feat: cross-asset / pairwise indicators (5 new) (#109)

* feat(core): add PairwiseBeta cross-asset indicator

Rolling OLS slope of one asset's log-returns on another's. Unlike Beta,
which regresses the raw inputs it is fed, PairwiseBeta differences
consecutive prices into log-returns internally -- the conventional way to
measure cross-asset beta, where a beta on price levels would be dominated
by the shared trend.

Two-series Indicator<Input = (f64, f64)>, exposed in Rust, Python, Node
and WASM, with unit/known-value/streaming tests and a pair fuzz target.

* feat(core): add PairSpreadZScore cross-asset indicator

Standardised log-spread ln(a) - beta*ln(b) of a pair, where beta is a
rolling-OLS hedge ratio and the spread is z-scored over its own look-back.
The canonical mean-reversion / statistical-arbitrage entry signal, with
independent beta_period and z_period windows.

Two-series Indicator<Input = (f64, f64)>, exposed in Rust, Python, Node
and WASM, with sign/known-value/streaming tests and a pair fuzz target.

* feat(core): add LeadLagCrossCorrelation cross-asset indicator

Reports the integer offset k in [-max_lag, max_lag] that maximises
|corr(a[t], b[t+k])|, answering which of two assets leads the other and by
how many bars. A positive lag means a leads b. Fully causal: a's window is
held centred while b's window slides across the buffered history, so every
lag is evaluated only against data already seen.

Struct output { lag, correlation }, exposed in Rust, Python, Node and WASM
with lead-detection/streaming tests and a pair fuzz driver.

* feat(core): add Cointegration (Engle-Granger + ADF) indicator

Rolling pairs-trading screen: an OLS hedge ratio of a on b, the spread
(residual) a - (alpha + beta*b), and an augmented Dickey-Fuller t-statistic
on the spread with configurable lags. A strongly negative statistic flags a
mean-reverting, tradeable spread. Includes a small Gaussian-elimination
solver for the augmented regression.

Struct output { hedge_ratio, spread, adf_stat }, exposed in Rust, Python,
Node and WASM with stationarity/hedge-ratio/streaming tests and a pair fuzz
driver.

* feat(core): add RelativeStrengthAB cross-asset indicator

Comparative relative strength of two assets: the ratio line a/b together
with its moving average and its RSI, the classic asset-vs-asset /
asset-vs-index rotation screen. Composes the existing Sma and Rsi over the
ratio; a zero denominator or non-finite price is skipped.

Struct output { ratio, ratio_ma, ratio_rsi }, exposed in Rust, Python, Node
and WASM with flat/rising-ratio/streaming tests and a pair fuzz driver.

* test(cointegration): cover ADF guard branches

The ADF helper's short-series and degrees-of-freedom guards and the
zero-dispersion (perfect AR) path are unreachable through the public
Cointegration API (period >= 2*adf_lags + 4), so exercise them with direct
unit tests on adf_no_constant. The second linear solve cannot be singular
once the coefficient solve on the same matrix has succeeded, so it now uses
expect() instead of a dead error branch.

bindings/node/__tests__/indicators.test.js

@@ -461,6 +461,8 @@ test('OpeningRange(2) breakout distance is signed close minus midpoint', () => {
 const pairFactories = {
   PearsonCorrelation: () => new wickra.PearsonCorrelation(14),
   Beta: () => new wickra.Beta(14),
+  PairwiseBeta: () => new wickra.PairwiseBeta(14),
+  PairSpreadZScore: () => new wickra.PairSpreadZScore(14, 14),
   SpearmanCorrelation: () => new wickra.SpearmanCorrelation(14),
 };
 
@@ -492,6 +494,85 @@ test('Beta perfect two-to-one', () => {
   assert.ok(Math.abs(out[out.length - 1] - 2) < 1e-9);
 });
 
+test('PairwiseBeta squared price is two', () => {
+  // b needs varying returns; a = b² ⇒ a's log-returns are exactly 2× b's.
+  const bench = Array.from({ length: 20 }, (_, i) => 100 + 10 * Math.sin(i * 0.5));
+  const asset = bench.map((v) => v * v);
+  const out = new wickra.PairwiseBeta(5).batch(asset, bench);
+  assert.ok(Math.abs(out[out.length - 1] - 2) < 1e-9);
+});
+
+test('PairSpreadZScore flat benchmark is sign of last move', () => {
+  // Flat b ⇒ hedge ratio 0 ⇒ spread = ln(a); z_period = 2 ⇒ z = sign of move.
+  const a = [100, 100, 110, 105, 130];
+  const b = [100, 100, 100, 100, 100];
+  const out = new wickra.PairSpreadZScore(2, 2).batch(a, b);
+  assert.ok(Math.abs(out[out.length - 1] - 1) < 1e-9);
+  assert.ok(Math.abs(out[out.length - 2] + 1) < 1e-9);
+});
+
+const llSignal = (t) =>
+  Math.sin(t * 0.4) + 0.4 * Math.sin(t * 1.1) + 0.2 * Math.cos(t * 0.27);
+
+test('LeadLagCrossCorrelation detects positive lead (object output)', () => {
+  const ll = new wickra.LeadLagCrossCorrelation(12, 5);
+  let last = null;
+  // b is a delayed by 3 ⇒ a leads b ⇒ lag = +3.
+  for (let t = 0; t < 60; t++) last = ll.update(llSignal(t), llSignal(t - 3));
+  assert.equal(last.lag, 3);
+  assert.ok(last.correlation > 0.99);
+});
+
+test('LeadLagCrossCorrelation batch is flat 2*n with last row matching', () => {
+  const n = 60;
+  const a = Array.from({ length: n }, (_, t) => llSignal(t));
+  const b = Array.from({ length: n }, (_, t) => llSignal(t - 3));
+  const out = new wickra.LeadLagCrossCorrelation(12, 5).batch(a, b);
+  assert.equal(out.length, 2 * n);
+  assert.equal(out[2 * (n - 1)], 3);
+  assert.ok(out[2 * (n - 1) + 1] > 0.99);
+});
+
+test('Cointegration detects mean-reverting pair (object output)', () => {
+  const n = 80;
+  const b = Array.from({ length: n }, (_, t) => 50 + 0.5 * t);
+  const a = b.map((v, t) => 2 * v + 1 + 0.5 * Math.sin(t * 0.6));
+  const co = new wickra.Cointegration(40, 1);
+  let last = null;
+  for (let i = 0; i < n; i++) last = co.update(a[i], b[i]);
+  assert.ok(Math.abs(last.hedgeRatio - 2) < 0.1);
+  assert.ok(last.adfStat < -2);
+});
+
+test('Cointegration batch is flat 3*n with last row matching', () => {
+  const n = 80;
+  const b = Array.from({ length: n }, (_, t) => 50 + 0.5 * t);
+  const a = b.map((v, t) => 2 * v + 1 + 0.5 * Math.sin(t * 0.6));
+  const out = new wickra.Cointegration(40, 1).batch(a, b);
+  assert.equal(out.length, 3 * n);
+  assert.ok(Math.abs(out[3 * (n - 1)] - 2) < 0.1);
+  assert.ok(out[3 * (n - 1) + 2] < -2);
+});
+
+test('RelativeStrengthAB constant ratio is flat (object output)', () => {
+  const rs = new wickra.RelativeStrengthAB(5, 5);
+  let last = null;
+  for (let i = 0; i < 30; i++) last = rs.update(200, 100); // ratio is a constant 2
+  assert.ok(Math.abs(last.ratio - 2) < 1e-12);
+  assert.ok(Math.abs(last.ratioMa - 2) < 1e-12);
+  assert.ok(Math.abs(last.ratioRsi - 50) < 1e-9);
+});
+
+test('RelativeStrengthAB batch is flat 3*n with last row matching', () => {
+  const n = 30;
+  const a = Array.from({ length: n }, () => 200);
+  const b = Array.from({ length: n }, () => 100);
+  const out = new wickra.RelativeStrengthAB(5, 5).batch(a, b);
+  assert.equal(out.length, 3 * n);
+  assert.ok(Math.abs(out[3 * (n - 1)] - 2) < 1e-12);
+  assert.ok(Math.abs(out[3 * (n - 1) + 2] - 50) < 1e-9);
+});
+
 test('SpearmanCorrelation monotone non-linear is 1', () => {
   const x = Array.from({ length: 10 }, (_, i) => i + 1);
   const y = x.map((v) => v ** 3);