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論文名稱 Title |
型態辨識應用於技術分析指標之研究 Pattern Recognition of Technical Analysis Indicators |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
57 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2011-06-18 |
繳交日期 Date of Submission |
2011-09-23 |
關鍵字 Keywords |
量化、編碼、交易策略、股價走勢、型態辨識、技術分析 trading strategy, pattern recognition, trend, technical analysis, encode |
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統計 Statistics |
本論文已被瀏覽 5766 次,被下載 151 次 The thesis/dissertation has been browsed 5766 times, has been downloaded 151 times. |
中文摘要 |
技術分析的基本概念建立在歷史將會不斷的重演之上,因此分析者能夠利用觀察過去的股價走勢來對未來的市場情勢進行推估,這樣的技術應用在近年來也更加廣泛。另一方面本文中的型態辨識技術的點子來自於臉部辨識的系統,在這樣的系統下,機器捕捉到被觀察者的臉部特徵並且利用編碼程序將所有的特徵量化形成一組足以描述目標形象的數據。透過比對這些數據的相似度,系統便可以確認被觀察者的身份。型態辨識應用了這樣的系統並且將編碼的想法轉移到了股價的走勢上,它將歷史的股價走勢高低點位等特徵利用量化的數字描述,藉此對未來的股價進行推測。在實際應用上,型態辨識可以作為一個交易策略的前製作業,搭配型態辨識出來的結果,交易策略也能夠進一步的修正出最有獲利能力的狀態。 這個模型取用的台灣加權指數作為分析的標的,總共提取了19個編碼,並可以區分為價格面向以及走勢面向。在經過比對以後就可以得出相似的預測結果。實證上利用了一個周頻的簡單策略這個模型達到了31.57%的年化報酬以及26.66%的年化風險。即便是在計入交易手續費以後,模型仍然具有14.94%的年化報酬以及26.72%的年化風險。 |
Abstract |
In recent years technical analysis has been used more and more frequently. The original concept of technical analysis is built on history will be continue to repeat itself. Therefore, analysts and investors could predict the market price by observing the historical data. The idea of pattern recognition technology comes from face recognition systems. In the system, the analyst captures the facial features from the entrant and then quantifies the features as codes. Through the process of recognition, the analyst can confirm the identity of the entrant. Pattern recognition applies the idea to extract information encoded in the stock market characteristics and recognize the market with historical data. In the application, pattern recognition can be regarded as a pre-operation of the technical analysis. Users analyze the current information through pattern recognition and can further build the strategy. This model has 19 codes captured from two dimensions; the first is price, and the second is the trend of ups and downs. The empirical results for the decade in the weekly frequency trading strategy are an annual return of 31.57% and annual risk of 26.66%. After the deduction of trading fees, the strategy has an annual return of 14.94% and annual risk of 26.72%. |
目次 Table of Contents |
論文審定書 i 摘要 ii Abstract iii List of Tables vi List of Figures vii I. Introduction 1 1.1 Background 1 1.2 Research Motivation and Purpose 2 1.3 Research Framework and Flow 2 II. Literature Review 4 2.1 Technical analysis 4 2.2 Face recognition 6 III. Methodology 10 3.1 Feature Extractor 11 3.1.1 Data collection 11 3.1.2 Time span of the snapshot 11 3.1.3 Factor extraction 15 3.2 The PCA process 21 3.2.1 Principal component analysis (PCA) 21 3.2.2 Dimension Reduction 22 3.3 Pattern Recognition 23 3.3.1 Target period 23 3.3.2 Euclidean distance 23 3.3.3 Calculate the similar sample period 25 3.4 Application 27 IV. Empirical Results 28 4.1 Characteristic of recognition model 28 4.1.1 The distribution of Euclidean distance 28 4.1.2 Code – Type_6 28 4.2 Similar period trend graph 30 4.2.1 Database 30 4.2.2 Recognition trend graph (Target period: 20110422) 31 4.2.3 Recognition trend graph (Target period: 20090422) 35 4.2.4 Recognition trend graph (Target period: 20020422) 35 4.3 Back test of the rising probability 37 4.3.1 Statistic of future price with different number of sample periods 37 4.3.2 Frequency of database refresh 38 4.3.3 Estimation of target period 39 4.3.4 Accuracy of the recognition process 40 4.3.5 Empirical performance of recognition process 40 V. Conclusion 45 5.1 Conclusion 45 5.2 Suggestions for Further Research 46 References 48 |
參考文獻 References |
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