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Reinforcement Learning Interpretability: Applications to Algorithmic Trading

Speakers: Hariom Tatsat, Bryan Yekelchik and Zach Coriarty

CQF Institute is proud to bring you a free online regional talk with Hariom Tatsat, Bryan Yekelchik and Zach Coriarty on 'Reinforcement Learning Interpretability: Applications to Algorithmic Trading'.

Please note: This talk will be hosted at 18:00 EDT - 19:00 EDT. 

Event Agenda 

17:30 - 18:00 EDT - Networking opportunities

18:00 - 19:00 EDT - CQF Institute Regional Talk: Hariom Tatsat, Bryan Yekelchik and Zach Coriarty

This event can earn you up to 2 CPD credits.

Abstract

Reinforcement Learning (RL) agents proved to be a force to be reckoned with in many complex games like Chess and Go. Financial firms are leveraging the power of RL, given it potential to automate all the steps involved in algorithmic trading. However, it is quite challenging to understand and interpret a RL based models. 

This talk focuses on an approach to understand and interpret Reinforcement Learning (RL) based trading strategies. We first briefly introduce the concept of reinforcement learning in the context of algorithmic trading, followed by demonstration of an RL- interpretability infrastructure. We then discuss possible derived outcomes of using this infrastructure when applied to trading a market instrument.

Speaker's Bio

Hariom Tatsat currently works as a Vice President in the Quantitative Analytics division of an investment bank in New York. Hariom has extensive experience as a Quant in the areas of predictive modelling, financial instrument pricing, and risk management in several global investment banks and financial organizations. He completed his MS at UC Berkeley, BE at IIT Kharagpur (India). He holds CQF and FRM certifications.   

Bryan Yekelchik currently works as a Data Scientist at a Fortune 500. He completed his M.S. at Lehigh University and B.S. at Bucknell University.

Zach Coriarty will graduate with a BS in Computer Science and Business from Lehigh University in May 2022. He has worked on various ML projects, including one that was implemented into the stack of a YCombinator-backed startup. Post graduation he is considering graduate school, but ultimately hopes to build impactful products with ML.