Title: Application of deep reinforcement learning for Indian stock trading automation Show Chinese title

Title: 深度强化学习在印度股票交易自动化中的应用

Abstract: In stock trading, feature extraction and trading strategy design are the two important tasks to achieve long-term benefits using machine learning techniques. Several methods have been proposed to design trading strategy by acquiring trading signals to maximize the rewards. In the present paper the theory of deep reinforcement learning is applied for stock trading strategy and investment decisions to Indian markets. The experiments are performed systematically with three classical Deep Reinforcement Learning models Deep Q-Network, Double Deep Q-Network and Dueling Double Deep Q-Network on ten Indian stock datasets. The performance of the models are evaluated and comparison is made. Show Chinese abstract

Abstract: 在股票交易中，特征提取和交易策略设计是使用机器学习技术实现长期收益的两个重要任务。 已经提出了几种方法，通过获取交易信号来设计交易策略以最大化奖励。 在本文中，深度强化学习的理论被应用于印度市场的股票交易策略和投资决策。 在十个印度股票数据集上，系统地进行了三种经典深度强化学习模型 Deep Q-Network、Double Deep Q-Network 和 Dueling Double Deep Q-Network 的实验。 评估了模型的性能并进行了比较。