fix(signal_v2-phase3b): force FP32 + predict_quantiles positional args

ChronosBoltPipeline.predict_quantiles takes `inputs` positional, not
`context` keyword. Use positional with TypeError fallback for older
chronos versions.

FP16 caused inf overflow on Korean stock prices (e.g. 280,000원 >
FP16 max 65,504). Force FP32 for prices to avoid this. Chronos model
itself handles internal scaling.

Verified end-to-end: 60-day daily fetch → Chronos predict → quantile
output. Example 005930: median=-0.59%, q10=-8.9%, q90=+6.4%, conf=0.0
(low conf is mathematically correct when median is near zero relative
to distribution width).

45/45 tests still pass.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

signal_v2/chronos_predictor.py

@@ -35,7 +35,9 @@ class ChronosPredictor:
             pipeline_cls = ChronosPipeline
 
         self._device = device or ("cuda" if torch.cuda.is_available() else "cpu")
-        dtype = torch.float16 if self._device == "cuda" else torch.float32
+        # Always use float32 — Korean stock prices (e.g. 280,000원) exceed FP16 max (~65,504)
+        # causing inf in quantile output. FP32 is safe for typical price magnitudes.
+        dtype = torch.float32
         logger.info("Loading Chronos pipeline: %s on %s (cls=%s)",
                     model_name, self._device, pipeline_cls.__name__)
         # Try `dtype` (newer API) first, fall back to `torch_dtype` (older)
@@ -76,6 +78,14 @@ class ChronosPredictor:
         # Modern API: predict_quantiles (ChronosBolt / Chronos-2)
         if hasattr(self._pipeline, "predict_quantiles"):
             quantile_levels = [0.1, 0.5, 0.9]
+            # ChronosBolt API: positional `inputs` (first arg). Older variants use `context`.
+            try:
+                quantiles_tensor, _ = self._pipeline.predict_quantiles(
+                    contexts,
+                    prediction_length=prediction_length,
+                    quantile_levels=quantile_levels,
+                )
+            except TypeError:
                 quantiles_tensor, _ = self._pipeline.predict_quantiles(
                     context=contexts,
                     prediction_length=prediction_length,