Dynamic Stock Trading with Gated-Convolutional-Attention Neural Network and Deep Reinforcement Learning

The stock market plays an imperative role in the entire financial market. The intricate and multifaceted nature of the stock market poses a challenge for investors seeking to establish a reliable and profitable trading approach. This paper aims to address this issue by leveraging two methodologies based on Deep Reinforcement Learning (DRL), namely Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG), incorporating Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU) architectures, along with an attention mechanism to boost the decision making based on time-series stock data. This adaptation enables the model to focus on essential features and time periods within the stock data, leading to more successful and higher-quality trading choices. Following extensive experimentation and analysis, our proposed RLbased trading demonstrates improved accuracy and profitability compared to similar approaches. The proposed methodology strives to offer investors a dependable and lucrative trading strategy, ultimately leading to a more prosperous and efficient stock trading experience.

Dynamic Stock Trading with Gated-Convolutional-Attention Neural Network and Deep Reinforcement Learning Keywords:

Stock markets , Trading Strategies , gated recurrent unit (GRU) , deep reinforcement learning (DRL) , deep q-network (DQN) , deep deterministic policy gradient (DDPG)