API Reference

This is the class and function reference of skfolio. Please refer to the full user guide for further details, as the class and function raw specifications may not be enough to give full guidelines on their uses.

skfolio.measures: Measures

Module that includes all Measures functions used across skfolio.

Base Classe

measures.BaseMeasure

Base Enum of measures

Classes

measures.PerfMeasure	Enumeration of performance measures
measures.RiskMeasure	Enumeration of risk measures
measures.ExtraRiskMeasure	Enumeration of other risk measures not used in convex optimization
measures.RatioMeasure	Enumeration of ratio measures

Functions

measures.mean(returns)	Compute the mean.
measures.get_cumulative_returns(returns[, ...])	Compute the cumulative returns from the returns.
measures.get_drawdowns(returns[, compounded])	Compute the drawdowns' series from the returns.
measures.variance(returns)	Compute the variance (second moment).
measures.semi_variance(returns[, ...])	Compute the semi-variance (second lower partial moment).
measures.standard_deviation(returns)	Compute the standard-deviation (square root of the second moment).
measures.semi_deviation(returns[, ...])	Compute the semi standard-deviation (semi-deviation) (square root of the second lower partial moment).
measures.third_central_moment(returns)	Compute the third central moment.
measures.fourth_central_moment(returns)	Compute the Fourth central moment.
measures.fourth_lower_partial_moment(returns)	Compute the fourth lower partial moment.
measures.cvar(returns[, beta])	Compute the historical CVaR (conditional value at risk).
measures.mean_absolute_deviation(returns[, ...])	Compute the mean absolute deviation (MAD).
measures.value_at_risk(returns[, beta])	Compute the historical value at risk (VaR).
measures.worst_realization(returns)	Compute the worst realization (worst return).
measures.first_lower_partial_moment(returns)	Compute the first lower partial moment.
measures.entropic_risk_measure(returns[, ...])	Compute the entropic risk measure.
measures.evar(returns[, beta])	Compute the EVaR (entropic value at risk) and its associated risk aversion.
measures.drawdown_at_risk(drawdowns[, beta])	Compute the Drawdown at risk.
measures.cdar(drawdowns[, beta])	Compute the historical CDaR (conditional drawdown at risk).
measures.max_drawdown(drawdowns)	Compute the maximum drawdown.
measures.average_drawdown(drawdowns)	Compute the average drawdown.
measures.edar(drawdowns[, beta])	Compute the EDaR (entropic drawdown at risk).
measures.ulcer_index(drawdowns)	Compute the Ulcer index.
measures.gini_mean_difference(returns)	Compute the Gini mean difference (GMD).
measures.owa_gmd_weights(n_observations)	Compute the OWA weights used for the Gini mean difference (GMD) computation.
measures.effective_number_assets(weights)	Computes the effective number of assets, defined as the inverse of the Herfindahl index [R3692bcdb1be2-1]:

skfolio.portfolio: Portfolio

Portfolio module. Portfolio and MultiPeriodPortfolio objects are returned by the predict method of Optimization estimators. They need to be homogenous to the convex optimization problems meaning that Portfolio is the dot product of the assets weights with the assets returns and MultiPeriodPortfolio is a list of Portfolio.

Base Classe

portfolio.BasePortfolio

Base Portfolio class for all portfolios in skfolio.

Classes

portfolio.Portfolio	Portfolio class.
portfolio.MultiPeriodPortfolio	Multi-Period Portfolio class.

skfolio.population: Population

Classes

population.Population

Population Class.

skfolio.optimization.naive: Naive Optimization Estimators

Classes

optimization.EqualWeighted	Equally Weighted estimator.
optimization.InverseVolatility	Inverse Volatility estimator.
optimization.Random	Random weight estimator.

skfolio.optimization.convex: Convex Optimization Estimators

Enum

optimization.ObjectiveFunction

Enumeration of objective functions.

Classes

optimization.ConvexOptimization	Base class for all convex optimization estimators in skfolio.
optimization.MeanRisk	Mean-Risk Optimization estimator.
optimization.RiskBudgeting	Risk Budgeting Optimization estimator.
optimization.MaximumDiversification	Maximum Diversification Optimization estimator.
optimization.DistributionallyRobustCVaR	Distributionally Robust CVaR.

skfolio.optimization.cluster: Clustering Optimization Estimators

Classes

optimization.BaseHierarchicalOptimization	Base Hierarchical Clustering Optimization estimator.
optimization.HierarchicalRiskParity	Hierarchical Risk Parity estimator.
optimization.HierarchicalEqualRiskContribution	Hierarchical Equal Risk Contribution estimator.
optimization.NestedClustersOptimization	Nested Clusters Optimization estimator.

skfolio.optimization.ensemble: Ensemble Optimization Estimators

Classes

optimization.BaseComposition	Handles parameter management for ensemble estimators.
optimization.StackingOptimization	Stack of optimizations with a final optimization.

skfolio.prior: Prior Estimators

skfolio package

Model Dataclass

prior.PriorModel

Prior model dataclass.

Base Class

prior.BasePrior

Base class for all prior estimators in skfolio.

Classes

prior.EmpiricalPrior	Empirical Prior estimator.
prior.BlackLitterman	Black & Litterman Prior Model estimator.
prior.FactorModel	Factor Model estimator.

Loading Matrix Classes for Factor Models

prior.BaseLoadingMatrix	Base class for all Loading Matrix estimators.
prior.LoadingMatrixRegression	Loading Matrix Regression estimator.

skfolio.moments.mu: Mu Estimators

skfolio package

Base Class

moments.BaseMu

Base class for all expected returns estimators in skfolio.

Classes

moments.EmpiricalMu	Empirical Expected Returns (Mu) estimator.
moments.EWMu	Exponentially Weighted Expected Returns (Mu) estimator.
moments.ShrunkMu	Shrinkage Expected Returns (Mu) estimator.
moments.EquilibriumMu	Equilibrium Expected Returns (Mu) estimator.
moments.ShrunkMuMethods	Shrinkage methods for the ShrunkMu estimator

skfolio.moments.covariance: Covariance Estimators

skfolio package

Base Class

moments.BaseCovariance

Base class for all covariance estimators in skfolio.

Classes

moments.EmpiricalCovariance	Empirical Covariance estimator.
moments.EWCovariance	Exponentially Weighted Covariance estimator.
moments.GerberCovariance	Gerber Covariance estimator.
moments.DenoiseCovariance	Covariance Denoising estimator.
moments.DetoneCovariance	Covariance Detoning estimator.
moments.LedoitWolf	LedoitWolf Covariance Estimator.
moments.OAS	Oracle Approximating Shrinkage Estimator as proposed in [Re9a22b087643-1].
moments.ShrunkCovariance	Covariance estimator with shrinkage.
moments.GraphicalLassoCV	Sparse inverse covariance with cross-validated choice of the l1 penalty.
moments.ImpliedCovariance	Implied Covariance estimator.

skfolio.distance: Distance Estimators

skfolio package

Base Class

distance.BaseDistance

Base class for all distance estimators in skfolio.

Classes

distance.PearsonDistance	Pearson Distance estimator.
distance.KendallDistance	Kendall Distance estimator.
distance.SpearmanDistance	Spearman Distance estimator.
distance.CovarianceDistance	Covariance Distance estimator.
distance.DistanceCorrelation	Distance Correlation estimator.
distance.MutualInformation	Mutual Information estimator.

skfolio.cluster: Cluster Estimators

skfolio package

Classes

cluster.HierarchicalClustering	Hierarchical Clustering.
cluster.LinkageMethod	Methods for calculating the distance between clusters in the linkage matrix.

skfolio.uncertainty_set: Uncertainty set Estimators

skfolio package

Model Dataclass

uncertainty_set.UncertaintySet

Ellipsoidal uncertainty set dataclass.

Base Classes

uncertainty_set.BaseMuUncertaintySet	Base class for all Mu Uncertainty Set estimators in skfolio.
uncertainty_set.BaseCovarianceUncertaintySet	Base class for all Covariance Uncertainty Set estimators in skfolio.

Classes

uncertainty_set.EmpiricalMuUncertaintySet	Empirical Mu Uncertainty Set.
uncertainty_set.EmpiricalCovarianceUncertaintySet	Empirical Covariance Uncertainty set.
uncertainty_set.BootstrapMuUncertaintySet	Bootstrap Mu Uncertainty set.
uncertainty_set.BootstrapCovarianceUncertaintySet	Bootstrap Covariance Uncertainty set.

skfolio.pre_selection: Pre-selection Transformers

skfolio package

Classes

pre_selection.DropCorrelated	Transformer for dropping highly correlated assets.
pre_selection.SelectKExtremes	Transformer for selecting the k best or worst assets.
pre_selection.SelectNonDominated	Transformer for selecting non dominated assets.
pre_selection.SelectComplete	Transformer to select assets with complete data across the entire observation period.
pre_selection.SelectNonExpiring	Transformer to select assets that do not expire within a specified lookahead period after the end of the observation period.

skfolio.model_selection: Model Selection

skfolio package

Base Classes

model_selection.BaseCombinatorialCV

Base class for all combinatorial cross-validators.

Classes

model_selection.CombinatorialPurgedCV	Combinatorial Purged Cross-Validation.
model_selection.WalkForward	Walk Forward Cross-Validator.

Functions

model_selection.cross_val_predict(estimator, X)	Generate cross-validated Portfolios estimates.
model_selection.optimal_folds_number(...[, ...])	Find the optimal number of folds (total folds and test folds) for a target training size and a target number of test paths.

skfolio.metrics: Metrics

skfolio package

Functions

metrics.make_scorer(score_func[, ...])

Make a scorer from a measure or from a custom score function.

skfolio.datasets: Datasets

Functions

datasets.load_sp500_dataset()	Load the prices of 20 assets from the S&P 500 Index composition.
datasets.load_sp500_index()	Load the prices of the S&P 500 Index.
datasets.load_factors_dataset()	Load the prices of 5 factor ETFs.
datasets.load_ftse100_dataset([data_home, ...])	Load the prices of 64 assets from the FTSE 100 Index composition.
datasets.load_nasdaq_dataset([data_home, ...])	Load the prices of 1455 assets from the NASDAQ Composite Index.

skfolio.preprocessing: Preprocessing

Functions

preprocessing.prices_to_returns(X[, y, ...])

Transforms a DataFrame of prices to linear or logarithmic returns.

skfolio.utils.stats: Stats

Functions

stats.NBinsMethod(value)	Enumeration of the Number of Bins Methods
stats.n_bins_freedman(x)	Compute the optimal histogram bin size using the Freedman-Diaconis rule [R8d5b646da1d1-1].
stats.n_bins_knuth(x)	Compute the optimal histogram bin size using Knuth's rule [R8c3fe88ee915-1].
stats.is_cholesky_dec(x)	Returns True if Cholesky decomposition can be computed.
stats.assert_is_square(x)	Raises an error if the matrix is not square.
stats.assert_is_symmetric(x)	Raises an error if the matrix is not symmetric.
stats.assert_is_distance(x)	Raises an error if the matrix is not a distance matrix.
stats.cov_nearest(cov[, higham, ...])	Compute the nearest covariance matrix that is positive definite and with a cholesky decomposition than can be computed.
stats.cov_to_corr(cov)	Convert a covariance matrix to a correlation matrix.
stats.corr_to_cov(corr, std)	Convert a correlation matrix to a covariance matrix given its standard-deviation vector.
stats.commutation_matrix(x)	Compute the commutation matrix.
stats.compute_optimal_n_clusters(distance, ...)	Compute the optimal number of clusters based on Two-Order Difference to Gap Statistic [Re0e718a4c413-1].
stats.rand_weights(n[, zeros])	Produces n random weights that sum to one from an uniform distribution (non-uniform distribution over a simplex)
stats.rand_weights_dirichlet(n)	Produces n random weights that sum to one from a dirichlet distribution (uniform distribution over a simplex)
stats.minimize_relative_weight_deviation(...)	Apply weight constraints to an initial array of weights by minimizing the relative weight deviation of the final weights from the initial weights.