kama

kama(close, length=None, fast=None, slow=None, drift=None, offset=None, **kwargs)

Kaufman’s Adaptive Moving Average (KAMA)

Developed by Perry Kaufman, 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.

Sources:

https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:kaufman_s_adaptive_moving_average https://www.tradingview.com/script/wZGOIz9r-REPOST-Indicators-3-Different-Adaptive-Moving-Averages/

Calculation:

Default Inputs:

length=10

Args:

close (pd.Series): Series of ‘close’s length (int): It’s period. Default: 10 fast (int): Fast MA period. Default: 2 slow (int): Slow MA period. Default: 30 drift (int): The difference period. Default: 1 offset (int): How many periods to offset the result. Default: 0

Kwargs:

fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method

Returns:

pd.Series: New feature generated.