Volume Profile

The full per-bin volume distribution over a rolling window — the raw histogram that Value Area reduces to POC / VAH / VAL.

Quick reference

Field	Value
Family	Market Profile
Input type	Candle (uses high, low, volume)
Output type	VolumeProfileOutput { price_low, price_high, bins }
Output range	bins[i] >= 0; sums to the window's total volume
Default parameters	VolumeProfile::classic() = 20-bar window, 50 bins
Warmup period	period
Interpretation	Where volume concentrated across price over the window.

Formula

Over the last period candles, the price domain spans the window's lowest low to its highest high, split into bin_count equal buckets of width w = (price_high - price_low) / bin_count:

bins[i] = Σ over candles of the volume attributed to bucket i

Each candle's volume is spread uniformly across the bins its [low, high] range touches (a single-print bar with low == high drops its whole volume into one bin) — the same bucketing convention as ValueArea. Where ValueArea collapses this distribution to the Point of Control and Value Area High/Low, VolumeProfile exposes the entire histogram for rendering or post-processing. See crates/wickra-core/src/indicators/volume_profile.rs.

Parameters

Name	Type	Default	Valid range	Description
period	usize	20 (classic)	>= 1	Rolling window length in candles. 0 errors with Error::PeriodZero.
bin_count	usize	50 (classic)	>= 1	Number of price buckets. 0 errors with Error::PeriodZero.

Inputs / Outputs

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

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

VolumeProfileOutput carries price_low: f64, price_high: f64 and bins: Vec<f64> (length bin_count, lowest bucket first).

Python update(candle) returns (price_low, price_high, bins_ndarray) or None during warmup; batch(high, low, volume) returns a (n, bin_count + 2) array whose columns are [price_low, price_high, bin_0, …] (warmup rows are NaN). Node update(high, low, volume) returns { priceLow, priceHigh, bins } or null; batch returns a flat Array<number> of n * (bin_count + 2).

Warmup

VolumeProfile::new(period, bin_count).warmup_period() == period. No value is emitted until the rolling window holds period candles. The unit test warms_up_over_period pins this.

Edge cases

• Single-print window. If every bar in the window trades at one price (price_high == price_low), the domain collapses and all volume lands in bin 0. Pinned by degenerate_single_price_window.
• Zero-volume bars. Bars with zero volume contribute nothing to the histogram. Pinned by zero_volume_bars_are_skipped.
• Volume conservation. The bins always sum to the window's total volume (within floating-point tolerance). Pinned by conserves_total_volume.
• Rolling eviction. When a new candle arrives the oldest leaves the window, so the price domain and histogram only reflect the last period candles. Pinned by rolling_window_drops_oldest.
• Reset. reset() clears the window and last output. Pinned by reset_clears_state.

Examples

Rust

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

fn flat(o: f64, h: f64, l: f64, c: f64, v: f64) -> Candle {
    Candle::new(o, h, l, c, v, 0).unwrap()
}

fn main() {
    let mut vp = VolumeProfile::new(2, 4).unwrap();
    vp.update(flat(10.0, 10.0, 10.0, 10.0, 100.0)); // seed (single print at 10)
    let out = vp.update(flat(10.0, 14.0, 10.0, 12.0, 80.0)).unwrap();
    println!("[{}, {}] {:?}", out.price_low, out.price_high, out.bins);
}

Output:

[10, 14] [120.0, 20.0, 20.0, 20.0]

Bar 0's 100 lands in bin 0 (price 10); bar 1 spans 10‑14 over all four bins, so its 80 splits 20 per bin → [120, 20, 20, 20]. This matches the reference_distribution unit test.

Python

import numpy as np
import wickra as ta

vp = ta.VolumeProfile(2, 4)
print(vp.update((10.0, 10.0, 10.0, 10.0, 100.0, 0)))  # None (warmup)
low, high, bins = vp.update((10.0, 14.0, 10.0, 12.0, 80.0, 1))
print(low, high, bins)

Output:

None
10.0 14.0 [120.  20.  20.  20.]

Node

const ta = require('wickra');
const vp = new ta.VolumeProfile(2, 4);
console.log(vp.update(10, 10, 100));        // null (warmup), args: high, low, volume
const out = vp.update(14, 10, 80);
console.log(out.priceLow, out.priceHigh, out.bins);

Output:

null
10 14 [ 120, 20, 20, 20 ]

Streaming vs batch (Python)

import numpy as np, wickra as ta

high = np.array([12.0, 13.0, 14.0, 15.0])
low = np.array([9.0, 10.0, 11.0, 12.0])
volume = np.array([30.0, 40.0, 50.0, 60.0])
profile = ta.VolumeProfile(4, 8).batch(high, low, volume)   # shape (4, 10)
assert profile.shape == (4, 8 + 2)

Interpretation

The histogram's tallest bin is the Point of Control — the price the market most agreed on over the window — and the contiguous bins around it that hold ~70% of volume are the Value Area. Use ValueArea when you only need those summary levels; reach for VolumeProfile when you want to render the full distribution, detect multi-modal (double-distribution) sessions, or compute custom value-area percentages yourself. Low-volume "gaps" between high-volume nodes often act as fast-travel zones for price.

Common pitfalls

• Reading bins without the price bounds. A bin index is only meaningful with price_low / price_high: bin i covers [price_low + i·w, price_low + (i+1)·w) where w = (price_high − price_low) / bin_count.
• Expecting a fixed price grid. The domain is recomputed each bar from the window's low/high, so bin boundaries shift as the window rolls — bins are not comparable across timestamps by index alone.
• Confusing it with TPO. Volume Profile weights by traded volume; TpoProfile counts time at price and ignores volume.

References

Market-profile volume distribution and the Point-of-Control / Value-Area concepts originate with J. Peter Steidlmayer's Market Profile work at the CBOT (1980s).

See also

• Indicator-ValueArea — POC / VAH / VAL from this distribution.
• Indicator-TpoProfile — the time-based (volume-agnostic) profile.
• Indicators-Overview — the full taxonomy.