Kaufman Adaptive Moving Average

KAMA adapts to market volatility, offering a more responsive moving average.

Method

tm.ta.kama(source, timeperiod)

Inputs

Parameter	Argument type	Description	Default Value
source	pd.Series	Input data series	-
timeperiod	int	Number of periods for the indicator	30

Outputs

Output	Type
kama	np.ndarray

Example usage

strategy.py

import tradomate as tm

@tm.strategy()
def my_strategy(config: tm.TradomateConfig, data: tm.TradomateData):

    # Trying out Kaufman Adaptive Moving Average
    kama = tm.ta.kama(data.close, timeperiod=30)

    # Get the last value and print
    last_value = kama.iloc[-1]
    tm.log(f"Last value of Kaufman Adaptive Moving Average is {last_value}")

    # Plot the values of kama
    tm.plot(kama, title="Kaufman Adaptive Moving Average", overlay=False)