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

Abstract

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.