Hanzhi Wang
Quantitative Researcher
Beijing, Singapore
Summary
👤
Senior🎓
Top SchoolHanzhi Wang is a quantitative researcher with a decade of experience blending rigorous mathematical foundations and practical trading research, currently pursuing an MS in Quantitative Finance at National University of Singapore. He has built and productionized portfolio optimizers and cost models—achieving multi-fold speedups with GPU-accelerated solvers and delivering 20–50% token-cost reduction rules for LLM usage—demonstrating a rare mix of algorithmic insight and systems pragmatism. His work spans industry and academic settings, from scenario-driven market forecasts for analog ICs to alpha strategy development that lifted model Sharpe by ~0.15 through alternative data. Hanzhi is fluent in Python, SQL and optimization tooling, and routinely translates domain heuristics into rule-based features and linear/integer programs. He combines hands-on backtesting and machine learning (LightGBM, clustering) with sensitivity analysis to craft robust, monitorable signals for production. Based in Beijing and Singapore, he brings both theoretical depth in probability and algebra and a track record of turning research into deployable, high-performance solutions.
10 years of coding experience
1 year of employment as a software developer
undergraduate visiting student Multi-/Interdisciplinary Studies General, undergraduate visiting student Multi-/Interdisciplinary Studies General at University of California, BerkeleyBachelor of Science - BS Applied Mathematics, Bachelor of Science - BS Applied Mathematics at Renmin University of ChinaMaster of Science - MS Quantitative Finance, Master of Science - MS Quantitative Finance at National University of SingaporeProgramming languages (2)
Github contributions (5)
wzskytop/PRSim-Code
May 2019 - Jul 2020
Contributions:16 commits, 16 pushes, 2 comments in 1 year 1 month
wanghzccls/wanghzccls.github.io
May 2021 - Mar 2025
Contributions:62 pushes, 1 branch in 3 years 10 months
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