Hariom Tatsat
Author, Machine Learning Blueprints for Finance
Hariom Tatsat currently works as a Vice President in the Quantitative Analytics division of an investment bank in 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, his BE at IIT Kharagpur (India).
Watch live: JUNE 7 @ 4:15PM – 4:45PM ET
Reinforcement Learning Interpretability: Applications to Algorithmic Trading
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.