PortfolioOptimisers.jl

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Randomised search cross validation ​

PortfolioOptimisers.RandomisedSearchCrossValidation Type
julia
struct RandomisedSearchCrossValidation{T1, T2, T3, T4, T5, T6, T7, T8, T9, T10} <:
       AbstractSearchCrossValidationEstimator
    p::T1
    cv::T2
    r::T3
    scorer::T4
    ex::T5
    n_iter::T6
    rng::T7
    seed::T8
    train_score::T9
    kwargs::T10
end

Randomised search cross-validation estimator for portfolio optimisation. Samples parameter sets from distributions or vectors, applies cross-validation splits, fits and scores each configuration, and selects the optimal parameters using the provided scoring strategy.

Fields

  • p: Parameter grid, supporting vectors, dictionaries, or distributions for random sampling. If all parameters in a grid are vectors they are sampled uniformly without replacement, if at least one of them is a distribution they are sampled with replacement. When sampling without replacement the sampling is performed up until the list of candidates is exhausted. If a vector of grids, each grid is sampled independently and then they're all concatenated.

  • cv: Cross-validation splitter.

  • r: Risk measure used for scoring.

  • scorer: Scoring function to select optimal parameter set.

  • ex: Parallel execution strategy.

  • n_iter: Number of random parameter sets to sample.

  • rng: Random number generator for reproducibility.

  • seed: Optional seed for RNG.

  • train_score: Whether to record training scores.

  • kwargs: Additional keyword arguments for customisation.

Constructors

julia
RandomisedSearchCrossValidation(p::Union{AbstractVector{<:Pair{<:AbstractString, <:Any}},
                                         AbstractVector{<:AbstractVector{<:Pair{<:AbstractString,
                                                                                <:Any}}},
                                         AbstractDict{<:AbstractString, <:Any},
                                         AbstractVector{<:AbstractDict{<:AbstractString,
                                                                       <:Any}}};
                                cv::SearchCV = KFold(),
                                r::AbstractBaseRiskMeasure = ConditionalValueatRisk(),
                                scorer::CrossValSearchScorer = HighestMeanScore(),
                                ex::FLoops.Transducers.Executor = FLoops.ThreadedEx(),
                                n_iter::Integer = 10,
                                rng::Random.AbstractRNG = Random.default_rng(),
                                seed::Option{<:Integer} = nothing,
                                train_score::Bool = false, kwargs::NamedTuple = (;))

Keyword arguments correspond to the fields above.

Validation

  • Parameter grid p must not be empty.

  • All parameter values must be vectors or distributions.

  • For nested parameter grids, each must not be empty.

  • n_iter must be non-empty, greater than zero, and finite.

Examples

julia
julia> RandomisedSearchCrossValidation(Dict("alpha" => [0.1, 0.2, 0.3],
                                            "beta" => Normal(1.0, 0.5)))
RandomisedSearchCrossValidation
            p ┼ Dict{String, Any}: Dict{String, Any}("alpha" => [0.1, 0.2, 0.3], "beta" => Distributions.Normal{Float64}(μ=1.0, σ=0.5))
           cv ┼ KFold
              │              n ┼ Int64: 5
              │    purged_size ┼ Int64: 0
              │   embargo_size ┴ Int64: 0
            r ┼ ConditionalValueatRisk
              │   settings ┼ RiskMeasureSettings
              │            │   scale ┼ Float64: 1.0
              │            │      ub ┼ nothing
              │            │     rke ┴ Bool: true
              │      alpha ┼ Float64: 0.05
              │          w ┴ nothing
       scorer ┼ HighestMeanScore()
           ex ┼ Transducers.ThreadedEx{@NamedTuple{}}: Transducers.ThreadedEx()
       n_iter ┼ Int64: 10
          rng ┼ Random.TaskLocalRNG: Random.TaskLocalRNG()
         seed ┼ nothing
  train_score ┼ Bool: false
       kwargs ┴ @NamedTuple{}: NamedTuple()

Related

source
PortfolioOptimisers.search_cross_validation Method
julia
search_cross_validation(opt::NonFiniteAllocationOptimisationEstimator,
                       rscv::RandomisedSearchCrossValidation,
                       rd::ReturnsResult)

Performs randomised search cross-validation for portfolio optimisation estimators. Samples parameter sets from distributions or vectors, applies cross-validation splits, fits and scores each configuration, and selects the optimal parameters using the provided scoring strategy.

Arguments

  • opt: Portfolio optimisation estimator to be tuned.

  • rscv: Randomised search cross-validation estimator specifying parameter grid, CV splitter, risk measure, scorer, execution strategy, number of iterations, RNG, and options.

  • rd: Returns result containing asset returns data.

Returns

  • SearchCrossValidationResult: Result type containing the optimal estimator, test and train scores, parameter grid, and selected index.

Validation

  • Sets RNG seed if provided.

  • Validates parameter grid and number of iterations.

  • Ensures sampled parameter sets are valid.

Details

  • Samples parameter sets from vectors or distributions.

  • Applies cross-validation splits to the returns data.

  • Fits the estimator for each sampled parameter set and split.

  • Scores each configuration using the specified risk measure and scoring function.

  • Selects the optimal parameter set based on cross-validation scores.

  • Returns a result object encapsulating the optimal estimator and score matrices.

Related

Examples

source