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

* 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%.

bindings/wasm/src/lib.rs

@@ -227,6 +227,16 @@ wasm_scalar_indicator!(WasmLinRegSlope, "LinRegSlope", wc::LinRegSlope, period:
 wasm_scalar_indicator!(WasmVerticalHorizontalFilter, "VerticalHorizontalFilter", wc::VerticalHorizontalFilter, period: usize);
 wasm_scalar_indicator!(WasmZScore, "ZScore", wc::ZScore, period: usize);
 wasm_scalar_indicator!(WasmLinRegAngle, "LinRegAngle", wc::LinRegAngle, period: usize);
+wasm_scalar_indicator!(WasmVariance, "Variance", wc::Variance, period: usize);
+wasm_scalar_indicator!(WasmCoefficientOfVariation, "CoefficientOfVariation", wc::CoefficientOfVariation, period: usize);
+wasm_scalar_indicator!(WasmSkewness, "Skewness", wc::Skewness, period: usize);
+wasm_scalar_indicator!(WasmKurtosis, "Kurtosis", wc::Kurtosis, period: usize);
+wasm_scalar_indicator!(WasmStandardError, "StandardError", wc::StandardError, period: usize);
+wasm_scalar_indicator!(WasmDetrendedStdDev, "DetrendedStdDev", wc::DetrendedStdDev, period: usize);
+wasm_scalar_indicator!(WasmRSquared, "RSquared", wc::RSquared, period: usize);
+wasm_scalar_indicator!(WasmMedianAbsoluteDeviation, "MedianAbsoluteDeviation", wc::MedianAbsoluteDeviation, period: usize);
+wasm_scalar_indicator!(WasmAutocorrelation, "Autocorrelation", wc::Autocorrelation, period: usize, lag: usize);
+wasm_scalar_indicator!(WasmHurstExponent, "HurstExponent", wc::HurstExponent, period: usize, chunks: usize);
 wasm_scalar_indicator!(WasmRviVolatility, "RVIVolatility", wc::RviVolatility, period: usize);
 wasm_scalar_indicator!(WasmLaguerreRsi, "LaguerreRSI", wc::LaguerreRsi, gamma: f64);
 wasm_scalar_indicator!(WasmConnorsRsi, "ConnorsRSI", wc::ConnorsRsi, period_rsi: usize, period_streak: usize, period_rank: usize);
@@ -462,6 +472,65 @@ wasm_scalar_indicator!(WasmEhlersStochastic, "EhlersStochastic", wc::EhlersStoch
 wasm_scalar_indicator!(WasmEmpiricalModeDecomposition, "EmpiricalModeDecomposition", wc::EmpiricalModeDecomposition, period: usize, fraction: f64);
 wasm_scalar_indicator!(WasmFama, "FAMA", wc::Fama, fast_limit: f64, slow_limit: f64);
 
+// ---------- Family 12: Two-series indicators (Pearson / Beta / Spearman) ----------
+
+macro_rules! wasm_pair_indicator {
+    ($name:ident, $js_name:literal, $rust_ty:ty) => {
+        #[wasm_bindgen(js_name = $js_name)]
+        pub struct $name {
+            inner: $rust_ty,
+        }
+
+        #[wasm_bindgen(js_class = $js_name)]
+        impl $name {
+            #[wasm_bindgen(constructor)]
+            pub fn new(period: usize) -> Result<$name, JsError> {
+                Ok($name {
+                    inner: <$rust_ty>::new(period).map_err(map_err)?,
+                })
+            }
+            pub fn update(&mut self, x: f64, y: f64) -> Option<f64> {
+                self.inner.update((x, y))
+            }
+            /// Batch over two equally-sized arrays. Returns one `f64` per
+            /// input position (`NaN` during warmup).
+            pub fn batch(&mut self, x: &[f64], y: &[f64]) -> Result<Float64Array, JsError> {
+                if x.len() != y.len() {
+                    return Err(JsError::new("x and y must be equal length"));
+                }
+                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(Float64Array::from(out.as_slice()))
+            }
+            pub fn reset(&mut self) {
+                self.inner.reset();
+            }
+            #[wasm_bindgen(js_name = isReady)]
+            pub fn is_ready(&self) -> bool {
+                self.inner.is_ready()
+            }
+            #[wasm_bindgen(js_name = warmupPeriod)]
+            pub fn warmup_period(&self) -> usize {
+                self.inner.warmup_period()
+            }
+        }
+    };
+}
+
+wasm_pair_indicator!(
+    WasmPearsonCorrelation,
+    "PearsonCorrelation",
+    wc::PearsonCorrelation
+);
+wasm_pair_indicator!(WasmBeta, "Beta", wc::Beta);
+wasm_pair_indicator!(
+    WasmSpearmanCorrelation,
+    "SpearmanCorrelation",
+    wc::SpearmanCorrelation
+);
+
 // ---------- KAMA (three params) ----------
 
 #[wasm_bindgen(js_name = KAMA)]