On the quest for robust technical trading strategies using multi-objective optimization

被引：2

作者：

Manuel Berutich, Jose ^{[1
]}

Luna, Francisco ^{[2
]}

Lopez, Francisco ^{[1
]}

机构：

[1] ETS Ingn Informat, Dept Lenguajes & Ciencias Comp, Malaga, Spain

[2] Univ Carlos III Madrid, Dept Informat, Madrid, Spain

来源：

AI COMMUNICATIONS | 2014年 / 27卷 / 04期

关键词：

Trading rule; robust optimization; evolutionary multi-objective optimization; ALGORITHMS;

D O I：

10.3233/AIC-140609

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a robust multi-market optimization methodology for technical trading strategies, whereby robustness is incorporated via the environmental variables of the problem. The search for the optimum parameters is conducted over several markets, in the hope of exposing the GA to differing conditions, increasing the robustness of the solutions produced. Our results show an improvement in terms of performance for the solutions generated under this robust method when compared to those offered by single-market optimization.

引用

页码：453 / 471

页数：19

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