TradingAgents: Multi-Agents LLM Financial Trading Framework

ArXiv ID: 2412.20138 “View on arXiv”

Authors: Unknown

Abstract

Significant progress has been made in automated problem-solving using societies of agents powered by large language models (LLMs). In finance, efforts have largely focused on single-agent systems handling specific tasks or multi-agent frameworks independently gathering data. However, the multi-agent systems’ potential to replicate real-world trading firms’ collaborative dynamics remains underexplored. TradingAgents proposes a novel stock trading framework inspired by trading firms, featuring LLM-powered agents in specialized roles such as fundamental analysts, sentiment analysts, technical analysts, and traders with varied risk profiles. The framework includes Bull and Bear researcher agents assessing market conditions, a risk management team monitoring exposure, and traders synthesizing insights from debates and historical data to make informed decisions. By simulating a dynamic, collaborative trading environment, this framework aims to improve trading performance. Detailed architecture and extensive experiments reveal its superiority over baseline models, with notable improvements in cumulative returns, Sharpe ratio, and maximum drawdown, highlighting the potential of multi-agent LLM frameworks in financial trading. TradingAgents is available at https://github.com/TauricResearch/TradingAgents.

Keywords: Multi-agent systems, Large Language Models (LLMs), Stock trading, Fundamental analysis, Risk management

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders
  • Why: The paper focuses on LLM agent architecture and backtesting metrics like Sharpe ratio and cumulative returns, with minimal advanced mathematics beyond basic statistics, making it highly empirical and implementation-focused.
  flowchart TD
    A["Research Goal<br>Explore multi-agent LLM framework<br>to replicate trading firm dynamics"] --> B["Methodology<br>Multi-agent system with specialized<br>roles & collaborative debates"]
    B --> C["Data/Inputs<br>Financial market data &<br>historical trading information"]
    C --> D["Computational Process<br>Agent debates & synthesis of<br>fundamental, technical & sentiment insights"]
    D --> E["Key Findings<br>Superior performance vs baselines<br>↑ Cumulative returns, ↑ Sharpe ratio,<br>↓ Maximum drawdown"]
    E --> F["Outcome<br>Validates multi-agent LLM<br>potential in financial trading"]