Linear Regression

Classic statistics classic volatility trend

Linear Regression plots a straight line that best fits the data prices.

Visual Example

Linear Regression — 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 Linear Regression indicator is a technical analysis tool that linear regression plots a straight line that best fits the data prices.

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 for statistical analysis of price series: linear regression, standard deviation, correlation coefficients, and other descriptive statistics used as indicator inputs.

Standard statistical measures provide the mathematical foundation for many technical indicators. Linear regression finds the best-fit line through price, standard deviation quantifies dispersion, and correlation coefficients measure how closely two series move together — all are essential for quantitative strategy construction.

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/statistics.rs):

\[ y = a + bx \]

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

Parameters

Parameter	Default	Description
`period`	14	Period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::LINREG;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import LINREG

ind = LINREG(14)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_linear_regression(series: pl.Series) -> pl.Series:
    ind = qw.LINREG(14)
    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_linear_regression, return_dtype=pl.Float64).alias("linear_regression")
    )
    .collect()
)

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

Edge Cases & Limitations

Warm-up: first 14 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://www.investopedia.com/terms/l/linearregression.asp

Implementation: quantwave-core/src/indicators/statistics.rs (LINREG / LINREG_METADATA). Parity: quantwave-core/tests/gold_standard/linreg.json

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