Investing in stocks is a common choice for financial management. Technical indicators (TIs) assist investors in determining the best trading time to make a fortune. Moving average (MA) and relative strength index (RSI) are the most common TIs. The proposed hybrid technique maximizes the capabilities of these two indicators. This study utilizes the quantum-inspired algorithm to assist effectively in searching for the optimal solution in the vast solution space. The proposed trading system contains four innovative features. First, the traditional usage restriction of MA and RSI is removed to increase their potential effectiveness and identify the most profitable trading strategy. Second, this research proposes an innovative hybrid indicator (HI) that combines MA and RSI to simultaneously achieve both benefits. HI eliminates the restriction of employing a single indicator at once. Third, an efficient quantum-inspired algorithm, the Global-best-guided Quantum-inspired Tabu Search Algorithm with Quantum NOT Gate (GNQTS), effectively and efficiently explores optimized parameters. Fourth, this study proposes 60 sliding windows to determine the optimal period for training and testing. The investment targets include well-known indices: DJIA, S&P 500, NASDAQ Composite Index, and NYA, as well as high-reputation companies on the US stock market: DJIA components. By removing the restrictions imposed by these two indicators and the use of HI, the experiment results demonstrate that GNQTS can discover optimal parameters to generate higher returns than state-of-the-art methods and buy-and-hold (B&H) strategies. The proposed hybrid strategy provides for the promising prospect of quantum-inspired applications and the utilization of multiple indicators.

投资股票是理财的常见选择。技术指标（TI）帮助投资者确定发财的最佳交易时机。移动平均线 (MA) 和相对强度指数 (RSI) 是最常见的 TI。所提出的混合技术最大化了这两个指标的能力。本研究利用量子启发算法有效地协助在广阔的解空间中搜索最优解。拟议的交易系统包含四个创新功能。首先，消除了 MA 和 RSI 的传统使用限制，以提高其潜在有效性并确定最有利可图的交易策略。其次，本研究提出了一种创新的混合指标（HI），将 MA 和 RSI 结合起来，以同时实现这两种好处。HI 消除了一次使用单一指标的限制。第三，一种高效的量子启发算法，带有量子非门（GNQTS）的全局最佳引导量子启发禁忌搜索算法，有效且高效地探索优化参数。第四，本研究提出了 60 个滑动窗口来确定训练和测试的最佳周期。投资标的包括知名指数：道琼斯工业平均指数、标普500指数、纳斯达克综合指数、NYA，以及美国股市信誉良好的公司：道琼斯工业平均指数成分股。通过消除这两个指标的限制以及使用 HI，实验结果表明，GNQTS 可以发现最佳参数，从而产生比最先进的方法和买入并持有 (B&H) 策略更高的回报。所提出的混合策略为量子启发的应用和多种指标的利用提供了广阔的前景。