K线能量计算的股市生命期态势预测方法

发布时间：2018-02-01 05:32

本文关键词： K线特征孕育成熟度爆发力能量股市生命期　出处：《计算机应用研究》2016年06期 　论文类型：期刊论文

【摘要】：股市中K线特征是股价涨跌的因果信息,基于支持向量机(SVM)的股价预测模型没有考虑K线特征知识,对于股价态势难以有效预测。提出基于K线能量计算的股市生命期支持向量机态势预测算法(LPFSVM),首先,提取典型K线特征,通过引入特征的孕育成熟度和爆发力定义,给出K线特征支持向量机算法(KLF-SVM);进而,在KLF-SVM算法基础上定义特征的能量计算模型给出一种K线能量计算的SVM股价预测算法。为了有效地预测态势,引入股价波动的生命期概念,通过K线组合特征判定股价所处的生命期的阶段,进而结合生命期阶段之间的时序影响关系给出一种基于生命期的股价态势预测算法。在上证和深证数据集上的实验结果表明,LPF-SVM算法对于股价上升波段和下跌波段的股价预测取得了很好的效果。
[Abstract]:K line feature in stock market is the causal information of stock price rise and fall. The stock price prediction model based on support vector machine (SVM) does not take account of K line feature knowledge. It is difficult to predict stock price situation effectively. This paper proposes a life-cycle support vector machine (LPFSVM-based) algorithm based on K-line energy calculation. Firstly, it extracts the typical K-line features. By introducing the definition of gestation maturity and explosive force, the K-line feature support vector machine (KLF-SVM) algorithm is presented. Then, based on the KLF-SVM algorithm, a SVM stock price prediction algorithm for K-line energy calculation is proposed, which defines the characteristic energy calculation model. The concept of life period of stock price fluctuation is introduced, and the stage of life period of stock price is determined by K-line combination characteristics. Furthermore, an algorithm of stock price situation prediction based on life cycle is proposed, which is based on the relationship between life stages. The experimental results on Shanghai Stock Exchange and Shenzhen Stock Exchange data set show that. The LPF-SVM algorithm is very effective for the stock price prediction in the rising and falling bands.
【作者单位】：合肥工业大学计算机与信息学院;
【基金】：国家自然科学基金资助项目(61175051;61070131;61175033)
【分类号】：TP18
【正文快照】： 0引言随着金融市场的发展,股票投资在人们生活中的地位越来越重要,使用高效的挑选方法可以帮助人们在股票投资中取得丰厚的回报。但是中国股市因为受法制建设不健全、市场机制不完善以及投资者心理不成熟等因素影响容易产生大的波动[1],使得股票价格难以有效地预测。对于股票

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