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Correlation Trend

Ehlers DSP trend correlation ehlers statistics

Calculates the Pearson correlation between price and a linear time ramp to identify trends.

Visual Example

Correlation Trend — 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 Correlation Trend indicator is a technical analysis tool that calculates the pearson correlation between price and a linear time ramp to identify trends.

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 confirm whether price is trending or cycling before applying directional strategies. High correlation indicates a strong trend; low correlation indicates a cycling market.

In 'Correlation As A Trend Indicator' (2020), Ehlers uses the Pearson correlation coefficient between price and a linear ramp to identify trend strength. A coefficient near +1.0 indicates a consistent uptrend, while -1.0 indicates a consistent downtrend. Unlike standard moving averages, this approach is independent of price amplitude and focuses purely on the linearity of the move.

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/correlation_trend.rs):

[ X_i = Price_{t-i}, Y_i = -i ] [ R = \frac{n \sum X_i Y_i - \sum X_i \sum Y_i}{\sqrt{(n \sum X_i^2 - (\sum X_i)^2)(n \sum Y_i^2 - (\sum Y_i)^2)}} ]

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

Parameters

Parameter Default Description
length 20 Correlation window length

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::CORRELATION_TREND;
use quantwave_core::traits::Next;

let mut ind = CORRELATION_TREND::new(20);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import CORRELATION_TREND

ind = CORRELATION_TREND(20)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_correlation_trend(series: pl.Series) -> pl.Series:
    ind = qw.CORRELATION_TREND(20)
    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_correlation_trend, return_dtype=pl.Float64).alias("correlation_trend")
    )
    .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/CORRELATION%20AS%20A%20TREND%20INDICATOR.pdf

Implementation: quantwave-core/src/indicators/correlation_trend.rs (CORRELATION_TREND / CORRELATION_TREND_METADATA). Parity: quantwave-core/tests/gold_standard/correlation_trend.json

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