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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/112358
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/112358

    Title: 金融大數據與深度學習平台之設計與實作
    Design and Implementation of the Big Data in Finance and Deep Learning Platform
    Authors: 陳昱銘
    Chen, Yu-Ming
    Contributors: 劉文卿
    Liou, Wen-Qing
    Chen, Yu-Ming
    Keywords: 金融大數據
    Deep learning
    Date: 2017
    Issue Date: 2017-08-31 12:03:16 (UTC+8)
    Abstract: 本研究主旨是希望提供一個智能金融演算法交易平台,以Django CMS作為網頁框架,區分成研發環境與交易環境,完整的功能包含用戶研發、用戶測試以及使用演算法服務。用戶研發與測試上採用IPython的互動式開發介面,利用JupyterHub進行管理與配置,能夠同時提供多個用戶存取平台,使得平台足以負載大規模用戶的使用;而演算法服務經由Celery包裝成任務,以利交付給後台進行分散式運算。搭上近年來深度學習的熱潮,平台額外擴充Tensorflow套件與GPU建置,支援多核及高速演算法運算。
    The purpose of this research is to provide a smartly algorithmic trading platform with financial data. I use Django CMS as a web framework and consisting of Develop environment and Trade environment. The entire functions of the platform include “User Research and Development”,” User Testing” and “Algorithmic Services”.

    “User Research and Development” and “User Testing” using IPython interactive development interface, with JupyterHub management and configuration, can simultaneously provide multiple user accessing and make the platform enough to support more and more users; “Algorithmic Services” using Celery to package algorithms into tasks can facilitate the delivery to the Server for distributed computing. By means of the growth of Deep Learning in recent years, the platform adds extra Tensorflow and GPU deployment to support multi-core and high-speed algorithm computing.

    In face of accessing large number of complex and structured financial data, I choose HAWQ as the database in this research. Its extremely massively parallel processing can alleviate the complexity of system and the bottlenecks of efficiency caused by accessing massive number of data. Combing HAWQ with Ambari can achieve the functions of creation, monitoring and management of Hadoop distributed cluster. The developers will do much more easily in deployment and maintenance.

    The traditional table design may not fit in with the new database HAWQ, so this research will design appropriate table, and evaluate its performance to ensure that data can be accessed effectively and quickly from programs.
    Reference: [1] KPMG. (2016). Fintech funding hits all-time high in 2015, despite pullback in Q4: KPMG and CB Insights. Available: https://home.kpmg.com/xx/en/home/media/press-releases/2016/03/kpmg-and-cb-insights.html
    [2] 金融監督委員會。2016。金融科技發展策略白皮書。Available:
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    [6] Richard J. Hillman. (2005). Securities Markets: Decimal Pricing Has Contributed to Lower Trading Costs and a More Challenging Trading Environment
    [7] Bin Li, Michael Wu, and Nan Lu. (2002). System for trading financial assets using volume weighted average price. U.S. Patent No. US20020194107 A1
    [8] Morton Glantz and Robert Kissell. (2013). Multi-Asset Risk Modeling: Techniques for a Global Economy in an Electronic and Algorithmic Trading Era.
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    A First Look at Comparative Performance.
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    [13] Alexey Grishchenko. (2016). Apache HAWQ: Next Step In Massively Parallel Processing. Available:
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    [25] Jupyter, http://jupyter.org/
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    [31] Tensorflow, https://www.tensorflow.org/
    [32] Django, https://www.djangoproject.com/
    [33] Django CMS, https://www.django-cms.org/en/
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    [38] Docker Spawner, https://github.com/jupyterhub/dockerspawner
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0104356039
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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