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John Rawlins 678-776-1343. COMPUTERIZED CYCLICAL FILTRATION ANALYSIS WITH HISTORICAL AND CURRENT PROJECTIONS. Analysis overview.
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John Rawlins678-776-1343 COMPUTERIZED CYCLICAL FILTRATION ANALYSIS WITH HISTORICAL AND CURRENT PROJECTIONS
Analysis overview • CPO methodology employs proprietary statistical techniques to obtain cyclical information from price data. Other proprietary frequency domain techniques are then employed to obtain the cycles embedded in the price data. It is our experiences that these cycles, projected into the future, have great predictive value and can be used in a trading system. Further more, we have automated the process using expert system technology that mimics the rules of logic used by a human trader. This approach is generalized and accessible and avoids “over fitting” the data like more obscure approaches such as neural networks. We have yielded good results with this approach over a wide variety of price series.
Mathematical Overview • CPO goes through an initial de-trending filter that is determined by its specific time interval. The de-trending adds considerable computational stability to the analysis. The properties of the analysis are such that, in general, the time function being analyzed is converted into a discreet set of time dependent segments, which, by themselves, are adaptable to direct Fourier computations with variable evaluation lengths. Algorithms are applied which slide forward and back to test the validity of each transform length until minimal differences are established. Further, as these length searches are effected, another search is conducted which seeks a characteristic waveform applicable of the time segment under analysis. Once these parameters are gleaned, then the inverse of the filter is applied to the converted signal, and the lengths and waveforms obtained in the search stated. Using the inverse of the parameters in the original time domain a time variant nonlinear filter is employed on the initial function to, essentially, remove the waveforms and lengths found in the nonlinear domain.
CPO trade automation • 1. Implement a comprehensive rule base system that includes cycle projection slope calculations and momentum confirmation tools. • 2. Exit rules based on slope changes in the CPO calculations, relationship with upper and lower sigma.(2 standard deviations above or below the cycle summation parameter ) or changes in our momentum program rules.
