feat: implement HOLD negative sampling and stratified undersampling in ML pipeline

Added HOLD candles as negative samples to increase training data from ~535 to ~3,200 samples. Introduced a negative_ratio parameter in generate_dataset_vectorized() for sampling HOLD candles alongside signal candles. Implemented stratified undersampling to ensure signal samples are preserved during training. Updated relevant tests to validate new functionality and maintain compatibility with existing tests.

- Modified dataset_builder.py to include HOLD negative sampling logic
- Updated train_model.py to apply stratified undersampling
- Added tests for new sampling methods

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

tests/test_bot.py

@@ -17,6 +17,7 @@ def config():
         "RISK_PER_TRADE": "0.02",
         "NOTION_TOKEN": "secret_test",
         "NOTION_DATABASE_ID": "db_test",
+        "DISCORD_WEBHOOK_URL": "",
     })
     return Config()