diff --git a/zipline/test_algorithms.py b/zipline/test_algorithms.py
index 8fa80b22..e771ca5e 100644
--- a/zipline/test_algorithms.py
+++ b/zipline/test_algorithms.py
@@ -271,39 +271,39 @@ class BatchTransformAlgorithm(TradingAlgorithm):
         self.history_return_arbitrary_fields = []
 
         self.return_price_class = ReturnPriceBatchTransform(
-            market_aware=False,
             refresh_period=self.refresh_period,
-            delta=timedelta(days=self.window_length)
+            window_length=self.window_length,
+            fillna=False
         )
 
         self.return_price_decorator = return_price_batch_decorator(
-            market_aware=False,
             refresh_period=self.refresh_period,
-            delta=timedelta(days=self.window_length)
+            window_length=self.window_length,
+            fillna=False
         )
 
         self.return_args_batch = return_args_batch_decorator(
-            market_aware=False,
             refresh_period=self.refresh_period,
-            delta=timedelta(days=self.window_length)
+            window_length=self.window_length,
+            fillna=False
         )
 
         self.return_price_market_aware = ReturnPriceBatchTransform(
-            market_aware=True,
             refresh_period=self.refresh_period,
-            window_length=self.window_length
+            window_length=self.window_length,
+            fillna=False
         )
 
         self.return_price_more_days_than_refresh = ReturnPriceBatchTransform(
-            market_aware=True,
             refresh_period=1,
-            window_length=3
+            window_length=3,
+            fillna=False
         )
 
         self.return_arbitrary_fields = return_data(
-            market_aware=True,
             refresh_period=1,
-            window_length=3
+            window_length=3,
+            fillna=False
         )
 
         self.set_slippage(FixedSlippage())
diff --git a/zipline/transforms/utils.py b/zipline/transforms/utils.py
index 0050241d..1785400d 100644
--- a/zipline/transforms/utils.py
+++ b/zipline/transforms/utils.py
@@ -321,19 +321,21 @@ class BatchTransform(EventWindow):
     def __init__(self,
                  func=None,
                  refresh_period=None,
-                 market_aware=True,
-                 delta=None,
-                 window_length=None):
+                 window_length=None,
+                 fillna=True,
+                 fill_method='ffill'):
 
-        super(BatchTransform, self).__init__(market_aware,
-                                             window_length=window_length,
-                                             delta=delta)
+        super(BatchTransform, self).__init__(True,
+                                             window_length=window_length)
 
         if func is not None:
             self.compute_transform_value = func
         else:
             self.compute_transform_value = self.get_value
 
+        self.fillna = fillna
+        self.fill_method = fill_method
+
         self.refresh_period = refresh_period
         self.window_length = window_length
         self.trading_days_since_update = 0
@@ -437,14 +439,15 @@ class BatchTransform(EventWindow):
 
                 # concatenate different sids into one df
                 df = pd.DataFrame.from_dict(values_per_sid)
-                # Fills in gaps of missing data during transform of multiple
-                # stocks.
-                # e.g. we may be missing minute data because of illiquidity
-                # of one stock
-                df = df.fillna(method='ffill')
-                # Drop any empty rows after the fill.
-                # This will drop a leading row of N/A
-                df = df.dropna()
+
+                if self.fillna:
+                    # Fills in gaps of missing data during transform
+                    # of multiple stocks. E.g. we may be missing
+                    # minute data because of illiquidity of one stock
+                    df = df.fillna(method=self.fill_method)
+                    # Drop any empty rows after the fill.
+                    # This will drop a leading row of N/A
+                    df = df.dropna()
 
                 fields[field_name] = df