The Kaufman’s Adaptive Moving Average (KAMA) was developed by analyst Perry Kaufman in an attempt to cancel out the noise of market volatility and inefficiency by using an efficiency ratio multiple.
Kaufman’s algorithm is a bid to cancel out “noise” in the data used to create a moving average line. The Exponential Moving Average (EMA) is imperfect in part because of its reliance on historical data – if the data is not current, it tells traders nothing about how an asset may trend in the future. Some traders also believe that EMAs are biased by virtue of weighting recent data more heavily, which can lead to false signals and potential losing trades.
Kaufman’s solution was to use an Efficiency Ratio, which aims to minimize the amount of short term volatility which is not part of an actual trend, and a Smoothing Constant, which uses short term alpha (Fast Alpha) and long term alpha (Slow Alpha) to balance out the AMA when the volatility conditions change.
The effect is that the AMA will follow low volatility price conditions closely but will “take a step back” when prices become more volatile, in an attempt to disregard the temporary noise and keep an eye on the overall trend.
Traders of all skill and experience levels can use moving averages, but additional confirmation of trading decisions – free of emotion and inherent bias – is a useful option to have in any trader’s tool kit. Augmenting a AMA with quality artificial intelligence tools from Tickeron can help traders identify trade ideas, confirm trends, and make better, more profitable trades more often.