Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents
Researchers have launched the Agent Trading Arena, a simulation where large language model agents trade in a virtual stock market, directly affecting prices. The study finds that these AI agents struggle with numbers when using only text data, often missing key patterns. However, giving them chart-based visuals and a reflection module for strategy review greatly improves their trading. Tests on real NASDAQ and CSI data show vision-enhanced agents outperform text-only and traditional models, especially in volatile markets.
What it examines
This paper introduces the Agent Trading Arena, a virtual stock market where large language model (LLM) agents compete in real-time trading. The study aims to evaluate LLMs' numerical reasoning and adaptability, comparing text and visual data inputs, and exploring how reflection modules improve trading strategies.
What it concludes
LLMs perform better in trading when using visual data and reflection modules, showing stronger reasoning and decision-making. The Agent Trading Arena is a useful testbed for financial AI research, with potential applications in automated trading, financial analysis, and adaptive decision-making. Future work should address real-world complexities and integration.
Evidence objects
Agent Trading Arena is a novel simulation platform where LLM-based agents compete in a virtual stock market, directly impacting prices through real-time bid-ask interactions, closely mimicking real-world trading dynamics.
key_findings bullet 1 · key_findings · validation V0
LLMs struggle with numerical reasoning using only text data, often overfitting to recent trends; however, providing chart-based visualizations and a reflection module dramatically boosts their trading performance and strategic reasoning.
key_findings bullet 2 · key_findings · validation V0
Vision-enhanced LLM agents consistently outperform traditional and text-only models on real NASDAQ and CSI datasets, especially in volatile markets, though bridging the gap to live trading remains a significant challenge.
key_findings bullet 3 · key_findings · validation V0
This paper presents the Agent Trading Arena, a novel simulation where LLM-based agents interact in a dynamic, competitive trading environment, directly influencing price dynamics. Integrating LLMs, real-time feedback, and visual reasoning modules is original, enabling realistic evaluation of agent adaptability and reasoningoffering compelling insights for AI-driven investment management and trading research.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
- … based agents in financial markets remains limited. Financial ecosystems naturally … the “train-test” gap of traditional financial simulations. Its modular design further supports …
Source row: 128 · abstract type: snippet