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Rolling window

TimeArrays.ta_rolling — Function
ta_rolling(f::Function, t_array::TimeArray, n::Int; kw...) -> TimeArray

Applies function f in a sliding window with window size n to the elements of t_array. Function f must accept a vector with elements of type V as input and return a new value, which will be assigned to the corresponding timestamp of each window.

Keyword arguments

  • observations::Integer = n: Number of observations in the rolling window to calculate the value (otherwise NaN).

Function signature:

f(x::AbstractVector{V})::Any = ...

Examples

julia> using Dates

julia> t_array = TimeArray([
           TimeTick(DateTime("2024-01-01"), 1.0),
           TimeTick(DateTime("2024-01-03"), 2.0),
           TimeTick(DateTime("2024-01-04"), 3.0),
           TimeTick(DateTime("2024-01-07"), 4.0),
           TimeTick(DateTime("2024-01-09"), 5.0),
       ]);

julia> ta_rolling(sum, t_array, 3)
5-element TimeArray{DateTime, Float64}:
 TimeTick(2024-01-01T00:00:00, NaN)
 TimeTick(2024-01-03T00:00:00, NaN)
 TimeTick(2024-01-04T00:00:00, 6.0)
 TimeTick(2024-01-07T00:00:00, 9.0)
 TimeTick(2024-01-09T00:00:00, 12.0)

julia> ta_rolling(sum, t_array, 3; observations = 1)
5-element TimeArray{DateTime, Float64}:
 TimeTick(2024-01-01T00:00:00, 1.0)
 TimeTick(2024-01-03T00:00:00, 3.0)
 TimeTick(2024-01-04T00:00:00, 6.0)
 TimeTick(2024-01-07T00:00:00, 9.0)
 TimeTick(2024-01-09T00:00:00, 12.0)
source
ta_rolling(f::Function, t_array::TimeArray, p::Period; kw...) -> TimeArray

Applies function f in a sliding window with window period p to the elements of t_array. Function f must accept a vector with elements of type V as input and return a new value, which will be assigned to the corresponding timestamp of each window.

Function signature:

f(x::AbstractVector{V})::Any = ...

Keyword arguments

  • observations::Integer = 1: Number of observations in the rolling window to calculate the value (otherwise NaN).
Note

The first elements of the t_array during period p will always be replaced by NaN values (not enough elements in the window).

Examples

julia> using Dates

julia> t_array = TimeArray([
           TimeTick(DateTime("2024-01-01"), 1.0),
           TimeTick(DateTime("2024-01-03"), 2.0),
           TimeTick(DateTime("2024-01-04"), 3.0),
           TimeTick(DateTime("2024-01-07"), 4.0),
           TimeTick(DateTime("2024-01-09"), 5.0),
       ]);

julia> ta_rolling(sum, t_array, Day(3))
5-element TimeArray{DateTime, Float64}:
 TimeTick(2024-01-01T00:00:00, NaN)
 TimeTick(2024-01-03T00:00:00, 3.0)
 TimeTick(2024-01-04T00:00:00, 5.0)
 TimeTick(2024-01-07T00:00:00, 4.0)
 TimeTick(2024-01-09T00:00:00, 9.0)

julia> ta_rolling(sum, t_array, Day(3); observations = 2)
5-element TimeArray{DateTime, Float64}:
 TimeTick(2024-01-01T00:00:00, NaN)
 TimeTick(2024-01-03T00:00:00, 3.0)
 TimeTick(2024-01-04T00:00:00, 5.0)
 TimeTick(2024-01-07T00:00:00, NaN)
 TimeTick(2024-01-09T00:00:00, 9.0)
source
TimeArrays.ta_sma — Function
ta_sma(t_array::TimeArray, n::Integer; kw...) -> TimeArray
ta_sma(t_array::TimeArray, p::Period; kw...) -> TimeArray

Applies Simple Moving Average algorithm with window size n or period p to the elements of t_array.

Keyword arguments

Examples

julia> using Dates

julia> t_array = TimeArray([
           TimeTick(DateTime("2024-01-02"), 1.0),
           TimeTick(DateTime("2024-01-03"), 2.0),
           TimeTick(DateTime("2024-01-05"), 3.0),
           TimeTick(DateTime("2024-01-06"), 4.0),
           TimeTick(DateTime("2024-01-09"), 5.0),
       ]);

julia> ta_sma(t_array, 2)
5-element TimeArray{DateTime, Float64}:
 TimeTick(2024-01-02T00:00:00, NaN)
 TimeTick(2024-01-03T00:00:00, 1.5)
 TimeTick(2024-01-05T00:00:00, 2.5)
 TimeTick(2024-01-06T00:00:00, 3.5)
 TimeTick(2024-01-09T00:00:00, 4.5)

julia> t_array = TimeArray([
           TimeTick(DateTime("2024-01-02"), 1.0),
           TimeTick(DateTime("2024-01-03"), 2.0),
           TimeTick(DateTime("2024-01-05"), 3.0),
           TimeTick(DateTime("2024-01-06"), 4.0),
           TimeTick(DateTime("2024-01-09"), 5.0),
       ]);

julia> ta_sma(t_array, Day(3))
5-element TimeArray{DateTime, Float64}:
 TimeTick(2024-01-02T00:00:00, NaN)
 TimeTick(2024-01-03T00:00:00, 1.5)
 TimeTick(2024-01-05T00:00:00, 2.5)
 TimeTick(2024-01-06T00:00:00, 3.5)
 TimeTick(2024-01-09T00:00:00, 5.0)
source
TimeArrays.ta_wma — Function
ta_wma(t_array::TimeArray, n::Integer; kw...) -> TimeArray
ta_wma(t_array::TimeArray, p::Period; kw...) -> TimeArray

Applies Weighted Moving Average algorithm with window size n or period p to the elements of t_array.

Keyword arguments

Examples

julia> using Dates

julia> t_array = TimeArray([
           TimeTick(DateTime("2024-01-02"), 1.0),
           TimeTick(DateTime("2024-01-03"), 2.0),
           TimeTick(DateTime("2024-01-05"), 3.0),
           TimeTick(DateTime("2024-01-06"), 4.0),
           TimeTick(DateTime("2024-01-09"), 5.0),
       ]);

julia> ta_wma(t_array, 3)
5-element TimeArray{DateTime, Float64}:
 TimeTick(2024-01-02T00:00:00, NaN)
 TimeTick(2024-01-03T00:00:00, NaN)
 TimeTick(2024-01-05T00:00:00, 2.333333333333333)
 TimeTick(2024-01-06T00:00:00, 3.333333333333333)
 TimeTick(2024-01-09T00:00:00, 4.333333333333333)

julia> ta_wma(t_array, Day(3); observations = 2)
5-element TimeArray{DateTime, Float64}:
 TimeTick(2024-01-02T00:00:00, NaN)
 TimeTick(2024-01-03T00:00:00, 1.6666666666666665)
 TimeTick(2024-01-05T00:00:00, 2.6666666666666665)
 TimeTick(2024-01-06T00:00:00, 3.6666666666666665)
 TimeTick(2024-01-09T00:00:00, NaN)
source
TimeArrays.ta_ema — Function
ta_ema(t_array::TimeArray, window::Integer) -> TimeArray

Applies Exponential Moving Average algorithm with window size n to the elements of t_array.

Examples

julia> using Dates

julia> t_array = TimeArray([
           TimeTick(DateTime("2024-01-02"), 1.0),
           TimeTick(DateTime("2024-01-03"), 2.0),
           TimeTick(DateTime("2024-01-05"), 3.0),
           TimeTick(DateTime("2024-01-06"), 4.0),
           TimeTick(DateTime("2024-01-09"), 5.0),
       ]);

julia> ta_ema(t_array, 3)
5-element TimeArray{DateTime, Float64}:
 TimeTick(2024-01-02T00:00:00, 1.0)
 TimeTick(2024-01-03T00:00:00, 1.5)
 TimeTick(2024-01-05T00:00:00, 2.25)
 TimeTick(2024-01-06T00:00:00, 3.125)
 TimeTick(2024-01-09T00:00:00, 4.0625)
source
TimeArrays.ta_lag — Function
ta_lag(t_array::TimeArray, n::Integer) -> TimeArray

Shifts the values of t_array elements by n positions forward. Displaced elements that remain without a value are set to NaN.

Examples

julia> using Dates

julia> t_array = TimeArray([
           TimeTick(DateTime("2024-03-01"), 1.0),
           TimeTick(DateTime("2024-03-02"), 2.0),
           TimeTick(DateTime("2024-03-03"), 3.0),
           TimeTick(DateTime("2024-03-04"), 4.0),
       ]);

julia> ta_lag(t_array, 2)
4-element TimeArray{DateTime, Float64}:
 TimeTick(2024-03-01T00:00:00, NaN)
 TimeTick(2024-03-02T00:00:00, NaN)
 TimeTick(2024-03-03T00:00:00, 1.0)
 TimeTick(2024-03-04T00:00:00, 2.0)
source
TimeArrays.ta_lead — Function
ta_lead(t_array::TimeArray{T,V}, n::Integer) -> TimeArray

Shifts the values of t_array elements by n positions backward. Displaced elements that remain without a value are set to NaN.

Examples

julia> using Dates

julia> t_array = TimeArray([
           TimeTick(DateTime("2024-03-01"), 1.0),
           TimeTick(DateTime("2024-03-02"), 2.0),
           TimeTick(DateTime("2024-03-03"), 3.0),
           TimeTick(DateTime("2024-03-04"), 4.0),
       ]);

julia> ta_lead(t_array, 2)
4-element TimeArray{DateTime, Float64}:
 TimeTick(2024-03-01T00:00:00, 3.0)
 TimeTick(2024-03-02T00:00:00, 4.0)
 TimeTick(2024-03-03T00:00:00, NaN)
 TimeTick(2024-03-04T00:00:00, NaN)
source