Research on Spread Arbitrage Strategy and Optimization Analysis Based on Deep Reinforcement Learning in High-Frequency Trading
Zu-He Xu （ Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215000, China ）
https://doi.org/10.37155/2972-4813-0301-5Abstract
This paper explores the implementation of spread arbitrage strategies in high-frequency trading (HFT) environments by constructing an adaptive trading system based on deep reinforcement learning (DRL). The study focuses on four core components: multi-dimensional data integration and preprocessing, reinforcement learning algorithm framework design, trading signal generation and execution mechanism, and strategy optimization with risk control. Acomprehensive high-frequency trading solution is proposed, which incorporates multi-level feature engineering, optimized state space design, refned execution decision-making, and dynamic parameter adjustment. This approach enhances the robustness and profitability of arbitrage strategies, providing both theoretical foundation and practical guidance for the feld of quantitative trading.
Keywords
Deep Reinforcement Learning; High-Frequency Trading; Spread ArbitrageFull Text
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Copyright © 2025 Zu-He Xu
Publishing time:2025-03-30
This work is licensed under a Creative Commons Attribution 4.0 International License
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