REvolution R Enterprise - The R Productivity Environment Getting Started Guide

This guide is intended as your introduction to REvolution R Enterprise and its R Productivity Environment. For new users, this interface includes a number of aids to usability including IntelliSense for automatic word completion, code snippets to simplify programming, and an Object Browser with editing and plotting capabilities. For experienced R programmers, this interface includes a full-featured integrated development environment with a built-in visual debugger. This Getting Started Guide walks you through the most useful features of the new interface.

Tags: REvolution R Enterprise for Windows, debugger, script editing, loading packages, R console

REvolution Computing, February 2010

High-Performance Risk Analysis with REvolution R

Illustrated are the substantial performance gains possible with REvolution R Enterprise and the Intel® Xeon® processor 5500 series when computing risk metrics with the CreditMetrics[4] algorithm. REvolution R Enterprise can substantially reduce computation time for the benchmark CreditMetrics analysis on multi-core workstations. The optimized numeric routines available in REvolution R Enterprise can transparently speed up a large number of compute-intensive tasks in R. High-performance numerics, commercial support, training and developer tools for R from REvolution Computing position the R language for rapid prototyping and production environments in quantitative finance.

CreditMetrics Benchmark Code

Patch to CreditMetrics 0.0.1: CreditMetrics.R.diff

Tags: REvolution R Enterprise, CreditMetrics, value at risk, portfolio performance benchmarks

REvolution Computing and Intel, 2009

Financial Applications with ParallelR

The use of statistical packages in finance has two functions. One, econometric analysis of large volumes of data, and two, programming financial models. A popular package for these purposes is R. In this article we will examine two canonical applications of parallel programming for option pricing.We use the ParallelR package developed by REvolution Computing.We price options using trees andMonte Carlo simulation. Both these approaches are commonly used for option pricing and are amenable to parallelization and grid computing. In this paper we demonstrate the application using the widely used mathematical/statistical R package.

Tags: Parallel Monte Carlo, option pricing on trees

Sanjiv R. Das and Brian Granger, Journal of Investment Management, 2009

Using the foreach Package

Much of parallel computing comes to doing three things: splitting the problem into pieces, executing the pieces in parallel, and combining the results back together. Using the foreach package, the iterators help you to split the problem into pieces, the %dopar% function executes the pieces in parallel, and the specified .combine function puts the results back together. This whitepaper demonstrates how simple things can be done in parallel quite easily using the foreach package, and given some ideas about how more complex problems can be solved.

Tags: parallel computing, ParallelR, .combine function, iterators package, parallel random forest, parallel apply, list comprehensions,

REvolution Computing, October 2009

Parallelized Backtesting with foreach

ParallelR and the foreach function provide a simple mechanism to speed up "embarrassingly parallel" problems, even on modest hardware like a dual-core laptop. In many cases, with just a simple conversion from the for syntax to the foreach syntax you can get significant speedups without having to worry about many of the housekeeping details of setting up worker R sessions. And for the really big problems, you just need to change one line of code to move your job onto a distributed cluster or grid.

Tags: automated trading rule, MACD oscillator, Sharpe Ratio

REvolution Computing, May 2009

A Benchmark Study of Large-Scale Chemical Classification using ParallelR

The world is awash with online digital data. Utilizing this data to yield knowledge is the big challenge. The raw data by itself is rather worthless. Modern data mining techniques have emerged as a potential solution, but they are sufficiently compute intensive for real world applications that conventional PCs and servers often cannot provide knowledge to us in a timely fashion. This is a major issue as CPU clock rates seem to have leveled off and data sets (and subsequent run times) are increasing exponentially.

In this paper, we will show that by utilizing an “off the shelf” parallel data mining R package, caretNWS, “knowledge workers” can use quad-core processor based systems to classify data with a minimum of effort and yet realize high performance. For the first time, knowledge workers can achieve scalable data mining without resorting to parallel programming.

Tags: High-performance computing, drug development, caretNWS package, datamining, random forest method

REvolution Computing, Pfizer Research Labs, AMD 2008

Using ParallelR for High Performance Monte Carlo Simulation on Multiprocessor Computers

Opportunities for utilizing high performance Financial Services systems are common throughout the financial world. Examples include: credit risk assessment, portfolio optimization, optimization of marketing strategies, and credit card fraud detection.

The specific example we consider is a simple, prototype portfolio optimization problem that is a model of the “efficient frontier” approach suggested by Markowitz. cf., [4]. Our intention here is to illustrate the general ideas behind the use of multiprocessors to accelerate general portfolio optimization rather than to present the design and analysis of a production portfolio optimizer applied to real market data. We present benchmark data showing how well our parallel portfolio optimizer scales as a function of the number of cores.

Tags: credit risk, portfolio optimization, Sleigh function, multi-core processor

REvolution Computing and HP, 2008