Black format

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",},
-            segment_kwargs={"test": ("2021-01-19 00:00:00", "2021-01-25 16:00:00",),},
+            handler_kwargs={
+                "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,