ENH: Change DollarVolume to AverageDollarVolume.

docs/source/appendix.rst

@@ -66,7 +66,8 @@ Pipeline API
 .. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingStdDev
    :members:
 
-.. autofunction:: zipline.pipeline.factors.DollarVolume
+.. autoclass:: zipline.pipeline.factors.AverageDollarVolume
+   :members:
 
 .. autoclass:: zipline.pipeline.filters.Filter
    :members: __and__, __or__

docs/source/whatsnew/0.8.4.txt

@@ -53,7 +53,7 @@ Enhancements
   window_length. (:issue:`884`).
 
 * Added a new built-in factor:
-  :class:`~zipline.pipeline.factors.DollarVolume`. (:issue:`910`).
+  :class:`~zipline.pipeline.factors.AverageDollarVolume`. (:issue:`927`).
 
 * Added :class:`~zipline.pipeline.factors.ExponentialWeightedMovingAverage` and
   :class:`~zipline.pipeline.factors.ExponentialWeightedMovingStdDev`

tests/pipeline/test_engine.py

@@ -54,7 +54,7 @@ from zipline.pipeline.loaders.equity_pricing_loader import (
 from zipline.pipeline.engine import SimplePipelineEngine
 from zipline.pipeline import CustomFactor
 from zipline.pipeline.factors import (
-    DollarVolume,
+    AverageDollarVolume,
     EWMA,
     EWMSTD,
     ExponentialWeightedMovingAverage,
@@ -951,9 +951,18 @@ class ParameterizedFactorTestCase(TestCase):
 
     def test_dollar_volume(self):
         results = self.engine.run_pipeline(
-            Pipeline(columns={'dv': DollarVolume()}),
-            self.dates[0],
+            Pipeline(
+                columns={
+                    'dv1': AverageDollarVolume(window_length=1),
+                    'dv5': AverageDollarVolume(window_length=5),
+                }
+            ),
+            self.dates[5],
             self.dates[-1],
-        )['dv'].unstack()
-        expected = (self.raw_data ** 2) * 2
-        assert_frame_equal(results, expected)
+        )
+
+        expected_1 = (self.raw_data[5:] ** 2) * 2
+        assert_frame_equal(results['dv1'].unstack(), expected_1)
+
+        expected_5 = rolling_mean((self.raw_data ** 2) * 2, window=5)[5:]
+        assert_frame_equal(results['dv5'].unstack(), expected_5)

zipline/pipeline/factors/__init__.py

@@ -8,7 +8,7 @@ from .events import (
     BusinessDaysUntilNextEarnings,
 )
 from .technical import (
-    DollarVolume,
+    AverageDollarVolume,
     EWMA,
     EWMSTD,
     ExponentialWeightedMovingAverage,
@@ -25,7 +25,7 @@ __all__ = [
     'BusinessDaysSincePreviousEarnings',
     'BusinessDaysUntilNextEarnings',
     'CustomFactor',
-    'DollarVolume',
+    'AverageDollarVolume',
     'EWMA',
     'EWMSTD',
     'ExponentialWeightedMovingAverage',

zipline/pipeline/factors/technical.py

@@ -130,12 +130,18 @@ class MaxDrawdown(CustomFactor, SingleInputMixin):
             out[i] = (peak - data[end, i]) / data[end, i]
 
 
-def DollarVolume():
+class AverageDollarVolume(CustomFactor):
     """
-    Returns a Factor computing the product of most recent close price and
-    volume.
+    Average Daily Dollar Volume
+
+    **Default Inputs:** [USEquityPricing.close, USEquityPricing.volume]
+
+    **Default Window Length:** None
     """
-    return USEquityPricing.close.latest * USEquityPricing.volume.latest
+    inputs = [USEquityPricing.close, USEquityPricing.volume]
+
+    def compute(self, today, assets, out, close, volume):
+        out[:] = nanmean(close * volume, axis=0)
 
 
 class _ExponentialWeightedFactor(SingleInputMixin, CustomFactor):