Junyi Zhou is a quantitative trader with a decade of experience building and deploying data-driven trading strategies, currently trading at Abelian Technologies after prior quant trading roles at Citi. He combines advanced machine learning and numerical skills in Python and C++ with solid SQL and auditing foundations from an early-career stint at EY, enabling rigorous model validation and risk control. A Carnegie Mellon Computational Finance M.S. graduate with a dual B.S./BBA in Mathematics and Accountancy from Notre Dame, he bridges quantitative research and production trading execution. Based in Chaoyang District, Beijing, Junyi is pragmatic about iteration—his GitHub motto “Keep going” reflects a persistent, experiment-driven approach to refining strategies and systems.
11 years of coding experience
Master of Science (M.S.), Computational Finance, Master of Science (M.S.), Computational Finance at Carnegie Mellon University - Tepper School of Business
Bachelor of Science (BS), Bachelor of Business Administration (BBA), Mathematics, Accountancy, Bachelor of Science (BS), Bachelor of Business Administration (BBA), Mathematics, Accountancy at University of Notre Dame
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