Experimental computational simulation environments are increasingly being developed by major financial institutions to model their analytic algorithms; this includes evaluation of algorithm stability, estimation of its optimal parameters, and the expected risk and performance profiles. These environments will have a profound impact on the way research is conducted in social sciences. Consequently, for the past few years, UCL has been working on various simulation environments for computational finance, specifically financial economics and algorithmic trading. Michal Galas introduces the key concepts underlying these environments, which rely on big data analytics to enable large-scale testing, optimization, and monitoring of algorithms running in the virtual or real mode.
Michal Galas is a chief programmer at the UK PhD Centre in Financial Computing and Analytics and a research associate in financial computing in the Computer Science Department at UCL. Michal is an experienced IT technical leader, enterprise-size systems architect, and research-groups manager with 10 years of experience in financial computing and algorithmic trading. Michal specializes in R&D of large-scale systems, including service-oriented financial platforms, event-driven analytical engines, and HPC- based simulation environments. Michal holds a BSc in computer science with artificial intelligence and an MSc in artificial intelligence from City University London, as well as a PhD in financial computing from UCL.
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