本研究利用雲端運算的技術，發展大量使用者使用的策略交易的系統。為滿足大量使用者的運算需求，本系統包括幾項特性：
1. 採用服務導向架構以充分使用雲端運算的特性。
2. 建立非同步事件控制機制以提供服務間非同步運算能力。
3. 採用集中式資料結構，提出收縮式肋骨網絡(SRN)資料結構，減少運算需求。
4. 提供基因演算模擬環境，讓使用者可以發展符合個人投資偏好的投資策略。

In this study, we designed a algorithmic trading system for large numbers of users on a cloud computing plateform. So the main features of the algorithmic trading system have been as follows.
1. The use of Service-Oriented architecture in order to fully use the characteristics of cloud computing.
2. The establishment of asynchronous event control mechanism to provide services to non-synchronous computing power.
3. A centralized data structure, proposed Systolic Ribs Network (SRN) data structure, reducing the computing needs.
4. To provide the genetic algorithm simulation environment that allows users to develop in line with the investment strategy personal investment preferences.

一、英文參考文獻
[1] Andy Scurto（2008）. Cloud Computing Extends SOA Capability. National Underwriter. P & C. Erlanger: Mar 10, 2008. Vol. 112, 18-19.
[2] Chu-Pong Tsao, Yi-Hsiu Chen, Wei-Shan Hsieh, Shiang-Chih Hsiao（2009）. The Research of Construct Knowledge Map using Concept of Cloud Computing, International Journal of Information and Technology ,Vol.4 NO.2.
[3] Conlon, S. J., J. G.Hale, et al.（2008）. Information Extraction Agents For Service-Oriented Architecture using Web Service Systems: A Framework, Journal of Computer Information Systems 48(3).
[4] Fama, E.F.（1970）. Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, Vol.25, 383-417.
[5] Gartner, I.（2008）. Gartner Says Contrasting Views on Cloud Computing Are Creating Confusion. Retrieved from http://gartner.com/it/page.jsp?id=766215
[6] Goldberg, D. E.（1989）. Genetic Algorithm in Search, Optimization, and Machine Learning. Addison-Wesley.
[7] Ian Foster, Yong Zhao, Ioan Raicu, Shiyong Lu（2009）. Cloud Computing and Grid Computing 360-Degree Compared. IEEE Xplore. United States : IEEE.
[8] J.H.Holland（2009）. Adaption in Natural and Artificial Systems. University of Michingan, United States.
[9] John Harauz, Lori M. Kaufman, Bruce Potter（2009）. Data Security in the World of Cloud Computing. IEEE Xplore. United States : IEEE.
[10] Lars N. Kestner（1975）. Quantitative Tranding Strategies. McGraw-Hill.Press, Ann Arbor.
[11] Tsai, Chun Hua（2009）. Danger in the clouds－Network Security (Unpublished master dissertation). University of Chengchi, Taipei.
[12] Marios D. Dikaiakos, George Pallis, Dimitrios Katsaros, Pankaj Mehra, Athena Vakali（2009）. Cloud Computing Distributed Internet Computing for IT and Scientific Research. IEEE INTERNET COMPUTING. United States : IEEE.
[13] Kevin Kelly（2006）. Moving markets Shifts in trading patterns are making technology ever more important. The Economist. Retrived from http://www.economist.com/node/5475381?story_id=E1_VQSVPRT.
[14] Nagarajan, V. Y. Wu, M. Liu, Q.G. Wang（2005）. Forecast Studies for Financial Markets using Technical Analysis. International Conference on Control and Automation. ICCA.
[15] National Institute of Standards and Technology(NIST)（2010）. Cloud Computing Forum & Workshop. Retrieved from http://www.nist.gov/itl/cloud.cfm.
[16] Natis, Y. V.（2003）. Service-Oriented Architecture Scenario. Retrieved from http://www.gartner.com/DisplayDocument?doc_cd=114358.
[17] Packard, N. H.（1987）. A Genetic Learning Algorithm for the Analysis of Complex Data, Complex System, Vol. 4, 543-572.
[18] Robert Pardo（2010）. The Evaluation and Optimization of Trading Strategies. John Wiley & Sons Inc.
[19] Rob Iati,（2009）. The Real Story of Trading Software Espionage. Retrieved from http://AdvancedTrading.com.
[20] Robert W. Colby（2003）. The Encyclopedia of Technical Market Indicators. McGraw-Hill.
[21] Stallman（2008）. Stallman: Cloud computing is 'stupidity'. The Guardian.
[22] Stamford（2008）. Gartner Says Cloud Computing Will Be as Influential as E-business. Retrieved from http://www.gartner.com/it/page.jsp?id=707508.
[23] W3C（2008）. Web Services Activity. Retrieved from http://www.w3.org/2002/ws/ .
二、中文參考文獻
[24] Robert D. Edwards, & John Magee（1998）。股價趨勢技術分析(上)。寰宇出版股份有限公司。
[25] Robert D. Edwards, & John Magee（1998）。股價趨勢技術分析(下)。寰宇出版股份有限公司。
[26] 杜金龍（2006）。最新技術指標－在台灣股市應用的訣竅。財訊出版社。
[27] 林昇甫、徐永吉（2009）。遺傳演算法及其應用。五南圖書出版公司。
[28] 林銘郎（2005）。技術指標通論。簡愛洋行。
[29] 林耀堂（2000）。遺傳程式規劃於股市擇時交易之應用（未出版碩士論文）。國立中央大學，桃園縣。
[30] 莊子德（2010）。應用歷史資料訓練之遺傳演算法於投資組合策略（未出版碩士論文）。國立東華大學，花蓮縣。
[31] 張清良（2008）。股票市場買賣研判指標的應用（未出版碩士論文）。國立中正大學，嘉義縣。
[32] 劉大成（1999）。結合遺傳演化與範例學習法進行台灣股市行為預測之研究（未出版碩士論文）。國立政治大學，台北市。
[33] 蔡宜龍（1990）。台灣股市市場技術分析指標有效性之衡量（未出版碩士論文）。國立成功大學，台南市。
[34] 劉明修（2008）。以夥伴關係管理概念建構一SOA之電子化採購系統（未出版碩士論文）。私立大同大學，台北市。
[35] 劉俊宏（2008）。雲端運算環境下學習社群服務導向架構平台之研究（未出版碩士論文）。國立政治大學，台北市。
[36] 賴育漣（2009，10月1日）。演算法交易漸成市場熱點。經濟日報。取自http://itrader168.blogspot.com/2009/09/blog-post_23.html。