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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 2026-06-25 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

The KAMA indicator is a technical analysis tool that kaufman's adaptive moving average adjusts its sensitivity based on market volatility.

This indicator is primarily used for identifying key market conditions. It provides a robust signal that can be easily integrated into both simple strategies and more complex machine learning feature pipelines. Compared to its alternatives, it offers a distinct balance of responsiveness and stability.

Traders often combine this with other metrics to confirm signals and avoid false positives during sideways market regimes. It remains a standard tool for systematic trading models.

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.

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.

QuantWave implements this indicator via the universal Next<T> trait, guaranteeing bit-identical results between Rust streaming, Python streaming, and Polars batch (.ta() / map_batches) surfaces.

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

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 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.