TST: Test correlation/regression factors with nonexistent asset

tests/pipeline/test_engine.py

@@ -41,7 +41,9 @@ from scipy.stats.stats import linregress, pearsonr, spearmanr
 from six import iteritems, itervalues
 from toolz import merge
 
+from zipline.assets import Equity
 from zipline.assets.synthetic import make_rotating_equity_info
+from zipline.errors import NonExistentAssetInTimeFrame
 from zipline.lib.adjustment import MULTIPLY
 from zipline.lib.labelarray import LabelArray
 from zipline.pipeline import CustomFactor, Pipeline
@@ -1431,6 +1433,59 @@ class ParameterizedFactorTestCase(WithTradingEnvironment, ZiplineTestCase):
                     ),
                 )
 
+    def test_correlation_and_regression_with_bad_asset(self):
+        """
+        Test that `RollingPearsonOfReturns`, `RollingSpearmanOfReturns` and
+        `RollingLinearRegressionOfReturns` raise the proper exception when
+        given a nonexistent target asset.
+        """
+        start_date_index = 6
+        end_date_index = 10
+        my_asset = Equity(0)
+
+        # This filter is arbitrary; the important thing is that we test each
+        # factor both with and without a specified mask.
+        my_asset_filter = AssetID().eq(1)
+
+        for mask in (NotSpecified, my_asset_filter):
+            pearson_factor = RollingPearsonOfReturns(
+                target=my_asset,
+                returns_length=3,
+                correlation_length=3,
+                mask=mask,
+            )
+            spearman_factor = RollingSpearmanOfReturns(
+                target=my_asset,
+                returns_length=3,
+                correlation_length=3,
+                mask=mask,
+            )
+            regression_factor = RollingLinearRegressionOfReturns(
+                target=my_asset,
+                returns_length=3,
+                regression_length=3,
+                mask=mask,
+            )
+
+            with self.assertRaises(NonExistentAssetInTimeFrame):
+                self.engine.run_pipeline(
+                    Pipeline(columns={'pearson_factor': pearson_factor}),
+                    self.dates[start_date_index],
+                    self.dates[end_date_index],
+                )
+            with self.assertRaises(NonExistentAssetInTimeFrame):
+                self.engine.run_pipeline(
+                    Pipeline(columns={'spearman_factor': spearman_factor}),
+                    self.dates[start_date_index],
+                    self.dates[end_date_index],
+                )
+            with self.assertRaises(NonExistentAssetInTimeFrame):
+                self.engine.run_pipeline(
+                    Pipeline(columns={'regression_factor': regression_factor}),
+                    self.dates[start_date_index],
+                    self.dates[end_date_index],
+                )
+
 
 class StringColumnTestCase(WithSeededRandomPipelineEngine,
                            ZiplineTestCase):

zipline/pipeline/factors/technical.py

@@ -26,7 +26,7 @@ from scipy.stats import linregress, pearsonr, spearmanr
 from zipline.pipeline.data import USEquityPricing
 from zipline.pipeline.filters import SingleAsset
 from zipline.pipeline.mixins import SingleInputMixin
-from zipline.pipeline.term import NotSpecified
+from zipline.pipeline.term import AssetExists, NotSpecified
 from zipline.utils.numpy_utils import ignore_nanwarnings
 from zipline.utils.input_validation import expect_types
 from zipline.utils.math_utils import (
@@ -183,9 +183,12 @@ class _RollingCorrelationOfReturns(CustomFactor, SingleInputMixin):
                 correlation_length,
                 mask=NotSpecified,
                 **kwargs):
-        if mask is not NotSpecified:
-            # Make sure we do not filter out the asset of interest.
-            mask = mask | SingleAsset(asset=target)
+        if mask is NotSpecified:
+            mask = AssetExists()
+
+        # Make sure we do not filter out the asset of interest.
+        mask = mask | SingleAsset(asset=target)
+
         return super(_RollingCorrelationOfReturns, cls).__new__(
             cls,
             target=target,
@@ -390,9 +393,12 @@ class RollingLinearRegressionOfReturns(CustomFactor, SingleInputMixin):
                 regression_length,
                 mask=NotSpecified,
                 **kwargs):
-        if mask is not NotSpecified:
-            # Make sure we do not filter out the asset of interest.
-            mask = mask | SingleAsset(asset=target)
+        if mask is NotSpecified:
+            mask = AssetExists()
+
+        # Make sure we do not filter out the asset of interest.
+        mask = mask | SingleAsset(asset=target)
+
         return super(RollingLinearRegressionOfReturns, cls).__new__(
             cls,
             target=target,