Kristen Netznik

Kristen Netznik is a Quantitative Researcher at Wolverine Trading in Chicago, Illinois, where she applies advanced quantitative methods and data analysis to develop and enhance trading strategies. In her current role, she focuses on systematic research, leveraging statistical modeling and computational techniques to identify market inefficiencies and optimize trading execution, primarily within the context of proprietary trading across global listed equities, options, and derivatives markets.

Prior to joining Wolverine Trading, Kristen held progressive roles at Tradebot, serving as a Senior Data Analyst and previously as a Financial Data Analyst. At Tradebot, she built her expertise in algorithmic trading and financial data analytics, contributing to the development of automated trading strategies and data-driven performance evaluation for equity markets.

Kristen holds a degree from the University of Missouri-Columbia. Throughout her career, she has been recognized for her analytical rigor, technical proficiency in financial modeling, and her ability to drive insights in high-frequency and quantitative trading environments. Her work focuses on the intersection of quantitative research and market-making, with a primary emphasis on liquid securities markets such as equities and derivatives.