"""Hierarchical Equal Risk Contribution estimator."""
# Copyright (c) 2023-2026
# Author: Hugo Delatte <hugo.delatte@skfoliolabs.com>
# SPDX-License-Identifier: BSD-3-Clause
# Weight constraints is a novel implementation, see docstring for more details.
from __future__ import annotations
import numpy as np
import pandas as pd
import scipy.cluster.hierarchy as sch
import sklearn.utils.metadata_routing as skm
import sklearn.utils.validation as skv
import skfolio.typing as skt
from skfolio.cluster import HierarchicalClustering
from skfolio.distance import BaseDistance, PearsonDistance
from skfolio.measures import ExtraRiskMeasure, RiskMeasure
from skfolio.optimization.cluster.hierarchical._base import (
BaseHierarchicalOptimization,
)
from skfolio.prior import BasePrior, EmpiricalPrior
from skfolio.typing import ArrayLike
from skfolio.utils.stats import minimize_relative_weight_deviation
from skfolio.utils.tools import check_estimator
[docs]
class HierarchicalEqualRiskContribution(BaseHierarchicalOptimization):
r"""Hierarchical Equal Risk Contribution estimator.
The Hierarchical Equal Risk Contribution is a portfolio optimization method
developed by Thomas Raffinot [2]_.
This algorithm uses a distance matrix to compute hierarchical clusters using the
Hierarchical Tree Clustering algorithm. It then computes, for each cluster, the
total cluster risk of an inverse-risk allocation.
The final step is the top-down recursive division of the dendrogram, where the
assets weights are updated using a naive risk parity within clusters.
It differs from the Hierarchical Risk Parity by exploiting the dendrogram shape
during the top-down recursive division instead of bisecting it.
.. note ::
The default linkage method is set to the Ward variance minimization algorithm,
which is more stable and has better properties than the single-linkage
method [4]_.
Also, the initial paper does not provide an algorithm for handling weight
constraints, and no standard solution currently exists.
In contrast to HRP (Hierarchical Risk Parity), where weight constraints
can be applied to the split factor at each bisection step, HERC
(Hierarchical Equal Risk Contribution) cannot incorporate weight constraints
during the intermediate steps of the allocation. Therefore, in HERC, the
weight constraints must be enforced after the top-down allocation has been
completed.
In skfolio, we minimize the relative deviation of the final weights from
the initial weights. This is formulated as a convex optimization problem:
.. math::
\begin{cases}
\begin{aligned}
&\min_{w} & & \Vert \frac{w - w_{init}}{w_{init}} \Vert_{2}^{2} \\
&\text{s.t.} & & \sum_{i=1}^{N} w_{i} = 1 \\
& & & w_{min} \leq w_i \leq w_{max}, \quad \forall i
\end{aligned}
\end{cases}
The reason for minimizing the relative deviation (as opposed to the absolute
deviation) is that we want to limit the impact on the risk contribution of
each asset. Since HERC allocates inversely to risk, adjusting the weights
based on relative deviation ensures that the assets' risk contributions
remain proportionally consistent with the initial allocation.
Parameters
----------
risk_measure : RiskMeasure or ExtraRiskMeasure, default=RiskMeasure.VARIANCE
:class:`~skfolio.meta.RiskMeasure` or :class:`~skfolio.meta.ExtraRiskMeasure`
of the optimization.
Can be any of:
* MEAN_ABSOLUTE_DEVIATION
* FIRST_LOWER_PARTIAL_MOMENT
* VARIANCE
* SEMI_VARIANCE
* CVAR
* EVAR
* WORST_REALIZATION
* CDAR
* MAX_DRAWDOWN
* AVERAGE_DRAWDOWN
* EDAR
* ULCER_INDEX
* GINI_MEAN_DIFFERENCE_RATIO
* VALUE_AT_RISK
* DRAWDOWN_AT_RISK
* ENTROPIC_RISK_MEASURE
* FOURTH_CENTRAL_MOMENT
* FOURTH_LOWER_PARTIAL_MOMENT
The default is `RiskMeasure.VARIANCE`.
prior_estimator : BasePrior, optional
:ref:`Prior estimator <prior>`.
The prior estimator is used to estimate the :class:`~skfolio.prior.ReturnDistribution`
containing estimates of expected asset returns, covariance matrix and
returns. The moments and returns estimations are used for the risk computation
and the returns estimation are used by the distance matrix estimator.
The default (`None`) is to use :class:`~skfolio.prior.EmpiricalPrior`.
distance_estimator : BaseDistance, optional
:ref:`Distance estimator <distance>`.
The distance estimator is used to estimate the codependence and the distance
matrix needed for the computation of the linkage matrix.
The default (`None`) is to use :class:`~skfolio.distance.PearsonDistance`.
hierarchical_clustering_estimator : HierarchicalClustering, optional
:ref:`Hierarchical Clustering estimator <hierarchical_clustering>`.
The hierarchical clustering estimator is used to compute the linkage matrix
and the hierarchical clustering of the assets based on the distance matrix.
The default (`None`) is to use
:class:`~skfolio.cluster.HierarchicalClustering`.
min_weights : float | dict[str, float] | array-like of shape (n_assets, ), default=0.0
Minimum assets weights (weights lower bounds). The default is 0.0 (no short
selling). Negative weights are not allowed. If a float is provided, it is
applied to each asset. `None` is equivalent to the default `0.0`. If a
dictionary is provided, its (key/value) pair must be the (asset name/asset
minimum weight) and the input `X` of the `fit` methods must be a DataFrame with
the asset names in columns. When using a dictionary, assets values that are not
provided are assigned the default minimum weight of `0.0`.
Example:
* `min_weights = 0.0` --> long only portfolio (default).
* `min_weights = {"SX5E": 0.1, "SPX": 0.2}`
* `min_weights = [0.1, 0.2]`
max_weights : float | dict[str, float] | array-like of shape (n_assets, ), default=1.0
Maximum assets weights (weights upper bounds). The default is 1.0 (each asset
is below 100%). Weights above 1.0 are not allowed. If a float is provided, it is
applied to each asset. `None` is equivalent to the default `1.0`. 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 asset names in columns. When using a dictionary, assets values that are not
provided are assigned the default maximum weight of `1.0`.
Example:
* `max_weights = 1.0` --> each weight must be below 100% (default).
* `max_weights = 0.5` --> each weight must be below 50%.
* `max_weights = {"SX5E": 0.8, "SPX": 0.9}`
* `max_weights = [0.8, 0.9]`
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 of 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 asset names in columns.
The default value is `0.0`.
.. warning::
Based on the above formula, the periodicity of the transaction costs
must match 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. A transaction cost is paid once per
rebalancing while a position earns its expected return on every period it
is held, so the one-off cost is converted by dividing it by the expected
investment duration (e.g. `0.001 / 21` for a 10 bps cost with daily
returns and a one-month expected holding period).
(See :ref:`Periodicity Convention <periodicity_convention>`)
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 of 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 asset names in columns.
The default value is `0.0`.
.. warning::
Based on the above formula, the periodicity of the management fees
must match 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. Unlike transaction costs, management fees
accrue with holding time, so a stated annual fee converts directly to the
return periodicity (e.g. `0.02 / 252` for a 2% annual fee on daily
returns).
.. 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 total cost. 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 asset names in columns.
The default (`None`) means no previous weights.
Additionally, when `fallback="previous_weights"`, failures will fall back to
these weights if provided.
solver : str, default="CLARABEL"
The solver used for the weights constraints optimization. The default is
"CLARABEL" which is written in Rust and has better numerical stability and
performance than ECOS and SCS.
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 to use the CVXPY default.
For more details about solver arguments, check the CVXPY documentation:
https://www.cvxpy.org/tutorial/advanced/index.html#setting-solver-options
portfolio_params : dict, 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`.
fallback : BaseOptimization | "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_failure : bool, 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,)
Weights of the assets.
distance_estimator_ : BaseDistance
Fitted `distance_estimator`.
hierarchical_clustering_estimator_ : HierarchicalClustering
Fitted `hierarchical_clustering_estimator`.
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 asset 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.
Notes
-----
All estimators should specify all parameters as explicit keyword arguments in
`__init__` (no `*args` or `**kwargs`), following scikit-learn conventions.
References
----------
.. [1] "Hierarchical clustering-based asset allocation",
The Journal of Portfolio Management,
Thomas Raffinot (2017).
.. [2] "The hierarchical equal risk contribution portfolio",
Thomas Raffinot (2018).
.. [3] "Application of two-order difference to gap statistic".
Yue, Wang & Wei (2009).
.. [4] "A review of two decades of correlations, hierarchies, networks and
clustering in financial markets",
Gautier Marti, Frank Nielsen, Mikołaj Bińkowski, Philippe Donnat (2020).
"""
def __init__(
self,
risk_measure: RiskMeasure | ExtraRiskMeasure = RiskMeasure.VARIANCE,
prior_estimator: BasePrior | None = None,
distance_estimator: BaseDistance | None = None,
hierarchical_clustering_estimator: HierarchicalClustering | None = None,
min_weights: skt.MultiInput | None = 0.0,
max_weights: skt.MultiInput | None = 1.0,
solver: str = "CLARABEL",
solver_params: dict | None = None,
transaction_costs: skt.MultiInput = 0.0,
management_fees: skt.MultiInput = 0.0,
previous_weights: skt.MultiInput | None = None,
portfolio_params: dict | None = None,
fallback: skt.Fallback = None,
raise_on_failure: bool = True,
):
super().__init__(
risk_measure=risk_measure,
prior_estimator=prior_estimator,
distance_estimator=distance_estimator,
hierarchical_clustering_estimator=hierarchical_clustering_estimator,
min_weights=min_weights,
max_weights=max_weights,
transaction_costs=transaction_costs,
management_fees=management_fees,
previous_weights=previous_weights,
portfolio_params=portfolio_params,
fallback=fallback,
raise_on_failure=raise_on_failure,
)
self.solver = solver
self.solver_params = solver_params
[docs]
def fit(
self, X: ArrayLike, y: None = None, **fit_params
) -> HierarchicalEqualRiskContribution:
"""Fit the Hierarchical Equal Risk Contribution estimator.
Parameters
----------
X : array-like of shape (n_observations, n_assets)
Price returns of the assets.
y : Ignored
Not used, present for API consistency by convention.
**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 : HierarchicalEqualRiskContribution
Fitted estimator.
"""
routed_params = skm.process_routing(self, "fit", **fit_params)
# Validate
if not isinstance(self.risk_measure, RiskMeasure | ExtraRiskMeasure):
raise TypeError(
"`risk_measure` must be of type `RiskMeasure` or `ExtraRiskMeasure`"
)
if self.risk_measure in [ExtraRiskMeasure.SKEW, ExtraRiskMeasure.KURTOSIS]:
# Because Skew and Kurtosis can take negative values
raise ValueError(
f"risk_measure {self.risk_measure} currently not supported in HERC"
)
self.prior_estimator_ = check_estimator(
self.prior_estimator,
default=EmpiricalPrior(),
check_type=BasePrior,
)
self.distance_estimator_ = check_estimator(
self.distance_estimator,
default=PearsonDistance(),
check_type=BaseDistance,
)
self.hierarchical_clustering_estimator_ = check_estimator(
self.hierarchical_clustering_estimator,
default=HierarchicalClustering(),
check_type=HierarchicalClustering,
)
# Fit the estimators
self.prior_estimator_.fit(X, y, **routed_params.prior_estimator.fit)
return_distribution = self.prior_estimator_.return_distribution_
returns = return_distribution.returns
# To keep the asset_names
if isinstance(X, pd.DataFrame):
returns = pd.DataFrame(returns, columns=X.columns)
# noinspection PyArgumentList
self.distance_estimator_.fit(returns, y, **routed_params.distance_estimator.fit)
distance = self.distance_estimator_.distance_
# To keep the asset_names
if isinstance(X, pd.DataFrame):
distance = pd.DataFrame(distance, columns=X.columns)
# noinspection PyArgumentList
self.hierarchical_clustering_estimator_.fit(
X=distance, y=None, **routed_params.hierarchical_clustering_estimator.fit
)
n_clusters = self.hierarchical_clustering_estimator_.n_clusters_
labels = self.hierarchical_clustering_estimator_.labels_
linkage_matrix = self.hierarchical_clustering_estimator_.linkage_matrix_
X = skv.validate_data(self, X)
n_assets = X.shape[1]
min_weights, max_weights = self._convert_weights_bounds(n_assets=n_assets)
assets_risks = self._unitary_risks(return_distribution=return_distribution)
weights = np.ones(n_assets)
clusters_weights = np.ones(n_clusters)
clusters = [np.argwhere(labels == i).flatten() for i in range(n_clusters)]
clusters_sets = [set(cluster_ids) for cluster_ids in clusters]
# Compute cluster total risk based on inverse-risk allocation
cluster_risks = []
for cluster_ids in clusters:
inv_risk_w = np.zeros(n_assets)
inv_risk_w[cluster_ids] = 1 / assets_risks[cluster_ids]
inv_risk_w /= inv_risk_w.sum()
cluster_risks.append(
self._risk(weights=inv_risk_w, return_distribution=return_distribution)
)
weights[cluster_ids] = inv_risk_w[cluster_ids]
cluster_risks = np.array(cluster_risks)
# Compute the cluster weights using the dendrogram structure.
# Recurse from the root until each of the defined cluster is reached and
# update the weights using the naive risk parity.
def _recurse(node):
# Stop when the cluster is reached
if set(node.pre_order()) in clusters_sets:
return
left_node = node.get_left()
right_node = node.get_right()
left_cluster_tree = set(left_node.pre_order())
right_cluster_tree = set(right_node.pre_order())
left_cluster = []
right_cluster = []
for i, cluster_ids in enumerate(clusters_sets):
if cluster_ids.issubset(left_cluster_tree):
left_cluster.append(i)
elif cluster_ids.issubset(right_cluster_tree):
right_cluster.append(i)
if not left_cluster or not right_cluster:
raise ValueError("Corrupted")
left_cluster = np.array(left_cluster)
right_cluster = np.array(right_cluster)
left_risk = np.sum(cluster_risks[left_cluster])
right_risk = np.sum(cluster_risks[right_cluster])
alpha = 1 - left_risk / (left_risk + right_risk)
clusters_weights[left_cluster] *= alpha
clusters_weights[right_cluster] *= 1 - alpha
_recurse(left_node)
_recurse(right_node)
root = sch.to_tree(linkage_matrix)
_recurse(root)
# Combine intra-cluster weights with inter-cluster weights
for i, cluster_ids in enumerate(clusters):
weights[cluster_ids] *= clusters_weights[i]
# Apply weights constraints
weights = minimize_relative_weight_deviation(
weights=weights,
min_weights=min_weights,
max_weights=max_weights,
solver=self.solver,
solver_params=self.solver_params,
)
self.weights_ = weights
return self
