diff --git a/scripts/data_collector/base.py b/scripts/data_collector/base.py
index 2efc2feadcb..5f5076fa4d9 100644
--- a/scripts/data_collector/base.py
+++ b/scripts/data_collector/base.py
@@ -305,7 +305,7 @@ def _executor(self, file_path: Path):
         df = self._normalize_obj.normalize(df)
         if df is not None and not df.empty:
             if self._end_date is not None:
-                _mask = pd.to_datetime(df[self._date_field_name]) <= pd.Timestamp(self._end_date)
+                _mask = pd.to_datetime(df[self._date_field_name], utc=True).dt.tz_convert(None) <= pd.Timestamp(self._end_date)
                 df = df[_mask]
             df.to_csv(self._target_dir.joinpath(file_path.name), index=False)
 
diff --git a/scripts/data_collector/yahoo/collector.py b/scripts/data_collector/yahoo/collector.py
index 82660f1112b..67d136adb33 100644
--- a/scripts/data_collector/yahoo/collector.py
+++ b/scripts/data_collector/yahoo/collector.py
@@ -392,8 +392,8 @@ def normalize_yahoo(
         columns = copy.deepcopy(YahooNormalize.COLUMNS)
         df = df.copy()
         df.set_index(date_field_name, inplace=True)
-        df.index = pd.to_datetime(df.index)
-        df.index = df.index.tz_localize(None)
+        df.index = pd.to_datetime(df.index, utc=True)
+        df.index = df.index.tz_convert(None)
         df = df[~df.index.duplicated(keep="first")]
         if calendar_list is not None:
             df = df.reindex(
@@ -458,6 +458,8 @@ def adjusted_price(self, df: pd.DataFrame) -> pd.DataFrame:
         df = df.copy()
         df.set_index(self._date_field_name, inplace=True)
         if "adjclose" in df:
+            df["adjclose"] = pd.to_numeric(df["adjclose"], errors="coerce")
+            df["close"] = pd.to_numeric(df["close"], errors="coerce")
             df["factor"] = df["adjclose"] / df["close"]
             df["factor"] = df["factor"].ffill()
         else:
@@ -465,6 +467,7 @@ def adjusted_price(self, df: pd.DataFrame) -> pd.DataFrame:
         for _col in self.COLUMNS:
             if _col not in df.columns:
                 continue
+            df[_col] = pd.to_numeric(df[_col], errors="coerce")
             if _col == "volume":
                 df[_col] = df[_col] / df["factor"]
             else:
@@ -500,6 +503,7 @@ def _manual_adj_data(self, df: pd.DataFrame) -> pd.DataFrame:
             # NOTE: retain original adjclose, required for incremental updates
             if _col in [self._symbol_field_name, "adjclose", "change"]:
                 continue
+            df[_col] = pd.to_numeric(df[_col], errors="coerce")
             if _col == "volume":
                 df[_col] = df[_col] * _close
             else: