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研究生： 蘇禧
Sushilata - Devi Mayanglambam
論文名稱： 證券投資基金交易策略基於湍流粒子群優化和移動平均收斂 - 發散
Trading Strategy of Mutual Funds Based on Turbulent Particle Swarm Optimization and Moving Average Convergence - Divergence
指導教授： 洪西進
Shi-Jinn Horng
口試委員: 鍾國亮
Kuo-Liang Chung
王獻
Xian Wang
吳有基
Yu-Chi Wu
學位類別： 碩士
Master
系所名稱： 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年： 2011
畢業學年度： 99
語文別： 英文
論文頁數： 76
中文關鍵詞： 共同基金 、粒子群優化算法 、粒子群優化湍流 、移動平均收斂發散
外文關鍵詞： Mutual funds, Particle Swarm Optimization, Turbulent Particle Swarm Optimization, Moving Average Convergence Divergence
相關次數： 點閱：340  下載：1 
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    • 共同基金是一種投資組合(包含股票、債券、基金以及貴重金屬交易)。這些投資組合是由專業的經理人來做管理，另外一方面公開發售給社會大眾。這讓投資人可以投資不同種類的公司，與投資單一公司比較起來擁有較低的風險。傳統上，投資人會使用人工的方式來分析基金過去的淨值，不過這種做法不但沒有說服力且花時間，另外也擁有很高的風險。因此，一個成功的交易策略不僅僅可以為投資人帶來良好的獲利，也可以讓我們預測基金未來發展。本篇論文使用最佳化演算法提出了一種有效率且簡單的交易策略模型。論文當中結合了Turbulent Particle Swarm Optimization (TPSO)以及一種技術指標Moving Average Convergence-Divergence (MACD)。為了驗證本架構穩定度以及效能，論文當中使用不同年份的歷史資料來做測試。要有良好的獲利，時間長度是非常重要的參數，根據我們的實驗結果得知，用七年的淨值歷史來做測試會有最好的結果。和原始的資料相比，使用七年的淨值歷史可提高38%的獲利；使用八年的交易歷史可提高22%的獲利。

    A mutual fund is a bundle of investments (whether stocks, bonds, money, art, precious metals). These investments are managed professionally and shares of the mutual fund are sold to the public. This allows mutual fund holders to diversify among many companies instead of owning a single stock. Mutual funds have become the most popular products for diversity of investment. Traditionally, people are used to analyze the historical data and the market manually, however the return is not convincing most of the time and the risk factor is very high. Hence, a successful trading strategy is necessary to achieve the profit and good market forecast. In this thesis, an efficient and simple trading strategy model is designed based on optimization algorithm, Turbulent Particle Swarm Optimization (TPSO) in combination with technical indicators namely Moving Average Convergence-Divergence (MACD). To check the stability and performance of the proposed technique, different window sizes (different time periods) of training data are used. From the experimental finding, it turns out that proper duration of training period is very important to achieve better profit and seven years training period gives the best performance in comparison with other window sizes. The performance of each fund on average has been improved more than the original about 38% and 22% for 7 and 8 years training period respectively in testing phase.

    DECLARATION I 摘要 II ABSTRACT III ACKNOWLEDGEMENTS IV LIST OF FIGURES VII LIST OF TABLES VIII CHAPTER 1 INTRODUCTION 1 1.1 INTRODUCTION 1 1.2 MOTIVATION 5 1.3 ORGANIZATION OF THESIS 6 CHAPTER 2 PARTICLE SWARM OPTIMIZATION 7 2.1 INTRODUCTION 7 2.2 APPLICATION OF PSO 11 2.3 ALGORITHM OF STANDARD PSO 12 2.4 TURBULENT PARTICLE SWARM OPTIMIZATION 13 CHAPTER 3 TECHNICAL INDICATORS 15 3.1 INTRODUCTION 15 3.2 EXPONENTIAL MOVING AVERAGES 16 3.3 MOVING AVERAGE CONVERGENCE-DIVERGENCE 18 3.3.1 Example of creating MACD 19 3.3.2 Market Psychology 23 3.4 TRADING STRATEGY 24 3.4.1 Algorithm of Trading Strategy 24 CHAPTER 4 PROPOSED METHOD 28 4.1 TURBULENT PARTICLE SWARM OPTIMIZATION AND MOVING AVERAGE CONVERGENCE AND DIVERGENCE (TPSO-MACD) 28 4.2 DETAILS OF PROPOSED METHOD 29 4.3 FLOWCHART OF TPSO-MACD 31 CHAPTER 5 DISCUSSION OF EXPERIMENT AND COMPARISON 32 5.1 EXPERIMENT DETAILS 32 5.2 EXPERIMENTAL DATA 36 5.3 COMPARISON 37 CHAPTER 6 CONCLUSION AND FUTURE WORK 43 6.1 CONCLUSION AND FUTURE WORK 43 REFERENCES 44 APPENDIX 47

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