This paper concerns extracting knowledge from limit order books and using it to predict significant changes in the price of the financial asset under study. Limit order books are encoded in a feature-based data representation. A binary Support Vector Machine classifier is trained to predict whether a particular limit order book leads to a significant change in the price in a few successive time instants, or not. An Evolutionary Algorithm is used to find the optimal parameter setting of the feature-based data representation, so that the performance of the classifier was better. Computational experiments performed on financial ultra-high frequency time series coming from the London Stock Exchange Rebuild Order Book database confirmed that the optimization of the feature-based data representation is very effective in improving the performance of the classification.
