support adding from date when updating pred (#703)

* support adding from date when updating pred

* fix updating data error

qlib/utils/__init__.py

@@ -578,7 +578,7 @@ def get_date_range(trading_date, left_shift=0, right_shift=0, future=False):
     return calendar
 
 
-def get_date_by_shift(trading_date, shift, future=False, clip_shift=True, freq="day"):
+def get_date_by_shift(trading_date, shift, future=False, clip_shift=True, freq="day", align: Optional[str] = None):
     """get trading date with shift bias wil cur_date
         e.g. : shift == 1,  return next trading date
                shift == -1, return previous trading date
@@ -587,14 +587,25 @@ def get_date_by_shift(trading_date, shift, future=False, clip_shift=True, freq="
         current date
     shift : int
     clip_shift: bool
+    align : Optional[str]
+        When align is None, this function will raise ValueError if `trading_date` is not a trading date
+        when align is "left"/"right", it will try to align to left/right nearest trading date before shifting when `trading_date` is not a trading date
 
     """
     from qlib.data import D
 
     cal = D.calendar(future=future, freq=freq)
-    if pd.to_datetime(trading_date) not in list(cal):
-        raise ValueError("{} is not trading day!".format(str(trading_date)))
-    _index = bisect.bisect_left(cal, trading_date)
+    trading_date = pd.to_datetime(trading_date)
+    if align is None:
+        if trading_date not in list(cal):
+            raise ValueError("{} is not trading day!".format(str(trading_date)))
+        _index = bisect.bisect_left(cal, trading_date)
+    elif align == "left":
+        _index = bisect.bisect_right(cal, trading_date) - 1
+    elif align == "right":
+        _index = bisect.bisect_left(cal, trading_date)
+    else:
+        raise ValueError(f"align with value `{align}` is not supported")
     shift_index = _index + shift
     if shift_index < 0 or shift_index >= len(cal):
         if clip_shift:

qlib/workflow/online/update.py

@@ -90,14 +90,24 @@ class DSBasedUpdater(RecordUpdater, metaclass=ABCMeta):
                    SZ300676   -0.001321
     """
 
-    def __init__(self, record: Recorder, to_date=None, hist_ref: int = 0, freq="day", fname="pred.pkl"):
+    def __init__(self, record: Recorder, to_date=None, from_date=None, hist_ref: int = 0, freq="day", fname="pred.pkl"):
         """
         Init PredUpdater.
 
+        Expected behavior in following cases:
+        - if `to_date` is greater than the max date in the calendar, the data will be updated to the latest date
+        - if there are data before `from_date` or after `to_date`, only the data between `from_date` and `to_date` are affected.
+
         Args:
             record : Recorder
             to_date :
                 update to prediction to the `to_date`
+                if to_date is None:
+                    data will updated to the latest date.
+            from_date :
+                the update will start from `from_date`
+                if from_date is None:
+                    the updating will occur on the next tick after the latest data in historical data
             hist_ref : int
                 Sometimes, the dataset will have historical depends.
                 Leave the problem to users to set the length of historical dependency
@@ -127,13 +137,16 @@ def __init__(self, record: Recorder, to_date=None, hist_ref: int = 0, freq="day"
             )
             to_date = latest_date
         self.to_date = to_date
+
         # FIXME: it will raise error when running routine with delay trainer
         # should we use another prediction updater for delay trainer?
         self.old_data: pd.DataFrame = record.load_object(fname)
-
-        # dropna is for being compatible to some data with future information(e.g. label)
-        # The recent label data should be updated together
-        self.last_end = self.old_data.dropna().index.get_level_values("datetime").max()
+        if from_date is None:
+            # dropna is for being compatible to some data with future information(e.g. label)
+            # The recent label data should be updated together
+            self.last_end = self.old_data.dropna().index.get_level_values("datetime").max()
+        else:
+            self.last_end = get_date_by_shift(from_date, -1, align="left")
 
     def prepare_data(self) -> DatasetH:
         """
@@ -187,6 +200,15 @@ def get_update_data(self, dataset: Dataset) -> pd.DataFrame:
         ...
 
 
+def _replace_range(data, new_data):
+    dates = new_data.index.get_level_values("datetime")
+    data = data.sort_index()
+    data = data.drop(data.loc[dates.min() : dates.max()].index)
+    cb_data = pd.concat([data, new_data], axis=0)
+    cb_data = cb_data[~cb_data.index.duplicated(keep="last")].sort_index()
+    return cb_data
+
+
 class PredUpdater(DSBasedUpdater):
     """
     Update the prediction in the Recorder
@@ -196,11 +218,9 @@ def get_update_data(self, dataset: Dataset) -> pd.DataFrame:
         # Load model
         model = self.rmdl.get_model()
         new_pred: pd.Series = model.predict(dataset)
-
-        cb_pred = pd.concat([self.old_data, new_pred.to_frame("score")], axis=0)
-        cb_pred = cb_pred.sort_index()
+        data = _replace_range(self.old_data, new_pred.to_frame("score"))
         self.logger.info(f"Finish updating new {new_pred.shape[0]} predictions in {self.record.info['id']}.")
-        return cb_pred
+        return data
 
 
 class LabelUpdater(DSBasedUpdater):
@@ -216,6 +236,5 @@ def __init__(self, record: Recorder, to_date=None, **kwargs):
 
     def get_update_data(self, dataset: Dataset) -> pd.DataFrame:
         new_label = SignalRecord.generate_label(dataset)
-        cb_data = pd.concat([self.old_data, new_label], axis=0)
-        cb_data = cb_data[~cb_data.index.duplicated(keep="last")].sort_index()
+        cb_data = _replace_range(self.old_data.sort_index(), new_label)
         return cb_data

qlib/workflow/online/utils.py

@@ -158,7 +158,7 @@ def online_models(self, exp_name: str = None) -> list:
         exp_name = self._get_exp_name(exp_name)
         return list(list_recorders(exp_name, lambda rec: self.get_online_tag(rec) == self.ONLINE_TAG).values())
 
-    def update_online_pred(self, to_date=None, exp_name: str = None):
+    def update_online_pred(self, to_date=None, from_date=None, exp_name: str = None):
         """
         Update the predictions of online models to to_date.
 
@@ -176,7 +176,7 @@ def update_online_pred(self, to_date=None, exp_name: str = None):
             if issubclass(cls, TSDatasetH):
                 hist_ref = kwargs.get("step_len", TSDatasetH.DEFAULT_STEP_LEN)
             try:
-                updater = PredUpdater(rec, to_date=to_date, hist_ref=hist_ref)
+                updater = PredUpdater(rec, to_date=to_date, from_date=from_date, hist_ref=hist_ref)
             except LoadObjectError as e:
                 # skip the recorder without pred
                 self.logger.warn(f"An exception `{str(e)}` happened when load `pred.pkl`, skip it.")

tests/rolling_tests/test_update_pred.py

@@ -21,11 +21,7 @@ def test_update_pred(self):
         """
         task = copy.deepcopy(CSI300_GBDT_TASK)
 
-        task["record"] = {
-            "class": "SignalRecord",
-            "module_path": "qlib.workflow.record_temp",
-            "kwargs": {"dataset": "<DATASET>", "model": "<MODEL>"},
-        }
+        task["record"] = ["qlib.workflow.record_temp.SignalRecord"]
 
         exp_name = "online_srv_test"
 
@@ -57,6 +53,27 @@ def test_update_pred(self):
 
         online_tool.update_online_pred(to_date=latest_date + pd.Timedelta(days=10))
 
+        good_pred = rec.load_object("pred.pkl")
+
+        mod_range = slice(latest_date - pd.Timedelta(days=20), latest_date - pd.Timedelta(days=10))
+        mod_range2 = slice(latest_date - pd.Timedelta(days=9), latest_date - pd.Timedelta(days=2))
+        mod_pred = good_pred.copy()
+
+        mod_pred.loc[mod_range] = -1
+        mod_pred.loc[mod_range2] = -2
+
+        rec.save_objects(**{"pred.pkl": mod_pred})
+        online_tool.update_online_pred(
+            to_date=latest_date - pd.Timedelta(days=10), from_date=latest_date - pd.Timedelta(days=20)
+        )
+
+        updated_pred = rec.load_object("pred.pkl")
+
+        # this range is not fixed
+        self.assertTrue((updated_pred.loc[mod_range] == good_pred.loc[mod_range]).all().item())
+        # this range is fixed now
+        self.assertTrue((updated_pred.loc[mod_range2] == -2).all().item())
+
     def test_update_label(self):
 
         task = copy.deepcopy(CSI300_GBDT_TASK)