feat(family-16): add ValueArea + InitialBalance + OpeningRange (#52)

* feat(family-16): add ValueArea + InitialBalance + OpeningRange

Opens family #16 (Market Profile) with the three OHLCV-compatible scalar /
multi-output indicators:

- ValueArea(period, bin_count, value_area_pct) -> {poc, vah, val}.
  Rolling bin-approximation volume profile over the last `period`
  candles. Each candle's volume is spread uniformly across [low, high];
  POC is the bin with highest cumulative volume; the value area expands
  symmetrically from POC and always absorbs the higher-volume neighbour
  next, until `value_area_pct` (default 0.70) of total volume is
  enclosed. Defaults (20, 50, 0.70).

- InitialBalance(period) -> {high, low}. Tracks session-opening high
  and low over the first `period` bars, then locks. Default period = 12
  (one-hour IB on 5-minute bars for US equities). Callers MUST invoke
  reset() at every session boundary, otherwise IB stays fixed for the
  lifetime of the instance.

- OpeningRange(period) -> {high, low, breakout_distance}. Same
  lock-after-N-bars semantics as IB with a shorter default period
  (6 = 30 min on 5-minute bars) and a third output that tracks
  close - or_mid (positive above the range mid, negative below).

Histogram-output Market Profile variants (Volume Profile, VPVR,
Composite Profile) are deferred because they need a new histogram
output API layer rather than fixed-arity scalars. Tick-data-only
variants (TPO Profile, Single Print, Order Flow Delta, Cumulative
Delta, Volume-Weighted Open) are out of scope because `wickra-data`
does not currently expose tick / L2 data.

All four bindings (Rust core, Python, Node, WASM) ship the new
indicators with parity tests; benches added; fuzz target extended.
Counter 71 -> 74 across 8 -> 9 families. cargo check --workspace
--all-features green.

* fix(family-16): cover cold paths in InitialBalance + ValueArea

InitialBalance::value() public getter had no test covering the post-update
Some(...) branch — extended accessors_and_metadata to call value() after one
update. ValueArea single-print bar path (c.high == c.low) was unreachable in
existing tests since the only single-print test used a uniform 100-price
window which exits early via the span == 0 guard; added a mixed-window test
that triggers the c.high <= c.low branch directly. The (None, None) arm of
the expansion match was by-construction unreachable (the loop condition
already requires at least one neighbour) and has been folded into an
if/else.

bindings/python/tests/test_streaming_vs_batch.py

@@ -159,3 +159,45 @@ def test_rolling_vwap_streaming_matches_batch(ohlc_series):
     assert streamer.is_ready()
     streamer.reset()
     assert not streamer.is_ready()
+
+
+def test_value_area_streaming_matches_batch(ohlc_series):
+    high, low, close = ohlc_series
+    volume = np.linspace(100.0, 200.0, num=close.size, dtype=np.float64)
+    batch = ta.ValueArea(20, 50, 0.70).batch(high, low, volume)
+
+    streamer = ta.ValueArea(20, 50, 0.70)
+    rows = []
+    for h, l, v in zip(high, low, volume):
+        mid = float((h + l) / 2.0)
+        out = streamer.update((mid, float(h), float(l), mid, float(v), 0))
+        rows.append([math.nan, math.nan, math.nan] if out is None else list(out))
+    streamed = np.array(rows, dtype=np.float64)
+    assert _equal_with_nan(batch, streamed)
+
+
+def test_initial_balance_streaming_matches_batch(ohlc_series):
+    high, low, _close = ohlc_series
+    batch = ta.InitialBalance(12).batch(high, low)
+
+    streamer = ta.InitialBalance(12)
+    rows = []
+    for h, l in zip(high, low):
+        mid = float((h + l) / 2.0)
+        out = streamer.update((mid, float(h), float(l), mid, 0.0, 0))
+        rows.append([math.nan, math.nan] if out is None else list(out))
+    streamed = np.array(rows, dtype=np.float64)
+    assert _equal_with_nan(batch, streamed)
+
+
+def test_opening_range_streaming_matches_batch(ohlc_series):
+    high, low, close = ohlc_series
+    batch = ta.OpeningRange(6).batch(high, low, close)
+
+    streamer = ta.OpeningRange(6)
+    rows = []
+    for h, l, c in zip(high, low, close):
+        out = streamer.update((float(c), float(h), float(l), float(c), 0.0, 0))
+        rows.append([math.nan, math.nan, math.nan] if out is None else list(out))
+    streamed = np.array(rows, dtype=np.float64)
+    assert _equal_with_nan(batch, streamed)