KAMA - The adaptive moving average
Making a moving average responsive to volatility changes results
in a dynamic, more accurate indicator.
Developed by Perry Kaufman
The Adaptive Moving Average (AMA) aka Kaufman Adaptive Moving Average (KAMA) was created by Perry Kaufman and first presented in his book Smarter Trading (1995). This moving average offered a significant advantage over previous attempts at ‘intelligent’ averages because it allowed the user greater control.
Introduction
Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements.
Calculation
There are several steps required to calculate Kaufman's Adaptive Moving Average. Let's first start with the settings recommended by Perry Kaufman, which are KAMA (10,2,30).
- 10 is the number of periods for the Efficiency Ratio (ER).
- 2 is the number of periods for the fastest EMA constant.
- 30 is the number of periods for the slowest EMA constant.
Before calculating KAMA, we need to calculate the Efficiency Ratio (ER) and the Smoothing Constant (SC). Breaking down the formula into bite size nuggets makes it easier to understand the methodology behind the indicator. Note that ABS stands for Absolute Value.
Efficiency Ratio (ER)
The ER is basically the price change adjusted for the daily volatility.
