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研究生： 何善豪
Ho, Shan Hao
論文名稱： 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
指導教授： 劉文卿
Liou, Wenqing
學位類別： 碩士
Master
系所名稱： 商學院 - 資訊管理學系
Department of Management Information System
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 47
中文關鍵詞： 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
外文關鍵詞： high-frequency trading, time series, data mining, artificial neural network, multilayer perceptron, backpropagation, distributed computing, cluster computing
相關次數： 點閱：149  下載：8 
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    • 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.

    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

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