I’ve been meaning to write about Freerisk.org for a while now, but only got reminded yesterday as I read the Wired article about Toby Segaran’s (and Jesper Anderson’s) new project.

Freerisk.org sucks in financial data from the SEC using the XBRL format, allows the community to add additional annotations, and then makes that data available to standard risk analysis algorithms and, this is the best part, available for others to apply their own algorithms. My first reaction was, this is what we want to be able to do in bioinformatics. Keep the data available, add annotations, and have this sandbox in which algorithms can be applied and developed.

The finance geek part of it is interesting enough, but I got interested in Freerisk for the general idea, especially coming from a field where there is a lot of data publicly available but not necessary sandoxes/platforms for analysis and testing out new algorithms, although there is a lot of intent. From the about page of Freerisk.org

Freerisk is a project with the goal of making freely available the data, algorithms and tools necessary to perform risk modeling. We believe that risk management is too important to society to be an arcane subject or competitive advantage.

You could easily replace “risk management” with biology or genomics, or something similar.

The pieces that Freerisk contains are even more interesting

• An open repository of financial data, including financial statements for public companies
• A standards-based API for querying financial data
• A distributed method for designing and running risk models
• Open-source tools for parsing and handling financial data
• Educational materials on risk-management

This is a hackers playground. We need something like this in the informatics community, especially as our data volumes grow. It’s just an ethos that we seem to lack in general, and part is due to the fact that we need to publish our data, but there is a broader community of analysts and developers this could appeal too. Resources like these are needed, not just for finance, but in many other areas. The key is to find enough interested people to contribute. We have some aspects in the bioinformatics space, but it’s somewhat fragmented and the analytics part is the weakness at this point.