mlsanityData to 7 Oct 2026

Practical Deep Reinforcement Learning Approach for Stock Trading

arXiv 1811.07522 ↗First posted 19 Nov 2018cs.LG q-fin.TR stat.MLcited by 49

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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.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Nov 2018.

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Citing papers per year: 2021: 11; 2022: 8; 2023: 10; 2024: 9; 2025: 6; 2026: 5.

49 papers.

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  1. 2023FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Designcited by 163
  2. 2021Recent Advances in Reinforcement Learning in Financecited by 102
  3. 2021FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Financecited by 57
  4. 2021Reinforcement Learning for Quantitative Tradingcited by 24
  5. 2025FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Tradingcited by 23
  6. 2021Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Reviewcited by 17
  7. 2023Dynamic Datasets and Market Environments for Financial Reinforcement Learningcited by 15
  8. 2025Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategycited by 15
  9. 2022A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networkscited by 12
  10. 2024The Evolution of Reinforcement Learning in Quantitative Finance: A Surveycited by 11
  11. 2024A Review of Reinforcement Learning in Financial Applicationscited by 11
  12. 2022Deep Reinforcement Learning Approach for Trading Automation in The Stock Marketcited by 9
37 more
  1. 2024Approximate Equivariance in Reinforcement Learningcited by 9
  2. 2022Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfittingcited by 5
  3. 2021On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Servicescited by 4
  4. 2024CNN-DRL with Shuffled Features in Financecited by 3
  5. 2024Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agentcited by 3
  6. 2024GUIDE: Real-Time Human-Shaped Agentscited by 3
  7. 2021A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rulescited by 2
  8. 2021Application of deep reinforcement learning for Indian stock trading automationcited by 2
  9. 2021High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learningcited by 2
  10. 2022Markov Decision Processes under Model Uncertaintycited by 2
  11. 2023Systematic Review on Reinforcement Learning in the Field of Fintechcited by 2
  12. 2023Deep Reinforcement Learning for ESG financial portfolio managementcited by 2
  13. 2021Adaptive learning for financial markets mixing model-based and model-free RL for volatility targetingcited by 1
  14. 2022Financial Vision Based Reinforcement Learning Trading Strategycited by 1
  15. 2022DDPG based on multi-scale strokes for financial time series trading strategycited by 1
  16. 2023Deep Policy Gradient Methods in Commodity Marketscited by 1
  17. 2023Discrete-Time Mean-Variance Strategy Based on Reinforcement Learningcited by 1
  18. 2024Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Executioncited by 1
  19. 2021Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applicationscited by 0
  20. 2021TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Executioncited by 0
  21. 2022Intelligent Systematic Investment Agent: an ensemble of deep learning and evolutionary strategiescited by 0
  22. 2022Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strengthcited by 0
  23. 2023Using a Deep Learning Model to Simulate Human Stock Trader's Methods of Chart Analysiscited by 0
  24. 2023Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Tradingcited by 0
  25. 2023An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Tradingcited by 0
  26. 2023Predictive AI for SME and Large Enterprise Financial Performance Managementcited by 0
  27. 2024Portfolio Management using Deep Reinforcement Learningcited by 0
  28. 2024Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learningcited by 0
  29. 2025Risk-averse policies for natural gas futures trading using distributional reinforcement learningcited by 0
  30. 2025Revisiting Ensemble Methods for Stock Trading and Crypto Trading Tasks at ACM ICAIF FinRL Contest 2023-2024cited by 0
  31. 2025Directly Learning Stock Trading Strategies Through Profit Guided Loss Functionscited by 0
  32. 2025Deep Reinforcement Learning in Factor Investmentcited by 0
  33. 2026The Enhanced Physics-Informed Kolmogorov-Arnold Networks: Applications of Newton's Laws in Financial Deep Reinforcement Learning (RL) Algorithmscited by 0
  34. 2026MultiHedge: Adaptive Coordination via Retrieval-Augmented Controlcited by 0
  35. 2026CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approachcited by 0
  36. 2026Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Tradingcited by 0
  37. 2026Deep Reinforcement Learning for Equity Trading: Benchmarking Actor-Critic Methods with Forward Retrainingcited by 0