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題名 運用於高頻交易策略規劃之分散式類神經網路框架
Distributed Framework of Artificial Neural Network for Planning High-Frequency Trading Strategies
作者 何善豪
Ho, Shan Hao
貢獻者 劉文卿
Liou, Wenqing
何善豪
Ho, Shan Hao
關鍵詞 高頻交易
時間序列
資料探勘
類神經網路
多層感知器
後向傳導
分散式運算
叢集運算
high-frequency trading
time series
data mining
artificial neural network
multilayer perceptron
backpropagation
distributed computing
cluster computing
日期 2013
上傳時間 25-Aug-2014 15:15:23 (UTC+8)
摘要 在這份研究中，我們提出一個類分散式神經網路框架，此框架為高頻交易系統研究下之子專案。在系統中，我們透過資料探勘程序發掘財務時間序列中的模式，其中所採用的資料探勘演算法之一即為類神經網路。我們實作一個在分散式平台上訓練類神經網路的框架。我們採用Apache Spark來建立底層的運算叢集，因為它提供高效能的記憶體內運算（in-memory computing）。我們分析一些分散式後向傳導演算法（特別是用來預測財務時間序列的），加以調整，並將其用於我們的框架。我們提供了許多細部的選項，讓使用者在進行類神經網路建模時有很高的彈性。
In this research, we introduce a distributed framework of artificial neural network (ANN) as a subproject under the research of a high-frequency trading (HFT) system. In the system, ANNs are used in the data mining process for identifying patterns in financial time series. We implement a framework for training ANNs on a distributed computing platform. We adopt Apache Spark to build the base computing cluster because it is capable of high performance in-memory computing. We investigate a number of distributed backpropagation algorithms and techniques, especially ones for time series prediction, and incorporate them into our framework with some modifications. With various options for the details, we provide the user with flexibility in neural network modeling.
參考文獻 縮短集合競價秒數提升交易效能. (2013, May 28). TWSE 臺灣證券交易所. Retrieved March 14, 2014, from http://www.twse.com.tw/ch/about/press_room/tsec_news_detail.php?id=11972 [Reducing Cycle Time of Call Auction to Increase Performance. (2013, May 28). TWSE Taiwan Stock Exchange. Retrieved March 14, 2014, from http://www.twse.com.tw/ch/about/press_room/tsec_news_detail.php?id=11972]
Andonie, R., Chronopoulos, A. T., Grosu, D., & Galmeanu, H. (1998, October). Distributed backpropagation neural networks on a PVM heterogeneous system. In Parallel and Distributed Computing and Systems Conference (PDCS`98) (p. 555).
Dahl, G., McAvinney, A., & Newhall, T. (2008, February). Parallelizing neural network training for cluster systems. In Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks (pp. 220-225). ACTA Press.
Feng, A. (2013). Spark and Hadoop at Yahoo: Brought to you by YARN [Slides]. Retrieved March 21, 2014, from http://ampcamp.berkeley.edu/wp-content/uploads/2013/07/andy-feng-ampcamp-3-presentation-Spark_on_YARN.pdf
Ganeshamoorthy, K., & Ranasinghe, D. N. (2008, May). On the performance of parallel neural network implementations on distributed memory architectures. In Cluster Computing and the Grid, 2008. CCGRID`08. 8th IEEE International Symposium on (pp. 90-97). IEEE.
Grant, J. (2013, November 12). Asia stock exchanges and watchdogs grapple with HFT dilemma. Financial Times. Retrieved March 14, 2014, from http://www.ft.com/cms/s/0/5ff181f6-4b4c-11e3-8c4c-00144feabdc0.html
GTSM to Re-adjust Securities Matching Time to 15 seconds Starting July 1, 2013. (2013, June 28). GreTai Securities Market. Retrieved March 14, 2014, from http://hist.gretai.org.tw/en/about/news/otc_news/otc_news_detail.php?doc_id=783
Gu, R., Shen, F., & Huang, Y. (2013, October). A parallel computing platform for training large scale neural networks. In Big Data, 2013 IEEE International Conference on (pp. 376-384). IEEE.
Haldane, A. (2010). Patience and finance. Remarks at the Oxford China Business Forum, Beijing.
Jones, R. D., Lee, Y. C., Barnes, C. W., Flake, G. W., Lee, K., Lewis, P. S., & Qian, S. (1990, June). Function approximation and time series prediction with neural networks. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on (pp. 649-665). IEEE.
Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215-236.
Kenett, D. Y., Ben-Jacob, E., & Stanley, H. E. (2013). How High Frequency Trading Affects a Market Index. Scientific reports, 3.
Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990, June). Stock market prediction system with modular neural networks. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on (pp. 1-6). IEEE.
Kingsley, T., Phadnis, K., & Stone, G. (2013, June 11). HFT: Perspectives from Asia-Part I. Bloomberg Tradebook. Retrieved March 14, 2014, from http://www.bloombergtradebook.com/blog/hft-perspectives-from-asia-part-i/
Kwong, R. (2011, November 18). Taiwan Stock Exchange plans IT upgrade. Financial Times. Retrieved March 14, 2014, from http://www.ft.com/cms/s/0/f9803820-0fa4-11e1-a468-00144feabdc0.html
Liu, Z., Li, H., & Miao, G. (2010, August). MapReduce-based backpropagation neural network over large scale mobile data. In Natural Computation (ICNC), 2010 Sixth International Conference on (Vol. 4, pp. 1726-1730). IEEE.
Pethick, M., Liddle, M., Werstein, P., & Huang, Z. (2003, November). Parallelization of a backpropagation neural network on a cluster computer. In International conference on parallel and distributed computing and systems (PDCS 2003).
Popper, N. (2012, October 14). High-Speed Trading No Longer Hurtling Forward. The New York Times. Retrieved March 14, 2014, from http://www.nytimes.com/2012/10/15/business/with-profits-dropping-high-speed-trading-cools-down.html
Price, M. (2013, October 7). Asia Goes Slow on High-Speed Trading. MoneyBeat - The Wall Street Journal. Retrieved March 14, 2014, from http://blogs.wsj.com/moneybeat/2013/10/07/asia-goes-slow-on-high-speed-trading/
Ranasinghe, D. (2014, April 2). Are markets rigged? Asia experts weigh in on debate. CNBC. Retrieved April 6, 2014, from http://www.cnbc.com/id/101546147
Scala Documentation. (n.d.). Scala Documentation. Retrieved March 21, 2014, from http://docs.scala-lang.org/
Sudhakar, V., & Murthy, C. S. R. (1998). Efficient mapping of backpropagation algorithm onto a network of workstations. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 28(6), 841-848.
Suresh, S., Omkar, S. N., & Mani, V. (2005). Parallel implementation of back-propagation algorithm in networks of workstations. Parallel and Distributed Systems, IEEE Transactions on, 16(1), 24-34.
White, H. (1988, July). Economic prediction using neural networks: The case of IBM daily stock returns. In Neural Networks, 1988., IEEE International Conference on (pp. 451-458). IEEE.
Xin, R. S., Rosen, J., Zaharia, M., Franklin, M. J., Shenker, S., & Stoica, I. (2013, June). Shark: SQL and rich analytics at scale. In Proceedings of the 2013 international conference on Management of data (pp. 13-24). ACM.
Yoon, H., Nang, J. H., & Maeng, S. R. (1990, October). A distributed backpropagation algorithm of neural networks on distributed-memory multiprocessors. In Frontiers of Massively Parallel Computation, 1990. Proceedings., 3rd Symposium on the (pp. 358-363). IEEE.
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, Franklin M. J., Shenker, S., & Stoica, I. (2012, April). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation (pp. 2-2). USENIX Association.
描述 碩士
國立政治大學
資訊管理研究所
100356019
102
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100356019
資料類型 thesis
dc.contributor.advisor 劉文卿zh_TW
dc.contributor.advisor Liou, Wenqingen_US
dc.contributor.author (Authors) 何善豪zh_TW
dc.contributor.author (Authors) Ho, Shan Haoen_US
dc.creator (作者) 何善豪zh_TW
dc.creator (作者) Ho, Shan Haoen_US
dc.date (日期) 2013en_US
dc.date.accessioned 25-Aug-2014 15:15:23 (UTC+8)-
dc.date.available 25-Aug-2014 15:15:23 (UTC+8)-
dc.date.issued (上傳時間) 25-Aug-2014 15:15:23 (UTC+8)-
dc.identifier (Other Identifiers) G0100356019en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/69191-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 100356019zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (摘要) 在這份研究中，我們提出一個類分散式神經網路框架，此框架為高頻交易系統研究下之子專案。在系統中，我們透過資料探勘程序發掘財務時間序列中的模式，其中所採用的資料探勘演算法之一即為類神經網路。我們實作一個在分散式平台上訓練類神經網路的框架。我們採用Apache Spark來建立底層的運算叢集，因為它提供高效能的記憶體內運算（in-memory computing）。我們分析一些分散式後向傳導演算法（特別是用來預測財務時間序列的），加以調整，並將其用於我們的框架。我們提供了許多細部的選項，讓使用者在進行類神經網路建模時有很高的彈性。zh_TW
dc.description.abstract (摘要) In this research, we introduce a distributed framework of artificial neural network (ANN) as a subproject under the research of a high-frequency trading (HFT) system. In the system, ANNs are used in the data mining process for identifying patterns in financial time series. We implement a framework for training ANNs on a distributed computing platform. We adopt Apache Spark to build the base computing cluster because it is capable of high performance in-memory computing. We investigate a number of distributed backpropagation algorithms and techniques, especially ones for time series prediction, and incorporate them into our framework with some modifications. With various options for the details, we provide the user with flexibility in neural network modeling.en_US
dc.description.tableofcontents Abstract .......................................................................................................................................i
Abstract in Chinese ....................................................................................................................ii
Table of Contents..................................................................................................................... iii
List of Tables .............................................................................................................................v
List of Figures ...........................................................................................................................vi
Introduction................................................................................................................................1
High-Frequency Trading in the Markets around the World ..........................................1
Research for High-Frequency Trading System..............................................................3
Strategy Planning with Artificial Neural Network ........................................................4
Training ANNs on a Distributed Computing Platform..................................................5
Literature Review.......................................................................................................................7
Time Series Prediction with ANN.................................................................................7
Distributed Backpropagation Algorithm........................................................................9
Technologies for Parallel and Distributed Computing ................................................11
Research Method .....................................................................................................................13
Overview of the HFT System ......................................................................................13
Architecture of the HFT system.......................................................................14
Data mining process in the HFT system..........................................................17
DISTRIBUTED ANN FOR HFT STRATEGIES iv
Conceptual Model of the Distributed Framework of ANN.........................................19
Single-model training session. .........................................................................21
Multi-model training session. ..........................................................................24
Architecture of the Distributed Framework of ANN...................................................26
Components for single-model training session................................................26
Components for multi-model training session.................................................30
Experiments .............................................................................................................................33
Training Time ..............................................................................................................33
Experiment Method. ........................................................................................33
Result. ..............................................................................................................34
Trading Simulation ......................................................................................................36
Experiment Method. ........................................................................................36
Testing for Firing Threshold............................................................................39
Testing for Voting Threshold...........................................................................40
Conclusion ...............................................................................................................................42
References................................................................................................................................43		zh_TW
dc.format.extent 420326 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100356019en_US
dc.subject (關鍵詞) 高頻交易zh_TW
dc.subject (關鍵詞) 時間序列zh_TW
dc.subject (關鍵詞) 資料探勘zh_TW
dc.subject (關鍵詞) 類神經網路zh_TW
dc.subject (關鍵詞) 多層感知器zh_TW
dc.subject (關鍵詞) 後向傳導zh_TW
dc.subject (關鍵詞) 分散式運算zh_TW
dc.subject (關鍵詞) 叢集運算zh_TW
dc.subject (關鍵詞) high-frequency tradingen_US
dc.subject (關鍵詞) time seriesen_US
dc.subject (關鍵詞) data miningen_US
dc.subject (關鍵詞) artificial neural networken_US
dc.subject (關鍵詞) multilayer perceptronen_US
dc.subject (關鍵詞) backpropagationen_US
dc.subject (關鍵詞) distributed computingen_US
dc.subject (關鍵詞) cluster computingen_US
dc.title (題名) 運用於高頻交易策略規劃之分散式類神經網路框架zh_TW
dc.title (題名) Distributed Framework of Artificial Neural Network for Planning High-Frequency Trading Strategiesen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 縮短集合競價秒數提升交易效能. (2013, May 28). TWSE 臺灣證券交易所. Retrieved March 14, 2014, from http://www.twse.com.tw/ch/about/press_room/tsec_news_detail.php?id=11972 [Reducing Cycle Time of Call Auction to Increase Performance. (2013, May 28). TWSE Taiwan Stock Exchange. Retrieved March 14, 2014, from http://www.twse.com.tw/ch/about/press_room/tsec_news_detail.php?id=11972]
Andonie, R., Chronopoulos, A. T., Grosu, D., & Galmeanu, H. (1998, October). Distributed backpropagation neural networks on a PVM heterogeneous system. In Parallel and Distributed Computing and Systems Conference (PDCS`98) (p. 555).
Dahl, G., McAvinney, A., & Newhall, T. (2008, February). Parallelizing neural network training for cluster systems. In Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks (pp. 220-225). ACTA Press.
Feng, A. (2013). Spark and Hadoop at Yahoo: Brought to you by YARN [Slides]. Retrieved March 21, 2014, from http://ampcamp.berkeley.edu/wp-content/uploads/2013/07/andy-feng-ampcamp-3-presentation-Spark_on_YARN.pdf
Ganeshamoorthy, K., & Ranasinghe, D. N. (2008, May). On the performance of parallel neural network implementations on distributed memory architectures. In Cluster Computing and the Grid, 2008. CCGRID`08. 8th IEEE International Symposium on (pp. 90-97). IEEE.
Grant, J. (2013, November 12). Asia stock exchanges and watchdogs grapple with HFT dilemma. Financial Times. Retrieved March 14, 2014, from http://www.ft.com/cms/s/0/5ff181f6-4b4c-11e3-8c4c-00144feabdc0.html
GTSM to Re-adjust Securities Matching Time to 15 seconds Starting July 1, 2013. (2013, June 28). GreTai Securities Market. Retrieved March 14, 2014, from http://hist.gretai.org.tw/en/about/news/otc_news/otc_news_detail.php?doc_id=783
Gu, R., Shen, F., & Huang, Y. (2013, October). A parallel computing platform for training large scale neural networks. In Big Data, 2013 IEEE International Conference on (pp. 376-384). IEEE.
Haldane, A. (2010). Patience and finance. Remarks at the Oxford China Business Forum, Beijing.
Jones, R. D., Lee, Y. C., Barnes, C. W., Flake, G. W., Lee, K., Lewis, P. S., & Qian, S. (1990, June). Function approximation and time series prediction with neural networks. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on (pp. 649-665). IEEE.
Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215-236.
Kenett, D. Y., Ben-Jacob, E., & Stanley, H. E. (2013). How High Frequency Trading Affects a Market Index. Scientific reports, 3.
Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990, June). Stock market prediction system with modular neural networks. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on (pp. 1-6). IEEE.
Kingsley, T., Phadnis, K., & Stone, G. (2013, June 11). HFT: Perspectives from Asia-Part I. Bloomberg Tradebook. Retrieved March 14, 2014, from http://www.bloombergtradebook.com/blog/hft-perspectives-from-asia-part-i/
Kwong, R. (2011, November 18). Taiwan Stock Exchange plans IT upgrade. Financial Times. Retrieved March 14, 2014, from http://www.ft.com/cms/s/0/f9803820-0fa4-11e1-a468-00144feabdc0.html
Liu, Z., Li, H., & Miao, G. (2010, August). MapReduce-based backpropagation neural network over large scale mobile data. In Natural Computation (ICNC), 2010 Sixth International Conference on (Vol. 4, pp. 1726-1730). IEEE.
Pethick, M., Liddle, M., Werstein, P., & Huang, Z. (2003, November). Parallelization of a backpropagation neural network on a cluster computer. In International conference on parallel and distributed computing and systems (PDCS 2003).
Popper, N. (2012, October 14). High-Speed Trading No Longer Hurtling Forward. The New York Times. Retrieved March 14, 2014, from http://www.nytimes.com/2012/10/15/business/with-profits-dropping-high-speed-trading-cools-down.html
Price, M. (2013, October 7). Asia Goes Slow on High-Speed Trading. MoneyBeat - The Wall Street Journal. Retrieved March 14, 2014, from http://blogs.wsj.com/moneybeat/2013/10/07/asia-goes-slow-on-high-speed-trading/
Ranasinghe, D. (2014, April 2). Are markets rigged? Asia experts weigh in on debate. CNBC. Retrieved April 6, 2014, from http://www.cnbc.com/id/101546147
Scala Documentation. (n.d.). Scala Documentation. Retrieved March 21, 2014, from http://docs.scala-lang.org/
Sudhakar, V., & Murthy, C. S. R. (1998). Efficient mapping of backpropagation algorithm onto a network of workstations. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 28(6), 841-848.
Suresh, S., Omkar, S. N., & Mani, V. (2005). Parallel implementation of back-propagation algorithm in networks of workstations. Parallel and Distributed Systems, IEEE Transactions on, 16(1), 24-34.
White, H. (1988, July). Economic prediction using neural networks: The case of IBM daily stock returns. In Neural Networks, 1988., IEEE International Conference on (pp. 451-458). IEEE.
Xin, R. S., Rosen, J., Zaharia, M., Franklin, M. J., Shenker, S., & Stoica, I. (2013, June). Shark: SQL and rich analytics at scale. In Proceedings of the 2013 international conference on Management of data (pp. 13-24). ACM.
Yoon, H., Nang, J. H., & Maeng, S. R. (1990, October). A distributed backpropagation algorithm of neural networks on distributed-memory multiprocessors. In Frontiers of Massively Parallel Computation, 1990. Proceedings., 3rd Symposium on the (pp. 358-363). IEEE.
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, Franklin M. J., Shenker, S., & Stoica, I. (2012, April). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation (pp. 2-2). USENIX Association.		zh_TW