Merge remote-tracking branch 'origin/main' into nested_decision_exe

tests/rolling_tests/test_update_pred.py

@@ -0,0 +1,117 @@
+import copy
+import unittest
+
+import fire
+import pandas as pd
+
+import qlib
+from qlib.config import REG_CN
+from qlib.data import D
+from qlib.model.trainer import task_train
+from qlib.tests import TestAutoData
+from qlib.tests.config import CSI300_GBDT_TASK
+from qlib.workflow.online.utils import OnlineToolR
+from qlib.workflow.online.update import LabelUpdater
+
+
+class TestRolling(TestAutoData):
+    _setup_kwargs = dict(expression_cache=None, dataset_cache=None)
+
+    def test_update_pred(self):
+        """
+        This test is for testing if it will raise error if the `to_date` is out of the boundary.
+        """
+        task = copy.deepcopy(CSI300_GBDT_TASK)
+
+        task["record"] = {
+            "class": "SignalRecord",
+            "module_path": "qlib.workflow.record_temp",
+        }
+
+        exp_name = "online_srv_test"
+
+        cal = D.calendar()
+        latest_date = cal[-1]
+
+        train_start = latest_date - pd.Timedelta(days=61)
+        train_end = latest_date - pd.Timedelta(days=41)
+        task["dataset"]["kwargs"]["segments"] = {
+            "train": (train_start, train_end),
+            "valid": (latest_date - pd.Timedelta(days=40), latest_date - pd.Timedelta(days=21)),
+            "test": (latest_date - pd.Timedelta(days=20), latest_date),
+        }
+
+        task["dataset"]["kwargs"]["handler"]["kwargs"] = {
+            "start_time": train_start,
+            "end_time": latest_date,
+            "fit_start_time": train_start,
+            "fit_end_time": train_end,
+            "instruments": "csi300",
+        }
+
+        rec = task_train(task, exp_name)
+
+        pred = rec.load_object("pred.pkl")
+
+        online_tool = OnlineToolR(exp_name)
+        online_tool.reset_online_tag(rec)  # set to online model
+
+        online_tool.update_online_pred(to_date=latest_date + pd.Timedelta(days=10))
+
+    def test_update_label(self):
+
+        task = copy.deepcopy(CSI300_GBDT_TASK)
+
+        task["record"] = {
+            "class": "SignalRecord",
+            "module_path": "qlib.workflow.record_temp",
+        }
+
+        exp_name = "online_srv_test"
+
+        cal = D.calendar()
+        shift = 10
+        latest_date = cal[-1 - shift]
+
+        train_start = latest_date - pd.Timedelta(days=61)
+        train_end = latest_date - pd.Timedelta(days=41)
+        task["dataset"]["kwargs"]["segments"] = {
+            "train": (train_start, train_end),
+            "valid": (latest_date - pd.Timedelta(days=40), latest_date - pd.Timedelta(days=21)),
+            "test": (latest_date - pd.Timedelta(days=20), latest_date),
+        }
+
+        task["dataset"]["kwargs"]["handler"]["kwargs"] = {
+            "start_time": train_start,
+            "end_time": latest_date,
+            "fit_start_time": train_start,
+            "fit_end_time": train_end,
+            "instruments": "csi300",
+        }
+
+        rec = task_train(task, exp_name)
+
+        pred = rec.load_object("pred.pkl")
+
+        online_tool = OnlineToolR(exp_name)
+        online_tool.reset_online_tag(rec)  # set to online model
+        online_tool.update_online_pred()
+
+        new_pred = rec.load_object("pred.pkl")
+        label = rec.load_object("label.pkl")
+        label_date = label.dropna().index.get_level_values("datetime").max()
+        pred_date = new_pred.dropna().index.get_level_values("datetime").max()
+
+        # The prediction is updated, but the label is not updated.
+        self.assertTrue(label_date < pred_date)
+
+        # Update label now
+        lu = LabelUpdater(rec)
+        lu.update()
+        new_label = rec.load_object("label.pkl")
+        new_label_date = new_label.index.get_level_values("datetime").max()
+        self.assertTrue(new_label_date == pred_date)  # make sure the label is updated now
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/storage_tests/test_storage.py

@@ -149,15 +149,15 @@ class TestStorage(TestAutoData):
 
         """
 
-        feature = FeatureStorage(instrument="SH600004", field="close", freq="day", provider_uri=self.provider_uri)
+        feature = FeatureStorage(instrument="SZ300677", field="close", freq="day", provider_uri=self.provider_uri)
 
         with self.assertRaises(IndexError):
             print(feature[0])
         assert isinstance(
-            feature[815][1], (float, np.float32)
+            feature[3049][1], (float, np.float32)
         ), f"{feature.__class__.__name__}.__getitem__(i: int) error"
-        assert len(feature[815:818]) == 3, f"{feature.__class__.__name__}.__getitem__(s: slice) error"
-        print(f"feature[815: 818]: \n{feature[815: 818]}")
+        assert len(feature[3049:3052]) == 3, f"{feature.__class__.__name__}.__getitem__(s: slice) error"
+        print(f"feature[3049: 3052]: \n{feature[3049: 3052]}")
 
         print(f"feature[:].tail(): \n{feature[:].tail()}")