected Shortfall).

max_evarfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the EVaR (Entropic Value at Risk).

max_max_drawdownfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the Maximum Drawdown.

max_average_drawdownfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the Average Drawdown.

max_cdarfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the CDaR (Conditional Drawdown at Risk).

max_edarfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the EDaR (Entropic Drawdown at Risk).

max_ulcer_indexfloat | array-like of shape (n_optimization), optional

Upper bound constraint on the Ulcer Index.

max_gini_mean_differencefloat | array-like of shape (n_optimization), optional

Upper bound constraint on the Gini Mean Difference.

min_returnfloat | array-like of shape (n_optimization), optional

Lower bound constraint on the expected return.

min_acceptable_returnfloat, optional

The minimum acceptable return used to distinguish “downside” and “upside” returns for the computation of lower partial moments:

First Lower Partial Moment

Semi-Variance

Semi-Deviation

The default (None) is to use the mean.

cvar_betafloat, default=0.95

CVaR (Conditional Value at Risk) confidence level. The default value is 0.95.

evar_betafloat, default=0

EVaR (Entropic Value at Risk) confidence level. The default value is 0.95.

cdar_betafloat, default=0.95

CDaR (Conditional Drawdown at Risk) confidence level. The default value is 0.95.

edar_betafloat, default=0.95

EDaR (Entropic Drawdown at Risk) confidence level. The default value is 0.95.

add_objectiveCallable[[cp.Variable], cp.Expression], optional

Add a custom objective to the existing objective expression. It is a function that must take as argument the weights w and returns a CVXPY expression.

add_constraintsCallable[[cp.Variable], cp.Expression|list[cp.Expression]], optional

Add a custom constraint or a list of constraints to the existing constraints. It is a function that must take as argument the weights w and returns a CVXPY expression or a list of CVXPY expressions.

overwrite_expected_returnCallable[[cp.Variable], cp.Expression], optional

Overwrite the expected return \(\mu \cdot w\) with a custom expression. It is a function that must take as argument the weights w and returns a CVXPY expression.

solverstr, default=”CLARABEL”

The solver to use. The default is “CLARABEL” which is written in Rust and has better numerical stability and performance than ECOS and SCS. Cvxpy will replace its default solver “ECOS” by “CLARABEL” in future releases. For more details about available solvers, check the CVXPY documentation: https://www.cvxpy.org/tutorial/advanced/index.html#choosing-a-solver

solver_paramsdict, optional

Solver parameters. For example, solver_params=dict(verbose=True). The default (None) is use {"tol_gap_abs": 1e-9, "tol_gap_rel": 1e-9} for “CLARABEL”, {"numerics/feastol": 1e-8, "limits/gap": 1e-8} for SCIP and the solver default otherwise. For more details about solver arguments, check the CVXPY documentation: https://www.cvxpy.org/tutorial/solvers

scale_objectivefloat, optional

Scale each objective element by this value. It can be used to increase the optimization accuracies in specific cases. The default (None) is set depending on the problem.

scale_constraintsfloat, optional

Scale each constraint element by this value. It can be used to increase the optimization accuracies in specific cases. The default (None) is set depending on the problem.

save_problembool, default=False

If this is set to True, the CVXPY Problem is saved in problem_. The default is False.

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, transaction_costs, management_fees, previous_weights and risk_free_rate.

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.

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.
problem_values_dict[str, float] | list[dict[str, float]] of size n_optimizations: Expression values retrieved from the CVXPY problem.
prior_estimator_BasePrior: Fitted prior_estimator.
mu_uncertainty_set_estimator_BaseMuUncertaintySet: Fitted mu_uncertainty_set_estimator if provided.
covariance_uncertainty_set_estimator_BaseCovarianceUncertaintySet: Fitted covariance_uncertainty_set_estimator if provided.
problem_: cvxpy.Problem: CVXPY problem used for the optimization. Only when save_problem is set to True.
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: 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 Mean-Risk 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.

References

[1]

“Portfolio Optimization: Theory and Application”, Chapter 7, 10, 13 and 14, Daniel P. Palomar (2025)

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

Fit the Mean-Risk 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:

selfMeanRisk: 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.