mlsanityData to 7 Oct 2026

Reinforcement Learning for Quantitative Trading

arXiv 2109.13851 ↗First posted 28 Sep 2021cs.LG q-fin.CPcited by 24

Not on the map. Why?

Abstract (arXiv)

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement learning (RL) has garnered significant interest in many domains such as robotics and video games, owing to its outstanding ability on solving complex sequential decision making problems. RL's impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks. It is a flourishing research direction to explore RL techniques' potential on QT tasks. This paper aims at providing a comprehensive survey of research efforts on RL-based methods for QT tasks. More concretely, we devise a taxonomy of RL-based QT models, along with a comprehensive summary of the state of the art. Finally, we discuss current challenges and propose future research directions in this exciting field.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 36 of its 150 references that are in the map. First posted on arXiv 28 Sep 2021.

Builds on

36 of 150 references.

References · 36

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2014Going Deeper with Convolutionscited by 4,231
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  5. 2015End-to-End Training of Deep Visuomotor Policiescited by 847
  6. 2019Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019cited by 270
  7. 2015Deep Reinforcement Learning in Large Discrete Action Spacescited by 158
  8. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 123
  9. 2018Temporal Relational Ranking for Stock Predictioncited by 100
  10. 2020Deep Learning for Financial Applications : A Surveycited by 90
  11. 2019Deep Reinforcement Learning for Tradingcited by 73
  12. 2017Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Predictioncited by 60
24 more
  1. 2020Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction Statescited by 55
  2. 2019Risk-Averse Trust Region Optimization for Reward-Volatility Reductioncited by 53
  3. 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
  4. 2018Market Making via Reinforcement Learningcited by 45
  5. 2019Get Real: Realism Metrics for Robust Limit Order Book Market Simulationscited by 40
  6. 2018Double Deep Q-Learning for Optimal Executioncited by 39
  7. 2019AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networkscited by 39
  8. 2016Cryptocurrency Portfolio Management with Deep Reinforcement Learningcited by 31
  9. 2019Enhancing Time Series Momentum Strategies Using Deep Neural Networkscited by 31
  10. 2019ABIDES: Towards High-Fidelity Market Simulation for AI Researchcited by 31
  11. 2021REST: Relational Event-driven Stock Trend Forecastingcited by 30
  12. 2021Universal Trading for Order Execution with Oracle Policy Distillationcited by 29
  13. 2019Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimizationcited by 22
  14. 2020Cost-Sensitive Portfolio Selection via Deep Reinforcement Learningcited by 21
  15. 2020Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Executioncited by 20
  16. 2016Predicting the direction of stock market prices using random forestcited by 14
  17. 2020Robust Market Making via Adversarial Reinforcement Learningcited by 13
  18. 2020Option Hedging with Risk Averse Reinforcement Learningcited by 13
  19. 2020DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysiscited by 12
  20. 2021MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Managementcited by 10
  21. 2019WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Seriescited by 8
  22. 2020Uncertainty-Aware Lookahead Factor Models for Quantitative Investingcited by 8
  23. 2020Bridging the gap between Markowitz planning and deep reinforcement learningcited by 5
  24. 2019Alpha Discovery Neural Network based on Prior Knowledgecited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 6; 2024: 8; 2025: 3; 2026: 4.

24 papers.

Sort:

By area:

  1. 2025Group size effects and collective misalignment in LLM multi-agent systemscited by 16
  2. 2023Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Setscited by 15
  3. 2021DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunitiescited by 14
  4. 2025LiveTradeBench: Seeking Real-World Alpha with Large Language Modelscited by 13
  5. 2022MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimizationcited by 10
  6. 2024LLM-Based World Models Can Make Decisions Solely, But Rigorous Evaluations are Neededcited by 10
  7. 2023PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Marketscited by 5
  8. 2024MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRUcited by 4
  9. 2025CTBench: Cryptocurrency Time Series Generation Benchmarkcited by 3
  10. 2022Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Tradingcited by 2
  11. 2026AlphaPROBE: Alpha Mining via Principled Retrieval and On-graph biased evolutioncited by 2
  12. 2023Robot path planning using deep reinforcement learningcited by 1
12 more
  1. 2024Configurable Mirror Descent: Towards a Unification of Decision Makingcited by 1
  2. 2024Deviations from the Nash equilibrium in a two-player optimal execution game with reinforcement learningcited by 1
  3. 2026Realistic Market Impact Modeling for Reinforcement Learning Trading Environmentscited by 1
  4. 2023Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Trainingcited by 0
  5. 2023Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Tradingcited by 0
  6. 2023Onflow: a model free, online portfolio allocation algorithm robust to transaction feescited by 0
  7. 2024Synergistic Formulaic Alpha Generation for Quantitative Trading based on Reinforcement Learningcited by 0
  8. 2024A priori Estimates for Deep Residual Network in Continuous-time Reinforcement Learningcited by 0
  9. 2024Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learningcited by 0
  10. 2024Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learningcited by 0
  11. 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
  12. 2026Indirect tipping: a social attack surface in AI agent populationscited by 0