Asset turnover

The turnover is used to measure the absolute weight change between the current weights and benchmark weights. They can be used to compute fees, or as a constraint. It can also be used as a risk measure, but we will detail that use in Risk Measures

PortfolioOptimisers.TurnoverEstimator Type

struct TurnoverEstimator{__T_w, __T_val, __T_dval, __T_fixed} <: AbstractEstimator

Estimator for turnover portfolio constraints.

TurnoverEstimator specifies turnover constraints for each asset in a portfolio, based on current portfolio weights w, asset-specific turnover values val, and a default value for assets not explicitly specified. Supports asset-specific turnover via dictionaries, pairs, or vectors of pairs.

This estimator can be converted into a concrete Turnover constraint using the turnover_constraints function, which maps the estimator's specifications to the assets in a given AssetSets object.

Fields

• w: Current portfolio weights vector.

• val: Default value to use for the estimator. If nothing, the estimator provides the default value.

• dval: Default value for assets not specified in val.

• fixed: Whether the estimator is fixed and does not update with new weights.

Constructors

TurnoverEstimator(;
    w::VecNum,
    val::EstValType,
    dval::Option{<:Number} = nothing,
    fixed::Bool = false
) -> TurnoverEstimator

Validation

• w is validated with assert_nonempty_finite_val.

• val is validated with assert_nonempty_nonneg_finite_val.

• dval, if not nothing, dval >= 0.

View parameters

When port_opt_view is called on this type, the following @vprop-tagged fields are automatically subset to the selected indices:

• w: Sliced to the selected indices via port_opt_view.

• val: Sliced to the selected indices via port_opt_view.

Examples

julia> TurnoverEstimator(; w = [0.2, 0.3, 0.5], val = Dict("A" => 0.1, "B" => 0.2), dval = 0.0)
TurnoverEstimator
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Dict{String, Float64}: Dict("B" => 0.2, "A" => 0.1)
   dval ┼ Float64: 0.0
  fixed ┴ Bool: false

Related

• Turnover

• AbstractEstimator

• VecNum

• EstValType

• Option

• turnover_constraints

• port_opt_view

source

PortfolioOptimisers.Turnover Type

struct Turnover{__T_w, __T_val, __T_fixed} <: AbstractResult

Container for turnover portfolio constraints.

Turnover stores the portfolio weights and turnover constraint values for each asset. The turnover constraint can be specified as a scalar (applied to all assets) or as a vector of per-asset values.

Mathematical definition

$$\begin{array}{rl}\mathit{T}\mathit{n}(\mathit{w})& :=|\mathit{w}-{\mathit{w}}_b|\end{array}$$

Where:

• $\mathit{T}\mathit{n}(\mathit{w})$: N × 1 turnover vector.

• $\mathit{w}$: N × 1 vector of current portfolio weights.

• ${\mathit{w}}_b$: N × 1 vector of benchmark portfolio weights.

• $|\cdot |$: Element-wise absolute value.

Fields

• w: Current portfolio weights vector.

• val: Default value to use for the estimator. If nothing, the estimator provides the default value.

• fixed: Whether the estimator is fixed and does not update with new weights.

Constructors

Turnover(;
    w::VecNum,
    val::Num_VecNum = 0.0,
    fixed::Bool = false
) -> Turnover

Keywords correspond to the struct's fields.

Validation

• !isempty(w).

• val:

  • AbstractVector: !isempty(val), length(w) == length(val), any(isfinite, val), all(x -> x >= 0, val).

  • Number: isfinite(val) and val >= 0.

View parameters

When port_opt_view is called on this type, the following @vprop-tagged fields are automatically subset to the selected indices:

• w: Sliced to the selected indices via port_opt_view.

• val: Sliced to the selected indices via port_opt_view.

Examples

julia> Turnover(; w = [0.2, 0.3, 0.5], val = [0.1, 0.2, 0.0])
Turnover
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Vector{Float64}: [0.1, 0.2, 0.0]
  fixed ┴ Bool: false

julia> Turnover(; w = [0.2, 0.3, 0.5], val = 0.02, fixed = true)
Turnover
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Float64: 0.02
  fixed ┴ Bool: true

Related

• set_turnover_constraints!

• _set_turnover_constraints!

• set_turnover_fees!

• TurnoverEstimator

• AbstractResult

• VecNum

• Num_VecNum

• turnover_constraints

• factory(tn::Turnover, w::VecNum)

• port_opt_view

source

PortfolioOptimisers.factory Method

factory(tn::Turnover, w::VecNum)

Create a new Turnover constraint with updated portfolio weights.

factory constructs a new Turnover object using the provided portfolio weights w and the turnover values from an existing Turnover constraint tn.

Arguments

• tn: Existing Turnover constraint object.

• w: New portfolio weights vector.

Returns

• tn::Turnover: New constraint object with updated weights and original turnover values.

Examples

julia> tn = Turnover(; w = [0.2, 0.3, 0.5], val = [0.1, 0.2, 0.0])
Turnover
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Vector{Float64}: [0.1, 0.2, 0.0]
  fixed ┴ Bool: false

julia> factory(tn, [0.0, 0.2, 0.8])
Turnover
      w ┼ Vector{Float64}: [0.0, 0.2, 0.8]
    val ┼ Vector{Float64}: [0.1, 0.2, 0.0]
  fixed ┴ Bool: false

julia> tn = Turnover(; w = [0.2, 0.3, 0.5], val = [0.1, 0.2, 0.0], fixed = true)
Turnover
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Vector{Float64}: [0.1, 0.2, 0.0]
  fixed ┴ Bool: true

julia> factory(tn, [0.0, 0.2, 0.8])
Turnover
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Vector{Float64}: [0.1, 0.2, 0.0]
  fixed ┴ Bool: true

Related

• Turnover

• TurnoverEstimator

• turnover_constraints

source

PortfolioOptimisers.factory Method

factory(tn::TurnoverEstimator, w::VecNum)

Create a new TurnoverEstimator with updated portfolio weights.

Constructs a new TurnoverEstimator object using the provided portfolio weights w and the turnover values and default value from an existing TurnoverEstimator tn.

Arguments

• tn: Existing TurnoverEstimator object. Supplies turnover values and default value.

• w: New portfolio weights vector.

Returns

• tn::TurnoverEstimator: New estimator object with the same values and default but updated weights.

Validation

• w is validated to be non-empty, finite, and numeric.

Examples

julia> tn = TurnoverEstimator(; w = [0.2, 0.3, 0.5], val = Dict("A" => 0.1, "B" => 0.2),
                              dval = 0.0)
TurnoverEstimator
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Dict{String, Float64}: Dict("B" => 0.2, "A" => 0.1)
   dval ┼ Float64: 0.0
  fixed ┴ Bool: false

julia> factory(tn, [0.1, 0.4, 0.5])
TurnoverEstimator
      w ┼ Vector{Float64}: [0.1, 0.4, 0.5]
    val ┼ Dict{String, Float64}: Dict("B" => 0.2, "A" => 0.1)
   dval ┼ Float64: 0.0
  fixed ┴ Bool: false

julia> tn = TurnoverEstimator(; w = [0.2, 0.3, 0.5], val = Dict("A" => 0.1, "B" => 0.2),
                              dval = 0.0, fixed = true)
TurnoverEstimator
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Dict{String, Float64}: Dict("B" => 0.2, "A" => 0.1)
   dval ┼ Float64: 0.0
  fixed ┴ Bool: true

julia> factory(tn, [0.1, 0.4, 0.5])
TurnoverEstimator
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Dict{String, Float64}: Dict("B" => 0.2, "A" => 0.1)
   dval ┼ Float64: 0.0
  fixed ┴ Bool: true

Related

• TurnoverEstimator

• Turnover

• factory(tn::Turnover, w::VecNum)

• turnover_constraints

source

PortfolioOptimisers.factory Method

factory(tn::VecTnE_Tn, w::VecNum)

Create new turnover constraints or estimators with updated portfolio weights.

Applies factory to each element in tn, constructing a new collection of turnover constraints or estimators with the provided portfolio weights w.

Arguments

• tn: Vector of turnover constraints or estimators.

• w: New portfolio weights vector.

Returns

• res::VecTnE_Tn: Vector of updated turnover constraints or estimators.

Examples

julia> tn1 = Turnover(; w = [0.2, 0.3, 0.5], val = [0.1, 0.2, 0.0]);

julia> tn2 = Turnover(; w = [0.2, 0.3, 0.5], val = [0.05, 0.1, 0.0]);

julia> factory([tn1, tn2], [0.1, 0.4, 0.5])
2-element Vector{Turnover{Vector{Float64}, Vector{Float64}, Bool}}:
 Turnover
      w ┼ Vector{Float64}: [0.1, 0.4, 0.5]
    val ┼ Vector{Float64}: [0.1, 0.2, 0.0]
  fixed ┴ Bool: false

 Turnover
      w ┼ Vector{Float64}: [0.1, 0.4, 0.5]
    val ┼ Vector{Float64}: [0.05, 0.1, 0.0]
  fixed ┴ Bool: false

Related

• VecTnE_Tn

• Turnover

• TurnoverEstimator

• factory(tn::Turnover, w::VecNum)

• factory(tn::TurnoverEstimator, w::VecNum)

source

PortfolioOptimisers.turnover_constraints Function

turnover_constraints(tn::TurnoverEstimator, sets::AssetSets; datatype::DataType = Float64,
                     strict::Bool = false)

Generate turnover portfolio constraints from a TurnoverEstimator and asset set.

turnover_constraints constructs a Turnover object representing turnover constraints for the assets in sets, using the specifications in tn. Supports scalar, vector, dictionary, pair, or custom turnover types for flexible assignment and validation.

Arguments

• tn: TurnoverEstimator specifying current weights, asset-specific turnover values, and default value.

• sets: AssetSets containing asset names or indices.

• datatype: Data type for default turnover values when dval is nothing.

• strict: If true, enforces strict matching between assets and turnover values (throws error on mismatch); if false, issues a warning.

Returns

• tn::Turnover: Object containing portfolio weights and turnover values aligned with sets.

Details

• Turnover values are extracted and mapped to assets using estimator_to_val.

Examples

julia> sets = AssetSets(; dict = Dict("nx" => ["A", "B", "C"]));

julia> tn = TurnoverEstimator(; w = [0.2, 0.3, 0.5], val = Dict("A" => 0.1, "B" => 0.2));

julia> turnover_constraints(tn, sets)
Turnover
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Vector{Float64}: [0.1, 0.2, 0.0]
  fixed ┴ Bool: false

Related

• TurnoverEstimator

• Turnover

• AssetSets

source

turnover_constraints(tn::Option{<:Turnover}, args...; kwargs...)

Propagate or pass through turnover portfolio constraints.

turnover_constraints returns the input Turnover object unchanged or nothing. This method is used to propagate already constructed turnover constraints, enabling composability and uniform interface handling in constraint generation workflows.

Arguments

• tn: An existing Turnover object.

• args...: Additional positional arguments (ignored).

• kwargs...: Additional keyword arguments (ignored).

Returns

• tn::Option{<:Turnover}: The input constraint object, unchanged.

Examples

julia> tn = Turnover(; w = [0.2, 0.3, 0.5], val = [0.1, 0.2, 0.0]);

julia> turnover_constraints(tn)
Turnover
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Vector{Float64}: [0.1, 0.2, 0.0]
  fixed ┴ Bool: false

Related

• TurnoverEstimator

• Turnover

• Option

source

turnover_constraints(tn::VecTnE_Tn, sets::AssetSets; datatype::DataType = Float64,
                     strict::Bool = false)

Broadcasts turnover_constraints over the vector.

Provides a uniform interface for processing multiple constraint estimators simultaneously.

Arguments

• tn: Vector of turnover constraints or estimators.

• sets: AssetSets containing asset names or indices.

• datatype: Data type for default turnover values when dval is nothing.

• strict: If true, enforces strict matching between assets and turnover values (throws error on mismatch); if false, issues a warning.

Returns

• res::VecTn: Vector of constructed turnover constraints.

Examples

julia> sets = AssetSets(; dict = Dict("nx" => ["A", "B", "C"]));

julia> tn1 = TurnoverEstimator(; w = [0.2, 0.3, 0.5], val = Dict("A" => 0.1, "B" => 0.2));

julia> tn2 = TurnoverEstimator(; w = [0.1, 0.4, 0.5], val = Dict("B" => 0.15, "C" => 0.3));

julia> turnover_constraints([tn1, tn2], sets)
2-element Vector{Turnover{Vector{Float64}, Vector{Float64}, Bool}}:
 Turnover
      w ┼ Vector{Float64}: [0.2, 0.3, 0.5]
    val ┼ Vector{Float64}: [0.1, 0.2, 0.0]
  fixed ┴ Bool: false

 Turnover
      w ┼ Vector{Float64}: [0.1, 0.4, 0.5]
    val ┼ Vector{Float64}: [0.0, 0.15, 0.3]
  fixed ┴ Bool: false

Related

• VecTnE_Tn

• AssetSets

source

PortfolioOptimisers.port_opt_view Method

port_opt_view(tn::VecTnE_Tn, i)

Create views of multiple turnover constraints or estimators for a subset of assets.

port_opt_view returns a vector of turnover constraint or estimator objects, each restricted to the indices or assets specified by i.

Arguments

• tn: Vector of turnover constraints or estimators.

• i: Index or indices specifying the subset of assets.

Returns

• res::VecTnE_Tn: Vector of turnover constraint or estimator objects, each restricted to the specified subset.

Details

• Applies port_opt_view to each element in tn.

• Supports both Turnover and TurnoverEstimator types.

• Enables composable and uniform processing of asset subsets for batch turnover constraints.

Examples

julia> tn1 = Turnover(; w = [0.2, 0.3, 0.5], val = [0.1, 0.2, 0.0], fixed = true);

julia> tn2 = TurnoverEstimator(; w = [0.2, 0.3, 0.5], val = Dict("A" => 0.1, "B" => 0.2),
                               dval = 0.0, fixed = true);

julia> PortfolioOptimisers.port_opt_view(concrete_typed_array([tn1, tn2]), 1:2)
2-element Vector{Union{Turnover{SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}, SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}, Bool}, TurnoverEstimator{SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}, Dict{String, Float64}, Float64, Bool}}}:
 Turnover
      w ┼ SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}: [0.2, 0.3]
    val ┼ SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}: [0.1, 0.2]
  fixed ┴ Bool: true

 TurnoverEstimator
      w ┼ SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}: [0.2, 0.3]
    val ┼ Dict{String, Float64}: Dict("B" => 0.2, "A" => 0.1)
   dval ┼ Float64: 0.0
  fixed ┴ Bool: true

Related

• TurnoverEstimator

• Turnover

• VecTnE_Tn

• turnover_constraints

• port_opt_view

• concrete_typed_array

source

PortfolioOptimisers.TnE_Tn Type

const TnE_Tn = Union{<:Turnover, <:TurnoverEstimator}

Alias for a turnover constraint or estimator.

Represents either a constructed turnover constraint or a turnover constraint estimator. Used for flexible dispatch in turnover constraint generation and processing.

Related

• Turnover

• TurnoverEstimator

source

PortfolioOptimisers.VecTnE_Tn Type

const VecTnE_Tn = AbstractVector{<:TnE_Tn}

Alias for a vector of turnover constraints or estimators.

Represents a collection of turnover constraints or estimators, enabling batch processing and broadcasting of turnover constraint generation.

Related

• TnE_Tn

• Turnover

• TurnoverEstimator

source

PortfolioOptimisers.VecTn Type

const VecTn = AbstractVector{<:Turnover}

Alias for a vector of turnover constraints.

Represents a collection of constructed turnover constraints for multiple portfolios or assets.

Related

• Turnover

source

PortfolioOptimisers.Tn_VecTn Type

const Tn_VecTn = Union{<:Turnover, <:VecTn}

Alias for a single turnover constraint or a vector of turnover constraints.

Enables flexible dispatch for functions that accept either a single turnover constraint or multiple constraints.

Related

• Turnover

• VecTn

source

PortfolioOptimisers.TnE_Tn_VecTnE_Tn Type

const TnE_Tn_VecTnE_Tn = Union{<:TnE_Tn, <:VecTnE_Tn}

Alias for a single turnover constraint/estimator or a vector of them.

Supports flexible dispatch for turnover constraint generation and processing, accepting either a single constraint/estimator or a collection.

Related

• TnE_Tn

• VecTnE_Tn

• Turnover

• TurnoverEstimator

source

PortfolioOptimisers.needs_previous_weights Method

needs_previous_weights(tn::TnE_Tn) -> Bool
needs_previous_weights(tn::VecTnE_Tn) -> Bool

Check if a turnover constraint or estimator requires previous portfolio weights.

Arguments

• tn: Turnover constraint or estimator.

Returns

• Bool: true if previous weights are needed, false otherwise.

Related

• TurnoverEstimator

• Turnover

source