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.
Cite
@article{arxiv.2509.12048,
title = {Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System},
author = {Hoon Sagong and Heesu Kim and Hanbeen Hong},
journal= {arXiv preprint arXiv:2509.12048},
year = {2025}
}
Comments
Accepted paper at International Conference on ICT Convergence 2025