An Optimized Input Genetic Algorithm Model for the Financial Market

By: David Ademola Oyemade  

The financial market has continued to attract increasing investment in the last decade. The daily turnover of the foreign exchange (FOREX) market for example, has increased from 4.0 trillion USD in 2010 to 5.1 trillion USD in 2016. However, with all the contributions of researchers towards solving the time series problem of the financial market, 89% of retail investors and traders lose money in the FOREX financial market. We propose an optimized input genetic algorithm (OIGA) model for profit optimization in the FOREX financial market. The model emerged from instances of FOREX expert advisers having different properties and used for live trading. In the genetic algorithm model, each of these expert advisers formed an individual producing a profit or loss value over a period to time. All the expert advisers combine to form the population. The model incorporates an evaluation and optimization method for the properties of the expert advisers at the input level. The proposed model will be compared with the existing genetic algorithm models. The results of the implementation and tests will be published. When fully implemented, the model will hopefully serve as a positive step towards providing profitable expert advisers as a solution to a real life problem.

Genetic Algorithm, Financial Market, FOREX, Model, Expert Advisers
Technologies in Knowledge Sharing
Paper Presentation in a Themed Session

Dr. David Ademola Oyemade

Lecturer, Mathematics and Computer Science, Federal University of Petroleum Resources

Dr. Oyemade is a Lecturer in the department of Mathematics/Computer Science, Federal University of Petroleum Resources, Effurun, Nigeria. He holds M.Sc. and Ph.D. in Computer Science from University of Benin, Nigeria. His research areas and interests include Software Engineering, Software Architecture and Learning Technologies. David is a member of ACM and IEEE Computer Society. He is also a member of Nigeria Computer Society (NCS).