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系統識別號 U0002-1507201913344600
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 (English-taught 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 2019-06-27
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 MOGA-based 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 non-dominated 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 non-dominated 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 Stop-Loss and Take-Profit 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 Rank-based 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 Non-Dominated 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. Non-dominated Pareto front of uptrend dataset. 46
Figure 11. Non-dominated Pareto front of sideway trend dataset. 48
Figure 12. Non-dominated 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 GGA-GTSP, MOGA-GTSP and BHS at uptrend datasets. 46
Table 17. Comparison of ROI between GGA-GTSP, MOGA-GTSP and BHS at sideway trend dataset. 49
Table 18. Comparison of ROI between GGA-GTSP, MOGA-GTSP and BHS at downtrend dataset. 51
Table 19. The comparison of optimized MOGA-GTSP, GGA-GTSP and BHS on the three trend datasets. 53


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