Alex Kwon
Quantitative Researcher
Miami, Florida, United States
Summary
🤩
Rockstar🎓
Top SchoolAlex Kwon is a quantitative researcher with six years of experience applying data-driven models to high-frequency markets, currently based in Miami and working at Citadel Securities. His career progressed from quantitative trading in New York to research roles spanning Hong Kong and Miami, reflecting a strong track record across global markets and time zones. He blends a Berkeley Computer Science education with hands-on trading experience and earlier leadership as an ROK Army Special Forces squad leader, a background that contributes discipline and operational rigor to research workflows. Alex has interned at top firms including Marshall Wace and Fidelity, and has taught as a lecturer at UC Berkeley, signaling both practical expertise and an ability to communicate complex concepts. Comfortable at the intersection of engineering and quantitative finance, he focuses on translating statistical insight into deployable trading strategies.
6 years of coding experience
2 years of employment as a software developer
Bachelor of Arts - BA Computer Science, Bachelor of Arts - BA Computer Science at University of California, BerkeleySFQC BAC AIT RASP, SFQC BAC AIT RASP at ROK Special Warfare SchoolPhillips Academy
Chinese, Korean, English
Github Skills (18)
prediction-model10
python10
trading10
finance10
machine-learning10
quantitative-finance10
algorithmic-trading10
statistical-learning9
stochastic-processes8
financial8
reveal5
slide5
data-science5
data-driven3
ml2
Programming languages (3)
Github contributions (5)
santiment/mlfinlab
Apr 2020 - Jun 2020
Package based on the work of Dr Marcos Lopez de Prado regarding his research with respect to Advances in Financial Machine Learning
Contributions:331 commits in 2 months
financialdata-sciencepackage-basedmlmachine-learning
brucewong0516/mlfinlab
Jun 2020 - Jun 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:77 commits in 15 days
pythonmanagersmachine-learningtradinglearning-by
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