Neighborhood evaluation in acquiring stock trading strategy using genetic algorithms

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.