文献类型：期刊文献

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

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

机构：[1] East China University of Science and Technology, School of Business and Research Center for Econophysics, Shanghai, China

年份：2025

起止页码：55

外文期刊名：Proceedings - 2025 IEEE 11th International Conference on Intelligent Data and Security, IDS 2025

收录：EI(收录号：20252818759218)

语种：英文

外文关键词：Architecture - Computation theory - Convolution - Cryptocurrency - Decision making - Deep learning - Deep reinforcement learning - Electronic trading - Network architecture - Signal processing

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. ? 2025 IEEE.