隨著時代的變遷，投資理財已成為生活中的一環，由於股票投資的獲利性高、變現容易且資訊獲得便利，股票趨勢或股票價格分析成為一個研究的主題。技術分析 (Technical Analysis) 是一股股價方析的主要方法之一，並且可以區分為數量分析及圖形分析兩個領域。數量分析是利用技術指標來判斷市場趨勢，進而做出買進或賣出的決策，而圖形分析是以圖形做為分析工具，其中可以細分為 K 線分析與型態分析。目前的文獻大多是致力於股價漲跌趨勢的正確率，或是漲跌幅度的準確率提升。然而在數量分析的研究中有許多技術指標，加上技術指標的組合結果過於複雜，並且沒有文獻可以佐證哪一 (幾) 個技術分析的組合對於投資者最有利，因此投資者與分析師無法有效地分析技術指標所帶來的意涵。此外，在圖型分析的研究中，對於 K 線樣式的定義沒有一套公正的評估標準，並且樣式出現與否的判定是根據分析人員的主觀判斷，因此研究結果的有效性與可靠性無從考證。本研究為了解決上述的問題，提出了一個結合影像檢索於 K 線圖影像的分析法，藉此驗證 K 線圖影像是否適合用於股價預測分析。本研究的預期達成目標為：(1) 驗證影像檢索技術適合用於 K 線圖分析，(2) 發展出適合於 K 線圖分析的影像特徵內容，(3) 訓練出一套通用的 K 線圖影像分類器，以及 (4) 投資策略的研擬。The investment activities have become an integral part of our lives. Because stock investment has higher profit and is easy to sell them for cash, stock price analysis has long been regarded as an important research topic. Technical Analysis, one of the major stock prediction methodology, includes quantitative analysis studies (QAS) and graphical analysis studies (GAS), in which QAS uses technical indicators to predict market trends and stock price and to decide the best time to buy/sell stocks. On the other hand, GAS can be subdivided into candlestick charts and pattern analysis. Much related work focuses on either improving the precision rate of stock price trends, or increasing the returns of investment. However, QAS contain various technical indicators and there is no an exact answer about how many and what technical indicators are more representative for stock prediction. In addition, it is difficult to allow the investors and analysts to grasp the underlying idea of technical indicators. Moreover, GAS has no formal definition and fair evaluation methods of candlestick chart patterns, thus the efficiency and reliability is difficult to trace. In order to improve above limitations, the aim of this research project is to propose a novel approach to bring the idea of image retrieval into candlestick charts analysis, and to verify whether candlestick charts are suitable for stock price analysis. This study is expected to achieve the following objectives: (1) To show that the image retrieval techniques are applicable for candlestick charts analysis. (2) To research and develop new images features to represent the visual content of candlestick charts. (3) To construct and compare general machine learning based classifiers using candlestick charts for stock prediction. (4) To draw up some useful investment strategies. 研究期間：10008 ~ 10107
