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Hilbert Transform - Dominant Cycle Period (HT_DCPERIOD)

Ehlers DSP cycle hilbert adaptive dsp

Identifies the period of the dominant cycle in the price data using the Hilbert Transform.

Visual Example

Hilbert Transform - Dominant Cycle Period (HT_DCPERIOD) — 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 Hilbert Transform - Dominant Cycle Period (HT_DCPERIOD) indicator is a technical analysis tool that identifies the period of the dominant cycle in the price data using the hilbert transform.

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 to dynamically adjust the lookback periods of other indicators (e.g., adaptive moving averages). Knowing the current dominant cycle length allows for more accurate smoothing and trend detection.

John Ehlers popularized the use of the Hilbert Transform to identify the dominant cycle in financial time series. The DCPERIOD indicator tracks the length of this cycle in bars, providing a crucial parameter for creating market-responsive technical indicators that adapt to changing volatility. — Rocket Science for Traders

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/cycle.rs):

\[ \text{DCPERIOD}_t = \text{Recalculated Dominant Cycle using Hilbert Transform} \]

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

Parameters

Parameter Default Description
(none) — No tunable parameters for this detector.

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::HT_DCPERIOD;
use quantwave_core::traits::Next;

let mut ind = HT_DCPERIOD::new(14);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import HT_DCPERIOD

ind = HT_DCPERIOD(14)
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.ht_dcperiod(14).alias("hilbert_transform_dominant_cycle_period_ht_dcperiod")
    )
    .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://www.tradingview.com/support/solutions/43000502011-hilbert-transform-dominant-cycle-period-ht-dcperiod/

Implementation: quantwave-core/src/indicators/cycle.rs (HT_DCPERIOD / HT_DCPERIOD_METADATA). Parity: quantwave-core/tests/gold_standard/ht_dcperiod.json

Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.