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/python/tests/test_input_validation.py

@@ -35,6 +35,72 @@ def test_unequal_length_candle_batch_raises(ohlc_series):
         ta.Aroon(14).batch(high, short)
 
 
+def test_pairwise_beta_rejects_bad_period():
+    with pytest.raises(ValueError):
+        ta.PairwiseBeta(0)
+    with pytest.raises(ValueError):
+        ta.PairwiseBeta(1)
+
+
+def test_unequal_length_pair_batch_raises(sine_prices):
+    a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
+    b = a[:-1]
+    with pytest.raises(ValueError):
+        ta.PairwiseBeta(20).batch(a, b)
+    with pytest.raises(ValueError):
+        ta.PairSpreadZScore(20, 20).batch(a, b)
+
+
+def test_pair_spread_zscore_rejects_bad_periods():
+    with pytest.raises(ValueError):
+        ta.PairSpreadZScore(1, 20)
+    with pytest.raises(ValueError):
+        ta.PairSpreadZScore(20, 1)
+
+
+def test_lead_lag_rejects_bad_params():
+    with pytest.raises(ValueError):
+        ta.LeadLagCrossCorrelation(1, 5)
+    with pytest.raises(ValueError):
+        ta.LeadLagCrossCorrelation(10, 0)
+
+
+def test_lead_lag_unequal_length_batch_raises(sine_prices):
+    a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
+    b = a[:-1]
+    with pytest.raises(ValueError):
+        ta.LeadLagCrossCorrelation(12, 5).batch(a, b)
+
+
+def test_cointegration_rejects_too_small_period():
+    # period must be >= 2*adf_lags + 4.
+    with pytest.raises(ValueError):
+        ta.Cointegration(3, 0)
+    with pytest.raises(ValueError):
+        ta.Cointegration(5, 1)
+
+
+def test_cointegration_unequal_length_batch_raises(sine_prices):
+    a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
+    b = a[:-1]
+    with pytest.raises(ValueError):
+        ta.Cointegration(20, 1).batch(a, b)
+
+
+def test_relative_strength_rejects_zero_periods():
+    with pytest.raises(ValueError):
+        ta.RelativeStrengthAB(0, 14)
+    with pytest.raises(ValueError):
+        ta.RelativeStrengthAB(20, 0)
+
+
+def test_relative_strength_unequal_length_batch_raises(sine_prices):
+    a = np.ascontiguousarray((sine_prices + 100.0).astype(np.float64))
+    b = a[:-1]
+    with pytest.raises(ValueError):
+        ta.RelativeStrengthAB(10, 14).batch(a, b)
+
+
 def test_roc_and_trix_have_default_periods():
     # ROC/TRIX gained constructor defaults matching the TA-Lib convention.
     assert ta.ROC().period == 10