PortfolioOptimisers.jl

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Clustering ​

PortfolioOptimisers.HierarchicalClustering Type
julia
struct HierarchicalClustering{T1, T2, T3, T4} <: AbstractClusteringResult
    clustering::T1
    S::T2
    D::T3
    k::T4
end

Result type for hierarchical clustering in PortfolioOptimisers.jl.

HierarchicalClustering stores the output of a hierarchical clustering algorithm, including the clustering object, similarity and distance matrices, and the number of clusters.

Fields

  • clustering: The hierarchical clustering object.

  • S: Similarity matrix used for clustering.

  • D: Distance matrix used for clustering.

  • k: Number of clusters.

Constructor

julia
HierarchicalClustering(; clustering::Clustering.Hclust, S::AbstractMatrix,
                       D::AbstractMatrix, k::Integer)

Keyword arguments correspond to the fields above.

Validation

  • !isempty(S).

  • !isempty(D).

  • size(S) == size(D).

  • k ≥ 1.

Related

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PortfolioOptimisers.clusterise Method
julia
clusterise(cle::AbstractClusteringResult, args...; kwargs...)

Return the clustering result as-is.

This function provides a generic interface for extracting or processing clustering results. By default, it simply returns the provided clustering result object unchanged. This allows for consistent downstream handling of clustering results in PortfolioOptimisers.jl workflows.

Arguments

  • cle::AbstractClusteringResult: The clustering result object.

  • args...: Additional positional arguments, ignored.

  • kwargs...: Additional keyword arguments, ignored.

Returns

  • The input cle object.

Related

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PortfolioOptimisers.SecondOrderDifference Type
julia
struct SecondOrderDifference <: AbstractOptimalNumberClustersAlgorithm end

Algorithm type for estimating the optimal number of clusters using the second-order difference method.

The SecondOrderDifference algorithm selects the optimal number of clusters by maximizing the second-order difference of a clustering evaluation metric (such as within-cluster sum of squares or silhouette score) across different cluster counts. This approach helps identify the "elbow" point in the metric curve.

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PortfolioOptimisers.StandardisedSilhouetteScore Type
julia
struct StandardisedSilhouetteScore{T1} <: AbstractOptimalNumberClustersAlgorithm
    metric::T1
end

Algorithm type for estimating the optimal number of clusters using the standardised silhouette score.

StandardisedSilhouetteScore selects the optimal number of clusters by maximizing the silhouette score, which measures how well each object lies within its cluster compared to other clusters. The score can be computed using different distance metrics.

Fields

  • metric: The distance metric used for silhouette calculation from Distances.jl, or nothing for the default.

Constructor

julia
StandardisedSilhouetteScore(; metric::Union{Nothing, <:Distances.SemiMetric} = nothing)

Keyword arguments correspond to the fields above.

Examples

julia
julia> StandardisedSilhouetteScore()
StandardisedSilhouetteScore
  metric ┴ nothing

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PortfolioOptimisers.OptimalNumberClusters Type
julia
struct OptimalNumberClusters{T1, T2} <: AbstractOptimalNumberClustersEstimator
    max_k::T1
    alg::T2
end

Estimator type for selecting the optimal number of clusters in PortfolioOptimisers.jl.

OptimalNumberClusters encapsulates the configuration for determining the optimal number of clusters, including the maximum allowed clusters and the algorithm used for selection.

Fields

  • max_k: Maximum number of clusters to consider. If nothing, computed as the sqrt(N), where N is the number of assets.

  • alg: Algorithm for selecting the optimal number of clusters. If an integer, defines the number of clusters directly.

Constructor

julia
OptimalNumberClusters(; max_k::Union{Nothing, <:Integer} = nothing,
                      alg::Union{<:Integer, <:AbstractOptimalNumberClustersAlgorithm} = SecondOrderDifference())

Keyword arguments correspond to the fields above.

Validation

  • max_k >= 1.

  • If alg is an integer, alg >= 1.

Examples

julia
julia> OptimalNumberClusters(; max_k = 10)
OptimalNumberClusters
  max_k ┼ Int64: 10
    alg ┴ SecondOrderDifference()

Related

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PortfolioOptimisers.HClustAlgorithm Type
julia
struct HClustAlgorithm{T1} <: AbstractClusteringAlgorithm
    linkage::T1
end

Algorithm type for hierarchical clustering in PortfolioOptimisers.jl.

HClustAlgorithm specifies the linkage method used for hierarchical clustering, such as :ward, :single, :complete, or :average.

Fields

  • linkage: Linkage method for hierarchical clustering from Clustering.jl.

Constructor

julia
HClustAlgorithm(; linkage::Symbol = :ward)

Keyword arguments correspond to the fields above.

Examples

julia
julia> HClustAlgorithm(; linkage = :average)
HClustAlgorithm
  linkage ┴ Symbol: :average

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PortfolioOptimisers.ClusteringEstimator Type
julia
struct ClusteringEstimator{T1, T2, T3, T4} <: AbstractClusteringEstimator
    ce::T1
    de::T2
    alg::T3
    onc::T4
end

Estimator type for clustering in PortfolioOptimisers.jl.

ClusteringEstimator encapsulates all configuration required for clustering, including the covariance estimator, distance estimator, clustering algorithm, and optimal number of clusters estimator.

Fields

  • ce: Covariance estimator.

  • de: Distance estimator.

  • alg: Clustering algorithm.

  • onc: Optimal number of clusters estimator.

Constructor

julia
ClusteringEstimator(; ce::StatsBase.CovarianceEstimator = PortfolioOptimisersCovariance(),
                    de::AbstractDistanceEstimator = Distance(; alg = CanonicalDistance()),
                    alg::AbstractClusteringAlgorithm = HClustAlgorithm(),
                    onc::AbstractOptimalNumberClustersEstimator = OptimalNumberClusters())

Keyword arguments correspond to the fields above.

Examples

julia
julia> ClusteringEstimator()
ClusteringEstimator
   ce ┼ PortfolioOptimisersCovariance
      │   ce ┼ Covariance
      │      │    me ┼ SimpleExpectedReturns
      │      │       │   w ┴ nothing
      │      │    ce ┼ GeneralCovariance
      │      │       │   ce ┼ StatsBase.SimpleCovariance: StatsBase.SimpleCovariance(true)
      │      │       │    w ┴ nothing
      │      │   alg ┴ Full()
      │   mp ┼ DefaultMatrixProcessing
      │      │       pdm ┼ Posdef
      │      │           │   alg ┴ UnionAll: NearestCorrelationMatrix.Newton
      │      │   denoise ┼ nothing
      │      │    detone ┼ nothing
      │      │       alg ┴ nothing
   de ┼ Distance
      │   power ┼ nothing
      │     alg ┴ CanonicalDistance()
  alg ┼ HClustAlgorithm
      │   linkage ┴ Symbol: :ward
  onc ┼ OptimalNumberClusters
      │   max_k ┼ nothing
      │     alg ┴ SecondOrderDifference()

Related

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PortfolioOptimisers.AbstractClusteringEstimator Type
julia
abstract type AbstractClusteringEstimator <: AbstractPhylogenyEstimator end

Abstract supertype for all clustering estimator types in PortfolioOptimisers.jl.

All concrete types implementing clustering-based estimation algorithms should subtype AbstractClusteringEstimator. This enables a consistent interface for clustering estimators throughout the package.

Related

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PortfolioOptimisers.AbstractClusteringAlgorithm Type
julia
abstract type AbstractClusteringAlgorithm <: AbstractPhylogenyAlgorithm end

Abstract supertype for all clustering algorithm types in PortfolioOptimisers.jl.

All concrete types implementing specific clustering algorithms should subtype AbstractClusteringAlgorithm. This enables flexible extension and dispatch of clustering routines.

Related

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PortfolioOptimisers.AbstractOptimalNumberClustersEstimator Type
julia
abstract type AbstractOptimalNumberClustersEstimator <: AbstractEstimator end

Abstract supertype for all optimal number of clusters estimator types in PortfolioOptimisers.jl.

All concrete types implementing algorithms to estimate the optimal number of clusters should subtype AbstractOptimalNumberClustersEstimator. This enables a consistent interface for cluster number estimation.

Related

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PortfolioOptimisers.AbstractOptimalNumberClustersAlgorithm Type
julia
abstract type AbstractOptimalNumberClustersAlgorithm <: AbstractAlgorithm end

Abstract supertype for all optimal number of clusters algorithm types in PortfolioOptimisers.jl.

All concrete types implementing specific algorithms for determining the optimal number of clusters should subtype AbstractOptimalNumberClustersAlgorithm. This enables flexible extension and dispatch of cluster number selection routines.

Related

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PortfolioOptimisers.AbstractClusteringResult Type
julia
abstract type AbstractClusteringResult <: AbstractPhylogenyResult end

Abstract supertype for all clustering result types in PortfolioOptimisers.jl.

All concrete types representing the result of a clustering estimation should subtype AbstractClusteringResult. This enables a consistent interface for clustering results throughout the package.

Related

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