
系統識別號 
U00020603201816055800 
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 
YuHsuan Chen 
學號 
605420057 
學位類別 
碩士 
語文別 
英文 
口試日期 
20180111 
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
stoploss and takeprofit 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 stoploss and takeprofit 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 stoploss and takeprofit points can increase return and reduce risk and to set stoploss and takeprofit points is an optimization problem, the second approach thus use not only grouping, trading strategy and weight but also stoploss and takeprofit 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 stoploss and takeprofit 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 StopLoss and TakeProfit 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 STOPLOSS AND TAKEPROFIT 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

參考文獻 
[1] Y. Chang and M. Lee, "Incorporating Markov decision process on genetic algorithms to formulate trading strategies for stock markets," Applied Soft Computing, Vol.52, No.10, pp. 1143–1153, 2016.
[2] T. J. Chang, S. C. Yang and K. J. Chang, "Portfolio optimization problems in different risk measures using genetic algorithm," Expert Systems with Applications, Vol. 36, pp. 1052910537, 2009.
[3] Y. W. ChangChien and Y. L. Chen, "Mining associative classification rules with stock trading data–A GAbased method," KnowledgeBased Systems, Vol.23, No.6, pp. 605–614, 2010.
[4] C. H. Chen, Y. H. Chen and M. E. Wu, "A GGAbased algorithm for group trading strategy portfolio optimization," The Multidisciplinary International Social Networks Conference, pp. 15, 2017.
[5] J. Chen, J. Hou, S. Wu and Y. ChangChien, "Constructing investment strategy portfolios by combination genetic algorithms," Expert Systems with Applications, Vol. 36, No.2, pp. 38243828, 2009.
[6] C. H. Chen, C. B. Lin and C. C. Chen, "Mining group stock portfolio by using grouping genetic algorithms," Evolutionary Computation (CEC), 2015 IEEE Congress on, pp. 738743, 2015.
[7] C. H. Chen, C. Y Lu, T. P Hong and J. H Su, "Using grouping genetic algorithm to mine diverse group stock portfolio," The IEEE Congress on Evolutionary Computation, pp. 4734  4738, 2016.
[8] C. H. Chen, W. Y. Shen, T. P. Hong and J. H. Su, "An islandbased algorithm for group stock portfolio optimization," Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, pp. 15, 2017.
[9] Y. H. Chou, S. Y. Kuo, C. Y. Chen and H. C. Chao, "A rulebased dynamic decisionmaking stock trading system based on quantuminspired tabu search algorithm", IEEE Access, Vol.2, pp. 883896, 2014.
[10] Y. H. Chou, S. Y. Kuo and Y. T. Lo, "Portfolio optimization based on funds standardization and genetic algorithm," IEEE Access, Vol. 5, pp. 2188521900, 2017.
[11] E. Falkenauer, "A new representation and operators for genetic algorithms applied to grouping problems," Evolutionary Computation, Vol. 2, pp. 123144, 1994.
[12] E. Falkenauer, "A hybrid grouping genetic algorithm for bin packing," Journal of Heuristics, Vol. 2, pp. 530, 1996.
[13] T. C. Fu, C. P. Chung and F. L. Chung, "Adopting genetic algorithms for technical analysis and portfolio management, "Computers and Mathematics with Applications, Vol.66,No.10, pp. 17431757, 2013.
[14] D. E. Goldberg, "Genetic algorithms in search, optimization, and machine learning," Addison Wesley, 1989.
[15] J. J. Grefenstette, "Optimization of control parameters for genetic algorithms," IEEE Transactions on System Man, and Cybernetics, Vol. 16, pp. 122128, 1986.
[16] F. Hassanzadeh, M. Collan and M. Modarres, "A practical approach to R&D portfolio selection using the fuzzy payoff method," IEEE Transactions on Fuzzy Systems, Vol. 20, pp. 615622, 2012.
[17] L. R. Z. Hoklie, "Resolving multi objective stock portfolio optimization problem using genetic algorithm," International Conference on Computer and Automation Engineering, pp. 4044, 2010.
[18] J. H. Holland, "Adaptation in natural and artificial systems," University of Michigan Press, 1975.
[19] K. M. Kaminski and A. W. Lo, "When do stoploss rules stop losses?," Journal of Financial Markets, Vol.18, pp. 234254, 2014.
[20] Y. Kim and D. Enke, "Developing a rule change trading system for the futures market using rough set analysis," Expert Systems with Applications, Vol.59, pp. 165173, 2016.
[21] R. Kumar and S. Bhattacharya, "Cooperative search using agents for cardinality constrained portfolio selection problem," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 42, pp. 1510  1518, 2012.
[22] S. Y. Kuo, C. Kuo and Y. H. Chou, "Dynamic stock trading system based on quantuminspired tabu search algorithm," The IEEE Congress on Evolutionary Computation, pp. 10291036, 2013.
[23] Y. Leu and T. I. Chiu, "An effective stock portfolio trading strategy using genetic algorithms and weighted fuzzy time series," The 16th NorthEast Asia Symposium on Nano, Information Technology and Reliability, pp. 7075, 2011.
[24] Y. J. Liu and W. G. Zhang, "Fuzzy portfolio optimization model under real constraints," Insurance: Mathematics and Economics, Vol. 53, No. 3, pp. 704711, 2013.
[25] A. W. Lo and A. Remorov, "Stoploss strategies with serial correlation, regime switching, and transaction costs," Journal of Financial Markets, Vol.34, pp. 115, 2017.
[26] K. Lwin, R. Qu and G. Kendall, "A learningguided multiobjective evolutionary algorithm for constrained portfolio optimization," Applied Soft Computing, Vol. 24, pp. 757772, 2014.
[27] H. Markowitz, "Portfolio selection, "Journal of Finance," Vol. 7, No. 1, pp. 7791, 1952.
[28] H. M. Markowitz, "Harry Markowitz: Selected Works," World Scientific Publishing Company, 2009.
[29] W. Nuij, V. Milea, F. Hogenboom, F. Frasincar and U. Kaymak "An automated framework for incorporating news into stock trading strategies," IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 4, pp. 823835, 2014.
[30] X. Qin, H. Wang, F. Li, J. Chen, X. Zhou, X. Du, S. Wang, "Optimizing parameters of algorithm trading strategies using MapReduce," International Conference on Fuzzy Systems and Knowledge Discovery, pp. 27382741, 2012.
[31] M. E. Wu, C. H. Wang and W. H. Chung, "Using trading mechanisms to investigate large futures data and their implications to market trends," Soft Computing, Vol. 21, No. 11, pp. 2821  2834, 2017.
[32] H. Yao, Z. Li and D. Li, "Multiperiod meanvariance portfolio selection with stochastic interest rate and uncontrollable liability," European Journal of Operational Research, Vol. 252, No. 3, pp. 837851, 2016.

論文使用權限 
同意紙本無償授權給館內讀者為學術之目的重製使用，於20230309公開。同意授權瀏覽/列印電子全文服務，於20230309起公開。 


