Application of deep reinforcement learning for Indian stock trading automation
This work addresses stock trading automation for Indian investors, but it is incremental as it applies existing methods to a new dataset.
The paper applied deep reinforcement learning to automate stock trading in Indian markets, testing three models on ten datasets and evaluating their performance.
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.