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

docker-compose.yml

@@ -5,8 +5,12 @@ services:
     restart: unless-stopped
     env_file:
       - .env
+    environment:
+      - TZ=Asia/Seoul
     volumes:
       - ./logs:/app/logs
+      - ./models:/app/models
+      - ./data:/app/data
     logging:
       driver: "json-file"
       options: