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KAMA

Classic moving-average adaptive smoothing classic

Kaufman's Adaptive Moving Average adjusts its sensitivity based on market volatility.

Visual Example

KAMA — annotated preview mapping to core implementation

Synthetic ideal per library logic. Generated 2026-07-01 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

Kaufman's Adaptive Moving Average adjusts its sensitivity based on market volatility.

Use as an adaptive moving average that is fast in trending markets and slow in choppy, sideways conditions. Reduces whipsaws that plague fixed-period moving averages in ranging markets.

Native Rust implementation with gold-standard or TA-Lib parity tests where applicable.

Perry Kaufman designed KAMA using an Efficiency Ratio that measures how directionally price has moved versus total path length. A high ratio (strong trend) produces a fast-reacting EMA; a low ratio (choppy market) produces a near-flat line, dramatically reducing false signals during consolidation. — New Trading Systems and Methods, 4th ed.

Typical applications:

  • Trend filter or signal line for systematic entries
  • Default lookback 10 — tune per asset volatility
  • Cross with faster oscillator for entry timing
  • Streaming and Polars paths are bit-identical for production parity

QuantWave implements this via the universal Next<T> trait — bit-identical across Rust streaming, Python streaming, and Polars .ta() batch plugins.

Formula / Specification

Implementation (quantwave-core/src/indicators/kama.rs):

[ ER = \frac{|Price - Price_{t-n}|}{\sum |Price - Price_{t-1}|} ] [ SC = [ER(FastSC - SlowSC) + SlowSC]^2 ] [ KAMA = KAMA_{t-1} + SC(Price - KAMA_{t-1}) ]

Gold-standard parity vectors: quantwave-core/tests/gold_standard/kama.json.

Parameters

Parameter Default Description
period 10 Efficiency Ratio lookback period
fast_period 2 Fastest smoothing period
slow_period 30 Slowest smoothing period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::KAMA;
use quantwave_core::traits::Next;

let mut ind = KAMA::new(10);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import KAMA

ind = KAMA(10)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave  # registers pl.col().ta

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("close").ta.kama(10).alias("kama")
    )
    .collect()
)

All surfaces are bit-identical via the single Next<T> implementation and proptests.

Edge Cases & Limitations

  • Warm-up: first 10 bars may return NaN or partial state per implementation.
  • Parameter sensitivity: smaller periods increase noise; larger periods increase lag.
  • Sudden gaps or bad ticks can distort rolling windows — consider pre-filtering.
  • Single-series indicators ignore volume unless otherwise documented.
  • Validated via proptests against gold-standard vectors where available.
  • No look-ahead bias; streaming and Polars batch paths are bit-identical.

Boundary Behavior

Condition Behavior
Warm-up Leading bars return NaN until warmup_bars is satisfied.
period > len When period exceeds series length, output is all NaN.
NaN inputs NaN in input propagates to output (NaN out).
Invalid params Non-positive period or missing required params raise ValueError.
Empty data Empty input returns an empty result series.

Sources & References

Primary Source: https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:kaufman_s_adaptive_moving_average

Implementation: quantwave-core/src/indicators/kama.rs (KAMA / KAMA_METADATA). Parity: quantwave-core/tests/gold_standard/kama.json

Provenance: Standards bulk upgrade 2026-07-01 IST — see docs/DOCUMENTATION_STANDARDS.md.