black format

examples/highfreq/highfreq_processor.py

@@ -70,7 +70,7 @@ class HighFreqNorm(Processor):
             columns=["FEATURE_%d" % i for i in range(12 * 240)],
         ).sort_index()
         return df_new_features
-    
+
     def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
         if fit_start_time:
             self.fit_start_time = fit_start_time

examples/highfreq/workflow.py

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

@@ -35,7 +35,7 @@ class Dataset(Serializable):
 
     def config(self, *arg, **kwargs):
         """
-        config is designed to configure and parameters that cannot be learned from the data 
+        config is designed to configure and parameters that cannot be learned from the data
         """
         super().config(*arg, **kwargs)
 
@@ -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
 
@@ -130,7 +130,7 @@ class DatasetH(Dataset):
 
         kwargs : dict
             Config of DatasetH, such as
-            
+
             - segments : dict
                 Config of segments which is same as 'segments' in self.__init__
 
@@ -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
@@ -151,16 +149,15 @@ class DatasetH(Dataset):
         ----------
         handler_kwargs : dict
             init arguments of DataHanlder, which could include the following arguments:
-            
+
             - init_type : Init Type of Handler
-            
+
             - enable_cache : wheter to enable cache
 
         """
         super().setup_data(**kwargs)
         if handler_kwargs is not None:
             self.handler.setup_data(**handler_kwargs)
-        
 
     def __repr__(self):
         return "{name}(handler={handler}, segments={segments})".format(
@@ -464,7 +461,6 @@ class TSDatasetH(DatasetH):
         cal = self.handler.fetch(col_set=self.handler.CS_RAW).index.get_level_values("datetime").unique()
         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).

qlib/data/dataset/handler.py

@@ -119,7 +119,7 @@ class DataHandler(Serializable):
             self.start_time = start_time
         if end_time:
             self.end_time = end_time
-        
+
     def setup_data(self, enable_cache: bool = False):
         """
         Set Up the data.
@@ -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"""
 
@@ -229,7 +230,7 @@ class ZScoreNorm(Processor):
 
         df.loc(axis=1)[self.cols] = normalize(df[self.cols].values)
         return df
-    
+
     def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
         if fit_start_time:
             self.fit_start_time = fit_start_time
@@ -280,6 +281,7 @@ class RobustZScoreNorm(Processor):
             self.fit_end_time = fit_end_time
         super().config(**kwargs)
 
+
 class CSZScoreNorm(Processor):
     """Cross Sectional ZScore Normalization"""