文献类型：会议论文

英文题名：AlphaSeek FinRL: A Hybrid Deep Learning Architecture for High-Frequency Cryptocurrency Trading

作者：Liu, Jun-Chi[1,2];Ma, Jun-Chao[1,2];Jiang, Zhi-Qiang[1,2]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Res Ctr Econophys, Shanghai, Peoples R China

会议论文集：11th International Conference on Intelligent Data and Security-IDS-Annual

会议日期：MAY 09-11, 2025

会议地点：New York City, NY

语种：英文

外文关键词：Cryptocurrency trading; Mamba state space models; temporal convolution networks; attention mechanisms; deep reinforcement learning

摘要：This paper presents a novel approach to cryptocurrency trading by introducing a hybrid deep learning architecture that combines state-of-the-art sequence modeling techniques with reinforcement learning. Our model integrates Mamba State Space Models (SSM), Temporal Convolution Networks (TCN), and multi-head attention mechanisms to capture complex temporal dependencies in market data, while leveraging Deep Q-Network variants for optimal decision making. We implement a sophisticated signal processing pipeline with adaptive smoothing and feature fusion mechanisms, followed by a reinforcement learning framework for trading strategy optimization. The proposed architecture demonstrates superior performance in capturing market dynamics and generating robust trading signals, as validated through comprehensive backtesting on high-frequency cryptocurrency data.