diff --git a/examples/nested_decision_execution/rl_dummy.py b/examples/nested_decision_execution/rl_dummy.py
index cd0961f66..c42e28be4 100644
--- a/examples/nested_decision_execution/rl_dummy.py
+++ b/examples/nested_decision_execution/rl_dummy.py
@@ -319,6 +319,7 @@ class RLStrategy(BaseStrategy):
         self.policy = policy
 
         # TODO: how to get inner frequency and trade len
+        # This should be no longer required when PA is provided by qlib.
         self.inner_frequency = "day"
         self.inner_trade_len = 1
 
@@ -432,6 +433,12 @@ class RlWorkflow(NestedDecisonExecutionWorkflow):
             min_cost=5
         )
         common_infra = CommonInfrastructure(trade_account=trade_account, trade_exchange=exchange)
+        executor = NestedExecutor(
+            time_per_step="week",
+            inner_executor=SimulatorExecutor(time_per_step="day", verbose=True),
+            inner_strategy=RLStrategy(Observation("day"), Action(), DummyPolicy()),
+            common_infra=common_infra
+        )
         strategy = init_instance_by_config({
             "class": "TopkDropoutStrategy",
             "module_path": "qlib.contrib.strategy.model_strategy",
@@ -442,12 +449,6 @@ class RlWorkflow(NestedDecisonExecutionWorkflow):
                 "n_drop": 5,
             },
         }, common_infra=common_infra)
-        executor = NestedExecutor(
-            time_per_step="week",
-            inner_executor=SimulatorExecutor(time_per_step="day", verbose=True),
-            inner_strategy=RLStrategy(Observation("day"), Action(), DummyPolicy()),
-            common_infra=common_infra
-        )
         report_dict = backtest_func(trade_start_time, trade_end_time, strategy, executor)
         print(report_dict)
 
@@ -463,7 +464,7 @@ class QlibOrderDataset(Dataset):
     def __len__(self):
         return len(self.orders)
 
-    def __getitem__(self, index):
+    def __getitem__(self, index) -> Order:
         return self.orders[index]
 
 
@@ -535,7 +536,7 @@ class Action:
     def validate(self, action: Any) -> bool:
         return self.action_space.contains(action)
 
-    def to_volume(self, action: Any, ep_state: EpisodicState):
+    def to_volume(self, action: Any, ep_state: EpisodicState) -> Any:
         exec_vol = ep_state.position / self.denominator * action
         if ep_state.cur_step + 1 >= ep_state.num_step:
             exec_vol = ep_state.position