diff --git a/tests/test_algorithm.py b/tests/test_algorithm.py
index fcc67017..3140caf3 100644
--- a/tests/test_algorithm.py
+++ b/tests/test_algorithm.py
@@ -4127,42 +4127,62 @@ class TestPanelData(ZiplineTestCase):
          pd.Timestamp('2015-12-24', tz='UTC'),),
     ])
     def test_panel_data(self, data_frequency, start_dt, end_dt):
+        trading_calendar = get_calendar('NYSE')
         if data_frequency == 'daily':
             history_freq = '1d'
-            df = create_daily_df_for_asset(get_calendar('NYSE'),
-                                           start_dt, end_dt)
+            create_df_for_asset = create_daily_df_for_asset
+            dt_transform = trading_calendar.minute_to_session_label
         elif data_frequency == 'minute':
             history_freq = '1m'
-            df = create_minute_df_for_asset(get_calendar('NYSE'),
-                                            start_dt, end_dt)
+            create_df_for_asset = create_minute_df_for_asset
 
-        panel = pd.Panel({1: df})
+            def dt_transform(dt):
+                return dt
 
-        price_record = pd.DataFrame(columns=['current', 'previous'])
+        sids = range(1, 3)
+        dfs = {}
+        for sid in sids:
+            dfs[sid] = create_df_for_asset(trading_calendar,
+                                           start_dt, end_dt, interval=sid)
+            dfs[sid]['prev_close'] = dfs[sid]['close'].shift(1)
+        panel = pd.Panel(dfs)
+
+        price_record = pd.Panel(items=sids,
+                                major_axis=panel.major_axis,
+                                minor_axis=['current', 'previous'])
 
         def initialize(algo):
             algo.first_bar = True
+            algo.equities = []
+            for sid in sids:
+                algo.equities.append(algo.sid(sid))
 
         def handle_data(algo, data):
-            price_record.loc[algo.get_datetime(), 'current'] = (
-                data.current(algo.sid(1), 'price')
+            price_record.loc[:, dt_transform(algo.get_datetime()),
+                             'current'] = (
+                data.current(algo.equities, 'price')
             )
             if algo.first_bar:
                 algo.first_bar = False
             else:
-                price_record.loc[algo.get_datetime(), 'previous'] = (
-                    data.history(algo.sid(1), 'price', 2, history_freq)[0]
+                price_record.loc[:, dt_transform(algo.get_datetime()),
+                                 'previous'] = (
+                    data.history(algo.equities, 'price',
+                                 2, history_freq).iloc[0]
                 )
 
+        def check_panels():
+            np.testing.assert_array_equal(
+                price_record.values.astype('float64'),
+                panel.loc[:, :, ['close',
+                                 'prev_close']].values.astype('float64')
+            )
+
         trading_algo = TradingAlgorithm(initialize=initialize,
                                         handle_data=handle_data)
         trading_algo.run(data=panel)
-        np.testing.assert_array_equal(
-            np.array(price_record.transpose(), dtype='float64'),
-            np.array([df['close'], df['close'].shift(1)], dtype='float64')
-        )
-
-        price_record.drop(price_record.index)
+        check_panels()
+        price_record.loc[:] = np.nan
 
         run_algorithm(
             start=start_dt,
@@ -4173,7 +4193,4 @@ class TestPanelData(ZiplineTestCase):
             data_frequency=data_frequency,
             data=panel
         )
-        np.testing.assert_array_equal(
-            np.array(price_record.transpose(), dtype='float64'),
-            np.array([df['close'], df['close'].shift(1)], dtype='float64')
-        )
+        check_panels()