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"]

signal_v2/tests/test_chronos_predictor.py

@@ -11,6 +11,8 @@ def mock_pipeline():
     with patch("chronos.BaseChronosPipeline") as cls:
         cls.__name__ = "BaseChronosPipeline"
         instance = MagicMock()
+        # ChronosBolt API: predict_quantiles returns (quantiles_tensor, mean_tensor)
+        # Modern (predict_quantiles) branch will be used since hasattr(MagicMock, "predict_quantiles") is True.
         cls.from_pretrained.return_value = instance
         yield instance
 
@@ -26,11 +28,16 @@ def _daily_ohlcv(close_seq):
              "close": c, "volume": 1000} for i, c in enumerate(close_seq)]
 
 
-def test_predict_batch_returns_prediction_dict(mock_pipeline, mock_torch_cpu):
+def _mk_quantiles_tensor(q10_price: float, q50_price: float, q90_price: float):
-    """mock pipeline → dict[ticker, ChronosPrediction]. last_close=100, samples=102 → ~+2% return."""
+    """Helper: build predict_quantiles return tensor shape [1, 1, 3]."""
     import torch
-    samples = np.full((100,), 102.0)
+    return torch.tensor([[[q10_price, q50_price, q90_price]]], dtype=torch.float32)
-    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))
+
+
+def test_predict_batch_returns_prediction_dict(mock_pipeline, mock_torch_cpu):
+    """mock predict_quantiles → dict[ticker, ChronosPrediction]. last_close=100, q50=102 → median≈+2%."""
+    quantiles = _mk_quantiles_tensor(101.5, 102.0, 102.5)  # narrow around 102
+    mock_pipeline.predict_quantiles.return_value = (quantiles, None)
 
     from signal_v2.chronos_predictor import ChronosPredictor, ChronosPrediction
     predictor = ChronosPredictor(model_name="mock-model")
@@ -43,11 +50,12 @@ def test_predict_batch_returns_prediction_dict(mock_pipeline, mock_torch_cpu):
 
 
 def test_conf_high_when_distribution_narrow(mock_pipeline, mock_torch_cpu):
-    """좁은 distribution → conf ≈ 1."""
+    """좁은 distribution (q90-q10 작음, median 0 아님) → conf ≈ 1."""
-    import torch
+    # last_close=100, q10=101.99, q50=102.00, q90=102.01
-    np.random.seed(42)
+    # returns: q10=0.0199, q50=0.02, q90=0.0201
-    samples = np.random.normal(102.0, 0.1, 100)
+    # spread = (0.0201 - 0.0199) / max(0.02, 0.001) = 0.0002/0.02 = 0.01 → conf = 1 - 0.005 = 0.995
-    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))
+    quantiles = _mk_quantiles_tensor(101.99, 102.0, 102.01)
+    mock_pipeline.predict_quantiles.return_value = (quantiles, None)
 
     from signal_v2.chronos_predictor import ChronosPredictor
     predictor = ChronosPredictor(model_name="mock-model")
@@ -58,10 +66,11 @@ def test_conf_high_when_distribution_narrow(mock_pipeline, mock_torch_cpu):
 
 def test_conf_low_when_distribution_wide(mock_pipeline, mock_torch_cpu):
     """넓은 distribution → conf ≈ 0."""
-    import torch
+    # last_close=100, q10=70, q50=100, q90=130
-    np.random.seed(42)
+    # returns: q10=-0.3, q50=0.0, q90=0.3
-    samples = np.random.normal(100.0, 30.0, 100)
+    # spread = (0.3 - (-0.3)) / max(0.0, 0.001) = 0.6 / 0.001 = 600 → conf = max(0, 1 - 300) = 0
-    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))
+    quantiles = _mk_quantiles_tensor(70.0, 100.0, 130.0)
+    mock_pipeline.predict_quantiles.return_value = (quantiles, None)
 
     from signal_v2.chronos_predictor import ChronosPredictor
     predictor = ChronosPredictor(model_name="mock-model")
@@ -71,14 +80,13 @@ def test_conf_low_when_distribution_wide(mock_pipeline, mock_torch_cpu):
 
 
 def test_return_computed_from_price_relative_to_last_close(mock_pipeline, mock_torch_cpu):
-    """price 예측 → last_close 대비 return 변환. last_close=100, samples=110 → return ~+10%."""
+    """price 예측 → last_close 대비 return 변환. last_close=100, q50=110 → return ≈ +10%."""
-    import torch
+    quantiles = _mk_quantiles_tensor(109.0, 110.0, 111.0)
-    samples = np.full((100,), 110.0)
+    mock_pipeline.predict_quantiles.return_value = (quantiles, None)
-    mock_pipeline.predict.return_value = torch.tensor(samples.reshape(1, 100, 1))
 
     from signal_v2.chronos_predictor import ChronosPredictor
     predictor = ChronosPredictor(model_name="mock-model")
-    # last close in the seq = 100
+    # last close = 100
     daily = {"005930": _daily_ohlcv(list(range(41, 101)))}  # last = 100
     result = predictor.predict_batch(daily)
     assert abs(result["005930"].median - 0.10) < 0.001