added tests to ensure:

- repeated calls with the same data window do not update batch transform
    windows.
    - repeated calls with the same data and same supplemental parameters do
    not update batch transform results
    - repeated calls with the same data and different supplemental params
    do update batch transform results

zipline/test_algorithms.py

@@ -273,6 +273,11 @@ def uses_ufunc(data, *args, **kwargs):
     return np.log(data)
 
 
+@batch_transform
+def price_multiple(data, multiplier, keyarg=1):
+    return data.price * multiplier * keyarg
+
+
 class BatchTransformAlgorithm(TradingAlgorithm):
     def initialize(self, *args, **kwargs):
         self.refresh_period = kwargs.pop('refresh_period', 1)
@@ -354,6 +359,12 @@ class BatchTransformAlgorithm(TradingAlgorithm):
             clean_nans=False
         )
 
+        self.price_multiple = price_multiple(
+            refresh_period=self.refresh_period,
+            window_length=self.window_length,
+            clean_nans=False
+        )
+
         self.iter = 0
 
         self.set_slippage(FixedSlippage())
@@ -370,6 +381,29 @@ class BatchTransformAlgorithm(TradingAlgorithm):
             self.return_not_full.handle_data(data))
         self.uses_ufunc.handle_data(data)
 
+        # check that calling transforms with the same arguments
+        # is idempotent
+        self.price_multiple.handle_data(data, 1, keyarg=1)
+
+        if self.price_multiple.full:
+            pre = len(self.price_multiple.ticks)
+            result1 = self.price_multiple.handle_data(data, 1, keyarg=1)
+            post = len(self.price_multiple.ticks)
+            assert pre == post, "batch transform is appending redundant events"
+            result2 = self.price_multiple.handle_data(data, 1, keyarg=1)
+            assert result1 is result2, "batch transform is not idempotent"
+
+            # check that calling transform with the same data, but
+            # different supplemental arguments results in new
+            # results.
+            result3 = self.price_multiple.handle_data(data, 2, keyarg=1)
+            assert result1 is not result3, \
+                "batch transform is not updating for new args"
+
+            result4 = self.price_multiple.handle_data(data, 1, keyarg=2)
+            assert result1 is not result4,\
+                "batch transform is not updating for new kwargs"
+
         new_data = deepcopy(data)
         for sid in new_data:
             new_data[sid]['arbitrary'] = 123

zipline/transforms/utils.py

@@ -228,8 +228,6 @@ class EventWindow(object):
         # Subclasses should override handle_add to define behavior for
         # adding new ticks.
         self.handle_add(event)
-        #if len(self.ticks) > self.window_length:
-        #    import nose.tools; nose.tools.set_trace()
         # Clear out any expired events.
         #
         #                              oldest               newest
@@ -406,9 +404,19 @@ class BatchTransform(EventWindow):
                       # functionality to zipline
                       if len(v)}
 
-        # append data frame to window. update() will call handle_add() and
-        # handle_remove() appropriately
-        self.update(event)
+        # only modify the trailing window if this is
+        # a new event. This is intended to make handle_data
+        # idempotent.
+        if event not in self.ticks:
+            # append data frame to window. update() will call handle_add() and
+            # handle_remove() appropriately, and self.updated
+            # will be modified based on the refresh_period
+            self.update(event)
+        else:
+            # we are recalculating based on an old event, so
+            # there is no change in the contents of the trailing
+            # window
+            self.updated = False
 
         # return newly computed or cached value
         return self.get_transform_value(*args, **kwargs)
@@ -449,7 +457,6 @@ class BatchTransform(EventWindow):
             # to call the user-defined batch-transform with the most
             # recent datapanel
             self.updated = True
-            self.trading_days_since_update = 0
         else:
             self.updated = False
 
@@ -516,10 +523,10 @@ class BatchTransform(EventWindow):
         if self.updated:
             # Create new pandas panel
             self.window = self.get_data()
+            # reset our counter for refresh_period
+            self.trading_days_since_update = 0
 
-        args_changed = args != self.last_args
-        args_changed = args_changed or kwargs != self.last_kwargs
-
+        args_changed = args != self.last_args or kwargs != self.last_kwargs
         if self.updated or args_changed:
             self.cached = self.compute_transform_value(
                 self.window,