# An Application of Deep Reinforcement Learning to Algorithmic Trading

arXiv: https://arxiv.org/abs/2004.06627 · HTML: https://mlsanity.com/paper/2004.06627/

## Key facts

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

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

- [Deep Reinforcement Learning for Active High Frequency Trading](https://mlsanity.com/paper/2101.07107/) (2021)
- [Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications](https://mlsanity.com/paper/2307.00908/) (2023)
- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading](https://mlsanity.com/paper/2406.14537/) (2024)
- [Risk-Sensitive Policy with Distributional Reinforcement Learning](https://mlsanity.com/paper/2212.14743/) (2022)
- [Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning](https://mlsanity.com/paper/2210.03469/) (2022)
- [Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets](https://mlsanity.com/paper/2107.08083/) (2021)
- [Language Model Guided Reinforcement Learning in Quantitative Trading](https://mlsanity.com/paper/2508.02366/) (2025)
- [A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules](https://mlsanity.com/paper/2101.03867/) (2021)
- [High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning](https://mlsanity.com/paper/2112.04755/) (2021)
- [Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation](https://mlsanity.com/paper/2502.17011/) (2025)
- [Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization](https://mlsanity.com/paper/2602.17098/) (2026)
- [A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT](https://mlsanity.com/paper/2404.03276/) (2024)
- [NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing](https://mlsanity.com/paper/2407.20281/) (2024)
- [Optimal Stop-Loss and Take-Profit Parameterization for Autonomous Trading Agent Swarm](https://mlsanity.com/paper/2604.27150/) (2026)
- [TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution](https://mlsanity.com/paper/2104.00620/) (2021)
- [An intelligent algorithmic trading based on a risk-return reinforcement learning algorithm](https://mlsanity.com/paper/2208.10707/) (2022)
- [A Deep Reinforcement Learning Trader without Offline Training](https://mlsanity.com/paper/2303.00356/) (2023)
- [Deep Reinforcement Learning for Robust Goal-Based Wealth Management](https://mlsanity.com/paper/2307.13501/) (2023)
- [An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading](https://mlsanity.com/paper/2309.00626/) (2023)
- [Risk-averse policies for natural gas futures trading using distributional reinforcement learning](https://mlsanity.com/paper/2501.04421/) (2025)
- [The Role of Deep Learning in Financial Asset Management: A Systematic Review](https://mlsanity.com/paper/2503.01591/) (2025)
- [Technical Indicator Networks (TINs): An Interpretable Neural Architecture Modernizing Classic al Technical Analysis for Adaptive Algorithmic Trading](https://mlsanity.com/paper/2507.20202/) (2025)
- [News-Aware Direct Reinforcement Trading for Financial Markets](https://mlsanity.com/paper/2510.19173/) (2025)
- [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)
- [Decomposable Reward Modeling and Realistic Environment Design for Reinforcement Learning-Based Forex Trading](https://mlsanity.com/paper/2604.00031/) (2026)
- [When Quotes Crumble: Detecting Transient Mechanical Liquidity Erosion in Limit Order Books](https://mlsanity.com/paper/2604.21993/) (2026)
- [A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis](https://mlsanity.com/paper/2606.10412/) (2026)
- [ViperQ: Order Flow Pattern Recognition via Auction Market Theory for Reinforcement Learning Trading](https://mlsanity.com/paper/2609.13825/) (2026)
- [Deep Reinforcement Learning for Equity Trading: Benchmarking Actor-Critic Methods with Forward Retraining](https://mlsanity.com/paper/2609.31870/) (2026)

## 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.