Black format

docs/conf.py

@@ -191,7 +191,15 @@ man_pages = [(master_doc, "qlib", u"QLib Documentation", [author], 1)]
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, "QLib", u"QLib Documentation", author, "QLib", "One line description of project.", "Miscellaneous",),
+    (
+        master_doc,
+        "QLib",
+        u"QLib Documentation",
+        author,
+        "QLib",
+        "One line description of project.",
+        "Miscellaneous",
+    ),
 ]
 
 

examples/benchmarks/TFT/libs/tft_model.py

@@ -721,7 +721,12 @@ class TemporalFusionTransformer:
         encoder_steps = self.num_encoder_steps
 
         # Inputs.
-        all_inputs = tf.keras.layers.Input(shape=(time_steps, combined_input_size,))
+        all_inputs = tf.keras.layers.Input(
+            shape=(
+                time_steps,
+                combined_input_size,
+            )
+        )
 
         unknown_inputs, known_combined_layer, obs_inputs, static_inputs = self.get_tft_embeddings(all_inputs)
 
@@ -861,7 +866,10 @@ class TemporalFusionTransformer:
             """Returns LSTM cell initialized with default parameters."""
             if self.use_cudnn:
                 lstm = tf.keras.layers.CuDNNLSTM(
-                    self.hidden_layer_size, return_sequences=True, return_state=return_state, stateful=False,
+                    self.hidden_layer_size,
+                    return_sequences=True,
+                    return_state=return_state,
+                    stateful=False,
                 )
             else:
                 lstm = tf.keras.layers.LSTM(

examples/highfreq/highfreq_handler.py

@@ -20,7 +20,10 @@ class HighFreqHandler(DataHandlerLP):
             new_l = []
             for p in proc_l:
                 p["kwargs"].update(
-                    {"fit_start_time": fit_start_time, "fit_end_time": fit_end_time,}
+                    {
+                        "fit_start_time": fit_start_time,
+                        "fit_end_time": fit_end_time,
+                    }
                 )
                 new_l.append(p)
             return new_l
@@ -30,7 +33,11 @@ class HighFreqHandler(DataHandlerLP):
 
         data_loader = {
             "class": "QlibDataLoader",
-            "kwargs": {"config": self.get_feature_config(), "swap_level": False, "freq": "1min",},
+            "kwargs": {
+                "config": self.get_feature_config(),
+                "swap_level": False,
+                "freq": "1min",
+            },
         }
         super().__init__(
             instruments=instruments,
@@ -61,7 +68,8 @@ class HighFreqHandler(DataHandlerLP):
 
             feature_ops = template_norm.format(
                 template_if.format(
-                    template_fillnan.format(template_paused.format("$close")), template_paused.format(price_field),
+                    template_fillnan.format(template_paused.format("$close")),
+                    template_paused.format(price_field),
                 ),
                 template_fillnan.format(template_paused.format("$close")),
             )
@@ -111,14 +119,24 @@ class HighFreqHandler(DataHandlerLP):
 
 class HighFreqBacktestHandler(DataHandler):
     def __init__(
-        self, instruments="csi300", start_time=None, end_time=None,
+        self,
+        instruments="csi300",
+        start_time=None,
+        end_time=None,
     ):
         data_loader = {
             "class": "QlibDataLoader",
-            "kwargs": {"config": self.get_feature_config(), "swap_level": False, "freq": "1min",},
+            "kwargs": {
+                "config": self.get_feature_config(),
+                "swap_level": False,
+                "freq": "1min",
+            },
         }
         super().__init__(
-            instruments=instruments, start_time=start_time, end_time=end_time, data_loader=data_loader,
+            instruments=instruments,
+            start_time=start_time,
+            end_time=end_time,
+            data_loader=data_loader,
         )
 
     def get_feature_config(self):
@@ -137,7 +155,8 @@ class HighFreqBacktestHandler(DataHandler):
         fields += [
             "Cut({0}, 240, None)".format(
                 template_if.format(
-                    template_fillnan.format(template_paused.format("$close")), template_paused.format(simpson_vwap),
+                    template_fillnan.format(template_paused.format("$close")),
+                    template_paused.format(simpson_vwap),
                 )
             )
         ]

examples/highfreq/highfreq_processor.py

@@ -65,6 +65,8 @@ class HighFreqNorm(Processor):
         feat = df_values[:, [0, 1, 2, 3, 4, 10]].reshape(-1, 6 * 240)
         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), index=idx, columns=["FEATURE_%d" % i for i in range(12 * 240)],
+            data=np.concatenate((feat, feat_1), axis=1),
+            index=idx,
+            columns=["FEATURE_%d" % i for i in range(12 * 240)],
         ).sort_index()
         return df_new_features

examples/highfreq/workflow.py

@@ -63,7 +63,13 @@ class HighfreqWorkflow(object):
                     "module_path": "highfreq_handler",
                     "kwargs": DATA_HANDLER_CONFIG0,
                 },
-                "segments": {"train": (start_time, train_end_time), "test": (test_start_time, end_time,),},
+                "segments": {
+                    "train": (start_time, train_end_time),
+                    "test": (
+                        test_start_time,
+                        end_time,
+                    ),
+                },
             },
         },
         "dataset_backtest": {
@@ -75,7 +81,13 @@ class HighfreqWorkflow(object):
                     "module_path": "highfreq_handler",
                     "kwargs": DATA_HANDLER_CONFIG1,
                 },
-                "segments": {"train": (start_time, train_end_time), "test": (test_start_time, end_time,),},
+                "segments": {
+                    "train": (start_time, train_end_time),
+                    "test": (
+                        test_start_time,
+                        end_time,
+                    ),
+                },
             },
         },
     }
@@ -140,11 +152,24 @@ class HighfreqWorkflow(object):
                 "start_time": "2021-01-19 00:00:00",
                 "end_time": "2021-01-25 16:00:00",
             },
-            segment_kwargs={"test": ("2021-01-19 00:00:00", "2021-01-25 16:00:00",),},
+            segment_kwargs={
+                "test": (
+                    "2021-01-19 00:00:00",
+                    "2021-01-25 16:00:00",
+                ),
+            },
         )
         dataset_backtest.init(
-            handler_kwargs={"start_time": "2021-01-19 00:00:00", "end_time": "2021-01-25 16:00:00",},
+            handler_kwargs={
-            segment_kwargs={"test": ("2021-01-19 00:00:00", "2021-01-25 16:00:00",),},
+                "start_time": "2021-01-19 00:00:00",
+                "end_time": "2021-01-25 16:00:00",
+            },
+            segment_kwargs={
+                "test": (
+                    "2021-01-19 00:00:00",
+                    "2021-01-25 16:00:00",
+                ),
+            },
         )
 
         ##=============get data=============

examples/run_all_model.py

@@ -34,7 +34,10 @@ exp_path = str(Path(os.getcwd()).resolve() / exp_folder_name)
 exp_manager = {
     "class": "MLflowExpManager",
     "module_path": "qlib.workflow.expm",
-    "kwargs": {"uri": "file:" + exp_path, "default_exp_name": "Experiment",},
+    "kwargs": {
+        "uri": "file:" + exp_path,
+        "default_exp_name": "Experiment",
+    },
 }
 if not exists_qlib_data(provider_uri):
     print(f"Qlib data is not found in {provider_uri}")

examples/workflow_by_code.py

@@ -81,7 +81,10 @@ if __name__ == "__main__":
         "strategy": {
             "class": "TopkDropoutStrategy",
             "module_path": "qlib.contrib.strategy.strategy",
-            "kwargs": {"topk": 50, "n_drop": 5,},
+            "kwargs": {
+                "topk": 50,
+                "n_drop": 5,
+            },
         },
         "backtest": {
             "verbose": False,

scripts/data_collector/yahoo/collector.py

@@ -39,7 +39,13 @@ class YahooData:
     INTERVAL_1d = "1d"
 
     def __init__(
-        self, timezone: str = None, start=None, end=None, interval="1d", delay=0, show_1min_logging: bool = False,
+        self,
+        timezone: str = None,
+        start=None,
+        end=None,
+        interval="1d",
+        delay=0,
+        show_1min_logging: bool = False,
     ):
         """
 
@@ -119,7 +125,11 @@ class YahooData:
             self._sleep()
             _remote_interval = "1m" if self._interval == self.INTERVAL_1min else self._interval
             return self.get_data_from_remote(
-                symbol, interval=_remote_interval, start=start_, end=end_, show_1min_logging=self._show_1min_logging,
+                symbol,
+                interval=_remote_interval,
+                start=start_,
+                end=end_,
+                show_1min_logging=self._show_1min_logging,
             )
 
         _result = None
@@ -428,7 +438,9 @@ class YahooNormalize:
     DAILY_FORMAT = "%Y-%m-%d"
 
     def __init__(
-        self, date_field_name: str = "date", symbol_field_name: str = "symbol",
+        self,
+        date_field_name: str = "date",
+        symbol_field_name: str = "symbol",
     ):
         """
 
@@ -446,7 +458,10 @@ class YahooNormalize:
 
     @staticmethod
     def normalize_yahoo(
-        df: pd.DataFrame, calendar_list: list = None, date_field_name: str = "date", symbol_field_name: str = "symbol",
+        df: pd.DataFrame,
+        calendar_list: list = None,
+        date_field_name: str = "date",
+        symbol_field_name: str = "symbol",
     ):
         if df.empty:
             return df
@@ -551,7 +566,9 @@ class YahooNormalize1min(YahooNormalize, ABC):
     CONSISTENT_1d = False
 
     def __init__(
-        self, date_field_name: str = "date", symbol_field_name: str = "symbol",
+        self,
+        date_field_name: str = "date",
+        symbol_field_name: str = "symbol",
     ):
         """
 

scripts/dump_bin.py

@@ -153,13 +153,22 @@ class DumpDataBase:
 
     @staticmethod
     def _read_calendars(calendar_path: Path) -> List[pd.Timestamp]:
-        return sorted(map(pd.Timestamp, pd.read_csv(calendar_path, header=None).loc[:, 0].tolist(),))
+        return sorted(
+            map(
+                pd.Timestamp,
+                pd.read_csv(calendar_path, header=None).loc[:, 0].tolist(),
+            )
+        )
 
     def _read_instruments(self, instrument_path: Path) -> pd.DataFrame:
         df = pd.read_csv(
             instrument_path,
             sep=self.INSTRUMENTS_SEP,
-            names=[self.symbol_field_name, self.INSTRUMENTS_START_FIELD, self.INSTRUMENTS_END_FIELD,],
+            names=[
+                self.symbol_field_name,
+                self.INSTRUMENTS_START_FIELD,
+                self.INSTRUMENTS_END_FIELD,
+            ],
         )
 
         return df

setup.py

@@ -70,10 +70,16 @@ with open(os.path.join(here, "README.md"), encoding="utf-8") as f:
 # Cython Extensions
 extensions = [
     Extension(
-        "qlib.data._libs.rolling", ["qlib/data/_libs/rolling.pyx"], language="c++", include_dirs=[NUMPY_INCLUDE],
+        "qlib.data._libs.rolling",
+        ["qlib/data/_libs/rolling.pyx"],
+        language="c++",
+        include_dirs=[NUMPY_INCLUDE],
     ),
     Extension(
-        "qlib.data._libs.expanding", ["qlib/data/_libs/expanding.pyx"], language="c++", include_dirs=[NUMPY_INCLUDE],
+        "qlib.data._libs.expanding",
+        ["qlib/data/_libs/expanding.pyx"],
+        language="c++",
+        include_dirs=[NUMPY_INCLUDE],
     ),
 ]
 
@@ -92,7 +98,9 @@ setup(
     # py_modules=['qlib'],
     entry_points={
         # 'console_scripts': ['mycli=mymodule:cli'],
-        "console_scripts": ["qrun=qlib.workflow.cli:run",],
+        "console_scripts": [
+            "qrun=qlib.workflow.cli:run",
+        ],
     },
     ext_modules=extensions,
     install_requires=REQUIRED,

tests/test_all_pipeline.py

@@ -78,7 +78,10 @@ port_analysis_config = {
     "strategy": {
         "class": "TopkDropoutStrategy",
         "module_path": "qlib.contrib.strategy.strategy",
-        "kwargs": {"topk": 50, "n_drop": 5,},
+        "kwargs": {
+            "topk": 50,
+            "n_drop": 5,
+        },
     },
     "backtest": {
         "verbose": False,
@@ -173,7 +176,9 @@ class TestAllFlow(TestAutoData):
     def test_1_backtest(self):
         analyze_df = backtest_analysis(TestAllFlow.PRED_SCORE, TestAllFlow.RID)
         self.assertGreaterEqual(
-            analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0], 0.10, "backtest failed",
+            analyze_df.loc(axis=0)["excess_return_with_cost", "annualized_return"].values[0],
+            0.10,
+            "backtest failed",
         )
 
 

tests/test_dump_data.py

@@ -40,7 +40,9 @@ class TestDumpData(unittest.TestCase):
         TestDumpData.STOCK_NAMES = list(map(lambda x: x.name[:-4].upper(), SOURCE_DIR.glob("*.csv")))
         provider_uri = str(QLIB_DIR.resolve())
         qlib.init(
-            provider_uri=provider_uri, expression_cache=None, dataset_cache=None,
+            provider_uri=provider_uri,
+            expression_cache=None,
+            dataset_cache=None,
         )
 
     @classmethod
@@ -52,7 +54,10 @@ class TestDumpData(unittest.TestCase):
 
     def test_1_dump_calendars(self):
         ori_calendars = set(
-            map(pd.Timestamp, pd.read_csv(QLIB_DIR.joinpath("calendars", "day.txt"), header=None).loc[:, 0].values,)
+            map(
+                pd.Timestamp,
+                pd.read_csv(QLIB_DIR.joinpath("calendars", "day.txt"), header=None).loc[:, 0].values,
+            )
         )
         res_calendars = set(D.calendar())
         assert len(ori_calendars - res_calendars) == len(res_calendars - ori_calendars) == 0, "dump calendars failed"

tests/test_get_data.py

@@ -26,7 +26,9 @@ class TestGetData(unittest.TestCase):
     def setUpClass(cls) -> None:
         provider_uri = str(QLIB_DIR.resolve())
         qlib.init(
-            provider_uri=provider_uri, expression_cache=None, dataset_cache=None,
+            provider_uri=provider_uri,
+            expression_cache=None,
+            dataset_cache=None,
         )
 
     @classmethod