Adaptive Cyclicality

Adaptive Cyclicality defines the cycles of the price evolution for any financial market. The Relative Cyclicality is computed by applying the "Cyclicality" mathematical transformation to the Adaptive Moving Average function introduced by Perry J. Kaufmann. The Adaptive Cyclicality can be used to generate automated long entry signals near the minimal price values. Also, it can be used to define limit conditions to generate automated exit signals near the maximal price values. The Adaptive Cyclicality transformation has a specific optimal period for each market and timeframe.

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The efficiency of the Adaptive Cyclicality indicator was proved during the time, having a very precise evolution, especially in the long-time intervals (H1, H4, D1, W1) for every market. For the small timeframes (M1, M5, M15, M30), the Adaptive Cyclicality is a very good filter of false signals of many other known indicators like RSI, CCI, MACD etc. The indicator can be successfully used for manual trading or can be automated by importing the specific data series. The Adaptive Cyclicality is an important part of the algorithms included in successful expert advisors like Dow Jones Predictor, Gold Predictor, Nasdaq Retractor, or DAX Retractor.