CFO

Chande Forecast Oscillator — the percentage difference between the close and the endpoint of an n-bar linear-regression forecast of the close.

Quick reference

Field	Value
Family	Price Oscillators
Input type	f64 (single close)
Output type	f64 (percent)
Output range	unbounded around zero (in percent)
Default parameters	period = 14
Warmup period	period (exact)
Interpretation	Positive: close above the linear forecast (overshoot). Negative: below (undershoot).

Formula

CFO_t = 100 · (close_t − LinearRegression(close, period)_t) / close_t

The LinearRegression term is the OLS endpoint forecast of the close over the last period bars. A positive CFO means price has run ahead of its own trend forecast; a negative CFO means it lags. For a constant or perfectly linear input the regression fits exactly and CFO is 0.

Parameters

Name	Type	Default	Constraint	Source
period	usize	14	>= 1	Cfo::new (cfo.rs:41)

period == 0 returns [Error::PeriodZero]. Python default comes from #[pyo3(signature = (period=14))]; the Node constructor takes period explicitly. The public class is CFO in both bindings.

Inputs / Outputs

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

A single f64 close in, an Option<f64> out (a percentage). Python maps this to float | None / a float64 np.ndarray with NaN warmup; Node to number | null / Array<number> with NaN warmup.

Warmup

warmup_period() returns period. CFO wraps a LinearRegression of the same period, which emits its first endpoint on input period (index period − 1); CFO emits on that bar. Pinned by warmup_emits_first_value_at_period (period 3: inputs 1–2 return None, input 3 emits).

Edge cases

• Constant series. The regression of a flat line equals the constant, so close − forecast = 0 ⇒ CFO = 0 (test constant_series_yields_zero).
• Perfectly linear series. The fit is exact, the close lands on the forecast, CFO = 0 (test perfect_linear_series_yields_zero).
• Zero close. The percentage form divides by close; on a zero close the indicator holds its previous value rather than emitting infinity (test zero_close_holds_value).
• Reset. reset() clears the regression and the held value.

Examples

Rust

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

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Perfectly linear input → CFO is 0 once warm.
    let prices: Vec<f64> = (1..=20).map(|i| f64::from(i) * 2.0).collect();
    let mut cfo = Cfo::new(5)?;
    println!("{:?}", cfo.batch(&prices).into_iter().flatten().last()); // Some(0.0)
    Ok(())
}

Python

import numpy as np
import wickra as ta

cfo = ta.CFO(14)
out = cfo.batch(np.array([...], dtype=float))  # 1-D percent series, NaN warmup

Node

const ta = require('wickra');
const cfo = new ta.CFO(14);
const out = cfo.update(101.5); // null during warmup, else a percent reading

Interpretation

CFO measures how far price has strayed from its own short-term trend forecast, in percent:

1. Overshoot / reversion. Large positive readings mark price running ahead of trend — a pullback candidate; large negative readings the reverse. Chande used band crossings (e.g. ±3 %) as fade triggers.
2. Trend confirmation. Persistent same-sign readings confirm the trend is intact; a flip through zero warns the close has crossed its forecast.

Because the output is a percentage, CFO is directly comparable across instruments at different price levels — unlike the price-unit Apo.

Common pitfalls

• Feeding non-price series. The division by close assumes a positive price; on a series that crosses zero the percentage form is ill-defined.
• Confusing CFO with the forecast itself. CFO is the deviation from the LinearRegression endpoint, not the endpoint — use LinearRegression directly if you want the forecast value.

References

• Tushar Chande & Stanley Kroll, The New Technical Trader, Wiley, 1994.

See also

• LinearRegression — the forecast CFO deviates from.
• LinRegSlope — the trend slope.
• Cmo — Chande's momentum oscillator companion.