Kaufman’s Adaptive Moving Average (KAMA)

get_kama(quotes, er_periods=10, fast_periods=2, slow_periods=30)


name type notes
quotes Iterable[Quote] Iterable of the Quote class or its sub-class.
See here for usage with pandas.DataFrame
er_periods int, default 10 Number of Efficiency Ratio (volatility) periods (E). Must be greater than 0.
fast_periods int, default 2 Number of Fast EMA periods. Must be greater than 0.
slow_periods int, default 30 Number of Slow EMA periods. Must be greater than fast_periods.

Historical quotes requirements

You must have at least 6×E or E+100 periods of quotes, whichever is more, to cover the convergence periods. Since this uses a smoothing technique, we recommend you use at least 10×E data points prior to the intended usage date for better precision.

quotes is an Iterable[Quote] collection of historical price quotes. It should have a consistent frequency (day, hour, minute, etc). See the Guide for more information.



Convergence warning: The first 10×E periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.


name type notes
date datetime Date
efficiency_ratio float, Optional Efficiency Ratio is the fractal efficiency of price changes
kama Decimal, Optional Kaufman’s adaptive moving average

More about Efficiency Ratio(ER): ER fluctuates between 0 and 1, but these extremes are the exception, not the norm. ER would be 1 if prices moved up or down consistently over the er_periods periods. ER would be zero if prices are unchanged over the er_periods periods.


See Utilities and Helpers for more information.


from stock_indicators import indicators

# This method is NOT a part of the library.
quotes = get_historical_quotes("SPY")

# Calculate KAMA(10,2,30)
results = indicators.get_kama(quotes, 10,2,30)

About Kaufman’s Adaptive Moving Average (KAMA)

Created by Perry Kaufman, KAMA is an volatility adaptive moving average of Close price over configurable lookback periods. [Discuss] 💬