kingchenc
2d0ee926c5
- Rust core: typical_price.rs ((H+L+C)/3), median_price.rs ((H+L)/2), weighted_close.rs ((H+L+2C)/4) — stateless per-bar OHLC transforms — and linreg.rs (LinearRegression — endpoint of a rolling ordinary-least-squares fit) and linreg_slope.rs (LinRegSlope — slope of that fit). Each with a full Indicator impl, runnable doctest and reference / property / warmup / reset / batch==streaming tests. - Python: PyTypicalPrice / PyMedianPrice / PyWeightedClose / PyLinearRegression / PyLinRegSlope PyO3 classes + module registration + .pyi stubs. - Node: explicit TypicalPriceNode / MedianPriceNode / WeightedCloseNode / LinearRegressionNode / LinRegSlopeNode; index.d.ts and index.js updated. - WASM: explicit WasmTypicalPrice / WasmMedianPrice / WasmWeightedClose; WasmLinearRegression / WasmLinRegSlope via the scalar macro. - Wiki: a new indicators/statistics/ folder with five Indicator-*.md pages, a new "Statistics" family in Indicators-Overview.md and Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 454 core tests, 25 data tests and 66 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.