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R / Finance 2014: Packaged Takeaways

by Joseph Rickert I was very happy to have been able to attend R / Finance 2014 which wrapped up a couple of weeks ago. In general, the talks were at a very high level of play, some dealing with brand new ideas and many presented at a significant level of technical or mathematical sophistication. Fortunately, most of the slides from the presentations are quite detailed and available at the conference site. Collectively, these presentations provide a view of the boundaries of the conceptual space imagined by the leaders in quantitative finance. Some of this space covers infrastructure issues involving ideas for pushing the limits of R (Some Performance Improvements for the R Engine) or building a new infrasturcture (New Ideas for Large Network Analysis) or (Building Simple Data Caches) for example. Others are involved with new computational tools (Solving Cone Constrained Convex Programs) or attempt to push the limits on getting some actionable insight from the mathematical abstrations: (Portfolio Inference withthei One Wierd Trick) or (Twinkle twinkle litle STAR: Smooth Transition AR Models in R) for example. But while the talks may be illuminating, the real take-aways from the conference are the R packages. These tools embody the work of the thought leaders in the field of computational finance and are the means for anyone sufficiently motivated to understand this cutting edge work.  By my count, 20 of the 44 tutorials and talks given at the conference were based on a particular R package. Some of the packages listed in the following table are well-established and others are work-in-progress sitting out on R-Forge or GitHub, providing opportunities for the interested to get involved. R Finance 2014 Talk Package Description Introduction to data.table   data.table Extension of the data frame An Example-Driven Hands-on introduction to Rcpp   Rcpp Functions to facilitate integrating R with C++ Portfolio Optimization: Utility, Computation, Equities Applications   fPortfolio Environment for reaching Financial Engineering and Computational Finance Re-Evaluation of the Low Risk Anomaly via Matching   cem Implementation of the Coarsened Exact matching Algorithm BCP Stability Analytics: New Directions in Tactical Asset Management   bcp Bayesian Analysis of Change Point Problems On the Persistence of Cointegration in Pairs Trading   egcm Engle-Granger Cointegration Models Tests for Robust Versus Least Squares Factor Model Fits   robust robust methods The R Package cccp: Solving Cone Constrained Convex Programs   cccp Solver for convex problems for cone constraints Twinkle, twinkle little STAR: Smooth Transition AR Models in R   Twinkle Modeling smooth transition models Asset Allocaton with Higher Order Moments and Factor Models   DEoptim /   PortfolioAnalytics Global optimization by differential evolution / Numerical methods for portfolio optimization Event Studies in R   eventstudies Event study and extreme event analysis An R package on Credit Default Swaps   CDS Provides tools for pricing credit default swaps New Ideas for Large Network Analysis, Implemented in R   IRL Implicitly restarted Lanczos methods for R Package “Intermediate and Long Memory Time Series   ILMTS   Simulate & Detect Intermediate and Long Memory Processes / in development Stochvol: Dealing with Stochastic Volatility in Time Series   stochvol Efficient Bayesian Inference for Stochastic Volatility (SV) Models Divide and Recombine for the Analysis of Large Complex Data with R   RHIPE Package for using R with Hadoop gpusvcalibration: Fast Stochastic Volatility Model Calibration using GPUs   gpusvcalibration Fast calibration of stochastic volatility models for option pricing models The FlexBayes Package   FlexBayes Provides an MCMC engine for the class of hierarchical feneralized linear models and connections to WinBUGS and OpenBUGS Building Simple Redis Data Caches   RcppReddis Rcpp bindings for Redis that connects R to the Redis key/value store Package pbo: Probability of Backtest Overfitting   pbo Uses Combinatorial Symmetric Cross Validation to implement performance tests.   Many of these packages / projects also have supplementary material that is worth chasing down. Be sure to take a look at Alexios Ghalanos recent post that provides an accessible introduction to his stellar keynote address. Many thanks to the organizers of the conference who, once again, did a superb job, and to the many professionals attending who graciously attempted to explain their ideas to a dilletante. My impression was that most of the attendies thoroughly enjoyed themselves and that the general sentiment was expressed by the last slide of Stephen Rush's presentation:    

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David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid