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.

目前，股票市场具有吸引力，但由于政治、经济及社会因素导致股价波动，开发高效且高精度的投资模型面临挑战。本文提出一种基于强化学习（reinforcement learning, RL）模型的创新性短期自动投资方案，旨在降低交易过程中的资本损失。另一方面，我们提出一种自适应数据窗口结构，以提升投资智能体（AI Agent）在原油、黄金及欧元这三个外汇市场中的学习能力与准确性。此外，该RL模型采用带有整流线性单元（rectified linear unit, ReLU）神经元的演员-评论家（actor-critic）神经网络生成专业化投资智能体，从而实现更高效的交易、降低不同时间段的投资损失并缩短模型学习时间。在初始条件变化的测试阶段，所提出的RL模型在欧元市场实现了0.03%的平均损失降低，黄金市场为0.25%，原油市场为0.13%。