Base JuMP Optimisation

PortfolioOptimisers.BaseJuMPOptimisationEstimator Type

julia

abstract type BaseJuMPOptimisationEstimator <: BaseOptimisationEstimator

Abstract supertype for base JuMP-based portfolio optimisation estimators.

These are configuration-level types (e.g., JuMPOptimiser) that define the optimisation problem setup for JuMP-based optimisers.

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PortfolioOptimisers.JuMPOptimisationEstimator Type

julia

abstract type JuMPOptimisationEstimator <: NonFiniteAllocationOptimisationEstimator

Abstract supertype for JuMP-based portfolio optimisation estimators.

JuMP optimisers formulate and solve portfolio optimisation problems using mathematical programming via the JuMP.jl framework.

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PortfolioOptimisers.RiskJuMPOptimisationEstimator Type

julia

abstract type RiskJuMPOptimisationEstimator <: JuMPOptimisationEstimator

Abstract supertype for risk-based JuMP portfolio optimisation estimators.

Subtype RiskJuMPOptimisationEstimator to implement optimisers that minimise or constrain risk measures as the primary objective.

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PortfolioOptimisers.ObjectiveFunction Type

julia

abstract type ObjectiveFunction <: AbstractEstimator

Abstract supertype for portfolio objective functions.

Subtype ObjectiveFunction to implement portfolio optimisation objectives such as minimum risk, maximum return, or maximum Sharpe ratio.

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PortfolioOptimisers.JuMPReturnsEstimator Type

julia

abstract type JuMPReturnsEstimator <: AbstractEstimator

Abstract supertype for JuMP-based returns estimators used in optimisation models.

JuMPReturnsEstimator types define how expected returns are incorporated into JuMP models.

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PortfolioOptimisers.JuMPConstraintEstimator Type

julia

abstract type JuMPConstraintEstimator <: AbstractConstraintEstimator

Abstract supertype for JuMP constraint estimators.

Subtype JuMPConstraintEstimator to implement custom constraints or objectives for JuMP-based portfolio optimisers.

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PortfolioOptimisers.CustomJuMPConstraint Type

julia

abstract type CustomJuMPConstraint <: JuMPConstraintEstimator

Abstract supertype for custom JuMP constraint implementations.

Implement add_custom_constraint! to define custom JuMP model constraints.

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PortfolioOptimisers.CustomJuMPObjective Type

julia

abstract type CustomJuMPObjective <: JuMPConstraintEstimator

Abstract supertype for custom JuMP objective implementations.

Implement add_custom_objective_term! to add custom terms to the JuMP model objective.

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PortfolioOptimisers.needs_previous_weights Method

julia

needs_previous_weights(_::CustomJuMPConstraint) -> Bool

Return false: custom JuMP constraints never require previous portfolio weights.

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PortfolioOptimisers.needs_previous_weights Method

julia

needs_previous_weights(_::CustomJuMPObjective) -> Bool

Return false: custom JuMP objectives never require previous portfolio weights.

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PortfolioOptimisers.JuMPOptimisationSolution Type

julia

struct JuMPOptimisationSolution{__T_w} <: OptimisationModelResult

Stores the solution (portfolio weights) from a JuMP optimisation model.

Fields

w: Portfolio weights vector assets × 1.

Constructors

julia

JuMPOptimisationSolution(; w::ArrNum) -> JuMPOptimisationSolution

Keywords correspond to the struct's fields.

Validation

!isempty(w).

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PortfolioOptimisers.BaseJuMPOptimisationResult Type

julia

abstract type BaseJuMPOptimisationResult <: AbstractResult

Abstract supertype for the embedded JuMP optimisation result core.

Mirrors BaseJuMPOptimisationEstimator: the factored-out struct holding the fields common to every JuMP-based optimisation result lives on this branch and is not part of the optimisation result hierarchy. The concrete core is JuMPOptimisationResult.

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PortfolioOptimisers.JuMPOptimisationResult Type

julia

struct JuMPOptimisationResult{__T_oe, __T_pa, __T_retcode, __T_sol, __T_model} <: BaseJuMPOptimisationResult

Shared field core for JuMP-based optimisation results.

Holds the fields common to every JuMP optimisation result. Embedded as the first field (jr) of each concrete JuMP result, analogous to how JuMPOptimiser is embedded as opt in each JuMP optimiser. The concrete result keeps only its unique fields plus the trailing fb.

Fields

oe: Type of the optimisation estimator that produced this result.
pa: Processed optimisation attributes.
retcode: Optimisation return code.
sol: Optimisation solution.
model: :JuMP.Model`: The JuMP optimisation model.

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Base.getproperty Method

julia

getproperty(jr::JuMPOptimisationResult, sym::Symbol) -> Any

Access properties of JuMPOptimisationResult. Virtual property :w extracts portfolio weights from sol; unknown properties forward to pa.

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PortfolioOptimisers.RiskJuMPOptimisationResult Type

julia

abstract type RiskJuMPOptimisationResult <: NonFiniteAllocationOptimisationResult

Abstract supertype for JuMP-based continuous optimisation results.

The JuMP half of the result split; mirrors RiskJuMPOptimisationEstimator. Concrete subtypes embed a JuMPOptimisationResult as their first field (jr) and add only their unique fields plus the trailing fb. The default getproperty resolves unique fields directly and delegates everything else (including :w and the pa fall-through) to jr; types with composed sub-result fields override it to forward into those first.

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Base.getproperty Method

julia

getproperty(
    r::RiskJuMPOptimisationResult,
    sym::Symbol
) -> Any

Default property access for RiskJuMPOptimisationResult: unique fields resolve directly; everything else delegates to the embedded JuMPOptimisationResult jr.

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PortfolioOptimisers.NonJuMPOptimisationResult Type

julia

abstract type NonJuMPOptimisationResult <: NonFiniteAllocationOptimisationResult

Abstract supertype for non-JuMP continuous optimisation results.

Groups the results that do not carry a JuMP model (naive, clustering, and meta-optimiser results). Mirrors the JuMP/non-JuMP split on the result side; the JuMP half is RiskJuMPOptimisationResult.

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PortfolioOptimisers.add_custom_objective_term! Function

julia

add_custom_objective_term!(args...; kwargs...)

Add a custom objective term to the JuMP model.

No-op fallback. Override this method for subtypes of CustomJuMPObjective to add custom penalty or reward terms to the JuMP model objective.

Arguments

args...: JuMP model and custom objective type (ignored in fallback).
kwargs...: Additional keyword arguments.

Returns

nothing.

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PortfolioOptimisers.add_custom_constraint! Function

julia

add_custom_constraint!(args...; kwargs...)

Add a custom constraint to the JuMP model.

No-op fallback. Override this method for subtypes of CustomJuMPConstraint to add custom constraints to the JuMP model.

Arguments

args...: JuMP model and custom constraint type (ignored in fallback).
kwargs...: Additional keyword arguments.

Returns

nothing.

Related

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PortfolioOptimisers.process_model Function

julia

process_model(model, retcode)

Extract the solution from an optimised JuMP model based on the return code.

On success, extracts the optimised weights from the model. On failure, returns an empty solution.

Arguments

model: Optimised JuMP model.
retcode: Optimisation return code (OptimisationSuccess or OptimisationFailure).

Returns

Solution object.

Related

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PortfolioOptimisers.optimise_JuMP_model! Function

julia

optimise_JuMP_model!(model::JuMP.Model, slv::Slv_VecSlv)

Attempt to optimise a JuMP model using one or more configured solvers.

Tries each solver in order, applying settings and checking for solution feasibility. Returns a JuMPResult with trial errors and success status.

Arguments

model: JuMP model to optimise.
slv: Single Solver or vector of Solver objects.

Returns

res::JuMPResult: Result object containing trial errors and success flag.

Details

For each solver, sets the optimizer and attributes, runs JuMP.optimize!, and checks solution feasibility.
If a solver fails, records the error and tries the next.
Stops at the first successful solution.

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julia

optimise_JuMP_model!(
    model::Model,
    opt::JuMPOptimisationEstimator
) -> Tuple{Union{OptimisationFailure{Dict{Any, Any}}, OptimisationSuccess{Dict{Any, Any}}}, JuMPOptimisationSolution{<:AbstractArray{var"#s21", N}} where {var"#s21"<:(Union{var"#s20", var"#s19"} where {var"#s20"<:Number, var"#s19"<:AbstractJuMPScalar}), N}}
optimise_JuMP_model!(
    model::Model,
    opt::JuMPOptimisationEstimator,
    datatype::DataType
) -> Tuple{Union{OptimisationFailure{Dict{Any, Any}}, OptimisationSuccess{Dict{Any, Any}}}, JuMPOptimisationSolution{<:AbstractArray{var"#s21", N}} where {var"#s21"<:(Union{var"#s20", var"#s19"} where {var"#s20"<:Number, var"#s19"<:AbstractJuMPScalar}), N}}

Attempt to solve the JuMP model using each solver in opt.opt.slv in order.

Tries each solver sequentially, checking feasibility and finite non-zero weights. Returns a (retcode, solution) tuple where retcode is OptimisationSuccess or OptimisationFailure and solution is a JuMPOptimisationSolution.

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PortfolioOptimisers.set_model_scales! Function

julia

set_model_scales!(model::JuMP.Model, so::Number, sc::Number)

Arguments

model::JuMP.Model: JuMP optimisation model.
so::Number: Objective scale factor.
sc::Number: Constraint scale factor.

Returns

nothing.

Related

JuMPOptimiser

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PortfolioOptimisers.set_initial_w! Function

julia

set_initial_w!(args...)
set_initial_w!(w::VecNum, wi::VecNum)

Set initial (warm-start) values for portfolio weight variables in the JuMP model.

The no-op fallback does nothing when wi is not provided. The two-argument method sets JuMP start values for each weight variable.

Arguments

w::VecNum: Vector of JuMP weight variables.
wi::VecNum: Vector of initial weight values.

Returns

nothing.

Related

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PortfolioOptimisers.set_w! Function

julia

set_w!(model::JuMP.Model, X::MatNum, wi::Option{<:VecNum_VecVecNum})

Create portfolio weight variables in the JuMP model and optionally set initial values.

Registers a vector of weight variables w of length size(X, 2) in the model. If wi is provided, sets the initial values via set_initial_w!.

Arguments

model::JuMP.Model: JuMP optimisation model.
X::MatNum: Asset returns matrix (shape: observations × assets).
wi: Optional initial weight values.

Returns

nothing.

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PortfolioOptimisers.set_portfolio_returns! Function

julia

set_portfolio_returns!(model::JuMP.Model, X::MatNum)

Compute and register portfolio returns expression X * w in the JuMP model.

If the expression already exists in the model, returns it directly (idempotent).

Arguments

model::JuMP.Model: JuMP optimisation model.
X::MatNum: Asset returns matrix.

Returns

The portfolio returns expression.

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PortfolioOptimisers.set_net_portfolio_returns! Function

julia

set_net_portfolio_returns!(model::JuMP.Model, X::MatNum)

Compute and register net portfolio returns (after fees) in the JuMP model.

Calls set_portfolio_returns! and subtracts fees if present.

Arguments

model::JuMP.Model: JuMP optimisation model.
X::MatNum: Asset returns matrix.

Returns

The net portfolio returns expression.

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PortfolioOptimisers.set_portfolio_returns_plus_one! Function

julia

set_portfolio_returns_plus_one!(model::JuMP.Model, X::MatNum)

Compute and register portfolio gross returns X .+ 1 in the JuMP model.

Used in drawdown and logarithmic return computations.

Arguments

model::JuMP.Model: JuMP optimisation model.
X::MatNum: Portfolio returns expression.

Returns

The gross returns expression X .+ 1.

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PortfolioOptimisers.set_portfolio_drawdowns_plus_one! Function

julia

set_portfolio_drawdowns_plus_one!(model::JuMP.Model, X::MatNum)

Compute and register absolute drawdowns plus one in the JuMP model.

Computes absolute_drawdown_arr(X) .+ 1 and registers it in the model.

Arguments

model::JuMP.Model: JuMP optimisation model.
X::MatNum: Portfolio returns expression.

Returns

The drawdowns-plus-one expression.

Related

set_portfolio_returns_plus_one!

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PortfolioOptimisers.set_risk_constraints! Function

julia

set_risk_constraints!(model, r, X, T, ...) -> nothing

Set risk constraints in the JuMP model for a given risk measure.

Generic function stub; concrete methods are defined in constraint and risk measure files. Each method configures the appropriate risk constraint expressions for a given risk measure type r.

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PortfolioOptimisers.get_constraint_scale Function

julia

get_constraint_scale(model::JuMP.Model)

Return the constraint scale expression model[:sc].

Asserts the scale has been registered (via set_model_scales!); errors otherwise.

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PortfolioOptimisers.has_Xap1 Function

julia

has_Xap1(model::JuMP.Model, prefix::Symbol = Symbol(""))

Return true if the gross portfolio returns model[Symbol(prefix, :Xap1)] have been registered (via set_portfolio_returns_plus_one!).

Related

get_Xap1

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PortfolioOptimisers.get_ret Function

julia

get_ret(model::JuMP.Model)

Return the portfolio expected-return expression model[:ret].

Asserts the return expression has been registered; errors otherwise.

Related

get_risk

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PortfolioOptimisers.get_net_X Function

julia

get_net_X(model::JuMP.Model, prefix::Symbol = Symbol(""))

Return the net portfolio returns expression model[Symbol(prefix, :net_X)].

Asserts it has been registered (via set_net_portfolio_returns!); errors otherwise.

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PortfolioOptimisers.get_ddap1 Function

julia

get_ddap1(model::JuMP.Model, prefix::Symbol = Symbol(""))

Return the drawdowns-plus-one expression model[Symbol(prefix, :ddap1)].

Asserts it has been registered (via set_portfolio_drawdowns_plus_one!); errors otherwise.

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PortfolioOptimisers.get_w Function

julia

get_w(model::JuMP.Model)

Return the portfolio weight variables model[:w].

Asserts the weights have been registered (via set_w!); errors otherwise.

Related

set_w!

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PortfolioOptimisers.get_objective_scale Function

julia

get_objective_scale(model::JuMP.Model)

Return the objective scale expression model[:so].

Asserts the scale has been registered (via set_model_scales!); errors otherwise.

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PortfolioOptimisers.get_k Function

julia

get_k(model::JuMP.Model)

Return the homogenisation variable model[:k].

k >= 0 is the auxiliary scaling variable used to homogenise fractional/ratio objectives (e.g. maximum ratio); recovered weights are w / k. Asserts :k has been registered; errors otherwise.

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PortfolioOptimisers.get_Xap1 Function

julia

get_Xap1(model::JuMP.Model, prefix::Symbol = Symbol(""))

Return the gross portfolio returns expression model[Symbol(prefix, :Xap1)] (X .+ 1).

Asserts it has been registered (via set_portfolio_returns_plus_one!); errors otherwise.

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PortfolioOptimisers.has_ddap1 Function

julia

has_ddap1(model::JuMP.Model, prefix::Symbol = Symbol(""))

Return true if the drawdowns-plus-one model[Symbol(prefix, :ddap1)] have been registered (via set_portfolio_drawdowns_plus_one!).

Related

get_ddap1

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PortfolioOptimisers.has_net_X Function

julia

has_net_X(model::JuMP.Model, prefix::Symbol = Symbol(""))

Return true if the net portfolio returns model[Symbol(prefix, :net_X)] have been registered (via set_net_portfolio_returns!).

Related

get_net_X

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PortfolioOptimisers.get_X Function

julia

get_X(model::JuMP.Model, prefix::Symbol = Symbol(""))

Return the portfolio returns expression model[Symbol(prefix, :X)].

Asserts it has been registered (via set_portfolio_returns!); errors otherwise.

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PortfolioOptimisers.get_risk Function

julia

get_risk(model::JuMP.Model)

Return the scalarised portfolio risk expression model[:risk].

Asserts the risk expression has been registered (via scalarise_risk_expression!); errors otherwise.

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PortfolioOptimisers.get_dd Function

julia

get_dd(model::JuMP.Model, prefix::Symbol = Symbol(""))

Return the cumulative-drawdown variables model[Symbol(prefix, :dd)].

Asserts they have been registered (via set_drawdown_constraints!); errors otherwise.

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PortfolioOptimisers.has_X Function

julia

has_X(model::JuMP.Model, prefix::Symbol = Symbol(""))

Return true if the portfolio returns model[Symbol(prefix, :X)] have been registered (via set_portfolio_returns!).

Related

get_X

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PortfolioOptimisers.has_dd Function

julia

has_dd(model::JuMP.Model, prefix::Symbol = Symbol(""))

Return true if the cumulative-drawdown variables model[Symbol(prefix, :dd)] have been registered (via set_drawdown_constraints!).

Related

get_dd

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PortfolioOptimisers.preg! Function

julia

preg!(model::JuMP.Model, prefix::Symbol, name::Symbol, val)

The single place the model-state namespacing convention lives: a nested risk build (e.g. risk tracking) passes a non-empty prefix so the shared infrastructure keys it creates (:X, :net_X, :W, :dd, …) do not collide with the outer model's; the default empty prefix reproduces the bare key. Pairs with the prefixed read accessors. See ADR 0004.

Related

get_w

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Base JuMP Optimisation ​

Base JuMP Optimisation