kingchenc
780a176072
Completes the F2 family (Advanced MAs) end to end: - Rust core: zlema.rs (Zero-Lag EMA over the de-lagged series 2·price − price[lag]), t3.rs (Tillson's six-EMA cascade with the volume-factor polynomial), vwma.rs (volume-weighted rolling mean with a zero-volume fallback to the unweighted mean). Each with a full Indicator impl, runnable doctest and reference-value / warmup / reset / batch==streaming / non-finite tests. - Python: PyZlema / PyT3 / PyVwma PyO3 classes + module registration + .pyi stubs (T3 defaults v=0.7). - Node: ZlemaNode via the scalar macro, explicit T3Node and VwmaNode classes; index.d.ts and index.js updated. - WASM: WasmZlema / WasmT3 via the scalar macro, explicit WasmVwma. - Wiki: Indicator-Zlema.md, Indicator-T3.md, Indicator-Vwma.md plus rows in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 232 core tests, 25 data tests and 33 doctests green.
@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.