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

arXiv: https://arxiv.org/abs/2502.13165 · HTML: https://mlsanity.com/paper/2502.13165/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 13 of 13)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [AppAgent: Multimodal Agents as Smartphone Users](https://mlsanity.com/paper/2312.13771/) (2023)
- [Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions](https://mlsanity.com/paper/2306.02224/) (2023)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model](https://mlsanity.com/paper/2403.02647/) (2024)
- [PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets](https://mlsanity.com/paper/2302.00586/) (2023)
- [Democratization of Retail Trading: Can Reddit's WallStreetBets Outperform Investment Bank Analysts?](https://mlsanity.com/paper/2301.00170/) (2022)

## Built on by (most cited first, 24 of 24)

- [BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks](https://mlsanity.com/paper/2508.08127/) (2025)
- [Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarking](https://mlsanity.com/paper/2505.11065/) (2025)
- [QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining](https://mlsanity.com/paper/2602.07085/) (2026)
- [CombatVLA: An Efficient Vision-Language-Action Model for Combat Tasks in 3D Action Role-Playing Games](https://mlsanity.com/paper/2503.09527/) (2025)
- [Profit Mirage: Revisiting Information Leakage in LLM-based Financial Agents](https://mlsanity.com/paper/2510.07920/) (2025)
- [Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Finance](https://mlsanity.com/paper/2601.13770/) (2026)
- [QuantAgents: Towards Multi-agent Financial System via Simulated Trading](https://mlsanity.com/paper/2510.04643/) (2025)
- [MVISU-Bench: Benchmarking Mobile Agents for Real-World Tasks by Multi-App, Vague, Interactive, Single-App and Unethical Instructions](https://mlsanity.com/paper/2508.09057/) (2025)
- [Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks](https://mlsanity.com/paper/2602.23330/) (2026)
- [How are AI agents used? Evidence from 177,000 MCP tools](https://mlsanity.com/paper/2603.23802/) (2026)
- [Toward Reliable Evaluation of LLM-Based Financial Multi-Agent Systems: Taxonomy, Coordination Primacy, and Cost Awareness](https://mlsanity.com/paper/2603.27539/) (2026)
- [Will LLMs be Professional at Fund Investment? DeepFund: A Live Arena Perspective](https://mlsanity.com/paper/2503.18313/) (2025)
- [PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Management](https://mlsanity.com/paper/2605.27887/) (2026)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [How Foundational Skills Influence VLM-based Embodied Agents:A Native Perspective](https://mlsanity.com/paper/2602.20687/) (2026)
- [TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding](https://mlsanity.com/paper/2512.06933/) (2025)
- [Argus: A Multi-Agent Sensitive Information Leakage Detection Framework Based on Hierarchical Reference Relationships](https://mlsanity.com/paper/2512.08326/) (2025)
- [PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents](https://mlsanity.com/paper/2512.14735/) (2025)
- [Don't Guess, Escalate: Towards Explainable Uncertainty-Calibrated AI Forensic Agents](https://mlsanity.com/paper/2512.16614/) (2025)
- [FLARE: Agentic Coverage-Guided Fuzzing for LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2604.05289/) (2026)
- [CSTrader: A Testbed for Language-Grounded Trading in a Community-Driven Virtual Asset Market](https://mlsanity.com/paper/2606.31461/) (2026)
- [Can Agentic Trading Systems Pay for Their Own Intelligence?](https://mlsanity.com/paper/2607.10286/) (2026)
- [OpenPM: Auditable Point-in-Time Evaluation for LLM Portfolio-Management Agents](https://mlsanity.com/paper/2608.09988/) (2026)
- [SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes](https://mlsanity.com/paper/2609.19705/) (2026)

## Abstract (arXiv)

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/).