臺灣博碩士論文加值系統

　　在加密貨幣日益增長的時代，比特幣為加密貨幣之首且在近年來掀起熱潮，也因此有更多投資人、金融機構開始投資比特幣和其他加密貨幣，甚至連帶影響到相關產業，例如：顯示卡等3C產品，其原因不外乎都大家都想藉由「挖礦」來獲利。在這風險波動非常大的市場裡每個人都想要賺取一筆資金，但加密貨幣沒有基本面能夠進行分析，只能藉由消息面和技術分析來判斷進場點時機，因此本篇論文研究的主題是能否利用日內技術指標策略在比特幣期貨市場裡獲利。
　　本文研擬出兩個技術策略在比特幣期貨市場上進行回測，由於先前有許多文獻在比特幣市場上使用單指標策略的績效都不是非常優異，因此本文的技術策略分別結合兩種不同的指標來判斷進場、出場時機。本文使用的資料為五分鐘日內資料，並且使用的技術指標有布林通道、相對強弱指數(RSI)、移動平均線(SMA)和滑異同移動平均線指標(MACD)。策略一結合布林通道與相對強弱指數(RSI)；策略二則結合移動平均線(SMA)和滑異同移動平均線指標(MACD)。研究結果顯示，兩個策略在比特幣期貨市場上皆有獲利，策略一為勝率高但獲利較小然而策略二則是低勝率但獲利明顯比策略一來的高。因此近一步研究也發現兩個策略在獲利方法上也不進相同，策略一主要獲利為日內交易而策略二則以跨日交易為主，總而言之，使用多種指標在比特幣市場上會比單指標績效來的好。

This paper develops two technical strategies for backtesting in the Bitcoin futures market. Since there are many previous studies documents that use single-index-indicator strategies in the Bitcoin market, the performance of the single-indicator strategies doesn’t has good perform, so this study develop the technical strategies in this paper by combining two different indicators. The data used in this paper is five-minute intraday data, and the technical indicators used are Bollinger Bands, Relative Strength Index (RSI), Moving Average (SMA) and Moving Average Convergence & Divergence (MACD). Strategy 1 combines Bollinger Bands and Relative Strength Index; strategy 2 combines moving average and Moving Average Convergence & Divergence. The results of the research show that both strategies are profitable in the Bitcoin futures market. Strategy 1 has a high winning rate but low profits, while strategy 2 has a low winning rate but the profit is significantly higher than strategy 1. Therefore, a further study results also found that the two strategies are not the same in terms of profitability. The profits of strategy 1 is are mainly from intraday trading and while the those of strategy 2 is from interday trading. In conclusion, the strategies based on using multiple indicators will perform better than those based on a single indicator in the Bitcoin market.

書名頁 i
論文口試委員審定書 ii
中文摘要 iii
英文摘要 iv
誌謝 v
目錄 vi
表目錄 vii
圖目錄 viii

第一章 緒論 1
1.1 研究動機與背景 1
1.1.1 比特幣背景及動機 1
1.1.2 比特幣期貨 3
1.2 研究問題 4

第二章 文獻回顧 7
2.1 技術分析 7
2.2 比特幣市場泡沫化 8
2.3 比特必市場效率 9

第三章 研究方法和資料 11
3.1 資料 11
3.2 研究方法 11
3.2.1 移動平均線 11
3.2.2 相對強弱指標 12
3.2.3 布林通道 12
3.2.3 滑異同移動平均線指標 13

第四章 實證結果 14
4.1 研究策略及績效 14
4.1.1 布林通道和相對強弱指數策略 14
4.1.2 簡單移動平均線和滑異同移動平均線指標策略 21

第五章 結論與建議 28

參考文獻 30

Anghel, D. G. (2020), “A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis”, Finance Research Letters, 101655.
Ahmed, S., Grobys, K., & Sapkota, N. (2020), “Profitability of technical trading rules among cryptocurrencies with privacy function”, Finance Research Letters, 35, 101495.
Bariviera, A. F. (2017), “The inefficiency of Bitcoin revisited: A dynamic approach”, Economics Letters, 161, 1-4.
Corbet, S., Lucey, B., & Yarovaya, L. (2018), “Datestamping the Bitcoin and Ethereum bubbles”, Finance Research Letters, 26, 81-88.
Corbet, S., Eraslan, V., Lucey, B., & Sensoy, A. (2019), “The effectiveness of technical trading rules in cryptocurrency markets”, Finance Research Letters, 31, 32-37.
Cheah, E. T., & Fry, J. (2015), “Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin”, Economics letters, 130, 32-36.
Demant, J., Munksgaard, R., & Houborg, E. (2018), “Personal use, social supply or redistribution? Cryptomarket demand on Silk Road 2 and Agora”, Trends in Organized Crime, 21(1), 42-61.
Fry, J., & Cheah, E. T. (2016), “Negative bubbles and shocks in cryptocurrency markets”, International Review of Financial Analysis, 47, 343-352.
Grobys, K., Ahmed, S., & Sapkota, N. (2020), “Technical trading rules in the cryptocurrency market”, Finance Research Letters, 32, 101396.
Gerritsen, D. F., Bouri, E., Ramezanifar, E., & Roubaud, D. (2020), “The profitability of technical trading rules in the Bitcoin market”, Finance Research Letters, 34, 101263.
Huang, J. Z., Huang, W., & Ni, J. (2019), “Predicting Bitcoin returns using high-dimensional technical indicators”, The Journal of Finance and Data Science, 5(3), 140-155.
Hiramoto, N., & Tsuchiya, Y. (2020), “Measuring dark web marketplaces via Bitcoin transactions: From birth to independence”, Forensic Science International: Digital Investigation, 35, 301086
Hudson, R., & Urquhart, A. (2019), “Technical trading and cryptocurrencies”, Annals of Operations Research, 1-30.
Kajtazi, A., & Moro, A. (2019), “The role of bitcoin in well diversified portfolios: A comparative global study”, International Review of Financial Analysis, 61, 143-157.
Kristoufek, L. (2018), “On Bitcoin markets (in) efficiency and its evolution”, Physica A: Statistical Mechanics and its Applications, 503, 257-262.
Lee, S., Yoon, C., Kang, H., Kim, Y., Kim, Y., Han, D., Son, S., & Shin, S. (2019), “Cybercriminal minds: an investigative study of cryptocurrency abuses in the dark web”, In Network and Distributed System Security Symposium (NDSS), 1–15.
Platanakis, E., & Urquhart, A. (2020), “Should investors include bitcoin in their portfolios? A portfolio theory approach”, The British Accounting Review, 52(4), 100837.
Resta, M., Pagnottoni, P., & De Giuli, M. E. (2020), “Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall?”, Risks,8(2), 44.
Shynkevich, A. (2021), “Impact of bitcoin futures on the informational efficiency of bitcoin spot market”, Journal of Futures Markets, 41(1), 115-134.
Urquhart, A. (2016), “The inefficiency of Bitcoin”, Economics Letters, 148, 80-82.
Zhang, W., Wang, P., Li, X., & Shen, D. (2018), “The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average”, Physica A: Statistical Mechanics and its Applications, 510, 658-670.