Hilbert Transform - Dominant Cycle Phase (HT_DCPHASE)
Calculates the phase angle (0 to 360 degrees) of the dominant cycle identified by the Hilbert Transform.
Visual Example

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 Phase (HT_DCPHASE) indicator is a technical analysis tool that calculates the phase angle (0 to 360 degrees) of the dominant cycle identified by 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 identify the current position within a market cycle. It is the core component for generating the Hilbert Sine Wave indicator, which signals trend vs. cycle regimes.
The Dominant Cycle Phase represents the instantaneous position within a detected cycle. By measuring the phase angle, traders can determine if the market is at a peak, trough, or mid-cycle, enabling more precise timing for entry and exit signals. — 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):
Gold-standard parity vectors: quantwave-core/tests/gold_standard/ht_dcphase.json.
Parameters
| Parameter | Default | Description |
|---|---|---|
| (none) | — | No tunable parameters for this detector. |
Usage Examples
Streaming (Rust)
use quantwave_core::indicators::HT_DCPHASE;
use quantwave_core::traits::Next;
let mut ind = HT_DCPHASE::new(14);
for price in &prices {
let value = ind.next(price);
}
Streaming (Python)
Polars Batch (Python)
import polars as pl
df = (
pl.read_csv('ohlcv.csv')
.lazy()
.with_columns(
pl.col("close").ta.ht_dcphase(14).alias("hilbert_transform_dominant_cycle_phase_ht_dcphase")
)
.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. |
Related Indicators & See Also
Sources & References
Primary Source: https://www.tradingview.com/support/solutions/43000502010-hilbert-transform-dominant-cycle-phase-ht-dcphase/
Implementation: quantwave-core/src/indicators/cycle.rs (HT_DCPHASE / HT_DCPHASE_METADATA).
Parity: quantwave-core/tests/gold_standard/ht_dcphase.json
Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.