Inertia

Donald Dorsey's Inertia — a LinearRegression smoothing of the RVI series. Preserves trend direction while damping the underlying ratio.

Quick reference

Field	Value
Family	Momentum Oscillators
Input type	Candle (uses open, high, low, close)
Output type	f64
Output range	unbounded; tracks the RVI ratio's scale
Default parameters	rvi_period = 14, linreg_period = 20
Warmup period	rvi_period + linreg_period − 1 (33 for defaults)
Interpretation	Slow trend-following variant of RVI; a cross of zero signals a trend change.

Formula

Inertia_t = LinearRegression(RVI(close − open, high − low; rvi_period), linreg_period)_t

Each bar's RVI value feeds a rolling least-squares regression; the endpoint of the fit is published. Dorsey's thesis is that a market's inertia — its tendency to keep doing what it is doing — is best read from the smoothed slope of vigor rather than the noisy ratio itself.

Parameters

Name	Type	Default	Constraint	Source
rvi_period	usize	14	>= 1	Inertia::new (inertia.rs:46)
linreg_period	usize	20	>= 1	inertia.rs:46

Either parameter == 0 returns [Error::PeriodZero]. Inertia::classic() returns (14, 20). Python defaults come from #[pyo3(signature = (rvi_period=14, linreg_period=20))]; the Node constructor takes both explicitly. The public class is Inertia in both bindings.

Inputs / Outputs

use wickra::{Indicator, Inertia, Candle};
// Inertia: Input = Candle, Output = f64
const _: fn(&mut Inertia, Candle) -> Option<f64> = <Inertia as Indicator>::update;

• Python. update(candle) returns float | None; batch(open, high, low, close) returns a 1-D float64 np.ndarray with NaN warmup.
• Node. update(open, high, low, close) returns number | null; batch(open, high, low, close) returns an Array<number> with NaN warmup.

Warmup

warmup_period() returns rvi_period + linreg_period − 1. The inner RVI emits at rvi_period candles; the regression then needs linreg_period − 1 more RVI values to fill its window. Pinned by warmup_emits_first_value_at_warmup_period ((3, 4) → warmup 6: first five None, sixth emits).

Edge cases

• Constant RVI. Identical candles give a constant RVI; the regression of a constant series equals that constant — e.g. RVI = 0.5/2.0 = 0.25 for a (c−o)=0.5, (h−l)=2.0 bar (test constant_rvi_yields_constant_inertia).
• Reset. reset() resets both the inner RVI and the regression.

Examples

Rust

use wickra::{Candle, Indicator, Inertia};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut inertia = Inertia::classic(); // (14, 20)
    let mut last = None;
    for i in 0..80 {
        let o = 100.0 + f64::from(i);
        let c = o + 0.5;
        last = inertia.update(Candle::new(o, c + 0.2, o - 0.2, c, 1.0, i64::from(i))?);
    }
    println!("{last:?}");
    Ok(())
}

Python

import wickra as ta
inertia = ta.Inertia(14, 20)
out = inertia.batch(open_, high, low, close)  # 1-D series, NaN warmup (33 rows)

Node

const ta = require('wickra');
const inertia = new ta.Inertia(14, 20);
const v = inertia.update(100, 100.7, 99.8, 100.5); // open, high, low, close

Interpretation

Inertia is RVI run through a regression smoother, so it trades responsiveness for stability:

1. Trend persistence. Positive Inertia ⇒ vigor is trending up ⇒ the market's "inertia" is bullish; negative ⇒ bearish. The slow smoothing makes the zero-line cross a higher-conviction trend-change signal than a raw RVI cross.
2. Pair with RVI. Use raw RVI for early reads and Inertia to confirm the trend has actually turned.

Common pitfalls

• Expecting RVI-speed signals. Inertia deliberately lags — its warmup is rvi_period + linreg_period − 1 bars and its turns trail RVI's.
• Reading absolute magnitude. Like RVI it is a ratio scale, not a bounded 0–100 oscillator.

References

• Donald Dorsey, "Inertia", Technical Analysis of Stocks & Commodities.

See also

• Rvi — the underlying vigor ratio.
• LinearRegression — the smoother applied.
• RVIVolatility — the unrelated volatility RVI.