This thesis presents Genetic Programming methodologies to find successful and understandable technical trading rules for financial markets. The methods when applied to the S&P500 consistently beat the buy-and-hold strategy over a 12-year period, even when considering transaction costs. Some of the methods described discover rules that beat the S&P500 with 99% significance. The work describes the use of a complexity-penalizing factor to avoid overfitting and improve comprehensibility of the rules produced by GPs. The effect of this factor on the returns for this domain area is studied and the results indicated that it increased the predictive ability of the rules. A restricted set of operators and domain knowledge were used to improve comprehensibility. In particular, arithmetic operators were eliminated and a number of technical indicators in addition to the widely used moving averages, such as trend lines and local maxima and minima were added. A new evaluation function that tests for consistency of returns in addition to total returns is introduced. Different cooperative coevolutionary genetic programming strategies for improving returns are studied and the results analyzed. We find that paired collaborator coevolution has the best results.
Worcester Polytechnic Institute
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Seshadri, Mukund, "Comprehensibility, Overfitting and Co-Evolution in Genetic Programming for Technical Trading Rules" (2003). Masters Theses (All Theses, All Years). 546.
comprehensiblity, technical analysis, Genetic Programming, overfitting, coevolution cooperative coevolution, Genetic programming (Computer science), Finance, Data processing, Stock price forecasting, Data processing