fix(mlx): remove double normalization in walk-forward validation

Add normalize=False parameter to MLXFilter.fit() so external callers
can skip internal normalization. Remove the external normalization +
manual _mean/_std reset hack from walk_forward_auc() in train_mlx_model.py.

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

scripts/train_mlx_model.py

@@ -219,17 +219,8 @@ def walk_forward_auc(
         y_tr_bal = y_tr[bal_idx]
         w_tr_bal = w_tr[bal_idx]
 
-        # 폴드별 정규화 (학습 데이터 기준으로 계산, 검증에도 동일 적용)
-        mean = X_tr_bal.mean(axis=0)
-        std = X_tr_bal.std(axis=0) + 1e-8
-        X_tr_norm = (X_tr_bal - mean) / std
-        X_val_norm = (X_val_raw - mean) / std
-
-        # DataFrame으로 래핑해서 MLXFilter.fit()에 전달
-        # fit() 내부 정규화가 덮어쓰지 않도록 이미 정규화된 데이터를 넘기고
-        # _mean=0, _std=1로 고정해 이중 정규화를 방지
-        X_tr_df = pd.DataFrame(X_tr_norm, columns=FEATURE_COLS)
-        X_val_df = pd.DataFrame(X_val_norm, columns=FEATURE_COLS)
+        X_tr_df = pd.DataFrame(X_tr_bal, columns=FEATURE_COLS)
+        X_val_df = pd.DataFrame(X_val_raw, columns=FEATURE_COLS)
 
         model = MLXFilter(
             input_dim=len(FEATURE_COLS),
@@ -239,9 +230,7 @@ def walk_forward_auc(
             batch_size=256,
         )
         model.fit(X_tr_df, pd.Series(y_tr_bal), sample_weight=w_tr_bal)
-        # fit()이 내부에서 다시 정규화하므로 저장된 mean/std를 항등 변환으로 교체
-        model._mean = np.zeros(len(FEATURE_COLS), dtype=np.float32)
-        model._std = np.ones(len(FEATURE_COLS), dtype=np.float32)
+        # fit() handles normalization internally, predict_proba() applies same mean/std
 
         proba = model.predict_proba(X_val_df)
         auc = roc_auc_score(y_val, proba) if len(np.unique(y_val)) > 1 else 0.5

src/mlx_filter.py

@@ -141,18 +141,24 @@ class MLXFilter:
         X: pd.DataFrame,
         y: pd.Series,
         sample_weight: np.ndarray | None = None,
+        normalize: bool = True,
     ) -> "MLXFilter":
         X_np = X[FEATURE_COLS].values.astype(np.float32)
         y_np = y.values.astype(np.float32)
 
-        # nan-safe 정규화: nanmean/nanstd로 통계 계산 후 nan → 0.0 대치
-        # (z-score 후 0.0 = 평균값, 신경망에 줄 수 있는 가장 무난한 결측 대치값)
-        mean_vals  = np.nanmean(X_np, axis=0)
-        self._mean = np.nan_to_num(mean_vals, nan=0.0)   # 전체-NaN 컬럼 → 평균 0.0
-        std_vals   = np.nanstd(X_np, axis=0)
-        self._std  = np.nan_to_num(std_vals, nan=1.0) + 1e-8  # 전체-NaN 컬럼 → std 1.0
-        X_np = (X_np - self._mean) / self._std
-        X_np = np.nan_to_num(X_np, nan=0.0)
+        if normalize:
+            # nan-safe 정규화: nanmean/nanstd로 통계 계산 후 nan → 0.0 대치
+            # (z-score 후 0.0 = 평균값, 신경망에 줄 수 있는 가장 무난한 결측 대치값)
+            mean_vals  = np.nanmean(X_np, axis=0)
+            self._mean = np.nan_to_num(mean_vals, nan=0.0)   # 전체-NaN 컬럼 → 평균 0.0
+            std_vals   = np.nanstd(X_np, axis=0)
+            self._std  = np.nan_to_num(std_vals, nan=1.0) + 1e-8  # 전체-NaN 컬럼 → std 1.0
+            X_np = (X_np - self._mean) / self._std
+            X_np = np.nan_to_num(X_np, nan=0.0)
+        else:
+            self._mean = np.zeros(X_np.shape[1], dtype=np.float32)
+            self._std = np.ones(X_np.shape[1], dtype=np.float32)
+            X_np = np.nan_to_num(X_np, nan=0.0)
 
         w_np = sample_weight.astype(np.float32) if sample_weight is not None else None
 

tests/test_ml_pipeline_fixes.py

@@ -77,3 +77,26 @@ def test_equity_curve_includes_unrealized_pnl():
 
     last = bt.equity_curve[-1]
     assert last["equity"] == 1050.0, f"Expected 1050.0 (1000+50), got {last['equity']}"
+
+
+def test_mlx_no_double_normalization():
+    """MLXFilter.fit()에 normalize=False를 전달하면 내부 정규화를 건너뛰어야 한다."""
+    pytest.importorskip("mlx.core")
+    import numpy as np
+    import pandas as pd
+    from src.mlx_filter import MLXFilter
+    from src.ml_features import FEATURE_COLS
+
+    n_features = len(FEATURE_COLS)
+    rng = np.random.default_rng(42)
+    X = pd.DataFrame(
+        rng.standard_normal((100, n_features)).astype(np.float32),
+        columns=FEATURE_COLS,
+    )
+    y = pd.Series(rng.integers(0, 2, 100).astype(np.float32))
+
+    model = MLXFilter(input_dim=n_features, hidden_dim=16, epochs=1, batch_size=32)
+    model.fit(X, y, normalize=False)
+
+    assert np.allclose(model._mean, 0.0), "normalize=False시 mean은 0이어야 한다"
+    assert np.allclose(model._std, 1.0), "normalize=False시 std는 1이어야 한다"