Paper: arXiv 2410.14927

Abstract

Leveraging Deep Reinforcement Learning (DRL) in automated stock trading has shown promising results, yet its application faces significant challenges, including the curse of dimensionality, inertia in trading actions, and insufficient portfolio diversification. Addressing these challenges, we introduce the Hierarchical Reinforced Trader (HRT), a novel trading strategy employing a bi-level Hierarchical Reinforcement Learning framework. The HRT integrates a Proximal Policy Optimization (PPO)-based High-Level Controller (HLC) for strategic stock selection with a Deep Deterministic Policy Gradient (DDPG)-based Low-Level Controller (LLC) tasked with optimizing trade executions to enhance portfolio value. In our empirical analysis, comparing the HRT agent with standalone DRL models and the S&P 500 benchmark during both bullish and bearish market conditions, we achieve a positive and higher Sharpe ratio. This advancement not only underscores the efficacy of incorporating hierarchical structures into DRL strategies but also mitigates the aforementioned challenges, paving the way for designing more profitable and robust trading algorithms in complex markets.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematics from deep reinforcement learning (PPO, DDPG) and hierarchical MDP formulations, indicating high complexity. It presents a backtest-ready empirical analysis with Sharpe ratios, comparisons to benchmarks, and visualizations on S&P 500 data, though real-world implementation details like slippage and latency are not deeply explored.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Develop robust automated trading system<br>addressing dimensionality, inertia, and diversification"] --> B["Methodology: Bi-Level HRT Architecture"]
  B --> C["Data Inputs<br>Historical Stock Market Data"]
  C --> D["High-Level Controller<br>PPO Agent: Strategic Stock Selection"]
  D --> E["Low-Level Controller<br>DDPG Agent: Execution Optimization"]
  E --> F["Computational Process<br>Bi-Level HRL Integration"]
  F --> G["Key Outcomes"]
  G --> H["Superior Sharpe Ratio vs. Benchmarks"]
  G --> I["Effective Mitigation of DRL Challenges"]

Cited References (23)

  1. FinRL-DeepSeek : LLM -Infused Risk-Sensitive Reinforcement Learning for Trading Agents (2025)
  2. Dynamic Programming and Optimal Control: Volume I (2012)
  3. FNSPID : A Comprehensive Financial News Dataset in Time Series (2024)
  4. Addressing Function Approximation Error in Actor-Critic Methods (2018)
  5. Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning (2023)
  6. LightGBM : A Highly Efficient Gradient Boosting Decision Tree (2017)
  7. Market Sentiment-Aware Deep Reinforcement Learning Approach for Stock Portfolio Allocation (2021)
  8. Continuous Control with Deep Reinforcement Learning (2015)
and 15 more
  1. Practical Deep Reinforcement Learning Approach for Stock Trading (2018)
  2. FinRL : Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance (2021)
  3. FinRL-Meta : Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning (2022)
  4. Dynamic Datasets and Market Environments for Financial Reinforcement Learning (2024) — link
  5. Portfolio Selection (1952)
  6. Asynchronous Methods for Deep Reinforcement Learning (2016)
  7. Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning (2022)
  8. Hierarchical Reinforcement Learning: A Comprehensive Survey (2021)
  9. EarnHFT : Efficient Hierarchical Reinforcement Learning for High Frequency Trading (2024)
  10. Proximal Policy Optimization Algorithms (2017)
  11. Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution (2020)
  12. Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy (2020)
  13. Qlib : An AI-Oriented Quantitative Investment Platform (2020)
  14. Instruct-FinGPT : Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models (2023)
  15. A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist (2024)

0 of 23 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.