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題名 基於微服務架構之即時建模的程式交易系統
Real-Time Modeling Program Trading System Based On Microservice Architecture
作者 萬恩福
Wan, En Fu
貢獻者 劉文卿
Liou, Wen Ching
萬恩福
Wan, En Fu
關鍵詞 程式交易
即時建模
適應性調整
時間序列模型
分散式運算
集成方法
Program Trading
Real-Time Modeling
Adaptation
Time Series Model
Distributed Computing
Ensemble Method
日期 2016
上傳時間 9-Aug-2016 10:45:02 (UTC+8)
摘要 本研究以預測台指期為例，透過時間序列作為預測模型，其貢獻在於以即時建模的方式，解決批次建模難以臨時調整模型之缺點，以分散式運算技術Storm，結合R之運算環境，在極短的時間內，平行建立大量單變數與多變數時間序列模型，改善以往建立模型時，為了找出較佳模型，所需反覆執行的建模過程，最後採取集成方法，將所有模型集結起來，以投票方式預測適應性訊號，並且透過適應性調整的機制，逐漸逼近最佳的預測準確度。
參考文獻 [1] Y. F. Wang, "Predicting stock price using fuzzy grey prediction system," Expert Systems with Applications, vol. 22, pp. 33–38, 2002/01/01 2002.
     [2] M. H. F. Zarandi, B. Rezaee, I. B. Turksen, and E. Neshat, "A type-2 fuzzy rule-based expert system model for stock price analysis," Expert Systems with Applications, vol. 36, pp. 139–154, 2009/01/01 2009.
     [3] R. J. Kuo, C. H. Chen, and Y. C. Hwang, "An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network," Fuzzy Sets and Systems, vol. 118, pp. 21-45, 2001/02/16 2001.
     [4] R. Tsaih, Y. Hsu, and C. C. Lai, "Forecasting S&P 500 stock index futures with a hybrid AI system," Decision Support Systems, vol. 23, pp. 161–174, 1998/06/01 1998.
     [5] M. M. Rounaghi and F. N. Zadeh, "Investigation of market efficiency and financial stability between S&P 500 and London stock exchange: Monthly and yearly forecasting of time series stock returns using ARMA model," Physica A: Statistical Mechanics and its Applications, vol. 456, pp. 10–21, 2016/08/01 2016.
     [6] V. Akgiray, "Conditional Heteroscedasticity in time series of stock returns: Evidence and forecasts," The Journal of Business, vol. 62, pp. 55-80, 1989/01 1989.
     [7] H. R. Stoll and R. E. Whaley, "The dynamics of stock index and stock index futures returns," Journal of Financial and Quantitative Analysis, vol. 25, pp. 441-468, 1990/12/01 1990.
     [8] Q. C. Chu, W.-l. G. Hsieh, and Y. Tse, "Price discovery on the S&P 500 index markets: An analysis of spot index, index futures, and SPDRs," International Review of Financial Analysis, vol. 8, pp. 21-34, 1999/01/01 1999.
     [9] D. A. Dickey and W. A. Fuller, "Distribution of the estimators for Autoregressive time series with a unit root," Journal of the American Statistical Association, vol. 74, pp. 427-431, 1979/06 1979.
     [10] G. E. Pelham and G. Jenkins, "Time series analysis, forecasting and control," 1990/01/11 1990.
     [11] R. F. Engle, "Autoregressive conditional Heteroscedasticity with estimates of the variance of United Kingdom inflation," Econometrica, vol. 50, pp. 987-1007, 1982/07 1982.
     [12] T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, vol. 31, pp. 307-327, 1986/04/01 1986.
     [13] S. Johansen, "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, vol. 12, pp. 231-254, 1988/06/01 1988.
     [14] 楊奕農, 時間序列分析, 2009.
     [15] C. A. Sims, "Macroeconomics and reality," Econometrica, vol. 48, pp. 1-48, 1980/01 1980.
     [16] R. F. Engle and C. W. J. Granger, "Co-Integration and error correction: Representation, estimation, and testing," Econometrica, vol. 55, pp. 251-276, 1987/03 1987.
     [17] K. Yang, H. Yoon, and C. Shahabi, "A feature subset selection technique for Multivariate time series," Advances in Knowledge Discovery and Data Mining, pp. 516-522, 2005.
     [18] D. Inc. (2016). Docker. Available: https://www.docker.com/
     [19] A. S. Foundation. (2016). Apache Kafka. Available: http://kafka.apache.org/
     [20] J. Kreps, L. Corp, and J. Rao, "Kafka: A distributed messaging system for log processing."
     [21] M. Zaharia, M. Chowdhury, T. Das, A. Dave, Justin, M. McCauley, et al., "Resilient distributed Datasets: A fault-tolerant abstraction for in-memory cluster computing."
     [22] M. Zaharia, T. Das, H. Li, T. Hunter, S. Shenker, and I. Stoica, "Discretized streams," Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles - SOSP `13, 2013.
     [23] A. S. Foundation. (2016). Apache Spark. Available: http://spark.apache.org/
     [24] A. S. Foundation. (2016). Apache Storm. Available: http://storm.apache.org/
     [25] A. Toshniwal, S. Taneja, A. Shukla, K. Ramasamy, J. M. Patel, S. Kulkarni, et al., "Storm@twitter," pp. 147-156, 2014/06/18 2014.
     [26] S. Urbanek, "Rserve A fast way to provide R Functionality to applications," 2003.
     [27] Kong. (2014). Microservices architecture pattern. Available: http://microservices.io/patterns/microservices.html
描述 碩士
國立政治大學
資訊管理學系
103356011
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0103356011
資料類型 thesis
dc.contributor.advisor 劉文卿zh_TW
dc.contributor.advisor Liou, Wen Chingen_US
dc.contributor.author (Authors) 萬恩福zh_TW
dc.contributor.author (Authors) Wan, En Fuen_US
dc.creator (作者) 萬恩福zh_TW
dc.creator (作者) Wan, En Fuen_US
dc.date (日期) 2016en_US
dc.date.accessioned 9-Aug-2016 10:45:02 (UTC+8)-
dc.date.available 9-Aug-2016 10:45:02 (UTC+8)-
dc.date.issued (上傳時間) 9-Aug-2016 10:45:02 (UTC+8)-
dc.identifier (Other Identifiers) G0103356011en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/99766-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 103356011zh_TW
dc.description.abstract (摘要) 本研究以預測台指期為例，透過時間序列作為預測模型，其貢獻在於以即時建模的方式，解決批次建模難以臨時調整模型之缺點，以分散式運算技術Storm，結合R之運算環境，在極短的時間內，平行建立大量單變數與多變數時間序列模型，改善以往建立模型時，為了找出較佳模型，所需反覆執行的建模過程，最後採取集成方法，將所有模型集結起來，以投票方式預測適應性訊號，並且透過適應性調整的機制，逐漸逼近最佳的預測準確度。zh_TW
dc.description.tableofcontents 摘要 I
     目錄 II
     圖目錄 V
     表目錄 VII
     第一章 、緒論 1
     第一節 、研究背景 1
     第二節 、研究動機與目的 1
     第三節 、研究流程 4
     第二章 、文獻探討 5
     第一節 、單根檢定 5
     第二節 、ARMA與ARIMA 6
     一 、ARMA(p, q) 6
     二 、ARIMA(p, d, q) 7
     三 、Box-Jenkins 7
     第三節 、GARCH 8
     第四節 、JOHANSEN共整合檢定 9
     一 、跡檢定（Trace Test） 10
     二 、最大特性根檢定（Maximum Eigenvalue Test) 10
     第五節 、VAR與VECM 10
     一 、VAR（Vector Autoregression） 10
     二 、VECM（Vector Error Correction Model） 11
     第六節 、CORONA 12
     第七節 、DOCKER 13
     第八節 、APACHE KAFKA 14
     第九節 、APACHE SPARK 16
     第十節 、APACHE STORM 19
     第十一節 、RSERVE 21
     第三章 、研究方法 23
     第一節 、研究架構 23
     第二節 、即時建模 24
     一 、集成方法（Ensemble Method） 24
     二 、模型等級 28
     第三節 、適性訊號 30
     一 、適性訊號產生 30
     二 、適應性調整（Adaptation） 31
     第四節 、兩階段建模 33
     第四章 、系統實作與測試 35
     第一節 、系統概述 35
     第二節 、系統實作 39
     一 、Corona微服務 39
     二 、兩階段建模微服務 42
     第三節 、系統測試 60
     一 、測試環境 60
     二 、測試結果 63
     第五章 、結論與未來展望 69
     第一節 、結論 69
     第二節 、未來展望 70
     參考文獻 71		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103356011en_US
dc.subject (關鍵詞) 程式交易zh_TW
dc.subject (關鍵詞) 即時建模zh_TW
dc.subject (關鍵詞) 適應性調整zh_TW
dc.subject (關鍵詞) 時間序列模型zh_TW
dc.subject (關鍵詞) 分散式運算zh_TW
dc.subject (關鍵詞) 集成方法zh_TW
dc.subject (關鍵詞) Program Tradingen_US
dc.subject (關鍵詞) Real-Time Modelingen_US
dc.subject (關鍵詞) Adaptationen_US
dc.subject (關鍵詞) Time Series Modelen_US
dc.subject (關鍵詞) Distributed Computingen_US
dc.subject (關鍵詞) Ensemble Methoden_US
dc.title (題名) 基於微服務架構之即時建模的程式交易系統zh_TW
dc.title (題名) Real-Time Modeling Program Trading System Based On Microservice Architectureen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] Y. F. Wang, "Predicting stock price using fuzzy grey prediction system," Expert Systems with Applications, vol. 22, pp. 33–38, 2002/01/01 2002.
     [2] M. H. F. Zarandi, B. Rezaee, I. B. Turksen, and E. Neshat, "A type-2 fuzzy rule-based expert system model for stock price analysis," Expert Systems with Applications, vol. 36, pp. 139–154, 2009/01/01 2009.
     [3] R. J. Kuo, C. H. Chen, and Y. C. Hwang, "An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network," Fuzzy Sets and Systems, vol. 118, pp. 21-45, 2001/02/16 2001.
     [4] R. Tsaih, Y. Hsu, and C. C. Lai, "Forecasting S&P 500 stock index futures with a hybrid AI system," Decision Support Systems, vol. 23, pp. 161–174, 1998/06/01 1998.
     [5] M. M. Rounaghi and F. N. Zadeh, "Investigation of market efficiency and financial stability between S&P 500 and London stock exchange: Monthly and yearly forecasting of time series stock returns using ARMA model," Physica A: Statistical Mechanics and its Applications, vol. 456, pp. 10–21, 2016/08/01 2016.
     [6] V. Akgiray, "Conditional Heteroscedasticity in time series of stock returns: Evidence and forecasts," The Journal of Business, vol. 62, pp. 55-80, 1989/01 1989.
     [7] H. R. Stoll and R. E. Whaley, "The dynamics of stock index and stock index futures returns," Journal of Financial and Quantitative Analysis, vol. 25, pp. 441-468, 1990/12/01 1990.
     [8] Q. C. Chu, W.-l. G. Hsieh, and Y. Tse, "Price discovery on the S&P 500 index markets: An analysis of spot index, index futures, and SPDRs," International Review of Financial Analysis, vol. 8, pp. 21-34, 1999/01/01 1999.
     [9] D. A. Dickey and W. A. Fuller, "Distribution of the estimators for Autoregressive time series with a unit root," Journal of the American Statistical Association, vol. 74, pp. 427-431, 1979/06 1979.
     [10] G. E. Pelham and G. Jenkins, "Time series analysis, forecasting and control," 1990/01/11 1990.
     [11] R. F. Engle, "Autoregressive conditional Heteroscedasticity with estimates of the variance of United Kingdom inflation," Econometrica, vol. 50, pp. 987-1007, 1982/07 1982.
     [12] T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, vol. 31, pp. 307-327, 1986/04/01 1986.
     [13] S. Johansen, "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, vol. 12, pp. 231-254, 1988/06/01 1988.
     [14] 楊奕農, 時間序列分析, 2009.
     [15] C. A. Sims, "Macroeconomics and reality," Econometrica, vol. 48, pp. 1-48, 1980/01 1980.
     [16] R. F. Engle and C. W. J. Granger, "Co-Integration and error correction: Representation, estimation, and testing," Econometrica, vol. 55, pp. 251-276, 1987/03 1987.
     [17] K. Yang, H. Yoon, and C. Shahabi, "A feature subset selection technique for Multivariate time series," Advances in Knowledge Discovery and Data Mining, pp. 516-522, 2005.
     [18] D. Inc. (2016). Docker. Available: https://www.docker.com/
     [19] A. S. Foundation. (2016). Apache Kafka. Available: http://kafka.apache.org/
     [20] J. Kreps, L. Corp, and J. Rao, "Kafka: A distributed messaging system for log processing."
     [21] M. Zaharia, M. Chowdhury, T. Das, A. Dave, Justin, M. McCauley, et al., "Resilient distributed Datasets: A fault-tolerant abstraction for in-memory cluster computing."
     [22] M. Zaharia, T. Das, H. Li, T. Hunter, S. Shenker, and I. Stoica, "Discretized streams," Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles - SOSP `13, 2013.
     [23] A. S. Foundation. (2016). Apache Spark. Available: http://spark.apache.org/
     [24] A. S. Foundation. (2016). Apache Storm. Available: http://storm.apache.org/
     [25] A. Toshniwal, S. Taneja, A. Shukla, K. Ramasamy, J. M. Patel, S. Kulkarni, et al., "Storm@twitter," pp. 147-156, 2014/06/18 2014.
     [26] S. Urbanek, "Rserve A fast way to provide R Functionality to applications," 2003.
     [27] Kong. (2014). Microservices architecture pattern. Available: http://microservices.io/patterns/microservices.html		zh_TW