Reinforcement Learning Framework for Quantitative Trading
On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance
Abstract (arXiv)
The inherent volatility and dynamic fluctuations within the financial stock market underscore the necessity for investors to employ a comprehensive and reliable approach that integrates risk management strategies, market trends, and the movement trends of individual securities. By evaluating specific data, investors can make more informed decisions. However, the current body of literature lacks substantial evidence supporting the practical efficacy of reinforcement learning (RL) agents, as many models have only demonstrated success in back testing using historical data. This highlights the urgent need for a more advanced methodology capable of addressing these challenges. There is a significant disconnect in the effective utilization of financial indicators to better understand the potential market trends of individual securities. The disclosure of successful trading strategies is often restricted within financial markets, resulting in a scarcity of widely documented and published strategies leveraging RL. Furthermore, current research frequently overlooks the identification of financial indicators correlated with various market trends and their potential advantages. This research endeavors to address these complexities by enhancing the ability of RL agents to effectively differentiate between positive and negative buy/sell actions using financial indicators. While we do not address all concerns, this paper provides deeper insights and commentary on the utilization of technical indicators and their benefits within reinforcement learning. This work establishes a foundational framework for further exploration and investigation of more complex scenarios.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 11 references that are in the map. First posted on arXiv 12 Nov 2024.
Builds on
4 of 11 references.
References · 4
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
- 2019Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Responsecited by 6
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024The Evolution of Reinforcement Learning in Quantitative Finance: A Survey
- 2025QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learning
- 2024A Review of Reinforcement Learning in Financial Applications
- 2026Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading
- 2023PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets
- 2025Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
- 2024Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior
- 2024Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity