diff --git a/scripts/train_mlx_model.py b/scripts/train_mlx_model.py
index d0f196d..7ecf913 100644
--- a/scripts/train_mlx_model.py
+++ b/scripts/train_mlx_model.py
@@ -45,7 +45,7 @@ def _split_combined(df: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame | None
     return xrp_df, btc_df, eth_df
 
 
-def train_mlx(data_path: str, time_weight_decay: float = 2.0) -> float:
+def train_mlx(data_path: str, time_weight_decay: float = 2.0, atr_sl_mult: float = 2.0, atr_tp_mult: float = 2.0) -> float:
     print(f"데이터 로드: {data_path}")
     raw = pd.read_parquet(data_path)
     print(f"캔들 수: {len(raw)}")
@@ -58,7 +58,8 @@ def train_mlx(data_path: str, time_weight_decay: float = 2.0) -> float:
 
     print("\n데이터셋 생성 중...")
     t0 = time.perf_counter()
-    dataset = generate_dataset_vectorized(df, btc_df=btc_df, eth_df=eth_df, time_weight_decay=time_weight_decay)
+    dataset = generate_dataset_vectorized(df, btc_df=btc_df, eth_df=eth_df, time_weight_decay=time_weight_decay,
+                                          atr_sl_mult=atr_sl_mult, atr_tp_mult=atr_tp_mult)
     t1 = time.perf_counter()
     print(f"데이터셋 생성 완료: {t1 - t0:.1f}초, {len(dataset)}개 샘플")
 
@@ -170,6 +171,8 @@ def walk_forward_auc(
     time_weight_decay: float = 2.0,
     n_splits: int = 5,
     train_ratio: float = 0.6,
+    atr_sl_mult: float = 2.0,
+    atr_tp_mult: float = 2.0,
 ) -> None:
     """Walk-Forward 검증: 슬라이딩 윈도우로 n_splits번 학습/검증 반복."""
     print(f"\n=== Walk-Forward 검증 ({n_splits}폴드, decay={time_weight_decay}) ===")
@@ -177,7 +180,8 @@ def walk_forward_auc(
     df, btc_df, eth_df = _split_combined(raw)
 
     dataset = generate_dataset_vectorized(
-        df, btc_df=btc_df, eth_df=eth_df, time_weight_decay=time_weight_decay
+        df, btc_df=btc_df, eth_df=eth_df, time_weight_decay=time_weight_decay,
+        atr_sl_mult=atr_sl_mult, atr_tp_mult=atr_tp_mult,
     )
     missing = [c for c in FEATURE_COLS if c not in dataset.columns]
     for col in missing:
@@ -260,12 +264,16 @@ def main():
     )
     parser.add_argument("--wf", action="store_true", help="Walk-Forward 검증 실행")
     parser.add_argument("--wf-splits", type=int, default=5, help="Walk-Forward 폴드 수")
+    parser.add_argument("--sl-mult", type=float, default=2.0, help="SL ATR 배수 (기본 2.0)")
+    parser.add_argument("--tp-mult", type=float, default=2.0, help="TP ATR 배수 (기본 2.0)")
     args = parser.parse_args()
 
     if args.wf:
-        walk_forward_auc(args.data, time_weight_decay=args.decay, n_splits=args.wf_splits)
+        walk_forward_auc(args.data, time_weight_decay=args.decay, n_splits=args.wf_splits,
+                         atr_sl_mult=args.sl_mult, atr_tp_mult=args.tp_mult)
     else:
-        train_mlx(args.data, time_weight_decay=args.decay)
+        train_mlx(args.data, time_weight_decay=args.decay,
+                  atr_sl_mult=args.sl_mult, atr_tp_mult=args.tp_mult)
 
 
 if __name__ == "__main__":
diff --git a/scripts/train_model.py b/scripts/train_model.py
index 9b084b0..788e4e7 100644
--- a/scripts/train_model.py
+++ b/scripts/train_model.py
@@ -54,8 +54,6 @@ def _cgroup_cpu_count() -> int:
 
 
 LOOKAHEAD = 24  # 15분봉 × 24 = 6시간 (dataset_builder.py와 동기화)
-ATR_SL_MULT = 1.5
-ATR_TP_MULT = 3.0
 MODEL_PATH = Path("models/lgbm_filter.pkl")
 PREV_MODEL_PATH = Path("models/lgbm_filter_prev.pkl")
 LOG_PATH = Path("models/training_log.json")
@@ -63,6 +61,8 @@ LOG_PATH = Path("models/training_log.json")
 
 def _process_index(args: tuple) -> dict | None:
     """단일 인덱스에 대해 피처+레이블을 계산한다. Pool worker 함수."""
+    ATR_SL_MULT = 1.5  # legacy values
+    ATR_TP_MULT = 3.0
     i, df_values, df_columns = args
     df = pd.DataFrame(df_values, columns=df_columns)
 
@@ -191,7 +191,7 @@ def _load_lgbm_params(tuned_params_path: str | None) -> tuple[dict, float]:
     return lgbm_params, weight_scale
 
 
-def train(data_path: str, time_weight_decay: float = 2.0, tuned_params_path: str | None = None):
+def train(data_path: str, time_weight_decay: float = 2.0, tuned_params_path: str | None = None, atr_sl_mult: float = 2.0, atr_tp_mult: float = 2.0):
     print(f"데이터 로드: {data_path}")
     df_raw = pd.read_parquet(data_path)
     print(f"캔들 수: {len(df_raw)}, 컬럼: {list(df_raw.columns)}")
@@ -218,6 +218,8 @@ def train(data_path: str, time_weight_decay: float = 2.0, tuned_params_path: str
         df, btc_df=btc_df, eth_df=eth_df,
         time_weight_decay=time_weight_decay,
         negative_ratio=5,
+        atr_sl_mult=atr_sl_mult,
+        atr_tp_mult=atr_tp_mult,
     )
 
     if dataset.empty or "label" not in dataset.columns:
@@ -335,6 +337,8 @@ def walk_forward_auc(
     n_splits: int = 5,
     train_ratio: float = 0.6,
     tuned_params_path: str | None = None,
+    atr_sl_mult: float = 2.0,
+    atr_tp_mult: float = 2.0,
 ) -> None:
     """Walk-Forward 검증: 슬라이딩 윈도우로 n_splits번 학습/검증 반복.
 
@@ -359,6 +363,8 @@ def walk_forward_auc(
         df, btc_df=btc_df, eth_df=eth_df,
         time_weight_decay=time_weight_decay,
         negative_ratio=5,
+        atr_sl_mult=atr_sl_mult,
+        atr_tp_mult=atr_tp_mult,
     )
     actual_feature_cols = [c for c in FEATURE_COLS if c in dataset.columns]
     X = dataset[actual_feature_cols].values
@@ -422,7 +428,7 @@ def walk_forward_auc(
     print(f"  폴드별: {[round(a, 4) for a in aucs]}")
 
 
-def compare(data_path: str, time_weight_decay: float = 2.0, tuned_params_path: str | None = None):
+def compare(data_path: str, time_weight_decay: float = 2.0, tuned_params_path: str | None = None, atr_sl_mult: float = 2.0, atr_tp_mult: float = 2.0):
     """기존 피처 vs OI 파생 피처 추가 버전 A/B 비교."""
     import warnings
 
@@ -449,6 +455,8 @@ def compare(data_path: str, time_weight_decay: float = 2.0, tuned_params_path: s
         df, btc_df=btc_df, eth_df=eth_df,
         time_weight_decay=time_weight_decay,
         negative_ratio=5,
+        atr_sl_mult=atr_sl_mult,
+        atr_tp_mult=atr_tp_mult,
     )
 
     if dataset.empty:
@@ -546,6 +554,8 @@ def main():
     )
     parser.add_argument("--compare", action="store_true",
                         help="OI 파생 피처 추가 전후 A/B 성능 비교")
+    parser.add_argument("--sl-mult", type=float, default=2.0, help="SL ATR 배수 (기본 2.0)")
+    parser.add_argument("--tp-mult", type=float, default=2.0, help="TP ATR 배수 (기본 2.0)")
     args = parser.parse_args()
 
     # --symbol 모드: 심볼별 디렉토리 경로 자동 결정
@@ -563,16 +573,20 @@ def main():
         args.data = "data/combined_15m.parquet"
 
     if args.compare:
-        compare(args.data, time_weight_decay=args.decay, tuned_params_path=args.tuned_params)
+        compare(args.data, time_weight_decay=args.decay, tuned_params_path=args.tuned_params,
+                atr_sl_mult=args.sl_mult, atr_tp_mult=args.tp_mult)
     elif args.wf:
         walk_forward_auc(
             args.data,
             time_weight_decay=args.decay,
             n_splits=args.wf_splits,
             tuned_params_path=args.tuned_params,
+            atr_sl_mult=args.sl_mult,
+            atr_tp_mult=args.tp_mult,
         )
     else:
-        train(args.data, time_weight_decay=args.decay, tuned_params_path=args.tuned_params)
+        train(args.data, time_weight_decay=args.decay, tuned_params_path=args.tuned_params,
+              atr_sl_mult=args.sl_mult, atr_tp_mult=args.tp_mult)
 
 
 if __name__ == "__main__":
diff --git a/scripts/tune_hyperparams.py b/scripts/tune_hyperparams.py
index 119b0f0..d7734e6 100755
--- a/scripts/tune_hyperparams.py
+++ b/scripts/tune_hyperparams.py
@@ -39,7 +39,7 @@ from src.dataset_builder import generate_dataset_vectorized, stratified_undersam
 # 데이터 로드 및 데이터셋 생성 (1회 캐싱)
 # ──────────────────────────────────────────────
 
-def load_dataset(data_path: str) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+def load_dataset(data_path: str, atr_sl_mult: float = 2.0, atr_tp_mult: float = 2.0) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     """
     parquet 로드 → 벡터화 데이터셋 생성 → (X, y, w) numpy 배열 반환.
     study 시작 전 1회만 호출하여 모든 trial이 공유한다.
@@ -64,7 +64,8 @@ def load_dataset(data_path: str) -> tuple[np.ndarray, np.ndarray, np.ndarray, np
     df = df_raw[base_cols].copy()
 
     print("\n데이터셋 생성 중 (1회만 실행)...")
-    dataset = generate_dataset_vectorized(df, btc_df=btc_df, eth_df=eth_df, time_weight_decay=0.0, negative_ratio=5)
+    dataset = generate_dataset_vectorized(df, btc_df=btc_df, eth_df=eth_df, time_weight_decay=0.0, negative_ratio=5,
+                                          atr_sl_mult=atr_sl_mult, atr_tp_mult=atr_tp_mult)
 
     if dataset.empty or "label" not in dataset.columns:
         raise ValueError("데이터셋 생성 실패: 샘플 0개")
@@ -527,6 +528,8 @@ def main():
     parser.add_argument("--train-ratio", type=float, default=0.6, help="학습 구간 비율 (기본: 0.6)")
     parser.add_argument("--min-recall",  type=float, default=0.35, help="최소 재현율 제약 (기본: 0.35)")
     parser.add_argument("--no-baseline", action="store_true",     help="베이스라인 측정 건너뜀")
+    parser.add_argument("--sl-mult", type=float, default=2.0, help="SL ATR 배수 (기본 2.0)")
+    parser.add_argument("--tp-mult", type=float, default=2.0, help="TP ATR 배수 (기본 2.0)")
     args = parser.parse_args()
 
     # --symbol 모드: 심볼별 디렉토리 경로 자동 결정
@@ -538,7 +541,7 @@ def main():
         args.data = "data/combined_15m.parquet"
 
     # 1. 데이터셋 로드 (1회)
-    X, y, w, source = load_dataset(args.data)
+    X, y, w, source = load_dataset(args.data, atr_sl_mult=args.sl_mult, atr_tp_mult=args.tp_mult)
 
     # 2. 베이스라인 측정
     if args.symbol:
diff --git a/src/backtester.py b/src/backtester.py
index c157bf0..e5577db 100644
--- a/src/backtester.py
+++ b/src/backtester.py
@@ -743,6 +743,8 @@ class WalkForwardBacktester:
                 signal_threshold=self.cfg.signal_threshold,
                 adx_threshold=self.cfg.adx_threshold,
                 volume_multiplier=self.cfg.volume_multiplier,
+                atr_sl_mult=self.cfg.atr_sl_mult,
+                atr_tp_mult=self.cfg.atr_tp_mult,
             )
         except Exception as e:
             logger.warning(f"  [{symbol}] 데이터셋 생성 실패: {e}")