wickra/docs/wiki/indicators/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	Statistics
Sub-category	Regression
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.