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.
4.5 KiB
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_{t−1} + 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 parameters — Adl::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 == lowcontributes0to the line (zero_range_bar_contributes_nothingpins this). - Close at the high. Every bar closing on its high has
MFM = +1, so ADL grows by exactlyvolumeeach bar (close_at_high_accumulates_full_volumepins this). - Candle validation.
Candle::newrejects invalid bars upstream. - Reset.
adl.reset()returns the running total to0.
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 = ((9−8)−(12−9))/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
- Indicator-Obv.md — cumulative signed volume.
- Indicator-ChaikinOscillator.md — an oscillator built on the ADL.
- Indicators-Overview.md — the full taxonomy.