# Practical Deep Reinforcement Learning Approach for Stock Trading

arXiv: https://arxiv.org/abs/1811.07522 · HTML: https://mlsanity.com/paper/1811.07522/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 30 of 49)

- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance](https://mlsanity.com/paper/2111.09395/) (2021)
- [Reinforcement Learning for Quantitative Trading](https://mlsanity.com/paper/2109.13851/) (2021)
- [FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading](https://mlsanity.com/paper/2502.11433/) (2025)
- [Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review](https://mlsanity.com/paper/2106.00123/) (2021)
- [Dynamic Datasets and Market Environments for Financial Reinforcement Learning](https://mlsanity.com/paper/2304.13174/) (2023)
- [Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy](https://mlsanity.com/paper/2511.12120/) (2025)
- [A Review of Reinforcement Learning in Financial Applications](https://mlsanity.com/paper/2411.12746/) (2024)
- [A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks](https://mlsanity.com/paper/2212.02721/) (2022)
- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [Deep Reinforcement Learning Approach for Trading Automation in The Stock Market](https://mlsanity.com/paper/2208.07165/) (2022)
- [Approximate Equivariance in Reinforcement Learning](https://mlsanity.com/paper/2411.04225/) (2024)
- [Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting](https://mlsanity.com/paper/2209.05559/) (2022)
- [On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services](https://mlsanity.com/paper/2111.01306/) (2021)
- [Markov Decision Processes under Model Uncertainty](https://mlsanity.com/paper/2206.06109/) (2022)
- [CNN-DRL with Shuffled Features in Finance](https://mlsanity.com/paper/2402.03338/) (2024)
- [Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent](https://mlsanity.com/paper/2407.14486/) (2024)
- [GUIDE: Real-Time Human-Shaped Agents](https://mlsanity.com/paper/2410.15181/) (2024)
- [A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules](https://mlsanity.com/paper/2101.03867/) (2021)
- [Application of deep reinforcement learning for Indian stock trading automation](https://mlsanity.com/paper/2106.16088/) (2021)
- [High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning](https://mlsanity.com/paper/2112.04755/) (2021)
- [Systematic Review on Reinforcement Learning in the Field of Fintech](https://mlsanity.com/paper/2305.07466/) (2023)
- [Deep Reinforcement Learning for ESG financial portfolio management](https://mlsanity.com/paper/2307.09631/) (2023)
- [Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning](https://mlsanity.com/paper/2312.15385/) (2023)
- [Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting](https://mlsanity.com/paper/2104.10483/) (2021)
- [Financial Vision Based Reinforcement Learning Trading Strategy](https://mlsanity.com/paper/2202.04115/) (2022)
- [DDPG based on multi-scale strokes for financial time series trading strategy](https://mlsanity.com/paper/2207.10071/) (2022)
- [Deep Policy Gradient Methods in Commodity Markets](https://mlsanity.com/paper/2308.01910/) (2023)
- [Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution](https://mlsanity.com/paper/2410.14927/) (2024)

## Abstract (arXiv)

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.