Volume-by-Time Profile

Mean traded volume in each intraday time bucket. Unlike the return profiles, the first bar already produces output (volume needs no prior bar).

Quick reference

Item	Value
Family	Seasonality & Session
Input type	Candle (uses volume, timestamp)
Output type	VolumeByTimeProfileOutput { bins } (length buckets)
Output range	>= 0 per bin
Default parameters	buckets = 24, utc_offset_minutes = 0
Warmup period	1
Interpretation	Intraday liquidity / participation curve

Formula

bucket = floor(minute_of_day * buckets / 1440)   (clamped to buckets-1)
bins[b] = mean volume over bars whose bucket == b   (0.0 if empty)

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

Parameters

Name	Type	Default	Constraint	Source	Description
buckets	usize	24	> 0	volume_by_time_profile.rs:39	Number of intraday slices.
utc_offset_minutes	i32	0	none	volume_by_time_profile.rs:40	Shifts the instant before bucketing.

VolumeByTimeProfile::new returns Error::PeriodZero for buckets == 0.

Inputs / Outputs

const _: fn(&mut wickra::VolumeByTimeProfile, wickra::Candle) -> Option<wickra::VolumeByTimeProfileOutput> =
    <wickra::VolumeByTimeProfile as wickra::Indicator>::update;

• Python. update((o,h,l,c,v,ts)) → ndarray of length buckets; batch(...) → (n, buckets) array.
• Node. update(...) → number[]; batch(...) → flat number[] length n*buckets.
• WASM. update(...) → Float64Array.

Warmup

warmup_period() == 1 — output is produced from the first bar.

Edge cases

• Emits from the first bar and means the volume (test emits_from_first_bar_and_means_volume).
• Last bucket clamped for a 23:59 bar (test last_bucket_clamped).
• Rejects buckets == 0 (test rejects_zero_buckets).
• Reset clears all buckets (test reset_clears_state).

Examples

Rust

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

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let hour = 3_600_000;
    let mut prof = VolumeByTimeProfile::new(24, 0)?;
    let out = prof.update(Candle::new(100.0, 100.0, 100.0, 100.0, 500.0, hour)?).unwrap();
    println!("{}", out.bins[1]); // 500 (01:00 bucket)
    Ok(())
}

Python

import wickra as ta

HOUR = 3_600_000
prof = ta.VolumeByTimeProfile(24, 0)
out = prof.update((100.0, 100.0, 100.0, 100.0, 500.0, HOUR))
print(out[1])  # 500.0

Node

const wickra = require('wickra');
const HOUR = 3_600_000;
const prof = new wickra.VolumeByTimeProfile(24, 0);
console.log(prof.update(100, 100, 100, 100, 500, HOUR)[1]); // 500

Streaming

prof = ta.VolumeByTimeProfile(48, -300)  # half-hourly, US Eastern
for o, h, l, c, v, ts in candle_feed:
    curve = prof.update((o, h, l, c, v, ts))
    # curve[b] is the mean volume for intraday slice b

Interpretation

1. Liquidity scheduling. The volume curve (typically a "U" — heavy at the open and close) tells execution algorithms when to be aggressive vs patient.
2. VWAP scheduling. A VWAP-execution schedule weights child orders by the expected bucket volume from this profile.

Common pitfalls

• Volume units. Means inherit whatever volume unit the candles carry (shares, contracts, base/quote) — keep it consistent across the feed.
• Bucket count vs bar interval. Match buckets to the bar interval so each bucket aggregates a meaningful number of bars.

See also

• VolumeProfile — volume by price, not time.
• TimeOfDayReturnProfile — return by time.
• Vwap — volume-weighted average price.
• Indicators-Overview — full taxonomy.