For Quants, by Quants
Inside all our software products you will find our proprietary language Qlang. Qlang is a computer language of our own invention, with unique features for financial engineering. It’s a typed, object-oriented language, with vector and matrix expansion, which compiles at runtime to optimized machine code.
We strongly believe in creating software that not only solves one task really well, but also can be used as a tool to create new solutions for a whole domain of tasks. Qlang is an important part of this strategy, together with our strong suite of API:s for C++, C# and COM.
Qlang aims to strike a balance between time spent developing and debugging, and run-time performance. Using Qlang, a quantitative developer can develop and deploy a new solution, complete with an easy-to-use user interface to traders and sales people in hours instead of weeks.
Realtime feeds are integrated into the solution and Qlang code executes in response to incoming ticks with or without throttling. The user interface has smart handling of massive bursts of financial data so that the visual threads can continue to serve the user without interruption. Quantlab users can communicate financial data and messages using the internal realtime feed IQC. Contributing data to external feeds is as easy as listening.
Quantitative developers can use Qlang to access our extensive financial and mathematical libraries or code your own functions in Qlang, which is compiled at run-time to efficient machine code.
Quantlab also includes extensive bi-directional API-support for calling or being called from C++, C# and COM+ libraries.
Power of Quantlab on the server
When running Quantlab Servers for large scale calculation problems, such as enterprise wide risk management, the Qlang code takes advantage of built-in multi thread and multi processor support. Our simulation based calculations are further optimized using vector math instructions and highly optimized memory handling.
Quantlab supports all major databases, and many of our clients use standard relational database queries to interact with meta- and result data. For best performance, database calls are kept at a minimum and only used for loading external data, handling of persistence and longtime storage.
TAKE QUANTLAB FOR A SPIN
Want to try for yourself?
Contact our sales department to get a test license of the Quantlab developer and runtime environment today! Also ask about our seminars on how to effectively solve financial engineering problems in Qlang.