3 November 2011 By Andrew Griffin
We've been overwhelmed since we went public with the OpenGamma Platform with the number of developers, analysts, and risk managers who want to combine our data management and calculation capabilities with the statistical power of the R environment.
Much like we decided to actively embrace Excel as a front-end for driving an OpenGamma Platform installation, we thought that if people wanted to use R, it was up to us to make sure that we had the best support for it possible.
As the first public sample of just what we're getting at, consider the ChartSeries3D component. The example they provide is plotting a year's worth of yield curves, a pretty ideal way to showcase that rather than pulling this data from a file, you can pull it directly from the Historical Time Series service in an OpenGamma Platform installation.
I wanted to start with perhaps the simplest thing possible: plot the market data points that form your yield curve definition over the course of a year.
All that from this relatively small sample of R code:
In the next installment, I'll show you how we've modified this example to not only plot the raw market data, but to actually fit the yield curve using the OpenGamma Analytics library from data loaded into the R environment, and then bring back the fully fitted yield curves for plotting.
And that's before we show you the real power of what we're doing: driving full shocks, stresses, and historical regressions of whole portfolios from R and using R to analyze the results. We think R users in finance are going to find this exciting.