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An algorithm based on Renormalization Group (RG) to analyze time seriesforecasting was proposed in cond-mat/0110285. In this paper we explicitly codeand test it. We choose in particular some financial time series (stocks,indexes and commodities) with daily data and compute one step ahead forecasts.We then construct some indicators to evaluate performances. The algorithm issupposed to prescribe the future development of the time series by using theself-similarity property intrinsically present in RG approach. This propertycould be potentially very attractive for the purpose of building winningtrading systems. We discuss some relevant points along this direction. Althoughcurrent performances have to be improved the algorithm seems quite reactive tovarious combinations of input parameters and different past values sequences.This makes it a potentially good candidate to detect sharp market movements. Wefinally mention current drawbacks and sketch how to improve them.

Using self-similarity and renormalization group to analyze time series