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%.

fuzz/fuzz_targets/indicator_update.rs

@@ -15,16 +15,18 @@
 
 use libfuzzer_sys::fuzz_target;
 use wickra_core::{
-    AdaptiveCycle, Alma, Apo, BatchExt, BollingerBands, CenterOfGravity, Cfo, Cmo, ConnorsRsi,
-    Coppock, CyberneticCycle, Decycler, DecyclerOscillator, Dema, DoubleBollinger, Dpo,
-    EhlersStochastic, ElderImpulse, Ema, EmpiricalModeDecomposition, Fama, FisherTransform,
-    Frama, HilbertDominantCycle, HistoricalVolatility, Hma, Indicator, InstantaneousTrendline,
-    InverseFisherTransform, Jma, Kama, Kst, LaguerreRsi, LinRegAngle, LinRegChannel,
-    LinRegSlope, LinearRegression, MaEnvelope, MacdIndicator, Mama, McGinleyDynamic, Mom,
-    PercentageTrailingStop, Pmo, Ppo, RenkoTrailingStop, Roc, RoofingFilter, Rsi, RviVolatility,
-    SineWave, Sma, Smma, StandardErrorBands, Stc, StdDev, StepTrailingStop, StochRsi,
-    SuperSmoother, T3, Tema, Tii, Trima, Trix, Tsi, UlcerIndex, VerticalHorizontalFilter, Vidya,
-    Wma, ZScore, ZeroLagMacd, Zlema,
+    AdaptiveCycle, Alma, Apo, Autocorrelation, BatchExt, Beta, BollingerBands, CenterOfGravity,
+    Cfo, Cmo, CoefficientOfVariation, ConnorsRsi, Coppock, CyberneticCycle, Decycler,
+    DecyclerOscillator, Dema, DetrendedStdDev, DoubleBollinger, Dpo, EhlersStochastic,
+    ElderImpulse, Ema, EmpiricalModeDecomposition, Fama, FisherTransform, Frama,
+    HilbertDominantCycle, HistoricalVolatility, Hma, HurstExponent, Indicator,
+    InstantaneousTrendline, InverseFisherTransform, Jma, Kama, Kst, Kurtosis, LaguerreRsi,
+    LinRegAngle, LinRegChannel, LinRegSlope, LinearRegression, MaEnvelope, MacdIndicator, Mama,
+    McGinleyDynamic, MedianAbsoluteDeviation, Mom, PearsonCorrelation, PercentageTrailingStop,
+    Pmo, Ppo, RSquared, RenkoTrailingStop, Roc, RoofingFilter, Rsi, RviVolatility, SineWave,
+    Skewness, Sma, Smma, SpearmanCorrelation, StandardError, StandardErrorBands, Stc, StdDev,
+    StepTrailingStop, StochRsi, SuperSmoother, T3, Tema, Tii, Trima, Trix, Tsi, UlcerIndex,
+    Variance, VerticalHorizontalFilter, Vidya, Wma, ZScore, ZeroLagMacd, Zlema,
 };
 
 /// Drive a single streaming + batch run through one scalar indicator. Marked
@@ -86,6 +88,18 @@ fuzz_target!(|data: Vec<f64>| {
     drive(|| LinRegAngle::new(14).unwrap(), &data);
     drive(|| VerticalHorizontalFilter::new(14).unwrap(), &data);
     drive(|| ZScore::new(14).unwrap(), &data);
+    drive(|| Variance::new(14).unwrap(), &data);
+    drive(|| CoefficientOfVariation::new(14).unwrap(), &data);
+    drive(|| Skewness::new(14).unwrap(), &data);
+    drive(|| Kurtosis::new(14).unwrap(), &data);
+    drive(|| StandardError::new(14).unwrap(), &data);
+    drive(|| DetrendedStdDev::new(14).unwrap(), &data);
+    drive(|| RSquared::new(14).unwrap(), &data);
+    drive(|| MedianAbsoluteDeviation::new(14).unwrap(), &data);
+    drive(|| Autocorrelation::new(14, 2).unwrap(), &data);
+    // HurstExponent needs `period >= 2 * chunks`; 16/4 is the cheapest fit
+    // that still exercises every code path.
+    drive(|| HurstExponent::new(16, 4).unwrap(), &data);
     drive(|| RviVolatility::new(10).unwrap(), &data);
     drive(|| LaguerreRsi::new(0.5).unwrap(), &data);
     drive(|| ConnorsRsi::classic(), &data);
@@ -186,4 +200,17 @@ fuzz_target!(|data: Vec<f64>| {
         }
         let _ = DoubleBollinger::new(20, 1.0, 2.0).unwrap().batch(&data);
     }
+
+    // Family 12: Two-series indicators — pair adjacent samples of `data`.
+    {
+        let mut p = PearsonCorrelation::new(14).unwrap();
+        let mut b = Beta::new(14).unwrap();
+        let mut s = SpearmanCorrelation::new(14).unwrap();
+        for w in data.windows(2) {
+            let pair = (w[0], w[1]);
+            let _ = p.update(pair);
+            let _ = b.update(pair);
+            let _ = s.update(pair);
+        }
+    }
 });