Keltner Channels

A pure composition of EMA on typical price plus ATR-scaled envelopes. The middle line is the trend filter, the bands are the volatility cone.

Quick reference

Item	Value
Family	Volatility & Bands
Input type	Candle (uses high, low, close)
Output type	KeltnerOutput { upper: f64, middle: f64, lower: f64 }
Output range	unbounded; lower ≤ middle ≤ upper
Default parameters	ema_period = 20, atr_period = 10, multiplier = 2.0
Warmup period	max(ema_period, atr_period) (20 for defaults) — exact first-emission index
Interpretation	trend-following envelope; tags signal momentum, not exhaustion

Formula

middle_t = EMA_{ema_period}( typical_price_t )           // tp = (H+L+C)/3
upper_t  = middle_t + multiplier * ATR_{atr_period}_t
lower_t  = middle_t - multiplier * ATR_{atr_period}_t

The middle line is an EMA of typical price, not of close (crates/wickra-core/src/indicators/keltner.rs:62, candle.typical_price()).

Parameters

Name	Type	Default	Constraint	Source
ema_period	usize	20	> 0	Keltner::new (keltner.rs:33)
atr_period	usize	10	> 0	Keltner::new (keltner.rs:33)
multiplier	f64	2.0	finite and > 0.0	Keltner::new (keltner.rs:34-36)

Python defaults from #[pyo3(signature = (ema_period=20, atr_period=10, multiplier=2.0))] in bindings/python/src/lib.rs. Keltner::classic() returns the same configuration.

Inputs / Outputs

use wickra::{Indicator, Keltner, Candle, KeltnerOutput};
// Keltner: Input = Candle, Output = KeltnerOutput
const _: fn(&mut Keltner, Candle) -> Option<KeltnerOutput> = <Keltner as Indicator>::update;

• Python streaming. Returns (upper, middle, lower) tuple or None.
• Python batch. Keltner.batch(high, low, close) returns a 2-D np.ndarray of shape (n, 3) with columns [upper, middle, lower]; warmup rows are NaN across all three columns.
• Node streaming. Returns a { upper, middle, lower } object or null.
• Node batch. keltner.batch(high, low, close) returns a flat Array<number> of length n * 3 interleaved per row: [u0, m0, l0, u1, m1, l1, …].

Warmup

warmup_period() reports max(ema_period, atr_period) — for the default (20, 10, 2.0) that is 20 — and that figure is exact: the first non-None output lands on candle warmup_period() (index warmup_period() - 1).

Keltner::update feeds the EMA and ATR sub-indicators unconditionally on every candle, then emits once both are ready. The two sub-indicators warm up in parallel over the same candle window, so the slower of the two (max(ema_period, atr_period)) governs the first emission. With the classic (20, 10, 2.0) configuration the first valid KeltnerOutput is the 20th candle (index 19). This is pinned by the first_emission_matches_warmup_period test in keltner.rs.

Edge cases

• Flat market. A constant-OHLC series produces upper == middle == lower because ATR collapses to 0. The pinned test flat_market_collapses_bands covers this.
• Trending market. When ATR rises, both bands widen symmetrically around the EMA centerline.
• Reset. reset() resets both the underlying EMA and ATR; the configured periods/multiplier are preserved.
• NaN / infinity. Candle::new rejects non-finite OHLC values up front; the indicator never receives them.
• Invalid params. ema_period == 0, atr_period == 0, or non-positive multiplier returns an error from Keltner::new.

Examples

Rust

use wickra::{BatchExt, Candle, Indicator, Keltner};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let candles = vec![
        Candle::new(10.0, 11.0, 9.0,  10.5, 1.0, 0)?,
        Candle::new(10.5, 12.0, 10.0, 11.5, 1.0, 0)?,
        Candle::new(11.5, 13.0, 11.0, 12.5, 1.0, 0)?,
        Candle::new(12.5, 14.0, 12.0, 13.5, 1.0, 0)?,
        Candle::new(13.5, 15.0, 13.0, 14.5, 1.0, 0)?,
    ];
    let mut k = Keltner::new(3, 3, 2.0)?;
    for (i, v) in k.batch(&candles).into_iter().enumerate() {
        println!("i={i} -> {:?}", v);
    }
    Ok(())
}

Output:

i=0 -> None
i=1 -> None
i=2 -> Some(KeltnerOutput { upper: 15.166666666666666, middle: 11.166666666666666, lower: 7.166666666666666 })
i=3 -> Some(KeltnerOutput { upper: 16.166666666666664, middle: 12.166666666666666, lower: 8.166666666666666 })
i=4 -> Some(KeltnerOutput { upper: 17.166666666666664, middle: 13.166666666666666, lower: 9.166666666666666 })

The first emission is at i = 2 (the 3rd candle), exactly max(ema=3, atr=3) = 3 — the value warmup_period() reports. The EMA and ATR sub-indicators are fed in parallel, so neither delays the other.

Python

import numpy as np
import wickra as ta

k = ta.Keltner(3, 3, 2.0)
h = np.array([11.0, 12.0, 13.0, 14.0, 15.0])
l = np.array([ 9.0, 10.0, 11.0, 12.0, 13.0])
c = np.array([10.5, 11.5, 12.5, 13.5, 14.5])
print(k.batch(h, l, c))

Output:

[[        nan         nan         nan]
 [        nan         nan         nan]
 [        nan         nan         nan]
 [        nan         nan         nan]
 [17.16666667 13.16666667  9.16666667]]

Node

const w = require('wickra');

const k = new w.Keltner(3, 3, 2.0);
const flat = k.batch(
  [11, 12, 13, 14, 15],
  [ 9, 10, 11, 12, 13],
  [10.5, 11.5, 12.5, 13.5, 14.5],
);
console.log('length:', flat.length);
console.log('row 4 [upper, middle, lower]:', flat.slice(12, 15));

Output:

length: 15
row 4 [upper, middle, lower]: [ 17.166666666666664, 13.166666666666666, 9.166666666666666 ]

Interpretation

• Trend filter. Persistent closes above the upper band signal trend continuation, much like Bollinger's "walking the band" pattern; Keltner is generally tighter than Bollinger on noisy series because ATR responds more smoothly than a rolling stddev.
• Squeeze cross-over. A common "squeeze" setup compares Bollinger bandwidth to Keltner channel width: when Bollinger fits inside Keltner, a volatility expansion is statistically more likely.
• Pullback entries. In a defined uptrend, pullbacks to the middle EMA line are a classic continuation entry; the lower band acts as the disaster stop.

Common pitfalls

• Typical price ≠ close. The middle EMA runs on (H + L + C) / 3, not on close. A pre-computed "EMA of close" panel will not equal the Keltner middle line and trying to align them at floating-point precision will fail.

References

• Chester W. Keltner, How to Make Money in Commodities, 1960. The original construction used a 10-day SMA of typical price with an envelope sized by the 10-day average range. The modern variant (EMA centerline + ATR envelope) is the form Wickra implements.
• Linda Bradford Raschke popularised the EMA + ATR rephrasing in the 1990s; this is the version most TA libraries ship today.

See also

• EMA — the centerline component.
• ATR — the envelope width component.
• Bollinger Bands — envelope using stddev rather than ATR; useful side-by-side comparison.
• Donchian Channels — envelope using rolling extrema with no smoothing.