black format

examples/high_freq/highfreq_handler.py

@@ -50,7 +50,6 @@ class HighFreqHandler(DataHandlerLP):
             drop_raw=drop_raw,
         )
 
-
     def get_feature_config(self):
         fields = []
         names = []
@@ -205,7 +204,9 @@ class HighFreqBacktestHandler(DataHandler):
         template_paused = "Select(Eq($paused, 0.0), {0})"
         template_fillnan = "FFillNan({0})"
 
-        fields += [template_fillnan.format(template_paused.format("$close")),]
+        fields += [
+            template_fillnan.format(template_paused.format("$close")),
+        ]
         names += ["$close0"]
         fields += [
             "If(Eq({1}, np.nan), 0, If(Or(Gt({2}, Mul(1.001, {4})), Lt({2}, Mul(0.999, {3}))), 0, {1}))".format(

examples/high_freq/highfreq_ops.py

@@ -44,6 +44,7 @@ class Date(ElemOperator):
         series = self.feature.load(instrument, start_index, end_index, freq)
         return pd.Series(_calendar[series.index], index=series.index)
 
+
 class Select(PairOperator):
     def __init__(self, condition, feature):
         super(Select, self).__init__(condition, feature, "select")
@@ -53,6 +54,7 @@ class Select(PairOperator):
         series_feature = self.feature_right.load(instrument, start_index, end_index, freq)
         return series_feature.loc[series_condition]
 
+
 class IsNull(ElemOperator):
     def __init__(self, feature):
         super(IsNull, self).__init__(feature, "isnull")

examples/high_freq/highfreq_processor.py

@@ -62,9 +62,9 @@ class HighFreqNorm(Processor):
         feat_1 = df_values[:, [5, 6, 7, 8, 9, 11]].reshape(-1, 6 * 240)
 
         df_new_features = pd.DataFrame(
-           data=np.concatenate((feat, feat_1), axis=1),
+            data=np.concatenate((feat, feat_1), axis=1),
-           index=idx,
+            index=idx,
-           columns=["FEATURE_%d" % i for i in range(12 * 240)],
+            columns=["FEATURE_%d" % i for i in range(12 * 240)],
         ).sort_index()
 
         return df_new_features

examples/high_freq/workflow.py

@@ -24,6 +24,7 @@ from qlib.data.data import Cal
 
 from highfreq_ops import DayFirst, DayLast, FFillNan, Date, Select, IsNull
 
+
 def save_dataset(dataset, path: [Path, str]):
     """
     save dataset to path
@@ -35,6 +36,7 @@ def save_dataset(dataset, path: [Path, str]):
     """
     dataset.to_pickle(path=path)
 
+
 def load_dataset(path: [Path, str], init_type=DataHandlerLP.IT_LS):
     """
     load dataset from path
@@ -57,17 +59,24 @@ def load_dataset(path: [Path, str], init_type=DataHandlerLP.IT_LS):
 
             The state of the object has been load by pickle
     """
-    fd = open(path, 'rb')
+    fd = open(path, "rb")
     dataset = pickle.load(fd)
     dataset.init(init_type=init_type)
     fd.close()
     return dataset
 
+
 if __name__ == "__main__":
 
     # use default data
     provider_uri = "/mnt/v-xiabi/data/qlib/high_freq"  # target_dir
-    qlib.init(provider_uri=provider_uri, custom_ops=[DayFirst, DayLast, FFillNan, Date, Select, IsNull], redis_port=233, region=REG_CN, auto_mount=False)
+    qlib.init(
+        provider_uri=provider_uri,
+        custom_ops=[DayFirst, DayLast, FFillNan, Date, Select, IsNull],
+        redis_port=233,
+        region=REG_CN,
+        auto_mount=False,
+    )
 
     MARKET = "csi300"
     BENCHMARK = "SH000300"
@@ -134,4 +143,3 @@ if __name__ == "__main__":
     Cal.get_calender_day(freq="1min")  # TO FIX: load the calendar day for cache
     dataset = init_instance_by_config(task["dataset"])
     dataset_backtest = init_instance_by_config(task["dataset_backtest"])
-

qlib/data/dataset/__init__.py

@@ -87,7 +87,6 @@ class DatasetH(Dataset):
         """
         super().__init__(handler, segments)
 
-
     def init(self, init_type: str = DataHandlerLP.IT_FIT_SEQ, enable_cache: bool = False):
         """
         Initialize the data of Qlib

qlib/data/dataset/handler.py

@@ -389,6 +389,7 @@ class DataHandlerLP(DataHandler):
 
         if self.drop_raw:
             del self._data
+
     # init type
     IT_FIT_SEQ = "fit_seq"  # the input of `fit` will be the output of the previous processor
     IT_FIT_IND = "fit_ind"  # the input of `fit` will be the original df

qlib/data/dataset/loader.py

@@ -94,7 +94,9 @@ class DLWParser(DataLoader):
         return exprs, names
 
     @abc.abstractmethod
-    def load_group_df(self, instruments, exprs: list, names: list, start_time=None, end_time=None, freq="day") -> pd.DataFrame:
+    def load_group_df(
+        self, instruments, exprs: list, names: list, start_time=None, end_time=None, freq="day"
+    ) -> pd.DataFrame:
         """
         load the dataframe for specific group
 
@@ -148,7 +150,9 @@ class QlibDataLoader(DLWParser):
         print("swap level", swap_level)
         super().__init__(config)
 
-    def load_group_df(self, instruments, exprs: list, names: list, start_time=None, end_time=None, freq="day") -> pd.DataFrame:
+    def load_group_df(
+        self, instruments, exprs: list, names: list, start_time=None, end_time=None, freq="day"
+    ) -> pd.DataFrame:
         if instruments is None:
             warnings.warn("`instruments` is not set, will load all stocks")
             instruments = "all"