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| Title: | 卷積神經網路結合技術指標交易策略在台灣加權指數期貨之應用
Applications of Trading Strategies for Convolution Neural Network and Technical Indicators in Taiwan Stock Price Index Futures |
| Authors: | 李杰穎
Lee, Chieh-Ying |
| Contributors: | 林士貴
王釧茹
Lin, Shih-Kuei
Wang, Chuan-Ju
李杰穎
Lee, Chieh-Ying |
| Keywords: | 卷積神經網路
技術指標
隨機指標
布林通道
Convolutional neural networks
Technical indicator
Stochastic oscillator
Boolinger band |
| Date: | 2019 |
| Issue Date: | 2019-07-01 10:48:28 (UTC+8) |
| Abstract: | 在金融科技(Financial Technology, FinTech)迅速發展之下，許多金融服務都嘗試結合最新的突破技術，例如:行動支付(轉帳)、數位銀行崛起……等等，讓我們隨時都能使用金融服務，而不需要親自再跑一趟實體銀行，由此可見，金融發展與科技息息相關，目前金融也正在朝全自動化、智能化為目標努力。在金融交易方面，已經可以達到透過技術指標建構模型自動下單的技術，本論文將卷積神經網路模型(Convolutional Neural Network, CNN)應用在技術指標交易策略上，將開盤價、最高價、收盤價、最低價及技術指標走勢轉換成圖像，希望憑藉卷積神經網路模型優異的圖像辨識能力，對獲利和虧損的交易策略進行特徵提取，達到提高交易策略準確率的目的。實證結果發現，不管交易策略在多單或是空單方面，在經過卷積神經網路模型訓練之後，都能有效的提高準確率。臺灣近期也越來越重視人工智能結合金融服務的發展，人工智能不但能達到全自動化的服務，也能帶給使用金融服務的客戶煥然一新的體驗，並達到更快速的產品和服務交付，提高金融服務的效率，本文期望透過將類神經網路結合技術指標交易策略的方法，使未來學界朝人工智能交易為目標發展之時，能夠有更多不同的想法。
In terms of financial trading, it is possible to achieve automatically placing orders through following several technical indicators. In this paper, we apply Convolutional Neural Networks to the technical trading strategy. We converted opening price, highest price, closing price, lowest price, and the trend of technical indicator into images. We hoped that by the excellent ability of image recognition of Convolutional Neural Networks, we can extract the features of profit strategies and loss strategies, and then improve the accuracy of trading strategies. The empirical results show that no matter long strategies or short strategies were used, after the training of Convolutional Neural Networks, the accuracy of gaining profits from the strategies can be improved effectively. In recent years, people in Taiwan pay more and more attention to the development of artificial intelligence combined with financial services. My expectation is to provide different ideas to scholars and experts who worked hard in this related area in this thesis, so that they might further develop new techniques in this area from different perspectives. |
| Reference: | [1] Akita, R., Yoshihara, A., Matsubara, T., and Uehara K., (2016). Deep Learning for Stock Prediction Using Numerical and Textual Information. Kobe University, Master’s Thesis.
[2] Alexander, S. S., (1961). Price Movement in Speculative Markets: Trends or Random Walks.
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[16] 吳永樂， (2014)。運用支持向量機和決策樹預測台指期走勢，國立政治大學，碩士論文。
[17] 賴嘉蔚，(2017)。卷積神經網路預測時間序列能力分析，國立政治大學，碩士論文。
[18] 劉昭雨，(2017)。卷積神經網路在金融技術指標之應用，國立東華大學，碩士論文。 |
| Description: | 碩士
國立政治大學
金融學系
106352034 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0106352034 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU201900103 |
| Appears in Collections: | [金融學系] 學位論文
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