近年來股票投資是不少人累積財富的方法，而選擇正確的投資目標可以有效地增加獲利，因此如何準確的預測股價變化是許多研究者努力研究的目標。然而市面上關於投資股票的書籍、方法及專業術語千百種，對於剛踏入投資領域的投資者，容易不知道從何開始。
因此本研究提出基於卷積神經網路(Convolutional Neural Network,CNN)的股票預測模型。本研究總共分成三個部分，根據在網路上容易取得的股票日線圖作為訓練資料後：第一部分以半年為單位將圖片分成三個部分做漸進整合後透過CNN提取股票圖型之長期特徵。第二部分使用物件偵測網路YOLO(You Only Look Once)對股票圖型進行技術分析提取形態特徵。第三部分將以上兩個階段提取的圖片特徵結合進行分類來預測未來半年之股票上漲或下跌的機率。本研究使用元大證券"越是贏"公開的股票歷史資料作為資料集，分別收集了台灣50及台灣中型100於2005年至2019年的歷史股價進行訓練，於最終的測試成果達到61.53%的準確率。

Stock investment has been a way for people to accumulate wealth. Choosing the right investment target can effectively increase profits. However, there are thousands of books, methods, and professional terms about investing in the stock market, which is not easy for investors who have just entered the investment field.
We propose a stock prediction model based on CNN(Convolutional Neural Networks). The stock prediction model is divided into three parts: the first part divides the stock daily chart picture into three parts in half-year units for progressive integration and extracts the long-term features of the stock pattern through CNN. The second part uses the object detection network YOLO (You Only Look Once) to perform technical analysis on stock patterns to extract pattern features. In the third part, we combine the image features extracted from the above two stages to predict the probability of stock rising or declining in the next six months. In this study, the final test results achieved an accuracy of 61.53%.

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