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Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

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HARL-TRADE: A hierarchical adaptive reinforcement learning framework for second-level high-frequency trading.

Hao Shi¹, Xinting Zhang², Desheng Wu²

¹School of Computer Science and Technology, University of the Chinese Academy of Sciences, Beijing, China.

Chaos (Woodbury, N.Y.)

|February 18, 2026

Summary

This summary is machine-generated.

This study introduces an adaptive hierarchical framework for high-frequency trading (HFT) using an attention-based meta-agent. It enhances adaptability in volatile markets, outperforming existing methods with significant returns.

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Area of Science:

Quantitative Finance
Artificial Intelligence
Algorithmic Trading

Background:

High-frequency trading (HFT) requires adaptive strategies for volatile market conditions.
Existing discrete sub-agent frameworks exhibit limited adaptability due to rigid market condition allocations.

Purpose of the Study:

To propose a novel hierarchical framework with an attention-based meta-agent for dynamic sub-agent coordination in HFT.
To improve adaptability and performance in navigating diverse market regimes.

Main Methods:

Developed a hierarchical framework incorporating an attention-based meta-agent.
Utilized market embeddings and reinforcement learning for optimal sub-agent weight adjustment.
Implemented dynamic sub-agent assignment and multi-head attention mechanisms.

Main Results:

The proposed framework achieved a 42.15% total return and a 4.19 Sharpe ratio on historical HFT data.
Demonstrated superior performance compared to state-of-the-art baselines.
Ablation studies confirmed the effectiveness of the dynamic assignment and attention mechanisms.

Conclusions:

The attention-based hierarchical framework offers superior adaptability and performance in high-frequency trading.
Dynamic coordination of sub-agents via a meta-agent effectively addresses market volatility and transitions.