## 2 Related Works
### Related Methods  
[PERSON] [15] utilized a linear segmentation method to cut historical data, identifying key turning points in stock prices. By calculating daily technical indicators and combining them with Support Vector Regression (SVR) and Takagi-Sugeno (TS) fuzzy rules, [PERSON] aimed to learn the trading points of stock market reversals. However, the method only considered economic technical indicators and did not account for other influencing factors, which may reduce the model's prediction accuracy when external factors affect the stock market. [PERSON] [16] used common technical indicators, such as Moving Averages (MAs), Stochastic Oscillators, and Bollinger Bands, to predict ETF50. To address sudden stock price drops, [PERSON] incorporated stop-loss strategies and combined various parameters to form 47 different trading strategies. [PERSON] [17] used historical data from the New York Stock Exchange and Dow Jones, transforming one-dimensional numerical data into two-dimensional images. These images included positions of peaks and troughs and trading signals marked as buy, sell, or hold. During the data transformation, [PERSON] employed 15 technical indicators and 15 different intervals, feeding this data into a deep convolutional neural network for model training. It compared LSTM, CNNs, and other models, finding that LSTM's annualized return rate slightly outperformed other models. [PERSON] and [PERSON] [18] used historical data from ETF50, calculating 12 technical indicators. They recalculated weights according to the top 20 stocks in the Taiwan 50, using these as training data for a Back Propagation Neural Network (BPNN) to predict stock prices. The model's performance was evaluated using a trading strategy to assess returns. [PERSON] [19] used historical data from Taiwan 50 and its largest constituent, TSMC, to calculate 12 technical indicators and train an LSTM model for stock price prediction. After training, the model's predictions were corrected using a calibration strategy, and the optimal trading strategy was devised by combining secondary trading strategies and price range correction methods. [PERSON] [20] selected the optimal stock portfolio using GAs, calculating returns and using the Sharpe ratio [21] as the fitness function value. This study used 70 fundamental financial indicators, showing that the final evolved investment portfolio outperformed the market. [PERSON] et al. [22] applied data mining techniques to historical financial market data to uncover the relationships between stocks and the market. By combining fuzzy sets and GAs, they established fuzzy sets based on stock rules and market application rules. GAs were used for evolution, and the Sharpe ratio was employed as the fitness function to evaluate the fitness value. Their experiment identified 13 highly correlated stocks, which proved beneficial for practical stock trading. Hypotheses:  
* Hypothesis 1: The LSTM model, when trained on historical data and adjusted for TSMC's weight in ETF50, provides more accurate stock price predictions compared to traditional technical analysis methods like MA and SVR. * Hypothesis 2: The combination of Genetic Fuzzy Systems (GFSs) and GAs optimizes stock trading strategies by improving decision-making rules, leading to enhanced returns and a better risk-adjusted performance compared to strategies based solely on technical indicators. * Hypothesis 3: Accounting for the influence of external factors, such as global economic trends and major events like the COVID-19 pandemic, further improves the predictive power and robustness of the model, particularly for volatile stocks such as TSMC. Methodology Overview is listed below and its flowchart is shown in Figure 1:
* Input Data: We utilized historical stock data from the Taiwan Stock Exchange, focusing on the ETF50, which includes companies such as TSMC. Technical indicators like Moving Averages, Stochastic Oscillators, and Bollinger Bands were calculated as inputs.