摘要
设计了一个新型的基于技术指标规则的启发式量化择时系统(TIR-HA),通过模拟退火算法从大量技术指标规则中选取优化的规则组合,并利用一个改进的多数投票方法将所选规则的信号综合,可以在深入挖掘大量指标规则择时能力的同时综合考虑指标的超前性、滞后性以及在不同时间段的预测能力。本文将该系统应用于中国股票市场沪深300指数成分股116只股票的日交易数据,实证结果显示算法的夏普比率、索提诺比率、择时准确率和单次交易平均年化收益率均显著高于多数投票组合规则择时策略和基于机器学习预测的择时策略。
This paper designs a novel heuristic system,TIR-HA,using simulated annealing to select the optimized combinations from a large number of technical indicator rules,and incorporating an adjusted voting method to combine the trading signals of selected rules.It takes into account of the leading and lagging characteristic of indicator rules while deeply mines the timing capacity of the combined ones.The TIR-HA system is then applied to the daily trading data of 116 component stocks of Chinese CSI300 index.The empirical results show that the Sharpe ratio sortino ratio timing accuracy and average annually return per trading of TIR-HA are significantly higher than the performance of timing strategies that based on majority voting and machine learning algorithms.
作者
孔傲
朱洪亮
郭文旌
KONG Ao;ZHU Hong-liang;GUO Wen-jing(School of Finance,Nanjing University of Finance and Economics,Nanjing 210023;School of Management and Engineening,Nanjing University,Nanjing 210093)
出处
《系统工程》
CSSCI
北大核心
2019年第1期111-122,共12页
Systems Engineering
基金
江苏省高等学校自然科学研究面上项目(17KJB120004
17KJB120005)
江苏省高校哲学社会科学研究项目(2017SJB0234
2017SJB0233)
教育部人文社科规划项目(17YJA790101)
教育部人文社会科学研究青年基金项目(17YJC630128)
国家自然科学基金(71471081
71501088
71671082)
关键词
技术指标规则
启发式算法
量化择时
模拟退火算法
Technical Indicator Rule
Heuristic Algorithm
Quantitative Timing
Simulated Annealing
