Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System

Hoon Sagong, Heesu Kim, Hanbeen Hong
9/15/2025
cs.LG

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Abstract

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.

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Cite this paper

@article{sagong2025hidarts,
  title  = {Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System},
  author = {Hoon Sagong and Heesu Kim and Hanbeen Hong},
  year   = {2025},
  eprint = {2509.12048},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.12048v1}
}

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