feat(signal_v2-phase3b): chronos_predictor + 4 mock tests

ChronosPredictor wraps HuggingFace ChronosPipeline. Batch predict
returns ChronosPrediction(median, q10, q90, conf, as_of) per ticker.
Confidence = 1 - clamp(spread/2, 0, 1) where spread = (q90-q10) / |median|.
Lazy import of chronos lib (heavy). GPU auto-detect with FP16.

44 tests pass.

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

signal_v2/chronos_predictor.py

@@ -0,0 +1,78 @@
+"""Chronos-2 zero-shot forecaster wrapper."""
+from __future__ import annotations
+import logging
+from dataclasses import dataclass
+from datetime import datetime
+from zoneinfo import ZoneInfo
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+KST = ZoneInfo("Asia/Seoul")
+
+
+@dataclass
+class ChronosPrediction:
+    median: float
+    q10: float
+    q90: float
+    conf: float
+    as_of: str
+
+
+class ChronosPredictor:
+    """HuggingFace Chronos-2 zero-shot forecaster."""
+
+    def __init__(self, model_name: str = "amazon/chronos-2", device: str | None = None):
+        from chronos import ChronosPipeline
+        import torch
+
+        self._device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        logger.info("Loading Chronos pipeline: %s on %s", model_name, self._device)
+        self._pipeline = ChronosPipeline.from_pretrained(
+            model_name,
+            device_map=self._device,
+            torch_dtype=torch.float16 if self._device == "cuda" else torch.float32,
+        )
+        logger.info("Chronos pipeline loaded.")
+
+    def predict_batch(
+        self,
+        daily_ohlcv_dict: dict[str, list[dict]],
+        prediction_length: int = 1,
+        num_samples: int = 100,
+    ) -> dict[str, ChronosPrediction]:
+        """종목별 1-day return 분포 예측."""
+        import torch
+
+        tickers = list(daily_ohlcv_dict.keys())
+        if not tickers:
+            return {}
+
+        contexts = [
+            torch.tensor([bar["close"] for bar in daily_ohlcv_dict[t]], dtype=torch.float32)
+            for t in tickers
+        ]
+        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"]
+            returns = (samples - last_close) / last_close
+            median = float(np.quantile(returns, 0.5))
+            q10 = float(np.quantile(returns, 0.1))
+            q90 = float(np.quantile(returns, 0.9))
+            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