## 1 Introduction  
This paper uses the \"Yuanta/P-shares Taiwan Top 50 ETF (Exchange Traded Fund)\" (ETF50) [1] as the research target. It tracks Taiwan's top 50 stock index and holds the same constituent stocks as the top 50 index in Taiwan. This paper primarily focuses on the technical analysis of stocks, using the performance of technical indicators to understand the current state of the stock market. To achieve higher returns, investors aim to buy stocks at low prices and sell them at high prices. In past years, different studies were proposed to determine the trading points for buying and selling, serving as a stock trading strategy [2]. However, during certain black swan [3] events or periods of extreme market volatility, traditional rules that simply apply stock technical indicators [4] may not yield satisfactory trading results. For example, the 2020 COVID-19 pandemic [5], the 2018 US-China trade war, and the 2011 US debt crisis have greatly impacted financial markets, making it challenging for investors to achieve good returns. The ETF50 considers 50 listed companies in Taiwan, among which TSMC (Taiwan Semiconductor Manufacturing Company) holds the most important position and determines much of the stock market trend. TSMC plays a pivotal role not only in Taiwan'seconomy but also in the global semiconductor industry. As the largest contract chip manufacturer in the world, TSMC competes with other major players such as Intel, Nvidia, Samsung, and Philips. In terms of technological advancements and production capacity, TSMC has consistently maintained a leadership position, particularly in advanced node technology, such as 5 nm and 3 nm processes, which are critical for manufacturing next-generation chips used in smartphones, AI, and autonomous vehicles. The COVID-19 pandemic significantly disrupted the global semiconductor supply chain. Lockdowns, workforce shortages, and surging demand for consumer electronics, such as laptops and smartphones, caused a global chip shortage that impacted industries from the automotive industry to telecommunications. TSMC, with its advanced manufacturing capabilities, was pivotal in addressing the shortage, but the gap between supply and demand persists. This situation highlighted the dependence of global industries on a few key manufacturers, like TSMC and Samsung, which continue to face pressure to ramp up production amidst geopolitical tensions and increasing global demand. Traditional stock prediction models, including time-series analysis and statistical methods, largely rely on historical data and the assumption that future market movements will remain within predictable bounds. However, black swan events defy these assumptions, making such models ineffective in extreme scenarios. The inability of these models to account for rare and unexpected events often results in flawed predictions, especially during periods of heightened market volatility. Consequently, there is a growing need to explore deep learning techniques, which can adapt to a wider range of data and learn more complex, nonlinear patterns, to better handle the unpredictability of black swan events. This paper uses a calibration procedure called \"Short-Term Bias Compensation\" (STBC) to adjust the model's stock price predictions, aiming to reduce the impact of sudden external factors on the model's prediction accuracy. We use fuzzy rules [6] evolved by genetic algorithms (GAs) to determine the stock trading strategy. The basic processing steps of this paper are as follows. 1. Predict Stock Prices: This paper employs a deep learning Long Short-Term Memory (LSTM) model [7] to predict stock prices. LSTM is particularly useful for handling time-series data, making it a powerful tool for financial market prediction [8]. By training on historical stock data through multiple neurons and multiple layers of the network, it learns the stock price trends to predict future stock prices. 2.