Research on OAC model for quantitative trading of digital currency
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.
国家科技期刊平台
NSTL
万方数据
