BUG: batch_transform was wrongly updating when days < refresh_period.

tests/test_transforms.py

@@ -327,6 +327,14 @@ class TestBatchTransform(TestCase):
         self.assertEqual(algo.history_return_price_market_aware[:2],
                          [None, None],
                          "First two iterations should return None")
+        self.assertEqual(algo.history_return_more_days_than_refresh[:3],
+                         [None, None, None],
+                         "First five iterations should return None")
+        self.assertTrue(isinstance(
+            algo.history_return_more_days_than_refresh[4],
+            pd.DataFrame),
+            "Sixth iteration should not be None"
+        )
 
         # test overloaded class
         for test_history in [algo.history_return_price_class,

zipline/test_algorithms.py

@@ -260,6 +260,7 @@ class BatchTransformAlgorithm(TradingAlgorithm):
         self.history_return_price_decorator = []
         self.history_return_args = []
         self.history_return_price_market_aware = []
+        self.history_return_more_days_than_refresh = []
 
         self.return_price_class = ReturnPriceBatchTransform(
             market_aware=False,
@@ -285,6 +286,12 @@ class BatchTransformAlgorithm(TradingAlgorithm):
             days=self.days
         )
 
+        self.return_price_more_days_than_refresh = ReturnPriceBatchTransform(
+            market_aware=True,
+            refresh_period=1,
+            days=3
+        )
+
         self.set_slippage(FixedSlippage())
 
     def handle_data(self, data):
@@ -297,6 +304,8 @@ class BatchTransformAlgorithm(TradingAlgorithm):
                 data, *self.args, **self.kwargs))
         self.history_return_price_market_aware.append(
             self.return_price_market_aware.handle_data(data))
+        self.history_return_more_days_than_refresh.append(
+            self.return_price_more_days_than_refresh.handle_data(data))
 
 
 class SetPortfolioAlgorithm(TradingAlgorithm):

zipline/transforms/utils.py

@@ -361,6 +361,7 @@ class BatchTransform(EventWindow):
         self.refresh_period = refresh_period
         self.days = days
         self.trading_days_since_update = 0
+        self.trading_days_total = 0
 
         self.full = False
         self.last_dt = None
@@ -397,49 +398,59 @@ class BatchTransform(EventWindow):
             self.last_dt = event.dt
             return
 
+        # update trading day counters
         if self.last_dt.day != event.dt.day:
             self.last_dt = event.dt
             self.trading_days_since_update += 1
+            self.trading_days_total += 1
 
-        if self.trading_days_since_update >= self.refresh_period:
-            # Create a pandas.Panel (i.e. 3d DataFrame) from the
-            # events in the current window.
-            #
-            # The resulting panel looks like this:
-            # index : field_name (e.g. price)
-            # major axis/rows : dt
-            # minor axis/colums : sid
-            #
-            # This Panel data structure ultimately gets passed to the
-            # user-overloaded get_value() method.
-            #
-            # self.ticks contains ndicts with data, dt keys.
-            # event parameter is an ndict with data, dt keys.
-            fields = {}
-            for field_name in ['price', 'volume']:
-                sids = self.ticks[0].data.keys()
-                # Skip non-existant fields
-                if field_name not in self.ticks[0].data[sids[0]]:
-                    continue
-
-                values_per_sid = {}
-
-                for sid in sids:
-                    values_per_sid[sid] = pd.Series(
-                        {tick.data[sid].dt: tick.data[sid][field_name]
-                         for tick in self.ticks}
-                    )
-
-                # concatenate different sids into one df
-                fields[field_name] = pd.DataFrame.from_dict(values_per_sid)
-
-            self.data = pd.Panel.from_dict(fields, orient='items')
-
+        if self.trading_days_since_update >= self.refresh_period and\
+            self.trading_days_total >= self.days:
+            # Create datapanel of running event window.
+            self.data = self.get_data()
+            # Setting updated to True will cause get_transform_value()
+            # 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
 
+    def get_data(self):
+        # Create a pandas.Panel (i.e. 3d DataFrame) from the
+        # events in the current window.
+        #
+        # The resulting panel looks like this:
+        # index : field_name (e.g. price)
+        # major axis/rows : dt
+        # minor axis/colums : sid
+        #
+        # This Panel data structure ultimately gets passed to the
+        # user-overloaded get_value() method.
+        #
+        # self.ticks contains ndicts with data, dt keys.
+        # event parameter is an ndict with data, dt keys.
+        fields = {}
+        for field_name in ['price', 'volume']:
+            sids = self.ticks[0].data.keys()
+            # Skip non-existant fields
+            if field_name not in self.ticks[0].data[sids[0]]:
+                continue
+
+            values_per_sid = {}
+
+            for sid in sids:
+                values_per_sid[sid] = pd.Series(
+                    {tick.data[sid].dt: tick.data[sid][field_name]
+                     for tick in self.ticks}
+                )
+
+            # concatenate different sids into one df
+                fields[field_name] = pd.DataFrame.from_dict(values_per_sid)
+
+        data = pd.Panel.from_dict(fields, orient='items')
+        return data
+
     def handle_remove(self, event):
         # since an event is expiring, we know the window is full
         self.full = True
@@ -449,6 +460,13 @@ class BatchTransform(EventWindow):
             "Either overwrite get_value or provide a func argument.")
 
     def get_transform_value(self, *args, **kwargs):
+        """Call user-defined batch-transform function passing all
+        arguments.
+
+        Note that this will only call the transform if the datapanel
+        has actually been updated. Otherwise, the previously, cached
+        value will be returned.
+        """
         if self.data is None:
             return None