基于k-means聚类的股票KDJ类指标综合分析方法

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

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。

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 inte- grated 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 pro- posed. Finally, using the 1364 Shanghai Stocks, some empirically analyzing results are given.