Research on OAC model for quantitative trading of digital currency

Abstract

In response to the challenges encountered in quantitative trading of digital currencies, characterized by the presence 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 capacity 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 elevating the quality and efficacy of trading strategies. Experimental findings revealed that, in the Bitcoin market, the cumulative 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 compared 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.

Keywords

• quantitative trading;deep reinforcement learning;attention mechanism;long short-term memory;digital currency