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

Rui Sun, Li Zhao, Zuoyou Jiang, Bo Yang, Yuxiao Bai, Mengting Chen, Jing Li and Zuo Bai

Papers from arXiv.org

Abstract: In financial trading, large language model (LLM)-based agents demonstrate significant potential, but their decisions can be sensitive to noisy and non-stationary market information. We propose ContestTrade, a multi-agent trading system with an internal competitive mechanism inspired by institutional investment workflows. The system consists of two specialized teams: (1) a Data Team that processes and condenses massive market data into diversified textual factors optimized for constrained LLM context windows, and (2) a Research Team that produces parallelized multipath trading decisions via tool-augmented deep research. The core design is a "Quantify-Predict-Allocate" contest mechanism within each team: agent outputs are scored only after market outcomes become observable, future utility is predicted from historical scores, and resources are allocated to agents with positive predicted utility. In a post-2024 A-share backtest, ContestTrade achieves higher backtested return and risk-adjusted performance than the evaluated baselines. We further describe the temporal protocol, implementation choices, and limitations to clarify the scope of these results.

Date: 2025-08, Revised 2026-07
New Economics Papers: this item is included in nep-ain and nep-cmp
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