Adjust rolling api (#1594)

* Intermediate version

* Fix yaml template & Successfully run rolling

* Be compatible with benchmark

* Get same results with previous linear model

* Black formatting

* Update black

* Update the placeholder mechanism

* Update CI

* Update CI

* Upgrade Black

* Fix CI and simplify code

* Fix CI

* Move the data processing caching mechanism into utils.

* Adjusting DDG-DA

* Organize import

examples/benchmarks/TRA/example.py

@@ -6,7 +6,6 @@ from qlib.utils import init_instance_by_config
 
 
 def main(seed, config_file="configs/config_alstm.yaml"):
-
     # set random seed
     with open(config_file) as f:
         config = yaml.safe_load(f)
@@ -30,7 +29,6 @@ def main(seed, config_file="configs/config_alstm.yaml"):
 
 
 if __name__ == "__main__":
-
     # set params from cmd
     parser = argparse.ArgumentParser(allow_abbrev=False)
     parser.add_argument("--seed", type=int, default=1000, help="random seed")

examples/benchmarks/TRA/src/dataset.py

@@ -96,7 +96,6 @@ class MTSDatasetH(DatasetH):
         drop_last=False,
         **kwargs,
     ):
-
         assert horizon > 0, "please specify `horizon` to avoid data leakage"
 
         self.seq_len = seq_len
@@ -111,7 +110,6 @@ class MTSDatasetH(DatasetH):
         super().__init__(handler, segments, **kwargs)
 
     def setup_data(self, handler_kwargs: dict = None, **kwargs):
-
         super().setup_data()
 
         # change index to <code, date>

examples/benchmarks/TRA/src/model.py

@@ -45,7 +45,6 @@ class TRAModel(Model):
         avg_params=True,
         **kwargs,
     ):
-
         np.random.seed(seed)
         torch.manual_seed(seed)
 
@@ -93,7 +92,6 @@ class TRAModel(Model):
         self.global_step = -1
 
     def train_epoch(self, data_set):
-
         self.model.train()
         self.tra.train()
 
@@ -146,7 +144,6 @@ class TRAModel(Model):
         return total_loss
 
     def test_epoch(self, data_set, return_pred=False):
-
         self.model.eval()
         self.tra.eval()
         data_set.eval()
@@ -204,7 +201,6 @@ class TRAModel(Model):
         return metrics, preds
 
     def fit(self, dataset, evals_result=dict()):
-
         train_set, valid_set, test_set = dataset.prepare(["train", "valid", "test"])
 
         best_score = -1
@@ -380,7 +376,6 @@ class LSTM(nn.Module):
             self.output_size = hidden_size
 
     def forward(self, x):
-
         x = self.input_drop(x)
 
         if self.training and self.noise_level > 0:
@@ -464,7 +459,6 @@ class Transformer(nn.Module):
         self.output_size = hidden_size
 
     def forward(self, x):
-
         x = self.input_drop(x)
 
         if self.training and self.noise_level > 0:
@@ -514,7 +508,6 @@ class TRA(nn.Module):
         self.predictors = nn.Linear(input_size, num_states)
 
     def forward(self, hidden, hist_loss):
-
         preds = self.predictors(hidden)
 
         if self.num_states == 1:

examples/benchmarks/TRA/workflow_config_tra_Alpha158.yaml

@@ -57,9 +57,7 @@ port_analysis_config: &port_analysis_config
         class: TopkDropoutStrategy
         module_path: qlib.contrib.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
             topk: 50
             n_drop: 5
     backtest:

examples/benchmarks/TRA/workflow_config_tra_Alpha158_full.yaml

@@ -51,9 +51,7 @@ port_analysis_config: &port_analysis_config
         class: TopkDropoutStrategy
         module_path: qlib.contrib.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
             topk: 50
             n_drop: 5
     backtest:

examples/benchmarks/TRA/workflow_config_tra_Alpha360.yaml

@@ -51,9 +51,7 @@ port_analysis_config: &port_analysis_config
         class: TopkDropoutStrategy
         module_path: qlib.contrib.strategy
         kwargs:
-            signal:
-                - <MODEL> 
-                - <DATASET>
+            signal: <PRED>
             topk: 50
             n_drop: 5
     backtest: