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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 張智星 | |
dc.contributor.author | Hao-Hsin Hsu | en |
dc.contributor.author | 許顥馨 | zh_TW |
dc.date.accessioned | 2021-06-08T03:31:10Z | - |
dc.date.copyright | 2019-08-16 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-13 | |
dc.identifier.citation | Stephen J. Brown, William N. Goetzmann, 'The Dow Theory: William Peter Hamilton’s”, 1998.
Mark Hulbert, 'VIEWPOINT; More Proof for the Dow Theory', The New York Times, 1998. Ross Levine, Sara Zervos, “Stock Markets, Banks, and Economic Growth”, The American Economic Review, 1998. Mark Kac, “Random Walk and the Theory of Brownian Motion”, The American Mathematical Monthly, 1947. Eugene F. Fama, “Random Walks in Stock Market Prices”, Financial Analysts Journal, 2019. Kenneth D. Garbade and William L. Silber, “Price Movements and Price Discovery in Futures and Cash Markets”, The MIT Press, 1983. Richard J. Bauer Jr., Julie R. Dahlquist, “Technical Market Indicators”, John Wiley & Sons, Inc, 1999. Peter Lakner, “Optimal trading strategy for an investor: the case of partial information”, Stochastic Processes and their Applications, 1998. Anna A.Obizhaeva, Jiang Wang, “Optimal trading strategy and supply/demand dynamics”, Journal of Financial Markets, 2013. Hendrik Bessembinder, Paul J. Seguin, “Price Volatility, Trading Volume, and Market Depth: Evidence from Futures Markets”, School of Business Administration, University of Washington, 1993. Robert S. Pindyck, “The Dynamics of Commodity Spot and Futures Markets: A Primer”, The Energy Journal, 2001. Chao-Ying Joanne Peng, Kuk Lida Lee, Gary M. Ingersoll, 'An Introduction to Logistic Regression”, Analysis and Reporting', Indiana University-Bloomington, 2002. Leo Breiman, 'RANDOM FORESTS', Statistics Department, University of California, Berkeley, 2001. Ho, Tin Kam, 'Random Decision Forest', Montreal, Canada, 1995. Ho, Tin Kam, 'The Random Subspace Method for Constructing Decision Forests'. IEEE, 1998. Sepp Hochreiter, Jürgen Schmidhuber, 'LONG SHORT-TERM MEMORY', Neural Computation, 1997. David W. Lu, “Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks”, University of Cornell, 2017. David M. Q. Nelson ; Adriano C. M. Pereira ; Renato A. de Oliveira, “Stock market's price movement prediction with LSTM neural networks”, IEEE, 2017. Sreelekshmy Selvin ; R Vinayakumar ; E. A Gopalakrishnan ; Vijay Krishna Menon ; K. P. Soman, “Stock price prediction using LSTM, RNN and CNN-sliding window model”, IEEE, 2017. 賴怡玲, “使用增強式學習法建立臺灣股價指數期貨當沖交易策略”, 國立臺灣大學資訊工程研究所碩士論文, 2009。 莊向峰, “基於行為經濟學與價量分析使用增強式學習法建立臺灣股票指數期貨交易策略”, 國立臺灣大學資訊網路與多媒體研究所碩士論文, 2018。 http://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://medium.com/@chih.sheng.huang821/ https://tradingstrategyguides.com/ http://wiki.mbalib.com/zh-tw/量价理论。 https://zh.wikipedia.org/wiki/效率市場假說。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21326 | - |
dc.description.abstract | 金融市場的動態預測一直是許多投資者以及研究者試圖解決的問題,本研究希望能透過機器學習建置一個能夠穩定判斷市場趨勢的模型,並利用該模型設計一套可以在台灣股票指數期貨中穩定獲利的策略。首先我們選用三種不同的機器學習模型:Logistic Regression、Random Forest、LSTM,將趨勢分成三種類別:上升趨勢、下跌趨勢、盤整趨勢,並從中選出最適合的模型。接著我們希望在趨勢中找出合適的進場時機點,因此我們先使用Dynamic Programming演算法標記歷史資料中所有的最佳進場時機點,同樣用上述三種機器學習模型學習如何判斷進場時機點並從中選出表現最佳者。最後將兩個模型合併建置出一套能夠在市場中獲利的策略,並用一般投資者常用的技術指標策略來與本研究設計的模型比較績效。 | zh_TW |
dc.description.abstract | Forecasting the dynamics of financial market has always been a problem that many investors and researchers try to solve. In this research, we tried to build a stable model to predict market trends through machine learning, and used this model to design a strategy that can make profits in Taiwan stock index futures. First, we selected three different machine learning models: logistic regression, random forest and LSTM, and divided the trend into three categories: upward trend, downward trend and consolidation trend. Next, we selected the most suitable model from them. Afterwards, we tried to find the timings of buying in these trends. Therefore, we used dynamic programming to label all of the best timings of buying from historical data. Then, we used the aforementioned three models for training to predict the timings of buying and chose the model with the best performance. Finally, we combined the two models together to build a profitable strategy, and used several technical indicator strategies to compare the model designed in this study. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T03:31:10Z (GMT). No. of bitstreams: 1 ntu-108-R06922119-1.pdf: 2448449 bytes, checksum: 0cef8bdbe8058bbcf0bb6ea5ba2570d6 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 摘要 ii ABSTRACT iii 目錄 iv 圖目錄 viii 表目錄 x 第1章 緒論 1 1.1 研究動機 1 1.2 研究目的 2 1.3 論文架構 2 第2章 文獻探討及背景知識 4 2.1 市場趨勢 4 2.1.1 基本趨勢 4 2.1.2 次要趨勢 6 2.1.3 短期變動趨勢 7 2.2 隨機漫步理論 7 2.3 效率市場假說 8 2.4 交易策略 8 2.5 期貨簡介 9 2.5.1 期貨種類 10 2.5.2 台灣加權股價指數期貨 11 2.6 道氏理論(The Dow Theory) 11 2.6.1 投機原理 12 2.6.2 三重運動原理 12 2.6.3 相互驗證理論 12 2.7 技術指標 13 2.8 風險分析 14 2.8.1 每日損益標準差 14 2.8.2 夏普指數(Sharp Ratio) 15 2.8.3 最大交易回落(Max Draw Down) 15 2.9 滑價 16 2.10 機器學習 16 2.10.1 羅吉斯回歸(Logistic Regression) 17 2.10.2 隨機森林(Random Forest) 18 2.10.3 長短期記憶(Long Short-Term Memory) 19 第3章 研究方法 21 3.1 系統架構 21 3.2 實驗資料 22 3.3 交易環境設定 22 3.4 資料前處理 24 3.4.1 資料標準化(Normalization) 24 3.4.2 資料切分 25 第4章 市場趨勢 27 4.1 ZigZag指標 27 4.2 實驗 29 4.2.1 趨勢判斷模型 30 4.2.2 模型效能評估 32 第5章 進場時機 35 5.1 動態規劃演算法 35 5.2 實驗 36 5.2.1 進場時機點模型 37 5.2.2 模型效能評估 39 第6章 實驗結果與分析 40 6.1 模型綜合比較 40 6.2 交易策略架構 40 6.3 績效評估 41 6.3.1 Rule-based交易策略模型 42 6.3.2 績效評估項目 42 6.3.3 績效評估結果 43 第7章 結論與未來展望 47 7.1 結論 47 7.2 未來展望 47 參考文獻 49 | |
dc.language.iso | zh-TW | |
dc.title | 機器學習應⽤於台灣股票指數期貨趨勢預測及交易策略建構 | zh_TW |
dc.title | Forecasting Taiwan Stock Index Futures Trends and Constructing
Trading Strategies Using Machine Learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 陳永耀 | |
dc.contributor.oralexamcommittee | 韓傳祥 | |
dc.subject.keyword | 市場趨勢,交易策略,台灣股票指數期貨,技術分析,機器學習, | zh_TW |
dc.subject.keyword | market trend,trading strategy,TX,indicator analysis,Machine learning, | en |
dc.relation.page | 50 | |
dc.identifier.doi | 10.6342/NTU201903325 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2019-08-13 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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