基于强化学习的金融交易系统研究与发展  

Review on Financial Trading System Based on Reinforcement Learning

作　　者：梁天新 杨小平[1] 王良[1] 韩镇远 LIANG Tian-Xin;YANG Xiao-Ping;WANG Liang;HAN Zhen-Yuan(School of Information, Renmin Universityof China, Beijing 100872, China)

机构地区：[1]中国人民大学信息学院,北京100872

出　　处：《软件学报》2019年第3期845-864,共20页Journal of Software

基　　金：国家自然科学基金(71531012)~~

摘　　要：近年来,强化学习在电子游戏、棋类、决策控制等领域取得了巨大进展,也带动着金融交易系统的迅速发展.金融交易问题已经成为强化学习领域的研究热点,特别是股票、外汇和期货等方面具有广泛的应用需求和学术研究意义.以金融领域常用的强化学习模型的发展为脉络,对交易系统、自适应算法、交易策略等方面的诸多研究成果进行了综述.最后讨论了强化学习在金融领域应用中存在的困难和挑战,并对今后强化学习交易系统发展趋势进行展望.In recent years, reinforcement learning has made great progress in the fields of electronic games, chess, and decision-making control. It has also driven the rapid development of financial transaction systems. The issue of financial transactions has become a hot topic in the field of reinforcement learning. Especially, it has wide application demand and academic research significance in the fields of stock, foreign exchange, and futures. This paper summarizes the research achievements of transaction systems, adaptive algorithms, and transaction strategies based on the progress of reinforcement learning models, which are commonly used in the financial field. Finally, the difficulties and challenges of reinforcement learning in financial trading system are discussed, and the future development trend is prospected.

关 键 词：强化学习 深度学习 金融交易系统 自适应算法 交易策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]