💡 The paper introduces QuantAgent, a multi-agent large language model framework designed specifically for high-frequency trading. High-frequency trading requires rapid and precise decisions based on short-term market signals, which is different from traditional financial applications that involve long-term semantic reasoning. Existing large language models are not well-suited for high-frequency trading due to their lack of structured reasoning capabilities and domain-specific tools.

To address this problem, the QuantAgent framework decomposes trading into four specialized agents: Indicator, Pattern, Trend, and Risk. Each agent is equipped with domain-specific tools and structured reasoning capabilities to capture distinct aspects of market dynamics over short temporal windows. The Indicator agent focuses on technical indicators, the Pattern agent focuses on chart patterns, the Trend agent focuses on trend-based features, and the Risk agent focuses on risk management.

The results show that QuantAgent outperforms strong neural and rule-based baselines in terms of predictive accuracy and cumulative return over 4-hour trading intervals. The evaluation was conducted across ten financial instruments, including Bitcoin and Nasdaq futures, using zero-shot evaluations. The findings suggest that combining structured financial priors with language-native reasoning can unlock new potential for real-time decision systems in high-frequency financial markets.

The main contribution of the paper is the introduction of a multi-agent large language model framework that is specifically designed for high-frequency trading. The framework's ability to decompose trading into specialized agents and leverage domain-specific tools and structured reasoning capabilities makes it well-suited for the high-speed and precision-critical demands of high-frequency trading. The results demonstrate the effectiveness of the QuantAgent framework and highlight its potential for use in real-world high-frequency trading applications.