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。

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

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

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