QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading
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.
68c77229ee0eed1697d6b6ae
Fei Xiong
68c77229ee0eed1697d6b6af
Xiang Zhang
claimed_verified
2025-09-15T15:07:49.281Z
656553d89bf6665f10e3a92d
/avatars/f55b09e249c48ef3fe484afa33a182ae.svg
xiang wyatt zhang
user
Wyattz23
68c77229ee0eed1697d6b6b0
Aosong Feng
68c77229ee0eed1697d6b6b1
Siqi Sun
68c77229ee0eed1697d6b6b2
Chenyu You
QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading
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.
QuantAgent, a multi-agent LLM framework, excels in high-frequency trading by leveraging specialized agents for technical indicators, chart patterns, trends, and risk, outperforming existing neural and rule-based systems.
Large Language Models LLMs Multi-agent LLM frameworks TradingAgent FINMEM High-Frequency Trading HFT structured reasoning technical indicators chart patterns trend-based features zero-shot evaluations predictive accuracy cumulative return financial instruments Bitcoin Nasdaq futures domain-specific tools traceable real-time decision systems