skfolio.optimization.DistributionallyRobustCVaR#

class skfolio.optimization.DistributionallyRobustCVaR(risk_aversion=1.0, cvar_beta=0.95, wasserstein_ball_radius=0.02, prior_estimator=None, min_weights=0.0, max_weights=1.0, budget=1, min_budget=None, max_budget=None, max_short=None, max_long=None, groups=None, linear_constraints=None, left_inequality=None, right_inequality=None, risk_free_rate=0.0, 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, previous_weights=None, raise_on_failure=True)[source]#

Distributionally Robust CVaR.

The Distributionally Robust CVaR model constructs a Wasserstein ball in the space of multivariate and non-discrete probability distributions centered at the uniform distribution on the training samples and finds the allocation that minimizes the CVaR of the worst-case distribution within this Wasserstein ball. Esfahani and Kuhn [1] proved that for piecewise linear objective functions, which is the case of CVaR [2], the distributionally robust optimization problem over a Wasserstein ball can be reformulated as finite convex programs.

Only piecewise linear functions are supported, which means that transaction costs and regularization are not permitted.

A solver like Mosek that can handle a high number of constraints is preferred.

Parameters:

cvar_betafloat, default=0.95

CVaR (Conditional Value at Risk) confidence level.

risk_aversionfloat, default=1.0

Risk aversion factor of the utility function: return - risk_aversion * cvar.

wasserstein_ball_radius: float, default=0.02

Radius of the Wasserstein ball.

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.

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_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.

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.

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.

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 to use {"tol_gap_abs": 1e-9, "tol_gap_rel": 1e-9} for the solver “CLARABEL” and the CVXPY default otherwise. For more details about solver arguments, check the CVXPY documentation: https://www.cvxpy.org/tutorial/advanced/index.html#setting-solver-options

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.

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.
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.
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 Distributionally Robust CVaR 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]

“Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations”. Esfahani and Kuhn (2018).

[2]

“Optimization of conditional value-at-risk”. Rockafellar and Uryasev (2000).

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

Fit the Distributionally Robust CVaR Optimization estimator.

Parameters:

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

selfDistributionallyRobustCVaR: 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()#

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.