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Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System

arXiv 2509.12048 ↗First posted 15 Sep 2025cs.LGcited by 0

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Abstract (arXiv)

Conventional autonomous trading systems struggle to balance computational efficiency and market responsiveness due to their fixed operating frequency. We propose Hi-DARTS, a hierarchical multi-agent reinforcement learning framework that addresses this trade-off. Hi-DARTS utilizes a meta-agent to analyze market volatility and dynamically activate specialized Time Frame Agents for high-frequency or low-frequency trading as needed. During back-testing on AAPL stock from January 2024 to May 2025, Hi-DARTS yielded a cumulative return of 25.17% with a Sharpe Ratio of 0.75. This performance surpasses standard benchmarks, including a passive buy-and-hold strategy on AAPL (12.19% return) and the S&P 500 ETF (SPY) (20.01% return). Our work demonstrates that dynamic, hierarchical agents can achieve superior risk-adjusted returns while maintaining high computational efficiency.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 10 references that are in the map. First posted on arXiv 15 Sep 2025.

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References · 5

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2020FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Financecited by 87
  4. 2021FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Financecited by 58
  5. 2020Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economicscited by 23

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