股市投資一直以來都是一項熱門的投資方式,其投資方法主要分成基本分析、技術分析、籌碼分析。在技術分析中,投資者會根據不同的技術指標來投資股票,但技術指標種類繁多,時常讓投資者不知道該使用哪種技術指標來投資,導致投資結果並不理想。本研究以TEJ資料庫做為股市資料來源,將以Hadoop平台為基礎讓股票先透過技術指標公式平行運算後,接著將K-means演算法套用於MapReduce框架上,藉此將股票作分群並同時提高運算效率,最後將分群結果定義決策後,再推薦投資者作買進或賣出之決策。本研究之決策支援系統將採用技術指標的公式及K-means演算法來建構其中的模式管理子系統部分,並將分析出的結果呈現出來供投資者作參考。
Stock market investment has always been a popular way to invest. The approach is divided into fundamental analysis, technical analysis, and chip analysis. In technical analysis, investors will invest in stocks based on different technical indicators, but there are various kinds of technical indicators that usually make the investors be confused of using which technical indicators to invest, leading to investment results are not satisfactory. In this study, the stock market data is sourced from TEJ database. This paper will be based on Hadoop platform, let the stock after the first through the parallel computing technology indicators formula, and set the K-means algorithm into the MapReduce framework, to cluster stocks and improve operation efficiency at the same time. At last, define the clustering results, and then recommend the investors to make decisions for buying or selling. The decision support system of this paper will be used technical indicators formulas and K-means algorithm to construct a section in which model management system, and analyze the results for investors to reference.