Goals / Deliverables

• Create state of the art QF library with ML ready applications
  • Easy to use and, thus, leveraging your (daily) work/research
  • Flows smoothly with Tensorflow (TF), PyTorch and Python
  • Applied for practical purposes in financial institutions, teaching and research
  • Has common system interfaces and could re-use already libraries, eg. Prof. Fries’ FinMathLib
• Aiming to become a leading modern analytics, research and teaching brand
  • Teaching and research (Conferences, Papers, Books)
  • Cooperation (creating models for leading banks, asset managers, insurers; leading
  universities)
• Establish Python/ML based solutions as industry standard – Open Source RaaS
  • Financial institutions look for solutions based on “understandable”, easy to use open source software – especially for areas that do not generate revenue in the first place (risk control, middle-/ backoffice, …)
  • Trend to combine classic “quant shops” with data analytics/data science units
  • RaaS – Risk as a Service (Well documented, open source risk models – regulatory proof)

• The QuantLab - ML aims to provide cutting edge numerical techniques that fit into the new era of combining classical QF with ML and work with the current ML set-up.
• The results are applied in practice, education and research. We emphasize the work on industry best practices and industry relevance.