Source code for skfolio.optimization.convex._distributionally_robust

"""Distributionally Robust CVaR Optimization estimator."""

# Copyright (c) 2023
# Author: Hugo Delatte <delatte.hugo@gmail.com>
# License: BSD 3 clause

import cvxpy as cp
import numpy as np
import numpy.typing as npt
import sklearn.utils.metadata_routing as skm

import skfolio.typing as skt
from skfolio.measures import RiskMeasure
from skfolio.optimization.convex._base import ConvexOptimization
from skfolio.prior import BasePrior, EmpiricalPrior
from skfolio.utils.tools import check_estimator


[docs] class DistributionallyRobustCVaR(ConvexOptimization): r"""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_beta : float, default=0.95 CVaR (Conditional Value at Risk) confidence level. risk_aversion : float, 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_estimator : BasePrior, optional :ref:`Prior estimator <prior>`. The prior estimator is used to estimate the :class:`~skfolio.prior.PriorModel` containing the estimation of assets expected returns, covariance matrix, returns and Cholesky decomposition of the covariance. The default (`None`) is to use :class:`~skfolio.prior.EmpiricalPrior`. min_weights : float | 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 minium 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_weights : float | 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]` budget : float | 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). Examples: * `budget = 1` --> fully invested portfolio. * `budget = 0` --> market neutral portfolio. * `budget = None` --> no constraints on the sum of weights. min_budget : float, 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_short : float, 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_long : float, optional Maximum long position. The long position is defined as the sum of positive weights. The default (`None`) means no maximum long position. max_budget : float, 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_constraints : array-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. Examples: * "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 groups : dict[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. Examples: * groups = {"SX5E": ["Equity", "Europe"], "SPX": ["Equity", "US"], "TLT": ["Bond", "US"]} * groups = [["Equity", "Equity", "Bond"], ["Europe", "US", "US"]] left_inequality : array-like of shape (n_constraints, n_assets), optional Left inequality matrix :math:`A` of the linear constraint :math:`A \cdot w \leq b`. right_inequality : array-like of shape (n_constraints, ), optional Right inequality vector :math:`b` of the linear constraint :math:`A \cdot w \leq b`. risk_free_rate : float, default=0.0 Risk-free interest rate. The default value is `0.0`. add_constraints : Callable[[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 CVPXY expression or a list of CVPXY expressions. overwrite_expected_return : Callable[[cp.Variable], cp.Expression], optional Overwrite the expected return :math:`\mu \cdot w` with a custom expression. It is a function that must take as argument the weights `w` and returns a CVPXY expression. solver : str, 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_params : dict, 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 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_objective : float, 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_constraints : float, 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_problem : bool, default=False If this is set to True, the CVXPY Problem is saved in `problem_`. The default is `False`. raise_on_failure : bool, default=True If this is set to True, an error is raised when the optimization fail otherwise it passes with a warning. portfolio_params : dict, optional Portfolio parameters passed to the portfolio evaluated by the `predict` and `score` methods. If not provided, the `name`, `transaction_costs`, `management_fees`, `previous_weights` and `risk_free_rate` are copied from the optimization model and passed to the portfolio. 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. 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). """ def __init__( self, risk_aversion: float = 1.0, cvar_beta: float = 0.95, wasserstein_ball_radius: float = 0.02, prior_estimator: BasePrior | None = None, min_weights: skt.MultiInput | None = 0.0, max_weights: skt.MultiInput | None = 1.0, budget: float | None = 1, min_budget: float | None = None, max_budget: float | None = None, max_short: float | None = None, max_long: float | None = None, groups: skt.Groups | None = None, linear_constraints: skt.LinearConstraints | None = None, left_inequality: skt.Inequality | None = None, right_inequality: skt.Inequality | None = None, risk_free_rate: float = 0.0, solver: str = "CLARABEL", solver_params: dict | None = None, scale_objective: float | None = None, scale_constraints: float | None = None, save_problem: bool = False, raise_on_failure: bool = True, add_objective: skt.ExpressionFunction | None = None, add_constraints: skt.ExpressionFunction | None = None, overwrite_expected_return: skt.ExpressionFunction | None = None, portfolio_params: dict | None = None, ): super().__init__( risk_measure=RiskMeasure.CVAR, prior_estimator=prior_estimator, min_weights=min_weights, max_weights=max_weights, budget=budget, min_budget=min_budget, max_budget=max_budget, max_short=max_short, max_long=max_long, groups=groups, linear_constraints=linear_constraints, left_inequality=left_inequality, right_inequality=right_inequality, risk_free_rate=risk_free_rate, cvar_beta=cvar_beta, solver=solver, solver_params=solver_params, scale_objective=scale_objective, scale_constraints=scale_constraints, save_problem=save_problem, raise_on_failure=raise_on_failure, add_objective=add_objective, add_constraints=add_constraints, overwrite_expected_return=overwrite_expected_return, portfolio_params=portfolio_params, ) self.risk_aversion = risk_aversion self.wasserstein_ball_radius = wasserstein_ball_radius
[docs] def fit( self, X: npt.ArrayLike, y: npt.ArrayLike | None = None, **fit_params ) -> "DistributionallyRobustCVaR": """Fit the Distributionally Robust CVaR Optimization estimator. Parameters ---------- X : array-like of shape (n_observations, n_assets) Price returns of the assets. y : array-like of shape (n_observations, n_factors), optional Price returns of factors. The default is `None`. **fit_params : dict 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 :ref:`Metadata Routing User Guide <metadata_routing>` for more details. Returns ------- self : DistributionallyRobustCVaR Fitted estimator. """ routed_params = skm.process_routing(self, "fit", **fit_params) self._check_feature_names(X, reset=True) # Used to avoid adding multiple times similar constrains linked to identical # risk models self.prior_estimator_ = check_estimator( self.prior_estimator, default=EmpiricalPrior(), check_type=BasePrior, ) self.prior_estimator_.fit(X, y, **routed_params.prior_estimator.fit) prior_model = self.prior_estimator_.prior_model_ n_observations, n_assets = prior_model.returns.shape # set solvers params if self.solver == "CLARABEL": self._set_solver_params(default={"tol_gap_abs": 1e-9, "tol_gap_rel": 1e-9}) else: self._set_solver_params(default=None) # set scale self._set_scale_objective(default=1) self._set_scale_constraints(default=1) a1 = -1 b1 = cp.Constant(self.risk_aversion) a2 = -1 - self.risk_aversion / (1 - self.cvar_beta) b2 = cp.Constant(self.risk_aversion * (1 - 1 / (1 - self.cvar_beta))) ones = np.ones(n_assets) w = cp.Variable(n_assets) u = cp.Variable((n_observations, n_assets)) v = cp.Variable((n_observations, n_assets)) lb = cp.Variable() tau = cp.Variable() s = cp.Variable(n_observations) factor = cp.Constant(1) # constraints constraints = self._get_weight_constraints( n_assets=n_assets, w=w, factor=factor ) constraints += [ u * self._scale_constraints >= cp.Constant(0), v * self._scale_constraints >= cp.Constant(0), b1 * tau * self._scale_constraints + a1 * (prior_model.returns @ w) * self._scale_constraints + cp.multiply(u, (1 + prior_model.returns)) @ ones * self._scale_constraints <= s * self._scale_constraints, b2 * tau * self._scale_constraints + a2 * (prior_model.returns @ w) * self._scale_constraints + cp.multiply(v, (1 + prior_model.returns)) @ ones * self._scale_constraints <= s * self._scale_constraints, ] for i in range(n_observations): # noinspection PyTypeChecker constraints.append( cp.norm(-u[i] - a1 * w, np.inf) * self._scale_constraints <= lb * self._scale_constraints ) # noinspection PyTypeChecker constraints.append( cp.norm(-v[i] - a2 * w, np.inf) * self._scale_constraints <= lb * self._scale_constraints ) # custom objectives and constraints custom_objective = self._get_custom_objective(w=w) constraints += self._get_custom_constraints(w=w) objective = cp.Minimize( cp.Constant(self.wasserstein_ball_radius) * lb * self._scale_objective + (1 / n_observations) * cp.sum(s) * self._scale_objective + custom_objective * self._scale_objective ) # problem problem = cp.Problem(objective, constraints) # results self._solve_problem(problem=problem, w=w, factor=factor) return self