R and Finance

May 8, 2014

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

by Joseph Rickert

R/Finance 2014 is just about a week away. Over the past four or five years this has become my favorite conference. It is small (300 people this year), exceptionally well-run, and always offers an eclectic mix of theoretical mathematics, efficient, practical computing, industry best practices and trading “street smarts”. This clip of Blair Hull delivering a keynote speech at R/Finance 2012 is an example of the latter. It ought to resonate with anyone who has followed some of the hype surrounding Michael Lewis recent book Flash Boys.


In any event, I thought it would be a good time to look at the relationship between R and Finance and to highlight some resources that are available to students, quants and data scientists looking to do computational finance with R.

First off, consider what computational finance has done for R. From the point of view of the development and growth of the R language, I think it is pretty clear that computational finance has played the role of the ultimate “Killer App” for R. This high stakes, competitive environment where a theoretical edge or a marginal computational advantage can mean big rewards has led to R package development in several areas including time series, optimization, portfolio analysis, risk management, high performance computing and big data. Additionally, challenges and crisis in the financial markets have helped accelerate R’s growth into big data. In this podcast, Michael Kane talks about the analysis of the 2010 Flash Crash he did with Casey King  and Richard Holowczak and describes using R with large financial datasets.

Conversely, I think that it is also clear that R has done quite a bit to further computational finance. R’s ability to facilitate rapid data analysis and visualization, its great number of available functions and algorithms and the ease with which it can interface to new data sources and other computing environments has made it a flexible tool that evolves  and adapts at a pace that matches developments in the financial industry. The list of packages in the Finance Task View on CRAN indicates the symbiotic relationship between the development of R and the needs of those working in computational finance. On the one hand, there are over 70 packages under the headings Finance and Risk Management that were presumably developed to directly respond to a problem in computational finance. But, the task view also mentions that packages in the  Econometrics, Multivariate, Optimization, Robust, SocialSciences and TimeSeries task views may also be useful to anyone working in computational finance. (The High Performance Computing and Machine Learning task views should probably also be mentioned.) The point is that while a good bit of R is useful to problems in computational finance, R has greatly benefited from the contributions of the computational finance community.

If you are just getting started with R and computational finance have a look at John Nolan’s R as a Tool in Computational Finance. Other resources for R and computational finance that you may find helpful are::

Package Vignettes
Several of the Finance related packages have very informative vignettes or associated websites. For example have a look at those for the packages portfolio, rugarch, rquantlib (check out the cool rotating distributions), PerformanceAnalytics, and MarkowitzR.

Quandl has become a major source for financial data, which can be easily accessed from R.

Relevant websites include the RMetrics site, The R Trader,  Burns Statistics and Guy Yollin’s repository of presentations

Three videos that.I found to be particularly interesting are recordings of the presentations “Finance with R” by  Ronald Hochreiter,  “Using R in Academic Finance” by Sanjiv Das and Portfolio Construction in R by Elliot Norma.

Over the past couple of years, RBloggers has  posted quite a few finance related applications. Prominent among these is the series on Quantitative Finance Applications in R by Daniel Harrison on the Revolutions Blog.

Books on R and Finance include the excellent RMetrics series of ebooks, Statistics and Data Analysis for Financial Engineering by David Ruppert, Financial Risk Modeling and Portfolio Optimization with R by Bernard Pfaff, Introduction to R for Quantitative Finance by Daróczi et al. and a brand new title Computational Finance: An Introductory Course with R by Agrimiro Arratia.

This August, Eric Zivot will teach the course Introduction to Computational Finance and Financial Econometrics which will emphasize R.

The R Journal
The R Journal frequently publishes finance related papers. The present issue: Volume 5/2, December 2013 contains three relevant papers. Performance Attribution for Equity Portfolios by Yang Lu, David Kane, Temporal Disaggregation of Time Series by Christoph Sax, Peter Steiner, and betategarch: Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCH Models by Genaro Sucarrat.

in addition to R/Finance (Chicago) and useR!2014 (Los Angeles) look for R based, computational finance expertise at the 8th R/RMetrics Workshop (Paris).

R-Sig-Finance is one of R’s most active special interest groups.

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

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