(3.232.129.123) 您好!臺灣時間:2021/02/26 22:01
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:陳非霆
研究生(外文):Chen, Fei-Ting
論文名稱:卷積深度Q-學習之ETF自動交易系統
論文名稱(外文):Convolutional Deep Q-learning for ETF Automated Trading System
指導教授:蔡炎龍
學位類別:碩士
校院名稱:國立政治大學
系所名稱:應用數學系
學門:數學及統計學門
學類:數學學類
論文種類:學術論文
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-learningDQNETF
外文關鍵詞:Deep learningNeural networkCNNQ-leanringDQNETF
相關次數:
  • 被引用被引用:0
  • 點閱點閱:826
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
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
[1] Anastasia Borovykh, Sander Bohte, and Cornelis W Oosterlee. Conditional time se- ries forecasting with convolutional neural networks. arXiv preprint arXiv:1703.04691, 2017.
[2] Guglielmo Maria Caporale, Juncal Cuñado, and Luis A Gil-Alana. Modelling long- run trends and cycles in financial time series data. Journal of Time Series Analysis, 34(3):405–421, 2013.
[3] Thira Chavarnakul and David Enke. Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34(2):1004–1017, 2008.
[4] Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška. The importance of experience replay database composition in deep reinforcement learning. In Deep Reinforcement Learning Workshop, NIPS, 2015.
[5] John Cristian Borges Gamboa. Deep learning for time-series analysis. arXiv preprint arXiv:1701.01887, 2017.
[6] Yoon Kim. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882, 2014.
[7] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.

[8] Ramon Lawrence. Using neural networks to forecast stock market prices. University of Manitoba, 1997.
[9] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553): 436–444, 2015.
[10] Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous control with deep rein- forcement learning. arXiv preprint arXiv:1509.02971, 2015.
[11] Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In International Conference on Machine Learning, pages 1928–1937, 2016.
[12] Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam, Mustafa Suleyman, Charles Beat- tie, Stig Petersen, et al. Massively parallel methods for deep reinforcement learning. arXiv preprint arXiv:1507.04296, 2015.
[13] Pierre Sermanet, David Eigen, Xiang Zhang, Michaël Mathieu, Rob Fergus, and Yann LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229, 2013.
[14] Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction, volume 1. MIT press Cambridge, 1998.
[15] Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Van Hasselt, Marc Lanctot, and Nando De Freitas. Dueling network architectures for deep reinforcement learning. arXiv preprint arXiv:1511.06581, 2015.
[16] Yudong Zhang and Lenan Wu. Stock market prediction of s&p 500 via combination of improved bco approach and bp neural network. Expert systems with applications, 36(5):8849–8854, 2009.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔