TimeSeries.jl

Installation

julia> Pkg.add("TimeSeries")

Additionally, the MarketData package includes some const objects that include TimeArray objects. These objects are historical price time series and can be used for testing, benchmarking or simply taking TimeSeries functionality through some paces.

julia> Pkg.add("MarketData")

Alternately, you can create some dummy data with this code block.

d = [Date(1980,1,1):Date(2015,1,1)];
t = TimeArray(d,rand(length(d)),["test"])

Package objectives

TimeSeries aims to provide a lightweight framework for working with time series data in Julia. There are less than 500 total lines of code in the src/ directory.

Quick tour of current API

julia> using TimeSeries, MarketData

julia> ohlc
500x4 TimeArray{Float64,2} 2000-01-03 to 2001-12-31

             Open    High    Low     Close
2000-01-03 | 104.88  112.5   101.69  111.94
2000-01-04 | 108.25  110.62  101.19  102.5
2000-01-05 | 103.75  110.56  103.0   104.0
2000-01-06 | 106.12  107.0   95.0    95.0
⋮
2001-12-26 | 21.35   22.3    21.14   21.49
2001-12-27 | 21.58   22.25   21.58   22.07
2001-12-28 | 21.97   23.0    21.96   22.43
2001-12-31 | 22.51   22.66   21.83   21.9

Split

julia> ohlc[1]
1x4 TimeArray{Float64,2} 2000-01-03 to 2000-01-03

             Open    High    Low     Close
2000-01-03 | 104.88  112.5   101.69  111.94

julia> ohlc[1:2]
2x4 TimeArray{Float64,2} 2000-01-03 to 2000-01-04

             Open    High    Low     Close
2000-01-03 | 104.88  112.5   101.69  111.94
2000-01-04 | 108.25  110.62  101.19  102.5

julia> ohlc[[Date(2000,1,3), Date(2000,1,14)]]
2x4 TimeArray{Float64,2} 2000-01-03 to 2000-01-14

             Open    High    Low     Close
2000-01-03 | 104.88  112.5   101.69  111.94
2000-01-14 | 100.0   102.25  99.38   100.44

julia> ohlc["Low"][1:2]
2x1 TimeArray{Float64,1} 2000-01-03 to 2000-01-04

             Low
2000-01-03 | 101.69
2000-01-04 | 101.19

julia> ohlc["Open", "Close"][1:2]
2x2 TimeArray{Float64,2} 2000-01-03 to 2000-01-04

             Open    Close
2000-01-03 | 104.88  111.94
2000-01-04 | 108.25  102.5

Apply

julia> op[1:3] .- cl[2:4]
2x1 TimeArray{Float64,1} 2000-01-04 to 2000-01-05

             Op.-Cl
2000-01-04 | 5.75
2000-01-05 | -0.25

julia> 2.*cl
500x1 TimeArray{Float64,1} 2000-01-03 to 2001-12-31

             Close
2000-01-03 | 223.88
2000-01-04 | 205.0
2000-01-05 | 208.0
2000-01-06 | 190.0
⋮
2001-12-26 | 42.98
2001-12-27 | 44.14
2001-12-28 | 44.86
2001-12-31 | 43.8

julia> percentchange(cl, method="log")
499x1 TimeArray{Float64,1} 2000-01-04 to 2001-12-31

             Close
2000-01-04 | -0.09
2000-01-05 | 0.01
2000-01-06 | -0.09
2000-01-07 | 0.05
⋮
2001-12-26 | 0.01
2001-12-27 | 0.03
2001-12-28 | 0.02
2001-12-31 | -0.02

julia> basecall(cl, cumsum)
500x1 TimeArray{Float64,1} 2000-01-03 to 2001-12-31

             Close
2000-01-03 | 111.94
2000-01-04 | 214.44
2000-01-05 | 318.44
2000-01-06 | 413.44
⋮
2001-12-26 | 23028.84
2001-12-27 | 23050.91
2001-12-28 | 23073.34
2001-12-31 | 23095.24i

Combine

julia> merge(op,cl)
500x2 TimeArray{Float64,2} 2000-01-03 to 2001-12-31

             Open    Close
2000-01-03 | 104.88  111.94
2000-01-04 | 108.25  102.5
2000-01-05 | 103.75  104.0
2000-01-06 | 106.12  95.0
⋮
2001-12-26 | 21.35   21.49
2001-12-27 | 21.58   22.07
2001-12-28 | 21.97   22.43
2001-12-31 | 22.51   21.9

julia> collapse(cl, last)
105x1 TimeArray{Float64,1} 2000-01-07 to 2001-12-31

             Close
2000-01-07 | 99.5
2000-01-14 | 100.44
2000-01-21 | 111.31
2000-01-28 | 101.62
⋮
2001-12-14 | 20.39
2001-12-21 | 21.0
2001-12-28 | 22.43
2001-12-31 | 21.9