Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System

2025-09-15 · Hoon Sagong, Heesu Kim, Hanbeen Hong 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.

📄 PDF Abstract BibTeX arXiv:2509.12048

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-agent Reinforcement Learning Computational Efficiency

EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading

2023-09-22 · Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia 외

High-frequency trading (HFT) uses computer algorithms to make trading decisions in short time scales (e.g., second-level), which is widely used in the Cryptocurrency (Crypto) market (e.g., Bitcoin). Reinforcement learnin…

Algorithmic Trading Hierarchical Reinforcement Learning Reinforcement Learning

Pseudo-Inverted Bottleneck Convolution for DARTS Search Space

2022-12-31 · Arash Ahmadian, Louis S. P. Liu, Yue Fei, Konstantinos N. Plataniotis 외

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based neural architecture search method. Since the introduction of DARTS, there has been little work done on adapting the acti…

Neural Architecture Search

Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning

2023-01-25 · Weiguang Han, Boyi Zhang, Qianqian Xie, Min Peng 외

Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair se…

Hierarchical Reinforcement Learning PAIR TRADING reinforcement-learning Reinforcement Learning (RL)

Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution

2024-10-19 · Zijie Zhao, Roy E. Welsch

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, an…

Deep Reinforcement Learning Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning

Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading

2026-05-03 · Polydoros Giannouris, Yuechen Jiang, Lingfei Qian, Yuyan Wang 외 arxiv

Many sequential decision-making problems exhibit hierarchical structure, where high-level semantic choices constrain downstream actions and feedback is delayed and ambiguous. Learning in such settings is challenging due …

Hierarchical Reinforcement Learning PAIR TRADING