The 1.0 version of the OpenGamma Platform is finally out.
It’s been in the making for longer than anticipated, but we believe it was essential to wait until we have all the main components ready to ship.
In this blog post, I’ll briefly go over the most significant changes. If you can’t wait to see it for yourself, head over to our developers’ site to download the Platform.
After Bloomberg’s recent announcement to open source its API, we’ve been able to include the Bloomberg module in our 1.0 release. This will allow you to load automatic reference data for exchange-traded securities, as well as load and update time series. Our integration module also now Open Sources our OpenGamma Live Data Adapter for pulling real-time streaming data into your OpenGamma environment from your Terminal, SAPI, or Managed B-Pipe instance. You can now download the Platform, hook it up to your existing Bloomberg infrastructure, and be doing live trading and risk analytics with real world data in a matter of minutes!
This means that when evaluating, you have two basic options. You can choose the classic “Examples” package, which works with entirely mock data, just as in 0.9. However, if you have access to a Bloomberg Terminal, SAPI, or Managed B-PIPE instance, you can use the “BloombergExamples” package to work with real data and get your own portfolios up and running in the system in a matter of minutes.
New HTML5 Web GUI
Our HTML5 Web GUI continues to improve. While we’re busy rewriting our Web Analytics Viewer from scratch (to make it easier to include in your custom portals and applications), there have been a number of improvements to the main Open Source Platform GUI:
- The Web Analytics Viewer now supports Market Data Snapshots as well as different Live Data configurations as data sources and fallback to Historical Time Series for market data;
- It also supports dynamic re-aggregation of your portfolio on the fly - without reconfiguring your View Definition;
- Many of our Data Master viewers (like Portfolios, Positions, and Securities) are far better integrated, and can pull in key historical time series directly into the viewer for Securities to make it quicker to do common tasks.
Finally, our whole GUI has been retrofitted with our new “Push REST” capabilities to notify all clients of data changes whenever they happen anywhere in your environment: reloading to get changes other people on a desk have made is a thing of the past.
We continue to improve our Database Masters. Aside from numerous performance improvements throughout (particularly our Historical Time Series database schema and Master code), we’ve completely rewritten our Batch Risk database and masters to allow far more types of data to be stored and queried with the most complex of View Definitions.
Our data masters have also been enhanced to allow runtime tagging of Securities, Trades, Positions, and Portfolios. This allows you to put in any custom attributes required on any of these data types, and incorporate that with other new elements like our expression language for dynamic portfolio filtering and aggregation exactly as the “native” data fields.
Asset Classes and Analytics
The OpenGamma Platform continues to extend its support for new asset classes and analytical methods.
Perhaps the most important change since 0.9.0 has been the amount of time that our Quantitative Development team has spent on making sure that we have a single source of all major market conventions for the G8 (and nearly the whole of the G20). Not only have we incorporated this into the Platform so that we have accurate cashflow determinations for assets in these currencies, but we have a booklet (forthcoming) that has all this information in one place as a guide you can use for your own development.
Notable new assets include:
- Caps/Floors (including CMS)
- Inflation products
- Additional types of IR Swaps
- Equity Variance Swaps
- FX Futures
- Digital FX Options
We’ve also extended the types of analytical methods available, in particular enhancing the number of different analytical methods available for IR Swaptions and applying our Local Volatility and SVI models to multiple asset classes.
But no matter how good our analytics library or data management may be, there will always be times when you won’t want to use it, but have a system that can generate sensitivities you want to aggregate with the rest of your portfolio (common examples are credit derivatives, ABS, and RMBS). To handle that, we now have full support for External Sensitivities.
You can now create an External Sensitivities Security, assign the appropriate risk factors to it, and use the following features:
- Yield curve sensitivity mapping
- Separate yield curve/credit/all sensitivities buckets
- DV01, CS01, Historical VaR
R Integration Module
We’ve found a large number of quants and risk managers using the R statistical programming environment to drive deep and custom statistical analysis of their portfolios. In keeping with our dedication to supporting the tools that end-users actually want to use, we have taken our industry-leading Excel Integration Module, stripped away the parts that aren’t Excel specific, created the OpenGamma Language Integration package, and used that to provide the same level of extremely deep, useful integration with the R statistical programming environment.
Everything that you would expect by now from a tight OpenGamma integration is there: you can pull in all types of data available in your OpenGamma Platform instance, and they all appear as native R objects; you can drive shocks and stress tests and historical simulations, including perturbing market data at either the individual ticker or tensor level. You can even create custom trades and portfolios from R to do what-if scenarios.
We think this is extremely powerful, and we’re thrilled that this is in the Open Source OpenGamma Platform! (also, we’re a sponsor of the R/Finance Conference in Chicago in May; if you’re in the area come on by and say hi!).
04/04/2012 Update: The R Installer is now available on the downloads page.
Perhaps the most immediate change developers will notice is the improved build system – it’s actually now a single-step process. We’re confident that you’ll find deployment a whole lot easier. We’ve also included better ant target names.
For those of you evaluating the platform independently, we’ve included sample data for new asset classes, and improved data import/export: there is a standard import format for security/portfolio data, as well as a framework for custom importers.
Finally, we’ve completely overhauled the configuration system. You’ll find that deployment and maintenance have been made significantly user-friendlier, using the distributed component management system.
As ever, we welcome your feedback; please add your comment below. For any technical questions, we recommend contacting our technical team through our forums. A special thank you to those of you already evaluating the Platform who have posted questions, comments and suggestions on the forums over the past months.