fix(ai_trade): Chronos confidence를 absolute spread 기반으로 통일 (F4)

코드 리뷰 F4: signal_generator의 hard gate(L79)는 absolute spread(0.6 threshold)를
쓰지만 chronos_predictor:106의 confidence는 relative spread (q90-q10)/max(|median|, 0.001).
zero-shot median≈0 케이스에서 spread가 폭증하여 conf=0으로 눌리고 결국 모든
매수 신호가 confidence_threshold(0.7)를 못 넘김.

산식 통일: conf = max(0, min(1, 1 - spread/_SPREAD_THRESHOLD)). _SPREAD_THRESHOLD=0.6
은 signal_generator hard gate와 동일.

- spread≈0 → conf≈1 (확신)
- spread=0.3 → conf=0.5 (중간)
- spread≥0.6 → conf=0 (거부)

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

ai_trade/chronos_predictor.py

@@ -10,6 +10,10 @@ import numpy as np
 logger = logging.getLogger(__name__)
 KST = ZoneInfo("Asia/Seoul")
 
+# F4: signal_generator hard gate와 동일한 absolute spread threshold.
+# zero-shot median≈0에서 conf가 0으로 폭락하던 relative 산식 (spread/abs(median)) 대체.
+_SPREAD_THRESHOLD = 0.6
+
 
 @dataclass
 class ChronosPrediction:
@@ -103,8 +107,8 @@ class ChronosPredictor:
                 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)))
+                spread = q90 - q10  # F4: absolute spread
+                conf = float(max(0.0, min(1.0, 1.0 - spread / _SPREAD_THRESHOLD)))
                 results[ticker] = ChronosPrediction(
                     median=median, q10=q10, q90=q90, conf=conf, as_of=now_iso,
                 )
@@ -124,8 +128,8 @@ class ChronosPredictor:
             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)))
+            spread = q90 - q10  # F4: absolute spread
+            conf = float(max(0.0, min(1.0, 1.0 - spread / _SPREAD_THRESHOLD)))
             results[ticker] = ChronosPrediction(
                 median=median, q10=q10, q90=q90, conf=conf, as_of=now_iso,
             )