diff --git a/pandas/core/window/indexers.py b/pandas/core/window/indexers.py
index f9a2eead9cb87..a81d712190f79 100644
--- a/pandas/core/window/indexers.py
+++ b/pandas/core/window/indexers.py
@@ -3,7 +3,10 @@
 
 import numpy as np
 
-from pandas.tseries.offsets import DateOffset
+from pandas.core.indexes.datetimes import DatetimeIndex
+
+from pandas.tseries.frequencies import to_offset
+from pandas.tseries.offsets import BusinessMixin, DateOffset
 
 BeginEnd = Tuple[np.ndarray, np.ndarray]
 
@@ -231,3 +234,30 @@ def get_window_bounds(
                 end[i] -= 1
 
         return start, end
+
+
+class BusinessWindowIndexer(VariableWindowIndexer):
+    """Calculate window bounds based on business frequencies."""
+
+    def __init__(self, index=None, offset=None, keys=None):
+        super().__init__(index, offset, keys)
+        self.offset = to_offset(self.offset)
+        if not isinstance(self.offset, BusinessMixin):
+            raise ValueError(
+                "BusinessWindowIndexer only supports Business frequencies."
+            )
+        if not isinstance(self.index, DatetimeIndex):
+            raise ValueError("BusinessWindowIndexer only supports DatetimeIndexes.")
+
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        window_size: int = 0,
+        min_periods: Optional[int] = None,
+        center: Optional[bool] = None,
+        closed: Optional[str] = None,
+        win_type: Optional[str] = None,
+    ) -> BeginEnd:
+        return super().get_window_bounds(
+            num_values, self.offset, min_periods, center, closed, win_type
+        )
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index ec300d5578881..0609bc8e1910a 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -395,6 +395,8 @@ def _get_window_indexer(self, index_as_array):
         -------
         VariableWindowIndexer or FixedWindowIndexer
         """
+        if isinstance(self.window, BaseIndexer):
+            return self.window
         if self.is_freq_type:
             return VariableWindowIndexer(index=index_as_array)
         return FixedWindowIndexer(index=index_as_array)
@@ -436,7 +438,14 @@ def _apply(
             check_minp = _use_window
 
         if window is None:
-            apply_window = self._get_window(**kwargs)  # type: int
+            apply_window = self._get_window(**kwargs)
+        else:
+            apply_window = window
+
+        if isinstance(apply_window, BaseIndexer):
+            # This value isn't significant for subclasses of BaseIndexer
+            # but is passed along to other validation checks.
+            apply_window = 0
 
         blocks, obj = self._create_blocks()
         block_list = list(blocks)
@@ -898,12 +907,8 @@ def _pop_args(win_type, arg_names, kwargs):
             # GH #15662. `False` makes symmetric window, rather than periodic.
             return sig.get_window(win_type, window, False).astype(float)
         elif isinstance(window, BaseIndexer):
-            return window.get_window_span(
-                win_type=self.win_type,
-                min_periods=self.min_periods,
-                center=self.center,
-                closed=self.closed,
-            )
+            # Defer calling `.get_window_bounds` until later?
+            return window
 
     def _get_roll_func(
         self, cfunc: Callable, check_minp: Callable, index: np.ndarray, **kwargs
diff --git a/pandas/tests/window/test_custom_indexer.py b/pandas/tests/window/test_custom_indexer.py
index 1d32d6619422a..07b78fb8fc395 100644
--- a/pandas/tests/window/test_custom_indexer.py
+++ b/pandas/tests/window/test_custom_indexer.py
@@ -1,4 +1,6 @@
-from pandas import Series
+from pandas import Series, date_range
+from pandas.core.window.indexers import BusinessWindowIndexer
+import pandas.util.testing as tm
 
 
 def test_custom_indexer_validates(
@@ -13,3 +15,11 @@ def test_custom_indexer_validates(
         min_periods=min_periods,
         closed=closed,
     )
+
+
+def test_rolling_business_day():
+    index = date_range("2019-09-06", "2019-09-13", freq="D")
+    s = Series(range(len(index)), index=index)
+    result = s.rolling(BusinessWindowIndexer(index=s.index, offset="B")).mean()
+    expected = Series([0, 1, 1.5, 2, 4, 5, 6, 7], index=index)
+    tm.assert_series_equal(result, expected)