智能科学与技术学报2024，Vol.6Issue(2)：220-231,12.DOI:10.11959/j.issn.2096-6652.202402
面向数字货币量化交易的OAC模型研究
Research on OAC model for quantitative trading of digital currency
摘要
Abstract
In response to the challenges encountered in quantitative trading of digital currencies,characterized by the pres-ence of a multitude of intricate factors and a high-dimensional factor state space,an enhanced optimistic actor-critic(OAC)model,referred to as OAC_LSTM_ATT,had been proposed.This model incorporated long short-term memory(LSTM)and a multi-head attention mechanism to optimize the network architecture of OAC,thereby augmenting its ca-pacity for modeling time-series data and generalization.Through this integration,the intelligent agent operating in the quantitative trading environment was capable of making more adaptable and precise trading decisions,consequently el-evating the quality and efficacy of trading strategies.Experimental findings revealed that,in the Bitcoin market,the cumu-lative return achieved was 16.36%,with a maximum drawdown of 9.08%,a Sharpe ratio of 0.014,and a volatility of 13.09%.Corresponding metrics in the Ethereum market amounted to 16.30%,8.56%,0.014,and 13.42%.When com-pared to models such as PPO,LSTM_PPO,A2C,OAC_LSTM_ATT demonstrates superior performance in terms of both effectiveness and stability,thereby offering valuable insights for the development of quantitative trading strategies.关键词
量化交易/深度强化学习/注意力机制长/短期记忆网络/数字货币Key words
quantitative trading/deep reinforcement learning/attention mechanism/long short-term memory/digital cur-rency分类
信息技术与安全科学引用本文复制引用
许波,贺一峻,李祥霞..面向数字货币量化交易的OAC模型研究[J].智能科学与技术学报,2024,6(2):220-231,12.基金项目
广东省哲学社会科学规划项目(No.GD24CGL08) （No.GD24CGL08）
广东省普通高校重点领域专项(No.2021ZDZX3006) （No.2021ZDZX3006）
广州市科技计划项目(No.202201011651) Project of Philosophy and Social Science Planning of Guangdong(No.GD24CGL08),Special Projects in Key Fields of Colleges and Universities in Guangdong Province(No.2021ZDZX3006),Science and Technology Projects in Guangzhou(No.202201011651) （No.202201011651）
