Multiobjective Optimization of Technical Market Indicators

Price prediction is a problem of great complexity and usually some technical indicators are used to predict the markets trends. The main difficulty in the use of technical indicators lies in deciding the parameters values. We proposed the use of Evolutionary Algorithms (EAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. We are working on several parallel implementations of Evolutionary Algorithms for finance, including grid computing and GPU implementations.