AbstractThe present paper approaches high-frequency trading from a computational science perspective, presenting a pattern recognition model to predict price changes of stock market assets. The technique is based on the feature-weighted Euclidean distance to the centroid of a training cluster. A set of micro technical indicators, traditionally employed by professional scalpers, is used in this setting. We describe procedures for removal of outliers, normalization of feature points, computation of weights of features, and classification of test points. The complexity of computation at each quote received is proportional to the number of features. In addition, processing of indicators is parallelizable and, therefore, suitable in high-frequency domains. Experiments are presented for different prediction time intervals and confidence thresholds. Predictions made 10 to 2000 milliseconds before a price change resulted in an accuracy that ranged monotonically from 97% to 75%. Finally, we observed an empirical relation between Euclidean distance in the feature space and prediction accuracy.