fix(signal_v2-phase3b): ChronosBolt predict_quantiles API support

ChronosBoltPipeline.predict() does not accept `context` kwarg; it
uses positional-only and is deterministic (no num_samples). Switch
to predict_quantiles(context, prediction_length, quantile_levels)
which returns (quantiles_tensor, mean_tensor).

Implementation: if hasattr(pipeline, "predict_quantiles") → modern
quantile branch. Else fall back to legacy sample-based predict (T5).

Tests: switch to predict_quantiles mock returning (quantiles, None)
with shape [1, 1, 3] for q10/q50/q90 directly.

45/45 tests pass.

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

signal_v2/chronos_predictor.py

@@ -55,7 +55,11 @@ class ChronosPredictor:
         prediction_length: int = 1,
         num_samples: int = 100,
     ) -> dict[str, ChronosPrediction]:
-        """종목별 1-day return 분포 예측."""
+        """종목별 1-day return 분포 예측.
+
+        ChronosBolt / Chronos-2 등 신모델은 predict_quantiles 사용 (deterministic).
+        Legacy ChronosPipeline (T5) 는 sample-based predict.
+        """
         import torch
 
         tickers = list(daily_ohlcv_dict.keys())
@@ -66,16 +70,43 @@ class ChronosPredictor:
             torch.tensor([bar["close"] for bar in daily_ohlcv_dict[t]], dtype=torch.float32)
             for t in tickers
         ]
+        now_iso = datetime.now(KST).isoformat()
+        results: dict[str, ChronosPrediction] = {}
+
+        # Modern API: predict_quantiles (ChronosBolt / Chronos-2)
+        if hasattr(self._pipeline, "predict_quantiles"):
+            quantile_levels = [0.1, 0.5, 0.9]
+            quantiles_tensor, _ = self._pipeline.predict_quantiles(
+                context=contexts,
+                prediction_length=prediction_length,
+                quantile_levels=quantile_levels,
+            )
+            quantiles_np = (
+                quantiles_tensor.cpu().numpy()
+                if hasattr(quantiles_tensor, "cpu")
+                else np.asarray(quantiles_tensor)
+            )
+            # shape: [num_series, prediction_length, 3]
+            for i, ticker in enumerate(tickers):
+                q10_price, q50_price, q90_price = quantiles_np[i, 0, :]
+                last_close = daily_ohlcv_dict[ticker][-1]["close"]
+                median = float((q50_price - last_close) / last_close)
+                q10 = float((q10_price - last_close) / last_close)
+                q90 = float((q90_price - last_close) / last_close)
+                spread = (q90 - q10) / max(abs(median), 0.001)
+                conf = float(max(0.0, min(1.0, 1.0 - spread / 2.0)))
+                results[ticker] = ChronosPrediction(
+                    median=median, q10=q10, q90=q90, conf=conf, as_of=now_iso,
+                )
+            return results
+
+        # Legacy API: sample-based predict (ChronosPipeline T5)
         forecasts = self._pipeline.predict(
             context=contexts,
             prediction_length=prediction_length,
             num_samples=num_samples,
         )
-        # Convert to numpy if tensor
         forecasts_np = forecasts.numpy() if hasattr(forecasts, "numpy") else np.asarray(forecasts)
-
-        now_iso = datetime.now(KST).isoformat()
-        results: dict[str, ChronosPrediction] = {}
         for i, ticker in enumerate(tickers):
             samples = forecasts_np[i, :, 0]
             last_close = daily_ohlcv_dict[ticker][-1]["close"]