Merge pull request #1367 from quantopian/smoothing-at-least

ENH: Added AtLeastN filter

docs/source/whatsnew/1.0.2.txt

@@ -31,6 +31,11 @@ Enhancements
   returns True if an asset produced a True for any/all days in the previous
   ``window_length`` days (:issue:`1358`).
 
+- Added new pipeline filter :class:`~zipline.pipeline.filters.AtLeastN`,
+  which takes another filter and an int N and returns True if an asset
+  produced a True on N or more days in the previous ``window_length``
+  days (:issue:`1367`).
+
 Bug Fixes
 ~~~~~~~~~
 

tests/pipeline/test_filter.py

@@ -30,7 +30,7 @@ from zipline.errors import BadPercentileBounds
 from zipline.pipeline import Filter, Factor, TermGraph
 from zipline.pipeline.classifiers import Classifier
 from zipline.pipeline.factors import CustomFactor
-from zipline.pipeline.filters import All, Any
+from zipline.pipeline.filters import All, Any, AtLeastN
 from zipline.testing import check_arrays, parameter_space, permute_rows
 from zipline.utils.numpy_utils import float64_dtype, int64_dtype
 from .base import BasePipelineTestCase, with_default_shape
@@ -498,6 +498,81 @@ class FilterTestCase(BasePipelineTestCase):
         check_arrays(results['3'], expected_3)
         check_arrays(results['4'], expected_4)
 
+    def test_at_least_N(self):
+
+        # With a window_length of K, AtLeastN should return 1
+        # if N or more 1's exist in the lookback window
+
+        # This smoothing filter gives customizable "stickiness"
+
+        data = array([[1, 1, 1, 1, 1, 1],
+                      [1, 1, 1, 1, 1, 1],
+                      [1, 1, 1, 1, 1, 0],
+                      [1, 1, 1, 1, 0, 0],
+                      [1, 1, 1, 0, 0, 0],
+                      [1, 1, 0, 0, 0, 0],
+                      [1, 0, 0, 0, 0, 0]], dtype=bool)
+
+        expected_1 = array([[1, 1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1, 0],
+                            [1, 1, 1, 1, 0, 0]], dtype=bool)
+
+        expected_2 = array([[1, 1, 1, 1, 1, 1],
+                            [1, 1, 1, 1, 1, 0],
+                            [1, 1, 1, 1, 0, 0],
+                            [1, 1, 1, 0, 0, 0]], dtype=bool)
+
+        expected_3 = array([[1, 1, 1, 1, 1, 0],
+                            [1, 1, 1, 1, 0, 0],
+                            [1, 1, 1, 0, 0, 0],
+                            [1, 1, 0, 0, 0, 0]], dtype=bool)
+
+        expected_4 = array([[1, 1, 1, 1, 0, 0],
+                            [1, 1, 1, 0, 0, 0],
+                            [1, 1, 0, 0, 0, 0],
+                            [1, 0, 0, 0, 0, 0]], dtype=bool)
+
+        class Input(Filter):
+            inputs = ()
+            window_length = 0
+
+        all_but_one = AtLeastN(inputs=[Input()],
+                               window_length=4,
+                               N=3)
+
+        all_but_two = AtLeastN(inputs=[Input()],
+                               window_length=4,
+                               N=2)
+
+        any_equiv = AtLeastN(inputs=[Input()],
+                             window_length=4,
+                             N=1)
+
+        all_equiv = AtLeastN(inputs=[Input()],
+                             window_length=4,
+                             N=4)
+
+        results = self.run_graph(
+            TermGraph({
+                'AllButOne': all_but_one,
+                'AllButTwo': all_but_two,
+                'AnyEquiv': any_equiv,
+                'AllEquiv': all_equiv,
+                'Any': Any(inputs=[Input()], window_length=4),
+                'All': All(inputs=[Input()], window_length=4)
+            }),
+            initial_workspace={Input(): data},
+            mask=self.build_mask(ones(shape=data.shape)),
+        )
+
+        check_arrays(results['Any'], expected_1)
+        check_arrays(results['AnyEquiv'], expected_1)
+        check_arrays(results['AllButTwo'], expected_2)
+        check_arrays(results['AllButOne'], expected_3)
+        check_arrays(results['All'], expected_4)
+        check_arrays(results['AllEquiv'], expected_4)
+
     @parameter_space(factor_len=[2, 3, 4])
     def test_window_safe(self, factor_len):
         # all true data set of (days, securities)

zipline/pipeline/filters/__init__.py

@@ -9,12 +9,13 @@ from .filter import (
     PercentileFilter,
     SingleAsset,
 )
-from .smoothing import All, Any
+from .smoothing import All, Any, AtLeastN
 
 __all__ = [
     'All',
     'Any',
     'ArrayPredicate',
+    'AtLeastN',
     'CustomFilter',
     'Filter',
     'Latest',

zipline/pipeline/filters/smoothing.py

@@ -33,3 +33,19 @@ class Any(CustomFilter):
 
     def compute(self, today, assets, out, arg):
         out[:] = (arg.sum(axis=0) > 0)
+
+
+class AtLeastN(CustomFilter):
+    """
+    A Filter requiring that assets produce True for at least N days in the
+    last ``window_length`` days.
+
+    **Default Inputs:** None
+
+    **Default Window Length:** None
+    """
+
+    params = ('N',)
+
+    def compute(self, today, assets, out, arg, N):
+        out[:] = (arg.sum(axis=0) >= N)