R

This post is a slightly revised (and “blogified”) version of the message Brian Peterson has sent to various R mailing lists.

Once again, R has been accepted as a mentoring organization for the Google Summer of Code (2012).  We invite students interested in this program to learn more about it.  A good starting point is the R GSoC wiki.

Students participating in the program receive US$5,000 for successful completion of a GSoC project, a great resume item, and an opportunity to work with R package authors.

You can find more information on the R/Finance conference website.  Hope to see you in Chicago in May!

The registration for R/Finance 2012 – which will take place May 11 and 12 in Chicago – is NOW OPEN!

Building on the success of the three previous conferences in 2009, 2010, and 2011, we expect more than 250 attendees from around the world.  R users from industry, academia, and government will join 40+ presenters covering all areas of finance with R.

Running DEoptim in parallel has been on the development team’s wishlist for awhile.  It had not been a priority though, because none of us have personally needed it.  An opportunity arose when Kris Boudt approached me about collaborating to add this functionality as part of a consultancy project for a financial services firm.

We were able to add and test the functionality within a week.  The latest revision of DEoptim on R-Forge has the capability to evaluate the objective function on multiple cores using foreach.  Very CPU-intensive problems will see speed increases in approximately linear time (less communication overhead).

I’m excited to share the call for papers for the upcoming R/Finance conference.  Even if you don’t submit a presentation, I hope to see you there!

Call for Papers:

R/Finance 2012: Applied Finance with R
May 11 and 12, 2012
University of Illinois, Chicago, IL, USA

The fourth annual R/Finance conference for applied finance using R will be held on May 11 and 12, 2012 in Chicago, IL, USA on the campus of the University of Illinois at Chicago. The two-day conference will cover topics including portfolio management, time series analysis, advanced risk tools, high-performance computing, market microstructure, and econometrics. All will be discussed within the context of using R as a primary tool for financial risk management, portfolio construction, and trading.

This is a guest post by Ilya Kipnis. He blogs at QuantStrat TradeR.

When trading stocks in a single currency, instrument metadata can be safely ignored because the multiplier is 1 and the currencies are all the same.  When doing analysis on fixed income products, options, futures, or other complex derivative instruments, the data defining the properties of these instruments becomes critical to tasks like accounting for value of trades, or comparing notional value between more than one instrument. The FinancialInstrument package provides a construct for storing metadata for tradeable contracts (referred to as instruments, e.g. stocks, futures, options, etc.) and their root representations.  It can be used to create any asset class and complex derivatives, across multiple currencies. 

The most recent issue of The R Journal was recently published.  If you’re not a regular reader, you should at least check out the following three contributed articles (listed in order of appearance).

• Rmetrics - timeDate Package
• Differential Evolution with DEoptim
• Analyzing an Electronic Limit Order Book

For those of you who don’t subscribe to the R-SIG-Finance mailing list:

1. You really should subscribe ;-)
2. Dirk Eddelbuettel announced the R/Finance 2011 presentations are now available.

I’ve included the entire announcement (with some hyperlinks) below.

The organizing committee for the R/Finance 2011 conference is pleased to announce the availability of presentation slides from the 3rd annual R/Finance conference.  This year’s two-day conference once again attracted over 200 participants from across the globe. Academics, students and industry professionals enjoyed almost 30 talks covering trading, optimization, risk management and more — all using R!

Dirk Eddelbuettel has recently released RQuantLib-0.3.7, which contains the necessary QuantLib builds to allow the CRAN servers to build the Windows binary.

This (thankfully) makes my post on how to build RQuantLib on 32-bit Windows unnecessary for casual users, but may be useful for those who want to develop RQuantLib on Windows.

This is the third post in the Backtesting in Excel and R series and it will show how to backtest a simple strategy in R.  It will follow the 4 steps Damian outlined in his post on how to backtest a simple strategy in Excel.

Step 1: Get the data
The getSymbols function in quantmod makes this step easy if you can use daily data from Yahoo Finance.  There are also “methods” (not in the strict sense) to pull data from other sources (FRED, Google, Oanda, R save files, databases, etc.).  You could also use them as a template to write a custom function for a particular vendor you use.

(This is a guest post by Damian from Skill Analytics and ETF Prophet)

Let me start by saying that I’m not an expert in backtesting in Excel – there are a load of very smart bloggers out there that have, as I would say, “mad skillz” at working with Excel including (but not limited to) Michael Stokes over at marketsci.com, Jeff Pietch over at etfprophet.com and the folks (David and Corey) over at cssanalytics.wordpress.com. All of these guys have been gracious enough, over the years, to share with me how to do backtests – so I am indebted to them. And I want to thank Josh here at FOSS Trading as well – because he’s been kind enough to help me in learning how to use R for testing.