Add 10 pairwise stat-arb indicators to Price Statistics (#154)

Adds ten pairwise `(f64, f64)` indicators to the **Price Statistics** family, completing the A1 stat-arb expansion block.

## Indicators

**Scalar output:**
- **RollingCorrelation** — rolling Pearson correlation of period-over-period *returns* (distinct from level-based `PearsonCorrelation`).
- **RollingCovariance** — rolling covariance of returns.
- **OuHalfLife** — Ornstein–Uhlenbeck half-life of mean reversion of the spread `a − b`.
- **SpreadHurst** — Hurst exponent of the spread (variance-of-lagged-differences fit) for regime detection.
- **DistanceSsd** — Gatev sum-of-squared-deviations between two start-normalised series.
- **BetaNeutralSpread** — rolling OLS regression residual `a − (α + β·b)`.
- **VarianceRatio** — Lo–MacKinlay variance-ratio test on the spread (two params: `period`, `q`).
- **GrangerCausality** — F-statistic for whether `b` predicts `a` (two params: `period`, `lag`).

**Struct output (custom bindings):**
- **KalmanHedgeRatio** — dynamic hedge ratio via a Kalman filter → `{ hedgeRatio, intercept, spread }`.
- **SpreadBollingerBands** — Bollinger bands on the spread → `{ middle, upper, lower, percentB }`.

## Notes
- No new traits or input families: all use the native `Indicator<Input = (f64, f64)>` (precedent `Beta`, `Cointegration`).
- Adds `Error::InvalidParameter` for floating-point constructor parameters (Kalman `delta`/`observation_var`, `num_std`).
- Full Python/Node/WASM bindings; the two struct-output indicators are hand-written, the rest use the pair macros.
- Indicator count 315 → 325; README, family rows, `__init__`, fuzz target, and CHANGELOG updated.

## Verification
- `cargo test --workspace --all-features` — green (2676 core lib + 308 doc).
- `cargo clippy --workspace --all-targets --all-features -- -D warnings` — clean.
- Node: `npm run build && npm test` — 410 passing (`index.d.ts`/`index.js` regenerated).
- Python: `pytest` — 684 passing.

fuzz/fuzz_targets/indicator_update_pair.rs

@@ -8,10 +8,7 @@
 //! panic.
 
 use libfuzzer_sys::fuzz_target;
-use wickra_core::{
-    Alpha, BatchExt, Cointegration, Indicator, InformationRatio, LeadLagCrossCorrelation,
-    PairSpreadZScore, PairwiseBeta, RelativeStrengthAB, TreynorRatio,
-};
+use wickra_core::{Alpha, BatchExt, BetaNeutralSpread, Cointegration, DistanceSsd, GrangerCausality, Indicator, InformationRatio, KalmanHedgeRatio, LeadLagCrossCorrelation, OuHalfLife, PairSpreadZScore, PairwiseBeta, RelativeStrengthAB, RollingCorrelation, RollingCovariance, SpreadBollingerBands, SpreadHurst, TreynorRatio, VarianceRatio};
 
 #[inline(never)]
 fn drive<I>(make: impl Fn() -> I, data: &[(f64, f64)])
@@ -41,6 +38,14 @@ fuzz_target!(|data: &[u8]| {
     drive(|| Alpha::new(10, 0.0).unwrap(), &pairs);
     drive(|| PairwiseBeta::new(10).unwrap(), &pairs);
     drive(|| PairSpreadZScore::new(10, 10).unwrap(), &pairs);
+    drive(|| RollingCorrelation::new(20).unwrap(), &pairs);
+    drive(|| RollingCovariance::new(20).unwrap(), &pairs);
+    drive(|| OuHalfLife::new(60).unwrap(), &pairs);
+    drive(|| SpreadHurst::new(60).unwrap(), &pairs);
+    drive(|| DistanceSsd::new(20).unwrap(), &pairs);
+    drive(|| BetaNeutralSpread::new(20).unwrap(), &pairs);
+    drive(|| VarianceRatio::new(60, 2).unwrap(), &pairs);
+    drive(|| GrangerCausality::new(60, 1).unwrap(), &pairs);
 
     // Struct-output pair indicator: drive update + batch directly (the generic
     // `drive` above only covers `Output = f64`).
@@ -61,4 +66,16 @@ fuzz_target!(|data: &[u8]| {
         let _ = rs.update(x);
     }
     let _ = RelativeStrengthAB::new(10, 14).unwrap().batch(&pairs);
+    let mut kalman_hedge_ratio = KalmanHedgeRatio::new(0.001, 0.001).unwrap();
+    for &x in &pairs {
+        let _ = kalman_hedge_ratio.update(x);
+    }
+    let _ = KalmanHedgeRatio::new(0.001, 0.001).unwrap().batch(&pairs);
+
+    let mut spread_bollinger_bands = SpreadBollingerBands::new(20, 2.0).unwrap();
+    for &x in &pairs {
+        let _ = spread_bollinger_bands.update(x);
+    }
+    let _ = SpreadBollingerBands::new(20, 2.0).unwrap().batch(&pairs);
+
 });