feat(data): expose CandleReader (CSV) natively in all 10 languages (#311)

Add the data-layer CSV candle reader to every binding so loading OHLCV
candles from a CSV no longer needs a per-language CSV/dataframe dependency.

- C ABI: wickra_candle_reader_new(bytes, len) / _count / _read / _free over
  an opaque CandleReader handle (parse the whole buffer up front, then drain).
- Native: Node/WASM CandleReader.read() -> Candle[], Python read() -> list[tuple].
- C-ABI languages: Go Read() []Candle, C# Candle[] Read(), Java Candle[] read(),
  R read() S3 generic (n x 6 matrix); C / C++ call the C ABI directly.
- Cross-language golden testdata/golden/data_csv*.csv pins the parsed candles
  bit-for-bit across every binding.

Verified locally across Rust (test+clippy+fmt), Node, WASM, Python, C#, Go,
Java, R, and the C/C++ cmake parity suite.

bindings/python/tests/test_data_layer.py

@@ -44,6 +44,18 @@ def test_tick_aggregator_matches_golden(gap_fill, fixture):
             assert abs(g[j] - w[j]) <= tol, f"row {i} col {j}: {g[j]} vs {w[j]}"
 
 
+def test_candle_reader_matches_golden():
+    with open(os.path.join(GOLDEN, "data_csv.csv")) as f:
+        text = f.read()
+    got = ta.CandleReader(text).read()
+    want = _read("data_csv_candles")
+    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]}"
+
+
 INPUT = _read("input")  # open,high,low,close,volume (timestamp = row index)