Stock market trading via actor-critic reinforcement learning and adaptable data structure

Currently, the stock market is attractive, and it is challenging to develop an efficient investment model with high accuracy due to changes in the values of the shares for political, economic, and social reasons. This paper presents an innovative proposal for a short-term, automatic investment model to reduce capital loss during trading, applying a reinforcement learning (RL) model. On the other hand, we propose an adaptable data window structure to enhance the learning and accuracy of investment agents in three foreign exchange markets: crude oil, gold, and the Euro. In addition, the RL model employs an actor-critic neural network with rectified linear unit (ReLU) neurons to generate specialized investment agents, enabling more efficient trading, minimizing investment losses across different time periods, and reducing the model's learning time. The proposed RL model obtained a reduction average loss of 0.03% in Euro, 0.25% in Gold, and 0.13% in Crude Oil in the test phase with varying initial conditions.