Accumulation/Distribution Line (AD)

Classic volume momentum classic accumulation distribution

A volume-based indicator designed to measure the cumulative flow of money into and out of a security.

Visual Example

Accumulation/Distribution Line (AD) — 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 volume-based indicator designed to measure the cumulative flow of money into and out of a security.

Use to confirm price trends or identify potential reversals through divergences. Rising AD confirms an uptrend; falling AD confirms a downtrend.

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

Developed by Marc Chaikin, the AD line uses the relationship between price and volume to determine whether a security is being accumulated or distributed. It is calculated by multiplying the Money Flow Multiplier by the period's volume and adding it to a cumulative total. — 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):

\[ \text{MFM} = \frac{(Close - Low) - (High - Close)}{High - Low} \\ \text{MFV} = \text{MFM} \times Volume \\ AD_t = AD_{t-1} + \text{MFV} \]

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

Parameters

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

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::AD;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import AD

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

Polars Batch (Python)

import polars as pl
import quantwave  # registers pl.col().ta

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("open").ta.ad("open", "high", "low", "close").alias("accumulation_distribution_line_ad")
    )
    .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/a/accumulationdistributioncurve.asp

Implementation: quantwave-core/src/indicators/volume.rs (AD / AD_METADATA). Parity: quantwave-core/tests/gold_standard/ad.json

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