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 2026-06-25 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

The Accumulation/Distribution Line (AD) indicator is a technical analysis tool that a volume-based indicator designed to measure the cumulative flow of money into and out of a security.

This indicator is primarily used for identifying key market conditions. It provides a robust signal that can be easily integrated into both simple strategies and more complex machine learning feature pipelines. Compared to its alternatives, it offers a distinct balance of responsiveness and stability.

Traders often combine this with other metrics to confirm signals and avoid false positives during sideways market regimes. It remains a standard tool for systematic trading models.

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

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

QuantWave implements this indicator via the universal Next<T> trait, guaranteeing bit-identical results between Rust streaming, Python streaming, and Polars batch (.ta() / map_batches) surfaces.

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

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	Output starts from bar 1; warmup_bars marks period-stability, not NaN.
period > len	Cumulative sum continues; period only affects smoothed variants.
NaN inputs	NaN inputs may produce NaN or skip depending on indicator.
Invalid params	Invalid 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 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.