# Application of deep reinforcement learning for Indian stock trading automation

arXiv: https://arxiv.org/abs/2106.16088 · HTML: https://mlsanity.com/paper/2106.16088/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding](https://mlsanity.com/paper/2004.05940/) (2020)
- [Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network](https://mlsanity.com/paper/1902.10948/) (2019)

## Built on by (most cited first, 2 of 2)

- [Explainable Reinforcement Learning on Financial Stock Trading using SHAP](https://mlsanity.com/paper/2208.08790/) (2022)
- [The Enhanced Physics-Informed Kolmogorov-Arnold Networks: Applications of Newton's Laws in Financial Deep Reinforcement Learning (RL) Algorithms](https://mlsanity.com/paper/2602.01388/) (2026)

## Abstract (arXiv)

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.