On-Balance Volume (OBV)

Classic volume momentum classic accumulation distribution

A momentum indicator that uses volume flow to predict changes in stock price.

Visual Example

On-Balance Volume (OBV) — annotated preview mapping to core implementation

Synthetic ideal per library logic. Generated 2026-07-01 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

A momentum indicator that uses volume flow to predict changes in stock price.

Use to identify accumulation by institutions. When price is flat but OBV is rising, a breakout to the upside is likely. Conversely, when price is flat but OBV is falling, a breakdown is likely.

Native Rust implementation with gold-standard or TA-Lib parity tests where applicable.

Introduced by Joe Granville in his 1963 book 'Granville's New Key to Stock Market Profits', OBV is one of the oldest and most respected volume indicators. It operates on the principle that volume precedes price, and that institutional money flow leaves a detectable trail in the volume data before the price move occurs. — StockCharts ChartSchool

Typical applications:

Fade extremes in ranges; trade with trend on recoveries from oversold/overbought
Use divergences as early warning — confirm with structure or volume
Parameter default N — shorten for sensitivity, lengthen for stability
Drop into build_feature_matrix() for ML research

QuantWave implements this via the universal Next<T> trait — bit-identical across Rust streaming, Python streaming, and Polars .ta() batch plugins.

Formula / Specification

Implementation (quantwave-core/src/indicators/volume.rs):

\[ OBV_t = OBV_{t-1} + \begin{cases} Volume & \text{if } Close_t > Close_{t-1} \\ 0 & \text{if } Close_t = Close_{t-1} \\ -Volume & \text{if } Close_t < Close_{t-1} \end{cases} \]

Gold-standard parity vectors: quantwave-core/tests/gold_standard/obv.json.

Parameters

Parameter	Default	Description
(none)	—	No tunable parameters for this detector.

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::OBV;
use quantwave_core::traits::Next;

let mut ind = OBV::new(14);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import OBV

ind = OBV(14)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_on_balance_volume_obv(series: pl.Series) -> pl.Series:
    ind = qw.OBV(14)
    return pl.Series([ind.next(float(v)) for v in series.to_list()])

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("close").map_batches(apply_on_balance_volume_obv, return_dtype=pl.Float64).alias("on_balance_volume_obv")
    )
    .collect()
)

All surfaces are bit-identical via the single Next<T> implementation and proptests.

Edge Cases & Limitations

Warm-up: first N bars may return NaN or partial state per implementation.
Parameter sensitivity: smaller periods increase noise; larger periods increase lag.
Sudden gaps or bad ticks can distort rolling windows — consider pre-filtering.
Single-series indicators ignore volume unless otherwise documented.
Validated via proptests against gold-standard vectors where available.
No look-ahead bias; streaming and Polars batch paths are bit-identical.

Boundary Behavior

Condition	Behavior
Warm-up	Leading bars return NaN until warmup_bars is satisfied.
period > len	When period exceeds series length, output is all NaN.
NaN inputs	NaN in input propagates to output (NaN out).
Invalid params	Non-positive period or missing required params raise ValueError.
Empty data	Empty input returns an empty result series.

Sources & References

Primary Source: https://www.investopedia.com/terms/o/onbalancevolume.asp

Implementation: quantwave-core/src/indicators/volume.rs (OBV / OBV_METADATA). Parity: quantwave-core/tests/gold_standard/obv.json

Provenance: Standards bulk upgrade 2026-07-01 IST — see docs/DOCUMENTATION_STANDARDS.md.