refactor(ml): add MLFilter.from_model(), fix validator initial_balance

- MLFilter.from_model() classmethod eliminates brittle __new__() private-attribute
  manipulation in backtester walk-forward model injection
- backtest_validator._check_invariants() now accepts cfg and uses cfg.initial_balance
  instead of a hardcoded 1000.0 for the negative-balance invariant check
- backtester.py walk-forward injection block simplified to use the new factory method

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

src/backtest_validator.py

@@ -30,7 +30,7 @@ def validate(trades: list[dict], summary: dict, cfg) -> dict:
     results: list[CheckResult] = []
 
     # 검증 1: 논리적 불변 조건
-    results.extend(_check_invariants(trades))
+    results.extend(_check_invariants(trades, cfg))
 
     # 검증 2: 통계적 이상 감지
     results.extend(_check_statistics(trades, summary))
@@ -47,7 +47,7 @@ def validate(trades: list[dict], summary: dict, cfg) -> dict:
     }
 
 
-def _check_invariants(trades: list[dict]) -> list[CheckResult]:
+def _check_invariants(trades: list[dict], cfg=None) -> list[CheckResult]:
     """논리적 불변 조건. 하나라도 위반 시 FAIL."""
     results = []
 
@@ -120,7 +120,7 @@ def _check_invariants(trades: list[dict]) -> list[CheckResult]:
     ))
 
     # 5. 잔고가 음수가 된 적 없음
-    balance = 1000.0  # cfg.initial_balance를 몰라도 trades에서 추적 가능
+    balance = cfg.initial_balance if cfg is not None else 1000.0
     min_balance = balance
     for t in trades:
         balance += t["net_pnl"]

src/backtester.py

@@ -317,16 +317,9 @@ class Backtester:
             self.ml_filters = {}
             for sym in self.cfg.symbols:
                 if sym in ml_models and ml_models[sym] is not None:
-                    mf = MLFilter.__new__(MLFilter)
-                    mf._disabled = False
-                    mf._onnx_session = None
-                    mf._lgbm_model = ml_models[sym]
-                    mf._threshold = self.cfg.ml_threshold
-                    mf._onnx_path = Path("/dev/null")
-                    mf._lgbm_path = Path("/dev/null")
-                    mf._loaded_onnx_mtime = 0.0
-                    mf._loaded_lgbm_mtime = 0.0
-                    self.ml_filters[sym] = mf
+                    self.ml_filters[sym] = MLFilter.from_model(
+                        ml_models[sym], threshold=self.cfg.ml_threshold
+                    )
                 else:
                     self.ml_filters[sym] = None
 

src/ml_filter.py

@@ -155,6 +155,21 @@ class MLFilter:
             logger.warning(f"ML 필터 예측 오류 (진입 차단): {e}")
             return False
 
+    @classmethod
+    def from_model(cls, model, threshold: float = 0.55) -> "MLFilter":
+        """외부에서 학습된 LightGBM 모델을 주입하여 MLFilter를 생성한다.
+        backtester walk-forward에서 사용."""
+        instance = cls.__new__(cls)
+        instance._disabled = False
+        instance._onnx_session = None
+        instance._lgbm_model = model
+        instance._threshold = threshold
+        instance._onnx_path = Path("/dev/null")
+        instance._lgbm_path = Path("/dev/null")
+        instance._loaded_onnx_mtime = 0.0
+        instance._loaded_lgbm_mtime = 0.0
+        return instance
+
     def reload_model(self):
         """외부에서 강제 리로드할 때 사용 (하위 호환)."""
         prev_backend = self.active_backend

tests/test_ml_pipeline_fixes.py

@@ -100,3 +100,16 @@ def test_mlx_no_double_normalization():
 
     assert np.allclose(model._mean, 0.0), "normalize=False시 mean은 0이어야 한다"
     assert np.allclose(model._std, 1.0), "normalize=False시 std는 1이어야 한다"
+
+
+def test_ml_filter_from_model():
+    """MLFilter.from_model()로 LightGBM 모델을 주입할 수 있어야 한다."""
+    from src.ml_filter import MLFilter
+    from unittest.mock import MagicMock
+
+    mock_model = MagicMock()
+    mock_model.predict_proba.return_value = [[0.3, 0.7]]
+
+    mf = MLFilter.from_model(mock_model, threshold=0.55)
+    assert mf.is_model_loaded()
+    assert mf.active_backend == "LightGBM"