Laurie McAughtry

Successfully deploying LLMs on the trading desk hinges on robust data infrastructure—not just model performance. This panel dives into the data engineering required to support LLMs in production, from clean input capture to curated datasets built for the speed and specificity of trading environments. 

• How to capture and process high-frequency trading data for LLM applications
• Best practices for data labeling, storage design, and real-time pipeline optimization
• Meeting governance, auditability, and compliance standards when working with sensitive data; and
• Practical strategies for making LLMs operationally viable in fast-moving, regulated trading settings 

In a world of shifting geopolitical risk, inflation shocks, and tariff swings, static trading strategies no longer suffice. This panel explores how firms are developing macro-aware algorithms that respond in real time to geopolitical shifts, economic data, and market sentiment—enabling faster, smarter decision-making at the point of execution. 

• How macro signals like inflation data, policy changes, and sentiment indicators are being integrated into trading models.
• Understanding the infrastructure and backtesting challenges behind building adaptive, real-time algorithms
• The role of human oversight and model governance in responsive, macro-driven strategies; and
• Reducing execution risk and improving performance during market-moving events 

This panel will explore how trading teams are leveraging real-time TCA to improve decision-making, prove best execution, and respond dynamically to market shifts. With growing demand for speed, precision, and transparency, join this panel to unlock deeper execution intelligence. 

• The latest tools, methodologies, and data strategies shaping next-generation TCA
• Pre-trade insight, intra-trade adjustment, or post-trade performance evaluation 

Panelists:

Mike Hewel, Director, Trading, Mercer Advisors