Klinger Volume Oscillator (KVO)

Stephen Klinger's long / short-term volume-force MACD with trend-aware cumulative-money-flow weighting. Each bar produces a "volume force" whose sign tracks the daily trend and whose magnitude scales with how the current accumulation horizon compares to the previous trend's.

Quick reference

Item	Value
Family	Volume
Input type	Candle (uses high, low, close, volume)
Output type	KvoOutput { kvo, signal }
Output range	unbounded (centred near zero)
Default parameters	fast = 34, slow = 55, signal = 13 (Klinger's defaults)
Warmup period	slow + signal - 1
Interpretation	Volume-force MACD; KVO crossing signal = trade trigger

Formula

dm_t   = high_t + low_t + close_t        (daily measurement)
trend  = sign(dm_t - dm_{t-1})

cm_t   = cm_{t-1} + dm_t       if trend unchanged
cm_t   = dm_{t-1} + dm_t       if trend just flipped

vf_t   = volume_t · trend · |2 · (dm_t / cm_t) - 1| · 100

KVO_t    = EMA(vf, fast)_t - EMA(vf, slow)_t
signal_t = EMA(KVO, signal)_t

See crates/wickra-core/src/indicators/kvo.rs.

Parameters

Name	Type	Default	Constraint	Description
fast	usize	34	> 0, < slow	Fast EMA period.
slow	usize	55	> 0, > fast	Slow EMA period.
signal	usize	13	> 0	Signal-line EMA period.

Inputs / Outputs

Indicator<Input = Candle, Output = KvoOutput> with two fields. Python: (n, 2) array, columns [kvo, signal]. Node: flat number[] of length n * 2.

Warmup

warmup_period() == slow + signal - 1. The slow EMA seeds at slow; the signal EMA then needs signal - 1 further KVO values.

Edge cases

• Constant input. dm flat → trend = 0 → vf = 0 → KVO → 0.
• Volume = 0. Bar contributes zero force.
• Trend-flip-on-equal. dm == dm_prev is treated as unchanged (sign 0), preserving the prior trend.
• Reset. Clears all three EMAs and the cumulative measure.

Examples

Rust

use wickra::{BatchExt, Candle, Indicator, Kvo};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let candles: Vec<Candle> = (0..120).map(|i| {
        let b = 100.0 + (f64::from(i) * 0.2).sin() * 5.0;
        Candle::new(b, b + 1.0, b - 1.0, b + 0.3, 1000.0, i as i64).unwrap()
    }).collect();
    let mut k = Kvo::classic();
    if let Some(o) = k.batch(&candles)[80] {
        println!("KVO={o:.2}");
    }
    Ok(())
}

Python

import numpy as np
import wickra as ta

n = 120
base = 100 + np.sin(np.linspace(0, 25, n)) * 5
k = ta.Kvo(34, 55, 13)
out = k.batch(base + 1, base - 1, base + 0.3, np.full(n, 1000.0))
print(out[80])

Node

const wickra = require('wickra');
const k = new wickra.Kvo(34, 55, 13);
// feed h, l, c, v

Streaming

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

let mut k = Kvo::classic();
let mut prev: Option<f64> = None;
let candle_stream: Vec<wickra::Candle> = Vec::new(); // your live OHLCV candle feed
for bar in candle_stream {
    if let Some(v) = k.update(bar) {
        if let Some(p) = prev {
            if p <= 0.0 && v > 0.0 { /* bullish zero-line cross */ }
            if p >= 0.0 && v < 0.0 { /* bearish zero-line cross */ }
        }
        prev = Some(v);
    }
}

Interpretation

• KVO above zero. Buying pressure dominates short-term.
• Signal-line crossover. Klinger's canonical signal — KVO crossing above signal is bullish; below is bearish.
• Divergence detection. Like other volume oscillators, KVO divergences vs price flag exhaustion.

Common pitfalls

• Comparing to MACD scales. KVO operates on volume-weighted forces; its absolute magnitude depends on raw volume scale. Threshold-based systems need per-instrument calibration.
• Trend-flip surprise. The dm trend-detection resets the cumulative measure on flips, which can produce sharp KVO jumps.

References

• Stephen J. Klinger, Volume Oscillator, Technical Analysis of Stocks & Commodities, December 1997.

See also

• Obv — simpler cumulative volume.
• ChaikinOscillator — alternative ADL-based volume oscillator.
• MacdIndicator — closely-related topology.
• Indicators-Overview — full taxonomy.