<a id="skfolio-optimization-riskbudgeting"></a>

# skfolio.optimization.RiskBudgeting

<a id="skfolio.optimization.RiskBudgeting"></a>

### *class* skfolio.optimization.RiskBudgeting(risk_measure=Variance, risk_budget=None, prior_estimator=None, min_weights=0.0, max_weights=1.0, transaction_costs=0.0, management_fees=0.0, previous_weights=None, groups=None, linear_constraints=None, left_inequality=None, right_inequality=None, risk_free_rate=0.0, min_return=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, raise_on_failure=True, add_objective=None, add_constraints=None, overwrite_expected_return=None, portfolio_params=None, fallback=None)

Risk Budgeting Optimization estimator.

The Risk Budgeting estimator solves the below convex problem:

> $$
> \begin{cases}
> \begin{aligned}
> & \min_{w,s} && \mathrm{Risk}(w) \\
> & \text{s.t.} && budget^{\top}\log(w) \ge 0 \\
> &             && \mathbf{1}^{\top} w = s \\
> &             && expected\_return(w) \ge s\, min\_return \\
> &             && A w \le s\, b \\
> &             && w \ge 0
> \end{aligned}
> \end{cases}

> $$

with $budget$ the risk budget vector and $min\_return$ the minimum
expected return constraint.

And $Risk$ 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 expected asset returns, covariance matrix and returns are estimated from the
[prior estimator](https://skfolio.org/user_guide/prior.html.md#prior).

* **Parameters:**
  **risk_measure** *RiskMeasure, 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
    <br/>
    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*
  : [Prior estimator](https://skfolio.org/user_guide/prior.html.md#prior).
    The prior estimator is used to estimate the [`ReturnDistribution`](https://skfolio.org/generated/skfolio.prior.ReturnDistribution.html.md#skfolio.prior.ReturnDistribution)
    containing estimates of expected asset returns, covariance matrix,
    returns and Cholesky decomposition of the covariance.
    The default (`None`) is to use [`EmpiricalPrior`](https://skfolio.org/generated/skfolio.prior.EmpiricalPrior.html.md#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 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).
    <br/>
    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%).
    <br/>
    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:
    $$
    total\_cost = \sum_{i=1}^{N} c_{i} \times |w_{i} - w\_prev_{i}|
    <br/>
    $$
    <br/>
    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:
    $$
    expected\_return = \mu^{T} \cdot w - total\_cost
    <br/>
    $$
    <br/>
    with $\mu$ the vector of assets’ expected returns and $w$ the
    vector of assets weights.
    <br/>
    For positions in `previous_weights` whose assets are no longer in the
    investment universe, transaction costs are calculated assuming full
    liquidation. These costs are included in both the optimization and
    `Portfolio.total_cost`. For assets absent from `X`, `transaction_costs`
    must be a single rate applied to all assets or a dictionary keyed by asset name.
    <br/>
    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`.
    <br/>
    #### WARNING
    Based on the above formula, the periodicity of the transaction costs
    must match 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. 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 [Periodicity Convention](https://skfolio.org/user_guide/data_preparation.html.md#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:
    $$
    total\_fee = \sum_{i=1}^{N} f_{i} \times w_{i}
    <br/>
    $$
    <br/>
    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:
    $$
    expected\_return = \mu^{T} \cdot w - total\_fee
    <br/>
    $$
    <br/>
    with $\mu$ the vector of assets’ expected returns and $w$ the vector
    of assets weights.
    <br/>
    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`.
    <br/>
    #### WARNING
    Based on the above formula, the periodicity of the management fees
    must match 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. 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).
    <br/>
    #### 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_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.
    For named positions in assets absent from `X`, these calculations assume
    full liquidation.
    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.

  **linear_constraints** *array-like of shape (n_constraints,), optional*
  : Linear constraints on portfolio weights or factor exposures.
    <br/>
    Constraint names can reference:
    > * Asset names: individual asset weights (e.g. `"SPX"`, `"AAPL"`)
    > * Group names: sums of weights in groups defined by `groups`
    > * Factor names: portfolio factor exposure (requires factor model prior)
    > * Factor families: sum of portfolio exposures to all factors in one family
    <br/>
    Supported equation patterns include:
    > * `"name <= value"` or `"name >= value"`
    > * `"name == value"`
    > * `"a * name1 + b * name2 <= c * name3 + d"`
    <br/>
    For example:
    > * `"SPX >= 0.10"` –> SPX weight >= 10%
    > * `"SX5E + SPX >= 0.2"` –> sum of SX5E and SPX weights >= 20%
    > * `"US == 0.7"` –> sum of weights in US group == 70%
    > * `"Equity == 3 * Bond"` –> sum of weights in Equity group == 3x sum of weights in Bond group
    > * `"Momentum <= 0.30"` –> portfolio Momentum exposure <= 30%
    > * `"style <= 0.50"` –> sum of all style factor exposures (Momentum, Value, Size, etc.) <= 50%
    <br/>
    Factor constraints require a prior estimator (e.g.
    [`TimeSeriesFactorModel`](https://skfolio.org/generated/skfolio.prior.TimeSeriesFactorModel.html.md#skfolio.prior.TimeSeriesFactorModel),
    [`CharacteristicsFactorModel`](https://skfolio.org/generated/skfolio.prior.CharacteristicsFactorModel.html.md#skfolio.prior.CharacteristicsFactorModel))
    that provides `loading_matrix`, `factor_names` and optionally `factor_families`
    in its [`FactorModel`](https://skfolio.org/generated/skfolio.prior.FactorModel.html.md#skfolio.prior.FactorModel).
    <br/>
    Asset, group, factor, and factor family names must be unique.

  **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.
    <br/>
    For example:
    > * `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 $A$ of the linear
    constraint $A \cdot w \leq b$.

  **right_inequality** *array-like of shape (n_constraints, ), optional*
  : Right inequality vector $b$ of the linear
    constraint $A \cdot w \leq b$.

  **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
    <br/>
    The default (`None`) is to use the mean.

  **cvar_beta** *float, default=0.95*
  : CVaR (Conditional Value at Risk) confidence level.
    The default value is `0.95`.

  **evar_beta** *float, default=0.95*
  : 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 must be a function taking the CVXPY weight variable `w` as its first
    positional argument and, optionally, the estimator instance as its second.
    It must return a CVXPY expression or a list of CVXPY expressions, evaluated
    when `fit` is called.
    <br/>
    For example, to require an effective number of assets of at least 20:
    ```pycon
    >>> import cvxpy as cp
    >>> from skfolio.optimization import RiskBudgeting
    >>> model = RiskBudgeting(add_constraints=lambda w: cp.sum_squares(w) <= 1 / 20)
    ```
    <br/>
    The optional second argument gives access to the estimator’s attributes,
    including quantities estimated during `fit`. For example, to cap each
    position size in risk units at 20 bps, using the volatilities estimated
    by the prior:
    ```pycon
    >>> import numpy as np
    >>> def position_risk_cap(w, model):
    ...     covariance = model.prior_estimator_.return_distribution_.covariance
    ...     vols = np.sqrt(np.diag(covariance))
    ...     return cp.multiply(vols, w) <= 0.002
    >>> model = RiskBudgeting(add_constraints=position_risk_cap)
    ```

  **overwrite_expected_return** *Callable[[cp.Variable], cp.Expression], optional*
  : Overwrite the expected return $\mu \cdot w$ with a custom CVXPY
    expression. It must be a function taking the CVXPY weight variable `w` as
    its first positional argument and, optionally, the estimator instance as
    its second. It must return a concave CVXPY expression, evaluated when
    `fit` is called. The custom expression replaces the expected return in the
    objective function and in the constraints where the expected return is
    used.
    <br/>
    For example, to adjust the expected return for volatility drag,
    approximating the portfolio geometric mean return:
    ```pycon
    >>> import cvxpy as cp
    >>> from skfolio.optimization import RiskBudgeting
    >>> def geometric_expected_return(w, model):
    ...     dist = model.prior_estimator_.return_distribution_
    ...     return dist.mu @ w - 0.5 * cp.quad_form(w, dist.covariance)
    >>> model = RiskBudgeting(overwrite_expected_return=geometric_expected_return)
    ```

  **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](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](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`.

  **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,) 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`](#skfolio.optimization.RiskBudgeting.fit)(X[, y])            | Fit the Risk Budgeting Optimization estimator.                                                                                  |
|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|
| [`fit_predict`](#skfolio.optimization.RiskBudgeting.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`](#skfolio.optimization.RiskBudgeting.get_metadata_routing)() | Get metadata routing of this object.                                                                                            |
| [`get_params`](#skfolio.optimization.RiskBudgeting.get_params)([deep])     | Get parameters for this estimator.                                                                                              |
| [`predict`](#skfolio.optimization.RiskBudgeting.predict)(X)             | Predict the `Portfolio` or a `Population` of portfolios on `X`.                                                                 |
| [`score`](#skfolio.optimization.RiskBudgeting.score)(X[, y])          | Prediction score using the Sharpe Ratio.                                                                                        |
| [`set_params`](#skfolio.optimization.RiskBudgeting.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

* <a id='r80d35056dba9-1'>**[1]**</a> “Constrained Risk Budgeting Portfolios: Theory, Algorithms, Applications”, Journal of Portfolio Management, Richard, J.-C., & Roncalli, T. (2019)
* <a id='r80d35056dba9-2'>**[2]**</a> “Portfolio Optimization: Theory and Application”, Chapter 11, Daniel P. Palomar (2025)

### Examples

For complete tutorials on risk budgeting optimization, see the
[Risk Budgeting](https://skfolio.org/auto_examples/risk_budgeting/index.html.md#risk-budgeting-examples) gallery.

```pycon
>>> from skfolio import RiskMeasure
>>> from skfolio.datasets import load_sp500_dataset
>>> from skfolio.optimization import RiskBudgeting
>>> from skfolio.preprocessing import prices_to_returns
>>>
>>> # Load historical prices and convert them to returns
>>> prices = load_sp500_dataset()
>>> X = prices_to_returns(prices)
>>>
>>> # Variance risk parity optimization
>>> model = RiskBudgeting(risk_measure=RiskMeasure.VARIANCE)
>>> model.fit(X)
RiskBudgeting()
>>> print(model.weights_)
[0.0422 0.0314 0.0343 ... 0.0473 0.0603 0.0565]
>>>
>>> # CVaR risk budgeting with custom asset budgets
>>> risk_budget = {asset: 1.0 for asset in X.columns}
>>> risk_budget["AAPL"] = 1.5
>>> risk_budget["GE"] = 0.2
>>> risk_budget["JPM"] = 0.2
>>> model = RiskBudgeting(
...     risk_measure=RiskMeasure.CVAR,
...     risk_budget=risk_budget,
... )
>>> model.fit(X)
RiskBudgeting(...)
>>> print(model.weights_)
[0.0623 0.0319 0.0347 ... 0.0502 0.0659 0.0595]
>>>
>>> portfolio = model.predict(X)
>>> print(portfolio.cvar)
0.0251...
```

<a id="skfolio.optimization.RiskBudgeting.fit"></a>

#### fit(X, y=None, \*\*fit_params)

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.

<a id="skfolio.optimization.RiskBudgeting.fit_predict"></a>

#### 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, factors=...)` then `predict(X)` separately.

If fitting fails and `raise_on_failure=False`, this returns a
`FailedPortfolio`.

* **Parameters:**
  **X** *array-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`.

<a id="skfolio.optimization.RiskBudgeting.get_metadata_routing"></a>

#### get_metadata_routing()

Get metadata routing of this object.

Please check [User Guide](https://skfolio.org/user_guide/metadata_routing.html.md#metadata-routing) on how the routing
mechanism works.

* **Returns:**
  **routing** *MetadataRequest*
  : A `MetadataRequest` encapsulating
    routing information.

<a id="skfolio.optimization.RiskBudgeting.get_params"></a>

#### get_params(deep=True)

Get parameters for this estimator.

* **Parameters:**
  **deep** *bool, default=True*
  : If True, will return the parameters for this estimator and
    contained subobjects that are estimators.
* **Returns:**
  **params** *dict*
  : Parameter names mapped to their values.

<a id="skfolio.optimization.RiskBudgeting.needs_previous_weights"></a>

#### *property* needs_previous_weights

Whether `previous_weights` must be propagated between folds/rebalances.

Used by `cross_val_predict` and `online_predict` to decide whether to run
sequentially and pass the weights from the previous rebalancing to the next.
This is `True` when `portfolio_params` sets `weight_drift=True`, or when
transaction costs, a maximum turnover, or a fallback depending on
`previous_weights` are present.

<a id="skfolio.optimization.RiskBudgeting.predict"></a>

#### 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:**
  **X** *array-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`.

<a id="skfolio.optimization.RiskBudgeting.score"></a>

#### 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:**
  **X** *array-like of shape (n_observations, n_assets)*
  : Price returns of the assets.

  **y** *Ignored*
  : Not used, present here for API consistency by convention.
* **Returns:**
  **score** *float*
  : The Sharpe Ratio of the portfolio if the prediction is a single `Portfolio`
    or the mean of all the portfolio Sharpe Ratios if the prediction is a
    `Population` of `Portfolio`.

<a id="skfolio.optimization.RiskBudgeting.set_params"></a>

#### 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:**
  **\*\*params** *dict*
  : Estimator parameters.
* **Returns:**
  **self** *estimator instance*
  : Estimator instance.