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研究生: 黃昱凱
Huang, Yu-Kai
論文名稱: 利用 Radius Neighbors Regressor 模型預測台灣股市加權指數並賦予強弱指標
Enhancing Stock Market Predictions with Dynamic Radius Neighbors Regressor: A Feature Weighted Approach
指導教授: 蔡芸琤
Tsai, Yun-Cheng
林順喜
Lin, Shun-Shii
口試委員: 蔡芸琤
Tsai, Yun-Cheng
林順喜
Lin, Shun-Shii
許軒
Hsu, Hsuan
邱嘉豪
Chiu, Chia-Hao
口試日期: 2024/07/02
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 64
中文關鍵詞: 股票機器學習Radius Neighbor Regressor
英文關鍵詞: Stock Market, Radius Neighbor Regressor, Machine Learning
研究方法: 實驗設計法主題分析
DOI URL: http://doi.org/10.6345/NTNU202401777
論文種類: 學術論文
相關次數: 點閱:62下載:0
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  • 股票投資是現代人在累積資產上不可或缺的工具,雖然投資理財有賺有賠,但是若能夠找到一套良好的交易策略,以及善用各種分析工具,達到長期穩定獲利也是一件可以期盼的事情。本論文使用Radius Neighbor Regressor之機器學習方法並結合個人之股票交易經驗,在特定時間窗口之大盤強弱指標以及大盤中長期的多空頭判斷上取得了良好的結果。
    在實作上,我們使用Radius Neighbor Regressor與個人交易經驗所挑選出的特徵值作為產生強弱指標的依據。資料收集樣本時間為2013/1/25~2023/12/21,總共2668個交易日。主要資料來源取自XQ全球贏家之資料庫,並且使用所經加權後的強弱指標分別在幾種預測時間的長短進行比較與分析。
    從實驗結果驗證,我們發現使用Radius Neighbor Regressor搭配個人交易經驗所挑選出的特徵值,在以60個交易日預測後20個交易日的結果準確率高達73%,且在傳統多空頭的分析上也得到了良好的結果。另外,還證明了在特徵值選擇上以個人交易經驗做選擇的優勢,最後也彌補了單純使用Radius Neighbor Regressor機器學習方法的缺點,得出最佳的一種大盤強弱指標之模型。

    Stock investment is an indispensable tool for modern people to accumulate wealth. Although investment and financial management come with risks, finding a good trading strategy and utilizing various analytical tools can lead to long-term stable profits. This thesis uses the Radius Neighbor Regressor machine learning method combined with personal stock trading experience to achieve good results in determining the strength and weakness indicators of the market and the long-term bullish or bearish trends within a specific time window.
    In practice, we use the Radius Neighbor Regressor and the features selected based on personal trading experience to generate strength and weakness indicators. The data collection period spans from January 25, 2013 to December 21, 2023, covering a total of 2668 trading days, with the main data source being the XQ Global Winner database. We then use the weighted strength and weakness indicators to conduct comparisons and analyses over various prediction periods.
    The experimental results confirm that using the Radius Neighbor Regressor combined with the features selected based on personal trading experience achieves an accuracy rate of up to 73% when predicting the results for the 20 trading days following a 60-day prediction period. This approach also yielded good results in traditional bullish and bearish analyses, demonstrating the advantage of selecting features based on personal trading experience. Ultimately, this method addresses the shortcomings of solely using the Radius Neighbor Regressor machine learning method, resulting in the optimal model for market strength and weakness indicators.

    摘要 i 誌謝 iii 第一章 緒論 1 1.1 研究背景 1 1.2 研究目的 1 第二章 文獻探討 3 第三章 Methodology 7 3.1 特徵專有名詞解析 8 3.1.1 籌碼面 8 3.1.1.1 投信 8 3.1.1.2 外資 9 3.1.1.3 自營商 10 3.1.1.4 券商分點 10 3.1.2 融資 13 3.1.3 均線及K棒 13 3.1.3.1 K棒(K線) 14 3.1.3.2 均線 14 3.1.4 量價關係 14 3.2 交易策略 20 3.2.1 股票交易策略的重要性 20 3.2.2 風險報酬比 21 3.2.3 交易的SOP(標準操作程序) 22 3.2.4 個人獨創之交易策略 26 3.3 資料集準備 29 3.4 資料預處理 29 3.4.1 特徵值選擇 29 3.4.2 特徵值標準化 29 3.4.3 創建時間窗口 30 3.4.4 資料集分割 30 3.5 模型選擇 30 3.5.1 Radius Neighbors Regressor 30 3.5.2 K-Nearest Neighbors (KNN) 31 3.5.3 Random Forest 32 第四章 實驗結果 36 4.1 Radius Neighbors Regressor半徑值選擇 36 4.1.1 低半徑值(5~16): 39 4.1.2 中半徑值(17~29): 40 4.1.3 高半徑值(30~59): 43 4.2 權重分配以及強弱指標實驗結果 45 4.3 權重分配以及強弱指標實驗結果 50 4.3.1 多空頭實驗結果 51 4.3.1 以強弱指標為主的每月實驗結果 54 4.3.2 不同時間窗口與不同特徵之實驗結果 56 4.4Radius Neighbors Regressor最佳模型套入台指期交易驗證結果 61 第四章 未來展望 63 參考文獻 64

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