Announcing the F# R Type Provider

August 1, 2012 at 2:52 pm | Posted in opensource | 24 Comments
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Here at BlueMountain we like to perform statistical analysis of data.  The stats package R is great for doing that.  We also like to use the data retrieval and processing capabilities of F#. F#’s interactive environment lends itself pretty well to data exploration, and we can also easily access our existing .NET-based libraries.  Once we are done, we can build and release production-supportable applications.

Nothing on the .NET platform competes with R for statistical functionality, so we set about bridging the gap between F# and R.  F# 3.0 provides a nice innovative mechanism for doing this, through Type Providers.

We have released an Open Source RProvider on github.  Here’s an example of how to use it:

// Pull in stock prices for some tickers then compute returns
let data = [
    for ticker in [ "MSFT"; "AAPL"; "VXX"; "SPX"; "GLD" ] ->
        ticker, getStockPrices ticker 255 |> R.log |> R.diff ]

// Construct an R data.frame then plot pairs of returns
let df = R.data_frame(namedParams data)

Any of the calls above that begin R. are actually evaluated inside the R engine.

This produces a lovely pair plot like this:

While we intend to continue to enhance the provider to meet our needs, we really hope others will do the same.  If you use F# and work in the statistical/econometrics space, please try it out.  If you use R and are looking for a robust environment in which to develop applications, also try it (and F#) out.  If you have ideas for improvements, please feel free to share them with us.  And if you develop enhancements/fixes, please submit a pull request!

The RProvider is built on the RDotNet project, which handles all the gnarly interop with unmanaged data structures used by R.DLL.  The Type Provider provides an easy-to-use layer on top of that to use R from F#.  Many thanks go to the RDotNet author, Kosei.

Mountain Lions

August 1, 2012 at 1:52 pm | Posted in Uncategorized | Leave a comment

We are very pleased to launch this blog, through which the members of the BlueMountain Quantitative Strategy team will share opinions, provide useful information about technology and announce open-source software.

You can read a bit about what we do by checking out this Waters Technology article:

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