此篇論文研究目的在於探討如何利用漲跌統計法、Suffix Tree資料結構和遺傳基因演算法等技術，來實現股價分析，以提高股價預測的精確度和穩定性。以IEEE論文和市面上的選股操作相關書籍作為主要資料來源。而研究方法係利用Suffix Tree資料結構和遺傳基因演算法，以獲得更加精確和穩定的預測結果。在回測歷史數據時，最高準確率可達61%以上。經過實驗後發現，不同的演算法適用於不同類型的股票。漲跌統計法適用於成長型股票，Suffix Tree適用於景氣循環股，遺傳基因演算法則適用於任何類型的股票，勝率雖不是最高，但預測結果較為穩定。因此，在實際應用中，選擇合適的演算法非常重要，可以提高股價預測的精度和可靠性。綜合以上所述，本篇論文探討了利用不同演算法實現股價分析的方法，並通過回測歷史數據獲得了一定的成果。未來的研究可以進一步探討不同演算法的適用範圍、準確度和穩定度，以提高股價預測的準確性和可靠性。;The purpose of this paper is to explore how to use techniques such as up and down statistical method, suffix tree data structure, and genetic algorithm to achieve stock price analysis, in order to improve the accuracy and stability of stock price prediction. The main sources of data were IEEE papers and stock selection related books on the market. The research method used suffix tree data structure and genetic algorithm to obtain more accurate and stable prediction results. In backtesting historical data, the highest accuracy rate can reach over 61%. After experiments, it was found that different algorithms are suitable for different types of stocks. The up and down statistical method is suitable for growth stocks, suffix tree is suitable for cyclical stocks, and genetic algorithm is suitable for any type of stocks, although the winning rate is not the highest, the prediction results are more stable. Therefore, choosing the appropriate algorithm is very important in practical applications, which can improve the accuracy and reliability of stock price prediction. In summary, this paper discusses the methods of implementing stock price analysis using different algorithms, and has achieved some results through backtesting historical data. Future research can further explore the applicable scope, accuracy, and stability of different algorithms to improve the accuracy and reliability of stock price prediction.
