Application of Mutation Only Genetic Algorithm for the Extraction of Investment Strategy in Financial Time Series

We use the recently introduced method of Mutation Only Genetic Algorithm (MOGA) to search for good strategies of investment in financial time series, as measured by the yield over a fixed period of investment. The rules for buy, sell or hold are introduced as conditional statements involving inequalities of various moving averages, and encoded in a string representation chromosomes in MOGA. The extraction of good investment strategies corresponds to the discovery of rules that are fit in the sense of evolutionary computation. The investment strategy is evaluated using the rate of overall return in both the training set and the test set, thereby converting the problem of discovering good investment strategies to an optimization problem in combinatorics. Stock data from NASDAQ, including Microsoft, Intel, and Dell are tested. We have compared the performance of the investment rules involving a single stock and that involving two stocks. Within the confme of limited data, we find that rules that allow buy, sell, hold and swap between two stocks are superior in all samples tested.