https://github.com/jtamos/temporal.jl

Time series implementation for the Julia language focused on efficiency and flexibility
https://github.com/jtamos/temporal.jl

data-analysis data-structures data-visualization econometrics economics finance io julia julia-language quantitative-finance quantitative-trading scientific-computing time-series time-series-analysis timeseries

Last synced: about 15 hours ago
JSON representation

Time series implementation for the Julia language focused on efficiency and flexibility

• Host: GitHub
• URL: https://github.com/jtamos/temporal.jl
• Owner: JTAmos
• License: other
• Created: 2016-02-25T06:48:49.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2023-03-03T01:11:31.000Z (over 3 years ago)
• Last Synced: 2026-03-31T17:10:39.619Z (4 months ago)
• Topics: data-analysis, data-structures, data-visualization, econometrics, economics, finance, io, julia, julia-language, quantitative-finance, quantitative-trading, scientific-computing, time-series, time-series-analysis, timeseries
• Language: Julia
• Homepage:
• Size: 3.08 MB
• Stars: 101
• Watchers: 9
• Forks: 31
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

README

          
[![Build Status](https://travis-ci.org/dysonance/Temporal.jl.svg?branch=master)](https://travis-ci.org/dysonance/Temporal.jl)

[![Coverage Status](https://coveralls.io/repos/github/dysonance/Temporal.jl/badge.svg?branch=master)](https://coveralls.io/github/dysonance/Temporal.jl?branch=master)

[![codecov.io](http://codecov.io/github/dysonance/Temporal.jl/coverage.svg?branch=master)](http://codecov.io/github/dysonance/Temporal.jl?branch=master)

[![](https://img.shields.io/badge/docs-latest-blue.svg)](https://dysonance.github.io/Temporal.jl/latest)

# Temporal

This package provides a flexible & efficient time series class, `TS`, for the [Julia](http://julialang.org/) programming language. While still early in development, the overarching goal is for the class to be able to slice & dice data with the rapid prototyping speed of [R](https://www.r-project.org/)'s [`xts`](https://github.com/joshuaulrich/xts) and [Python](https://www.python.org/)'s [`pandas`](http://pandas.pydata.org/) packages, while retaining the performance one expects from Julia.

[See the documentation](http://dysonance.github.io/Temporal.jl/latest/) for a more in-depth look at the package and some of the pain points it may solve when doing technical research with time series data.

Below is a brief teaser with a minimal use case illustrating a small subset of features. This example also makes use of the [Indicators.jl](https://github.com/dysonance/Indicators.jl) package, which provides a series of financial market technical analysis indicators with wrappers for the `Temporal.TS` type. Visualization is offered through the [Plots.jl](http://docs.juliaplots.org/latest/) package, which Temporal leverages through [RecipesBase.jl](https://github.com/JuliaPlots/RecipesBase.jl).

```julia

using Temporal, Plots, Indicators

crude = quandl("CHRIS/CME_CL1")

gasoline = quandl("CHRIS/CME_RB1")

prices = [crude["2012/2019", :Settle] gasoline["2012/2019", :Settle]]

prices.fields = [:Crude, :Gasoline]

prices = dropnan(prices)

daily_returns = diff(log(prices))

cumulative_returns = cumprod(1 + daily_returns)

spread = cumulative_returns[:,1] - cumulative_returns[:,2]

spread = [spread sma(spread, n=200)]

spread.fields = Symbol.(["Spread", "SMA (200)"])

gr()

ℓ = @layout[ a{0.7h}; b{0.3h} ]

plot(cumulative_returns, c=[:black :purple], layout=ℓ, subplot=1)

plot!(spread, c=[:blue :orange], layout=ℓ, subplot=2)

```

![alt text](https://raw.githubusercontent.com/dysonance/Temporal.jl/master/examples/crack_spread.png)