mlsanityData to 7 Oct 2026

Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review

arXiv 2106.00123 ↗First posted 31 May 2021cs.LG q-fin.TRcited by 17

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Abstract (arXiv)

Algorithmic stock trading has become a staple in today's financial market, the majority of trades being now fully automated. Deep Reinforcement Learning (DRL) agents proved to be to a force to be reckon with in many complex games like Chess and Go. We can look at the stock market historical price series and movements as a complex imperfect information environment in which we try to maximize return - profit and minimize risk. This paper reviews the progress made so far with deep reinforcement learning in the subdomain of AI in finance, more precisely, automated low-frequency quantitative stock trading. Many of the reviewed studies had only proof-of-concept ideals with experiments conducted in unrealistic settings and no real-time trading applications. For the majority of the works, despite all showing statistically significant improvements in performance compared to established baseline strategies, no decent profitability level was obtained. Furthermore, there is a lack of experimental testing in real-time, online trading platforms and a lack of meaningful comparisons between agents built on different types of DRL or human traders. We conclude that DRL in stock trading has showed huge applicability potential rivalling professional traders under strong assumptions, but the research is still in the very early stages of development.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 12 of its 61 references that are in the map. First posted on arXiv 31 May 2021.

Builds on

12 of 61 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  5. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  6. 2016Deep Reinforcement Learning from Self-Play in Imperfect-Information Gamescited by 153
  7. 2019Deep Reinforcement Learning for Tradingcited by 73
  8. 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
  9. 2019AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networkscited by 39
  10. 2019Enhancing Time Series Momentum Strategies Using Deep Neural Networkscited by 31
  11. 2018Financial Trading as a Game: A Deep Reinforcement Learning Approachcited by 22
  12. 2020Deep learning for Stock Market Predictioncited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 3; 2024: 6; 2025: 2; 2026: 3.

17 papers.

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  1. 2023FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Designcited by 163
  2. 2023Dynamic Datasets and Market Environments for Financial Reinforcement Learningcited by 15
  3. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  4. 2022A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networkscited by 12
  5. 2024A Review of Reinforcement Learning in Financial Applicationscited by 11
  6. 2024Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Modelscited by 9
  7. 2021Quantum Machine Learning for Financecited by 7
  8. 2021FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Financecited by 6
  9. 2025Language Model Guided Reinforcement Learning in Quantitative Tradingcited by 3
  10. 2024CNN-DRL for Scalable Actions in Financecited by 2
  11. 2024MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Tradingcited by 1
  12. 2024Reinforcement Learning Pair Trading: A Dynamic Scaling approachcited by 1
5 more
  1. 2024Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learningcited by 0
  2. 2025Directly Learning Stock Trading Strategies Through Profit Guided Loss Functionscited by 0
  3. 2026Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Makingcited by 0
  4. 2026Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processescited by 0
  5. 2026Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Tradingcited by 0