Files
wickra/docs/wiki/indicators/volume/Indicator-Adl.md
T
kingchenc d2f99efd78 F13c: restructure the indicator catalogue into eight families
The original taxonomy was four classical families plus a statistics group,
with the F1-F12 expansion slotted in as sub-categories. This regroups the
whole 71-indicator catalogue into eight top-level families, each with at
least five members:

  Moving Averages (12), Momentum Oscillators (13), Trend & Directional (9),
  Price Oscillators (5), Volatility & Bands (12), Trailing Stops (5),
  Volume (9), Price Statistics (7).

- Wiki: docs/wiki/indicators/ reorganised into eight family folders; all 71
  indicator pages moved with `git mv`. Every internal cross-link is
  normalised to `../<family>/Indicator-X.md`, each page's `Family` field is
  set to its new family, and two pre-existing `../Indicator-Chaining.md`
  links (should have been `../../`) are corrected. A link check confirms
  every relative wiki link resolves.
- Indicators-Overview.md fully rewritten around the eight families;
  Home.md indicator reference and the README family table follow suit.
- Warmup-Periods.md gains the eight F13 indicators; CHANGELOG records the
  46-indicator expansion (25 -> 71) and the eight-family taxonomy.
- Tests: Node indicators.test.js and Python test_new_indicators.py cover
  all eight new indicators (Node 91/91, Python 117/117 green).

cargo fmt + clippy (core/wickra/data/wasm/node) clean; 508 core tests,
25 data tests and 74 doctests green.
2026-05-22 21:21:56 +02:00

4.5 KiB
Raw Blame History

ADL

Accumulation/Distribution Line — a cumulative volume-flow line that weights each bar's volume by where its close fell within the range.

Quick reference

Field Value
Family Volume
Input type Candle (uses high, low, close, volume)
Output type f64
Output range unbounded (drifts with cumulative volume)
Default parameters none (no parameters)
Warmup period 1
Interpretation Running buying/selling pressure; slope and divergence matter.

Formula

MFM_t = ((close  low)  (high  close)) / (high  low)   (money-flow multiplier, 1..+1)
MFV_t = MFM_t · volume_t                                  (money-flow volume)
ADL_t = ADL_{t1} + MFV_t

The money-flow multiplier asks where in the bar's range did price close? A close on the high gives +1 (full accumulation), on the low 1 (full distribution), in the middle 0. Scaling by volume and running the cumulative total gives a line whose slope reflects sustained buying or selling pressure. A bar with high == low carries no positional information and contributes 0.

Parameters

ADL takes no parametersAdl::new() in Rust, wickra.ADL() in Python, new ta.ADL() in Node.

Inputs / Outputs

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

impl Indicator for Adl {
    type Input = Candle;
    type Output = f64;
    // update(&mut self, input: Candle) -> Option<f64>
}

ADL is a candle-input indicator: it reads high, low, close and volume. In Python the streaming update accepts a 6-tuple or a dict; the batch helper takes high, low, close, volume numpy arrays. Node and WASM expose update(high, low, close, volume) and the matching batch.

Warmup

Adl::new().warmup_period() == 1. ADL is cumulative — it emits a value from the very first candle.

Edge cases

  • Zero-range bar. A bar with high == low contributes 0 to the line (zero_range_bar_contributes_nothing pins this).
  • Close at the high. Every bar closing on its high has MFM = +1, so ADL grows by exactly volume each bar (close_at_high_accumulates_full_volume pins this).
  • Candle validation. Candle::new rejects invalid bars upstream.
  • Reset. adl.reset() returns the running total to 0.

Examples

Rust

use wickra::{BatchExt, Candle, Indicator, Adl};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut adl = Adl::new();
    let out = adl.batch(&[
        Candle::new(8.0, 10.0, 8.0, 10.0, 100.0, 0)?,  // close at high
        Candle::new(10.0, 12.0, 8.0, 9.0, 200.0, 1)?,
    ]);
    println!("{:?}", out);
    Ok(())
}

Output:

[Some(100.0), Some(0.0)]

Bar 1 closes at its high (MFM = +1), adding +100. Bar 2 has MFM = ((98)(129))/4 = 0.5, adding 100, so the line returns to 0. This matches the reference_values test in crates/wickra-core/src/indicators/adl.rs.

Python

import numpy as np
import wickra as ta

adl = ta.ADL()
high = np.array([10.0, 12.0])
low = np.array([8.0, 8.0])
close = np.array([10.0, 9.0])
volume = np.array([100.0, 200.0])
print(adl.batch(high, low, close, volume))

Output:

[100.   0.]

Node

const ta = require('wickra');
const adl = new ta.ADL();
console.log(adl.batch([10, 12], [8, 8], [10, 9], [100, 200]));

Output:

[ 100, 0 ]

Interpretation

Adl is read by slope and by divergence, never by absolute level (the total drifts arbitrarily with cumulative volume). A rising ADL confirms that an up-move is backed by accumulation; a falling ADL while price rises is a bearish divergence — the rally is not being bought into. ChaikinOscillator is the standard way to turn the ADL into a bounded, tradeable oscillator.

Common pitfalls

  • Reading the absolute value. Only the slope and divergences are meaningful; the level depends on where you started the stream.
  • Feeding it scalar prices. It needs the full OHLCV bar.

References

Marc Chaikin's Accumulation/Distribution Line; the money-flow-multiplier formulation here matches the standard definition (StockCharts, TA-Lib's AD).

See also