Automating and Optimizing Financial Signal Discovery with Multi-Agent Systems

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 Agentic AI is bridging the gap between human expertise and automated efficiency. We designed a system of signal-discovery agents using NeMo Agent Toolkit to change signal discovery from a manual grind into a self-evolving, autonomous loop. The result is a painless process for designing, testing, and optimizing the agentic system. The system coordinates three specialized agents: - Signal agent: Identifies potential alpha signals from market data. - Code agent: Translates signal descriptions into executable Python code. - Evaluation agent: Runs backtests, applies logical evaluation, and iteratively refines signal suggestions. NeMo Agent Toolkit multi-agent signal discovery loop The NeMo Agent Toolkit provides the orchestration layer to build intelligent agents that plan complex research tasks, use tools to execute Python code, and reflect on backtesting results to refine their own hypotheses. Our proposed system consists of three specialized agents that interact in a continuous cycle of creation, execution, and refinement. The NeMo Agent Toolkit manages the “handoffs” between these agents, ensuring that context (such as signal definitions or backtest results) is preserved across the workflow. The signal generator This agent acts as the creative brain, using nemotron-3-nano-30b-a3b through NVIDIA NIM to hypothesize new signal expressions. We designed the following system prompt: “You are a senior quantitative researcher at a top hedge fund. Generate {config.num_signals} unique stock selection signals based on the request” to define the core objective of the agent. To ensure it produced logically sound results rather than “hallucinating” math, we provided it with a structured library of mathematical calculators as building blocks. Each calculator is a dictionary containing a name, signature, and meaning. In our example, there are 66 different operators covering operations from arithmetic, math (abs, clip, power), rank, and time series (log return, momentum, delta). For example, Rank_Add, normalizes two different sets of data (like price and volume) into a shared 0-to-1 percentile scale and sums them together. We provide the interpretation and the Python code, enabling…