feat(data-layer): Resampler (candle resampling) in all 10 languages (#310)

* feat(data-layer): Resampler (candle resampling) in all 10 languages

Second data-layer feature (F3): resample candles into a higher timeframe.

- Native (Node.js/WASM): new Resampler(timeframe) -> update(o,h,l,c,v,ts):
  Candle|null + flush(): Candle|null. Python the same -> tuple|None.
- C ABI: wickra_resampler_new/update/flush/free (update has the multi-output
  shape so the generators auto-emit it; flush is bespoke). Go Update -> (Candle,
  bool) + Flush; C# Candle? Update/Flush; Java Candle update/flush; R update()
  generic + a flush() S3 method (extends base::flush); C/C++ direct.
- Cross-language golden (testdata/golden/data_resampled.csv): the shared input
  candles resampled into 5-unit buckets, the final partial bucket via flush,
  pinned bit-for-bit across every binding.

Verified locally in all 10 (3 candles for the 5-unit smoke; 16 for the golden).
The WickraCandle output record is shared with the tick aggregator (deduped).

* test(node): exclude data-layer types from the indicator completeness contract

The Resampler exposes update(), so the completeness test flagged it as an
indicator and required batch/reset/isReady/warmupPeriod, which a data-layer type
does not have. Exclude TickAggregator and Resampler like the bar builders.

bindings/python/tests/test_data_layer.py

@@ -42,3 +42,24 @@ def test_tick_aggregator_matches_golden(gap_fill, fixture):
         for j in range(6):
             tol = 1e-9 * max(1.0, abs(w[j]))
             assert abs(g[j] - w[j]) <= tol, f"row {i} col {j}: {g[j]} vs {w[j]}"
+
+
+INPUT = _read("input")  # open,high,low,close,volume (timestamp = row index)
+
+
+def test_resampler_matches_golden():
+    r = ta.Resampler(5)
+    got = []
+    for i, (o, h, l, c, v) in enumerate(INPUT):
+        candle = r.update(o, h, l, c, v, i)
+        if candle is not None:
+            got.append(candle)
+    f = r.flush()
+    if f is not None:
+        got.append(f)
+    want = _read("data_resampled")
+    assert len(got) == len(want)
+    for i, (g, w) in enumerate(zip(got, want)):
+        for j in range(6):
+            tol = 1e-9 * max(1.0, abs(w[j]))
+            assert abs(g[j] - w[j]) <= tol, f"row {i} col {j}: {g[j]} vs {w[j]}"