ENH: Added new kwarg to batch_transform: create_panel.

tests/test_transforms.py

@@ -13,6 +13,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections import deque
+
 import pytz
 import numpy as np
 import pandas as pd
@@ -354,6 +356,9 @@ class TestBatchTransform(TestCase):
             self.assertIn('price', data.items)
             self.assertIn('ignore', data.items)
 
+        for data in algo.history_return_ticks[wl:]:
+            self.assertTrue(isinstance(data, deque))
+
         # test overloaded class
         for test_history in [algo.history_return_price_class,
                              algo.history_return_price_decorator]:

zipline/test_algorithms.py

@@ -72,6 +72,7 @@ The algorithm must expose methods:
 
 """
 from copy import deepcopy
+import numpy as np
 
 from zipline.algorithm import TradingAlgorithm
 from zipline.finance.slippage import FixedSlippage
@@ -271,6 +272,7 @@ class BatchTransformAlgorithm(TradingAlgorithm):
         self.history_return_sid_filter = []
         self.history_return_field_filter = []
         self.history_return_field_no_filter = []
+        self.history_return_ticks = []
 
         self.return_price_class = ReturnPriceBatchTransform(
             refresh_period=self.refresh_period,
@@ -328,6 +330,13 @@ class BatchTransformAlgorithm(TradingAlgorithm):
             clean_nans=True
         )
 
+        self.return_ticks = return_data(
+            refresh_period=self.refresh_period,
+            window_length=self.window_length,
+            clean_nans=True,
+            create_panel=False
+        )
+
         self.iter = 0
 
         self.set_slippage(FixedSlippage())
@@ -340,6 +349,8 @@ class BatchTransformAlgorithm(TradingAlgorithm):
         self.history_return_args.append(
             self.return_args_batch.handle_data(
                 data, *self.args, **self.kwargs))
+        self.history_return_ticks.append(
+            self.return_ticks.handle_data(data))
 
         new_data = deepcopy(data)
         for sid in new_data:
@@ -354,7 +365,6 @@ class BatchTransformAlgorithm(TradingAlgorithm):
                 self.return_nan.handle_data(data))
         else:
             nan_data = deepcopy(data)
-            import numpy as np
             for sid in nan_data.iterkeys():
                 nan_data[sid].price = np.nan
             self.history_return_nan.append(

zipline/transforms/utils.py

@@ -345,7 +345,8 @@ class BatchTransform(EventWindow):
                  window_length=None,
                  clean_nans=True,
                  sids=None,
-                 fields=None):
+                 fields=None,
+                 create_panel=True):
         """Instantiate new batch_transform object.
 
         :Arguments:
@@ -367,6 +368,12 @@ class BatchTransform(EventWindow):
                 Which fields to include in the moving window
                 (e.g. 'price'). If not supplied, fields will be
                 extracted from incoming events.
+            create_panel : bool <default=True>
+                If False, will create a pandas panel every refresh
+                period and pass it to the user-defined function.
+                If True, will pass the underlying deque reference
+                directly to the function which will be significantly
+                faster.
         """
 
         super(BatchTransform, self).__init__(True,
@@ -378,6 +385,7 @@ class BatchTransform(EventWindow):
             self.compute_transform_value = self.get_value
 
         self.clean_nans = clean_nans
+        self.create_panel = create_panel
 
         self.sids = sids
         if isinstance(self.sids, (str, int)):
@@ -528,8 +536,11 @@ class BatchTransform(EventWindow):
             return None
 
         if self.updated:
-            self.cached = self.compute_transform_value(self.get_data(),
-                                                       *args, **kwargs)
+            # Either create new pandas panel or pass ticks dequeue
+            # directly
+            data = self.get_data() if self.create_panel else self.ticks
+            self.cached = self.compute_transform_value(data, *args,
+                                                       **kwargs)
 
         return self.cached