动态调整交易频率的分层智能体系统，提升股票交易收益与效率
Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System
- 分层结构：元智能体根据市场波动决定激活高频或低频交易代理
- 回测表现：苹果股票2024年1月至2025年5月累计收益25.17%，夏普比0.75
- 适合量化交易研究者，尤其关注高效自适应系统设计
传统自主交易系统因固定运行频率，在计算效率与市场响应之间难以平衡。我们提出Hi-DARTS，一种分层多智能体强化学习框架。该框架由元智能体分析市场波动，按需动态激活专用的时间窗代理进行高频或低频交易。在2024年1月至2025年5月期间对AAPL股票的回测中，Hi-DARTS实现累计收益25.17%，夏普比0.75。该性能优于基准策略，包括苹果股票的被动买入持有策略（12.19%收益）和标普500指数基金SPY（20.01%收益）。结果表明，动态分层智能体可在保持高计算效率的同时获得更优的风险调整收益。
原文摘要 · Abstract (English)
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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