|Table of Contents|

Application of SVM Optimized by Genetic Algorithmin Quantization Timing Selection(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2017年01期
Page:
72-
Research Field:
计算机工程
Publishing date:

Info

Title:
Application of SVM Optimized by Genetic Algorithmin Quantization Timing Selection
Author(s):
Huang Hongyun1; Wu Libin2; Li Shizheng1
(1.School of Finance,Anhui University of Finance and Economics,Bengbu 233000,China)(2.Institute of Statistics and Applied Mathematics,Anhui University of Finance and Economics,Bengbu 233000,China)
Keywords:
genetic algorithm support; support vector machine; quantitative investment; timing selection; LIBSVM Toolbox
PACS:
TP183; F830.91
DOI:
10.3969/j.issn.1672-1292.2017.01.011
Abstract:
For quantitative investment caused by inaccurate trading signal judgment during the process of the timing of difficult problems,the excellent non-linear separable ability is used to support vector machine(SVM)based on historical price quantity information(opening price,closing price,the highest and the lowest price,volume and short long term moving average)model of quantitative timing. In the specific application of strategy model,in order to determine LIBSVM ToolBox in the "c" and "g" parameter,this paper optimize them through the genetic algorithm,then uses MATLAB software to achieve the(Shanghai pudong development bank)for individual stocks from January 4,2012 to 2012 on January 22,the strategy of back,finally the csi 300 index as the benchmark from the annualized yield,sharpe ratio,the angle of information ratio and maximum retracement back to the measurement results are analyzed. It is concluded that the GA-SVM can more accurately judge the conclusion of trading signals.

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