ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism

• URL: http://arxiv.org/abs/2508.00554v3
• Date: Mon, 18 Aug 2025 06:13:10 GMT
• Title: ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism
• Authors: Li Zhao, Rui Sun, Zuoyou Jiang, Bo Yang, Yuxiao Bai, Mengting Chen, Xinyang Wang, Jing Li, Zuo Bai,
• Abstract summary: Large language model (LLM)-based agents demonstrate significant potential in financial trading.<n>High sensitivity to market noise undermines the performance of LLM-based trading systems.<n>We propose a novel multi-agent system featuring an internal competitive mechanism inspired by modern corporate management structures.
• Score: 8.46483000946212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In financial trading, large language model (LLM)-based agents demonstrate significant potential. However, the high sensitivity to market noise undermines the performance of LLM-based trading systems. To address this limitation, we propose a novel multi-agent system featuring an internal competitive mechanism inspired by modern corporate management structures. The system consists of two specialized teams: (1) Data Team - responsible for processing and condensing massive market data into diversified text factors, ensuring they fit the model's constrained context. (2) Research Team - tasked with making parallelized multipath trading decisions based on deep research methods. The core innovation lies in implementing a real-time evaluation and ranking mechanism within each team, driven by authentic market feedback. Each agent's performance undergoes continuous scoring and ranking, with only outputs from top-performing agents being adopted. The design enables the system to adaptively adjust to dynamic environment, enhances robustness against market noise and ultimately delivers superior trading performance. Experimental results demonstrate that our proposed system significantly outperforms prevailing multi-agent systems and traditional quantitative investment methods across diverse evaluation metrics. ContestTrade is open-sourced on GitHub at https://github.com/FinStep-AI/ContestTrade.

Related papers

• Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks [6.55184070677326]
  We propose a multi-agent trading framework that decomposes investment analysis into fine-grained tasks.<n> Experimental results show that fine-grained task decomposition significantly improves risk-adjusted returns.<n>We exploit low correlation with the stock index and the variance of each system's output to achieve superior performance.
  arXiv  Detail & Related papers  (2026-02-26T18:37:36Z)
• When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents [74.55061622246824]
  Agent Market Arena (AMA) is the first lifelong, real-time benchmark for evaluating Large Language Model (LLM)-based trading agents.<n>AMA integrates verified trading data, expert-checked news, and diverse agent architectures within a unified trading framework.<n>It evaluates agents across GPT-4o, GPT-4.1, Claude-3.5-haiku, Claude-sonnet-4, and Gemini-2.0-flash.
  arXiv  Detail & Related papers  (2025-10-13T17:54:09Z)
• MARS: Optimizing Dual-System Deep Research via Multi-Agent Reinforcement Learning [82.14973479594367]
  Large Language Models (LLMs) for complex reasoning tasks require innovative approaches that bridge intuitive and deliberate cognitive processes.<n>This paper introduces a Multi-Agent System for Deep ReSearch (MARS) enabling seamless integration of System 1's fast, intuitive thinking with System 2's deliberate reasoning.
  arXiv  Detail & Related papers  (2025-10-06T15:42:55Z)
• Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading [57.28635022507172]
  TiMi is a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment.<n>We propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection.
  arXiv  Detail & Related papers  (2025-10-06T13:08:55Z)
• QuantAgents: Towards Multi-agent Financial System via Simulated Trading [40.636918662488505]
  QuantAgents is a multi-agent system integrating simulated trading.<n> QuantAgents comprises four agents: a simulated trading analyst, a risk control analyst, a market news analyst, and a manager.<n>Our system incentivizes agents to receive feedback on two fronts: performance in real-world markets and predictive accuracy in simulated trading.
  arXiv  Detail & Related papers  (2025-10-06T09:45:57Z)
• OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System [61.12400636463362]
  OnePiece is a unified framework that seamlessly integrates LLM-style context engineering and reasoning into both retrieval and ranking models.<n>OnePiece has been deployed in the main personalized search scenario of Shopee and achieves consistent online gains across different key business metrics.
  arXiv  Detail & Related papers  (2025-09-22T17:59:07Z)
• TradingGroup: A Multi-Agent Trading System with Self-Reflection and Data-Synthesis [15.865159423176982]
  TradingGroup is a multi-agent trading system designed to address limitations through a self-reflective architecture and an end-to-end data-synthesis pipeline.<n> TradingGroup consists of specialized agents for news sentiment analysis, financial report interpretation, stock trend forecasting, trading style adaptation, and a trading decision making agent.<n>Specifically, we design self-reflection mechanisms for the stock forecasting, style, and decision-making agents to distill past successes and failures for similar reasoning in analogous future scenarios.
  arXiv  Detail & Related papers  (2025-08-25T00:29:58Z)
• MASCA: LLM based-Multi Agents System for Credit Assessment [0.3277163122167434]
  We introduce MASCA, an LLM-driven multi-agent system designed to enhance credit evaluation by mirroring real-world decision-making processes.<n>We also present a signaling game theory perspective on hierarchical multi-agent systems, offering theoretical insights into their structure and interactions.
  arXiv  Detail & Related papers  (2025-07-30T15:19:38Z)
• R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization [19.301233249500655]
  Financial markets pose fundamental challenges for asset return prediction due to their high dimensionality, non-stationarity, and persistent volatility.<n>Despite advances in large language models and multi-agent systems, current quantitative research pipelines suffer from limited automation, weak interpretability, and fragmented coordination across key components such as factor mining and model innovation.<n>We propose R&D-Agent for Quantitative Finance, in short RD-Agent(Q), the first data-centric multi-agent framework designed to automate the full-stack research and development of quantitative strategies via coordinated factor-model co-optimization.
  arXiv  Detail & Related papers  (2025-05-21T06:20:56Z)
• A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
  Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making.<n>With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems.<n>We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
  arXiv  Detail & Related papers  (2025-04-12T01:27:49Z)
• Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models [75.4890331763196]
  Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems.<n>LLM-based Agentic RS (LLM-ARS) can offer more interactive, context-aware, and proactive recommendations.
  arXiv  Detail & Related papers  (2025-03-20T22:37:15Z)
• Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents [69.58565132975504]
  Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks.<n>We present the Agent Trading Arena, a virtual zero-sum stock market in which LLM-based agents engage in competitive multi-agent trading.
  arXiv  Detail & Related papers  (2025-02-25T08:41:01Z)
• HedgeAgents: A Balanced-aware Multi-agent Financial Trading System [20.48571388047213]
  Large Language Models (LLMs) and Agent-based models exhibit promising potential in real-time market analysis and trading decisions.<n>They still experience a significant -20% loss when confronted with rapid declines or frequent fluctuations.<n>This paper introduces an innovative multi-agent system, HedgeAgents, aimed at bolstering system via hedging robustness'' strategies.
  arXiv  Detail & Related papers  (2025-02-17T04:13:19Z)
• TradingAgents: Multi-Agents LLM Financial Trading Framework [4.293484524693143]
  TradingAgents proposes a novel stock trading framework inspired by trading firms.<n>It features LLM-powered agents in specialized roles such as fundamental analysts, sentiment analysts, technical analysts, and traders with varied risk profiles.<n>By simulating a dynamic, collaborative trading environment, this framework aims to improve trading performance.
  arXiv  Detail & Related papers  (2024-12-28T12:54:06Z)
• LMAgent: A Large-scale Multimodal Agents Society for Multi-user Simulation [66.52371505566815]
  Large language models (LLMs)-based AI agents have made significant progress, enabling them to achieve human-like intelligence.<n>We present LMAgent, a very large-scale and multimodal agents society based on multimodal LLMs.<n>In LMAgent, besides chatting with friends, the agents can autonomously browse, purchase, and review products, even perform live streaming e-commerce.
  arXiv  Detail & Related papers  (2024-12-12T12:47:09Z)
• Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research [17.43528917594047]
  We propose a novel multi-agent collaboration system designed to enhance decision-making in financial investment research. We focus on three sub-tasks: fundamentals, market sentiment, and risk analysis, by analyzing the 2023 SEC 10-K forms of 30 companies listed on the Dow Jones Index. Our findings reveal significant performance variations based on the configurations of AI agents for different tasks.
  arXiv  Detail & Related papers  (2024-11-07T15:28:20Z)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
  We propose using scorable negotiation to evaluate Large Language Models (LLMs) To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities. We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
  arXiv  Detail & Related papers  (2023-09-29T13:33:06Z)
• MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy [68.7563053122698]
  We propose a reliable object detection and segmentation system with MultiModal Redundancy (MMRNet) This is the first system that introduces the concept of multimodal redundancy to address sensor failure issues during deployment. We present a new label-free multi-modal consistency (MC) score that utilizes the output from all modalities to measure the overall system output reliability and uncertainty.
  arXiv  Detail & Related papers  (2022-10-19T19:15:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.