Kaufman’s Adaptive Moving Average (KAMA)

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

Parameters

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.

Return

KAMAResults[KAMAResult]

This method returns a time series of all available indicator values for the quotes provided.
KAMAResults is just a list of KAMAResult.
It always returns the same number of elements as there are in the historical quotes.
It does not return a single incremental indicator value.
The first N-1 periods will have None values since there’s not enough data to calculate.

⚞ 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.

KAMAResult

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.

Utilities

See Utilities and Helpers for more information.

Example

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] 💬