StockAgent

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Overview

Multi-agent LLM system that simulates investor trading behavior in a realistic stock-market environment to study how external factors affect decisions and outcomes.

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Best For Professions:

quant researchers data scientists machine learning engineers academic researchers traders

StockAgent is an open-source, LLM-driven multi-agent framework for simulating stock trading behavior in a market environment that aims to resemble real-world conditions. The system models investor-like agents reacting to information such as macroeconomics, policy changes, company fundamentals, and global events, enabling analysis of trading behaviors and profitability effects under different external-factor scenarios. The authors also position StockAgent as addressing test-set leakage concerns in agent-based trading simulations by reducing reliance on prior memorized knowledge of the evaluation data. It’s primarily a research and experimentation framework (not a live-broker trading bot) for evaluating LLMs and studying emergent trading patterns in controlled simulations.

Autonomy level

55%

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Some of the use cases of StockAgent:

Running agent-based stock trading simulations to test how news/events/fundamentals influence investor behavior.
Benchmarking different LLMs in a trading decision-making loop under consistent simulation rules.
Studying behavioral patterns (e.g., reaction to shocks) and profitability impacts across scenarios.
Building academic or internal prototypes for market-simulation experiments without touching real capital.

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