Wanis Abro is a quantitative researcher with eight years' experience applying machine learning to trading and market microstructure, currently based in Dubai and working at Qube Research & Technologies. Trained at ENSTA Paris and Sorbonne (Master in Quantitative Finance), he has a track record in mid-term frequency strategies, inventory and portfolio optimization, and event-driven index trading from roles at Qube and SGCIB. His background spans both research and front-office execution—building repo pricing and hedging models, validating market-risk parameters, and tuning multi-asset hedges for large portfolios. Comfortable with statistical methods and Bayesian experimental design, he blends academic rigor with production-focused implementation in Python/R. Notably, he has repeatedly solved practical trading frictions such as order smoothing during index reweighting, reflecting an eye for subtle market-structure opportunities.
7 years of coding experience
3 years of employment as a software developer
2-year intensive program preparing for the national competitive exam for entry to engineering school, 2-year intensive program preparing for the national competitive exam for entry to engineering school at Lycée Saint Louis
Engineering School / Quantitative Finance, Engineering School / Quantitative Finance at ENSTA
Master 2 MMMEF (Modélisation et Méthodes Mathématiques en Economie et Finance) Quantitative Finance major, Master 2 MMMEF (Modélisation et Méthodes Mathématiques en Economie et Finance) Quantitative Finance major at University of Paris I: Panthéon-Sorbonne
english / fluent, french / mother tongue, spanish / moderate, italian / moderate
MlFinlab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
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