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(such as list or an object having __iter__() ) of the Quote class or its sub-class. • Need help 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 ofKAMAResult
. - 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 haveNone
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_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]