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结合量子 LSTM预测与强化学习进行 FX交易

文章 arXiv papers · 作者: Jun-Hao Chen et al.
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总结

本文介绍一种仅做多的 USD/TWD交易智能体，将量子长短期记忆网络（QLSTM）的短期趋势预测与量子异步优势行动者评论家（QA3C）相结合。其状态包含 QLSTM特征和技术指标，奖励函数则旨在支持趋势跟随和风险控制。报告的设置采用长度为四的序列和八个 QA3C工作器，数据覆盖 2000至2025，并划分为训练集和测试集。

作者报告称，该智能体约五年的收益率为 11.87%，最大回撤为 0.92%，并表示其表现优于若干货币 ETF。研究将这些结果作为证据，说明这种混合方法或适用于利润较小且风险限额较严的交易。本文仅简要介绍实验，没有提供详细比较、统计不确定性或足以评估稳健性的信息。文中还指出，量子方法通过经典模拟运行，且策略经过简化，因此这些发现并未证明量子硬件带来优势，也不能自动推广至其他市场。

核心观点

  • 该智能体将 QLSTM短期趋势预测与 QA3C强化学习结合，用于 USD/TWD交易。
  • 其状态包含预测特征和技术指标，奖励函数则考虑趋势跟随与风险控制。
  • 据报告，该仅做多策略约五年收益率为 11.87%，最大回撤为 0.92%。
  • 量子组件通过经典模拟运行，作者称策略经过简化。

标签

外汇机器学习趋势跟踪风险管理技术指标回测
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# Quantum-Enhanced Forecasting for Deep Reinforcement Learning in Algorithmic Trading


# Quantum-Enhanced Forecasting for Deep Reinforcement Learning in Algorithmic Trading









The convergence of quantum-inspired neural networks and deep reinforcement learning offers a promising avenue for financial trading. We implemented a trading agent for USD/TWD by integrating Quantum Long Short-Term Memory (QLSTM) for short-term trend prediction with Quantum Asynchronous Advantage Actor-Critic (QA3C), a quantum-enhanced variant of the classical A3C. Trained on data from 2000-01-01 to 2025-04-30 (80\% training, 20\% testing), the long-only agent achieves 11.87\% return over around 5 years with 0.92\% max drawdown, outperforming several currency ETFs. We detail state design (QLSTM features and indicators), reward function for trend-following/risk control, and multi-core training. Results show hybrid models yield competitive FX trading performance. Implications include QLSTM's effectiveness for small-profit trades with tight risk and future enhancements. Key hyperparameters: QLSTM sequence length$=$4, QA3C workers$=$8. Limitations: classical quantum simulation and simplified strategy. \footnote{The views expressed in this article are those of the authors and do not represent the views of Wells Fargo. This article is for informational purposes only. Nothing contained in this article should be construed as investment advice. Wells Fargo makes no express or implied warranties and expressly disclaims all legal, tax, and accounting implications related to this article.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。