feat(ao-histogram): add Awesome Oscillator Histogram

AO - SMA(AO, sma_period). A configurable variant of the existing
AcceleratorOscillator (which fixes fast=5, slow=34, sma=5).
Three parameters; defaults match Bill Williams' Accelerator.

Touchpoints: awesome_oscillator_histogram.rs + mod.rs + lib.rs
re-export, PyAoHist + __init__.py + test_new_indicators CANDLE_SCALAR
+ test_known_values flat reference, AwesomeOscillatorHistogramNode +
index.d.ts/index.js + indicators.test.js factory + reference,
WasmAoHist, candle-fuzz target, README + CHANGELOG.

bindings/python/python/wickra/__init__.py

@@ -57,6 +57,7 @@ from ._wickra import (
     StochRSI,
     UltimateOscillator,
     APO,
+    AwesomeOscillatorHistogram,
     PPO,
     DPO,
     Coppock,
@@ -139,6 +140,7 @@ __all__ = [
     "StochRSI",
     "UltimateOscillator",
     "APO",
+    "AwesomeOscillatorHistogram",
     "PPO",
     "DPO",
     "Coppock",

bindings/python/src/lib.rs

@@ -812,6 +812,68 @@ impl PyKama {
     }
 }
 
+// ============================== AwesomeOscillatorHistogram ==============================
+
+#[pyclass(
+    name = "AwesomeOscillatorHistogram",
+    module = "wickra._wickra",
+    skip_from_py_object
+)]
+#[derive(Clone)]
+struct PyAoHist {
+    inner: wc::AwesomeOscillatorHistogram,
+}
+
+#[pymethods]
+impl PyAoHist {
+    #[new]
+    #[pyo3(signature = (fast=5, slow=34, sma_period=5))]
+    fn new(fast: usize, slow: usize, sma_period: usize) -> PyResult<Self> {
+        Ok(Self {
+            inner: wc::AwesomeOscillatorHistogram::new(fast, slow, sma_period).map_err(map_err)?,
+        })
+    }
+    fn update(&mut self, candle: &Bound<'_, PyAny>) -> PyResult<Option<f64>> {
+        let c = extract_candle(candle)?;
+        Ok(self.inner.update(c))
+    }
+    fn batch<'py>(
+        &mut self,
+        py: Python<'py>,
+        high: PyReadonlyArray1<'py, f64>,
+        low: PyReadonlyArray1<'py, f64>,
+    ) -> PyResult<Bound<'py, PyArray1<f64>>> {
+        let h = high
+            .as_slice()
+            .map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
+        let l = low
+            .as_slice()
+            .map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
+        if h.len() != l.len() {
+            return Err(PyValueError::new_err("high and low must be equal length"));
+        }
+        let mut out = Vec::with_capacity(h.len());
+        for i in 0..h.len() {
+            let candle = wc::Candle::new(l[i], h[i], l[i], l[i], 0.0, 0).map_err(map_err)?;
+            out.push(self.inner.update(candle).unwrap_or(f64::NAN));
+        }
+        Ok(out.into_pyarray(py))
+    }
+    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 {
+        let (f, s, k) = self.inner.periods();
+        format!("AwesomeOscillatorHistogram(fast={f}, slow={s}, sma_period={k})")
+    }
+}
+
 // ============================== APO ==============================
 
 #[pyclass(name = "APO", module = "wickra._wickra", skip_from_py_object)]
@@ -4540,6 +4602,7 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_class::<PyHma>()?;
     m.add_class::<PyKama>()?;
     m.add_class::<PyApo>()?;
+    m.add_class::<PyAoHist>()?;
     m.add_class::<PyCci>()?;
     m.add_class::<PyRoc>()?;
     m.add_class::<PyWilliamsR>()?;

bindings/python/tests/test_known_values.py

@@ -66,6 +66,16 @@ def test_rsi_wilder_textbook_first_value():
     assert math.isclose(out[14], 70.464, abs_tol=0.05)
 
 
+def test_awesome_oscillator_histogram_flat_series_converges_to_zero():
+    # Flat median price -> AO = 0 -> SMA(AO) = 0 -> AOHist = 0.
+    n = 50
+    high = np.full(n, 11.0)
+    low = np.full(n, 9.0)
+    out = ta.AwesomeOscillatorHistogram(3, 5, 3).batch(high, low)
+    # warmup = slow + sma - 1 = 5 + 3 - 1 = 7.
+    np.testing.assert_allclose(out[6:], 0.0, atol=1e-12)
+
+
 def test_apo_constant_series_converges_to_zero():
     # Both EMAs reproduce a constant exactly, so APO = 0 after warmup.
     out = ta.APO(3, 5).batch(np.full(30, 42.0, dtype=np.float64))

bindings/python/tests/test_new_indicators.py

@@ -139,6 +139,10 @@ CANDLE_SCALAR = {
         lambda: ta.AcceleratorOscillator(5, 34, 5),
         lambda ind, h, l, c, v: ind.batch(h, l),
     ),
+    "AwesomeOscillatorHistogram": (
+        lambda: ta.AwesomeOscillatorHistogram(5, 34, 5),
+        lambda ind, h, l, c, v: ind.batch(h, l),
+    ),
     "BalanceOfPower": (
         # The streaming 6-tuple feeds open == close, so batch matches with
         # the close column standing in for open.