feat: implement 15-minute timeframe upgrade for model training and data processing

- Introduced a new markdown document detailing the plan to transition the entire pipeline from a 1-minute to a 15-minute timeframe, aiming to improve model AUC from 0.49-0.50 to over 0.53.
- Updated key parameters across multiple scripts, including `LOOKAHEAD` adjustments and default data paths to reflect the new 15-minute interval.
- Modified data fetching and training scripts to ensure compatibility with the new timeframe, including changes in `fetch_history.py`, `train_model.py`, and `train_and_deploy.sh`.
- Enhanced the bot's data stream configuration to operate on a 15-minute interval, ensuring real-time data processing aligns with the new model training strategy.
- Updated training logs to capture new model performance metrics under the revised timeframe.

src/ml_filter.py

@@ -44,6 +44,11 @@ class MLFilter:
         self._try_load()
 
     def _try_load(self):
+        # 로드 여부와 무관하게 두 파일의 현재 mtime을 항상 기록한다.
+        # 이렇게 해야 로드하지 않은 쪽 파일이 나중에 변경됐을 때만 리로드가 트리거된다.
+        self._loaded_onnx_mtime = _mtime(self._onnx_path)
+        self._loaded_lgbm_mtime = _mtime(self._lgbm_path)
+
         # ONNX 우선 시도
         if self._onnx_path.exists():
             try:
@@ -53,8 +58,6 @@ class MLFilter:
                     providers=["CPUExecutionProvider"],
                 )
                 self._lgbm_model = None
-                self._loaded_onnx_mtime = _mtime(self._onnx_path)
-                self._loaded_lgbm_mtime = 0.0
                 logger.info(
                     f"ML 필터 로드: ONNX ({self._onnx_path}) "
                     f"| 임계값={self._threshold}"
@@ -68,8 +71,6 @@ class MLFilter:
         if self._lgbm_path.exists():
             try:
                 self._lgbm_model = joblib.load(self._lgbm_path)
-                self._loaded_lgbm_mtime = _mtime(self._lgbm_path)
-                self._loaded_onnx_mtime = 0.0
                 logger.info(
                     f"ML 필터 로드: LightGBM ({self._lgbm_path}) "
                     f"| 임계값={self._threshold}"