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研究生：	陳柏全 Chen, Po-Chuan
論文名稱：	即時爬取網路資料實現股市效益分析預測系統 A Real-Time Stock Market Efficiency Analytical Predictive System by Crawling Internet Data
指導教授：	陳響亮 Chen, Shang-Liang
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 製造資訊與系統研究所 Institute of Manufacturing Information and Systems
論文出版年：	2025
畢業學年度：	113
語文別：	英文
論文頁數：	172
中文關鍵詞：	樹梅派、演算法、台灣股市、模擬交易、XGBoost
外文關鍵詞：	Raspberry Pi, Algorithm, Taiwan Stock Market, Simulation Trading, XGBoost
相關次數：	點閱：51 下載：0
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摘要	2
Abstract	3
Acknowledgement	4
Table of Contents	0
List of Figures	4
List of Tables	5
List of Abbreviations	7
CHAPTER ONE	11
INTRODUCTION	11
1.1	Research Background	11
1.2	Research Motivation	12
1.3	Research Purpose	13
CHAPTER TWO	15
LITERATURE REVIEW	15
2.1 Stock Technical Analysis	15
2.2 Related Researches about Technical Analysis	17
2.3 Trend	18
2.4 Ties between Taiwan and World Stock Index	19
2.5 Algorithms	19
2.5.1 Support Vector Machines (SVM)	19
2.5.2 Extreme Gradient Boosting (XGBoost)	21
2.5.4 LSTM	24
CHAPTER THREE	28
RESEARCH METHOD	28
3.1 Data Collection	28
3.2 Stock Data Update Process	31
3.3 Raspberry Pi Zero 2 Routine	33
3.4 Main Computers’ tasks	34
3.5 SQL server	36
3.6 System Web Page Overview	36
3.7 System Modules	38
3.8 Information Flow	39
3.9 Weighted Ensemble Method	44
3.10 System Energy Consumption	45
3.11 Hardware Limitation	46
3.12 Investment Risk Disclosure	47
CHAPTER FOUR	48
EXPERIMENT METHOD	48
4.1 Experiment Design	48
4.2 Python Libraries and Algorithms	49
4.2.1 Major Python Libraries	49
4.3 RSI Pattern Backtesting	50
4.4 Algorithm Predictions	53
4.5 Algorithm prediction in trends	58
4.6 Simulation trading conditions	62
4.7 Trading Strategies	63
4.8 Real-Time stock analysis	68
4.9 Simulation trading based on ER and EL	69
CHAPTER FIVE	72
RESULTS AND DISCUSSION	72
5.1 Simulation results and discussion	72
5.2 Highest Return under ER and EL Strategy	76
5.3 Simulation trading based on algorithm predictions.	77
5.4 Simulation trading During a Volatile Marker Period	80
5.5 Considering the ER and EL to simulate trading	81
5.6 Considering weighted Algorithm predicted ER and EL to simulate trading	82
5.7 Using Algorithm-Weighted ER and EL as Selling Conditions	83
5.8 Correlation between Company Fundamentals and Returns of the ER-EL Strategy	84
5.9 Examine Operation Efficiency Ratio	86
5.10 Summary and Insights from Simulation Results	88
5.11 Stock Rating Framework for Investment Decision-Making	89
5.12 MVC Web Page	90
5.13 Summary of Chapter Five	91
CHAPTER SIX	93
FIELD TEST	93
6.1 Applying the Analytical Predictive System to Short a Stock	93
6.3 Trading a stock based on Analytical Predictive System	94
6.4 Actual Trading Opportunity Based on Algorithm Prediction	95
6.5 System Optimization	96
6.6 Suggestions for Future Research	97
6.7 Conclusions	99
References	100
Appendix	102
Appendix A  Algorithm Training Model Python Script	102
XGBoost Training Model Python Script	102
SVM Training Model Python Script	104
LSTM Training Model Python Script	107
Appendix B Python Script for Calculating Algorithm prediction Accuracies and Sample Size	110
Appendix C Simulation Trading Python Script	160
Appendix D List of Ensemble-Predicted Probability Abbreviation	165
Appendix E System Environment	166
                                    

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校內：2027-08-31公開
校外：2027-08-31公開電子論文尚未授權公開，紙本請查館藏目錄

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