Int. J. Data Mining, Modelling and Management, Vol. 3, No. 4, 2011 319 Copyright © 2011 Inderscience Enterprises Ltd. Applying genetic algorithms to Wall Street Laura Nuñez-Letamendia* Department of Finance, IE Business School, C/ Castellon de la Plana, 8, 28006, Madrid, Spain E-mail: laura.nunez@ie.edu *Corresponding author Joaquin Pacheco and Silvia Casado Department of Applied Economics, University of Burgos, Plaza Infanta Elena s/n Burgos 09001, Spain E-mail: jpacheco@ubu.es E-mail: scasado@ubu.es Abstract: Genetic algorithms (GAs) can be applied to a wide range of problems in the field of finance. The purpose of this paper is to make GAs accessible to practitioners, academicians and students who are interested in financial markets. By describing a simple application consisting in tuning a technical trading system for the Dow Jones we illustrate step by step how the reader can implement its own trading system with the help of the powerful tool, the GA. To show how this technique can easily be extended to other type of applications in the financial domain, some examples are brought up at the end of the paper. Keywords: evolutionary computation; genetic algorithms; GAs; Dow Jones; trading systems; moving averages; support-resistance levels. Reference to this paper should be made as follows: Nuñez-Letamendia, L., Pacheco, J. and Casado, S. (2011) ‘Applying genetic algorithms to Wall Street’, Int. J. Data Mining, Modelling and Management, Vol. 3, No. 4, pp.319–340. Biographical notes: Laura Nuñez-Letamendia is a Professor of Finance at IE Business School. She received her PhD in Economics and Finance at Universidad Autonoma de Madrid. Her current research interests include genetic algorithms and other metaheuristics and their applications to real-life problems in economics and finance: trading systems design, economic forecasting, patter recognition of financial distress in companies, etc. Joaquín A. Pacheco is a Full Professor of Statistics and Operations Research as well as the Chair of the Department of Applied Economics at the University of Burgos. He received his PhD in Mathematics (operations research) from the Complutense University of Madrid. His current research interests include metaheuristics and their application to pattern recognition, logistics, and other real-life problems. Silvia Casado is a Mathematics Instructor at the University of Burgos and a member of the Metaheuristics Group in the Department of Applied Economics there. Her research interests include metaheuristics, optimisation-simulation