PortfolioOptimisers.jl

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Base Uncertainty Sets ​

PortfolioOptimisers.BoxUncertaintySet Type
julia
struct BoxUncertaintySet{T1, T2} <: AbstractUncertaintySetResult
    lb::T1
    ub::T2
end

Represents a box uncertainty set for risk or prior statistics in portfolio optimisation. Stores lower and upper bounds for the uncertain quantity, such as expected returns or covariance.

Fields

  • lb: Lower bound array for the uncertainty set.

  • ub: Upper bound array for the uncertainty set.

Constructor

julia
BoxUncertaintySet(; lb::ArrNum, ub::ArrNum)

Keyword arguments correspond to the fields above.

Validation

  • !isempty(lb).

  • !isempty(ub).

  • size(lb) == size(ub).

Examples

julia
julia> BoxUncertaintySet(; lb = [0.1, 0.2], ub = [0.3, 0.4])
BoxUncertaintySet
  lb ┼ Vector{Float64}: [0.1, 0.2]
  ub ┴ Vector{Float64}: [0.3, 0.4]

Related

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PortfolioOptimisers.BoxUncertaintySetAlgorithm Type
julia
struct BoxUncertaintySetAlgorithm <: AbstractUncertaintySetAlgorithm end

Algorithm for constructing box uncertainty sets in portfolio optimisation. Box uncertainty sets model uncertainty by specifying lower and upper bounds for risk or prior statistics.

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PortfolioOptimisers.MuEllipsoidalUncertaintySet Type
julia
struct MuEllipsoidalUncertaintySet <: AbstractEllipsoidalUncertaintySetResultClass end

Represents the class identifier for mean ellipsoidal uncertainty sets in portfolio optimisation.

Used to distinguish ellipsoidal uncertainty sets that encode uncertainty for mean statistics, such as expected returns.

Related Types

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PortfolioOptimisers.SigmaEllipsoidalUncertaintySet Type
julia
struct SigmaEllipsoidalUncertaintySet <: AbstractEllipsoidalUncertaintySetResultClass end

Represents the class identifier for covariance ellipsoidal uncertainty sets in portfolio optimisation.

Used to distinguish ellipsoidal uncertainty sets that encode uncertainty for covariance statistics, such as covariance matrices.

Related Types

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PortfolioOptimisers.NormalKUncertaintyAlgorithm Type
julia
struct NormalKUncertaintyAlgorithm{T1} <: AbstractUncertaintyKAlgorithm
    kwargs::T1
end

Algorithm for computing the scaling parameter k for ellipsoidal uncertainty sets under the assumption of normally distributed returns in portfolio optimisation.

Fields

  • kwargs: Named tuple of keyword arguments for quantile calculation.

Constructor

julia
NormalKUncertaintyAlgorithm(; kwargs::NamedTuple = (;))

Keyword arguments correspond to the field above.

Validation

  • kwargs must be a valid NamedTuple.

Examples

julia
julia> NormalKUncertaintyAlgorithm()
NormalKUncertaintyAlgorithm
  kwargs ┴ @NamedTuple{}: NamedTuple()

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PortfolioOptimisers.GeneralKUncertaintyAlgorithm Type
julia
struct GeneralKUncertaintyAlgorithm <: AbstractUncertaintyKAlgorithm end

Algorithm for computing the scaling parameter k for ellipsoidal uncertainty sets using a general formula sqrt((1 - q) / q), this ignores the distribution of the underlying data.

Related Types

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PortfolioOptimisers.ChiSqKUncertaintyAlgorithm Type
julia
struct ChiSqKUncertaintyAlgorithm <: AbstractUncertaintyKAlgorithm end

Algorithm for computing the scaling parameter k for ellipsoidal uncertainty sets using the chi-squared distribution in portfolio optimisation.

Related Types

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PortfolioOptimisers.EllipsoidalUncertaintySet Type
julia
struct EllipsoidalUncertaintySet{T1, T2, T3} <: AbstractUncertaintySetResult
    sigma::T1
    k::T2
    class::T3
end

Represents an ellipsoidal uncertainty set for risk or prior statistics in portfolio optimisation. Stores a covariance matrix, a scaling parameter, and a class identifier for the uncertain quantity, such as expected returns or covariance.

Fields

  • sigma: Covariance matrix for the uncertainty set.

  • k: Scaling parameter for the ellipsoidal.

  • class: Identifier for the type of ellipsoidal uncertainty set (e.g., mean or covariance).

Constructor

julia
EllipsoidalUncertaintySet(; sigma::MatNum, k::Number,
                      class::AbstractEllipsoidalUncertaintySetResultClass)

Keyword arguments correspond to the fields above.

Validation

  • !isempty(sigma).

  • size(sigma, 1) == size(sigma, 2).

  • k > 0.

Examples

julia
julia> EllipsoidalUncertaintySet([1.0 0.2; 0.2 1.0], 2.5, SigmaEllipsoidalUncertaintySet())
EllipsoidalUncertaintySet
  sigma ┼ 2×2 Matrix{Float64}
      k ┼ Float64: 2.5
  class ┴ SigmaEllipsoidalUncertaintySet()

Related

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PortfolioOptimisers.EllipsoidalUncertaintySetAlgorithm Type
julia
struct EllipsoidalUncertaintySetAlgorithm{T1, T2} <: AbstractUncertaintySetAlgorithm
    method::T1
    diagonal::T2
end

Algorithm for constructing ellipsoidal uncertainty sets in portfolio optimisation. Ellipsoidal uncertainty sets model uncertainty by specifying an ellipsoidal region for risk or prior statistics, typically using a covariance matrix and a scaling parameter.

Fields

  • method: Algorithm or value used to determine the scaling parameter for the ellipsoidal.

  • diagonal: Indicates whether to use only the diagonal elements of the covariance matrix.

Constructor

julia
EllipsoidalUncertaintySetAlgorithm(;
                               method::Num_UcSK = ChiSqKUncertaintyAlgorithm(),
                               diagonal::Bool = true)

  • method: Sets the scaling algorithm or value for the ellipsoidal.

  • diagonal: Sets whether to use only diagonal elements.

Examples

julia
julia> EllipsoidalUncertaintySetAlgorithm()
EllipsoidalUncertaintySetAlgorithm
    method ┼ ChiSqKUncertaintyAlgorithm()
  diagonal ┴ Bool: true

Related

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PortfolioOptimisers.ucs Method
julia
ucs(uc::Option{<:Tuple{<:Option{<:AbstractUncertaintySetResult},
                       <:Option{<:AbstractUncertaintySetResult}}}, args...; kwargs...)

Returns the argument(s) unchanged. This is a no-op function used to handle cases where no uncertainty sets, or a tuple of pre-processed sets is not nothing.

Arguments

  • uc: Tuple of uncertainty sets, or nothing.

  • args...: Additional positional arguments (ignored).

  • kwargs...: Additional keyword arguments (ignored).

Returns

  • uc::Option{<:Tuple{<:Option{<:AbstractUncertaintySetResult}, <:Option{<:AbstractUncertaintySetResult}}}: The input, unchanged.

Related

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PortfolioOptimisers.ucs Method
julia
ucs(uc::AbstractUncertaintySetEstimator, rd::ReturnsResult; kwargs...)

Constructs an uncertainty set from a given estimator and returns data.

Arguments

  • uc: Uncertainty set estimator. Used to construct the uncertainty set.

  • rd: ReturnsResult. Contains the returns data and associated metadata.

  • kwargs...: Additional keyword arguments passed to the estimator.

Returns

  • uc::Tuple{<:AbstractUncertaintySetResult, <:AbstractUncertaintySetResult}: Expected returns and covariance uncertainty sets.

Details

  • Calls the estimator on the returns data and metadata in rd.

  • Passes rd.X, rd.F, and relevant metadata (iv, ivpa) to the estimator.

  • Additional keyword arguments are forwarded.

  • Used for compatibility with ReturnsResult objects.

Related

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PortfolioOptimisers.mu_ucs Method
julia
mu_ucs(uc::Option{<:AbstractUncertaintySetResult}, args...; kwargs...)

Returns the argument unchanged. This is a no-op function used to handle cases where no expected returns uncertainty set is not nothing.

Arguments

  • uc: Expected returns uncertainty set or nothing.

  • args...: Additional positional arguments (ignored).

  • kwargs...: Additional keyword arguments (ignored).

Returns

  • uc::Option{<:AbstractUncertaintySetResult}: The input, unchanged.

Related

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PortfolioOptimisers.mu_ucs Method
julia
mu_ucs(uc::AbstractUncertaintySetEstimator, rd::ReturnsResult; kwargs...)

Constructs an expected returns uncertainty set from a given estimator and returns data.

Arguments

  • uc: Uncertainty set estimator. Used to construct the expected returns uncertainty set.

  • rd: ReturnsResult. Contains the returns data and associated metadata.

  • kwargs...: Additional keyword arguments passed to the estimator.

Returns

  • uc::AbstractUncertaintySetResult: Expected returns uncertainty set.

Details

  • Calls the estimator on the returns data and metadata in rd.

  • Passes rd.X, rd.F, and relevant metadata (iv, ivpa) to the estimator.

  • Additional keyword arguments are forwarded.

  • Used for compatibility with ReturnsResult objects.

Related

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PortfolioOptimisers.sigma_ucs Method
julia
sigma_ucs(uc::Option{<:AbstractUncertaintySetResult}, args...; kwargs...)

Returns the argument unchanged. This is a no-op function used to handle cases where no covariance uncertainty set is not nothing.

Arguments

  • uc: Covariance uncertainty set or nothing.

  • args...: Additional positional arguments (ignored).

  • kwargs...: Additional keyword arguments (ignored).

Returns

  • uc::Option{<:AbstractUncertaintySetResult}: The input, unchanged.

Related

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

Defines the abstract interface for uncertainty set estimators in portfolio optimisation. Subtypes of this abstract type are responsible for constructing and estimating uncertainty sets for risk or prior statistics, such as box or ellipsoidal uncertainty sets.

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

Defines the abstract interface for algorithms that construct uncertainty sets in portfolio optimisation. Subtypes implement specific methods for generating uncertainty sets, such as box or ellipsoidal uncertainty sets, which are used to model uncertainty in risk or prior statistics.

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PortfolioOptimisers.AbstractUncertaintySetResult Type
julia
abstract type AbstractUncertaintySetResult <: AbstractResult end

Abstract type for results produced by uncertainty set algorithms in portfolio optimisation.

Represents the interface for all result types that encode uncertainty sets for risk or prior statistics, such as box or ellipsoidal uncertainty sets. Subtypes store the output of uncertainty set estimation or construction algorithms.

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

Defines the abstract interface for algorithms that compute the scaling parameter k for ellipsoidal uncertainty sets in portfolio optimisation.

Subtypes implement specific methods for generating the scaling parameter, which controls the size of the ellipsoidal region representing uncertainty in risk or prior statistics.

Related Types

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PortfolioOptimisers.AbstractEllipsoidalUncertaintySetResultClass Type
julia
abstract type AbstractEllipsoidalUncertaintySetResultClass <: AbstractUncertaintySetResult end

Defines the abstract interface for ellipsoidal uncertainty set result classes in portfolio optimisation.

Subtypes of this abstract type represent the class or category of ellipsoidal uncertainty sets, such as those for mean or covariance statistics. Used to distinguish between different types of ellipsoidal uncertainty set results.

Related Types

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PortfolioOptimisers.ucs_selector Function
julia
ucs_selector(risk_ucs::Nothing, prior_ucs::Nothing)
ucs_selector(risk_ucs::UcSE_UcS, prior_ucs::Any)
ucs_selector(risk_ucs::Nothing, prior_ucs::UcSE_UcS)

Function for selecting uncertainty sets from risk measure or prior result instances.

Arguments

  • risk_ucs: Risk measure uncertainty set estimator or result, or nothing.

  • prior_ucs: Prior result uncertainty set estimator or result, or nothing.

Returns

Based on the argument types, returns one of the following:

  • nothing: If both risk_ucs and prior_ucs are nothing.

  • risk_ucs::UcSE_UcS: If risk_ucs is not nothing.

  • prior_ucs::UcSE_UcS: If risk_ucs is nothing but prior_ucs is not nothing.

Related

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PortfolioOptimisers.k_ucs Function
julia
k_ucs(km::NormalKUncertaintyAlgorithm, q::Number, X::MatNum, sigma_X::MatNum)
k_ucs(::GeneralKUncertaintyAlgorithm, q::Number, args...)
k_ucs(::ChiSqKUncertaintyAlgorithm, q::Number, X::ArrNum, args...)
k_ucs(type::Number, args...)

ArrNum Computes the scaling parameter k for ellipsoidal uncertainty sets in portfolio optimisation.

Arguments

  • km: Scaling algorithm instance.

  • q: Quantile or confidence level.

  • X: Data matrix (returns).

  • sigma_X: Covariance matrix.

  • args...: Additional arguments.

  • type: Number value for direct scaling.

Returns

  • k::Number: Scaling parameter.

Details

  • Uses different algorithms to compute the scaling parameter:

    • Normal: 1 - q-th quantile of the Mahalanobis distances.

    • General: formula sqrt((1 - q) / q).

    • Chi-squared: 1 - q-th quantile of the chi-squared distribution.

    • Number: returns the provided value directly.

  • Supports multiple dispatch for extensibility.

Related

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