
系統識別號 
U00021507201913344600 
中文論文名稱

多目標群組交易策略組合最佳化技術 
英文論文名稱

Multiobjective Group Trading Strategy Portfolio Optimization Techniques 
校院名稱 
淡江大學 
系所名稱(中) 
資訊工程學系全英語碩士班 
系所名稱(英) 
Master’s Program, Department of Computer Science and Information Engineering (Englishtaught program 
學年度 
107 
學期 
2 
出版年 
108 
研究生中文姓名 
穆吉 
研究生英文姓名 
Munkhjargal Gankhuyag 
電子信箱 
muji.sgs@gmail.com 
學號 
606785052 
學位類別 
碩士 
語文別 
英文 
第二語文別 
中文 
口試日期 
20190627 
論文頁數 
63頁 
口試委員 
指導教授陳俊豪 委員吳牧恩 委員陳朝鈞

中文關鍵字 
群組交易策略組合
多目標遺傳演算法
交易策略
交易策略組合
組合最佳化

英文關鍵字 
Group trading strategy portfolio
multiobjective genetic algorithm
trading strategy
trading strategy portfolio

學科別分類 
學科別＞應用科學＞資訊工程

中文摘要 
在多變的金融市場中，多樣的技術分析和基本指標被用來產生交易策略並模組化用，旨在用來確定買賣股票時能夠做出適合的交易決策。在文獻中曾提出了群組交易策略組合最佳化方法來最佳化一個群組交易策略組合並制定最佳交易決策且其屬於單目標最佳化問題。然而，在現實的情況中，投資者必須同時考量多個目標來制定交易決策。故本篇論文提出一個多目標為基礎的演算法來找出柏拉圖解並提供給使用者建置更有用的交易策略。其中，每一柏拉圖解即為一群組交易策略組合。 為了最佳化一群組交易策略組合，根據所選擇的技術指標，演算法先產生候選交易策略。然後，再利用所設計之排名函數選出交易策略子集合。根據此交易策略子集合，所提的方法接著產生初始母體與用來記錄柏拉圖解的空集合。在編碼方式上，每個可能的群組交易策略組合由三部分來表達，分別為：群組、權重與交易策略。在演化過程中，兩個目標函數被用來評估每個染色體的適合度並找尋柏拉圖解。第一個目標函數將用來評估染色體的獲利與風險。第二個目標函數則用來衡量染色體的群組與權重平衡度。遺傳運算則接續用來產生新的染色體，包含：交配、突變與反轉運算。在實驗部分，我們透過三個不同趨勢的股價資料來展示所提的方法的有效性，分別為：上升趨勢、盤整與下降趨勢。 
英文摘要 
A variety of technical analyses techniques and fundamental indicators have been used to form trading strategies and modeled to determine the appropriate trading decisions for when to sell or buy stocks at unstable challenging financial market. A group trading strategy optimization portfolio algorithm was presented in the literature to find out an optimal group trading strategy portfolio to make trading decisions, and it belongs to the single objective optimization problem. However, in the real situation, traders have confronted to make decision by considering multiobjective goals. Hence, this thesis proposes a MOGAbased algorithm to find a set of Pareto solutions for investors to make more useful trading plans, and each solution is a group trading strategy portfolio. To optimize a GTSP, the candidate trading strategies are first produced according to the chosen technical indices. Then, a subset of the candidate trading strategies is selected using the determined ranking functions. Based on the subset of the trading strategies, the population is initialized as determined chromosome, and nondominated set is initialized as empty. In the encoding scheme, the grouping, weighting and trading strategy parts are utilized to represent a possible GTSP. The two objective functions are used to evaluate the fitness values of chromosomes to discover nondominated solutions. The first objective function is used to evaluate the return and risk of a GTSP in the chromosome. The second objective function is utilized to reveals the grouping and weight balances of the trading strategy groups. The genetic operators, including crossover, mutation, and inversion are executed on the population to generate new offspring. In the experiment, the proposed algorithm is evaluated on three datasets with different trends, namely uptrend, sideway trend and downtrend, to show the effectiveness of the proposed approach. 
論文目次 
Contents
CHAPTER 1 1
INTRODUCTION 1
1.1 Problem Definition 1
1.2 Contributions 2
1.3 Reader’s Guide 3
CHAPTER 2 4
LITERATURE REVIEWS 4
2.1 Review of Stock Portfolio Optimization with Trading Strategies 4
2.2 Review of Trading Strategies Optimization 6
2.3 Review of StopLoss and TakeProfit Strategies 9
CHAPTER 3 10
DEFINITION OF PROBLEM AND FRAMEWORK OF THE PROPOSED APPROACH 10
3.1 Definition of Problem 10
3.2 Framework of the Proposed Approach 11
CHAPTER 4 14
COMPONENTS OF PROPOSED APPROACH 14
4.1 Encoding Schema 14
4.2 The Two Objective Functions 16
4.3 The Rankbased Fitness assignment 19
4.4 Genetic Operations 22
CHAPTER 5 25
PROPOSED ALGORITHM 25
5.1 Pseudo Code of the Proposed Approach 25
5.2 Steps of the Proposed Algorithm 27
5.3 An Example 29
CHAPTER 6 41
EXPERIMENTAL RESULTS 41
6.1 Data Descriptions 41
6.2 Experimental Result Evaluation on Uptrend Dataset. 45
6.3 Experimental Result Evaluation on Sideway Trend Dataset 48
6.4 Experimental Result Evaluation on Downtrend Dataset 50
6.5 Evaluation on the Derived NonDominated Solutions 53
CHAPTER 7 55
CONCLUSIONS AND FUTURE WORK 55
REFERENCES 56
APPENDIX 60
List of figures
Figure 1. Framework of the proposed approach. 12
Figure 2. Encoding schema for a GTSP. 14
Figure 3. An initial chromosome. 15
Figure 4. The ranking results of the 15 chromosomes. 19
Figure 5. The results of assign fitness of the 15 chromosomes. 21
Figure 6. The average fitness values of the 15 chromosomes. 22
Figure 7. The uptrend datasets. 42
Figure 8. The sideway trend datasets. 42
Figure 9. The downtrend datasets. 43
Figure 10. Nondominated Pareto front of uptrend dataset. 46
Figure 11. Nondominated Pareto front of sideway trend dataset. 48
Figure 12. Nondominated Pareto front of downtrend dataset. 51
List of Tables
Table 1. The crossover operator on the weight part. 23
Table 2. The multiobjective genetic algorithm. 25
Table 3. Selected strategies and related information used in the example. 29
Table 4. The objective function 1 of the ten chromosomes. 32
Table 5. The portfolio returns of the ten chromosomes. 32
Table 6. Normalized MDD for every strategy. 33
Table 7. The risk of ten chromosomes. 34
Table 8. The objective function 2 of the ten chromosomes. 34
Table 9. The group balances of all chromosomes. 35
Table 10. The weight balance of all chromosomes. 35
Table 11. The multiobjective fitness values of all chromosomes. 36
Table 12. The ranking results of all the 15 chromosomes. 37
Table 13. The fitness values of all the 15 chromosomes. 38
Table 14. The resulting average fitness values of the 15 chromosomes. 38
Table 15. The trading rules generated using the ten technical indicators. 44
Table 16. Comparison of ROI between GGAGTSP, MOGAGTSP and BHS at uptrend datasets. 46
Table 17. Comparison of ROI between GGAGTSP, MOGAGTSP and BHS at sideway trend dataset. 49
Table 18. Comparison of ROI between GGAGTSP, MOGAGTSP and BHS at downtrend dataset. 51
Table 19. The comparison of optimized MOGAGTSP, GGAGTSP and BHS on the three trend datasets. 53

參考文獻 
REFERENCES
[1] Kim Youngmin, Enke David, “Developing a rule change trading system for the futures market using rough set analysis,” Expert Systems with Applications, Vol. 59, pp. 165173, 2016.
[2] Y. Kim, W. Ahn, K.J. Oh and D. Enke, “An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms,” Applied Soft Computing Journal, Vol.55, pp.127140, 2017.
[3] Y.Chen and X.Wang, “A hybrid stock trading system using genetic network programming and mean conditional valueatrisk.” European Journal of Operational Research, Vol. 240, pp.861871, 2015.
[4] Bahar. H., Zarandi, M., Esfahanipour, A. “A hybrid expert system for generating stock trading signals,” International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol. 115, pp. 1295 – 1300, 2016.
[5] A. B. Prasetijo, T. A. Saputro, I. P. Windasari and Y. E. Windarto, "Buy/sell signal detection in stock trading with bollinger bands and parabolic SAR: With web application for proofing trading strategy," 4th International Conference on Information Technology, Computer, and Electrical Engineering, pp. 4144, 2017.
[6] L. Wang, "Dynamical models of stock prices based on technical trading rules—part III: Application to Hong Kong stocks," IEEE Transactions on Fuzzy Systems, Vol. 23, no. 5, pp. 16801697, 2015
[7] Y. Ma and R. Han, "Research on stock trading strategy based on deep neural network," 18th International Conference on Control, Automation and Systems, pp. 9296, 2018.
[8] R. RuizCruz and A. D. DiazGonzalez, "Investment portfolio trading based on Markov chain and fuzzy logic," IEEE Latin American Conference on Computational Intelligence, pp. 16, 2018.
[9] C. H. Chen, Y. H. Chen, J. C. W. Lin and M. E. Wu, "An effective approach for obtaining a group trading strategy portfolio using grouping genetic algorithm," IEEE Access, Vol. 7, pp. 73137325, 2019.
[10] J. Pinto, R. F. Neves and N. Horta, “Multiobjective optimization of investment strategies based on evolutionary computation techniques, in volatile environments,” Proceedings of the 16th International Conference on Enterprise Information System, pp. 480488, 2014.
[11] D. J. Bodas Sagi, F. J. Soltero, J. I. Hidalgo, P. Fernández and F. Fernandez, "A technique for the optimization of the parameters of technical indicators with MultiObjective Evolutionary Algorithms," IEEE Congress on Evolutionary Computation, pp. 18, 2012.
[12] Y. H. Chou, S. Y. Kuo and C. Kuo, "A dynamic stock trading system based on a multiobjective quantuminspired tabu search algorithm," The IEEE International Conference on Systems, Man, and Cybernetics, pp. 112119, 2014.
[13] D. Lohpetch and D. Corne, "Multiobjective algorithms for financial trading: multiobjective outtrades singleobjective," IEEE Congress of Evolutionary Computation, pp. 192199, 2011.
[14] A.C Briza and P.C Naval, “Stock trading system based on the multiobjective particle swarm optimization of technical indicators on endofday market data,” Applied Soft Computing, 2010.
[15] M. Rajabi and H. Khaloozadeh, "Investigation and comparison of the performance of multiobjective evolutionary algorithms based on decomposition and dominance in portfolio optimization," Electrical Engineering Iranian Conference, pp. 923929, 2018.
[16] T. Murata and H. Ishibuchi, "MOGA: multiobjective genetic algorithms," IEEE International Conference on Evolutionary Computation, pp. 289294, 1995.
[17] 1.2 R. de Almeida, G. ReynosoMeza and M. T. A. Steiner, "Multiobjective optimization approach to stock market technical indicators," The IEEE Congress on Evolutionary Computation, pp. 36703677, 2016.
[18] D.A. Silva, F.N. Rui and N. Horta, “Portfolio optimization using fundamental indicators based on multiobjective EA” pp.3956, 2016.
[19] Hoklie and L. R. Zuhal, "Resolving multi objective stock portfolio optimization problem using genetic algorithm," The 2nd International Conference on Computer and Automation Engineering, pp. 4044, 2010.
[20] R. Drezewski, K. Doroz, “An agentbased coevolutionary multiobjective algorithm for portfolio optimization,” Symmetry, 2017.
[21] 1.2 K. Lwin, R. Qu, G. Kendall, “A learningguided multiobjective evolutionary algorithm for constrained portfolio optimization,” Applied Soft Computing, Vol. 24, pp.757772, 2014.
[22] S. Babaei, M.M Sepehri, E. Babaei, “Multiobjective portfolio optimization considering the dependence structure of asset returns,” European Journal of Operational Research, 2015.
[23] R. Ramadhiani, M. Yan, G. F. Hertono and B. D. Handari, "Implementation of enew local search based multiobjective optimization algorithm and multiobjective covariance based artificial bee colony algorithm in stocks portfolio optimization problem," 2nd International Conference on Informatics and Computational Sciences, pp. 16, 2018.
[24] W. Si, J. Li, P. Ding and R. Rao, "A multiobjective deep reinforcement learning approach for stock index future’s intraday trading," 10th International Symposium on Computational Intelligence and Design, pp. 431436, 2017.
[25] G. A. V. Pai, "Multiobjective metaheuristics for managing futures portfolio risk," IEEE Symposium Series on Computational Intelligence, pp. 12041211, 2018.
[26] Zh.Huiming and J. Watada, “A fuzzy index tracking multiobjective approach to stock data analytics,” 4th International Conference on Computer and Information Sciences, 2018.
[27] Z. Liu, Z. Liu, Y. Song, Z. Gong and H. Chen, "Predicting stock trend using multiobjective diversified echo state network," Seventh International Conference on Information Science and Technology, pp. 181186, 2017.
[28] C. M. Fonseca and P. J. Fleming, "Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization," The International Confidence on Genetic Algorithms, pp. 416423, 1993.
[29] I. Ucar, A. M. Ozbayoglu and M. Ucar, "Developing a two level options trading strategy based on option pair optimization of spread strategies with evolutionary algorithms," IEEE Congress on Evolutionary Computation, pp. 25262531, 2015.
[30] R. Ji, M. A. Lejeune and S. Y. Prasad, "Dynamic portfolio optimization with riskaversion adjustment utilizing technical indicators," 20th International Conference on Information Fusion, pp. 18, 2017.
[31] K. M. Kaminski and A. W. Lo, "When do stoploss rules stop losses?" Journal of Financial Markets, Vol.18, pp.234254, 2014
[32] 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

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20240716公開。同意授權瀏覽/列印電子全文服務，於20240716起公開。 


