F12: add price transforms and rolling linear regression

- Rust core: typical_price.rs ((H+L+C)/3), median_price.rs ((H+L)/2),
  weighted_close.rs ((H+L+2C)/4) — stateless per-bar OHLC transforms — and
  linreg.rs (LinearRegression — endpoint of a rolling ordinary-least-squares
  fit) and linreg_slope.rs (LinRegSlope — slope of that fit). Each with a
  full Indicator impl, runnable doctest and reference / property / warmup /
  reset / batch==streaming tests.
- Python: PyTypicalPrice / PyMedianPrice / PyWeightedClose /
  PyLinearRegression / PyLinRegSlope PyO3 classes + module registration +
  .pyi stubs.
- Node: explicit TypicalPriceNode / MedianPriceNode / WeightedCloseNode /
  LinearRegressionNode / LinRegSlopeNode; index.d.ts and index.js updated.
- WASM: explicit WasmTypicalPrice / WasmMedianPrice / WasmWeightedClose;
  WasmLinearRegression / WasmLinRegSlope via the scalar macro.
- Wiki: a new indicators/statistics/ folder with five Indicator-*.md pages,
  a new "Statistics" family in Indicators-Overview.md and Home.md.

cargo fmt + clippy (core/wickra/data/wasm/node) clean; 454 core tests,
25 data tests and 66 doctests green.

bindings/python/src/lib.rs

@@ -3577,6 +3577,285 @@ impl PyAtrTrailingStop {
     }
 }
 
+// ============================== Typical Price ==============================
+
+#[pyclass(name = "TypicalPrice", module = "wickra._wickra")]
+#[derive(Clone)]
+struct PyTypicalPrice {
+    inner: wc::TypicalPrice,
+}
+
+#[pymethods]
+impl PyTypicalPrice {
+    #[new]
+    fn new() -> Self {
+        Self {
+            inner: wc::TypicalPrice::new(),
+        }
+    }
+    fn update(&mut self, candle: &Bound<'_, PyAny>) -> PyResult<Option<f64>> {
+        let c = extract_candle(candle)?;
+        Ok(self.inner.update(c))
+    }
+    /// Batch over numpy columns high, low, close (all equal length).
+    fn batch<'py>(
+        &mut self,
+        py: Python<'py>,
+        high: PyReadonlyArray1<'py, f64>,
+        low: PyReadonlyArray1<'py, f64>,
+        close: 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))?;
+        let c = close
+            .as_slice()
+            .map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
+        if h.len() != l.len() || l.len() != c.len() {
+            return Err(PyValueError::new_err(
+                "high, low, close must be equal length",
+            ));
+        }
+        let mut out = Vec::with_capacity(h.len());
+        for i in 0..h.len() {
+            let candle = wc::Candle::new(c[i], h[i], l[i], c[i], 0.0, 0).map_err(map_err)?;
+            out.push(self.inner.update(candle).unwrap_or(f64::NAN));
+        }
+        Ok(out.into_pyarray_bound(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 {
+        "TypicalPrice()".to_string()
+    }
+}
+
+// ============================== Median Price ==============================
+
+#[pyclass(name = "MedianPrice", module = "wickra._wickra")]
+#[derive(Clone)]
+struct PyMedianPrice {
+    inner: wc::MedianPrice,
+}
+
+#[pymethods]
+impl PyMedianPrice {
+    #[new]
+    fn new() -> Self {
+        Self {
+            inner: wc::MedianPrice::new(),
+        }
+    }
+    fn update(&mut self, candle: &Bound<'_, PyAny>) -> PyResult<Option<f64>> {
+        let c = extract_candle(candle)?;
+        Ok(self.inner.update(c))
+    }
+    /// Batch over numpy columns high, low (both equal length).
+    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_bound(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 {
+        "MedianPrice()".to_string()
+    }
+}
+
+// ============================== Weighted Close ==============================
+
+#[pyclass(name = "WeightedClose", module = "wickra._wickra")]
+#[derive(Clone)]
+struct PyWeightedClose {
+    inner: wc::WeightedClose,
+}
+
+#[pymethods]
+impl PyWeightedClose {
+    #[new]
+    fn new() -> Self {
+        Self {
+            inner: wc::WeightedClose::new(),
+        }
+    }
+    fn update(&mut self, candle: &Bound<'_, PyAny>) -> PyResult<Option<f64>> {
+        let c = extract_candle(candle)?;
+        Ok(self.inner.update(c))
+    }
+    /// Batch over numpy columns high, low, close (all equal length).
+    fn batch<'py>(
+        &mut self,
+        py: Python<'py>,
+        high: PyReadonlyArray1<'py, f64>,
+        low: PyReadonlyArray1<'py, f64>,
+        close: 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))?;
+        let c = close
+            .as_slice()
+            .map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
+        if h.len() != l.len() || l.len() != c.len() {
+            return Err(PyValueError::new_err(
+                "high, low, close must be equal length",
+            ));
+        }
+        let mut out = Vec::with_capacity(h.len());
+        for i in 0..h.len() {
+            let candle = wc::Candle::new(c[i], h[i], l[i], c[i], 0.0, 0).map_err(map_err)?;
+            out.push(self.inner.update(candle).unwrap_or(f64::NAN));
+        }
+        Ok(out.into_pyarray_bound(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 {
+        "WeightedClose()".to_string()
+    }
+}
+
+// ============================== Linear Regression ==============================
+
+#[pyclass(name = "LinearRegression", module = "wickra._wickra")]
+#[derive(Clone)]
+struct PyLinearRegression {
+    inner: wc::LinearRegression,
+}
+
+#[pymethods]
+impl PyLinearRegression {
+    #[new]
+    #[pyo3(signature = (period=14))]
+    fn new(period: usize) -> PyResult<Self> {
+        Ok(Self {
+            inner: wc::LinearRegression::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 slice = prices
+            .as_slice()
+            .map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
+        Ok(flatten(self.inner.batch(slice)).into_pyarray_bound(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!("LinearRegression(period={})", self.inner.period())
+    }
+}
+
+// ============================== Linear Regression Slope ==============================
+
+#[pyclass(name = "LinRegSlope", module = "wickra._wickra")]
+#[derive(Clone)]
+struct PyLinRegSlope {
+    inner: wc::LinRegSlope,
+}
+
+#[pymethods]
+impl PyLinRegSlope {
+    #[new]
+    #[pyo3(signature = (period=14))]
+    fn new(period: usize) -> PyResult<Self> {
+        Ok(Self {
+            inner: wc::LinRegSlope::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 slice = prices
+            .as_slice()
+            .map_err(|_| PyValueError::new_err(NON_CONTIGUOUS))?;
+        Ok(flatten(self.inner.batch(slice)).into_pyarray_bound(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!("LinRegSlope(period={})", self.inner.period())
+    }
+}
+
 // ============================== Module ==============================
 
 #[pymodule]
@@ -3640,5 +3919,10 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_class::<PyChandelierExit>()?;
     m.add_class::<PyChandeKrollStop>()?;
     m.add_class::<PyAtrTrailingStop>()?;
+    m.add_class::<PyTypicalPrice>()?;
+    m.add_class::<PyMedianPrice>()?;
+    m.add_class::<PyWeightedClose>()?;
+    m.add_class::<PyLinearRegression>()?;
+    m.add_class::<PyLinRegSlope>()?;
     Ok(())
 }