Ilya Kipnis is a volatility trader and quantitative researcher with 12 years of experience building data-driven trading systems, statistical analytics, and open-source R tools that have driven multi-million dollar outcomes in energy and trading desks. He designs and trades systematic volatility strategies—running both aggressive and conservative variants since 2017—that notably mitigated losses during choppy markets and captured upside in crises like March 2020. Comfortable across R, Python and SQL, he has a track record of speeding analytics pipelines from days to hours and scaling analyses to tens of millions of records for firms such as BP, Carlyle and Bank of America. An active open-source author, his long-running QuantStratTrader blog is widely read by practitioners and cited by Quantocracy, revealing reproducible code and practical research. With an MS in Statistics and a background in operations research and financial engineering, he blends academic rigor with production-first engineering to turn complex signals into deployable strategies.
12 years of coding experience
7 years of employment as a software developer
Cherry Hill East
Info Systems Engineering Operations Research Financial Engineering Economics, Info Systems Engineering Operations Research Financial Engineering Economics at Lehigh University
Bootcamp Program Data Science, Bootcamp Program Data Science at Thinkful
M.S. Statistics, M.S. Statistics at Rutgers University
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