feat: implement ML filter with LightGBM for trading signal validation

- Added MLFilter class to load and evaluate LightGBM model for trading signals.
- Introduced retraining mechanism to update the model daily based on new data.
- Created feature engineering and label building utilities for model training.
- Updated bot logic to incorporate ML filter for signal validation.
- Added scripts for data fetching and model training.

Made-with: Cursor

src/ml_filter.py

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+from pathlib import Path
+import joblib
+import pandas as pd
+from loguru import logger
+
+
+class MLFilter:
+    """
+    LightGBM 모델을 로드하고 진입 여부를 판단한다.
+    모델 파일이 없으면 항상 진입을 허용한다 (폴백).
+    """
+
+    def __init__(self, model_path: str = "models/lgbm_filter.pkl", threshold: float = 0.60):
+        self._model_path = Path(model_path)
+        self._threshold = threshold
+        self._model = None
+        self._try_load()
+
+    def _try_load(self):
+        if self._model_path.exists():
+            try:
+                self._model = joblib.load(self._model_path)
+                logger.info(f"ML 필터 모델 로드 완료: {self._model_path}")
+            except Exception as e:
+                logger.warning(f"ML 필터 모델 로드 실패: {e}")
+                self._model = None
+
+    def is_model_loaded(self) -> bool:
+        return self._model is not None
+
+    def should_enter(self, features: pd.Series) -> bool:
+        """
+        확률 >= threshold 이면 True (진입 허용).
+        모델 없으면 True 반환 (폴백).
+        """
+        if not self.is_model_loaded():
+            return True
+        try:
+            X = features.to_frame().T
+            proba = self._model.predict_proba(X)[0][1]
+            logger.debug(f"ML 필터 확률: {proba:.3f} (임계값: {self._threshold})")
+            return bool(proba >= self._threshold)
+        except Exception as e:
+            logger.warning(f"ML 필터 예측 오류 (폴백 허용): {e}")
+            return True
+
+    def reload_model(self):
+        """재학습 후 모델을 핫 리로드한다."""
+        self._try_load()
+        logger.info("ML 필터 모델 리로드 완료")