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.

当前股票市场具备投资吸引力，但受政治、经济与社会因素引发的个股价值波动影响，开发高精度高效能投资模型颇具挑战。为此，本文提出一种创新性的短期自动化投资模型方案，通过引入强化学习（Reinforcement Learning, RL）以降低交易过程中的资本损失。另一方面，本文针对原油、黄金与欧元三类交易市场，提出可自适应调整的数据窗口结构，以提升投资智能体（AI Agent）的学习性能与预测精度。进一步地，所提RL模型采用搭载修正线性单元（Rectified Linear Unit, ReLU）神经元的演员-评论家神经网络，生成专用投资智能体，从而实现更高效的交易操作，最小化不同交易时段的投资损失，并缩短模型的学习时长。在初始条件各异的测试阶段中，所提RL模型实现的平均损失降幅分别为：欧元市场0.03%、黄金市场0.25%、原油市场0.13%。