feat(alma): add Arnaud Legoux Moving Average

Gaussian-weighted moving average with configurable centre (offset in
[0, 1]) and kernel width (sigma > 0). Pre-computes normalised weights
at construction so each update is a single rolling window dot product.

Reference: Arnaud Legoux and Dimitrios Kouzis-Loukas, 2009.

Touchpoints:
- crates/wickra-core: alma.rs + mod.rs + lib.rs re-export
- bindings/python: PyAlma + __init__.py + test_new_indicators +
  test_known_values reference
- bindings/node: AlmaNode + index.d.ts/index.js + indicators.test.js
  factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers ALMA(9, 0.85, 6.0)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry

bindings/python/python/wickra/__init__.py

@@ -38,6 +38,7 @@ from ._wickra import (
     ZLEMA,
     T3,
     VWMA,
+    ALMA,
     # Momentum
     RSI,
     MACD,
@@ -119,6 +120,7 @@ __all__ = [
     "ZLEMA",
     "T3",
     "VWMA",
+    "ALMA",
     # Momentum
     "RSI",
     "MACD",

bindings/python/src/lib.rs

@@ -812,6 +812,67 @@ impl PyKama {
     }
 }
 
+// ============================== ALMA ==============================
+
+#[pyclass(name = "ALMA", module = "wickra._wickra", skip_from_py_object)]
+#[derive(Clone)]
+struct PyAlma {
+    inner: wc::Alma,
+}
+
+#[pymethods]
+impl PyAlma {
+    #[new]
+    #[pyo3(signature = (period=9, offset=0.85, sigma=6.0))]
+    fn new(period: usize, offset: f64, sigma: f64) -> PyResult<Self> {
+        Ok(Self {
+            inner: wc::Alma::new(period, offset, sigma).map_err(map_err)?,
+        })
+    }
+    fn update(&mut self, value: f64) -> Option<f64> {
+        self.inner.update(value)
+    }
+    fn batch<'py>(
+        &mut self,
+        py: Python<'py>,
+        prices: PyReadonlyArray1<'py, f64>,
+    ) -> PyResult<Bound<'py, PyArray1<f64>>> {
+        let s = prices
+            .as_slice()
+            .map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
+        Ok(flatten(self.inner.batch(s)).into_pyarray(py))
+    }
+    #[getter]
+    fn period(&self) -> usize {
+        self.inner.period()
+    }
+    #[getter]
+    fn offset(&self) -> f64 {
+        self.inner.offset()
+    }
+    #[getter]
+    fn sigma(&self) -> f64 {
+        self.inner.sigma()
+    }
+    fn reset(&mut self) {
+        self.inner.reset();
+    }
+    fn is_ready(&self) -> bool {
+        self.inner.is_ready()
+    }
+    fn warmup_period(&self) -> usize {
+        self.inner.warmup_period()
+    }
+    fn __repr__(&self) -> String {
+        format!(
+            "ALMA(period={}, offset={}, sigma={})",
+            self.inner.period(),
+            self.inner.offset(),
+            self.inner.sigma()
+        )
+    }
+}
+
 // ============================== CCI ==============================
 
 #[pyclass(name = "CCI", module = "wickra._wickra", skip_from_py_object)]
@@ -4494,6 +4555,7 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_class::<PyTema>()?;
     m.add_class::<PyHma>()?;
     m.add_class::<PyKama>()?;
+    m.add_class::<PyAlma>()?;
     m.add_class::<PyCci>()?;
     m.add_class::<PyRoc>()?;
     m.add_class::<PyWilliamsR>()?;

bindings/python/tests/test_known_values.py

@@ -66,6 +66,28 @@ def test_rsi_wilder_textbook_first_value():
     assert math.isclose(out[14], 70.464, abs_tol=0.05)
 
 
+def test_alma_constant_series_yields_the_constant():
+    # ALMA's Gaussian weights are normalised, so any constant series is
+    # reproduced exactly after warmup.
+    out = ta.ALMA(9, 0.85, 6.0).batch(np.full(30, 42.0, dtype=np.float64))
+    assert np.all(np.isnan(out[:8]))
+    np.testing.assert_allclose(out[8:], 42.0, atol=1e-12)
+
+
+def test_alma_reference_value_period_3():
+    # ALMA(period=3, offset=0.85, sigma=6) on [10, 20, 30].
+    # m = 0.85 * 2 = 1.7; s = 3 / 6 = 0.5; 2*s^2 = 0.5.
+    out = ta.ALMA(3, 0.85, 6.0).batch(np.array([10.0, 20.0, 30.0]))
+    assert math.isnan(out[0]) and math.isnan(out[1])
+    # Independently compute the expected Gaussian-weighted sum.
+    w = np.exp(-((np.arange(3, dtype=np.float64) - 1.7) ** 2) / 0.5)
+    expected = float(np.dot([10.0, 20.0, 30.0], w) / w.sum())
+    assert math.isclose(out[2], expected, abs_tol=1e-12)
+    # Sanity: heavy offset toward the newest sample lifts the average above
+    # the simple mean of 20.
+    assert out[2] > 20.0
+
+
 def test_macd_constant_series_converges_to_zero():
     out = ta.MACD().batch(np.full(200, 100.0))
     # Last row's MACD and signal must be ~0.

bindings/python/tests/test_new_indicators.py

@@ -44,6 +44,7 @@ SCALAR = [
     (ta.SMMA, (14,)),
     (ta.TRIMA, (20,)),
     (ta.ZLEMA, (14,)),
+    (ta.ALMA, (9, 0.85, 6.0)),
     (ta.T3, (5, 0.7)),
     (ta.MOM, (10,)),
     (ta.CMO, (14,)),