diff --git a/tests/pipeline/test_engine.py b/tests/pipeline/test_engine.py
index 339f2f9b34..b4a9e78fc6 100644
--- a/tests/pipeline/test_engine.py
+++ b/tests/pipeline/test_engine.py
@@ -62,6 +62,7 @@
     MaxDrawdown,
     SimpleMovingAverage,
 )
+from zipline.pipeline.term import NotSpecified
 from zipline.testing import (
     make_rotating_equity_info,
     make_simple_equity_info,
@@ -95,6 +96,14 @@ def compute(self, today, assets, out, close):
         out[:] = assets
 
 
+class OpenPrice(CustomFactor):
+    window_length = 1
+    inputs = [USEquityPricing.open]
+
+    def compute(self, today, assets, out, open):
+        out[:] = open
+
+
 def assert_multi_index_is_product(testcase, index, *levels):
     """Assert that a MultiIndex contains the product of `*levels`."""
     testcase.assertIsInstance(
@@ -354,6 +363,62 @@ def test_numeric_factor(self):
             DataFrame(expected_avg, index=dates, columns=self.assets),
         )
 
+    def test_masked_factor(self):
+        """
+        Test that a Custom Factor computes the correct values when passed a
+        mask. The mask/filter should be applied prior to computing any values,
+        as opposed to computing the factor across the entire universe of
+        assets. Any assets that are filtered out should be filled with missing
+        values.
+        """
+        loader = self.loader
+        dates = self.dates[5:10]
+        assets = self.assets
+        asset_ids = self.asset_ids
+        num_dates = len(dates)
+        constants = self.constants
+        open = USEquityPricing.open
+        engine = SimplePipelineEngine(
+            lambda column: loader, self.dates, self.asset_finder,
+        )
+
+        # These are the expected values for the OpenPrice factor. If we pass
+        # OpenPrice a mask, any assets that are filtered out should have all
+        # NaN values. Otherwise, we expect its computed values to be the
+        # asset's open price.
+        values = array([constants[open]] * num_dates, dtype=float)
+        missing_values = array([nan] * num_dates)
+
+        for asset_id in asset_ids:
+            mask = AssetID() <= asset_id
+            factor1 = OpenPrice(mask=mask)
+
+            # Test running our pipeline both with and without a second factor.
+            # We do not explicitly test the resulting values of the second
+            # factor; we just want to implicitly ensure that the addition of
+            # another factor to the pipeline term graph does not cause any
+            # unexpected exceptions when calling `run_pipeline`.
+            for factor2 in (None,
+                            RollingSumDifference(mask=NotSpecified),
+                            RollingSumDifference(mask=mask)):
+                if factor2 is None:
+                    columns = {'factor1': factor1}
+                else:
+                    columns = {'factor1': factor1, 'factor2': factor2}
+                pipeline = Pipeline(columns=columns)
+                results = engine.run_pipeline(pipeline, dates[0], dates[-1])
+                factor1_results = results['factor1'].unstack()
+
+                expected = {
+                    asset: values if asset.sid <= asset_id else missing_values
+                    for asset in assets
+                }
+
+                assert_frame_equal(
+                    factor1_results,
+                    DataFrame(expected, index=dates, columns=assets),
+                )
+
     def test_rolling_and_nonrolling(self):
         open_ = USEquityPricing.open
         close = USEquityPricing.close
diff --git a/zipline/pipeline/engine.py b/zipline/pipeline/engine.py
index c871530db8..39a4776c16 100644
--- a/zipline/pipeline/engine.py
+++ b/zipline/pipeline/engine.py
@@ -242,8 +242,11 @@ def _mask_and_dates_for_term(self, term, workspace, graph, dates):
         Load mask and mask row labels for term.
         """
         mask = term.mask
-        offset = graph.extra_rows[mask] - graph.extra_rows[term]
-        return workspace[mask][offset:], dates[offset:]
+        mask_offset = graph.extra_rows[mask] - graph.extra_rows[term]
+        dates_offset = (
+            graph.extra_rows[self._root_mask_term] - graph.extra_rows[term]
+        )
+        return workspace[mask][mask_offset:], dates[dates_offset:]
 
     @staticmethod
     def _inputs_for_term(term, workspace, graph):
diff --git a/zipline/pipeline/graph.py b/zipline/pipeline/graph.py
index 6bcbe8c045..3cf4a62e5c 100644
--- a/zipline/pipeline/graph.py
+++ b/zipline/pipeline/graph.py
@@ -104,7 +104,8 @@ def offset(self):
         zipline.pipeline.engine.SimplePipelineEngine._inputs_for_term
         zipline.pipeline.engine.SimplePipelineEngine._mask_and_dates_for_term
         """
-        return {(term, dep): self.extra_rows[dep] - term.extra_input_rows
+        return {(term, dep): self.extra_rows[dep] - max(term.extra_input_rows,
+                                                        self.extra_rows[term])
                 for term in self
                 for dep in term.dependencies}
 
diff --git a/zipline/pipeline/mixins.py b/zipline/pipeline/mixins.py
index 4e8a27b5e3..da9297b88d 100644
--- a/zipline/pipeline/mixins.py
+++ b/zipline/pipeline/mixins.py
@@ -70,6 +70,7 @@ class CustomTermMixin(object):
     def __new__(cls,
                 inputs=NotSpecified,
                 window_length=NotSpecified,
+                mask=NotSpecified,
                 dtype=NotSpecified,
                 missing_value=NotSpecified,
                 **kwargs):
@@ -88,6 +89,7 @@ def __new__(cls,
             cls,
             inputs=inputs,
             window_length=window_length,
+            mask=mask,
             dtype=dtype,
             missing_value=missing_value,
             **kwargs
@@ -104,7 +106,6 @@ def _compute(self, windows, dates, assets, mask):
         Call the user's `compute` function on each window with a pre-built
         output array.
         """
-        # TODO: Make mask available to user's `compute`.
         compute = self.compute
         missing_value = self.missing_value
         params = self.params
@@ -113,14 +114,18 @@ def _compute(self, windows, dates, assets, mask):
             # TODO: Consider pre-filtering columns that are all-nan at each
             # time-step?
             for idx, date in enumerate(dates):
+                col_mask = mask[idx]
+                masked_out = out[idx][col_mask]
+                masked_assets = assets[col_mask]
+
                 compute(
                     date,
-                    assets,
-                    out[idx],
-                    *(next(w) for w in windows),
+                    masked_assets,
+                    masked_out,
+                    *(next(w)[:, col_mask] for w in windows),
                     **params
                 )
-        out[~mask] = missing_value
+                out[idx][col_mask] = masked_out
         return out
 
     def short_repr(self):