淡江大學覺生紀念圖書館 (TKU Library)
進階搜尋


系統識別號 U0002-1509202011352600
中文論文名稱 以MOEA/D為基礎之群組交易策略組合最佳化技術
英文論文名稱 MOEA/D-based 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 108
學期 2
出版年 109
研究生中文姓名 葉重佑
研究生英文姓名 Chong-You Yee
學號 607420154
學位類別 碩士
語文別 中文
口試日期 2020-07-16
論文頁數 53頁
口試委員 指導教授-鄭建富
共同指導教授-陳俊豪
委員-林威成
委員-呂學展
中文關鍵字 交易策略  群組交易策略組合  多目標最佳化演算法  SPEA  MOEAD 
英文關鍵字 Trading strategy  group trading strategy portfolio  multi-objective genetic algorithm  SPEA  MOEA/D 
學科別分類
中文摘要 金融市場中,股票投資一直都是個熱門的項目,什麼時候該進行買入或賣出才能避開風險又獲得最大的收益一直都是一個很困難的問題。交易策略是常用來解決這個問題的方法且現有文獻已有許多方法被提出用來產生交易策略或交易策略組合,以追求最大的報酬。其中,群組交易策略組合最佳化方法更提供了投資者更多元的選擇機制,令使用者可以彈性更換組合內的策略。然而,一個群組交易策略組合無法滿足所有使用者的需求。故為了符合許多不同的面相,根據兩目標函數,本論文首先提出SPEA為基礎的群組交易組合最佳化方法來解決這個問題。第一個目標函數用來評估策略組合的風險與報酬,第二個目標函數用來評估群組內的交易策略是否相似。為求得更有效之柏拉圖解集合,我們進一步提出基於MOEA/D的群組交易組合最佳化方法。最後,透過真實資料集,實驗顯示所提的方法的是有效的且優於現有方法。
英文摘要 In the financial market, stock investment is always a hot topic, and when to buy and sell is always a difficult problem for investor to avoid risk and to maximize the return. Trading strategies are a common method to be used to handle this problem, and there are many approaches have been proposed for obtaining trading strategies or trading strategy portfolio to maximize the profit. In addition, the group trading strategy portfolio (GTSP) optimization approach provides investors a friendly mechanism for investors to replace any trading strategy that they do not satisfy with in a given trading strategy portfolio. However, a GTSP cannot meet the needs of all users. In order to meet many different aspects, in this thesis, based on the two objective functions, we first propose a SPEA-based GTSP optimization approach. The first objective function is used to evaluate the risk and return. The second objective function is used to evaluate whether the trading strategies in the group and weights of groups are similar. Then, to reach a better Pareto front, we further propose the MOEA/D-based GTSP optimization approach. At last, experiments on a real dataset were conducted to show the proposed approaches are effective and better than the existing previous approach
論文目次 目錄
第一章 簡介 1
1.1動機 1
1.2貢獻 2
1.3讀者指南 2
第二章 背景知識與文獻回顧 3
2.1多目標最佳化技術 4
2.1.1多目標問題 4
2.1.2多目標遺傳演算法(MOGA) 4
2.1.3 強健式柏拉圖進化演算法(SPEA) 5
2.1.4 基於分解的多目標進化演算法(MOEA/D) 6
2.2多目標策略最佳化技術 7
2.3群組交易策略最佳化技術 7
第三章 以SPEA為基礎的GTSP最佳化技術 9
3.1動機 9
3.2方法架構圖 9
3.3策略產生方法 10
3.4編碼方式 13
3.5目標函數 16
3.6虛擬碼 19
3.7範例 20
第四章 以MOEA/D為基礎的GTSP最佳化技術 26
4.1動機 26
4.2方法架構圖 26
4.3策略產生方法 27
4.4編碼方式 27
4.5目標函數 28
4.6虛擬碼 29
4.7範例 30
第五章 實驗結果 34
5.1實驗數據與環境設定 34
5.2方法一的實驗分析 35
5.2.1 Pareto Front 35
5.2.2與現有方法的獲利比較 37
5.2.3不同參數的影響 38
5.3方法二的實驗分析 39
5.3.1 Pareto Font 39
5.3.2與現有方法的獲利比較 41
5.3.3不同參數的影響 42
第六章 結論與未來展望 43
參考文獻 44
附錄 英文論文 48


圖目錄
圖 1 MOGA流程 5
圖 2 SPEA流程 6
圖 3 MOEA/D流程 7
圖 4方法一架構 9
圖 5交易策略產生流程 11
圖 6 染色體編碼方式 13
圖 7 染色體範例 13
圖 8 交配過程 14
圖 9 突變過程 15
圖 10 反轉過程 15
圖 11 適應度計算過程 23
圖 12修剪過程 24
圖 13 MOEA/D方法流程 26
圖 14新增的編碼部分 27
圖 15 編碼範例 28
圖 16 分布情況 30
圖 17 鄰居資訊 30
圖 18 更新EP的過程 32
圖 19 股價序列圖 34
圖 20方法一收斂過程 35
圖 21 柏拉圖最優解比較圖 36
圖 22 不同參數之比較 38
圖 23方法二收斂過程 39
圖 24 不同方法的柏拉圖集合比較 40
圖 25方法二的不同參數比較 42


表目錄
表 1 多目標優化相關之文獻整理(依年份排序) 3
表 2 一組交易訊息 12
表 3 染色體範例 20
表 4染色體目標函數值 22
表 5 染色體的適應度值 23
表 6 一些經過優化後的染色體 25
表 7 染色體資訊 31
表 8進化流程 31
表 9優化後的染色體 33
表 10 與現有方法的比較 37
表 11三個方法的比較資訊 41

參考文獻 參考文獻
[1] C. H. Chen, C. Y. Lu, T. P. Hong and J. H. Su,"Using grouping genetic algorithm to mine diverse group stock portfolio," 2016 IEEE Congress on Evolutionary Computation (CEC),2016
[2] C. H. Chen and C. Y. Hsieh,"Actionable Stock Portfolio Mining by Using Genetic Algorithms,"JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 32,pp. 1657-1678,2016
[3] C. H. Chen,C. C. Chenb and Y. Nojima,"An efficient and effective approach for mining a group stock portfolio using mapreduce," Intelligent Data Analysis, vol. 21, no. S1, pp. S217-S232, 2017
[4] C. H. Chen,J. Coupe and T. P. Hong ,"Optimizing Diverse Group Stock Portfolio without Setting a Number of Groups,"2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA),2017
[5] C. H. Chen, C. Y. LU, T. P. HONG, C. W. LIN,AND M. GAETA,"An Effective Approach for the Diverse Group Stock Portfolio Optimization Using Grouping Genetic Algorithm," IEEE Access ,Volume 7 ,pp.155871 - 155884,2019
[6] C. H. Chen and C. H. Yu,"A Series-based group stock portfolio optimization approach using the grouping genetic algorithm with symbolic aggregate Approximations,"Knowledge-Based Systems,Volume 125, pp.146-163, 2017
[7] D. Cheong, Y. M. Kim, H. W. Byun, K. J. Oh, T. Y. Kim,"Using genetic algorithm to support clustering-based portfolio optimization by investor information," Applied Soft Computing,Volume 61,pp.593-602,2017
[8] C. H. Chen, C. B. Lin, C. C. Chen, "Mining group stock portfolio by using grouping genetic algorithms", The IEEE Congress on Evolutionary Computation, pp. 738-743, 2015
[9] V. Bevilacqua, V. Pacelli, S. Saladino,"A novel multi objective genetic algorithm for the portfolio optimization,"Adv. Intell. Comput., pp. 186-193,2012
[10] R. Saborido, A.B. Ruiz, J.D. Bermúdez, E. Vercher and M. Luque,"Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection,"Appl. Soft Comput., 39, pp. 48-63,2016
[11] Y. Kessaci,"A Multi-Objective Continuous Genetic Algorithm for Financial Portfolio Optimization Problem,"GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion,pp.151–152,2017
[12] Ucar, İ., Ozbayoglu, A.M., Ucar, M.,"Developing a Two Level Options Trading Strategy Based on Option Pair Optimization of Spread Strategies with Evolutionary Algorithms", 2015 IEEE Congress on Evolutionary Computation,pp. 25-28 ,2015
[13] C. H. Chen, Y. H. Chen, and M. E. Wu,"A GGA-based Algorithm for Group Trading Strategy Portfolio Optimization," MISNC '17: Proceedings of the 4th Multidisciplinary International Social Networks Conference,pp. 1-5,2017
[14] J. H. Syu, M. E. Wu ; S. H. Lee and J. M. Ho," Modified ORB Strategies with Threshold Adjusting on Taiwan Futures Market,"2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr),2019
[15] C. H. Chen,M. Gankhuyag,T. Hong,M.E. Wu and J. M. T. Wu,"A Multiobjective-Based Group Trading Strategy Portfolio Optimization Technique,"International Conference on Genetic and Evolutionary Computing,pp.87-93,2020
[16] Y.H. Chang and M.S. Lee,"Incorporating Markov decision process on genetic algorithms to formulate trading strategies for stock markets,"Applied Soft Computing,Volume 52, pp.1143-1153,2017
[17] 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. 7313-7325, 2019
[18] H. Ishibuchi,Y. Sakane,N. Tsukamoto,Y. Nojima,"Evolutionary Many-Objective Optimization by NSGA-II and MOEA/D with Large Populations,"2009 IEEE International Conference on Systems, Man and Cybernetics,2009
[19] Q. Zhang,H. Li,"MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition,"IEEE Transactions on Evolutionary Computation,Volume 11,Issue 6,pp. 712 – 731.2007
[20] P. C. Chang,S. H. Chen,Q. Zhang,J. L. Lin,"MOEA/D for flowshop scheduling problems,"2008 IEEE Congress on Evolutionary Computation,2008
[21] R. Wang,J. Xiong,H. Ishibuchi,G. Wu,T. Zhang,"On the effect of reference point in MOEA/D for multi-objective optimization,"Applied Soft Computing,Volume 58, pp. 25-34,2017
[22] Z. Dong,X. Wang,L. Tang,"MOEA/D with a self-adaptive weight vector adjustment strategy based on chain segmentation,"Information Sciences,Volume 521, pp. 209-230,2020
[23] Q. Jiang,L. Wang,X. Hei,G. Yu,Y. Lin,X. Lu,"MOEA/D-ARA+SBX: A new multi-objective evolutionary algorithm based on decomposition with artificial raindrop algorithm and simulated binary crossover,"Knowledge-Based Systems,Volume 107, pp. 197-218,2016
[24] J. Ji,Y. Guo,D. Gong,W. Tang,"MOEA/D-based participant selection method for crowdsensing with social awareness,"Applied Soft Computing,Volume 87,2020
[25] C. Wang,W. Zhao,W. Li,L. Yu,"Multi-objective optimisation of electro–hydraulic braking system based on MOEA/D algorithm,"IET Intelligent Transport Systems,Volume 13,Issue 1,pp. 183–193,2019
[26] V.Ho-HuuS,.Hartjes,H.G.Visser,R.Curran,"An improved MOEA/D algorithm for bi-objective optimization problems with complex Pareto fronts and its application to structural optimization,"Expert Systems with Applications,Volume 92, pp. 430-446,2018
[27] J. Zhang,G. Liu,C. Luo,H. Hu,J. Huang,"MOEA/D-DE based bivariate control sequence optimization of a variable-rate fertilizer applicator,"Computers and Electronics in Agriculture,Volume 167,2019
[28] Y. Y. Tan,Y. C. Jiao,H. Li,X. K. Wang,"A modification to MOEA/D-DE for multiobjective optimization problems with complicated Pareto sets,"Information Sciences,Volume 213, pp. 14-38,2012
[29] A. Wahid,X. Gao,P. Andreae,"Multi-objective clustering ensemble for high-dimensional data based on Strength Pareto Evolutionary Algorithm (SPEA-II),"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA),2015
[30] R. Gharari,N. Poursalehi,M. Abbasi,M. Aghaie,"Implementation of Strength Pareto Evolutionary Algorithm II in the Multiobjective Burnable Poison Placement Optimization of KWU Pressurized Water Reactor,"Nuclear Engineering and Technology,Volume 48, Issue 5 ,pp.1126-1139,2016
[31] R. Shi,K. Y. Lee,"Multi-Objective Optimization of Electric Vehicle Fast Charging Stations with SPEA-II,"IFAC-PapersOnLine,Volume 48, Issue 30, pp. 535-540, 2015
[32] S. Jiang,S. Yang,"A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization," IEEE Transactions on Evolutionary Computation,Volume 21,Issue 3,pp. 329–346,2017
[33] W. Sheng,Y. Liu,X. Meng,T. Zhang,"An Improved Strength Pareto Evolutionary Algorithm 2 with application to the optimization of distributed generations,"Computers & Mathematics with Applications,Volume 64, Issue 5, pp. 944-955,2012
[34] X. Yuan,B. Zhang,P. Wang,J. Liang,Y. Yuan,Y. Huang,X. Lei,"Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm,"Energy,Volume 122, pp. 70-82,2017
[35] A. Konaka, D. W. Coitb, A.E. Smith, "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering & System Safety, Volume 91, Issue 9, pp. 992-1007, 2006
[36] A. Tahmassebi, A. H. Gandomi and A. Meyer-Baese, "Stock Risk Assessment via Multi-Objective Genetic Programming," Journal of Postdoctoral Research(JPR),Vol.6,No.3,2018
[37] J.H. Lee and N. Sabbaghi," Multi‑objective optimization case study for algorithmic trading strategies in foreign exchange markets," Digit Finance,2019
[38] F. Delgrange,J.P. Katoen,T. Quatmann and M. Randour," Simple Strategies in Multi-Objective MDPs," Tools and Algorithms for the Construction and Analysis of Systems ,pp 346-364,2020
[39] E. Zitzler and L. Thiele, "Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach," IEEE Transactions on Evolutionary Computation , Volume 3, Issue 4, pp.257-271, 1999
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2025-07-31公開。
  • 同意授權瀏覽/列印電子全文服務,於2025-07-31起公開。


  • 若您有任何疑問,請與我們聯絡!
    圖書館: 請來電 (02)2621-5656 轉 2486 或 來信