用量子增强模型提升量化交易收益，5年稳赚11.87%且最大回撤仅0.92%
Quantum-Enhanced Forecasting for Deep Reinforcement Learning in Algorithmic Trading
- 结合量子LSTM与量子A3C，实现趋势预测与策略优化
- 5年期回测收益11.87%，最大回撤0.92%，优于多数货币ETF
- 适合关注小利润高风控的量化交易研究者
量子神经网络与深度强化学习的融合为金融交易提供了新路径。本文构建了一个针对美元/新台币（USD/TWD）的交易代理，整合量子长短期记忆网络（QLSTM）进行短期趋势预测，并采用量子增强型异步优势演员-评论家（QA3C）算法优化决策。模型在2000-01-01至2025-04-30的数据上训练（80%用于训练，20%测试），长期持有策略实现约5年总回报率11.87%，最大回撤仅为0.92%，超越多个货币型ETF表现。文中详述状态设计（包括QLSTM特征与技术指标）、面向趋势追踪与风险控制的奖励函数，以及多核训练机制。结果表明，混合模型在外汇交易中具备竞争力。关键超参数：QLSTM序列长度=4，QA3C工作线程数=8。局限性在于使用经典模拟实现量子计算，且策略设计较为简化。展望未来可拓展至更复杂场景。
原文摘要 · Abstract (English)
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.
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