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

# skfolio.optimization.ConvexOptimization

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

### *class* skfolio.optimization.ConvexOptimization(risk_measure=Variance, prior_estimator=None, min_weights=0.0, max_weights=1.0, budget=1.0, min_budget=None, max_budget=None, max_short=None, max_long=None, cardinality=None, group_cardinalities=None, threshold_long=None, threshold_short=None, transaction_costs=0.0, management_fees=0.0, previous_weights=None, target_weights=None, groups=None, linear_constraints=None, left_inequality=None, right_inequality=None, l1_coef=0.0, l2_coef=0.0, mu_uncertainty_set_estimator=None, covariance_uncertainty_set_estimator=None, risk_free_rate=0.0, 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, add_objective=None, add_constraints=None, overwrite_expected_return=None, portfolio_params=None, fallback=None, raise_on_failure=True)

Base class for all convex optimization estimators in skfolio.

All risk measures that have a convex formulation are defined in class methods with
naming convention: `_{risk_measure}_risk`. That naming convention is used for
dynamic lookup.

CVX expressions that are shared among multiple risk measures are cached in a
dictionary named `_cvx_cache`.
This is to avoid cvx expression duplication and improve performance and convergence.

* **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_RATIO
    <br/>
    The default is `RiskMeasure.VARIANCE`.

  **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]`

  **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).
    <br/>
    For example:
    > * `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_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.

  **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.

  **cardinality** *int, optional*
  : Specifies the cardinality constraint to limit the number of invested assets
    (non-zero weights). This feature requires a mixed-integer solver. For an
    open-source option, we recommend using SCIP by setting `solver="SCIP"`.
    To install it, use: `pip install cvxpy[SCIP]`. For commercial solvers,
    supported options include MOSEK, GUROBI, or CPLEX.

  **group_cardinalities** *dict[str, int], optional*
  : A dictionary specifying cardinality constraints for specific groups of assets.
    The keys represent group names (strings), and the values specify the maximum
    number of assets allowed in each group. You must provide the groups using the
    `groups` parameter. This requires a mixed-integer solver (see `cardinality`
    for more details).

  **threshold_long** *float | dict[str, float] | array-like of shape (n_assets, ), optional*
  : Specifies the minimum weight threshold for assets in the portfolio to be
    considered as a long position. Assets with weights below this threshold
    will not be included as part of the portfolio’s long positions. This
    constraint can help eliminate insignificant allocations.
    This requires a mixed-integer solver (see `cardinality` for more details).
    It follows the same format as `min_weights` and `max_weights`.

  **threshold_short** *float | dict[str, float] | array-like of shape (n_assets, ), optional*
  : Specifies the maximum weight threshold for assets in the portfolio to be
    considered as a short position. Assets with weights above this threshold
    will not be included as part of the portfolio’s short positions. This
    constraint can help control the magnitude of short positions.
    This requires a mixed-integer solver (see `cardinality` for more details).
    It follows the same format as `min_weights` and `max_weights`.

  **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.

  **l1_coef** *float, default=0.0*
  : L1 regularization coefficient.
    It is used to penalize the objective function by the L1 norm:
    $$
    l1\_coef \times \Vert w \Vert_{1} = l1\_coef \times \sum_{i=1}^{N} |w_{i}|
    <br/>
    $$
    <br/>
    Increasing this coefficient will reduce the number of non-zero weights
    (cardinality). It tends to increase robustness (out-of-sample stability) but
    reduces diversification.
    The default value is `0.0`.

  **l2_coef** *float, default=0.0*
  : L2 regularization coefficient.
    It is used to penalize the objective function by the L2 norm:
    $$
    l2\_coef \times \Vert w \Vert_{2}^{2} = l2\_coef \times \sum_{i=1}^{N} w_{i}^2
    <br/>
    $$
    <br/>
    It tends to increase robustness (out-of-sample stability).
    The default value is `0.0`.

  **mu_uncertainty_set_estimator** *BaseMuUncertaintySet, optional*
  : [Mu Uncertainty set estimator](https://skfolio.org/user_guide/uncertainty_set.html.md#uncertainty-set-estimator).
    If provided, the expected asset returns are modelled with a norm-ball
    uncertainty set. It is called worst-case optimization and is a class of robust
    optimization. It reduces the instability that arises from the estimation errors
    of the expected returns.
    The worst-case portfolio expected return is:
    $$
    w^T\hat{\mu} - \kappa_{\mu}\lVert L_{\mu}^Tw\rVert_{q}
    <br/>
    $$
    <br/>
    with $\kappa$ the radius of the uncertainty set (confidence region),
    $L$ its linear geometry map and $q$ the dual norm. For an
    ellipsoidal set with shape matrix $S$, $L$ is a square-root factor
    satisfying $S = L L^T$ and $q$ is $2$.
    The default (`None`) means that no uncertainty set is used.

  **covariance_uncertainty_set_estimator** *BaseCovarianceUncertaintySet, optional*
  : [Covariance Uncertainty set estimator](https://skfolio.org/user_guide/uncertainty_set.html.md#uncertainty-set-estimator).
    If provided, covariance estimation uncertainty is included in the optimized
    variance. This approach is known as worst-case optimization, a form of robust
    optimization. It reduces sensitivity to covariance estimation errors.
    Covariance uncertainty is applied when `risk_measure=RiskMeasure.VARIANCE` or
    when `max_variance` is set.
    The default (`None`) means that no uncertainty set is used.

  **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_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 MeanRisk
    >>> model = MeanRisk(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 = MeanRisk(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 MeanRisk
    >>> 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 = MeanRisk(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 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/solvers](https://www.cvxpy.org/tutorial/solvers)

  **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`.

  **mu_uncertainty_set_estimator_** *BaseMuUncertaintySet*
  : Fitted `mu_uncertainty_set_estimator` if provided.

  **covariance_uncertainty_set_estimator_** *BaseCovarianceUncertaintySet*
  : Fitted `covariance_uncertainty_set_estimator` if provided.

  **problem_: cvxpy.Problem**
  : CVXPY problem used for the optimization. Only when `save_problem` is set to
    `True`.

  **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_predict`](#skfolio.optimization.ConvexOptimization.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.ConvexOptimization.get_metadata_routing)() | Get metadata routing of this object.                                                                                              |
| [`get_params`](#skfolio.optimization.ConvexOptimization.get_params)([deep])     | Get parameters for this estimator.                                                                                                |
| [`predict`](#skfolio.optimization.ConvexOptimization.predict)(X)             | Predict the `Portfolio` or a `Population` of portfolios on `X`.                                                                   |
| [`score`](#skfolio.optimization.ConvexOptimization.score)(X[, y])          | Prediction score using the Sharpe Ratio.                                                                                          |
| [`set_params`](#skfolio.optimization.ConvexOptimization.set_params)(\*\*params) | Set the parameters of this estimator.                                                                                             |

| **fit**   |    |
|-----------|----|

### Notes

All estimators should specify all parameters as explicit keyword arguments in
`__init__` (no `*args` or `**kwargs`), following scikit-learn conventions.

<a id="skfolio.optimization.ConvexOptimization.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.ConvexOptimization.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.ConvexOptimization.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.ConvexOptimization.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.ConvexOptimization.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.ConvexOptimization.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.ConvexOptimization.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.