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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曹承礎(Seng-Cho Chou) | |
| dc.contributor.author | Zi-Ting Tseng | en |
| dc.contributor.author | 曾子庭 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:50:26Z | - |
| dc.date.copyright | 2021-03-22 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-12-23 | |
| dc.identifier.citation | [1] Kuo Chuen, D. L., Guo, L., Wang, Y. (2017). Cryptocurrency: A New Investment Opportunity? SSRN Electronic Journal, 20 (3) 16-40. https://doi.org/10.2139/ssrn.2994097 [2] Wikipedia contributors. (n.d.). Cryptocurrency. Wikipedia. https://en.wikipedia.org/wiki/Cryptocurrency [3] Shen, D., Urquhart, A., Wang, P. (2019, January). Does twitter predict Bitcoin? Economics Letters, 118–122. https://doi.org/10.1016/j.econlet.2018.11.007 [4] Gogo, J. (2020, August 13). Bitcoin and Gold Correlation Reaches Record High 70%. Bitcoin News. https://news.bitcoin.com/bitcoin-and-gold-correlation-reaches-record-high-70-bolstering-btcs-store-of-value-credentials/ [5] Lamon, C., Nielsen, E., Redondo, E. (2017). Cryptocurrency Price Prediction Using News and Social Media Sentiment. [6] Shah, D., Zhang, K. (2014). Bayesian regression and Bitcoin. 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton). https://doi.org/10.1109/allerton.2014.7028484 [7] Nakamoto, S. (2008) Bitcoin: A Peer-to-Peer Electronic Cash System. https://bitcoin.org/bitcoin.pdf [8] Tsai, C. F., Hsiao, Y. C. (2010, December). Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decision Support Systems, 258–269. https://doi.org/10.1016/j.dss.2010.08.028 [9] Zhai Y., Hsu A., Halgamuge S.K. (2007) Combining News and Technical Indicators in Daily Stock Price Trends Prediction. In: Liu D., Fei S., Hou Z., Zhang H., Sun C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_132 [10] Wang, Yu Chen, Runyu. (2020). Cryptocurrency Price Prediction based on Multiple Market Sentiment. 2020 Proceedings of the 53rd Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2020.136. [11] Madan, I. (2014). Automated Bitcoin Trading via Machine Learning Algorithms. [12] Greaves, A.S., Au, B. (2015). Using the Bitcoin Transaction Graph to Predict the Price of Bitcoin. [13] Jang, H., Lee, J. (2018). An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information. IEEE Access, 5427–5437. https://doi.org/10.1109/access.2017.2779181 [14] Mcnally, S., Roche, J., Caton, S. (2018). Predicting the Price of Bitcoin Using Machine Learning. 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), 339-343. https://doi.org/ 10.1109/PDP2018.2018.00060 [15] Jiang, Z., Liang, J. (2017). Cryptocurrency portfolio management with deep reinforcement learning. 2017 Intelligent Systems Conference (IntelliSys), 905-913. https://doi.org/10.1109/IntelliSys.2017.8324237 [16] Hochreiter, S., Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735 [17] Chen, T., Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2939672.2939785 [18] Nie, F., Huang, H., Cai, X., Ding, C. (2010). Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization. NIPS. [19] Bitcoin Exchange | Cryptocurrency Exchange. (2017). Binance. https://www.binance.com/en [20] Sin, E., Wang, L. (2017). Bitcoin price prediction using ensembles of neural networks. 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 666-671. https://doi.org/ 10.1109/FSKD.2017.8393351 [21] JAMES CHEN. (Jun 25, 2019). Indicators https://www.investopedia.com/terms/i/indicator.asp | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21865 | - |
| dc.description.abstract | 大多數加密貨幣市場的研究論文中，通常都是使用研究人員自行選擇的特徵變數來預測價格或對趨勢進行分類。不過，如果相對於股票市場、外匯市場等其他市場而言，加密貨幣其實是不太受市場影響的。因此，我們假設可以僅藉由交易數據中的價格、數量和交易次數來獲取足夠的資訊量，以便於預測比特幣的價格。我們在這篇論文中提出了一個框架，該框架可以依序進行比特幣的價格預測、價格趨勢和交易的建議。每個過程都可以分做一個模組來解釋。在第一個模組中，我們應用LSTM機器學習方法來預測每隔30分鐘的未來24個價格。接下來，我們建構一種轉換價格到趨勢的方法，以識別價格的走向。用一個值來代表未來24個價格的上漲或下跌。最後，我們可以通過我們提出的交易策略，基於趨勢，給出交易建議。而為了檢驗訓練模型的通用性，我們選擇了2020年中，5種不同的振幅趨勢波動情況，在每個月中的每個30分鐘時，進行交易決策。結果表明，即使趨勢線下跌了4000美元，經由一系列的模組，我們交易後的利潤率達到了將近70％。 | zh_TW |
| dc.description.abstract | Mostly, studies on the cryptocurrency market usually use characteristic variables hand-selected by researchers to predict prices or classify trends. However, compared to other markets such as the stock market or the foreign exchange market, the cryptocurrency is actually independent without market influences. Therefore, we assume that there is enough information to be obtained by only using the price, quantity and number of transactions in the transaction data to predict the price of Bitcoin. In this paper, we propose a framework making Bitcoin price prediction, price trend, and trading suggestion. Each process is a module. In the first module, we apply the LSTM to predict the next 24 prices with 30 minutes time interval. Next, we construct a method about transforming prices to a price movement, which is a value to represent the rise or fall of the next 24 prices. Finally, through the trading strategy we proposed, we can identify a trading action based on the trend. In addition, in order to test the versatility of models, we select 5 different amplitude fluctuations in 2020, and made trading decisions every 30 minutes in a month. The results, through a series of modules, show that even if the trendline fell by $4,000, our profit rate after trading still reached nearly 70%. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:50:26Z (GMT). No. of bitstreams: 1 U0001-2312202011560200.pdf: 5243516 bytes, checksum: 1d65168d65eedbe3b322bb3bd83df96f (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | ACKNOWLEDGEMENT ii 中文摘要 iii ABSTRACT iv CONTENTS i LIST OF FIGURES iii LIST OF TABLES iv Chapter 1 Introduction 5 1.1 Background 5 1.2 Motivation 7 1.3 Objectives 8 1.4 Thesis organization 10 Chapter 2 Related Work 11 2.1 Cryptocurrency Introduction 11 2.2 Fundamental Technical Indicators 12 2.2.1 Stock Market 12 2.2.2 Cryptocurrency market 13 2.3 Technology Analysis of Cryptocurrency 14 2.3.1 The Prediction of Exact Price 14 2.3.2 The Prediction of Price Trend 15 2.3.3 The Profitable Strategy Application 16 2.4 Other Model and Methods 16 2.4.1 LSTM 17 2.4.2 Moving Average 18 Chapter 3 Methodology 21 3.1 Data Preprocessing 21 3.1.1 Dataset 21 3.1.2 Feature Selection 23 3.1.3 Training on Trial 24 3.2 Training Module 25 3.2.1 Parameter Generator 25 3.2.2 Machine Learning Architecture 27 3.3 Transformation Module 29 3.3.1 Trendline Generator 29 3.4 Evaluation Module 31 3.4.1 Trading Strategy 31 3.4.2 Portfolio of Parameter Management 33 Chapter 4 Experiment and Discussion 39 4.1 Fine-tune of Modules 39 4.1.1 Feature Selection and Its Parameters 39 4.1.2 Transformation from Price to Trend 42 4.1.3 Parameters in Trading Strategy 47 4.2 Discussion 49 4.2.1 RMSE Estimation 49 4.2.2 Model Update 50 Chapter 5 Conclusion and Future Works 54 5.1 Conclusion 54 5.2 Future Works 55 Reference 57 | |
| dc.language.iso | en | |
| dc.title | 僅使用交易數據進行機器學習的比特幣價格趨勢預測研究 | zh_TW |
| dc.title | Bitcoin Price Trend Prediction with Machine Learning Using Only Transaction Data | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 109-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳建錦(Chien Chin Chen),盧信銘(Hsin-Min Lu) | |
| dc.subject.keyword | 比特幣,機器學習,交易策略,價格預測,趨勢預測, | zh_TW |
| dc.subject.keyword | Bitcoin,Machine Learning,Trading Strategy,Price Prediction,Trend Prediction, | en |
| dc.relation.page | 59 | |
| dc.identifier.doi | 10.6342/NTU202004450 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-12-24 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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