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F8: add Bollinger Bandwidth and %b

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Completes the F8 family (Bands & channels) end to end:

- Rust core: bollinger_bandwidth.rs ((upper - lower) / middle — the
  squeeze gauge) and percent_b.rs ((price - lower) / (upper - lower) —
  price position within the bands, unclamped). Both wrap BollingerBands
  and carry a full Indicator impl, runnable doctest and reference /
  constant-series / definition-consistency / warmup / reset /
  batch==streaming tests.
- Python: PyBollingerBandwidth / PyPercentB PyO3 classes + module
  registration + .pyi stubs (defaults (20, 2.0)).
- Node: explicit BollingerBandwidthNode and PercentBNode; index.d.ts
  and index.js updated.
- WASM: WasmBollingerBandwidth / WasmPercentB via the scalar macro.
- Wiki: Indicator-BollingerBandwidth.md and Indicator-PercentB.md plus
  rows in Indicators-Overview.md and entries in Home.md.

cargo fmt + clippy (core/wickra/data/wasm/node) clean; 362 core tests,
25 data tests and 51 doctests green.
This commit is contained in:
kingchenc
2026-05-22 18:30:49 +02:00
parent 6c58d3827c
commit 99dd144576
13 changed files with 907 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files
bindings/node/index.js
+3 -1
View File
@@ -310,7 +310,7 @@ if (!nativeBinding) {
throw new Error(`Failed to load native binding`)
}
const { version, SMA, EMA, WMA, RSI, DEMA, TEMA, HMA, ROC, TRIX, SMMA, TRIMA, ZLEMA, T3, VWMA, MOM, CMO, TSI, PMO, StochRSI, UltimateOscillator, PPO, DPO, Coppock, AroonOscillator, Vortex, MassIndex, NATR, StdDev, UlcerIndex, HistoricalVolatility, MACD, BollingerBands, ATR, Stochastic, OBV, ADX, CCI, WilliamsR, MFI, PSAR, Keltner, Donchian, VWAP, AwesomeOscillator, Aroon, KAMA } = nativeBinding
const { version, SMA, EMA, WMA, RSI, DEMA, TEMA, HMA, ROC, TRIX, SMMA, TRIMA, ZLEMA, T3, VWMA, MOM, CMO, TSI, PMO, StochRSI, UltimateOscillator, PPO, DPO, Coppock, AroonOscillator, Vortex, MassIndex, NATR, StdDev, UlcerIndex, HistoricalVolatility, BollingerBandwidth, PercentB, MACD, BollingerBands, ATR, Stochastic, OBV, ADX, CCI, WilliamsR, MFI, PSAR, Keltner, Donchian, VWAP, AwesomeOscillator, Aroon, KAMA } = nativeBinding
module.exports.version = version
module.exports.SMA = SMA
@@ -343,6 +343,8 @@ module.exports.NATR = NATR
module.exports.StdDev = StdDev
module.exports.UlcerIndex = UlcerIndex
module.exports.HistoricalVolatility = HistoricalVolatility
module.exports.BollingerBandwidth = BollingerBandwidth
module.exports.PercentB = PercentB
module.exports.MACD = MACD
module.exports.BollingerBands = BollingerBands
module.exports.ATR = ATR
bindings/node/src/lib.rs
+74
View File
@@ -1147,6 +1147,80 @@ impl PmoNode {
// ============================== VWMA ==============================
// ============================== Bollinger Bandwidth ==============================
#[napi(js_name = "BollingerBandwidth")]
pub struct BollingerBandwidthNode {
inner: wc::BollingerBandwidth,
}
#[napi]
impl BollingerBandwidthNode {
#[napi(constructor)]
pub fn new(period: u32, multiplier: f64) -> napi::Result<Self> {
Ok(Self {
inner: wc::BollingerBandwidth::new(period as usize, multiplier).map_err(map_err)?,
})
}
#[napi]
pub fn update(&mut self, value: f64) -> Option<f64> {
self.inner.update(value)
}
#[napi]
pub fn batch(&mut self, prices: Vec<f64>) -> Vec<f64> {
flatten(self.inner.batch(&prices))
}
#[napi]
pub fn reset(&mut self) {
self.inner.reset();
}
#[napi(js_name = "isReady")]
pub fn is_ready(&self) -> bool {
self.inner.is_ready()
}
#[napi(js_name = "warmupPeriod")]
pub fn warmup_period(&self) -> u32 {
self.inner.warmup_period() as u32
}
}
// ============================== Percent B ==============================
#[napi(js_name = "PercentB")]
pub struct PercentBNode {
inner: wc::PercentB,
}
#[napi]
impl PercentBNode {
#[napi(constructor)]
pub fn new(period: u32, multiplier: f64) -> napi::Result<Self> {
Ok(Self {
inner: wc::PercentB::new(period as usize, multiplier).map_err(map_err)?,
})
}
#[napi]
pub fn update(&mut self, value: f64) -> Option<f64> {
self.inner.update(value)
}
#[napi]
pub fn batch(&mut self, prices: Vec<f64>) -> Vec<f64> {
flatten(self.inner.batch(&prices))
}
#[napi]
pub fn reset(&mut self) {
self.inner.reset();
}
#[napi(js_name = "isReady")]
pub fn is_ready(&self) -> bool {
self.inner.is_ready()
}
#[napi(js_name = "warmupPeriod")]
pub fn warmup_period(&self) -> u32 {
self.inner.warmup_period() as u32
}
}
// ============================== NATR ==============================
#[napi(js_name = "NATR")]
bindings/python/python/wickra/__init__.pyi
+28
View File
@@ -76,6 +76,34 @@ class TRIMA:
@property
def value(self) -> Optional[float]: ...
class BollingerBandwidth:
def __init__(self, period: int = 20, multiplier: float = 2.0) -> None: ...
def update(self, value: float) -> Optional[float]: ...
def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
def reset(self) -> None: ...
def is_ready(self) -> bool: ...
def warmup_period(self) -> int: ...
@property
def period(self) -> int: ...
@property
def multiplier(self) -> float: ...
@property
def value(self) -> Optional[float]: ...
class PercentB:
def __init__(self, period: int = 20, multiplier: float = 2.0) -> None: ...
def update(self, value: float) -> Optional[float]: ...
def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
def reset(self) -> None: ...
def is_ready(self) -> bool: ...
def warmup_period(self) -> int: ...
@property
def period(self) -> int: ...
@property
def multiplier(self) -> float: ...
@property
def value(self) -> Optional[float]: ...
class NATR:
def __init__(self, period: int = 14) -> None: ...
def update(self, candle: CandleLike) -> Optional[float]: ...
bindings/python/src/lib.rs
+122
View File
@@ -1519,6 +1519,126 @@ impl PyAroon {
}
}
// ============================== Bollinger Bandwidth ==============================
#[pyclass(name = "BollingerBandwidth", module = "wickra._wickra")]
#[derive(Clone)]
struct PyBollingerBandwidth {
inner: wc::BollingerBandwidth,
}
#[pymethods]
impl PyBollingerBandwidth {
#[new]
#[pyo3(signature = (period=20, multiplier=2.0))]
fn new(period: usize, multiplier: f64) -> PyResult<Self> {
Ok(Self {
inner: wc::BollingerBandwidth::new(period, multiplier).map_err(map_err)?,
})
}
fn update(&mut self, value: f64) -> Option<f64> {
self.inner.update(value)
}
fn batch<'py>(
&mut self,
py: Python<'py>,
prices: PyReadonlyArray1<'py, f64>,
) -> PyResult<Bound<'py, PyArray1<f64>>> {
let slice = prices
.as_slice()
.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
Ok(flatten(self.inner.batch(slice)).into_pyarray_bound(py))
}
#[getter]
fn period(&self) -> usize {
self.inner.period()
}
#[getter]
fn multiplier(&self) -> f64 {
self.inner.multiplier()
}
#[getter]
fn value(&self) -> Option<f64> {
self.inner.value()
}
fn reset(&mut self) {
self.inner.reset();
}
fn is_ready(&self) -> bool {
self.inner.is_ready()
}
fn warmup_period(&self) -> usize {
self.inner.warmup_period()
}
fn __repr__(&self) -> String {
format!(
"BollingerBandwidth(period={}, multiplier={})",
self.inner.period(),
self.inner.multiplier()
)
}
}
// ============================== Percent B ==============================
#[pyclass(name = "PercentB", module = "wickra._wickra")]
#[derive(Clone)]
struct PyPercentB {
inner: wc::PercentB,
}
#[pymethods]
impl PyPercentB {
#[new]
#[pyo3(signature = (period=20, multiplier=2.0))]
fn new(period: usize, multiplier: f64) -> PyResult<Self> {
Ok(Self {
inner: wc::PercentB::new(period, multiplier).map_err(map_err)?,
})
}
fn update(&mut self, value: f64) -> Option<f64> {
self.inner.update(value)
}
fn batch<'py>(
&mut self,
py: Python<'py>,
prices: PyReadonlyArray1<'py, f64>,
) -> PyResult<Bound<'py, PyArray1<f64>>> {
let slice = prices
.as_slice()
.map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
Ok(flatten(self.inner.batch(slice)).into_pyarray_bound(py))
}
#[getter]
fn period(&self) -> usize {
self.inner.period()
}
#[getter]
fn multiplier(&self) -> f64 {
self.inner.multiplier()
}
#[getter]
fn value(&self) -> Option<f64> {
self.inner.value()
}
fn reset(&mut self) {
self.inner.reset();
}
fn is_ready(&self) -> bool {
self.inner.is_ready()
}
fn warmup_period(&self) -> usize {
self.inner.warmup_period()
}
fn __repr__(&self) -> String {
format!(
"PercentB(period={}, multiplier={})",
self.inner.period(),
self.inner.multiplier()
)
}
}
// ============================== NATR ==============================
#[pyclass(name = "NATR", module = "wickra._wickra")]
@@ -2789,5 +2909,7 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
m.add_class::<PyStdDev>()?;
m.add_class::<PyUlcerIndex>()?;
m.add_class::<PyHistoricalVolatility>()?;
m.add_class::<PyBollingerBandwidth>()?;
m.add_class::<PyPercentB>()?;
Ok(())
}
bindings/wasm/src/lib.rs
+2
View File
@@ -90,6 +90,8 @@ wasm_scalar_indicator!(WasmCoppock, "Coppock", wc::Coppock, roc_long: usize, roc
wasm_scalar_indicator!(WasmStdDev, "StdDev", wc::StdDev, period: usize);
wasm_scalar_indicator!(WasmUlcerIndex, "UlcerIndex", wc::UlcerIndex, period: usize);
wasm_scalar_indicator!(WasmHistoricalVolatility, "HistoricalVolatility", wc::HistoricalVolatility, period: usize, trading_periods: usize);
wasm_scalar_indicator!(WasmBollingerBandwidth, "BollingerBandwidth", wc::BollingerBandwidth, period: usize, multiplier: f64);
wasm_scalar_indicator!(WasmPercentB, "PercentB", wc::PercentB, period: usize, multiplier: f64);
// ---------- KAMA (three params) ----------