Quantum Machine Learning, Quantitative Trading, Reinforcement Learning, Deep Learning

2025-09-11

AI Summary

This study addresses the challenge of simultaneously achieving high returns and low risk in short-term USD/TWD foreign exchange trading. We propose a quantum-inspired hybrid intelligent trading framework: (1) a Quantum Long Short-Term Memory (QLSTM) network to model nonlinear exchange rate dynamics and forecast short-term trends; and (2) a Quantum-enhanced Asynchronous Advantage Actor–Critic (QA3C) algorithm, trained in parallel across multiple processes, to optimize trading policies. Innovatively, we construct the state space by fusing technical indicators and design a trend-following reward mechanism to enhance strategy robustness. Empirical evaluation over a five-year backtest yields a cumulative return of 11.87% with a maximum drawdown of only 0.92%, significantly outperforming mainstream currency ETFs. Results demonstrate the efficacy and superior risk-control capability of quantum-inspired models in high-frequency, low-margin forex trading.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Developing quantum-enhanced reinforcement learning for financial trading
• Integrating QLSTM and QA3C for USD/TWD trend prediction
• Achieving high returns with minimal drawdown in FX markets

Innovation

• Quantum LSTM for trend prediction
• Quantum A3C for reinforcement learning
• Hybrid quantum-classical model training