mlsanityData to 14 Oct 2026

An Application of Deep Reinforcement Learning to Algorithmic Trading

arXiv 2004.06627 ↗First posted 7 Apr 2020q-fin.TR cs.AI cs.LGcited by 30

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Abstract (arXiv)

This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets. It proposes a novel DRL trading strategy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. Denominated the Trading Deep Q-Network algorithm (TDQN), this new trading strategy is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand. The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data. In order to objectively assess the performance of trading strategies, the research paper also proposes a novel, more rigorous performance assessment methodology. Following this new performance assessment approach, promising results are reported for the TDQN strategy.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Apr 2020.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 4; 2024: 4; 2025: 6; 2026: 8.

30 papers.

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  1. 2021Deep Reinforcement Learning for Active High Frequency Tradingcited by 21
  2. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  3. 2024The Evolution of Reinforcement Learning in Quantitative Finance: A Surveycited by 11
  4. 2024MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Tradingcited by 7
  5. 2022Risk-Sensitive Policy with Distributional Reinforcement Learningcited by 6
  6. 2022Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learningcited by 4
  7. 2021Robust Risk-Sensitive Reinforcement Learning Agents for Trading Marketscited by 3
  8. 2025Language Model Guided Reinforcement Learning in Quantitative Tradingcited by 3
  9. 2021A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rulescited by 2
  10. 2021High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learningcited by 2
  11. 2025Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluationcited by 2
  12. 2026Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimizationcited by 2
18 more
  1. 2024A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoTcited by 1
  2. 2024NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicingcited by 1
  3. 2026Optimal Stop-Loss and Take-Profit Parameterization for Autonomous Trading Agent Swarmcited by 1
  4. 2021TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Executioncited by 0
  5. 2022An intelligent algorithmic trading based on a risk-return reinforcement learning algorithmcited by 0
  6. 2023A Deep Reinforcement Learning Trader without Offline Trainingcited by 0
  7. 2023Deep Reinforcement Learning for Robust Goal-Based Wealth Managementcited by 0
  8. 2023An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Tradingcited by 0
  9. 2025Risk-averse policies for natural gas futures trading using distributional reinforcement learningcited by 0
  10. 2025The Role of Deep Learning in Financial Asset Management: A Systematic Reviewcited by 0
  11. 2025Technical Indicator Networks (TINs): An Interpretable Neural Architecture Modernizing Classic al Technical Analysis for Adaptive Algorithmic Tradingcited by 0
  12. 2025News-Aware Direct Reinforcement Trading for Financial Marketscited by 0
  13. 2026The Enhanced Physics-Informed Kolmogorov-Arnold Networks: Applications of Newton's Laws in Financial Deep Reinforcement Learning (RL) Algorithmscited by 0
  14. 2026Decomposable Reward Modeling and Realistic Environment Design for Reinforcement Learning-Based Forex Tradingcited by 0
  15. 2026When Quotes Crumble: Detecting Transient Mechanical Liquidity Erosion in Limit Order Bookscited by 0
  16. 2026A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysiscited by 0
  17. 2026ViperQ: Order Flow Pattern Recognition via Auction Market Theory for Reinforcement Learning Tradingcited by 0
  18. 2026Deep Reinforcement Learning for Equity Trading: Benchmarking Actor-Critic Methods with Forward Retrainingcited by 0