Kaufman’s Adaptive Moving Average (KAMA)
get_kama(quotes, er_periods=10, fast_periods=2, slow_periods=30)
||Iterable[Quote]||Iterable(such as list or an object having
• Need help with pandas.DataFrame?
||int, default 10||Number of Efficiency Ratio (volatility) periods (
||int, default 2||Number of Fast EMA periods. Must be greater than 0.|
||int, default 30||Number of Slow EMA periods. Must be greater than
Historical quotes requirements
You must have at least
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.
- This method returns a time series of all available indicator values for the
KAMAResultsis just a list of
- 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-1periods will have
Nonevalues since there’s not enough data to calculate.
Convergence warning: The first
10×Eperiods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.
||float, Optional||Efficiency Ratio is the fractal efficiency of price changes|
||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
See Utilities and Helpers for more information.
from stock_indicators import indicators # This method is NOT a part of the library. quotes = get_history_from_feed("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]