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研究生： 陳非霆
Chen, Fei-Ting
論文名稱： 卷積深度Ｑ-學習之ETF自動交易系統
Convolutional Deep Q-learning for ETF Automated Trading System
指導教授： 蔡炎龍
學位類別： 碩士
Master
系所名稱： 理學院 - 應用數學系
Department of Mathematical Sciences
論文出版年： 2017
畢業學年度： 106
語文別： 中文
論文頁數： 48
中文關鍵詞： 深度學習 、增強學習 、卷積神經網路 、Q-learning 、DQN 、ETF
外文關鍵詞： Deep learning, Neural network, CNN, Q-leanring, DQN, ETF
相關次數： 點閱：168  下載：36 
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    • 本篇文章使用了增強學習與捲積深度學習結合的DQCN模型製作交易系統，希望藉由此交易系統能自行判斷是否買賣ETF，由於ETF屬於穩定性高且手續費高的衍生性金融商品，所以該系統不即時性的做買賣，採用每二十個開盤日進行一次買賣，並由這20個開盤日進行買賣的預測，希望該系統能最大化我們未來的報酬。
    DQN是一種增強學習的模型，並在其中使用深度學習進行動作價值的預測，利用增強學習的自我更新動作價值的機制，再用深度學習強大的學習能力成就了人工智慧，並在其取得良好的成效。

    In this paper, we used DCQN model, which is combined with reinforcement learning and CNN to train a trading system and hope the trading system could judge whether buy or sell ETFs. Since ETFs is a derivative financial good with high stability and related fee, the system does not perform real-time trading and it performs every 20 trading day. The system predicts value of action based on data in the last 20 opening days to maximize our future rewards.
    DQN is a reinforcement learning model, using deep learning to predict value of actions in model. Combined with the RL's mechanism, which updates value of actions, and deep learning, which has a strong ability of learning, to finish an artificial intelligence. We got a perfect effect.

    1 Introduction 1
    2 Deep Learning 3
    2.1 NeuralNetworkandNeuron 5
    2.1.1 ActivationFunction 7
    2.1.2 LossFunction 9
    2.1.3 GradientDescentMethod 10
    2.2 ConvolutionalNeuralNetwork 11
    3 Reinforcement Learning 14
    3.1 Introduction 15
    3.2 Temporal-DifferencePrediction 18
    3.3 Q-Learning 20
    3.4 DeepQ-Learning 21
    3.5 Policy-BasedMethod 24
    3.6 Actor-Critic 26
    3.7 AsynchronousAdvantageActor-Critic(A3C) 28
    4 Exchange-Traded Fund 30
    4.1 ExchangeTradedFunds 31
    4.2 AdvantageofETF 32
    4.3 ExampleofETF 33
    5 Automated Trading System 35
    5.1 ETFdata 35
    5.2 AutomatedTradingSystem 37
    5.2.1 Introduction 37
    5.2.2 Definition 38
    5.2.3 InitialParameterSettlement 40
    5.2.4 NeuralNetwork 41
    5.2.5 DCQN 42
    5.3 Result 43
    6 Conclusion 45
    Bibliography 47

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