Successful Price Cycle Forecasts for S&P Futures Using TF3, a Pattern Recognition Algorithms Based on the KNN Method

Summary

Basing on the perceived stationary internal structure of market movements on appropriate time scales, a series of interrelated pattern recognition programs was designed to compare specific features of current cycle “legs” with a selected universe of analogous prior market features periods which are then queried to obtain a prediction as to the future of the current cycle leg. Similarities are determined by a K-Nearest-Neighbor (KNN) method. This procedure yields good results in simulated S&P futures trading and demonstrates the hypothesized stationary of market responses to stimuli.