Source code for skfolio.optimization.convex._risk_budgeting

"""Risk Budgeting Optimization estimator."""

# Copyright (c) 2023
# Author: Hugo Delatte <delatte.hugo@gmail.com>
# License: BSD 3 clause
# The optimization features are derived
# from Riskfolio-Lib, Copyright (c) 2020-2023, Dany Cajas, Licensed under 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 args_names, check_estimator


[docs] class RiskBudgeting(ConvexOptimization): r"""Risk Budgeting Optimization estimator. The Risk Budgeting estimator solves the below convex problem: .. math:: \begin{cases} \begin{aligned} &\min_{w} & & risk_{i}(w) \\ &\text{s.t.} & & b^T \cdot log(w) \ge c \\ & & & w^T \cdot \mu \ge min\_return \\ & & & A \cdot w \ge b \\ & & & w \ge 0 \end{aligned} \end{cases} with :math:`b` the risk budget vector and :math:`c` an auxiliary variable of the log barrier. And :math:`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 and additional constraints can also be added to the optimization problem (see the parameters description). Limitations are imposed on some constraints including long only weights to ensure convexity. The assets expected returns, covariance matrix and returns are estimated from the :ref:`prior estimator <prior>`. Parameters ---------- risk_measure : RiskMeasure, default=RiskMeasure.VARIANCE :class:`~skfolio.meta.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_budget : dict[str, float] | array-like of shape (n_assets,), optional Risk budget allocated to each asset. If a dictionary is provided, its (key/value) pair must be the (asset name/asset risk budget) and the input `X` of the `fit` method must be a DataFrame with the assets names in columns. The default (`None`) is to use the identity vector, reducing the risk budgeting to a risk-parity (each asset contributing equally to the total risk). 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]` transaction_costs : float | 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: .. math:: total\_cost = \sum_{i=1}^{N} c_{i} \times |w_{i} - w\_prev_{i}| with :math:`c_{i}` the transaction cost of asset i, :math:`w_{i}` its weight and :math:`w\_prev_{i}` its previous weight (defined in `previous_weights`). The float :math:`total\_cost` is impacting the portfolio expected return in the optimization: .. math:: expected\_return = \mu^{T} \cdot w - total\_cost with :math:`\mu` the vector af assets' expected returns and :math:`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 :math:`\mu`. For example, if the input `X` is composed of **daily** returns, the `transaction_costs` need to be expressed as **daily** costs. (See :ref:`sphx_glr_auto_examples_1_mean_risk_plot_6_transaction_costs.py`) management_fees : float | 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: .. math:: total\_fee = \sum_{i=1}^{N} f_{i} \times w_{i} with :math:`f_{i}` the management fee of asset i and :math:`w_{i}` its weight. The float :math:`total\_fee` is impacting the portfolio expected return in the optimization: .. math:: expected\_return = \mu^{T} \cdot w - total\_fee with :math:`\mu` the vector af assets expected returns and :math:`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 :math:`\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 :math:`\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_weights : float | 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. 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`. min_return : float | array-like of shape (n_optimization), optional Lower bound constraint on the expected return. min_acceptable_return : float, 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_beta : float, default=0.95 CVaR (Conditional Value at Risk) confidence level. evar_beta : float, default=0 EVaR (Entropic Value at Risk) confidence level. cvar_beta : float, default=0.95 CVaR (Conditional Value at Risk) confidence level. The default value is `0.95`. evar_beta : float, default=0 EVaR (Entropic Value at Risk) confidence level. The default value is `0.95`. cdar_beta : float, default=0.95 CDaR (Conditional Drawdown at Risk) confidence level. The default value is `0.95`. edar_beta : float, default=0.95 EDaR (Entropic Drawdown at Risk) confidence level. The default value is `0.95`. add_objective : Callable[[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_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. """ def __init__( self, risk_measure: RiskMeasure = RiskMeasure.VARIANCE, risk_budget: np.ndarray | None = None, prior_estimator: BasePrior | None = None, min_weights: skt.MultiInput | None = 0.0, max_weights: skt.MultiInput | None = 1.0, transaction_costs: skt.MultiInput = 0.0, management_fees: skt.MultiInput = 0.0, previous_weights: skt.MultiInput | 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, min_return: skt.Target | None = None, min_acceptable_return: skt.Target | None = None, cvar_beta: float = 0.95, evar_beta: float = 0.95, cdar_beta: float = 0.95, edar_beta: float = 0.95, 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=risk_measure, prior_estimator=prior_estimator, min_weights=min_weights, max_weights=max_weights, budget=1, transaction_costs=transaction_costs, management_fees=management_fees, previous_weights=previous_weights, groups=groups, linear_constraints=linear_constraints, left_inequality=left_inequality, right_inequality=right_inequality, risk_free_rate=risk_free_rate, min_acceptable_return=min_acceptable_return, cvar_beta=cvar_beta, evar_beta=evar_beta, cdar_beta=cdar_beta, edar_beta=edar_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.min_return = min_return self.risk_budget = risk_budget def _validation(self) -> None: if not isinstance(self.risk_measure, RiskMeasure): raise TypeError("risk_measure must be of type `RiskMeasure`") if self.min_weights < 0: raise ValueError( "Risk Budgeting must have non negative `min_weights` constraint" " otherwise the problem becomes non-convex." )
[docs] def fit(self, X: npt.ArrayLike, y=None, **fit_params) -> "RiskBudgeting": """Fit the Risk Budgeting 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`. Returns ------- self : RiskBudgeting Fitted estimator. """ routed_params = skm.process_routing(self, "fit", **fit_params) self._check_feature_names(X, reset=True) # Validate self._validation() # 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) # Risk budget risk_budget = self.risk_budget if risk_budget is None: risk_budget = np.ones(n_assets) else: risk_budget = self._clean_input( self.risk_budget, n_assets=n_assets, fill_value=1e-10, name="risk_budget", ) risk_budget[risk_budget == 0] = 1e-10 # Variables w = cp.Variable(n_assets) factor = cp.Variable() c = cp.Variable(nonneg=True) # Expected returns expected_return = ( self._cvx_expected_return(prior_model=prior_model, w=w) - self._cvx_transaction_cost(prior_model=prior_model, w=w, factor=factor) - self._cvx_management_fee(prior_model=prior_model, w=w) ) # risk budgeting constraint constraints = [ risk_budget @ cp.log(w) * self._scale_constraints >= c * self._scale_constraints ] # weight constraints constraints += self._get_weight_constraints( n_assets=n_assets, w=w, factor=factor ) parameters_values = [] # min_return constraint if self.min_return is not None: parameter = cp.Parameter(nonneg=False) constraints += [ expected_return * self._scale_constraints >= parameter * factor * self._scale_constraints ] parameters_values.append((parameter, self.min_return)) # risk and risk constraints risk_func = getattr(self, f"_{self.risk_measure.value}_risk") args = {} for arg_name in args_names(risk_func): if arg_name == "prior_model": args[arg_name] = prior_model elif arg_name == "w": args[arg_name] = w elif arg_name == "factor": if self.risk_measure in [RiskMeasure.FIRST_LOWER_PARTIAL_MOMENT]: args[arg_name] = factor else: args[arg_name] = cp.Constant(1) else: args[arg_name] = getattr(self, arg_name) risk, constraints_i = risk_func(**args) constraints += constraints_i # custom objectives and constraints custom_objective = self._get_custom_objective(w=w) constraints += self._get_custom_constraints(w=w) objective = cp.Minimize( risk * self._scale_objective + custom_objective * self._scale_objective ) # problem # noinspection PyTypeChecker problem = cp.Problem(objective, constraints) # results self._solve_problem( problem=problem, w=w, factor=factor, parameters_values=parameters_values, expressions={ "expected_return": expected_return, "risk": risk, "factor": factor, }, ) return self