Ryan Ahern of Exa presents Exa Snapshot, a point-in-time web search that lets agents query the web as it appeared on any past date, and shows how teams can backtest predictive signals and financial models without overfitting.
Ryan Ahern of Exa explains how version-controlled search through Exa Snapshot lets you view the web as it existed on a past date, enabling backtests of AI agents while avoiding look-ahead bias. A hands-on acquisition-prediction exercise illustrates feature tuning and the dangers of overfitting without holdout sets.
Morgan Stanley's AlphaLab is an open-sourced multi-agent system designed to automate quantitative research. Initially, AlphaLab 1.0 automated code generation, backtesting, and experimentation. Facing challenges, AlphaLab 2.0 evolves to prioritize building robust, verifiable environments, which serve as reinforcement learning signals, enabling the system to meta-optimize itself. This shift redefines the human role from performing research to designing these critical environments.
Explore how Weights & Biases (W&B) enhances reliability, reproducibility, and explainability in large-scale, agent-driven quantitative research. This video demonstrates two core applications: debugging multi-agent alpha research pipelines with W&B Weave to identify root causes and iterate on forecasts, and automating strategy optimization using W&B Models to tune agent weights and gain insights from performance convergence and parallel coordinate plots.