Files
wickra/bindings/node
kingchenc 81962485af F9: add Accumulation/Distribution Line and Volume-Price Trend
Completes the F9 family (Cumulative volume) end to end:

- Rust core: adl.rs (Accumulation/Distribution Line — cumulative
  range-weighted volume) and vpt.rs (Volume-Price Trend — cumulative
  volume scaled by percentage price change). Each with a full Indicator
  impl, runnable doctest and reference / cumulative-property / warmup /
  reset / batch==streaming tests.
- Python: PyAdl / PyVolumePriceTrend PyO3 classes + module registration
  + .pyi stubs (no parameters, like OBV/VWAP).
- Node: explicit AdlNode and VolumePriceTrendNode; index.d.ts and
  index.js updated.
- WASM: WasmAdl and WasmVolumePriceTrend.
- Wiki: Indicator-Adl.md and Indicator-VolumePriceTrend.md plus rows in
  Indicators-Overview.md and entries in Home.md.

cargo fmt + clippy (core/wickra/data/wasm/node) clean; 373 core tests,
25 data tests and 53 doctests green.
2026-05-22 18:38:21 +02:00
..
2026-05-22 16:22:52 +02:00

@wickra/wickra

Node.js bindings for the Wickra streaming-first technical indicators library.

Install

Once published, install per platform via the precompiled native package:

npm install @wickra/wickra

Build from source

cd bindings/node
npm install
npm run build
npm test

The native module is built via napi-rs. The build script produces a wickra.<platform>-<arch>.node binary in the package root that index.js loads at runtime.

Usage

import { SMA, RSI, MACD, version } from '@wickra/wickra';

console.log('wickra', version());

// Batch:
const prices = Array.from({ length: 1000 }, (_, i) => 100 + Math.sin(i * 0.1) * 5);
const rsi = new RSI(14).batch(prices);

// Streaming:
const macd = new MACD(12, 26, 9);
for (const p of livePriceStream) {
  const v = macd.update(p);
  if (v && v.histogram > 0) console.log('bullish crossover candidate');
}

See index.d.ts for the full TypeScript surface.