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/label_builder.py

@@ -0,0 +1,29 @@
+from typing import Optional
+
+
+def build_labels(
+    future_closes: list[float],
+    future_highs: list[float],
+    future_lows: list[float],
+    take_profit: float,
+    stop_loss: float,
+    side: str,
+) -> Optional[int]:
+    """
+    진입 이후 미래 캔들을 순서대로 확인해 TP/SL 도달 여부를 판단한다.
+    LONG: high >= TP → 1, low <= SL → 0
+    SHORT: low <= TP → 1, high >= SL → 0
+    둘 다 미도달 → None (학습 데이터에서 제외)
+    """
+    for high, low in zip(future_highs, future_lows):
+        if side == "LONG":
+            if high >= take_profit:
+                return 1
+            if low <= stop_loss:
+                return 0
+        else:  # SHORT
+            if low <= take_profit:
+                return 1
+            if high >= stop_loss:
+                return 0
+    return None