Chartist trading strategy with the application of Artificial Intelligence instruments

Abstract

Over the past decades, chart experiments have established a new frontier for technical analysis. The new technology achieved gives us the opportunity to implement automated trading systems for the financial market. These are able to capture the best performance while minimizing time effort. Since the timing effect is one of the most essential elements in investment trading, this thesis aims to build a system that filters the most promising and profitable assets with the support of the template matching technique, studying how Artificial Intelligence can achieve an astonishing result with the recognition of graphical patterns in price charts.