Base optimisation

PortfolioOptimisers.AbstractOptimisationEstimator Type

julia

abstract type AbstractOptimisationEstimator <: AbstractEstimator

Abstract supertype for all portfolio optimisation estimators in PortfolioOptimisers.jl.

All optimisers and optimisation components should subtype AbstractOptimisationEstimator to participate in the optimisation dispatch system.

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PortfolioOptimisers.BaseOptimisationEstimator Type

julia

abstract type BaseOptimisationEstimator <: AbstractOptimisationEstimator

Abstract supertype for base portfolio optimisation estimators.

BaseOptimisationEstimator is the parent for all internal optimiser components that configure the optimisation problem but are not directly invokable as top-level optimisers.

Related Types

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PortfolioOptimisers.OptimisationEstimator Type

julia

abstract type OptimisationEstimator <: AbstractOptimisationEstimator

Abstract supertype for portfolio optimisation estimators that produce portfolio weights.

Subtype OptimisationEstimator to implement concrete portfolio optimisers. All optimisers that can be invoked with optimise should subtype this.

Related Types

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PortfolioOptimisers.NonFiniteAllocationOptimisationEstimator Type

julia

abstract type NonFiniteAllocationOptimisationEstimator <: OptimisationEstimator

Abstract supertype for portfolio optimisation estimators that produce continuous (non-integer) portfolio weights.

Related Types

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PortfolioOptimisers.OptimisationAlgorithm Type

julia

abstract type OptimisationAlgorithm <: AbstractAlgorithm

Abstract supertype for optimisation algorithms used by portfolio optimisers.

Related Types

AbstractAlgorithm

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PortfolioOptimisers.OptimisationResult Type

julia

abstract type OptimisationResult <: AbstractResult

Abstract supertype for portfolio optimisation result types.

All concrete optimisation result types should subtype OptimisationResult.

Related Types

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PortfolioOptimisers.NonFiniteAllocationOptimisationResult Type

julia

abstract type NonFiniteAllocationOptimisationResult <: OptimisationResult

Abstract supertype for continuous (non-integer allocation) optimisation results.

Related Types

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PortfolioOptimisers.OptimisationReturnCode Type

julia

abstract type OptimisationReturnCode <: AbstractResult

Abstract supertype for optimisation return codes.

Concrete subtypes indicate whether an optimisation succeeded or failed.

Related Types

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PortfolioOptimisers.OptimisationModelResult Type

julia

abstract type OptimisationModelResult <: AbstractResult

Abstract supertype for intermediate optimisation model results.

Related Types

OptimisationResult

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PortfolioOptimisers.OptimisationSuccess Type

julia

struct OptimisationSuccess{__T_res} <: OptimisationReturnCode

Indicates that a portfolio optimisation completed successfully.

Fields

res: Optional result or message from the solver (default: nothing).

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PortfolioOptimisers.OptimisationFailure Type

julia

struct OptimisationFailure{__T_res} <: OptimisationReturnCode

Indicates that a portfolio optimisation failed.

Fields

res: Optional error message or diagnostic information (default: nothing).

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PortfolioOptimisers.JuMPWeightFinaliserFormulation Type

julia

abstract type JuMPWeightFinaliserFormulation <: AbstractAlgorithm

Abstract supertype for JuMP-based weight finaliser formulations.

Defines the interface for norm types used when adjusting portfolio weights to satisfy bounds via a JuMP model.

Related Types

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PortfolioOptimisers.RelativeErrorWeightFinaliser Type

julia

struct RelativeErrorWeightFinaliser <: JuMPWeightFinaliserFormulation

Minimises the L1 norm of relative weight deviations when enforcing weight bounds.

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PortfolioOptimisers.SquaredRelativeErrorWeightFinaliser Type

julia

struct SquaredRelativeErrorWeightFinaliser <: JuMPWeightFinaliserFormulation

Minimises the L2 norm (squared) of relative weight deviations when enforcing weight bounds.

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PortfolioOptimisers.AbsoluteErrorWeightFinaliser Type

julia

struct AbsoluteErrorWeightFinaliser <: JuMPWeightFinaliserFormulation

Minimises the L1 norm of absolute weight deviations when enforcing weight bounds.

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PortfolioOptimisers.SquaredAbsoluteErrorWeightFinaliser Type

julia

struct SquaredAbsoluteErrorWeightFinaliser <: JuMPWeightFinaliserFormulation

Minimises the L2 norm (squared) of absolute weight deviations when enforcing weight bounds.

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PortfolioOptimisers.WeightFinaliser Type

julia

abstract type WeightFinaliser <: AbstractAlgorithm

Abstract supertype for weight finaliser strategies.

A WeightFinaliser enforces weight bounds after the optimisation has produced unconstrained weights.

Related Types

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PortfolioOptimisers.IterativeWeightFinaliser Type

julia

struct IterativeWeightFinaliser{__T_iter} <: WeightFinaliser

Iteratively projects weights into the feasible region defined by weight bounds.

IterativeWeightFinaliser repeatedly clips and redistributes portfolio weights until they satisfy the given lower and upper bounds, or the maximum number of iterations iter is reached.

Fields

iter: Maximum number of iterations.

Constructors

julia

IterativeWeightFinaliser(;
    iter::Integer = 100
) -> IterativeWeightFinaliser

Keywords correspond to the struct's fields.

Validation

iter > 0.

Examples

julia

julia> IterativeWeightFinaliser()
IterativeWeightFinaliser
  iter ┴ Int64: 100

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PortfolioOptimisers.JuMPWeightFinaliser Type

julia

struct JuMPWeightFinaliser{__T_slv, __T_sc, __T_so, __T_alg} <: WeightFinaliser

Uses a JuMP optimisation model to enforce weight bounds.

JuMPWeightFinaliser solves a small optimisation problem to find the closest feasible weights (in the sense of the chosen error formulation) that satisfy the given bounds. Falls back to IterativeWeightFinaliser if the JuMP model fails.

Fields

slv: Solver or vector of solvers for the JuMP model.
sc: Scale factor applied to constraints.
so: Scale factor applied to the objective.
alg: Error formulation (L1/L2 relative or absolute).

Constructors

julia

JuMPWeightFinaliser(;
    slv::Slv_VecSlv,
    sc::Number = 1.0,
    so::Number = 1.0,
    alg::JuMPWeightFinaliserFormulation = RelativeErrorWeightFinaliser()
) -> JuMPWeightFinaliser

Keywords correspond to the struct's fields.

Validation

If slv is a VecSlv: !isempty(slv).
sc > 0, so > 0.

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PortfolioOptimisers._optimise Function

julia

_optimise(opt, rd, args...; dims, str_names, save, kwargs...)

Internal dispatch function for portfolio optimisation.

Called by optimise to perform the actual optimisation. Each optimisation estimator type implements its own overload. Returns the estimator-specific result type.

Arguments

opt: Optimisation estimator (e.g. MeanRisk, RiskBudgeting, etc.).
rd::ReturnsResult: Returns data.
dims::Int: Observation dimension.
str_names::Bool: Whether to use string names in the JuMP model.
save::Bool: Whether to save the JuMP model in the result.
kwargs...: Additional keyword arguments.

Returns

Estimator-specific optimisation result.

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PortfolioOptimisers.optimise Method

julia

optimise(opt::OptimisationEstimator, args...; kwargs...) -> OptimisationResult
optimise(opt::OptimisationResult, args...; kwargs...) -> OptimisationResult

Run portfolio optimisation using the given estimator opt and return an OptimisationResult.

If opt returns an OptimisationFailure, the fallback estimator is tried automatically until either a successful result is obtained or all fallbacks are exhausted.

Passing an OptimisationResult directly returns it unchanged (pass-through method).

Arguments

opt: Optimisation estimator (e.g. a JuMPOptimisationEstimator subtype).
args: Additional positional arguments (ignored).
kwargs: Additional keyword arguments (ignored).

Returns

OptimisationResult: The optimisation result.

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PortfolioOptimisers.optimise Method

julia

optimise(
    opt::OptimisationEstimator,
    args...;
    kwargs...
) -> RiskBudgetingResult{__T_oe, __T_pa, __T_prb, __T_retcode, __T_sol, __T_model, Nothing} where {__T_oe<:Union{Type{RelaxedRiskBudgeting{var"#s2376", var"#s2375", var"#s2371", var"#s2370", Nothing}} where {var"#s2376", var"#s2375"<:FactorRiskBudgeting, var"#s2371", var"#s2370"}, Type{RelaxedRiskBudgeting{var"#s2376", var"#s2375", var"#s2371", var"#s2370", Nothing}} where {var"#s2376", var"#s2375"<:AssetRiskBudgeting, var"#s2371", var"#s2370"}}, __T_pa<:ProcessedJuMPOptimiserAttributes, __T_prb<:Union{ProcessedAssetRiskBudgetingAttributes, ProcessedFactorRiskBudgetingAttributes{_A, _B, __T_rr} where {_A, _B, __T_rr<:Regression}}, __T_retcode<:Union{OptimisationFailure{Dict{Any, Any}}, OptimisationSuccess{Dict{Any, Any}}}, __T_sol<:(JuMPOptimisationSolution{<:AbstractArray{var"#s20", N}} where {var"#s20"<:(Union{var"#s19", var"#s18"} where {var"#s19"<:Number, var"#s18"<:AbstractJuMPScalar}), N}), __T_model<:Union{Nothing, Model}}

High level optimisation function that wraps around estimator-specific optimisation functions. This takes care of fallback methods if the primary optimisation fails. It returns the first successful optimisation result but stores all fallback results in the fb field of the result.

Arguments

opt::OptimisationEstimator: The optimisation estimator to use.
args: Additional positional arguments passed to the optimisation function.
kwargs: Additional keyword arguments passed to the optimisation function.

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PortfolioOptimisers.calc_net_returns Function

fees takes precedence over res.fees if both are provided

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PortfolioOptimisers.assert_special_nco_requirements Method

julia

assert_special_nco_requirements(opt)

Assert that the optimiser meets special requirements for Nested Clustered Optimisation (NCO).

The default implementation does nothing. Overridden for estimators (e.g. Stacking) that have requirements which must be validated before NCO can proceed.

Arguments

opt: Optimisation estimator, result, or vector thereof.

Returns

nothing.

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PortfolioOptimisers.needs_previous_weights Method

julia

needs_previous_weights(opt)

Return true if the optimiser requires the previous period's weights.

The default returns false. Overridden for optimisers that contain turnover constraints, tracking error constraints, or other time-dependent components that require the previous optimisation's weights.

Arguments

opt: Optimisation estimator, result, risk measure, fee structure, or vector thereof.

Returns

Bool: true if previous weights are needed.

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PortfolioOptimisers.is_time_dependent Method

julia

is_time_dependent(opt)

Return true if the optimiser has time-dependent constraints or objectives.

The default returns false. Overridden for estimators that must be updated between periods (e.g. when constraints depend on the current time step).

Arguments

opt: Optimisation estimator, result, or vector thereof.

Returns

Bool: true if the estimator is time-dependent.

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PortfolioOptimisers.update_time_dependent_estimator Method

julia

update_time_dependent_estimator(opt, args...)

Update the estimator for the current time period.

The default returns the estimator unchanged. Overridden for estimators that need to be updated between periods (e.g. sliding window constraints, time-varying parameters).

Arguments

opt: Optimisation estimator or result.
args...: Additional arguments (e.g. current period index, returns data).

Returns

Updated estimator.

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PortfolioOptimisers.opt_weight_bounds Function

julia

opt_weight_bounds(wf, wb, w)

Compute optimised weight bounds from the finaliser, bounds, and current weights.

Adjusts the weight bounds based on the weight finaliser algorithm and the current weight allocation, used in hierarchical weight allocation.

Arguments

wf: Weight finaliser algorithm.
wb: Weight bounds.
w: Current portfolio weights.

Returns

Updated weight bounds.

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PortfolioOptimisers.finalise_weight_bounds Function

julia

finalise_weight_bounds(wf::WeightFinaliser, wb::WeightBounds, w::VecNum)

Apply weight finalisation to enforce bounds and determine the optimisation return code.

Runs opt_weight_bounds with the given finaliser and bounds, then returns a success or failure return code based on whether all weights are finite.

Arguments

wf::WeightFinaliser: Weight finaliser algorithm.
wb::WeightBounds: Weight bounds configuration.
w::VecNum: Portfolio weights to finalise.

Returns

(retcode, w): Tuple of return code and adjusted weights.

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PortfolioOptimisers.opt_view Method

julia

opt_view(opt, i, args...)

Return a view or subset of an optimisation estimator for a given cluster index i.

Default fallback returns the estimator unchanged. Overridden for composite estimators (e.g. JuMPOptimiser, HierarchicalRiskParity) to slice all sub-estimators for the i-th cluster.

Arguments

opt: Optimisation estimator or result.
i: Cluster or asset index.
args...: Additional arguments (e.g. asset returns matrix).

Returns

Sliced or unchanged optimisation estimator.

Related

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Base optimisation ​

Base optimisation