WalkForward

PortfolioOptimisers.WalkForwardEstimator Type

julia

abstract type WalkForwardEstimator <: SequentialCrossValidationEstimator

Abstract supertype for all walk-forward cross-validation estimators in PortfolioOptimisers.jl.

Walk-forward estimators split time series data into sequential training and testing windows, advancing the test window forward at each step. Subtypes implement index-based or date-based walk-forward schemes.

Related

source

PortfolioOptimisers.WalkForwardResult Type

julia

struct WalkForwardResult{__T_train_idx, __T_test_idx} <: SequentialCrossValidationResult

Result type produced by WalkForwardEstimator subtypes after splitting time series data.

Stores the train and test index vectors for each fold of the walk-forward cross-validation.

Fields

train_idx: Training set indices.
test_idx: Test set indices.

Constructors

julia

WalkForwardResult(; train_idx::VecVecInt, test_idx::VecVecInt) -> WalkForwardResult

Keywords correspond to the struct's fields.

Validation

!isempty(train_idx) (sufficient data to cover training + testing periods).
!isempty(test_idx) (sufficient data to cover training + testing periods).
length(train_idx) == length(test_idx).

Related

source

PortfolioOptimisers.WFCVER Type

julia

const WFCVER = Union{<:WalkForwardEstimator, <:WalkForwardResult}

Alias for a walk-forward cross-validation estimator or result.

Matches either a WalkForwardEstimator or a WalkForwardResult.

Related

source

PortfolioOptimisers.DateAdjusterEstimator Type

julia

abstract type DateAdjusterEstimator <: AbstractEstimator

Abstract supertype for date adjustment estimators in walk-forward cross-validation.

Subtypes implement specific strategies for adjusting dates used in walk-forward splits.

Related

DateWalkForward

source

PortfolioOptimisers.DatesUnionPeriod Type

julia

const DatesUnionPeriod = Union{<:Dates.Period, <:Dates.CompoundPeriod}

Alias for a Dates period or compound period.

Used internally to accept either simple date periods (e.g., Dates.Month(1)) or compound periods (e.g., Dates.Month(1) + Dates.Day(1)) as date offsets in walk-forward cross-validation.

Related

source

PortfolioOptimisers.DateAdjType Type

julia

const DateAdjType = Union{<:Function, <:DateAdjusterEstimator}

Alias for a date adjustment function or estimator.

Matches either a plain Function or a DateAdjusterEstimator for adjusting dates in walk-forward cross-validation.

Related

source

PortfolioOptimisers.IndexWalkForward Type

julia

struct IndexWalkForward{__T_train_size, __T_test_size, __T_purged_size, __T_expand_train, __T_reduce_test} <: WalkForwardEstimator

Implements index-based walk-forward cross-validation for time series, supporting purging and flexible train/test windowing.

Fields

train_size: Training window size.
test_size: Test window size.
purged_size: Number of observations to purge between train and test sets.
expand_train: Whether to expand the training window over time.
reduce_test: Whether to allow the last test window to be smaller.

Constructors

julia

IndexWalkForward(
    train_size::Integer,
    test_size::Integer;
    purged_size::Integer = 0,
    expand_train::Bool = false,
    reduce_test::Bool = false,
) -> IndexWalkForward

Positional and keyword arguments correspond to the struct's fields.

Validation

train_size, test_size, and purged_size must be non-empty, non-negative, and finite.
Ensures train_size + purged_size < T where T is the total number of observations.

Examples

julia

julia> IndexWalkForward(100, 20; purged_size = 5, expand_train = true, reduce_test = false)
IndexWalkForward
    train_size ┼ Int64: 100
     test_size ┼ Int64: 20
   purged_size ┼ Int64: 5
  expand_train ┼ Bool: true
   reduce_test ┴ Bool: false

Related

source

Base.split Method

julia

Base.split(iwf::IndexWalkForward, rd::ReturnsResult) -> WalkForwardResult

Split the returns data rd into sequential walk-forward folds using integer observation indices. Each fold advances the test window by test_size observations.

Arguments

iwf::IndexWalkForward: Index-based walk-forward cross-validation estimator.
rd::ReturnsResult: Returns data to split.

Returns

WalkForwardResult: Result containing train and test index ranges for each fold.

Related

source

PortfolioOptimisers.DateWalkForward Type

julia

struct DateWalkForward{__T_train_size, __T_test_size, __T_period, __T_period_offset, __T_purged_size, __T_adjuster, __T_previous, __T_expand_train, __T_reduce_test} <: WalkForwardEstimator

Implements date-based walk-forward cross-validation for time series, supporting flexible windowing, purging, and custom date adjustment.

Fields

train_size: Training window size.
test_size: Test window size.
period: Time period for date-based walk-forward cross-validation.
period_offset: Offset applied to the walk-forward period.
purged_size: Number of observations to purge between train and test sets.
adjuster: Function for adjusting walk-forward dates.
previous: Whether to include the previous period in the training window.
expand_train: Whether to expand the training window over time.
reduce_test: Whether to allow the last test window to be smaller.

Constructors

julia

DateWalkForward(
    train_size::IntPeriodDateRange,
    test_size::Integer;
    period::DatesUnionPeriod = Dates.Day(1),
    period_offset::Option{<:DatesUnionPeriod} = nothing,
    purged_size::Integer = 0,
    adjuster::DateAdjType = identity,
    previous::Bool = false,
    expand_train::Bool = false,
    reduce_test::Bool = false,
) -> DateWalkForward

Positional and keyword arguments correspond to the struct's fields.

Validation

test_size and purged_size must be non-empty, non-negative, and finite.
If train_size is an integer, it must be non-empty, non-negative, and finite.

Examples

julia

julia> DateWalkForward(252, 21; period = Dates.Day(1), purged_size = 5, expand_train = true)
DateWalkForward
     train_size ┼ Int64: 252
      test_size ┼ Int64: 21
         period ┼ Dates.Day: Dates.Day(1)
  period_offset ┼ nothing
    purged_size ┼ Int64: 5
       adjuster ┼ typeof(identity): identity
       previous ┼ Bool: false
   expand_train ┼ Bool: true
    reduce_test ┴ Bool: false

Related

source

PortfolioOptimisers.special_div Method

julia

special_div(a::Integer, b::Integer)

Perform integer division with a special case for zero divisor.

Returns zero if b is zero, otherwise computes div(a, b). Used internally in walk-forward date calculations to avoid division by zero.

Arguments

a: Dividend.
b: Divisor.

Returns

div(a, b) or zero if b == 0.

Related

DateWalkForward

source

Base.split Method

julia

Base.split(dwf::DateWalkForward{<:Integer}, rd::ReturnsResult) -> WalkForwardResult

Split the returns data rd into sequential walk-forward folds using date-aligned indices, where train_size is specified as an integer number of date-range steps.

The timestamp vector rd.ts must not be nothing. Training and test windows are aligned to the period date range and advanced by test_size steps at a time.

Arguments

dwf::DateWalkForward{<:Integer}: Date-based walk-forward estimator with an integer train_size.
rd::ReturnsResult: Returns data containing the matrix X and timestamp vector ts.

Returns

WalkForwardResult: Result containing train and test index ranges for each fold.

Related

source

Base.split Method

julia

Base.split(dwf::DateWalkForward{<:Any}, rd::ReturnsResult) -> WalkForwardResult

Split the returns data rd into sequential walk-forward folds using date-aligned indices, where train_size is specified as a date Period (e.g., Dates.Month(6)).

The timestamp vector rd.ts must not be nothing. Training windows are defined by subtracting train_size from the split date, allowing calendar-based window lengths.

Arguments

dwf::DateWalkForward{<:Any}: Date-based walk-forward estimator with a Period train_size.
rd::ReturnsResult: Returns data containing the matrix X and timestamp vector ts.

Returns

WalkForwardResult: Result containing train and test index ranges for each fold.

Related

source

PortfolioOptimisers.n_splits Function

julia

n_splits(cv, rd::ReturnsResult)
n_splits(cv)

Return the number of cross-validation splits (folds) that would be produced by cv for the given returns data rd.

Arguments

cv: A cross-validation estimator or result (e.g. KFold, IndexWalkForward, DateWalkForward, CombinatorialCrossValidation, MultipleRandomised, or their corresponding result types).
rd::ReturnsResult: Returns data used to determine the number of splits.

Returns

Integer: The number of folds.

Related

source

julia

n_splits(dwf::DateWalkForward{<:Integer}, rd::ReturnsResult) -> Integer

Return the number of walk-forward folds that would be produced by dwf for the given returns data rd when the training window size is specified as an integer number of date-range steps.

Arguments

dwf::DateWalkForward{<:Integer}: Date-based walk-forward estimator with an integer train_size.
rd::ReturnsResult: Returns data containing a timestamp vector ts.

Returns

Integer: The number of folds.

Related

source

julia

n_splits(dwf::DateWalkForward{<:Any}, rd::ReturnsResult) -> Integer

Return the number of walk-forward folds that would be produced by dwf for the given returns data rd when the training window size is specified as a date Period.

Arguments

dwf::DateWalkForward{<:Any}: Date-based walk-forward estimator with a Period train_size.
rd::ReturnsResult: Returns data containing a timestamp vector ts.

Returns

Integer: The number of folds.

Related

source

PortfolioOptimisers.n_splits Method

julia

n_splits(dwf::DateWalkForward{<:Integer}, rd::ReturnsResult) -> Integer

Return the number of walk-forward folds that would be produced by dwf for the given returns data rd when the training window size is specified as an integer number of date-range steps.

Arguments

dwf::DateWalkForward{<:Integer}: Date-based walk-forward estimator with an integer train_size.
rd::ReturnsResult: Returns data containing a timestamp vector ts.

Returns

Integer: The number of folds.

Related

source

PortfolioOptimisers.n_splits Method

julia

n_splits(dwf::DateWalkForward{<:Any}, rd::ReturnsResult) -> Integer

Return the number of walk-forward folds that would be produced by dwf for the given returns data rd when the training window size is specified as a date Period.

Arguments

dwf::DateWalkForward{<:Any}: Date-based walk-forward estimator with a Period train_size.
rd::ReturnsResult: Returns data containing a timestamp vector ts.

Returns

Integer: The number of folds.

Related

source

PortfolioOptimisers.IntPeriodDateRange Type

julia

const IntPeriodDateRange = Union{<:Integer, <:DatesUnionPeriod}

Alias for an integer or date period used to specify window sizes.

Matches either a plain integer (number of observations) or a date period (DatesUnionPeriod) for walk-forward cross-validation splits.

Related

source

WalkForward ​

WalkForward