摘要
针对传统交易策略无法有效长期消除市场噪声和非线性影响的问题,提出一种基于注意力机制的异步优势动作评价(squeeze-and-excitation asynchronous advantage actor-critic,SE-A3C)量化交易策略。以历史技术指标因子为环境状态,利用卷积网络和注意力机制模块提取数据特征,判断交易动作,并采用异步训练的方式将多智能体与环境进行交互,有效提升策略的自适应能力。采用该策略对沪深300和上证50股指期货进行交易,结果表明:在测试阶段,沪深300的收益率为12.23%,胜率为58.82%,最大回撤率为2.47%;上证50的收益率为18.82%,胜率为57.56%,最大回撤率为1.05%。
Aiming at the inability of traditional trading strategies to effectively eliminate market noise and non-linear effects in the long term,an squeeze-and-excitation asynchronous advantage actor-critic(SE-A3C)quantitative trading strategy based on the attention mechanism is proposed.Taking historical technical indicator factors as the environmental state,using convolutional network and attention mechanism modules to extract data features,determine transaction actions,and use asynchronous training to interact with the environment by multi-agents,effectively improving the adaptive ability of strategies.This strategy trades CSI 300 and SSE 50 stock index futures.In the testing phase,the yield of CSI 300 is 12.23%,the winning rate is 58.82%,the maximum drawdown is 2.47%,and the yield of SSE 50 is 18.82%,the winning rate is 57.56%,the maximum drawdown is 1.05%.
作者
符甲鑫
刘磊
钱成
FU Jiaxin;LIU Lei;QIAN Cheng(College of Science,Hohai University,Nanjing 211100,China;School of Mathematics,Southeast University,Nanjing 211189,China)
出处
《南通大学学报（自然科学版）》
CAS
2023年第2期43-49,74,共8页
Journal of Nantong University(Natural Science Edition)
基金
国家自然科学基金面上项目(61773152)。
关键词
深度强化学习
异步优势动作评价
注意力机制
定量交易
deep reinforcement learning
asynchronous advantage actor-critic(A3C)
attention mechanism
quantitative trading
