Backfills a batch transform panel with supplemental data.

For the case where the window isn't covered by the data streaming
through the simulator.

e.g. in a case where the stocks being iterated over change every
quarter, the supplemental data will fill in the 'gap' missing from
the transform since the 'new' stocks were not streaming before
the beginning of the quarter.

Of note, test cases are covered by internal suites, but this could
use tests with completely mocked data.

zipline/transforms/utils.py

@@ -381,6 +381,12 @@ class BatchTransform(EventWindow):
         self.updated = False
         self.cached = None
 
+        # Data panel that provides bar information to fill in the window,
+        # when no bar ticks are available from the data source generator
+        # Used in universes that 'rollover', e.g. one that has a different
+        # set of stocks per quarter
+        self.supplemental_data = None
+
     def handle_data(self, data, *args, **kwargs):
         """
         New method to handle a data frame as sent to the algorithm's
@@ -466,6 +472,16 @@ class BatchTransform(EventWindow):
         data = pd.Panel(data_dict, major_axis=self.field_names,
                         minor_axis=self.sids)
 
+        if self.supplemental_data:
+            # item will be a date stamp
+            for item in data.items:
+                try:
+                    data[item] = self.supplemental_data[item].combine_first(
+                        data[item])
+                except KeyError:
+                    # Only filling in data available in supplemental data.
+                    pass
+
         data = data.swapaxes(0, 1)
 
         if self.clean_nans:
@@ -478,6 +494,11 @@ class BatchTransform(EventWindow):
             # This will drop a leading row of N/A
             data = data.dropna(axis=1)
 
+        # Hold on to a reference to the data,
+        # so that it's easier to find the current data when stepping
+        # through with a debugger
+        self.curr_data = data
+
         return data
 
     def handle_remove(self, event):