Kavli Affiliate: Xiang Zhang
| First 5 Authors: Fei Xiong, Fei Xiong, , ,
| Summary:
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 random prediction
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.
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