Evolutionary Machine Learning for Financial Markets
Machine Learning (ML) and Artificial Intelligence (AI) become increasingly important to many areas of Science and Technology. The increased interest for the use of ML to develop financial quantitative strategies raises new challenges, as data are time series, dynamic, stochastic and noisy. The proposed approach is to generate a large number of signals and to apply consensus methods to obtain models with increased performance. This requires advanced model generation techniques and high level of automation. We present a generative algorithm that operates over an algebra of portfolios and applies mathematical and ML operations to generate “expressions”. Grammar-like derivations are used to generate hypotheses that are backtested, filtered and analyzed.