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系統識別號 U0002-0603201816055800
中文論文名稱 群組交易策略組合最佳化技術之研究
英文論文名稱 A Study on Group Trading Strategy Portfolio Optimization Techniques
校院名稱 淡江大學
系所名稱(中) 資訊工程學系資訊網路與多媒體碩士班
系所名稱(英) Master’s Program in Networking and Multimedia, Department of Computer Science and Information Engine
學年度 106
學期 1
出版年 107
研究生中文姓名 陳諭萱
研究生英文姓名 Yu-Hsuan Chen
學號 605420057
學位類別 碩士
語文別 英文
口試日期 2018-01-11
論文頁數 105頁
口試委員 指導教授-陳俊豪
委員-林威成
委員-吳牧恩
中文關鍵字 群組策略交易組合  群組遺傳演算法  投資組合優化  交易策略  交易策略組合  停損停利點 
英文關鍵字 Group trading strategy portfolio  grouping genetic algorithm  portfolio optimization  trading strategy  trading strategy portfolio  stop-loss and take-profit points. 
學科別分類
中文摘要 在股票市場中,如何找出合適的買賣時機使得投資標的可獲利並降低風險是投資人重視的議題。為解決此問題,透過基本面、技術面或籌碼面指標組合而成的交易策略常用於決定標的買賣時機。由於技術面指標直接與股價相關且容易使用,故本論文主要著重於如何利用技術指標建立有效之交易策略。回顧文獻,相關研究議題包含;交易策略的制定、技術指標的參數最佳化、交易策略組合等。因現有技術所提供的交易策略組合有其限制,為了提昇交易策略組合的使用彈性與有效性,本論文首先定義群組交易策略組合最佳化問題,進而利用群組遺傳演算法設計兩群組交易策略組合最佳化方法。
在方法一中,每個群組交易策略組合透過群組、策略與資金權重三部分進行染色體編碼。每一染色體則透過利潤、風險、群組平衡與權重平衡四因子進行適合度評估。實驗透過上漲趨勢、盤整趨勢及下跌趨勢的資料集搭配兩交易策略集合與停損停利點進行有效性評估,實驗結果證實所提的方法可提供有效的群組交易策略組合。接著,因方法一實驗顯示使用停損停利點可提升利潤與降低風險,又設定合適的停損停利點是最佳化問題,故方法二在編碼上,除了使用群組、策略與資金權重,額外增加停損停利部分進行染色體編碼,之後透過群組遺傳演算法找出最佳之群組交易策略組合與其合適的停損停利點。實驗結果亦指出利用方法二所得的群組交易策略組合報酬明顯優於方法一。最後,本論文將所提的方法應用至群組股票投資組合上,實驗數據也顯示加入群組交易策略組合能有效降低股票投資組合風險且能的到更穩定的報酬。
英文摘要 In stock markets, how to determine an appropriate trading time for buying or selling stocks to make the return and risk of them being maximized and minimized is always an important issue for investors. The common way to deal with this problem is using trading strategies formed by fundamental, technical or chip analysis. Since there is a direct correlation between technical indicators and stock prices and technical indicators are much easy to use, hence, this thesis focus on how to establish efficient trading strategies by technical indicators. Literatures showed that there are lots of research topics, for example, including how to form trading strategies, parameter optimization for trading strategies, and trading strategy portfolio optimization. Because the trading strategy portfolios provided by existing approaches have limitations, to increase the flexibility and effectiveness of them, firstly, this thesis defines the group trading strategy portfolio optimization problem. Then, two group trading strategy portfolio optimization approaches are proposed using the grouping genetic algorithm.
In the first approach, a group trading strategy portfolio is encoded into a chromosome using three parts, the grouping, trading strategy and weight parts. The fitness function composes of four factors that are profit, risk, group balance and weight balance is utilized to assess the quality of a chromosome. Experiments were conducted on the uptrend, sideway trend and downtrend datasets with two sets of trading strategies and stop-loss and take-profit points to evaluate the effectiveness of the proposed approach. The experimental results show that the proposed approach can provide useful group trading strategy portfolio. Because the results also indicated that the first approach with stop-loss and take-profit points can increase return and reduce risk and to set stop-loss and take-profit points is an optimization problem, the second approach thus use not only grouping, trading strategy and weight but also stop-loss and take-profit part to encode a group trading strategy portfolio. Then, the grouping genetic algorithm is employed to optimize a group trading strategy portfolio and get its appropriate stop-loss and take-profit points. Experimental results reveal that the return of the second approach is better than the first approach. At last, the proposed approaches are applied on a group stock portfolio. The results show that stock portfolio with the group trading strategy can actually increase its ability to reduce risk and get more stable profit.
論文目次 CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Contributions 4
1.3 Reader’s Guide 5
CHAPTER 2 RELATED WORK 6
2.1 Review of the Trading Strategy Portfolio Optimization 6
2.2 Review of the Trading Strategy Parameter Optimization 7
2.3 Review of the Stock Portfolio Optimization with Trading Strategies 7
2.4 Literatures Related to Stop-Loss and Take-Profit Strategies 8
2.5 GGA and Grouping Problem 8
CHAPTER 3 PROBLEM DEFINITION 10
CHAPTER 4 AN EFFECTIVE APPROACH FOR OBTAINING A GROUP TRADING STRATEGY PORTFOLIO USING GROUPING GENETIC ALGORITHM 12
4.1 Motivation 12
4.2 Framework of the First Approach 13
4.3 The Four Components of the Proposed Approach 16
4.3.1 Encoding Scheme 16
4.3.2 Fitness Function and Reproduction 17
4.3.3 Genetic Operations 20
4.4 The Pseudo Code of the First Approach 22
4.5 An Example 23
CHAPTER 5 A SOPHISTICATED OPTIMIZATION ALGORITHM FOR OBTAINING A GROUP TRADING STRATEGY PORTFOLIO WITH APPROPRIATE STOP-LOSS AND TAKE-PROFIT POINTS 30
5.1 Motivation 30
5.2 Framework of the Second Approach 31
5.3 The Four Components of the Proposed Approach 34
5.3.1 Encoding Scheme 34
5.3.2 Fitness Function and Reproduction 37
5.3.3 Genetic Operations 39
5.4 The Pseudo Code of the Second Approach 41
5.5 An Example 43
CHAPTER 6 EXPERIMENTAL RESULTS 52
6.1 Data Descriptions 52
6.2 Experimental Results for Approach (I) 55
6.2.1 Experimental Results on the Three Datasets 56
6.2.1.1 Analysis of Uptrend Dataset 56
6.2.1.2 Analysis of Sideway Trend Dataset 61
6.2.1.3 Analysis of Downtrend Dataset 67
6.2.2 The Optimized GTSP 73
6.3 Experimental Results for Approach (II) 75
6.3.1 Analysis of Uptrend Dataset 75
6.3.1.1 Comparisons of the Proposed Approach and BHS 76
6.3.1.2 Comparisons of the Proposed Approach and the Previous Approach without SLTP Points 77
6.3.1.3 Comparisons of the Proposed Approach and the Previous Approach with Predefined SLTP Points 79
6.3.2 Analysis of Sideway Trend Dataset 82
6.3.2.1 Comparisons of the Proposed Approach and BHS 82
6.3.2.2 Comparisons of the Proposed Approach and the Previous Approach without SLTP Points 84
6.3.2.3 Comparisons of the Proposed Approach and the Previous Approach with Predefined SLTP Points 85
6.3.3 Analysis of Downtrend Dataset 88
6.3.3.1 Comparisons of the Proposed Approach and BHS 89
6.3.3.2 Comparisons of the Proposed Approach and the Previous Approach without SLTP Points 90
6.3.3.3 Comparisons of the Proposed Approach and the Previous Approach with Predefined SLTP Points 92
6.4 Application on a Group Stock Portfolio 95
CHAPTER 7 CONCLUSION AND FUTURE WORKS 98
REFERENCES 100
APPENDIX (I) 103
APPENDIX (II) 105


List of Figures
Figure 1. An example to explain the flexibility of a group trading strategy portfolio. 13
Figure 2. Data preprocessing procedure to form trading strategies. 14
Figure 3. Framework of the first approach. 15
Figure 4. Encoding schema of a GTSP. 16
Figure 5. A possible chromosome 17
Figure 6. Crossover on weight part. 21
Figure 7. The pseudo code of the first approach. 22
Figure 8. Framework of the second approach. 31
Figure 9. The Flowchart of trading strategies procedure. 33
Figure 10. Encoding schema of a GTSP and its SLTP points. 34
Figure 11. Chromosome representation 35
Figure 12. Crossover on weight part. 40
Figure 13. The pseudo code of the first approach. 42
Figure 14. Pseudo code of trading strategy procedure 43
Figure 15. The uptrend dataset. 53
Figure 16. The sideway trend dataset. 53
Figure 17. The downtrend of the dataset. 54



List of Tables
Table 1. Strategies and related information used in this example. 23
Table 2. The portfolio return of the ten chromosomes. 25
Table 3. Normalized MDD for every strategy. 26
Table 4. The risk of ten chromosomes. 26
Table 5. The group balances of all chromosomes. 27
Table 6. The weight balance of all chromosomes. 27
Table 7. The fitness values of all chromosomes. 27
Table 8.Trading strategy TS and stock price series 36
Table 9. Selected strategies and their related data. 44
Table 10. The portfolio return of the ten chromosomes. 46
Table 11. Normalized MDD for every trading strategy. 47
Table 12. The risk of ten chromosomes. 48
Table 13. The group balances of all chromosomes. 48
Table 14. The weight balance of all chromosomes. 49
Table 15. The fitness values of all chromosomes. 49
Table 16. The trading rules generated using the ten technical indicators. 55
Table 17. Returns of the GTSPs derived from one year training periods on one year testing periods on the uptrend dataset using the TOP15. 56
Table 18. Returns of the GTSPs derived from two year training periods on one year testing periods on the uptrend dataset using the TOP15. 57
Table 19. Returns of the GTSPs derived from three year training periods on one year testing periods on the uptrend dataset using the TOP15. 57
Table 20. Comparison returns of the derived GTSPs and BHS on the uptrend dataset using the TOP15. 58
Table 21. Returns of the GTSPs derived from one year training periods on one year testing periods on the uptrend dataset using TOP555. 59
Table 22. Returns of the GTSPs derived from two year training periods on one year testing periods on the uptrend dataset using TOP555. 60
Table 23. Returns of the GTSPs derived from three year training periods on one year testing periods on the uptrend dataset using TOP555. 60
Table 24. Comparison returns of the derived GTSPs and BHS on uptrend dataset using the TOP555. 61
Table 25. Returns of GTSPs trained from one year training periods on one year testing periods on the sideway trend dataset using TOP15. 62
Table 26. Returns of GTSPs trained from two year training periods on one year testing periods on the sideway trend dataset using TOP15. 62
Table 27. Returns of GTSPs trained from three year training periods on one year testing periods on the sideway trend dataset using TOP15. 63
Table 28. Comparison returns of the derived GTSPs and BHS on sideway trend dataset using the TOP15. 63
Table 29. Returns of GTSPs trained from one year training periods on one year testing periods on the sideway trend dataset using TOP555. 65
Table 30. Returns of GTSPs trained from two year training periods on one year testing periods on the sideway trend dataset using TOP555. 65
Table 31. Returns of GTSPs trained from three year training periods on one year testing periods on the sideway trend dataset using TOP555. 66
Table 32. Comparison returns of the derived GTSPs with BHS on sideway trend dataset using the TOP555. 66
Table 33. Returns of GTSPs trained from one year training periods on one year testing periods on the downtrend dataset using TOP15. 67
Table 34. Returns of GTSPs trained from two year training periods on one year testing periods on the downtrend dataset using TOP15. 68
Table 35. Returns of GTSPs trained from three year training periods on one year testing periods on the downtrend dataset using TOP15. 68
Table 36. Comparison returns of the derived GTSPs with BHS on downtrend dataset using the TOP15. 69
Table 37. Returns of GTSPs trained from one year training periods on one year testing periods on the downtrend dataset using TOP555. 70
Table 38. Returns of GTSPs trained from two year training periods on one year testing periods on the downtrend dataset using TOP555. 71
Table 39. Returns of GTSPs trained from three year training periods on one year testing periods on the downtrend dataset using TOP555. 71
Table 40. Comparison returns of the derived GTSPs with BHS on downtrend dataset using the TOP555. 72
Table 41. The optimized GTSPs by the proposed approach. 73
Table 42. Comparison of the proposed approach and BHS on the uptrend dataset. 76
Table 43. Comparison of the proposed approach and the previous approach without SLTP points on the uptrend dataset. 78
Table 44. Comparison of the proposed approach and the previous approach with SLTP points on the uptrend dataset. 79
Table 45. Comparison returns of the proposed approach and BHS on the sideway trend dataset. 82
Table 46. Comparison returns of the proposed approach and the previous approach without SLTP points on the sideway trend dataset. 84
Table 47. Comparison returns of the proposed approach and the previous approach with SLTP points on the sideway trend dataset. 86
Table 48. Comparison returns of the proposed approach and BHS on the downtrend dataset. 89
Table 49. Comparison returns of the previous approach and the previous approach without SLTP points on the downtrend dataset. 91
Table 50. Comparison returns of the proposed approach and the previous approach with SLTP points on the downtrend dataset. 93
Table 51. Comparison results of the GSPs with BHS and the proposed approach in terms of minimum returns. 95
Table 52. Comparison results of the GSPs with BHS and the proposed approach in terms of variance of returns. 96


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