Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market

TL;DR

This paper explores a hybrid quantum-classical reinforcement learning approach for sector rotation in Taiwan's stock market, revealing that quantum models excel in training rewards but struggle with real-world investment metrics due to reward design issues.

Contribution

It introduces a novel hybrid quantum-classical RL framework with empirical evaluation, highlighting the challenges of applying quantum models to financial decision-making.

Findings

Quantum-enhanced models achieve higher training rewards.

Classical models outperform quantum models in real investment metrics.

Reward design issues cause mismatch between training success and real-world performance.

Abstract

We propose a hybrid quantum-classical reinforcement learning framework for sector rotation in the Taiwan stock market. Our system employs Proximal Policy Optimization (PPO) as the backbone algorithm and integrates both classical architectures (LSTM, Transformer) and quantum-enhanced models (QNN, QRWKV, QASA) as policy and value networks. An automated feature engineering pipeline extracts financial indicators from capital share data to ensure consistent model input across all configurations. Empirical backtesting reveals a key finding: although quantum-enhanced models consistently achieve higher training rewards, they underperform classical models in real-world investment metrics such as cumulative return and Sharpe ratio. This discrepancy highlights a core challenge in applying reinforcement learning to financial domains -- namely, the mismatch between proxy reward signals and true investment objectives. Our analysis suggests that current reward designs may incentivize overfitting to short-term volatility rather than optimizing risk-adjusted returns. This issue is compounded by the inherent expressiveness and optimization instability of quantum circuits under Noisy Intermediate-Scale Quantum (NISQ) constraints. We discuss the implications of this reward-performance gap and propose directions for future improvement, including reward shaping, model regularization, and validation-based early stopping. Our work offers a reproducible benchmark and critical insights into the practical challenges of deploying quantum reinforcement learning in real-world finance.