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

Chart pattern analysis uses knowledge extracted from graphical information of price movements. There are two representative 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 pattern. We formulate rule-based chart pattern search as an optimization problem for a genetic algorithm. The suggested genetic algorithm includes a considerable amount of problem-specific manipulation. The algorithm successfully found attractive patterns working on the Korean stock market. We studied the rules used in the found patterns, noting that they are rising-support patterns. In addition, the automated pattern generation uses designs at a higher level of abstraction.