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.

docs/wiki/indicators/statistics/Indicator-LinearRegression.md

@@ -0,0 +1,152 @@
+# LinearRegression
+
+> Linear Regression — the endpoint of a rolling ordinary-least-squares fit
+> over the last `period` prices.
+
+## Quick reference
+
+| Field | Value |
+|-------|-------|
+| Family | Statistics |
+| Sub-category | Regression |
+| Input type | `f64` (price) |
+| Output type | `f64` |
+| Output range | unbounded (price scale) |
+| Default parameters | `period = 14` (Python) |
+| Warmup period | `period` |
+| Interpretation | A low-lag smoothed price — the trend line extrapolated to now. |
+
+## Formula
+
+Over the last `period` inputs, indexed `x = 0, 1, …, period − 1`:
+
+```
+b (slope)     = (n·Σxy − Σx·Σy) / (n·Σxx − (Σx)²)
+a (intercept) = (Σy − b·Σx) / n
+LinearReg     = a + b·(period − 1)
+```
+
+The indicator fits a straight line to the window by ordinary least squares,
+then reports that line's value at the most recent bar. Because it
+extrapolates the *local trend* forward rather than averaging it away, it lags
+a same-period [`Sma`](../trend/Indicator-Sma.md) noticeably less. This is
+TA-Lib's `LINEARREG`.
+
+## Parameters
+
+`period` — the regression window. Must be at least `2` (a line needs two
+points). The Python binding defaults it to `14`; the Rust and Node
+constructors require it explicitly.
+
+## Inputs / Outputs
+
+From `crates/wickra-core/src/indicators/linreg.rs`:
+
+```rust
+impl Indicator for LinearRegression {
+    type Input = f64;
+    type Output = f64;
+    // update(&mut self, input: f64) -> Option<f64>
+}
+```
+
+`LinearRegression` is a **scalar** indicator: it consumes one `f64` price per
+step. Because `Input = f64` it can sit inside a [`Chain`](../../Indicator-Chaining.md).
+
+## Warmup
+
+`LinearRegression::new(14).warmup_period() == 14`. The first value lands once
+the window holds a full `period` prices — on input index `period − 1`.
+
+## Edge cases
+
+- **`period < 2`.** Rejected at construction — a regression line is undefined
+  for fewer than two points.
+- **Perfect line.** Fed a perfectly linear series, the fit *is* that line, so
+  the endpoint equals the current value (`perfect_line_returns_current_value`
+  pins this).
+- **Constant series.** A flat input returns that constant.
+- **Reset.** `lr.reset()` clears the rolling window.
+
+## Examples
+
+### Rust
+
+```rust
+use wickra::{BatchExt, Indicator, LinearRegression};
+
+fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let mut lr = LinearRegression::new(3)?;
+    // Fit over [1, 2, 9]: the least-squares line is y = 4x, endpoint 4·2 = 8.
+    let out = lr.batch(&[1.0, 2.0, 9.0]);
+    println!("{:?}", out);
+    Ok(())
+}
+```
+
+Output:
+
+```
+[None, None, Some(8.0)]
+```
+
+This matches the `reference_values` test in
+`crates/wickra-core/src/indicators/linreg.rs`.
+
+### Python
+
+```python
+import numpy as np
+import wickra as ta
+
+lr = ta.LinearRegression(3)
+print(lr.batch(np.array([1.0, 2.0, 9.0])))
+```
+
+Output:
+
+```
+[nan nan  8.]
+```
+
+### Node
+
+```javascript
+const ta = require('wickra');
+const lr = new ta.LinearRegression(3);
+console.log(lr.batch([1, 2, 9]));
+```
+
+Output:
+
+```
+[ NaN, NaN, 8 ]
+```
+
+## Interpretation
+
+Read `LinearRegression` as a low-lag moving average: it tracks price more
+closely than an SMA of the same period because it projects the window's trend
+to the current bar instead of centring on the window. A shorter `period`
+hugs price; a longer one is a smoother trend line. Pair it with
+[`LinRegSlope`](Indicator-LinRegSlope.md) to read the same fit's steepness.
+
+## Common pitfalls
+
+- **Confusing it with an SMA.** It is a *projected* fit, not a centred
+  average, so it leads an SMA of the same period.
+- **Tiny periods.** `period = 2` is allowed but the "fit" just passes through
+  the last two points; use a meaningful window.
+
+## References
+
+Ordinary least-squares linear regression applied to a rolling price window;
+the endpoint formulation matches TA-Lib's `LINEARREG`.
+
+## See also
+
+- [Indicator-LinRegSlope.md](Indicator-LinRegSlope.md) — the slope of the same
+  rolling fit.
+- [Indicator-Sma.md](../trend/Indicator-Sma.md) — the centred average it is
+  often compared against.
+- [Indicators-Overview.md](../../Indicators-Overview.md) — the full taxonomy.