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/python/wickra/__init__.py

@@ -361,6 +361,7 @@ from ._wickra import (
     # Data layer
     TickAggregator,
     Resampler,
+    CandleReader,
     # Market Profile
     CompositeProfile,
     HighLowVolumeNodes,
@@ -908,6 +909,7 @@ __all__ = [
     # Data layer
     "TickAggregator",
     "Resampler",
+    "CandleReader",
     # Market Profile
     "CompositeProfile",
     "HighLowVolumeNodes",

bindings/python/src/lib.rs

@@ -28288,6 +28288,7 @@ fn _wickra(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> {
     // Data layer.
     m.add_class::<PyTickAggregator>()?;
     m.add_class::<PyResampler>()?;
+    m.add_class::<PyCandleReader>()?;
     // Candlestick patterns.
     m.add_class::<PyDoji>()?;
     m.add_class::<PyHammer>()?;
@@ -28666,3 +28667,32 @@ impl PyResampler {
             .map(|c| (c.open, c.high, c.low, c.close, c.volume, c.timestamp)))
     }
 }
+
+// ===== Data layer: CSV candle reader =====
+
+/// Parse OHLCV candles from a CSV string (header `timestamp,open,high,low,close,
+/// volume`; a leading UTF-8 BOM is stripped).
+#[pyclass(name = "CandleReader", module = "wickra._wickra", skip_from_py_object)]
+#[derive(Clone)]
+struct PyCandleReader {
+    candles: Vec<wc::Candle>,
+}
+
+#[pymethods]
+impl PyCandleReader {
+    #[new]
+    fn new(csv: &str) -> PyResult<Self> {
+        let mut reader =
+            wickra_data::csv::CandleReader::from_reader(csv.as_bytes()).map_err(map_data_err)?;
+        let candles = reader.read_all().map_err(map_data_err)?;
+        Ok(Self { candles })
+    }
+
+    /// Return every parsed candle as `(open, high, low, close, volume, timestamp)`.
+    fn read(&self) -> Vec<CandleTuple> {
+        self.candles
+            .iter()
+            .map(|c| (c.open, c.high, c.low, c.close, c.volume, c.timestamp))
+            .collect()
+    }
+}

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)