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feat(family-12): add 13 Statistik/Regression indicators (#51)

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* feat(family-12): add 13 Statistik/Regression indicators

Brings the Price Statistics family to 20 indicators (7 → 20) and the
total catalogue to 84 (71 → 84). Every indicator ships in the Rust
core plus Python, Node, and WASM bindings with full streaming ↔ batch
parity, fuzz coverage, and benches.

Scalar (f64 → f64):
- Variance, CoefficientOfVariation: rolling population variance and
  its dimensionless ratio with the mean. O(1) updates.
- Skewness, Kurtosis: rolling Pearson skewness and excess kurtosis,
  derived from running sums of x, x², x³, x⁴ via the binomial
  identities — also O(1) per bar.
- StandardError, DetrendedStdDev: standard error of estimate (n − 2)
  and population StdDev (n) of OLS residuals, sharing the LinReg
  O(1) sliding sums.
- RSquared: coefficient of determination of the rolling OLS fit; the
  trend-quality filter, clamped to [0, 1].
- MedianAbsoluteDeviation: robust dispersion estimator; O(period log
  period) per emission via two in-place sorts of a reusable scratch
  buffer.
- Autocorrelation(period, lag): rolling lag-k Pearson autocorrelation.
- HurstExponent(period, chunks): R/S-analysis trend-persistence
  estimator clamped to [0, 1].

Pair indicators (Input = (f64, f64)):
- PearsonCorrelation: rolling cross-series Pearson, O(1).
- Beta: rolling OLS slope of asset vs. benchmark (CAPM).
- SpearmanCorrelation: rolling rank correlation with mid-rank tie
  handling; O(period log period).

Touchpoints:
- crates/wickra-core: 13 new indicator modules + mod.rs / lib.rs
  re-exports.
- bindings/python: pyclasses + add_class registration + __init__.py
  import & __all__ updates. The pair indicators expose
  update(x, y) and batch(x, y) over two equally-sized numpy arrays.
- bindings/node: scalar indicators via node_scalar_indicator! macro;
  pair indicators via new node_pair_indicator! macro; explicit
  structs for Autocorrelation and HurstExponent (two-arg ctors).
  index.js extended with the new exports.
- bindings/wasm: scalar wrappers via wasm_scalar_indicator!; pair
  wrappers via new wasm_pair_indicator! macro.
- fuzz: every scalar drove through the generic helper; pair
  indicators stress-tested by pairing adjacent samples of the fuzz
  input.
- Python tests (test_new_indicators.py): added to SCALAR
  parametrisation, plus algebraic reference values
  (variance of [2,4,6] = 8/3, MAD ignoring outlier = 0, monotone
  non-linear Spearman = 1, two-to-one Beta = 2, etc.) and a
  streaming-vs-batch test for the pair indicators.
- Node tests (indicators.test.js): extended the scalar factories
  map and added a pair-indicator section with the same algebraic
  reference values.
- crates/wickra/benches: bench_scalar entries for all 10 single-
  input new indicators.
- README: counter 71 → 84; Price Statistics family-table row
  expanded with the 13 new indicators.
- CHANGELOG: Unreleased section documents the family addition.

Wiki drafts (ghost-ignored, manual sync to wickra.wiki at release
time): indicator-ideas/families/wiki/family-12-statistik-regression/
contains 13 deep-dive pages plus _Sidebar / Indicators-Overview /
Warmup-Periods / Home fragments for the curator merge.

cargo check --workspace --all-features: clean.

* fix(family-12): remove unreachable defensive guards in hurst_exponent

The three guards (m < 2 continue, end > buf.len() break, denom == 0.0
return) are by-construction unreachable given the constructor invariant
period >= 2 * chunks: m = period / k for k in 1..=chunks always
satisfies m >= 2 and end = (c+1) * m <= k * m <= period = buf.len(),
and m_1 = period and m_2 = period / 2 are always distinct so the slope
denominator is strictly positive. Removing them brings codecov/patch
back to 100%.
This commit is contained in:
kingchenc
2026-05-25 23:42:05 +02:00
committed by GitHub
parent 5aa0949bce
commit 05fcdd9a5e
26 changed files with 4303 additions and 42 deletions
Download Patch File Download Diff File Expand all files Collapse all files
bindings/node/src/lib.rs
+156
View File
@@ -177,6 +177,162 @@ impl RviVolatilityNode {
}
}
node_scalar_indicator!(VarianceNode, "Variance", wc::Variance);
node_scalar_indicator!(
CoefficientOfVariationNode,
"CoefficientOfVariation",
wc::CoefficientOfVariation
);
node_scalar_indicator!(SkewnessNode, "Skewness", wc::Skewness);
node_scalar_indicator!(KurtosisNode, "Kurtosis", wc::Kurtosis);
node_scalar_indicator!(StandardErrorNode, "StandardError", wc::StandardError);
node_scalar_indicator!(DetrendedStdDevNode, "DetrendedStdDev", wc::DetrendedStdDev);
node_scalar_indicator!(RSquaredNode, "RSquared", wc::RSquared);
node_scalar_indicator!(
MedianAbsoluteDeviationNode,
"MedianAbsoluteDeviation",
wc::MedianAbsoluteDeviation
);
// ============================== Autocorrelation (period + lag) ==============================
#[napi(js_name = "Autocorrelation")]
pub struct AutocorrelationNode {
inner: wc::Autocorrelation,
}
#[napi]
impl AutocorrelationNode {
#[napi(constructor)]
pub fn new(period: u32, lag: u32) -> napi::Result<Self> {
Ok(Self {
inner: wc::Autocorrelation::new(period as usize, lag as usize).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
}
}
// ============================== HurstExponent (period + chunks) ==============================
#[napi(js_name = "HurstExponent")]
pub struct HurstExponentNode {
inner: wc::HurstExponent,
}
#[napi]
impl HurstExponentNode {
#[napi(constructor)]
pub fn new(period: u32, chunks: u32) -> napi::Result<Self> {
Ok(Self {
inner: wc::HurstExponent::new(period as usize, chunks as usize).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
}
}
// ============================== Two-series indicators (Pearson / Beta / Spearman) ==============================
macro_rules! node_pair_indicator {
($wrapper:ident, $node_name:literal, $rust_ty:ty) => {
#[napi(js_name = $node_name)]
pub struct $wrapper {
inner: $rust_ty,
}
#[napi]
impl $wrapper {
#[napi(constructor)]
pub fn new(period: u32) -> napi::Result<Self> {
Ok(Self {
inner: <$rust_ty>::new(period as usize).map_err(map_err)?,
})
}
#[napi]
pub fn update(&mut self, x: f64, y: f64) -> Option<f64> {
self.inner.update((x, y))
}
/// Batch over two equally-sized arrays. Returns a length-`n` array
/// with `NaN` for warmup positions.
#[napi]
pub fn batch(&mut self, x: Vec<f64>, y: Vec<f64>) -> napi::Result<Vec<f64>> {
if x.len() != y.len() {
return Err(NapiError::new(
Status::InvalidArg,
"x and y must be equal length".to_string(),
));
}
let mut out = Vec::with_capacity(x.len());
for i in 0..x.len() {
out.push(self.inner.update((x[i], y[i])).unwrap_or(f64::NAN));
}
Ok(out)
}
#[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
}
}
};
}
node_pair_indicator!(
PearsonCorrelationNode,
"PearsonCorrelation",
wc::PearsonCorrelation
);
node_pair_indicator!(BetaNode, "Beta", wc::Beta);
node_pair_indicator!(
SpearmanCorrelationNode,
"SpearmanCorrelation",
wc::SpearmanCorrelation
);
// ============================== MACD ==============================
/// MACD triple: macd line, signal line, histogram.