In quantitative finance, researchers build algorithms to trade assets, derivatives, and other financial instruments. A key part of that work is finding signals: patterns in messy market data that may help predict future returns. These signals can come from price and volume data, economic indicators, fundamentals, or alternative sources like news sentiment.
For years, quant firms have uncovered and tested signals largely by hand. Traditionally, quantitative researchers have to manually hypothesize, code, backtest, and refine hundreds of potential signals. The workflow is often fragmented, moving between data scientists, developers, and analysts, which creates significant lag in a market that moves in milliseconds. Now, AI can help automate parts of this workflow and speed up the research cycle. The quantitative signal discovery agent developer example from NVIDIA shows how to automate signal discovery using agentic architecture built with the NVIDIA Nemotron family of open models and the NVIDIA NeMo Agent Toolkit open-source library.
In this blog post, we walk you through a complete example of building an agentic system for signal discovery using the NeMo Agent Toolkit.
Agentic systems for signal discovery
