Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System

• URL: http://arxiv.org/abs/2509.12048v1
• Date: Mon, 15 Sep 2025 15:31:47 GMT
• Title: Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System
• Authors: Hoon Sagong, Heesu Kim, Hanbeen Hong,
• Abstract summary: Hi-DARTS is a hierarchical multi-agent reinforcement learning framework.<n>It balances computational efficiency and market responsiveness.<n>Hi-DARTS yielded a cumulative return of 25.17% with a Sharpe Ratio of 0.75.
• Score: 1.764813029493129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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