R / Finance 2014: Packaged Takeaways
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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 takeaways 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 wellestablished and others are workinprogress sitting out on RForge or GitHub, providing opportunities for the interested to get involved.
R Finance 2014 Talk 
Package 
Description 
Introduction to data.table 
Extension of the data frame 

An ExampleDriven Handson introduction to Rcpp 
Functions to facilitate integrating R with C++ 

Portfolio Optimization: Utility, Computation, Equities Applications 
Environment for reaching Financial Engineering and Computational Finance 

ReEvaluation of the Low Risk Anomaly via Matching 
Implementation of the Coarsened Exact matching Algorithm 

BCP Stability Analytics: New Directions in Tactical Asset Management 
Bayesian Analysis of Change Point Problems 

On the Persistence of Cointegration in Pairs Trading 
EngleGranger Cointegration Models 

Tests for Robust Versus Least Squares Factor Model Fits 
robust methods 

The R Package cccp: Solving Cone Constrained Convex Programs 
Solver for convex problems for cone constraints 

Twinkle, twinkle little STAR: Smooth Transition AR Models in R 
Modeling smooth transition models 

Asset Allocaton with Higher Order Moments and Factor Models 
Global optimization by differential evolution / Numerical methods for portfolio optimization 

Event Studies in R 
Event study and extreme event analysis 

An R package on Credit Default Swaps 
Provides tools for pricing credit default swaps 

New Ideas for Large Network Analysis, Implemented in R 
Implicitly restarted Lanczos methods for R 

Package “Intermediate and Long Memory Time Series 

Simulate & Detect Intermediate and Long Memory Processes / in development 
Stochvol: Dealing with Stochastic Volatility in Time Series 
Efficient Bayesian Inference for Stochastic Volatility (SV) Models 

Divide and Recombine for the Analysis of Large Complex Data with R 
Package for using R with Hadoop 

gpusvcalibration: Fast Stochastic Volatility Model Calibration using GPUs 
Fast calibration of stochastic volatility models for option pricing models 

The FlexBayes Package 
Provides an MCMC engine for the class of hierarchical feneralized linear models and connections to WinBUGS and OpenBUGS 

Building Simple Redis Data Caches 
Rcpp bindings for Redis that connects R to the Redis key/value store 

Package pbo: Probability of Backtest Overfitting 
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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