Hudson River Trading (HRT) is a global trading firm that leverages deep learning to navigate the complexities of the world's financial markets. Every day, our models process petabytes of high-fidelity data, seeking to extract signal from trillions of events across thousands of interconnected products. Operating at this scale requires a unique intersection of frontier machine learning research and high-performance engineering.

In this talk, we will discuss our approach to building and deploying large-scale market models—foundation models designed to capture the latent structures of price discovery. We’ll share insights into the research hurdles of training on massive, non-stationary datasets and the engineering constraints of performing real-time inference at microsecond scale. Beyond prediction, we will touch on the challenges of maintaining robustness amidst highly dynamic market conditions and rapid regime shifts. Join us for a look into how we apply the next generation of deep learning to navigate one of the world’s most competitive and data-rich environments.