BUG: Use args and kwargs in Rolling.apply

doc/source/whatsnew/v1.1.0.rst

@@ -814,6 +814,8 @@ Groupby/resample/rolling
 - Bug in :meth:`GroupBy.first` and :meth:`GroupBy.last` where None is not preserved in object dtype (:issue:`32800`)
 - Bug in :meth:`Rolling.min` and :meth:`Rolling.max`: Growing memory usage after multiple calls when using a fixed window (:issue:`30726`)
 - Bug in :meth:`GroupBy.agg`, :meth:`GroupBy.transform`, and :meth:`GroupBy.resample` where subclasses are not preserved (:issue:`28330`)
+- Bug in :meth:`GroupBy.rolling.apply` ignores args and kwargs parameters (:issue:`33433`)
+
 
 Reshaping
 ^^^^^^^^^

pandas/core/window/rolling.py

@@ -1302,6 +1302,8 @@ def apply(
             use_numba_cache=engine == "numba",
             raw=raw,
             original_func=func,
+            args=args,
+            kwargs=kwargs,
         )
 
     def _generate_cython_apply_func(self, args, kwargs, raw, offset, func):

pandas/tests/window/test_apply.py

@@ -4,7 +4,7 @@
 from pandas.errors import NumbaUtilError
 import pandas.util._test_decorators as td
 
-from pandas import DataFrame, Series, Timestamp, date_range
+from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range
 import pandas._testing as tm
 
 
@@ -139,3 +139,28 @@ def test_invalid_kwargs_nopython():
         Series(range(1)).rolling(1).apply(
             lambda x: x, kwargs={"a": 1}, engine="numba", raw=True
         )
+
+
+@pytest.mark.parametrize("args_kwargs", [[None, {"par": 10}], [(10,), None]])
+def test_rolling_apply_args_kwargs(args_kwargs):
+    # GH 33433
+    def foo(x, par):
+        return np.sum(x + par)
+
+    df = DataFrame({"gr": [1, 1], "a": [1, 2]})
+
+    idx = Index(["gr", "a"])
+    expected = DataFrame([[11.0, 11.0], [11.0, 12.0]], columns=idx)
+
+    result = df.rolling(1).apply(foo, args=args_kwargs[0], kwargs=args_kwargs[1])
+    tm.assert_frame_equal(result, expected)
+
+    result = df.rolling(1).apply(foo, args=(10,))
+
+    midx = MultiIndex.from_tuples([(1, 0), (1, 1)], names=["gr", None])
+    expected = Series([11.0, 12.0], index=midx, name="a")
+
+    gb_rolling = df.groupby("gr")["a"].rolling(1)
+
+    result = gb_rolling.apply(foo, args=args_kwargs[0], kwargs=args_kwargs[1])
+    tm.assert_series_equal(result, expected)