A Genetic Algorithm for Rule-based Chart Pattern Search in Stock Market Prices

Abstract

Chart pattern analysis uses knowledge extracted from graph- ical information of price movements. There are two repre- sentative types of problems in chart pattern analysis: the matching problem and the search problem. There have been extensive studies on chart pattern matching. However, chart pattern search has not yet drawn much interest. Instead of automatic search, most studies use chart patterns manually designed by financial experts. In this paper, we suggest an automatic algorithm that searches a rule-based chart pat- tern. We formulate rule-based chart pattern search as an optimization problem for a genetic algorithm. The sug- gested genetic algorithm includes a considerable amount of problem-specific manipulation. The algorithm successfully fond attractive patterns working on the Korean stock mar- ket. We studied the rules used in the found patterns, noting that they are rising-support patterns. In addition, the au- tomated pattern generation uses designs at a higher level of abstraction.

Myoung Hoon Ha

Postdoctoral Researcher

My research interests include deep learning, reinforcement learning, representation learning, and adversarial robustness.