Hey, it’s Matt. Welcome back to AI Street. This week:
HRT on Building “Foundation Models for Automated Trading”
Top Papers on AI in Finance Q4 2025: SSRN
JPMorgan’s Dimon: banks must invest in AI or get left behind. + More News
Hudson River Trading is building foundation-style models trained on decades of global market data, applying techniques similar to those used in frontier language models for automated trading.
The firm is training these models on more than two decades of data spanning equities, futures, and cryptocurrencies, totaling over 100 terabytes. That translates into “something like trillions of tokens, in the same realm as what you train frontier language models on,” said Marc Khoury, a researcher on HRT’s AI team, speaking at an academic conference.
At a high level, HRT’s goal is to model markets as sequences of interactions. Electronic markets generate detailed streams of activity, including full limit order books, executed trades, and order-level events such as placements, cancellations, and fills. According to Khoury, much of the predictive signal lies in how these sequences evolve over time, especially during fast-moving conditions.