format code

examples/online_srv/online_management_simulate.py

@@ -113,7 +113,7 @@ class OnlineSimulationExample:
         self.rolling_gen = RollingGen(
             step=rolling_step, rtype=RollingGen.ROLL_SD, ds_extra_mod_func=None
         )  # The rolling tasks generator, ds_extra_mod_func is None because we just need to simulate to 2018-10-31 and needn't change the handler end time.
-        self.trainer = DelayTrainerRM(self.exp_name, self.task_pool) # Also can be TrainerR, TrainerRM, DelayTrainerR
+        self.trainer = DelayTrainerRM(self.exp_name, self.task_pool)  # Also can be TrainerR, TrainerRM, DelayTrainerR
         self.rolling_online_manager = OnlineManager(
             RollingStrategy(exp_name, task_template=tasks, rolling_gen=self.rolling_gen),
             trainer=self.trainer,

qlib/model/ens/ensemble.py

@@ -16,9 +16,9 @@ class Ensemble:
     For example: {Rollinga_b: object, Rollingb_c: object} -> object
 
     When calling this class:
-    
+
         Args:
-            ensemble_dict (dict): the ensemble dict like {name: things} waiting for merging 
+            ensemble_dict (dict): the ensemble dict like {name: things} waiting for merging
 
         Returns:
             object: the ensemble object
@@ -103,6 +103,7 @@ class AverageEnsemble(Ensemble):
         Returns:
             pd.DataFrame: the complete result of averaging and standardizing.
     """
+
     def __call__(self, ensemble_dict: dict) -> pd.DataFrame:
         # need to flatten the nested dict
         ensemble_dict = flatten_dict(ensemble_dict, sep=FLATTEN_TUPLE)

qlib/model/ens/group.py

@@ -64,7 +64,7 @@ class Group:
         else:
             raise NotImplementedError(f"Please specify valid `_ens_func`.")
 
-    def __call__(self, ungrouped_dict: dict, n_jobs:int=1, verbose:int=0, *args, **kwargs) -> dict:
+    def __call__(self, ungrouped_dict: dict, n_jobs: int = 1, verbose: int = 0, *args, **kwargs) -> dict:
         """
         Group the ungrouped_dict into different groups.
 

qlib/model/trainer.py

@@ -240,13 +240,13 @@ class DelayTrainerR(TrainerR):
         """
         Given a list of Recorder and return a list of trained Recorder.
         This class will finish real data loading and model fitting.
- 
+
         Args:
             recs (list): a list of Recorder, the tasks have been saved to them
             end_train_func (Callable, optional): the end_train method which needs at least `recorder`s and `experiment_name`. Defaults to None for using self.end_train_func.
             experiment_name (str): the experiment name, None for use default name.
             kwargs: the params for end_train_func.
- 
+
         Returns:
             List[Recorder]: a list of Recorders
         """

qlib/utils/serial.py

@@ -8,7 +8,6 @@ import dill
 from typing import Union
 
 
-
 class Serializable:
     """
     Serializable will change the behaviors of pickle.

qlib/workflow/online/manager.py

@@ -41,8 +41,8 @@ class OnlineManager(Serializable):
     It also provides a history recording of which models are online at what time.
     """
 
-    STATUS_SIMULATING = "simulating" # when calling `simulate`
-    STATUS_NORMAL = "normal" # the normal status
+    STATUS_SIMULATING = "simulating"  # when calling `simulate`
+    STATUS_NORMAL = "normal"  # the normal status
 
     def __init__(
         self,
@@ -210,7 +210,9 @@ class OnlineManager(Serializable):
     SIM_LOG_LEVEL = logging.INFO + 1
     SIM_LOG_NAME = "SIMULATE_INFO"
 
-    def simulate(self, end_time, frequency="day", task_kwargs={}, model_kwargs={}, signal_kwargs={}) -> Union[pd.Series, pd.DataFrame]:
+    def simulate(
+        self, end_time, frequency="day", task_kwargs={}, model_kwargs={}, signal_kwargs={}
+    ) -> Union[pd.Series, pd.DataFrame]:
         """
         Starting from the current time, this method will simulate every routine in OnlineManager until the end time.
 

qlib/workflow/online/strategy.py

@@ -118,7 +118,7 @@ class RollingStrategy(OnlineStrategy):
     def get_collector(self, process_list=[RollingGroup()], rec_key_func=None, rec_filter_func=None, artifacts_key=None):
         """
         Get the instance of `Collector <../advanced/task_management.html#Task Collecting>`_ to collect results. The returned collector must distinguish results in different models.
-        
+
         Assumption: the models can be distinguished based on the model name and rolling test segments.
         If you do not want this assumption, please implement your method or use another rec_key_func.
 

qlib/workflow/task/collect.py

@@ -98,7 +98,7 @@ class MergeCollector(Collector):
     def __init__(self, collector_dict: Dict[str, Collector], process_list: List[Callable] = [], merge_func=None):
         """
         Init MergeCollector.
- 
+
         Args:
             collector_dict (Dict[str,Collector]): the dict like {collector_key, Collector}
             process_list (List[Callable]): the list of processors or the instance of processor to process dict.

qlib/workflow/task/utils.py

@@ -20,7 +20,7 @@ def get_mongodb() -> Database:
 
     """
     Get database in MongoDB, which means you need to declare the address and the name of a database at first.
-    
+
     For example:
 
         Using qlib.init():