Format

qlib/contrib/eva/alpha.py

@@ -8,9 +8,7 @@ import pandas as pd
 from typing import Tuple
 
 
-def calc_ic(
+def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False) -> Tuple[pd.Series, pd.Series]:
-    pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False
-) -> Tuple[pd.Series, pd.Series]:
     """calc_ic.
 
     Parameters
@@ -29,9 +27,7 @@ def calc_ic(
     """
     df = pd.DataFrame({"pred": pred, "label": label})
     ic = df.groupby(date_col).apply(lambda df: df["pred"].corr(df["label"]))
-    ric = df.groupby(date_col).apply(
+    ric = df.groupby(date_col).apply(lambda df: df["pred"].corr(df["label"], method="spearman"))
-        lambda df: df["pred"].corr(df["label"], method="spearman")
-    )
     if dropna:
         return ic.dropna(), ric.dropna()
     else:

qlib/data/dataset/loader.py

@@ -143,8 +143,8 @@ class QlibDataLoader(DLWParser):
 
     def load_group_df(self, instruments, exprs: list, names: list, start_time=None, end_time=None) -> pd.DataFrame:
         if instruments is None:
-            warnings.warn('`instruments` is not set, will load all stocks')
+            warnings.warn("`instruments` is not set, will load all stocks")
-            instruments = 'all'
+            instruments = "all"
         if isinstance(instruments, str):
             instruments = D.instruments(instruments, filter_pipe=self.filter_pipe)
         elif self.filter_pipe is not None:
@@ -161,7 +161,9 @@ class StaticDataLoader(DataLoader):
     DataLoader that supports loading data from file or as provided.
     """
 
-    def __init__(self, feature_path_or_obj: Union[str, pd.DataFrame], label_path_or_obj: Union[str, pd.DataFrame] = None):
+    def __init__(
+        self, feature_path_or_obj: Union[str, pd.DataFrame], label_path_or_obj: Union[str, pd.DataFrame] = None
+    ):
         """
         Parameters
         ----------
@@ -192,22 +194,18 @@ class StaticDataLoader(DataLoader):
             df = self._data.loc(axis=0)[:, instruments]
         if start_time is None and end_time is None:
             return df  # NOTE: avoid copy by loc
-        return df.loc[pd.Timestamp(start_time):pd.Timestamp(end_time)]
+        return df.loc[pd.Timestamp(start_time) : pd.Timestamp(end_time)]
 
     def _maybe_load_raw_data(self):
         if self._data is not None:
             return
         self._data = load_dataset(self._feature_path_or_obj)
         if self._label_path_or_obj is not None:
-            self._data = pd.concat(
+            self._data = pd.concat({"feature": self._data, "label": load_dataset(self._label_path_or_obj)}, axis=1)
-                {"feature": self._data, "label": load_dataset(self._label_path_or_obj)}, axis=1
-            )
         if not isinstance(self._data.columns, pd.MultiIndex):
             self._data.columns = pd.MultiIndex.from_arrays(
                 [
-                    np.array(["feature", "label"])[
+                    np.array(["feature", "label"])[self._data.columns.str.contains("^LABEL").astype(int)],
-                        self._data.columns.str.contains("^LABEL").astype(int)
-                    ],
                     self._data.columns,
                 ]
             )

qlib/utils/__init__.py

@@ -702,10 +702,10 @@ def load_dataset(path_or_obj):
     if isinstance(path_or_obj, pd.DataFrame):
         return path_or_obj
     _, extension = os.path.splitext(path_or_obj)
-    if extension == '.h5':
+    if extension == ".h5":
         return pd.read_hdf(path_or_obj)
-    elif extension == '.pkl':
+    elif extension == ".pkl":
         return pd.read_pickle(path_or_obj)
-    elif extension == '.csv':
+    elif extension == ".csv":
         return pd.read_csv(path_or_obj, parse_dates=True, index_col=[0, 1])
-    raise ValueError(f'unsupported file type `{extension}`')
+    raise ValueError(f"unsupported file type `{extension}`")

qlib/workflow/record_temp.py

@@ -166,22 +166,23 @@ class SigAnaRecord(SignalRecord):
             "Rank IC": ric.mean(),
             "Rank ICIR": ric.mean() / ric.std(),
         }
-        objects = {
+        objects = {"ic.pkl": ic, "ric.pkl": ric}
-            'ic.pkl': ic,
-            'ric.pkl': ric
-        }
         if self.ana_long_short:
             long_short_r, long_avg_r = calc_long_short_return(pred.iloc[:, 0], label.iloc[:, 0])
-            metrics.update({
+            metrics.update(
-                'Long-Short Ann Return': long_short_r.mean() * self.ann_scaler,
+                {
-                'Long-Short Ann Sharpe': long_short_r.mean() / long_short_r.std() * self.ann_scaler ** 0.5,
+                    "Long-Short Ann Return": long_short_r.mean() * self.ann_scaler,
-                'Long-Avg Ann Return': long_avg_r.mean() * self.ann_scaler,
+                    "Long-Short Ann Sharpe": long_short_r.mean() / long_short_r.std() * self.ann_scaler ** 0.5,
-                'Long-Avg Ann Sharpe': long_avg_r.mean() / long_avg_r.std() * self.ann_scaler ** 0.5,
+                    "Long-Avg Ann Return": long_avg_r.mean() * self.ann_scaler,
-            })
+                    "Long-Avg Ann Sharpe": long_avg_r.mean() / long_avg_r.std() * self.ann_scaler ** 0.5,
-            objects.update({
+                }
-                'long_short_r.pkl': long_short_r,
+            )
-                'long_avg_r.pkl': long_avg_r,
+            objects.update(
-            })
+                {
+                    "long_short_r.pkl": long_short_r,
+                    "long_avg_r.pkl": long_avg_r,
+                }
+            )
         self.recorder.log_metrics(**metrics)
         self.recorder.save_objects(**objects, artifact_path=self.get_path())
         pprint(metrics)
@@ -189,7 +190,7 @@ class SigAnaRecord(SignalRecord):
     def list(self):
         paths = [self.get_path("ic.pkl"), self.get_path("ric.pkl")]
         if self.ana_long_short:
-            paths.extend([self.get_path('long_short_r.pkl'), self.get_path('long_avg_r.pkl')])
+            paths.extend([self.get_path("long_short_r.pkl"), self.get_path("long_avg_r.pkl")])
         return paths