Wanis Abro
Quantitative Researcher
Dubai, Dubai, United Arab Emirates
Summary
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Rockstar🎓
Top SchoolWanis 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.
8 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 LouisEngineering School / Quantitative Finance, Engineering School / Quantitative Finance at ENSTAMaster 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
Github Skills (6)
finance10
trading10
algorithmic-trading9
quantitative-finance9
python9
machine-learning9
Programming languages (2)
Github contributions (5)
rk-anantharaju/mlfinlab
May 2020 - May 2020
MlFinlab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
Contributions:10 commits in 19 days
machine-learning
sulta22/hello-world
Dec 2018 - Dec 2018
Contributions:7 pushes, 1 branch in 1 day
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