Technical analysis mainly focuses on analyzing the chart patterns, which is a non-trivial task. Because one time scale alone cannot be applied to all analytical processes, the identification of typical patterns on a stock price chart requires considerable knowledge and experience. The last two decades has seen attempts to solve such non-linear financial forecasting problems using AI technologies such as neural networks, fuzzy logic, genetic algorithms and expert systems but these, although accurate, lack explanatory power or are dependent on domain experts. This paper introduces a case based reasoning (CBR) system that provides an explainable method of financial forecasting  that is not dependent on the inputs of domain experts. This study proposes an algorithm, PXtract, which identifies and analyses possible chart patterns, makes dynamic use of different time windows, and introduces a wavelet multi-resolution analysis incorporated within a radial basis function neural network (RBFNN) matching method that can be used to automate the chart pattern matching process.