diff --git a/src/quant_platform_kit/common/history.py b/src/quant_platform_kit/common/history.py
new file mode 100644
index 0000000..9e992a0
--- /dev/null
+++ b/src/quant_platform_kit/common/history.py
@@ -0,0 +1,33 @@
+from __future__ import annotations
+
+import pandas as pd
+
+
+def normalize_history_frame(history, *, label: str, close_column: str = "close") -> pd.DataFrame:
+    if isinstance(history, pd.DataFrame):
+        frame = history.copy()
+    elif isinstance(history, pd.Series):
+        frame = history.to_frame(name=close_column)
+    else:
+        frame = pd.DataFrame(list(history))
+
+    if frame.empty:
+        raise ValueError(f"{label} must contain close history")
+
+    normalized_close = str(close_column or "close").strip() or "close"
+    lower_columns = {str(column).strip().lower(): column for column in frame.columns}
+    if normalized_close not in frame.columns:
+        close_match = lower_columns.get(normalized_close.lower())
+        if close_match is not None:
+            frame = frame.rename(columns={close_match: normalized_close})
+        elif len(frame.columns) == 1:
+            frame = frame.rename(columns={frame.columns[0]: normalized_close})
+        else:
+            columns = ", ".join(str(column) for column in frame.columns)
+            raise ValueError(f"{label} must include a {normalized_close} column; got columns: {columns or '<none>'}")
+
+    frame[normalized_close] = pd.to_numeric(frame[normalized_close], errors="coerce")
+    frame = frame.dropna(subset=[normalized_close]).reset_index(drop=True)
+    if frame.empty:
+        raise ValueError(f"{label} close history is empty after normalization")
+    return frame
diff --git a/tests/test_common_history.py b/tests/test_common_history.py
new file mode 100644
index 0000000..9f1b864
--- /dev/null
+++ b/tests/test_common_history.py
@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+import unittest
+
+import pandas as pd
+
+from quant_platform_kit.common.history import normalize_history_frame
+
+
+class CommonHistoryTests(unittest.TestCase):
+    def test_normalize_history_frame_accepts_dataframe_with_close_column(self) -> None:
+        frame = pd.DataFrame([{"close": 1.0}, {"close": 2.0}])
+
+        normalized = normalize_history_frame(frame, label="benchmark_history")
+
+        self.assertEqual(list(normalized["close"]), [1.0, 2.0])
+
+    def test_normalize_history_frame_accepts_dataframe_with_close_case_variant(self) -> None:
+        frame = pd.DataFrame([{"Close": 1.0}, {"Close": 2.0}])
+
+        normalized = normalize_history_frame(frame, label="benchmark_history")
+
+        self.assertEqual(list(normalized["close"]), [1.0, 2.0])
+
+    def test_normalize_history_frame_accepts_series(self) -> None:
+        series = pd.Series([1.0, 2.0, 3.0], name="close")
+
+        normalized = normalize_history_frame(series, label="benchmark_history")
+
+        self.assertEqual(list(normalized["close"]), [1.0, 2.0, 3.0])
+
+    def test_normalize_history_frame_accepts_single_column_iterable(self) -> None:
+        history = [1.0, 2.0, 3.0]
+
+        normalized = normalize_history_frame(history, label="benchmark_history")
+
+        self.assertEqual(list(normalized["close"]), [1.0, 2.0, 3.0])
+
+    def test_normalize_history_frame_rejects_missing_close_data(self) -> None:
+        with self.assertRaisesRegex(ValueError, "benchmark_history must include a close column"):
+            normalize_history_frame([{"open": 1.0, "high": 1.0}], label="benchmark_history")
+
+
+if __name__ == "__main__":
+    unittest.main()