Base moments

Abstract moment types and fallbacks

Some optimisations and constraints make use of summary statistics. These types and functions form the base for moment estimation in PortfolioOptimisers.jl.

They also provide generic fallbacks for the various functionality in the library.

PortfolioOptimisers.AbstractExpectedReturnsEstimator Type

julia

abstract type AbstractExpectedReturnsEstimator <: AbstractEstimator

Abstract supertype for all expected returns estimator types.

All concrete and/or abstract types that implement expected returns estimation should be subtypes of AbstractExpectedReturnsEstimator.

Interfaces

In order to implement a new expected returns estimator which will work seamlessly with the library, subtype AbstractExpectedReturnsEstimator with all necessary parameters–-including observation weights–-as part of the struct, and implement the following methods:

Expected returns

Statistics.mean(me::AbstractExpectedReturnsEstimator, X::MatNum; kwargs...) -> ArrNum: Expected returns estimation.

Arguments

me: Expected returns estimator.
X: Data matrix observations × features if the dims keyword does not exist or dims = 1, features × observations when dims = 2.
kwargs...: Additional keyword arguments passed to the mean estimator.

Returns

mu::ArrNum: Expected returns vector features x 1 if the dims keyword does not exist or dims = 2, 1 x features if dims = 1.

Factory

PortfolioOptimisers.factory(me::AbstractExpectedReturnsEstimator, w::PortfolioOptimisers.ObsWeights) -> AbstractExpectedReturnsEstimator: Factory method for creating instances of the estimator with new observation weights.

Arguments

me: Expected returns estimator.
w: Observation weights vector observations × 1.

Returns

me: New expected returns estimator of the same type as the argument, with the appropriate weights applied.

Examples

julia

julia> struct MyExpectedReturnsEstimator{T1} <:
              PortfolioOptimisers.AbstractExpectedReturnsEstimator
           w::T1
           function MyExpectedReturnsEstimator(w::PortfolioOptimisers.Option{<:PortfolioOptimisers.ObsWeights})
               PortfolioOptimisers.assert_nonempty_nonneg_finite_val(w, :w)
               return new{typeof(w)}(w)
           end
       end

julia> function MyExpectedReturnsEstimator(;
                                           w::PortfolioOptimisers.Option{<:PortfolioOptimisers.ObsWeights} = nothing)
           return MyExpectedReturnsEstimator(w)
       end
MyExpectedReturnsEstimator

julia> function PortfolioOptimisers.factory(::MyExpectedReturnsEstimator,
                                            w::PortfolioOptimisers.ObsWeights)
           return MyExpectedReturnsEstimator(; w = w)
       end

julia> function Statistics.mean(est::MyExpectedReturnsEstimator, X::PortfolioOptimisers.MatNum;
                                dims::Int = 1, kwargs...)
           PortfolioOptimisers.assert_dims(dims)
           if dims == 2
               X = X'
           end
           w = isnothing(est.w) ? fill(one(eltype(X)), size(X, 1)) : est.w
           X = X .* w
           mu = sum(X; dims = 1) / sum(w)
           return isone(dims) ? reshape(mu, 1, :) : reshape(mu, :, 1)
       end

julia> mean(MyExpectedReturnsEstimator(), [1.0 2.0; 0.3 0.7; 0.5 1.1]; dims = 2)
3×1 Matrix{Float64}:
 1.5
 0.5
 0.8

julia> PortfolioOptimisers.factory(MyExpectedReturnsEstimator(), StatsBase.Weights([1, 2, 3]))
MyExpectedReturnsEstimator
  w ┴ StatsBase.Weights{Int64, Int64, Vector{Int64}}: [1, 2, 3]

Related

Base moments ​

Abstract moment types and fallbacks ​

Windowed estimator generation ​

FullMoment and semi moments ​

Base moments

Abstract moment types and fallbacks

Windowed estimator generation

FullMoment and semi moments