---
title: "Quantitative Signal Discovery Agent"
publisher: "nvidia"
type: "blueprint"
updated: "2026-05-21T18:07:46.430Z"
description: "Automate and scale the discovery, testing, and refinement of trading signals for quantitative research."
canonical: "https://build.nvidia.com/nvidia/quantitative-signal-discovery-agent"
---

The Quantitative Signal Discovery Agent developer example demonstrates how to automate signal discovery—a core workflow in quantitative trading—using an agentic architecture built with [NVIDIA Nemotron](https://developer.nvidia.com/nemotron) family of open models and the NVIDIA NeMo Agent Toolkit open-source library. 

The system coordinates three specialized agents:

* Signal agent: Identifies potential alpha signals from real market data.  
* Code agent: Translates signal descriptions into executable Python code.  
* Evaluation agent: Runs backtests, applies logical evaluation, and iteratively refines signal suggestions. 

Together, these agents form a closed-loop system that continuously mines, tests, and improves trading signals. Using NVIDIA NeMo Agent Toolkit, developers can streamline orchestration, track performance and identify bottlenecks, and simplify deployment of this multi-agent workflow for quantitative research.

## Architecture Diagram
![Architecture Diagram](https://assets.ngc.nvidia.com/products/api-catalog/quantitative-signal-discovery-agent/diagram.jpg)

### Key Features
* **Autonomous Multi-Agent Orchestration:** Coordinate Signal, Code, and Eval Agents using the NeMo Agent Toolkit, enabling automation of complex, multi-stage reasoning  and continuous signal discovery.  
* **Self-Correcting Code Generation:** Generate executable Python from signal logic with built-in error-handling, iterative refinement, and reliability at scale.  
* **Reduced Latency and Cost:** Deploys lightweight NIM microservices optimized for throughput and efficiency—reducing GPU demand, inference cost, and energy use without compromising performance.  
* **Accelerated Backtesting and Strategy Evaluation:** Iterate faster using NVIDIA CUDA-X Data Science libraries for signal testing, validation, and selection.  
* **Scalable, Observable Experimentation:** Gain experiment tracking and performance monitoring with NeMo Agent Toolkit, supporting reproducible research.  
* **Modular Integration and Deployment:** Integrate seamlessly with existing quant pipelines using a modular design supporting on-premises, hybrid cloud, and edge deployments.

## Minimum System Requirements

### Hardware Requirements
* GPU: 1x NVIDIA GPU with 48GB+ VRAM (e.g., A100 80GB, H100) for local NIM deployment  
* RAM: 16 GB  
* Storage: 5 GB free disk space (for S\&P 500 data and model outputs)  
* Note: If using the hosted NIM API at build.nvidia.com, no local GPU is required. The workflow runs CPU-only and calls the NIM inference endpoint remotely. Additionally, a Milvus vector database instance is required for product catalog embeddings, and Arize Phoenix is recommended for distributed tracing and observability across the multi-agent workflow.

### OS
* Linux (Ubuntu 22.04+), macOS, or Windows

### Deployment Options
* Python virtual environment (pip or uv)  
* Jupyter Notebook

## Software Used in This Blueprint
**NVIDIA Technology**

* NVIDIA NeMo Agent Toolkit (NAT) — Multi-agent workflow orchestration, composing Signal, Code, and Eval agents in a closed-loop optimization pipeline  
* NVIDIA NIM (nemotron-3-nano-30b-a3b) — Open, efficient MoE model with 1M context, excelling in coding, reasoning, instruction following, tool calling, and more  
* Arize Phoenix (with NVIDIA NAT integration) — LLM observability, tracing, and debugging

**3rd Party Software**
* LangChain — LLM framework for agent-model interaction  
* pandas — DataFrame operations for price-volume data processing  
* NumPy — Array operations across signal computation and evaluation  
* SciPy — Statistical analysis (Spearman correlation, t-tests, p-values)  
* yfinance — S&P 500 price-volume data downloaded from Yahoo Finance  
* Arize Phoenix — Open-source LLM observability and tracing platform  
* OpenInference — LangChain instrumentation for trace capture  
* Jupyter — Interactive notebook environment for exploration and visualization

## License

By using this software, you are agreeing to the terms and conditions of the license and acceptable use policy.

GOVERNING TERMS: This developer example is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product Specific Terms for AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).

This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use. 

Please report security vulnerabilities or NVIDIA AI concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).