Neighborhood evaluation in acquiring stock trading strategy using genetic algorithms
Neighborhood evaluation in acquiring stock trading strategy using genetic algorithms
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DOI：
10.1109/socpar.2010.5686733
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发表时间：
2010-12
期刊：
影响因子：
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通讯作者：
K. Matsui;Haruo Sato
中科院分区：
文献类型：
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作者：
K. Matsui;Haruo Sato
我们提出了一种新的方法来评估个人在遗传算法（GAs）的算法交易在股票市场。在我们以前的工作中，我们提出了一个有效的方法来获取交易策略在股票市场。但在遗传搜索中有过拟合的倾向。我们的新方法，即邻域评价，涉及评价的遗传个体在适应度景观以及自己的相邻点。我们在东京证券交易所第一部分的20家公司的股票交易，近十一年来，我们的方法的性能进行了检查，并显示了附近的评价的有效性。
We propose a new method to evaluate individuals in genetic algorithms (GAs) for algorithmic trading in stock markets. In our previous work, we presented an effective method to acquire trading strategy in stock markets. However, it had a tendency of overfitting in genetic searches. Our new approach, namely neighborhood evaluation, involves evaluation for neighboring points of genetic individuals in fitness landscape as well as themselves. We examine the performance of our method in stock trading of twenty companies in the first section of Tokyo Stock Exchange for recent eleven years, and show the effectiveness of the neighborhood evaluation.