PortfolioOptimisers.jl

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Base Risk Constraints ​

PortfolioOptimisers.scalarise_risk_expression! Function
julia
scalarise_risk_expression!(model::Model, _::SumScalariser)

Collapse the risk_vec expression array stored in model into a single scalar risk JuMP expression.

The SumScalariser method sums all entries into a linear or quadratic expression. The LogSumExpScalariser method introduces auxiliary variables and exponential cone constraints to encode a log-sum-exp scalarisation. The MaxScalariser method introduces a variable and linear constraints to encode the maximum over all entries.

Arguments

Returns

  • nothing.

Related

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PortfolioOptimisers.set_risk_constraints! Method
julia
set_risk_constraints!(
    model::Model,
    r::RiskMeasure,
    opt::JuMPOptimisationEstimator,
    pr::AbstractPriorResult,
    pl::Union{Nothing, AbstractPhylogenyConstraintResult, AbstractVector{<:AbstractPhylogenyConstraintResult}},
    fees::Union{Nothing, Fees},
    args...;
    kwargs...
)

Dispatch to index-aware set_risk_constraints! for a single risk measure or iterate over a vector of risk measures.

The single-measure overload calls set_risk_constraints!(model, 1, r, ...). The vector overload calls set_risk_constraints!(model, i, rs[i], ...) for each element.

Arguments

  • model::JuMP.Model: The JuMP optimisation model.

  • r: A RiskMeasure instance, or rs a vector of risk measures.

  • opt::JuMPOptimisationEstimator: JuMP optimisation estimator.

  • pr: Prior result.

  • pl: Optional phylogeny constraints.

  • fees: Optional fees structure.

Returns

  • nothing.

Related

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PortfolioOptimisers.set_risk_upper_bound! Function
julia
set_risk_upper_bound!(args...)

Add an upper-bound constraint on a risk expression to model.

The fall-through method (args...) does nothing. The Front_NumVec overload records the expression and its frontier bound vector in model[:risk_frontier] for later use in Pareto frontier solves. The Number overload adds the constraint sc * (r_expr - ub * k) <= 0 directly to the model.

Arguments

  • model::JuMP.Model: The JuMP optimisation model.

  • r_expr::JuMP.AbstractJuMPScalar: The risk JuMP expression to bound.

  • ub: Upper bound; a scalar number or a frontier specification.

  • key::Symbol: Symbol used to name the constraint in the model.

Returns

  • nothing.

Related

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PortfolioOptimisers.set_risk_expression! Function
julia
set_risk_expression!(
    model::Model,
    r_expr::AbstractJuMPScalar,
    scale::Number,
    rke::Bool
)

Push a scaled risk expression onto the risk_vec array in model.

If rke is false the function does nothing. Otherwise it initialises risk_vec if needed and appends scale * r_expr.

Arguments

  • model::JuMP.Model: The JuMP optimisation model.

  • r_expr::JuMP.AbstractJuMPScalar: The risk JuMP expression to add.

  • scale::Number: Scaling factor applied to the expression.

  • rke::Bool: When false this method is a no-op.

Returns

  • nothing.

Related

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PortfolioOptimisers.set_risk_bounds_and_expression! Function
julia
set_risk_bounds_and_expression!(
    model::Model,
    opt::RiskJuMPOptimisationEstimator,
    r_expr::AbstractJuMPScalar,
    settings::RiskMeasureSettings,
    key
)

Apply an upper-bound constraint and register the risk expression for the objective.

Calls set_risk_upper_bound! with settings.ub and set_risk_expression! with settings.scale and settings.rke.

Arguments

  • model::JuMP.Model: The JuMP optimisation model.

  • opt::RiskJuMPOptimisationEstimator: Risk-based optimisation estimator.

  • r_expr::JuMP.AbstractJuMPScalar: Risk JuMP expression.

  • settings::RiskMeasureSettings: Settings carrying upper bound, scale, and rke flag.

  • key::Symbol: Symbol used to name constraints or expressions in the model.

Returns

  • nothing.

Related

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PortfolioOptimisers.set_drawdown_constraints! Function
julia
set_drawdown_constraints!(
    model::Model,
    X::AbstractMatrix{<:Union{var"#s19", var"#s18"} where {var"#s19"<:Number, var"#s18"<:AbstractJuMPScalar}}
) -> Any

Add portfolio drawdown tracking variables and constraints to model.

Creates the dd variable array (length T + 1) together with three constraints: cdd_start (initial drawdown is zero), cdd_geq_0 (drawdowns are non-negative), and cdd (drawdown recurrence relation). Returns the dd array; returns the existing one if already present in model.

Arguments

  • model::JuMP.Model: The JuMP optimisation model.

  • X::MatNum: Asset returns matrix (T × N).

Returns

  • dd: JuMP variable array of length T + 1 tracking portfolio drawdowns.

Related

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