跳到主要內容

臺灣博碩士論文加值系統

(44.213.60.33) 您好!臺灣時間:2024/07/17 04:10
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

: 
twitterline
研究生:蕭仲君
研究生(外文):Chung-Chun Hsiao
論文名稱:使用技術指標預測比特幣價格之研究
論文名稱(外文):The prediction of Bitcoin price based on technical indicators
指導教授:劉英和劉英和引用關係
指導教授(外文):Ying-Ho Liu
口試委員:侯佳利陳偉銘
口試委員(外文):Jia-Li HouWei-Ming Chen
口試日期:2018-07-17
學位類別:碩士
校院名稱:國立東華大學
系所名稱:資訊管理碩士學位學程
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:33
中文關鍵詞:比特幣機器學習技術指標移動視窗
外文關鍵詞:BitcoinMachine learningTechnical indicatorSliding window
相關次數:
  • 被引用被引用:7
  • 點閱點閱:775
  • 評分評分:
  • 下載下載:146
  • 收藏至我的研究室書目清單書目收藏:0
隨著區塊鏈的快速發展,加密貨幣的接受度漸漸提升,從比特幣問世至今,受到越來越多人的關注,成為近年來熱門的虛擬貨幣,許多人更將之視為投資工具;精準的預測價格有助於投資決策與投資計畫的擬定,然而,比特幣的價格常常有大幅波動,導致預測的困難度增加。有鑒於技術指標能有效地預測股價的事實,本研究利用24個預測股市的技術指標值,例如: 指數平滑異同移動平均線(MACD)、相對強弱指標(RSI)、KD隨機指標、William's % R等,作為預測比特幣的特徵值。最後,計算出來的技術指標值以及歷史數據會分別輸入到不同的分類模型和預測模型,來預測比特幣的趨勢以及價格。研究中使用移動視窗的方式進行實驗,使訓練時間與測試時間相近,維持訓練資料和測試資料的相關性。本研究期望能透過機器學習的技術,藉由輸入多種技術指標,探究其預測比特幣價格及其漲跌的能力。
With the rapid development of the blockchain technique, the acceptance of cryptocurrencies has gradually increased. Since the emergence of Bitcoin, more and more people have paid attention to it. Bitcoin has become a popular virtual currency in recent years. Accurate prediction of Bitcoin price helps to make effective investment decisions and plans. However, the price of Bitcoin often fluctuates greatly, which results in difficulty in prediction. In view of the fact that the technical indicators can effectively predict the stock price, this study uses 24 technical indicators to predict Bitcoin price. The technical indicators derived from historical Bitcoin transactions are used to construct classification models and prediction models for predicting Bitcoin's trend and price. We adopted the sliding window scheme to conduct experiments. The results show that the technical indicators can effectively predict Bitcoin's trend and price.
摘要 I
Abstract III
目錄 V
圖目錄 VII
表目錄 IX
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 2
第二章 文獻探討 5
2.1. 使用的技術指標預測股價的相關文獻 5
2.2. 預測比特幣的相關文獻 6
2.3. 分類模型 7
2.3.1. 單純貝式分類器(Naïve Bayes Classifiers) 7
2.3.2. 貝式網路(Bayesian Network) 7
2.3.3. 決策樹(Decision Tree, DT) 8
2.3.4. 支援向量機(Support Vector Machine, SVM) 9
2.3.5. 類神經網路(Neural Network, NN) 9
2.3.6. k-近鄰分類(K-Nearest-Neighbor, KNN) 10
2.3.7. 邏輯迴歸(Logistic Regression) 11
2.3.8. 自適應增強(AdaBoost) 12
2.4. 預測模型 12
2.4.1. 線性迴歸(Linear Regression) 12
2.4.2. k-近鄰分類(K-Nearest-Neighbor, KNN) 13
2.4.3. 決策樹(Decision Tree, DT) 13
2.4.4. 支援向量迴歸(Support Vector Regression, SVR) 13
2.4.5. 類神經網路(Neural Network, NN) 14
第三章 研究方法 15
3.1 研究架構 15
3.2 技術指標 15
3.3 移動視窗法 22
第四章 實驗結果 23
4.1. 實驗環境 23
4.2. 實驗資料 23
4.3. 資料處理 23
4.4. 實驗結果 25
4.4.1. 最佳參數 25
4.4.2. 分類準確率 26
4.4.3. 預測準確率 27
第五章 結論 29
參考文獻 31
[1] K. Hegazy and S. Mumford, Comparaitive Automated Bitcoin Trading Strategies, http://cs229.stanford.edu/proj2016/report/MumfordHegazy-ComparitiveAutomatedBitcoinTradingStrategies-report.pdf, retrieved on 2018/1/23.
[2] A.S. Hayes, Cryptocurrency value formation: An empirical study leading to a cost of production model for valuing bitcoin, Telematics and Informatics, Vol.34, pp.1308-1321, 2017
[3] Bitcoin Chart, https://bitcoincharts.com, retrieved on 2018/1/15.
[4] R. Peachavanish, Stock Selection and Trading Based on Cluster Analysis of Trend and Momentum Indicators, the International MultiConference of Engineers and Computer Scientists, Vol.1, 2016
[5] I. Madan, S. Saluja, and A. Zhao, Automated Bitcoin Trading via Machine Learning Algorithms, National College of Ireland, 2015
[6] A. Kar, Stock Prediction using Artificial Neural Networks, https://people.eecs.berkeley.edu/~akar/IITK_website/EE671/report_stock.pdf, retrieved on 2017/12/20.
[7] L.P. Ni, Z.W. Ni, and Y.Z. Gao, Stock trend prediction based on fractal feature selection and support vector machine, Expert Systems with Applications, Vol.38, pp. 5569-5576, 2011
[8] N. I. Indera, I. M. Yassin, A. Zabidi, and Z. I. Rizman, Non-linear autorgressive with exogeneous input (NARX) bitcoin price prediction model using pso-optimized parameters and moving average technical indicators, Journal of Fundamental and Applied Sciences, 2017
[9] Y. Zhai, A. Hsu, and S. K. Halgamuge, Combining News and Technical Indicators in Daily Stock Price Trends Prediction, Advances in Neural Networks – ISNN 2007, pp.1087-1096, 2007
[10] X. Di, Stock Trend Prediction with Technical Indicators using SVM, 2014, https://pdfs.semanticscholar.org/af39/96f74a477b649f4cf5c87645dae12b6232b9.pdf, retrieved on 2017/12/20.
[11] M.C. Lee, Using support vector machine with a hybrid feature selection method to the stock trend prediction, Expert Systems with Applications, Vol.36, pp.10896-10904, 2009
[12] L.A. Teixeira and A.L.I. Oliveira, A method for automatic stock trading combining technical analysis and nearest neighbor classification, Expert Systems with Applications, Vol.37, pp.6885-6890, 2010
[13] S. McNally, Predicting the price of Bitcoin using Machine Learning, National College of Ireland, 2016
[14] K. Zbikowski, Application of Machine Learning Algorithms for Bitcoin Automated Trading, Studies in Big Data Machine Intelligence and Big Data in Industry, pp.161-168, 2016
[15] T. Kimoto, K Asakawa, M. Yoda, and M. Takeoka, Stock Market Prediction System with Modular Neural Networks, 1990 IJCNN International Joint Conference, Vol.1, pp.1-6, 1990
[16] CoinGecko, https://www.coingecko.com/en, retrieved on 2018/7/16.
[17] 貝式網路示意圖,https://commons.wikimedia.org/wiki/File:%E5%9C%96%E4%BA%8C%EF%BC%9A%E4%B8%80%E5%80%8B%E7%B0%A1%E5%96%AE%E7%9A%84%E8%B2%9D%E6%B0%8F%E7%B6%B2%E8%B7%AF%E4%BE%8B%E5%AD%90.jpg, retrieved on 2018/1/23.
[18] 決策樹示意圖, https://www.tutorialspoint.com/data_mining/dm_dti.htm, retrieved on 2017/12/14.
[19] 支援向量機示意圖, https://docs.opencv.org/2.4/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html, retrieved on 2017/12/25.
[20] 類神經網路示意圖, http://blog.csdn.net/KangRoger/article/details/55681297, retrieved on 2017/12/14.
[21] k-近鄰分類示意圖, https://sflscientific.com/data-science-blog/2016/6/4/time-series-analysis-fitbit-using-dtw-and-knn, retrieved on 2017/12/20.
[22] 線性迴歸示意圖, https://en.wikipedia.org/wiki/Linear_regression, retrieved on 2018/1/17.
[23] 邏輯函數示意圖, http://thchou.blogspot.tw/2009/03/logistic-regression.html, retrieved on 2018/1/18.
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
無相關期刊