wickra/docs/wiki/indicators/price-statistics/Indicator-LinRegSlope.md

LinRegSlope

Linear Regression Slope — the slope of a rolling ordinary-least-squares fit over the last period prices.

Quick reference

Field	Value
Family	Price Statistics
Input type	f64 (price)
Output type	f64
Output range	unbounded around zero (price units per bar)
Default parameters	period = 14 (Python)
Warmup period	period
Interpretation	How steeply price trends; positive up, negative down, zero flat.

Formula

Over the last period inputs, indexed x = 0, 1, …, period − 1:

b = (n·Σxy − Σx·Σy) / (n·Σxx − (Σx)²)

LinRegSlope fits a straight line to the window by ordinary least squares — the same fit as LinearRegression — but reports the slope b instead of the endpoint. The slope is in price units per bar: positive while price trends up, negative while it trends down, near zero when it is ranging. This is TA-Lib's LINEARREG_SLOPE.

Parameters

period — the regression window. Must be at least 2 (a line needs two points). The Python binding defaults it to 14; the Rust and Node constructors require it explicitly.

Inputs / Outputs

From crates/wickra-core/src/indicators/linreg_slope.rs:

impl Indicator for LinRegSlope {
    type Input = f64;
    type Output = f64;
    // update(&mut self, input: f64) -> Option<f64>
}

LinRegSlope is a scalar indicator: it consumes one f64 price per step. Because Input = f64 it can sit inside a Chain.

Warmup

LinRegSlope::new(14).warmup_period() == 14. The first value lands once the window holds a full period prices — on input index period − 1.

Edge cases

• period < 2. Rejected at construction — a regression line is undefined for fewer than two points.
• Perfect line. Fed a series rising by a fixed step, the slope is exactly that step (perfect_line_returns_its_step pins this).
• Constant series. A flat input returns a slope of 0.
• Falling series. A descending input returns a negative slope.
• Reset. ls.reset() clears the rolling window.

Examples

Rust

use wickra::{BatchExt, Indicator, LinRegSlope};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut ls = LinRegSlope::new(3)?;
    // Fit over [1, 2, 9]: the least-squares line is y = 4x, slope 4.
    let out = ls.batch(&[1.0, 2.0, 9.0]);
    println!("{:?}", out);
    Ok(())
}

Output:

[None, None, Some(4.0)]

This matches the reference_values test in crates/wickra-core/src/indicators/linreg_slope.rs.

Python

import numpy as np
import wickra as ta

ls = ta.LinRegSlope(3)
print(ls.batch(np.array([1.0, 2.0, 9.0])))

Output:

[nan nan  4.]

Node

const ta = require('wickra');
const ls = new ta.LinRegSlope(3);
console.log(ls.batch([1, 2, 9]));

Output:

[ NaN, NaN, 4 ]

Interpretation

LinRegSlope is a momentum gauge: its sign is the trend direction and its magnitude is the trend's steepness in price-per-bar. A slope crossing zero marks a trend change; a slope that flattens while price still rises warns the trend is losing pace. Unlike a difference-based oscillator it uses every bar in the window, so it is less jumpy.

Common pitfalls

• Comparing slopes across instruments. The slope is in the instrument's own price units per bar — normalise (e.g. divide by price) to compare.
• Tiny periods. period = 2 reduces the slope to the last simple difference; use a meaningful window.

References

The slope of an ordinary least-squares fit to a rolling price window; matches TA-Lib's LINEARREG_SLOPE.

See also

• Indicator-LinearRegression.md — the endpoint of the same rolling fit.
• Indicator-Mom.md — raw price-difference momentum, the unsmoothed cousin.
• Indicators-Overview.md — the full taxonomy.