It’s been more than a month since R/Finance 2014, and my job has finally slowed down enough to allow me to write down my thoughts (though I’m writing this over two days during my train to and from Chicago).
The comments below are based on my personal experience. If I don’t comment on a seminar or presentation, it doesn’t mean I didn’t like it or it wasn’t good; it may have been over my head or I may have been distracted with my duties as a committee member.
This is a guest post by Ross Bennett. Ross is currently enrolled in the University of Washington Master of Science in Computational Finance & Risk Management program with an expected graduation date of December 2014. He worked on the PortfolioAnalytics package as part of the Google Summer of Code 2013 project and continues to work on the package as a Research Assistant at the University of Washington.
His work on the package focused on implementing a portfolio specification to separate and modularize assets, constraints, and objectives.
As announced on the R-SIG-Finance mailing list, registration for R/Finance 2014 is now open! The conference will take place May 17 and 18 in Chicago.
Building on the success of the previous conferences in 2009-2013, we expect more than 250 attendees from around the world. R users from industry, academia, and government will joining 30+ presenters covering all areas of finance with R.
We are very excited about the four keynote presentations given by Bob McDonald, Bill Cleveland, Alexios Ghalanos, and Luke Tierney.
The complaint I hear most frequently about quantstrat is that it’s slow, especially for large data. Some of this slow performance is due to quantstrat treating all strategies as path-dependent by default. Path dependence requires rules to be re-evaluated for each timestamp with a signal. More signals equates to longer run-times.
If your strategy is not path-dependent, you can get a fairly substantial performance improvement by turning path-dependence off. If your strategy truly is path-dependent, keep reading…
We’re getting ready for this year’s R/Finance conference. Here’s the call for papers. I hope to see you there!
R/Finance 2014: Applied Finance with R
May 16 and 17, 2014
University of Illinois at Chicago
The sixth annual R/Finance conference for applied finance using R will be held on May 16 and 17, 2014 in Chicago, IL, USA at the University of Illinois at Chicago. The conference will cover topics including portfolio management, time series analysis, advanced risk tools, high-performance computing, market microstructure, and econometrics.
It’s been one week since the 5th Annual R/Finance conference, and I finally feel sufficiently recovered enough to share my thoughts. The conference is a two-day whirlwind of applied quantitative finance, fantastic networking, and general geekery.
The comments below are based on my personal experience. If I don’t comment on a seminar or presentation, it doesn’t mean I didn’t like it or it wasn’t good; it may have been over my head or I may have been distracted with my duties as a committee member.
The registration for R/Finance 2013 – which will take place May 17 and 18 in Chicago – is NOW OPEN!
Building on the success of the previous conferences in 2009, 2010, 2011 and 2012, we expect more than 250 attendees from around the world. R users from industry, academia, and government will joining 30+ presenters covering all areas of finance with R.
We are very excited about the four keynotes by Sanjiv Das, Attilio Meucci, Ryan Sheftel, and Ruey Tsay.
If you haven’t signed up for the Introduction to Computational Finance and Financial Econometrics course taught by Eric Zivot on Coursera, it’s not too late. The second week just started and the first assignments aren’t due until September 18th.
Join me in getting a good refresher on basic statistics, simulation and bootstrapping, linear algebra, and learning more about portfolio optimization, efficient portfolios, and risk budgeting.
The Google Summer of Code (2012) project to extend xts has produced a very promising new plot.xts function. Michael Weylandt, the project’s student, wrote R-SIG-Finance to request impressions, feedback, and bug reports. The function is housed in the xtsExtra package of the xts project on R-Forge.
Please try xtsExtra::plot.xts and let us know what you think. A sample of the eye-candy produced by the code in Michael’s email is below. Granted, this isn’t a one-liner, but it’s certainly impressive!
You have a problem: R is single-threaded, but your code would be faster if it could simultaneously run on more than one core. You have access to a cluster and/or your computer has multiple cores. Parallel R, by Q. Ethan McCallum and Stephen Weston, can help you put this extra computing power to use.
The book describes 6 approaches to distributed computing. Thoughts on each approach follow:
snow The chapter starts by showing you how to create a socket cluster on a single machine (later sections discuss MPI clusters, and socket clusters of several machines).