# Reinforcement Learning for Quantitative Trading

arXiv: https://arxiv.org/abs/2109.13851 · HTML: https://mlsanity.com/paper/2109.13851/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 150 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 30 of 36)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Deep Reinforcement Learning in Large Discrete Action Spaces](https://mlsanity.com/paper/1512.07679/) (2015)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [Deep Reinforcement Learning for Trading](https://mlsanity.com/paper/1911.10107/) (2019)
- [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://mlsanity.com/paper/1712.02136/) (2017)
- [Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States](https://mlsanity.com/paper/2002.05780/) (2020)
- [Risk-Averse Trust Region Optimization for Reward-Volatility Reduction](https://mlsanity.com/paper/1912.03193/) (2019)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [Double Deep Q-Learning for Optimal Execution](https://mlsanity.com/paper/1812.06600/) (2018)
- [Get Real: Realism Metrics for Robust Limit Order Book Market Simulations](https://mlsanity.com/paper/1912.04941/) (2019)
- [AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks](https://mlsanity.com/paper/1908.02646/) (2019)
- [Cryptocurrency Portfolio Management with Deep Reinforcement Learning](https://mlsanity.com/paper/1612.01277/) (2016)
- [ABIDES: Towards High-Fidelity Market Simulation for AI Research](https://mlsanity.com/paper/1904.12066/) (2019)
- [Enhancing Time Series Momentum Strategies Using Deep Neural Networks](https://mlsanity.com/paper/1904.04912/) (2019)
- [REST: Relational Event-driven Stock Trend Forecasting](https://mlsanity.com/paper/2102.07372/) (2021)
- [Universal Trading for Order Execution with Oracle Policy Distillation](https://mlsanity.com/paper/2103.10860/) (2021)
- [Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization](https://mlsanity.com/paper/1901.08740/) (2019)
- [Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning](https://mlsanity.com/paper/2003.03051/) (2020)
- [Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution](https://mlsanity.com/paper/2012.12620/) (2020)
- [Predicting the direction of stock market prices using random forest](https://mlsanity.com/paper/1605.00003/) (2016)
- [Robust Market Making via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2003.01820/) (2020)
- [Option Hedging with Risk Averse Reinforcement Learning](https://mlsanity.com/paper/2010.12245/) (2020)

## Built on by (most cited first, 24 of 24)

- [Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets](https://mlsanity.com/paper/2305.19004/) (2023)
- [Group size effects and collective misalignment in LLM multi-agent systems](https://mlsanity.com/paper/2510.22422/) (2025)
- [DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities](https://mlsanity.com/paper/2201.09058/) (2021)
- [LiveTradeBench: Seeking Real-World Alpha with Large Language Models](https://mlsanity.com/paper/2511.03628/) (2025)
- [MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization](https://mlsanity.com/paper/2210.01774/) (2022)
- [LLM-Based World Models Can Make Decisions Solely, But Rigorous Evaluations are Needed](https://mlsanity.com/paper/2411.08794/) (2024)
- [PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets](https://mlsanity.com/paper/2302.00586/) (2023)
- [MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU](https://mlsanity.com/paper/2410.20679/) (2024)
- [CTBench: Cryptocurrency Time Series Generation Benchmark](https://mlsanity.com/paper/2508.02758/) (2025)
- [AlphaPROBE: Alpha Mining via Principled Retrieval and On-graph biased evolution](https://mlsanity.com/paper/2602.11917/) (2026)
- [Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading](https://mlsanity.com/paper/2210.15448/) (2022)
- [Robot path planning using deep reinforcement learning](https://mlsanity.com/paper/2302.09120/) (2023)
- [Configurable Mirror Descent: Towards a Unification of Decision Making](https://mlsanity.com/paper/2405.11746/) (2024)
- [Deviations from the Nash equilibrium in a two-player optimal execution game with reinforcement learning](https://mlsanity.com/paper/2408.11773/) (2024)
- [Realistic Market Impact Modeling for Reinforcement Learning Trading Environments](https://mlsanity.com/paper/2603.29086/) (2026)
- [Indirect tipping: a social attack surface in AI agent populations](https://mlsanity.com/paper/2609.25194/) (2026)
- [Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training](https://mlsanity.com/paper/2304.11043/) (2023)
- [Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading](https://mlsanity.com/paper/2307.09377/) (2023)
- [Onflow: a model free, online portfolio allocation algorithm robust to transaction fees](https://mlsanity.com/paper/2312.05169/) (2023)
- [Synergistic Formulaic Alpha Generation for Quantitative Trading based on Reinforcement Learning](https://mlsanity.com/paper/2401.02710/) (2024)
- [A priori Estimates for Deep Residual Network in Continuous-time Reinforcement Learning](https://mlsanity.com/paper/2402.16899/) (2024)
- [Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning](https://mlsanity.com/paper/2408.02295/) (2024)
- [Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning](https://mlsanity.com/paper/2412.17908/) (2024)
- [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)

## 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.