QuantAgent：基于价格驱动的多智能体大语言模型在高频交易中的应用

QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading

September 12, 2025

作者: Fei Xiong, Xiang Zhang, Aosong Feng, Siqi Sun, Chenyu You

cs.AI

摘要

近期，大型语言模型（LLMs）在金融推理与市场理解方面展现出了显著的能力。诸如TradingAgent和FINMEM等多智能体LLM框架，通过融合基本面与情绪分析输入，增强了这些模型在长期投资任务中的战略决策能力。然而，此类系统难以满足高频交易（HFT）对高速、精准决策的严苛要求。HFT依赖于基于结构化、短期信号的快速且风险意识强的决策，这些信号包括技术指标、图表形态及趋势特征，与传统的金融LLM应用所擅长的长期语义推理截然不同。为此，我们推出了QuantAgent，这是首个专为高频算法交易设计的多智能体LLM框架。该系统将交易分解为四个专业智能体——指标、形态、趋势和风险，每个智能体配备领域专用工具和结构化推理能力，以捕捉短时间窗口内市场的不同动态。在涵盖比特币和纳斯达克期货等十种金融工具的零样本评估中，QuantAgent在4小时交易区间内的预测准确性和累计收益上均表现出色，超越了强大的神经网络和基于规则的基线模型。我们的研究表明，将结构化金融先验知识与语言本机推理相结合，为高频金融市场中可追踪的实时决策系统开辟了新的潜力。

English

Recent advances in Large Language Models (LLMs) have demonstrated impressive capabilities in financial reasoning and market understanding. Multi-agent LLM frameworks such as TradingAgent and FINMEM augment these models to long-horizon investment tasks, leveraging fundamental and sentiment-based inputs for strategic decision-making. However, such systems are ill-suited for the high-speed, precision-critical demands of High-Frequency Trading (HFT). HFT requires rapid, risk-aware decisions based on structured, short-horizon signals, including technical indicators, chart patterns, and trend-based features, distinct from the long-term semantic reasoning typical of traditional financial LLM applications. To this end, we introduce QuantAgent, the first multi-agent LLM framework explicitly designed for high-frequency algorithmic trading. The system decomposes trading into four specialized agents, Indicator, Pattern, Trend, and Risk, each equipped with domain-specific tools and structured reasoning capabilities to capture distinct aspects of market dynamics over short temporal windows. In zero-shot evaluations across ten financial instruments, including Bitcoin and Nasdaq futures, QuantAgent demonstrates superior performance in both predictive accuracy and cumulative return over 4-hour trading intervals, outperforming strong neural and rule-based baselines. Our findings suggest that combining structured financial priors with language-native reasoning unlocks new potential for traceable, real-time decision systems in high-frequency financial markets.