highfrequency

Background

The highfrequency package is the go-to package for the analysis of intraday price data. The package was created as a merger of the RTAQ and realized packages in 2012. The package is in need of a thorough update.

Related work

What other R packages with similar functionality already exist? Why aren't they good enough?

Details of your coding project

• Update examples and functions where needed to be compatible with the millisecond data from TAQ.
• Improve documentation throughout the package, possibly by using the R-package 'roxygen2'
• Set up a test environment using the R-package 'testthat'
• Include a spotdrift estimation function, already in contact with the author of DriftBurstHypothesis
• Clean-up different notations, e.g. camelCase vs. underscore vs. dot
• Support for data.table
• Support for tibbles, which allows purrr-like rolling-window forecasting
• Allow external regressors, e.g. the VIX, in models that are implemented in the package, e.g. harModel
• Support for other high-frequency providers than TAQ

Expected impact

Mentors, please explain how this project will produce a useful package for the R community.

Mentors

Students, please contact mentors below after completing at least one of the tests below.

MENTORS: fill in this part. each project needs 2 mentors. One should be an expert R programmer with previous package development experience, and the other can be a domain expert in some other field or application area (optimization, bioinformatics, machine learning, data viz, etc). Ideally one of the two mentors should have previous experience with GSOC (either as a student or mentor). Please provide contact info for each mentor, along with qualifications. Example:

• Toby Hocking toby.hocking@r-project.org is the author of R packages X and Y.
• Other Dev other.dev@gmail.com is an expert in machine learning, and has previous GSOC experience with NAME_OF_OPEN_SOURCE_ORGANIZATION in 2015-2016.

Tests

Students, please do one or more of the following tests before contacting the mentors above.

MENTORS: write several tests that potential students can do to demonstrate their capabilities for this particular project. Ask some hard questions that will give you insight about how the students write code to solve problems. You'll see that the harder the questions that you ask, the easier it will be for you to choose between the students that apply for your project! Please modify the suggestions below to make them specific for your project.

• Easy: something that any useR should be able to do, e.g. download some existing package listed in the Related Work, and run it on some example data.
• Medium: something a bit more complicated. You can encourage students to write a script or some functions that show their R coding abilities.
• Hard: Can the student write a package with Rd files, tests, and vigettes? If your package interfaces with non-R code, can the student write in that other language?

Solutions of tests

Students, please post a link to your test results here.