Reinforcement Learning Framework for Quantitative Trading

ArXiv ID: 2411.07585 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Reinforcement Learning (RL), Trading Strategies, Financial Indicators, Market Trends, Risk Management, Equities (Stocks)

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers
• Why: The paper introduces RL concepts like MDP and uses basic financial indicators (e.g., SMA) without deep mathematical derivations or novel algorithms, keeping math complexity moderate. However, it lacks concrete backtesting results, statistical metrics, or implementation details (e.g., specific code or datasets), focusing instead on conceptual framework and challenges, resulting in low empirical rigor.

  flowchart TD
    A["Research Goal<br>Enhance RL agents' ability to differentiate<br>buy/sell actions using financial indicators"] --> B["Data & Inputs<br>Stock Market Data + Financial Indicators"]
    B --> C["Methodology<br>RL Framework Integration<br>Technical Indicators + Risk Management"]
    C --> D["Computational Process<br>Agent Training & Market Simulation"]
    D --> E{"Outcomes & Findings"}
    E --> F1["Foundation for Complex Scenarios"]
    E --> F2["Deeper Insights on Indicator Utilization"]
    E --> F3["Improved Action Differentiation"]