HedgeAgents: A Balanced-aware Multi-agent Financial Trading System

TL;DR

An innovative multi-agent system, HedgeAgents, aimed at bolstering system robustness via ''hedging'' strategies and observing with delight that HedgeAgents can even formulate investment experience comparable to those of human experts (https://hedgeagents.github.io/).

Abstract

As automated trading gains traction in the financial market, algorithmic investment strategies are increasingly prominent. While Large Language Models (LLMs) and Agent-based models exhibit promising potential in real-time market analysis and trading decisions, they still experience a significant -20% loss when confronted with rapid declines or frequent fluctuations, impeding their practical application. Hence, there is an imperative to explore a more robust and resilient framework. This paper introduces an innovative multi-agent system, HedgeAgents, aimed at bolstering system robustness via ``hedging'' strategies. In this well-balanced system, an array of hedging agents has been tailored, where HedgeAgents consist of a central fund manager and multiple hedging experts specializing in various financial asset classes. These agents leverage LLMs' cognitive capabilities to make decisions and coordinate through three types of conferences. Benefiting from the powerful understanding of LLMs, our HedgeAgents attained a 70% annualized return and a 400% total return over a period of 3 years. Moreover, we have observed with delight that HedgeAgents can even formulate investment experience comparable to those of human experts (https://hedgeagents.github.io/).

Figures (12)

• Figure 1: Our method outperformed all baselines on the PRUDEX benchmark sun2023prudexcompass across six dimensions. Marks on the inner circle represent the market average, while the outer layer details the measures evaluated.
• Figure 2: Performance of state-of-the-art models on Total Return (TR), Sharpe Ratio (SR) and Cumulative Returns (CR) in real-world scenarios, with the majority exhibiting negative scores.
• Figure 3: Our HedgeAgents comprise 3 hedging agents and 1 manager. Each agent is equipped with 23 tools and possesses 3 types of memory to execute 8 actions. Furthermore, collaboration among multiple agents can be categorized into three types of conferences: budget allocation, experience sharing, and extreme market conference.
• Figure 4: Cumulative Returns Comparison of all baselines and our HedgeAgents.
• Figure 5: Ablation analysis on several LLM backbones, from open-source to closed-source models.