Format

docs/component/data.rst

@@ -292,6 +292,7 @@ The ``Processor`` module in ``Qlib`` is designed to be learnable and it is respo
 - ``Fillna``: `processor` that handles N/A values, which will fill the N/A value by 0 or other given number.
 - ``MinMaxNorm``: `processor` that applies min-max normalization.
 - ``ZscoreNorm``: `processor` that applies z-score normalization.
+- ``RobustZScoreNorm``: `processor` that applies robust z-score normalization.
 - ``CSZScoreNorm``: `processor` that applies cross sectional z-score normalization.
 - ``CSRankNorm``: `processor` that applies cross sectional rank normalization.
 

examples/workflow_by_code_gats.py

@@ -17,7 +17,6 @@ from qlib.utils import exists_qlib_data
 from qlib.utils import init_instance_by_config
 
 
-
 if __name__ == "__main__":
 
     # use default data

examples/workflow_by_code_hats.py

@@ -90,7 +90,6 @@ if __name__ == "__main__":
         # "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
     }
 
-
     model = init_instance_by_config(task["model"])
     dataset = init_instance_by_config(task["dataset"])
     model.fit(dataset, save_path="benchmarks/HATS/model_hat.pkl")

qlib/contrib/model/pytorch_hats.py

@@ -261,10 +261,12 @@ class HATS(Model):
             self.logger.info("Loading pretrained model...")
             if self.base_model == "LSTM":
                 from ...contrib.model.pytorch_lstm import LSTMModel
+
                 pretrained_model = LSTMModel()
                 pretrained_model.load_state_dict(torch.load("benchmarks/LSTM/model_lstm_csi300.pkl"))
             elif self.base_model == "GRU":
                 from ...contrib.model.pytorch_gru import GRUModel
+
                 pretrained_model = GRUModel()
                 pretrained_model.load_state_dict(torch.load("benchmarks/GRU/model_gru_csi300.pkl"))
             model_dict = self.HATS_model.state_dict()
@@ -461,7 +463,9 @@ class GraphAttention(nn.Module):
             h = self.fcs[k](features)
 
             nbr_h = torch.cat(tuple([h[row] for row in rows]), dim=0)
-            self_h = torch.cat(tuple([h[mappings[nodes[i]]].repeat(len(row), 1) for (i, row) in enumerate(rows)]), dim=0)
+            self_h = torch.cat(
+                tuple([h[mappings[nodes[i]]].repeat(len(row), 1) for (i, row) in enumerate(rows)]), dim=0
+            )
             cat_h = torch.cat((self_h, nbr_h), dim=1)
 
             e = self.leakyrelu(self.a[k](cat_h))

qlib/contrib/model/pytorch_sfm.py

@@ -31,6 +31,7 @@ from ...model.base import Model
 from ...data.dataset import DatasetH
 from ...data.dataset.handler import DataHandlerLP
 
+
 class SFM_Model(nn.Module):
     def __init__(self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu"):
         super().__init__()
@@ -99,8 +100,9 @@ class SFM_Model(nn.Module):
             x_c = torch.matmul(x * B_W[0], self.W_c) + self.b_c
             x_o = torch.matmul(x * B_W[0], self.W_o) + self.b_o
 
-            i = self.inner_activation(x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)) # not sure whether I am doing in the right unsquuze
+            i = self.inner_activation(
-            
+                x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)
+            )  # not sure whether I am doing in the right unsquuze
 
             ste = self.inner_activation(x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste))
             fre = self.inner_activation(x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre))
@@ -292,7 +294,7 @@ class SFM(Model):
             freq_dim=self.freq_dim,
             dropout_W=self.dropout_W,
             dropout_U=self.dropout_U,
-            device=self.device
+            device=self.device,
         )
         if optimizer.lower() == "adam":
             self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr)
@@ -436,6 +438,7 @@ class SFM(Model):
 
     def cal_ic(self, pred, label):
         return torch.mean(pred * label)
+
     def predict(self, dataset):
         if not self._fitted:
             raise ValueError("model is not fitted yet!")
@@ -465,8 +468,10 @@ class SFM(Model):
 
         return pd.Series(np.concatenate(preds), index=index)
 
+
 class AverageMeter(object):
     """Computes and stores the average and current value"""
+
     def __init__(self):
         self.reset()