Update baselines

exps/trading/baselines.py

@@ -67,6 +67,18 @@ def extend_transformer_settings(alg2configs, name):
     return alg2configs
 
 
+def remove_PortAnaRecord(alg2configs):
+    alg2configs = copy.deepcopy(alg2configs)
+    for key, config in alg2configs.items():
+        xlist = config["task"]["record"]
+        new_list = []
+        for x in xlist:
+            if x["class"] != "PortAnaRecord":
+                new_list.append(x)
+        config["task"]["record"] = new_list
+    return alg2configs
+
+
 def retrieve_configs():
     # https://github.com/microsoft/qlib/blob/main/examples/benchmarks/
     config_dir = (lib_dir / ".." / "configs" / "qlib").resolve()
@@ -105,6 +117,12 @@ def retrieve_configs():
             )
         )
     alg2configs = extend_transformer_settings(alg2configs, "TSF")
+    alg2configs = remove_PortAnaRecord(alg2configs)
+    print(
+        "There are {:} algorithms : {:}".format(
+            len(alg2configs), list(alg2configs.keys())
+        )
+    )
     return alg2configs
 
 

exps/trading/organize_results.py

@@ -223,7 +223,7 @@ if __name__ == "__main__":
         info_dict["heads"],
         info_dict["values"],
         info_dict["names"],
-        space=12,
+        space=14,
         verbose=True,
         sort_key=True,
     )

lib/trade_models/quant_transformer.py

@@ -8,16 +8,12 @@ import os, math, random
 from collections import OrderedDict
 import numpy as np
 import pandas as pd
+from typing import Text, Union
 import copy
 from functools import partial
 from typing import Optional, Text
 
-from qlib.utils import (
-    unpack_archive_with_buffer,
-    save_multiple_parts_file,
-    get_or_create_path,
-    drop_nan_by_y_index,
-)
+from qlib.utils import get_or_create_path
 from qlib.log import get_module_logger
 
 import torch
@@ -308,10 +304,10 @@ class QuantTransformer(Model):
             torch.cuda.empty_cache()
         self.fitted = True
 
-    def predict(self, dataset, segment="test"):
+    def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
         if not self.fitted:
             raise ValueError("The model is not fitted yet!")
-        x_test = dataset.prepare(segment, col_set="feature")
+        x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
         index = x_test.index
 
         self.model.eval()