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AM Detector

Ehlers DSP cycle ehlers dsp amplitude frequency

Recovers market volatility from the amplitude-modulated whitened price spectrum.

Visual Example

AM Detector — 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

Recovers market volatility from the amplitude-modulated whitened price spectrum.

Use to extract the instantaneous amplitude and frequency of market cycles. The AM output measures cycle energy for position sizing; the FM output tracks cycle period for adaptive indicator tuning.

Part of QuantWave's Ehlers digital signal processing suite. Designed for low-lag cycle and trend work — pair with Roofing Filter or SuperSmoother on noisy inputs.

Ehlers adapts AM and FM demodulation techniques from radio engineering in Cycle Analytics for Traders to extract cycle amplitude and instantaneous frequency from market data. The amplitude envelope measures how energetic the current cycle is, while FM reveals whether the cycle period is expanding or contracting.

Typical applications:

  • Use for cycle timing in mean-reverting regimes
  • Gate with Hurst exponent or ADX before taking cycle signals
  • Allow 4+ bars warm-up for filter state to stabilise
  • Chain with Roofing Filter when input is noisy

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/amfm.rs):

\[ Deriv = |Close - Open|, Envel = \max(Deriv, 4), Volatil = \text{Avg}(Envel, 8) \]

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

Parameters

Parameter Default Description
highest_len 4 Envelope lookback length
avg_len 8 Smoothing length

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::AM_DETECTOR;
use quantwave_core::traits::Next;

let mut ind = AM_DETECTOR::new(4);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import AM_DETECTOR

ind = AM_DETECTOR(4)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_am_detector(series: pl.Series) -> pl.Series:
    ind = qw.AM_DETECTOR(4)
    return pl.Series([ind.next(float(v)) for v in series.to_list()])

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("close").map_batches(apply_am_detector, return_dtype=pl.Float64).alias("am_detector")
    )
    .collect()
)

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

Edge Cases & Limitations

  • Recursive DSP filters require a warm-up period; first N bars may be unstable or raw-pass-through.
  • Designed for cyclic/mean-reverting regimes; trending markets can produce lag or drift.
  • Parameter period (or equivalent) controls cutoff — too small adds noise, too large adds lag.
  • Prefer chaining with other Ehlers tools (Roofing Filter, SuperSmoother) on noisy inputs.
  • Validated via proptests against gold-standard vectors where available.
  • No look-ahead bias; suitable for live streaming and batch feature pipelines.

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://github.com/lavs9/quantwave/blob/main/references/Ehlers%20Papers/AMFM.pdf

Implementation: quantwave-core/src/indicators/amfm.rs (AM_DETECTOR / AM_DETECTOR_METADATA). Parity: quantwave-core/tests/gold_standard/am_detector.json

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