Hierarchical Risk Parity

PortfolioOptimisers.HierarchicalRiskParity Type

struct HierarchicalRiskParity{__T_opt, __T_r, __T_sca, __T_fb} <: ClusteringOptimisationEstimator

Hierarchical Risk Parity (HRP) portfolio optimiser.

HierarchicalRiskParity implements the Hierarchical Risk Parity algorithm of López de Prado (2016). It clusters assets using hierarchical clustering, then allocates weights by recursively bisecting the dendrogram and applying inverse-risk weighting within each cluster.

Fields

• opt: Base hierarchical optimiser configuration (prior, clustering, bounds, fees, etc.).

• r: Risk measure or vector of risk measures used to define the inter-cluster risk budget.

• sca: Scalariser for combining multiple risk measures.

• fb: Fallback optimiser.

Constructors

HierarchicalRiskParity(;
    opt::HierarchicalOptimiser = HierarchicalOptimiser(),
    r::OptRM_VecOptRM = Variance(),
    sca::Scalariser = SumScalariser(),
    fb::Option{<:OptE_Opt} = nothing
) -> HierarchicalRiskParity

Keywords correspond to the struct's fields.

Validation

• If r is a vector: !isempty(r).

Examples

julia> HierarchicalRiskParity()
HierarchicalRiskParity
  opt ┼ HierarchicalOptimiser
      │       pe ┼ EmpiricalPrior
      │          │        ce ┼ PortfolioOptimisersCovariance
      │          │           │   ce ┼ Covariance
      │          │           │      │    me ┼ SimpleExpectedReturns
      │          │           │      │       │   w ┴ nothing
      │          │           │      │    ce ┼ GeneralCovariance
      │          │           │      │       │   ce ┼ StatsBase.SimpleCovariance: StatsBase.SimpleCovariance(true)
      │          │           │      │       │    w ┴ nothing
      │          │           │      │   alg ┴ Full()
      │          │           │   mp ┼ DenoiseDetoneAlgMatrixProcessing
      │          │           │      │     pdm ┼ Posdef
      │          │           │      │         │      alg ┼ UnionAll: NearestCorrelationMatrix.Newton
      │          │           │      │         │   kwargs ┴ @NamedTuple{}: NamedTuple()
      │          │           │      │      dn ┼ nothing
      │          │           │      │      dt ┼ nothing
      │          │           │      │     alg ┼ nothing
      │          │           │      │   order ┴ DenoiseDetoneAlg()
      │          │        me ┼ SimpleExpectedReturns
      │          │           │   w ┴ nothing
      │          │   horizon ┴ nothing
      │      cle ┼ ClustersEstimator
      │          │    ce ┼ PortfolioOptimisersCovariance
      │          │       │   ce ┼ Covariance
      │          │       │      │    me ┼ SimpleExpectedReturns
      │          │       │      │       │   w ┴ nothing
      │          │       │      │    ce ┼ GeneralCovariance
      │          │       │      │       │   ce ┼ StatsBase.SimpleCovariance: StatsBase.SimpleCovariance(true)
      │          │       │      │       │    w ┴ nothing
      │          │       │      │   alg ┴ Full()
      │          │       │   mp ┼ DenoiseDetoneAlgMatrixProcessing
      │          │       │      │     pdm ┼ Posdef
      │          │       │      │         │      alg ┼ UnionAll: NearestCorrelationMatrix.Newton
      │          │       │      │         │   kwargs ┴ @NamedTuple{}: NamedTuple()
      │          │       │      │      dn ┼ nothing
      │          │       │      │      dt ┼ nothing
      │          │       │      │     alg ┼ nothing
      │          │       │      │   order ┴ DenoiseDetoneAlg()
      │          │    de ┼ Distance
      │          │       │   power ┼ nothing
      │          │       │     alg ┴ CanonicalDistance()
      │          │   alg ┼ HClustAlgorithm
      │          │       │   linkage ┴ Symbol: :ward
      │          │   onc ┼ OptimalNumberClusters
      │          │       │   max_k ┼ nothing
      │          │       │     alg ┼ SecondOrderDifference
      │          │       │         │   alg ┼ StandardisedValue
      │          │       │         │       │   mv ┼ MeanValue
      │          │       │         │       │      │   w ┴ nothing
      │          │       │         │       │   sv ┼ StdValue
      │          │       │         │       │      │           w ┼ nothing
      │          │       │         │       │      │   corrected ┴ Bool: true
      │      slv ┼ nothing
      │       wb ┼ WeightBounds
      │          │   lb ┼ Float64: 0.0
      │          │   ub ┴ Float64: 1.0
      │     fees ┼ nothing
      │     sets ┼ nothing
      │       wf ┼ IterativeWeightFinaliser
      │          │   iter ┴ Int64: 100
      │      brt ┼ Bool: false
      │   cle_pr ┼ Bool: true
      │   strict ┴ Bool: false
    r ┼ Variance
      │   settings ┼ RiskMeasureSettings
      │            │   scale ┼ Float64: 1.0
      │            │      ub ┼ nothing
      │            │     rke ┴ Bool: true
      │      sigma ┼ nothing
      │       chol ┼ nothing
      │         rc ┼ nothing
      │        alg ┴ SquaredSOCRiskExpr()
  sca ┼ SumScalariser()
   fb ┴ nothing

Related

• ClusteringOptimisationEstimator

• HierarchicalOptimiser

• HierarchicalEqualRiskContribution

• SchurComplementHierarchicalRiskParity

source

PortfolioOptimisers.optimise Function

optimise(hrp::HierarchicalRiskParity{<:Any, <:Any, <:Any, <:Nothing},
         rd::ReturnsResult = ReturnsResult(); dims::Int = 1, kwargs...) -> HierarchicalResult

Arguments

• hrp: The hierarchical risk parity optimiser to use.

• rd: The returns result to use. If isa(hrp.opt.pe, AbstractPriorResult), rd is not necessary if doing a standalone optimisation, but may be required/desired by fallbacks and/or clusterisation.

• dims: The dimension along which observations advance in time.

• kwargs: Additional keyword arguments passed to the optimisation function.

source