format code

examples/model_rolling/task_manager_rolling.py

@@ -119,7 +119,8 @@ def task_collecting(task_pool, exp_name):
         return False
 
     artifact = ens_workflow(
-        RecorderCollector(exp_name=exp_name, rec_key_func=rec_key, rec_filter_func=my_filter), RollingGroup(), 
+        RecorderCollector(exp_name=exp_name, rec_key_func=rec_key, rec_filter_func=my_filter),
+        RollingGroup(),
     )
     print(artifact)
 

examples/online_srv/task_manager_rolling_with_updating.py

@@ -70,9 +70,18 @@ task_xgboost_config = {
     "record": record_config,
 }
 
-class RollingOnlineExample:
 
-    def __init__(self, exp_name="rolling_exp", task_pool="rolling_task", provider_uri="~/.qlib/qlib_data/cn_data", region="cn", task_url="mongodb://10.0.0.4:27017/", task_db_name="rolling_db", rolling_step=550):
+class RollingOnlineExample:
+    def __init__(
+        self,
+        exp_name="rolling_exp",
+        task_pool="rolling_task",
+        provider_uri="~/.qlib/qlib_data/cn_data",
+        region="cn",
+        task_url="mongodb://10.0.0.4:27017/",
+        task_db_name="rolling_db",
+        rolling_step=550,
+    ):
         self.exp_name = exp_name
         self.task_pool = task_pool
         mongo_conf = {
@@ -84,9 +93,9 @@ class RollingOnlineExample:
         self.rolling_gen = RollingGen(step=rolling_step, rtype=RollingGen.ROLL_SD)
         self.trainer = TrainerRM(self.exp_name, self.task_pool)
         self.task_manager = TaskManager(self.task_pool)
-        self.rolling_online_manager = RollingOnlineManager(experiment_name=exp_name, rolling_gen=self.rolling_gen, trainer=self.trainer)
-
-        
+        self.rolling_online_manager = RollingOnlineManager(
+            experiment_name=exp_name, rolling_gen=self.rolling_gen, trainer=self.trainer
+        )
 
     def print_online_model(self):
         print("========== print_online_model ==========")
@@ -99,7 +108,6 @@ class RollingOnlineExample:
             if self.rolling_online_manager.get_online_tag(rec) == self.rolling_online_manager.NEXT_ONLINE_TAG:
                 print(rid)
 
-
     # This part corresponds to "Task Generating" in the document
     def task_generating(self):
 
@@ -114,11 +122,9 @@ class RollingOnlineExample:
 
         return tasks
 
-
     def task_training(self, tasks):
         self.trainer.train(tasks)
 
-
     # This part corresponds to "Task Collecting" in the document
     def task_collecting(self):
         print("========== task_collecting ==========")
@@ -141,7 +147,6 @@ class RollingOnlineExample:
         )
         print(artifact)
 
-
     # Reset all things to the first status, be careful to save important data
     def reset(self):
         print("========== reset ==========")
@@ -150,7 +155,6 @@ class RollingOnlineExample:
         for rid in exp.list_recorders():
             exp.delete_recorder(rid)
 
-
     # Run this firstly to see the workflow in Task Management
     def first_run(self):
         print("========== first_run ==========")
@@ -163,7 +167,6 @@ class RollingOnlineExample:
         latest_rec, _ = self.rolling_online_manager.list_latest_recorders()
         self.rolling_online_manager.reset_online_tag(list(latest_rec.values()))
 
-
     def routine(self):
         print("========== routine ==========")
         self.print_online_model()
@@ -178,7 +181,7 @@ if __name__ == "__main__":
 
     ####### to update the models and predictions after the trading time, use the command below
     # python task_manager_rolling_with_updating.py after_day
-    
+
     ####### to define your own parameters, use `--`
     # python task_manager_rolling_with_updating.py first_run --exp_name='your_exp_name' --rolling_step=40
     fire.Fire(RollingOnlineExample)

examples/online_srv/update_online_pred.py

@@ -71,12 +71,14 @@ def update_online_pred(experiment_name="online_srv"):
 
     online_manager.update_online_pred()
 
-def main(provider_uri = "~/.qlib/qlib_data/cn_data", region=REG_CN, experiment_name="online_srv"):
+
+def main(provider_uri="~/.qlib/qlib_data/cn_data", region=REG_CN, experiment_name="online_srv"):
     provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
     qlib.init(provider_uri=provider_uri, region=region)
     first_train(experiment_name)
     update_online_pred(experiment_name)
 
+
 if __name__ == "__main__":
     ## to train a model and set it to online model, use the command below
     # python update_online_pred.py first_train