black format

examples/highfreq/workflow.py

@@ -177,8 +177,8 @@ class HighfreqWorkflow(object):
         dataset_backtest.setup_data(handler_kwargs={})
 
         ##=============get data=============
-        xtest, = dataset.prepare(["test"])
+        (xtest,) = dataset.prepare(["test"])
-        backtest_test, = dataset_backtest.prepare(["test"])
+        (backtest_test,) = dataset_backtest.prepare(["test"])
 
         print(xtest, backtest_test)
         return

examples/rolling_process_data/workflow.py

@@ -105,7 +105,7 @@ class RollingDataWorkflow(object):
                     handler_kwargs={
                         "start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
                         "end_time": datetime(test_end_time[0] + rolling_offset, *test_end_time[1:]),
-                        "processor_kwargs":{
+                        "processor_kwargs": {
                             "fit_start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
                             "fit_end_time": datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]),
                         },
@@ -126,7 +126,9 @@ class RollingDataWorkflow(object):
                     },
                 )
                 dataset.setup_data(
-                    handler_kwargs={"init_type": DataHandlerLP.IT_FIT_SEQ,}
+                    handler_kwargs={
+                        "init_type": DataHandlerLP.IT_FIT_SEQ,
+                    }
                 )
 
             dtrain, dvalid, dtest = dataset.prepare(["train", "valid", "test"])

qlib/data/dataset/__init__.py

@@ -117,7 +117,7 @@ class DatasetH(Dataset):
         self.segments = segments.copy()
         super().__init__(**kwargs)
 
-    def config(self, handler_kwargs:dict = None, segments:dict = None, **kwargs):
+    def config(self, handler_kwargs: dict = None, segments: dict = None, **kwargs):
         """
         Initialize the DatasetH
 
@@ -141,8 +141,6 @@ class DatasetH(Dataset):
         if segments is not None:
             self.segments = segments.copy()
 
-        
-
     def setup_data(self, handler_kwargs: dict = None, **kwargs):
         """
         Setup the Data
@@ -161,7 +159,6 @@ class DatasetH(Dataset):
         if handler_kwargs is not None:
             self.handler.setup_data(**handler_kwargs)
 
-
     def __repr__(self):
         return "{name}(handler={handler}, segments={segments})".format(
             name=self.__class__.__name__, handler=self.handler, segments=self.segments
@@ -465,7 +462,6 @@ class TSDatasetH(DatasetH):
         cal = sorted(cal)
         self.cal = cal
 
-
     def _prepare_seg(self, slc: slice, **kwargs) -> TSDataSampler:
         # Dataset decide how to slice data(Get more data for timeseries).
         start, end = slc.start, slc.stop

qlib/data/dataset/handler.py

@@ -407,7 +407,7 @@ class DataHandlerLP(DataHandler):
         if self.drop_raw:
             del self._data
 
-    def config(self, processor_kwargs:dict = None, **kwargs):
+    def config(self, processor_kwargs: dict = None, **kwargs):
         """
         configuration of data.
         # what data to be loaded from data source

qlib/data/dataset/loader.py

@@ -53,6 +53,7 @@ class DataLoader(abc.ABC):
         """
         pass
 
+
 class DLWParser(DataLoader):
     """
     (D)ata(L)oader (W)ith (P)arser for features and names

qlib/data/dataset/processor.py

@@ -202,6 +202,7 @@ class MinMaxNorm(Processor):
             self.fit_end_time = fit_end_time
         super().config(**kwargs)
 
+
 class ZScoreNorm(Processor):
     """ZScore Normalization"""
 
@@ -280,6 +281,7 @@ class RobustZScoreNorm(Processor):
             self.fit_end_time = fit_end_time
         super().config(**kwargs)
 
+
 class CSZScoreNorm(Processor):
     """Cross Sectional ZScore Normalization"""