Big data has become a critical source of alpha as both volumes of information and the processing power to analyze it continue to increase exponentially. The modern quant is witnessing a seismic shift in core competencies as the field transitions from statistical modeling to data science and financial engineering.

To accommodate the evolution of the quant, Trading Show Chicago is unveiling Advanced Analytics, a brand-new stream of content for 2018. From extracting alpha from alternative data sources to applying deep learning to predict market behavior, this track will address the most critical developments fueling the technological arms race in quantitative research and trading.



  • Big data sweet spot – how are you finding, evaluating and applying alternative?
  • From risk to reward – aligning IT, human capital and strategy to mitigate risk and maximize investment performance
  • Deep neural networks – using DNNs to predict market behavior
  • Anomaly detection – machine learning-based approaches for detecting outliers in financial datasets

Brian Peterson

Partner & Head of Algorithmic Trading

Howard Getson


Andrew Curto

Head of Research & Strategy

Yam Peleg


Michael Mescher


Adrian De Valois- Franklin


Brian Miller

Founding Partner

Ernie Chan