7e4e9315c2
- 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
36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
import pytest
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import json
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from pathlib import Path
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from unittest.mock import AsyncMock, MagicMock, patch
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from src.retrainer import Retrainer
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@pytest.mark.asyncio
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async def test_retrain_calls_train(tmp_path):
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"""재학습 시 train 함수가 호출되는지 확인"""
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ml_filter = MagicMock()
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r = Retrainer(ml_filter=ml_filter, data_path=str(tmp_path / "data.parquet"))
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with patch("src.retrainer.fetch_and_save", new_callable=AsyncMock) as mock_fetch, \
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patch("src.retrainer.run_training", return_value=0.72) as mock_train, \
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patch("src.retrainer.get_current_auc", return_value=0.65):
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await r.retrain()
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mock_fetch.assert_called_once()
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mock_train.assert_called_once()
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@pytest.mark.asyncio
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async def test_retrain_rollback_when_worse(tmp_path):
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"""새 모델이 기존보다 나쁘면 롤백"""
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ml_filter = MagicMock()
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r = Retrainer(ml_filter=ml_filter, data_path=str(tmp_path / "data.parquet"))
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with patch("src.retrainer.fetch_and_save", new_callable=AsyncMock), \
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patch("src.retrainer.run_training", return_value=0.55), \
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patch("src.retrainer.get_current_auc", return_value=0.70), \
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patch("src.retrainer.rollback_model") as mock_rollback:
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await r.retrain()
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mock_rollback.assert_called_once()
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