TST: Ensure batch_order_target_percent orders like order_target_percent

tests/test_algorithm.py

@@ -98,6 +98,7 @@ from zipline.testing import (
     to_utc,
     trades_by_sid_to_dfs,
 )
+from zipline.testing import RecordBatchBlotter
 from zipline.testing.fixtures import (
     WithDataPortal,
     WithLogger,
@@ -171,6 +172,7 @@ from zipline.test_algorithms import (
     set_benchmark_algo,
     no_handle_data,
 )
+from zipline.testing.predicates import assert_equal
 from zipline.utils.api_support import ZiplineAPI, set_algo_instance
 from zipline.utils.calendars import get_calendar, register_calendar
 from zipline.utils.context_tricks import CallbackManager
@@ -1737,6 +1739,75 @@ def handle_data(context, data):
         )
         test_algo.run(self.data_portal)
 
+    def test_batch_order_target_percent_matches_multi_order(self):
+        weights = pd.Series([.3, .7])
+
+        multi_blotter = RecordBatchBlotter(self.SIM_PARAMS_DATA_FREQUENCY,
+                                           self.asset_finder)
+        multi_test_algo = TradingAlgorithm(
+            script=dedent("""\
+                from collections import OrderedDict
+                from six import iteritems
+
+                from zipline.api import sid, order_target_percent
+
+
+                def initialize(context):
+                    context.assets = [sid(0), sid(3)]
+                    context.placed = False
+
+                def handle_data(context, data):
+                    if not context.placed:
+                        for asset, weight in iteritems(OrderedDict(zip(
+                            context.assets, {weights}
+                        ))):
+                            order_target_percent(asset, weight)
+
+                        context.placed = True
+
+            """).format(weights=list(weights)),
+            blotter=multi_blotter,
+            env=self.env,
+        )
+        multi_stats = multi_test_algo.run(self.data_portal)
+        self.assertFalse(multi_blotter.order_batch_called)
+
+        batch_blotter = RecordBatchBlotter(self.SIM_PARAMS_DATA_FREQUENCY,
+                                           self.asset_finder)
+        batch_test_algo = TradingAlgorithm(
+            script=dedent("""\
+                from collections import OrderedDict
+
+                from zipline.api import sid, batch_order_target_percent
+
+
+                def initialize(context):
+                    context.assets = [sid(0), sid(3)]
+                    context.placed = False
+
+                def handle_data(context, data):
+                    if not context.placed:
+                        batch_order_target_percent(OrderedDict(zip(
+                            context.assets, {weights}
+                        )))
+                        context.placed = True
+
+            """).format(weights=list(weights)),
+            blotter=batch_blotter,
+            env=self.env,
+        )
+        batch_stats = batch_test_algo.run(self.data_portal)
+        self.assertTrue(batch_blotter.order_batch_called)
+
+        for stats in (multi_stats, batch_stats):
+            stats.orders = stats.orders.apply(
+                lambda orders: [toolz.dissoc(o, 'id') for o in orders]
+            )
+            stats.transactions = stats.transactions.apply(
+                lambda txns: [toolz.dissoc(txn, 'order_id') for txn in txns]
+            )
+        assert_equal(multi_stats, batch_stats)
+
     def test_order_dead_asset(self):
         # after asset 0 is dead
         params = SimulationParameters(