English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113311/144292 (79%)
Visitors : 50939710      Online Users : 948
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/69191
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/69191


    Title: 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
    Authors: 何善豪
    Ho, Shan Hao
    Contributors: 劉文卿
    Liou, Wenqing
    何善豪
    Ho, Shan Hao
    Keywords: 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
    Date: 2013
    Issue Date: 2014-08-25 15:15:23 (UTC+8)
    Abstract: 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.
    Reference: 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.
    Description: 碩士
    國立政治大學
    資訊管理研究所
    100356019
    102
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0100356019
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

    Files in This Item:

    File SizeFormat
    601901.pdf410KbAdobe PDF2166View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback