Reinforcement Learning for Quantitative Trading

2021/09/28 by Shuo Sun, Sun, Shuo, Rundong Wang +3 · 6 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Computational Finance (q-fin.CP) #Data Stream Mining Techniques #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Stock Market Forecasting Methods #cs.LG #q-fin.CP

paper · pdf · doi:10.48550/arxiv.2109.13851

arxiv created 2021/09/28 · openalex publication_date 2021/09/28 · arxiv updated 2021/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Common risk factors in the returns on stocks and bonds
• The Pricing of Options and Corporate Liabilities
• Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency
• Learning to trade via direct reinforcement
• Continuous control with deep reinforcement learning
• Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization
• A Comprehensive Survey on Graph Neural Networks

Cited by

• LiveTradeBench: Seeking Real-World Alpha with Large Language Models
• Group size effects and collective misalignment in LLM multi-agent systems
• CTBench: Cryptocurrency Time Series Generation Benchmark
• A comparative study of Bitcoin and Ripple cryptocurrencies trading using Deep Reinforcement Learning algorithms
• Realistic Market Impact Modeling for Reinforcement Learning Trading Environments
• Stock Market Forecasting: From Traditional Predictive Models to Large Language Models

Related

• Deep Reinforcement Learning for Quantitative Trading
• Reinforcement Learning Applied to Trading Systems: A Survey
• Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review
• Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration
• Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading