feat(bindings): expose RollingVWAP in Python, Node and WASM (R4)

The rolling-window VWAP indicator (`wickra_core::RollingVwap`) was only
available in the Rust crate, even though the README's Volume-family
table already advertised "VWAP (cumulative + rolling)" as a cross-
language feature. Users on Python, Node or in the browser had to fall
back to the cumulative `VWAP` or re-implement the rolling variant
themselves.

This commit closes the gap end-to-end:

- Python: `wickra.RollingVWAP(period)` — same constructor / `update` /
  `batch` / `reset` / `is_ready` / `warmup_period` surface as `VWAP`,
  plus a `period` property and a typed `__repr__`. The `__init__.py`
  re-exports it and `__all__` lists it; the `.pyi` stub matches.
- Node: `RollingVWAP(period)` — napi class with the same lifecycle,
  exported from `index.js` and declared in `index.d.ts`.
- WASM: `RollingVWAP(period)` — wasm-bindgen class with the same
  `Float64Array` I/O as `VWAP`.

Tests added:

- Python: `test_rolling_vwap_streaming_matches_batch` — exercises
  `update == batch` plus the full lifecycle on the shared OHLC fixture.
- Node: `RollingVWAP` row in the `candleScalar` parity table — covered
  by the generic streaming-vs-batch + lifecycle harness.
- WASM: dedicated `wasm-bindgen-test` mirrors the Python test.

The wiki page `Indicator-Vwap.md` drops the "Rust-only" caveat and
gains Python / Node / WASM examples.

bindings/python/tests/test_streaming_vs_batch.py

@@ -115,3 +115,23 @@ def test_obv_streaming_matches_batch(ohlc_series):
         rows.append(streamer.update((float(c), float(c), float(c), float(c), float(v), 0)))
     streamed = np.array([math.nan if x is None else x for x in rows], dtype=np.float64)
     assert _equal_with_nan(batch, streamed)
+
+
+def test_rolling_vwap_streaming_matches_batch(ohlc_series):
+    # RollingVWAP(20) on the shared OHLC series. Provides finite-memory VWAP
+    # parity coverage now that the indicator is exposed across all bindings.
+    high, low, close = ohlc_series
+    volume = np.linspace(100.0, 200.0, num=close.size, dtype=np.float64)
+    batch = ta.RollingVWAP(20).batch(high, low, close, volume)
+
+    streamer = ta.RollingVWAP(20)
+    rows = []
+    for h, l, c, v in zip(high, low, close, volume):
+        rows.append(streamer.update((float(c), float(h), float(l), float(c), float(v), 0)))
+    streamed = np.array([math.nan if x is None else x for x in rows], dtype=np.float64)
+    assert _equal_with_nan(batch, streamed)
+    assert streamer.period == 20
+    assert streamer.warmup_period() == 20
+    assert streamer.is_ready()
+    streamer.reset()
+    assert not streamer.is_ready()