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研究生: 鄭子敬
Cheng, Tzu-Ching
論文名稱: 使用技術分析評估加密貨幣的獲利能力-以比特幣為例
Using Technical Analysis Strategies to Assess the Profitability of Bitcoin
指導教授: 王惠嘉
Wang, Hei-Chia
學位類別: 碩士
Master
系所名稱: 管理學院 - 工業與資訊管理學系碩士在職專班
Department of Industrial and Information Management (on the job class)
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 43
中文關鍵詞: 集成學習技術分析比特幣
外文關鍵詞: ensemble learning, technical analysis, bitcoin
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  • 2021年,隨著以比特幣(BTC)為首的加密貨幣牛市行情崛起,引起許多人的關注,為了想從中獲利不少投資人便開始進入加密貨幣交易市場,當中不乏投資經驗豐富的老手、 其他產業的投資專家或者無經驗卻想賺錢的投機客,屬於高風險高報酬的市場。由於影響加密貨幣的因素眾多其價格波動的趨勢也難以被預測,因此本研究將使用技術分析指標中的ORB(Opening Range Breakout)作為買賣比特幣的交易策略,配合近幾年因科技進步發展而被廣泛用於各項領域的深度學習為基礎,進一步改善單一深度學習模型可能發生過度擬合問題,本研究將使用集成學習方法結合三種深度學習模型,讓模型的預測能力能夠更加精確,最終將混和預測模型與技術分析指標,用於模擬比特幣的交易策略,評估方法的獲利能力。結果顯示ORB指標使用在比特幣交易具有強大的收益能力,本研究所提出的Ensemble-ORB方法,從2019年起至2022年底的總報酬為83705美金,累計回報率為505%,兩項指標皆以1.5倍的差距勝於 Chen et al. (2020)提出CNN-ORB的方法,證明技術指標的交易策略在比特幣市場上是有利可圖的,且所提出之方法可幫助投資者在買賣時給予建議,避免在價格波動劇烈的比特幣市場上發生極大的虧損。

    In 2021, with the rise of the cryptocurrency bull market led by Bitcoin (BTC), it caught the attention of many people. To profit from this trend, numerous investors, including experienced traders, investment experts from other industries, and even speculative individuals without prior experience, began entering the cryptocurrency trading market. It is a high-risk, high-reward market. Due to the multitude of factors influencing cryptocurrency and the difficulty in predicting price fluctuations, this study utilizes the Opening Range Breakout (ORB) from technical analysis indicators as a trading strategy for buying and selling Bitcoin. With the rapid advancement of technology in recent years, deep learning has been widely applied in various fields. In order to improve the issue of overfitting that may occur in a single deep learning model, this study will employ ensemble learning by combining three different deep learning models to enhance the predictive accuracy of the model. Ultimately, the hybrid prediction model, along with the technical analysis indicators, is used to simulate a Bitcoin trading strategy and evaluate its profitability. The results demonstrate that the ORB indicator possesses strong profit-generating abilities in Bitcoin trading. The proposed Ensemble-ORB method in this study achieved a total return of $83,705 and a cumulative return rate of 505% from 2019 to the end of 2022. Both metrics outperformed Chen et al.'s (2020) CNN-ORB method by a factor of 1.5, proving that technical indicators' trading strategies are profitable in the Bitcoin market. Moreover, the proposed method can assist investors by providing advice during buying and selling, helping them avoid significant losses in the highly volatile Bitcoin market.

    摘要 I 目錄 VI 表目錄 VIII 圖目錄 IX 第1章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 2 1.3 論文架構 4 第2章 文獻探討 5 2.1 技術分析(Technical analysis) 5 2.2 深度學習( Deep Learning) 7 2.3 集成學習(Ensemble Learning) 10 2.4 效益評估 11 2.5 小結 13 第3章 研究方法 14 3.1 研究架構 14 3.2 資料蒐集模組 15 3.3 資料前處理模組 16 3.4 集成學習模組 21 3.5 效益評估模組 25 3.6 小結 26 第4章 實驗結果與討論 27 4.1 系統環境建置 27 4.2 參數設定 27 4.3 實驗結果 30 4.3.1 實驗一:CNN-ORB 分類精確度、總報酬、累計報酬率之比較 30 4.3.2 實驗二:LSTM-ORB 分類精確度、總報酬、累計報酬率之比較 32 4.3.3 實驗三:TCN-ORB 分類精確度、總報酬、累計報酬率之比較 34 4.3.4 實驗四:Ensemble-ORB 分類精確度、總報酬、累計報酬率之比較 36 第5章 結論及未來研究方向 39 5.1 研究成果 39 5.2 未來研究方向 39 參考文獻 41

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