A Deep Reinforcement Learning Model for Portfolio Management based on Weight Adjustment

Abstract

Existing portfolio management models based on deep reinforcement learning adjust funds dynamically by constructing an action space with specific trading behaviors. However, this method cannot effectively adapt to complex investment environments. This paper proposes an LSTM-ER-DCPPO portfolio management model based on the improved PPO algorithm. The model provides stable policy learning, enhanced strategy exploration, and trend identification, enabling more precise capital allocation through continuous asset weight output. To evaluate the model, a simulated market trading environment based on real stock data was created. The experimental results show that the LSTM-ER-DCPPO model achieved an annualized return of 58.62%, a maximum drawdown of 8.53%, a Calmar ratio of 6.873, and a Sharpe ratio of 2.434, outperforming other benchmark models.