Update Q models

notebooks/Q/workflow-demo.py

@@ -1,41 +1,75 @@
 import os
 import sys
-import qlib
+import ruamel.yaml as yaml
 import pprint
 import numpy as np
 import pandas as pd
+from pathlib import Path
 
+import qlib
 from qlib import config as qconfig
 from qlib.utils import init_instance_by_config
+from qlib.workflow import R
+from qlib.data.dataset import DatasetH
+from qlib.data.dataset.handler import DataHandlerLP
 
-qlib.init(provider_uri='~/.qlib/qlib_data/cn_data', region=qconfig.REG_CN)
+qlib.init(provider_uri="~/.qlib/qlib_data/cn_data", region=qconfig.REG_CN)
 
 dataset_config = {
-            "class": "DatasetH",
-            "module_path": "qlib.data.dataset",
+    "class": "DatasetH",
+    "module_path": "qlib.data.dataset",
+    "kwargs": {
+        "handler": {
+            "class": "Alpha360",
+            "module_path": "qlib.contrib.data.handler",
             "kwargs": {
-                "handler": {
-                    "class": "Alpha158",
-                    "module_path": "qlib.contrib.data.handler",
-                    "kwargs": {
-                        "start_time": "2008-01-01",
-                        "end_time": "2020-08-01",
-                        "fit_start_time": "2008-01-01",
-                        "fit_end_time": "2014-12-31",
-                        "instruments": "csi300",
-                    },
-                },
-                "segments": {
-                    "train": ("2008-01-01", "2014-12-31"),
-                    "valid": ("2015-01-01", "2016-12-31"),
-                    "test": ("2017-01-01", "2020-08-01"),
-                },
+                "start_time": "2008-01-01",
+                "end_time": "2020-08-01",
+                "fit_start_time": "2008-01-01",
+                "fit_end_time": "2014-12-31",
+                "instruments": "csi300",
+                "infer_processors": [
+                    {"class": "RobustZScoreNorm", "kwargs": {"fields_group": "feature", "clip_outlier": True}},
+                    {"class": "Fillna", "kwargs": {"fields_group": "feature"}},
+                ],
+                "learn_processors": [
+                    {"class": "DropnaLabel"},
+                    {"class": "CSRankNorm", "kwargs": {"fields_group": "label"}},
+                ],
+               "label": ["Ref($close, -2) / Ref($close, -1) - 1"]
             },
-        }
+        },
+        "segments": {
+            "train": ("2008-01-01", "2014-12-31"),
+            "valid": ("2015-01-01", "2016-12-31"),
+            "test": ("2017-01-01", "2020-08-01"),
+        },
+    },
+}
 
 if __name__ == "__main__":
-  dataset = init_instance_by_config(dataset_config)
-  pprint.pprint(dataset_config)
-  pprint.pprint(dataset)
-  import pdb; pdb.set_trace()
-  print('Complete')
+
+    qlib_root_dir = (Path(__file__).parent / '..' / '..' / '.latent-data' / 'qlib').resolve()
+    demo_yaml_path = qlib_root_dir / 'examples' / 'benchmarks' / 'GRU' / 'workflow_config_gru_Alpha360.yaml'
+    print('Demo-workflow-yaml: {:}'.format(demo_yaml_path))
+    with open(demo_yaml_path, 'r') as fp:
+      config = yaml.safe_load(fp)
+    pprint.pprint(config['task']['dataset'])
+    
+    dataset = init_instance_by_config(dataset_config)
+    pprint.pprint(dataset_config)
+    pprint.pprint(dataset)
+
+    df_train, df_valid, df_test = dataset.prepare(
+        ["train", "valid", "test"],
+        col_set=["feature", "label"],
+        data_key=DataHandlerLP.DK_L,
+    )
+
+    x_train, y_train = df_train["feature"], df_train["label"]
+
+    import pdb
+
+    pdb.set_trace()
+    print("Complete")
+