feat(cfo): add Chande Forecast Oscillator

100 * (close - LinReg(close, period)) / close. Positive when close
overshoots the linear forecast, negative when it undershoots. Holds
the previous value if the close is zero (percentage form undefined).
Single param period (default 14).

Touchpoints: cfo.rs + mod.rs + lib.rs re-export, PyCfo + __init__.py
+ test_new_indicators SCALAR + test_known_values linear reference,
CfoNode + index.d.ts/index.js + indicators.test.js factory + reference,
WasmCfo via scalar macro, scalar-fuzz target, README + CHANGELOG.

bindings/python/python/wickra/__init__.py

@@ -58,6 +58,7 @@ from ._wickra import (
     UltimateOscillator,
     APO,
     AwesomeOscillatorHistogram,
+    CFO,
     PPO,
     DPO,
     Coppock,
@@ -141,6 +142,7 @@ __all__ = [
     "UltimateOscillator",
     "APO",
     "AwesomeOscillatorHistogram",
+    "CFO",
     "PPO",
     "DPO",
     "Coppock",

bindings/python/src/lib.rs

@@ -874,6 +874,54 @@ impl PyAoHist {
     }
 }
 
+// ============================== CFO ==============================
+
+#[pyclass(name = "CFO", module = "wickra._wickra", skip_from_py_object)]
+#[derive(Clone)]
+struct PyCfo {
+    inner: wc::Cfo,
+}
+
+#[pymethods]
+impl PyCfo {
+    #[new]
+    #[pyo3(signature = (period=14))]
+    fn new(period: usize) -> PyResult<Self> {
+        Ok(Self {
+            inner: wc::Cfo::new(period).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()
+    }
+    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!("CFO(period={})", self.inner.period())
+    }
+}
+
 // ============================== APO ==============================
 
 #[pyclass(name = "APO", module = "wickra._wickra", skip_from_py_object)]
@@ -4603,6 +4651,7 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_class::<PyKama>()?;
     m.add_class::<PyApo>()?;
     m.add_class::<PyAoHist>()?;
+    m.add_class::<PyCfo>()?;
     m.add_class::<PyCci>()?;
     m.add_class::<PyRoc>()?;
     m.add_class::<PyWilliamsR>()?;

bindings/python/tests/test_known_values.py

@@ -76,6 +76,12 @@ def test_awesome_oscillator_histogram_flat_series_converges_to_zero():
     np.testing.assert_allclose(out[6:], 0.0, atol=1e-12)
 
 
+def test_cfo_perfect_linear_series_yields_zero():
+    # LinReg of a perfectly linear series fits exactly, so CFO = 0 after warmup.
+    out = ta.CFO(5).batch(np.arange(1.0, 21.0, dtype=np.float64) * 2.0)
+    np.testing.assert_allclose(out[4:], 0.0, atol=1e-9)
+
+
 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

@@ -52,6 +52,7 @@ SCALAR = [
     (ta.StochRSI, (14, 14)),
     (ta.PPO, (12, 26)),
     (ta.APO, (12, 26)),
+    (ta.CFO, (14,)),
     (ta.DPO, (20,)),
     (ta.Coppock, (14, 11, 10)),
     (ta.StdDev, (20,)),