Merge pull request #1325 from quantopian/lwma

ENH: Adds LinearWeightedMovingAverage factor

tests/pipeline/test_technical.py

@@ -16,6 +16,7 @@ from zipline.pipeline.factors import (
     Aroon,
     FastStochasticOscillator,
     IchimokuKinkoHyo,
+    LinearWeightedMovingAverage,
     RateOfChangePercentage,
 )
 from zipline.testing import ExplodingObject, parameter_space
@@ -380,3 +381,35 @@ class TestRateOfChangePercentage(ZiplineTestCase):
         out = np.zeros(len(assets))
         rocp.compute(today, assets, out, data)
         assert_equal(out, np.full((len(assets),), expected))
+
+
+class TestLinearWeightedMovingAverage(ZiplineTestCase):
+    def test_wma1(self):
+        wma1 = LinearWeightedMovingAverage(
+            inputs=(USEquityPricing.close,),
+            window_length=10
+        )
+
+        today = pd.Timestamp('2015')
+        assets = np.arange(5, dtype=np.int64)
+
+        data = np.ones((10, 5))
+        out = np.zeros(data.shape[1])
+
+        wma1.compute(today, assets, out, data)
+        assert_equal(out, np.ones(5))
+
+    def test_wma2(self):
+        wma2 = LinearWeightedMovingAverage(
+            inputs=(USEquityPricing.close,),
+            window_length=10
+        )
+
+        today = pd.Timestamp('2015')
+        assets = np.arange(5, dtype=np.int64)
+
+        data = np.arange(50, dtype=float).reshape((10, 5))
+        out = np.zeros(data.shape[1])
+
+        wma2.compute(today, assets, out, data)
+        assert_equal(out, np.array([30.,  31.,  32.,  33.,  34.]))

zipline/pipeline/factors/__init__.py

@@ -23,6 +23,7 @@ from .technical import (
     ExponentialWeightedMovingStdDev,
     FastStochasticOscillator,
     IchimokuKinkoHyo,
+    LinearWeightedMovingAverage,
     MaxDrawdown,
     RateOfChangePercentage,
     Returns,
@@ -47,6 +48,7 @@ __all__ = [
     'FastStochasticOscillator',
     'IchimokuKinkoHyo',
     'Latest',
+    'LinearWeightedMovingAverage',
     'MaxDrawdown',
     'RateOfChangePercentage',
     'RecarrayField',

zipline/pipeline/factors/technical.py

@@ -364,6 +364,35 @@ class ExponentialWeightedMovingAverage(_ExponentialWeightedFactor):
         )
 
 
+class LinearWeightedMovingAverage(CustomFactor, SingleInputMixin):
+    """
+    Weighted Average Value of an arbitrary column
+
+    **Default Inputs**: None
+
+    **Default Window Length**: None
+    """
+    # numpy's nan functions throw warnings when passed an array containing only
+    # nans, but they still returns the desired value (nan), so we ignore the
+    # warning.
+    ctx = ignore_nanwarnings()
+
+    def compute(self, today, assets, out, data):
+        num_days = data.shape[0]
+
+        # Initialize weights array
+        weights = arange(1, num_days + 1, dtype=float).reshape(num_days, 1)
+
+        # Compute normalizer
+        normalizer = (num_days * (num_days + 1)) / 2
+
+        # Weight the data
+        weighted_data = data * weights
+
+        # Compute weighted averages
+        out[:] = nansum(weighted_data, axis=0) / normalizer
+
+
 class ExponentialWeightedMovingStdDev(_ExponentialWeightedFactor):
     """
     Exponentially Weighted Moving Standard Deviation