Merge pull request #1052 from quantopian/empty-pipeline-bug

BUG: Empty pipeline failed to tz_localize

tests/pipeline/test_pipeline_algo.py

@@ -566,3 +566,43 @@ class PipelineAlgorithmTestCase(TestCase):
             # TradingAlgorithm.
             overwrite_sim_params=False,
         )
+
+    def test_empty_pipeline(self):
+
+        # For ensuring we call before_trading_start.
+        count = [0]
+
+        def initialize(context):
+            pipeline = attach_pipeline(Pipeline(), 'test')
+
+            vwap = VWAP(window_length=10)
+            pipeline.add(vwap, 'vwap')
+
+            # Nothing should have prices less than 0.
+            pipeline.set_screen(vwap < 0)
+
+        def handle_data(context, data):
+            pass
+
+        def before_trading_start(context, data):
+            context.results = pipeline_output('test')
+            self.assertTrue(context.results.empty)
+            count[0] += 1
+
+        algo = TradingAlgorithm(
+            initialize=initialize,
+            handle_data=handle_data,
+            before_trading_start=before_trading_start,
+            data_frequency='daily',
+            get_pipeline_loader=lambda column: self.pipeline_loader,
+            start=self.dates[0],
+            end=self.dates[-1],
+            env=self.env,
+        )
+
+        algo.run(
+            source=self.make_source(),
+            overwrite_sim_params=False,
+        )
+
+        self.assertTrue(count[0] > 0)

zipline/pipeline/engine.py

@@ -384,6 +384,23 @@ class SimplePipelineEngine(object):
         If mask[date, asset] is True, then result.loc[(date, asset), colname]
         will contain the value of data[colname][date, asset].
         """
+        if not mask.any():
+            # Manually handle the empty DataFrame case. This is a workaround
+            # to pandas failing to tz_localize an empty dataframe with a
+            # MultiIndex. It also saves us the work of applying a known-empty
+            # mask to each array.
+            #
+            # Slicing `dates` here to preserve pandas metadata.
+            empty_dates = dates[:0]
+            empty_assets = array([], dtype=object)
+            return DataFrame(
+                data={
+                    name: array([], dtype=arr.dtype)
+                    for name, arr in iteritems(data)
+                },
+                index=MultiIndex.from_arrays([empty_dates, empty_assets]),
+            )
+
         resolved_assets = array(self._finder.retrieve_all(assets))
         dates_kept = repeat_last_axis(dates.values, len(assets))[mask]
         assets_kept = repeat_first_axis(resolved_assets, len(dates))[mask]