feat(phase-3): Chronos zero-shot 예측 + 피처 빌더

- backend/app/models/chronos.py: amazon/chronos-t5-small (env CHRONOS_MODEL
  override 가능). lazy singleton, cuda + bf16 자동, q10/median/q90 + raw
  samples 반환 (앙상블 가중평균용).
- backend/app/models/features.py: 종목별 학습/추론 피처 DataFrame.
  OHLCV + TA(rsi/macd/atr/bb/sma/ema/vol_z) + 외인기관거래대금 + macro
  (kospi/kosdaq/usdkrw/us10y + r1) + sentiment(v_sentiment_daily, 3d rolling).
  학습용은 with_targets=True 로 y_close_h{1,3,5}, y_ret_h*, y_dir_h*
  (±0.3% flat band) 추가.
- pyproject.toml: chronos-forecasting 1.4.1, accelerate 0.30.1, joblib 1.4.2.

이 단계까지는 코드만. 실제 모델 다운로드는 첫 ping/predict 호출 시점에.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

backend/app/models/chronos.py

@@ -0,0 +1,118 @@
+"""Chronos zero-shot 시계열 예측 어댑터.
+
+모델: amazon/chronos-t5-small (46M, 빠르고 RTX 3070 Ti 에 충분히 들어감).
+환경변수 CHRONOS_MODEL 로 base/large 로 바꿀 수 있음.
+
+입력: 종가 시계열 (list[float], 최소 32 step).
+출력: horizon 일 quantile forecast (q10/median/q90).
+
+lazy singleton 으로 첫 호출 시 모델 로드. 디바이스는 settings.model_device 따라.
+"""
+from __future__ import annotations
+
+import logging
+import os
+import threading
+from dataclasses import dataclass
+
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+MODEL_NAME = os.environ.get("CHRONOS_MODEL", "amazon/chronos-t5-small")
+
+_lock = threading.Lock()
+_state: dict[str, object] = {"loaded": False, "pipe": None, "device": None}
+
+
+@dataclass
+class ChronosForecast:
+    horizon: int
+    median: list[float]
+    q10: list[float]
+    q90: list[float]
+    samples: list[list[float]]  # raw samples for ensemble downstream
+
+
+def _resolve_device() -> str:
+    import torch  # lazy
+
+    pref = (settings.model_device or "auto").lower()
+    if pref == "cuda":
+        return "cuda" if torch.cuda.is_available() else "cpu"
+    if pref == "cpu":
+        return "cpu"
+    return "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def _load() -> None:
+    global _state
+    with _lock:
+        if _state["loaded"]:
+            return
+        import torch
+        from chronos import ChronosPipeline
+
+        token = settings.huggingface_token or None
+        if token:
+            os.environ.setdefault("HUGGINGFACE_HUB_TOKEN", token)
+            os.environ.setdefault("HF_TOKEN", token)
+
+        device = _resolve_device()
+        # bf16 은 RTX 30xx 이상에서 지원. cpu 에선 fp32.
+        dtype = torch.bfloat16 if device == "cuda" else torch.float32
+        logger.info("loading Chronos %s on %s (dtype=%s)", MODEL_NAME, device, dtype)
+        pipe = ChronosPipeline.from_pretrained(
+            MODEL_NAME,
+            device_map=device,
+            torch_dtype=dtype,
+        )
+        _state.update({"loaded": True, "pipe": pipe, "device": device})
+
+
+def forecast(
+    series: list[float],
+    *,
+    horizon: int = 5,
+    num_samples: int = 30,
+) -> ChronosForecast:
+    """series 의 마지막 시점 이후 horizon 일 예측.
+
+    series 는 일봉 종가. 최소 32개 권장 (그보다 짧으면 Chronos 분위 안정성 떨어짐).
+    """
+    if len(series) < 32:
+        raise ValueError(
+            f"series too short ({len(series)}) for Chronos forecast (need >=32)"
+        )
+    _load()
+    import numpy as np
+    import torch
+
+    pipe = _state["pipe"]
+    context = torch.tensor([float(x) for x in series], dtype=torch.float32)
+    with torch.no_grad():
+        samples = pipe.predict(
+            context=context,
+            prediction_length=horizon,
+            num_samples=num_samples,
+        )
+    # samples: (1, num_samples, prediction_length)
+    arr = samples[0].cpu().float().numpy()
+    q10 = np.quantile(arr, 0.10, axis=0).tolist()
+    median = np.quantile(arr, 0.50, axis=0).tolist()
+    q90 = np.quantile(arr, 0.90, axis=0).tolist()
+    return ChronosForecast(
+        horizon=horizon,
+        median=[float(x) for x in median],
+        q10=[float(x) for x in q10],
+        q90=[float(x) for x in q90],
+        samples=[[float(x) for x in row] for row in arr.tolist()],
+    )
+
+
+def ping() -> dict[str, object]:
+    try:
+        _load()
+        return {"status": "ok", "model": MODEL_NAME, "device": _state["device"]}
+    except Exception as exc:  # noqa: BLE001
+        return {"status": "failed", "model": MODEL_NAME, "error": str(exc)}