Hudson River Trading (HRT) is a quantitative automated trading company that trades hundreds of millions of shares each day broken up into over a million trades and spread across thousands of symbols. It trades on over 200 markets worldwide, and accounts for around 10% of US equities volume. To provide price discovery and market making services for public markets, HRT employs state-of-the-art techniques from machine learning and optimization to understand and react to market data.
In this talk we will provide an overview of the unique challenges in this domain and the breadth of techniques employed at HRT. A fundamental challenge is the massive, heterogeneous, unevenly spaced, noisy, and bursty nature of financial datasets. Researchers at HRT use tools like multi-task learning, sequence modeling, and large language models to build some of the most predictive models in the world for these datasets. Given strong predictions about the future prices of financial products, HRT employs a variety of optimization techniques spanning from Bayesian optimization to quasi-newton methods to portfolio optimization to make trading decisions. Come to learn more about opportunities to make an impact in this fast paced and competitive industry.