Google Summer of Code will be open for students on Monday, April 22.  The R Project has once again been selected as a mentoring organization , and a variety of mentors have proposed a number of projects for students to work on during this summer.  Here’s a bit about the program, and more on the R-related projects that are lining up for students this summer.

About Google Summer of Code

The concept is relatively simple – Google brings together students with mentors to work on open-source projects of their choosing.  Mentors get code written for their project, but no money; students get paid $5,000, equivalent to a nice summer internship.

If you’re a student and you’re interested on something R-related, pick something you’re interested in working on (whether a mentor has submitted an interesting idea you want to pursue, or if you have an idea and want a mentor).  With an idea in hand, submit a project application directly to Google.   Google will award a certain number of student slots to the R project, and projects will be ranked and slots allocated by the GSOC-R administrators and mentors.

Finance-related Projects

Like last year, there are a few proposed R projects that are finance-related.  I’ve already introduced a project earlier, but I’ll touch on it again, briefly.  In finance, returns are not necessarily independent and identically distributed (IID) but standard tools for analyzing performance are based on this assumption.  Practitioners may apply those techniques with little understanding about how sensitive those measures are to violations of IID normality.  This project will develop some different approaches for addressing autocorrelation observed in financial data that have recently been discussed in the literature. The student will develop related functions that will be ultimately be included in PerformanceAnalytics.  Take a look at the references and the proposal for more details.

A second project is focused on extending the functionality of the Meucci package created in last year’s GSoC.  This will add more research by Attilio Meucci, who is a thought leader in risk and portfolio management.  This year we will focus on his paper, ‘Fully Integrated Liquidity and Market Risk Model‘  and several other papers that lead up to it.  The student will also spend some time on general improvement of the Meucci package, and will work on functionalizing some of the capabilities for inclusion in other, related packages.  David Ardia and Brian Peterson have volunteered to co-mentor this project.

Another project will improve the usability of objectives and constraints in PortfolioAnalytics.  Again, this is work that was conceived after a GSOC project last summer and will include normalizing the constraint interfaces to all supported types and allow for support of complex constraints even in optimization engines that do not support them directly.  This should extend PortfolioAnalytics to make it more general, easier to use, and more consistent across the many solvers available. This work will fall into three broad areas: constraints, utility functions, and ‘example’ functionality.  Doug Martin and Guy Yollin from the University of Washington have volunteered to mentor the project this year.

Josh Ulrich and Michael Waylandt have proposed another xts-related project this year as well.  This will focus on improvements to data construction, subsetting, and manipulation for xts time series objects.  The general goal here would be for a data.frame-like ability for different column classes, while retaining the power and speed of the xts time series capabilities and its methods for rbind and cbind.  This will be particularly helpful for use cases such as in order management, where order types or other character-based metadata may be carried along with numeric data.

And Eric Zivot has submitted a proposal for extending FactorAnalytics, a package in development for use in estimation, risk analysis and performance analysis of linear factor models for asset returns and portfolios.

There are several other very interesting projects proposed for the R Project organization as well.  Take a look – these are in various states of needing students or mentors.

Students, start your proposal…

Students should also take a look at the R Project’s proposal template as a starting point.  Proposals are expected to be more detailed than they were last year, and may run to ten or more pages.  In short, this is a competitive process and you will need to put your best foot forward.  I should also note that the process is very iterative – you’ll get feedback as time goes on and will be expected to be responsive to the questions people ask.  Project mentors usually also propose a test – some task that they think is representative of the summer’s work that will help demonstrate your skills and fitness for the project.

Or, consider bringing something new to the table.  This is an active, dynamic group of people who have a broad set of interests, and the process can accommodate well-proposed ideas that garner support.

Good luck, and I hope to hear from you soon.