Revolutions

R/Finance 2010 presentations

The presentations from April's successful R/Finance 2010 conference in Chicago are now available online. (Revolution Analytics is a proud sponsor of the conference.) There's some amazing content here for anyone looking for the cutting-edge of financial engineering, with presentations from practitioners and researchers at institutions like Invesco Asset Management, Black Mesa Capital, and some of the leading academic institutions from around the world. Here's just a selection of titles available for download:

• Trading with R: Idea to Execution in 50 Minutes with IBrokers and R 
• Risk Modeling with R
• Testing, Monitoring and Dating Structural Change in FX Regimes
• Portfolio Optimization with Conditional Value-at-Risk Budgets
• Simulation-based Estimation of Continuous Time Models
• Follow the link below for the complete list and to download the presentations.

R/Finance 2010: Agenda

New blog from Rmetrics Foundation

The Rmetrics Foundation (the sharp minds behind the Rmetrics suite of packages for financial analysis in R) have just launched a new blog where you can keep up with the latest Rmetrics news. Amongst the recent news: a ne eBook about data management of Indian financial market data, and a new interface between Rmetrics and AMPL. You can also follow Rmetrics on Twitter for updates.

Rmetrics Foundattion: Blog

R/Finance 2010 ... and unicorns

At the Information Management blogs, Steve Miller has posted a great roundup of last weekend's R/Finance 2010 conference in Chicago. Here's Steve's overall take:

This year's conference was even better than the 2009 inaugural, the in-excess-of-200 participants consumed by more than 20 consecutive high-powered presentations over the fast-paced day and a half. And while I'm a quantitative finance welterweight at best, there was plenty to pique my interest, including the latest developments to scale R for size and performance.

Check of the rest of Steve's post for a great review of the other talks at the conference.

As Steve mentions, analysis of large data sets was a big focus of the conference with at least six presentations on the topic, including my own. I talked about a research project we've been working on at REvolution for a while, to make data processing and statistical analysis techniques for huge data sets available in REvolution R, breaking the bottlenecks of single-CPU processing, slow disk I/O processing, and being limited to RAM on just one machine. I deviated from the pre-advertised title, and the title in the slides, "A Herd of Unicorns" (download as PDF), may require a little explanation out of context. The "unicorn" here is something powerful and (at least today) mythical: the combination of analytic algorithms for really large data sets, and a flexible programming environment that enables modern statistical analysis: exploration, data manipulation, visualization, model evaluation. In other words, the R environment. And if you had the freedom to do large-scale data analysis in R, while making the use of the power of multiple machine in a cluster or in the cloud then that would be, well, a herd of unicorns. We're working hard to make that fantasy a reality, soon. 

Information Management: R/Finance 2010: Applied Finance with R

Speeding up simulations with Amazon EC2

Over at Cerebral Mastication, JD Long tells a characteristically entertaining and informative story about how he uses R to run stochastic simulations of insurance portfolios and reinsurance treaties. A typical job involves 10,000 simulations, and when each estimate takes over 20 seconds you're talking some serious time to get the job done. 

Fortunately, this is the kind of problem that lends itself to parallelization: with a multiprocessor machine running R, you can run as many simulations as you have processors at the same time, and cut the total computation time by the same factor. JD describes how he boots up an 8-core instance running R on Amazon EC2, and then uses the multicore package on Linux to reduce the computation time by a factor of 8.

Sadly for Windows users, multicore doesn't run on Windows. But as Boris Shor points out in a comment to JD's post, REvolution R Enterprise allows you to do parallel programming like this (using the foreach operator) on a multiprocessor Windows machine (or even a cluster of many machines).

Cerebral Mastication: Using the R multicore package in Linux with wild an passionate abandon

Registration open for R/Finance 2010

Registrations are now open for the R/Finance 2010 conference, to be help April 16-17 in Chicago. Last year's meeting was a great success, and this year's looks to be just as good, with some great keynotes lined up:

• Analysis of Integrated and Co-integrated Time Series with R (Bernhard Pfaff)
• Leverage Space Portfolio Model (Ralph Vince)
• Signal Extraction (Marc Wildi0
• Applied Econometrics with R (Achim Zeileis)

There's also a great series of pre-conference tutorials lined up:

• Rcpp / RInside: Extending and Embedding R with C++ for Fun and Profit. (Dirk Eddelbuettel)
• Trading with R: Idea to Execution in 50 Minutes with IBrokers and R (Jeff Ryan) 
• Complex Portfolio Optimization with Generalized Business Objectives: Advanced Portfolio Optimization Techniques (Peter Carl & Brian Peterson)
• GPU Programming with R: An Introduction To GPU Programming with R (Josh Buckner & Mark Seligman) 

The tutorials are a steal at $50 each and sell out fast. Register for the conference and tutorials now at the link below.

(REvolution Computing is a proud sponsor of R/Finance 2010.)

R/Finance 2010: Conference and Tutorial Registration

R/Finance 2010, April 16-17 in Chicago

Today is the last day to submit abstracts for the R/Finance 2010 conference to be held in Chicago on April 16-17. If you're not planning on speaking, but are interested in applications of R in Finance, be sure to add this to your calendar -- last year's conference was an outstanding event. Here's some more information about the conference from the website:

The two-day conference will cover topics including portfolio management, time series analysis, advanced risk tools, high-performance computing, and econometrics. All will be discussed within the context of using R as a primary tool for financial risk management and trading. We strongly encourage R users, as well as package authors to submit an abstract for presentation at this years conference.

The 2010 conference will build upon the success of last year's event. Including traditional keynotes from leading names in the R and finance community, presentation of contributed papers, short "lightning-style" presentations as well as the chance to meet and discuss colloboratively the future of the R in Finance community.

R/Finance 2010: Applied Finance with R

Singapore, February 19-20: Computational Topics in Finance

With all of the winter snows in the US this weekend, a trip to equatorial climes sounds pretty good right about now. That makes this email announcement from Rmetrics leader Diethelm Wuertz all the more tempting:

Conference on 'Computational Topics in Finance'
February 19/20, 2010, National University of Singapore

Dear R/Rmetrics Community,

We would like to announce the first 'Computational Topics in Finance' conference, taking place on February 19/20, 2010, at the National University of Singapore.

The conference will bring together developers, practitioners, and users from academia, finance and insurance, providing a platform for common discussions and exchange of ideas. The topics will include using R/Rmetrics in finance, but the conference is by no means confined to R.

Preceding the conference, February 19/20, 2010, we will be giving a two-day 'Basic R for Finance' course.

You can find out more about both events on our website, http://www.rmetrics.org/.

We would like to invite you to take part in the conference, and we are now accepting submissions; please send your one-page abstracts to submissions@rmetrics.org. The submission deadline is February 10, 2010.

We look forward to seeing you in Singapore.

Wishing you merry Christmas and a happy new year,

Diethelm Wuertz
Juri Hinz
Mahendra Mehta
David Scott

There's also a poster you can download and print with more information.

Rmetrics.org: Singapore Conference 2010 - Computational Topics in Finance

Chicago R Users Fall Meetup, Thu Oct 29

All R users in the Chicagoland area are welcome to join the R User's Fall Meetup, taking place this Thursday evening at 5:30PM at Jak's Tap. As well as being a place to meet and mingle with local R users, there will also be a series of lightning talks. Refreshments will be provided by the fine folks at International Center for Futures and Derivatives at the University of Illinois at Chicago and the organizers of the R/Finance conferences. Full details from the R-sig-finance mailing list at the link below.

R-sig-finance mailing list: R CMD --meetup=Chicago --when=Oct 29 --where=Jak'sTap

Google uses R to predict economic activity

Predicting economic activity is an important business. For example, Wall Street considers Government reports of retail activity and property sales as leading indicators of the economy as a whole, and the Dow Jones Index sometimes rises (or falls!) markedly based on the reported numbers.

One problem: those government reports are released weeks after the fact (the August housing report was released only last Thursday), and even then only with preliminary data to be revised upwards or downwards later. Is there a way to get a jump on these reports and get a measure of economic activity in real time, predicting the outcomes of the official reports?

Google's Chief Economist studied just this question with a research paper (PDF) announced in a Google Research blog post back in April. The key insight is that the volume of Google searches for particular keywords is correlated with related economic indicators. For example, here's a comparison of searches for the keyword "Ford" with actual monthly sales of Ford vehicles:

Varian asks the question: can we use Google Insights data like this to predict the economic variables, even before they are reported?

Seems like you can. Varian used R to fit seasonal autoregressive models to retail sales, automotive sales, home sales, and passenger arrival data, and in each case made better predictions by including Google Trends data as a predictor than without. You can try this yourself by downloading R and using Google Insights for Search to download the trend data -- Varian has helpfully provided the R code for the Ford model in the paper. (Just be sure to log into Google Insights with your Google Account to enable downloading of trends data as a CSV file.)

Now, Google has taken this one step further by introducing Google Domestic Trends, where you can track the ups of downs of searches in 23 categories including air travel, real estate, and retail trade. Will Wall Street use these indices of search activity (and R!) to predict the economic reports before they come out? We'll know it's working when the Housing Report no longer sends the Dow into a tailspin.

Google Research Blog: Predicting the Present with Google Trends (with thanks to Roger Koenker for pointing out the existence of this report)

Interactive stock visualizations with R

Jeroen Ooms, who recently completed his Masters in Statistics at Utrech University, has created an outstanding web-based drag-and-drop application for visualizing financial data. With his "StockPlot" t application, you can select any stock from a number of world exchanges (including NASDAQ, DAX, FTSE), and drag it to a worksheet to see a time-series of the stock price. You can arrange up to four charts on the same worksheet for comparison purposes, and control the timeframe and appearance of each chart. This video makes the process clearer, or you can just try it out.

The graphs themselves are created on demand in R using the ggplot2 package, with the webserver calling R via RApache. The interactive front-end was created by Jeroen using the ExtJS framework. According to Jeroen, the entire application took around a month to create. Jeroen has used similar techniques to build several interactive applications around R, which you can find at his website. You can find more details about the implementation of these applications in his presentation at this year's UseR conference.

It's nice to see more and more interactive applications based on R appearing. I agree with Jeroen, who says: "I think the connection between R and the web is very powerful and believe that in five years from now, companies and universities will replace their expensive inefficient (commercial) statistical software packages by server based web applications." Keep up the great Jeroen, and thanks for encouraging others to do the same.

jeroenooms.com: StockPlot web application