基于k-means聚类的股票KDJ类指标综合分析方法

股票技术分析是证券分析的常用手段之一，目前的股票技术分析主要存在2个问题：1)都是从某个角度进行单维度分析，投资决策有较大偏差；2)任何单一的技术指标都有其局限性，需要相互补充才能更好进行投资决策。针对这些问题，本文讨论如何利用数据挖掘技术进行股票多维度综合分析问题。首先，分析数据挖掘应用到股票分析中可以解决的问题及可能面临的挑战；其次，提出一种基于数据挖掘聚类方法的选股模型；最后，对1364只上证股票进行实证分析，形成对股票的随机指标K、D、J等的综合挖掘结果。

K-means-based KDJ Integrated Analyzing Methods for Stock Transactions

Stock technical analysis is one of the means of securities analysis. There are two main problems in the current stock technical analysis. Firstly, one technical index is always analyzed in a dimension, and so the general investors are difficult to put them together to form an investment decision; secondly, any single technical index has its limitations, and so they need been integrated to make better investment decisions. In response to these major issues, this article discusses how to use the data mining technology for multi-dimensional comprehensive analysis of stocks. First of all, it analyzes the problems that data mining can solve in stock analysis and its possible challenges. Secondly, a stock selection model based on data mining clustering methods is proposed. Finally, using the 1364 Shanghai Stocks, some empirically analyzing results are given.