Keltner Channels

get_keltner(quotes, ema_periods=20, multiplier=2.0, atr_periods=10)

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?
`ema_periods`	int, default 20	Number of lookback periods (`E`) for the center line moving average. Must be greater than 1 to calculate.
`multiplier`	float, default 2.0	ATR Multiplier. Must be greater than 0.
`atr_periods`	int, default 10	Number of lookback periods (`A`) for the Average True Range. Must be greater than 1 to calculate.

Historical quotes requirements

You must have at least 2×N or N+100 periods of quotes, whichever is more, where N is the greater of E or A periods, to cover the convergence periods. Since this uses a smoothing technique, we recommend you use at least N+250 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

KeltnerResults[KeltnerResult]

This method returns a time series of all available indicator values for the quotes provided.
KeltnerResults is just a list of KeltnerResult.
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 N+250 periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.

KeltnerResult

name	type	notes
`date`	datetime	Date
`upper_band`	float, Optional	Upper band of Keltner Channel
`center_line`	float, Optional	EMA of Close price
`lower_band`	float, Optional	Lower band of Keltner Channel
`width`	float, Optional	Width as percent of Centerline price. `(upper_band-lower_band)/center_line`

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 Keltner(20)
results = indicators.get_keltner(quotes, 20,2.0,10)

About Keltner Channels

Created by Chester W. Keltner, Keltner Channels are based on an EMA centerline and ATR band widths. See also STARC Bands for an SMA centerline equivalent. [Discuss]