[1]黄宏运,吴礼斌,李诗争.GA优化的SVM在量化择时中的应用[J].南京师范大学学报(工程技术版),2017,17(01):072.[doi:10.3969/j.issn.1672-1292.2017.01.011]
 Huang Hongyun,Wu Libin,Li Shizheng.Application of SVM Optimized by Genetic Algorithmin Quantization Timing Selection[J].Journal of Nanjing Normal University(Engineering and Technology),2017,17(01):072.[doi:10.3969/j.issn.1672-1292.2017.01.011]
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GA优化的SVM在量化择时中的应用
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
17卷
期数:
2017年01期
页码:
072
栏目:
计算机工程
出版日期:
2017-03-30

文章信息/Info

Title:
Application of SVM Optimized by Genetic Algorithmin Quantization Timing Selection
文章编号:
1672-1292(2017)01-0072-08
作者:
黄宏运1吴礼斌2李诗争1
(1.安徽财经大学金融学院,安徽 蚌埠 233000)(2.安徽财经大学统计与应用数学学院,安徽 蚌埠 233000)
Author(s):
Huang Hongyun1Wu Libin2Li 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)
关键词:
遗传算法支持向量机量化投资择时LIBSVM工具箱
Keywords:
genetic algorithm supportsupport vector machinequantitative investmenttiming selectionLIBSVM Toolbox
分类号:
TP183; F830.91
DOI:
10.3969/j.issn.1672-1292.2017.01.011
文献标志码:
A
摘要:
针对量化投资过程中因交易信号判断不准确而导致的择时难问题,利用具有优良非线性可分能力的支持向量机建立基于历史价量信息(开盘价、收盘价、最高价、最低价、成交量和短长期移动平均指数)的量化择时模型. 在策略模型的具体应用中,为了确定LIBSVM ToolBox中的“-c”和“-g”参数,本文首先通过遗传算法对其寻优,然后利用MATLAB软件实现了对个股(浦发银行)自2012年1月4日至2016年6月22日的策略回测,最后以沪深300指数为基准从年化收益率、相关绩效指标和最大回撤等角度对回测结果进行了分析,得出GA-SVM可被有效运用到量化择时中去的结论.
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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备注/Memo

备注/Memo:
收稿日期:2016-10-18.
基金项目:国家自然科学基金(11301001)、安徽高等学校省级自然科学基金(KJ2013Z001)、安徽财经大学校级重点研究项目(ACKY1402ZD).
通讯联系人:吴礼斌,副教授,研究方向:计量金融与数理统计. E-mail:wlb1158@163.com
更新日期/Last Update: 1900-01-01