Sergei Nasibian
Sergei Nasibian is a Quantitative Strategist at Rothesay in London, where he designs and implements systematic trading and risk management models. He previously worked as a Data Scientist at McKinsey & Company and as a Senior Analyst at Yandex Eats, developing data-driven strategies across diverse domains. Sergei holds a degree with honors in Mathematics from Lomonosov Moscow State University, specializing in probability theory and stochastic processes. His research experience includes entropy-based change-point detection methods developed during a collaboration with Ulm University. Sergei is passionate about translating advanced mathematical concepts into practical, production-ready tools using open-source Python libraries, and he enjoys exploring intersections between machine learning, statistical modeling, and financial markets.
Session
Financial and other real-world time series often experience abrupt regime changes that can break assumptions and invalidate models. This talk shows how to use k-nearest neighbor entropy estimators combined with clustering algorithms, implemented entirely in Python, to detect these change-points early. We’ll explore practical examples with financial market data, discuss strengths and limitations, and provide reusable open-source code. Attendees will leave with tools to make their time series models more robust to sudden structural changes.