refactor online serving rolling api

examples/benchmarks/LightGBM/workflow_config_lightgbm_configurable_dataset.yaml

@@ -78,4 +78,4 @@ task:
         - class: PortAnaRecord
           module_path: qlib.workflow.record_temp
           kwargs: 
-            config: *port_analysis_config
+            config: *port_analysis_config

examples/model_rolling/requirements.txt

@@ -0,0 +1 @@
+xgboost

qlib/utils/__init__.py

@@ -570,9 +570,11 @@ def get_pre_trading_date(trading_date, future=False):
 
 
 def transform_end_date(end_date=None, freq="day"):
-    """get previous trading date
+    """handle the end date with various format
+
     If end_date is -1, None, or end_date is greater than the maximum trading day, the last trading date is returned.
     Otherwise, returns the end_date
+
     ----------
     end_date: str
         end trading date

qlib/workflow/online/strategy.py

@@ -10,6 +10,7 @@
 from qlib.data.data import D
 from qlib.log import get_module_logger
 from qlib.model.ens.group import RollingGroup
+from qlib.utils import transform_end_date
 from qlib.workflow.online.utils import OnlineTool, OnlineToolR
 from qlib.workflow.recorder import Recorder
 from qlib.workflow.task.collect import Collector, RecorderCollector
@@ -118,6 +119,7 @@ def __init__(
             task_template = [task_template]
         self.task_template = task_template
         self.rg = rolling_gen
+        assert issubclass(self.rg.__class__, RollingGen), "The rolling strategy relies on the feature if RollingGen"
         self.tool = OnlineToolR(self.exp_name)
         self.ta = TimeAdjuster()
 
@@ -174,28 +176,20 @@ def prepare_tasks(self, cur_time) -> List[dict]:
         Returns:
             List[dict]: a list of new tasks.
         """
+        # TODO: filter recorders by latest test segments is not a necessary
         latest_records, max_test = self._list_latest(self.tool.online_models())
         if max_test is None:
             self.logger.warn(f"No latest online recorders, no new tasks.")
             return []
-        calendar_latest = D.calendar(end_time=cur_time)[-1] if cur_time is None else cur_time
+        calendar_latest = transform_end_date(cur_time)
         self.logger.info(
             f"The interval between current time {calendar_latest} and last rolling test begin time {max_test[0]} is {self.ta.cal_interval(calendar_latest, max_test[0])}, the rolling step is {self.rg.step}"
         )
-        if self.ta.cal_interval(calendar_latest, max_test[0]) >= self.rg.step:
-            old_tasks = []
-            tasks_tmp = []
-            for rec in latest_records:
-                task = rec.load_object("task")
-                old_tasks.append(deepcopy(task))
-                test_begin = task["dataset"]["kwargs"]["segments"]["test"][0]
-                # modify the test segment to generate new tasks
-                task["dataset"]["kwargs"]["segments"]["test"] = (test_begin, calendar_latest)
-                tasks_tmp.append(task)
-            new_tasks_tmp = task_generator(tasks_tmp, self.rg)
-            new_tasks = [task for task in new_tasks_tmp if task not in old_tasks]
-            return new_tasks
-        return []
+        res = []
+        for rec in latest_records:
+            task = rec.load_object("task")
+            res.extend(self.rg.gen_following_tasks(task, calendar_latest))
+        return res
 
     def _list_latest(self, rec_list: List[Recorder]):
         """

qlib/workflow/online/update.py

@@ -105,6 +105,8 @@ def __init__(self, record: Recorder, to_date=None, hist_ref: int = 0, freq="day"
         if to_date == None:
             to_date = D.calendar(freq=freq)[-1]
         self.to_date = pd.Timestamp(to_date)
+        # FIXME: it will raise error when running routine with delay trainer
+        # should we use another predicition updater for delay trainer?
         self.old_pred = record.load_object("pred.pkl")
         self.last_end = self.old_pred.index.get_level_values("datetime").max()
 

qlib/workflow/task/gen.py

@@ -5,6 +5,7 @@
 """
 import abc
 import copy
+import pandas as pd
 from typing import List, Union, Callable
 
 from qlib.utils import transform_end_date
@@ -139,6 +140,53 @@ def __init__(self, step: int = 40, rtype: str = ROLL_EX, ds_extra_mod_func: Unio
         self.test_key = "test"
         self.train_key = "train"
 
+    def _update_task_segs(self, task, segs):
+        # update segments of this task
+        task["dataset"]["kwargs"]["segments"] = copy.deepcopy(segs)
+        if self.ds_extra_mod_func is not None:
+            self.ds_extra_mod_func(task, self)
+
+    def gen_following_tasks(self, task: dict, test_end: pd.Timestamp) -> List[dict]:
+        """
+        generating following rolling tasks for `task` until test_end
+
+        Parameters
+        ----------
+        task : dict
+            Qlib task format
+        test_end : pd.Timestamp
+            the latest rolling task includes `test_end`
+
+        Returns
+        -------
+        List[dict]:
+            the following tasks of `task`(`task` itself is excluded)
+        """
+        t = copy.deepcopy(task)
+        prev_seg = t["dataset"]["kwargs"]["segments"]
+        while True:
+            segments = {}
+            try:
+                for k, seg in prev_seg.items():
+                    # decide how to shift
+                    # expanding only for train data, the segments size of test data and valid data won't change
+                    if k == self.train_key and self.rtype == self.ROLL_EX:
+                        rtype = self.ta.SHIFT_EX
+                    else:
+                        rtype = self.ta.SHIFT_SD
+                    # shift the segments data
+                    segments[k] = self.ta.shift(seg, step=self.step, rtype=rtype)
+                if segments[self.test_key][0] > test_end:
+                    break
+            except KeyError:
+                # We reach the end of tasks
+                # No more rolling
+                break
+
+            prev_seg = segments
+            self._update_task_segs(t, segments)
+            yield t
+
     def generate(self, task: dict) -> List[dict]:
         """
         Converting the task into a rolling task.
@@ -191,43 +239,23 @@ def generate(self, task: dict) -> List[dict]:
         """
         res = []
 
-        prev_seg = None
-        test_end = None
-        while True:
-            t = copy.deepcopy(task)
-
-            # calculate segments
-            if prev_seg is None:
-                # First rolling
-                # 1) prepare the end point
-                segments: dict = copy.deepcopy(self.ta.align_seg(t["dataset"]["kwargs"]["segments"]))
-                test_end = transform_end_date(segments[self.test_key][1])
-                # 2) and init test segments
-                test_start_idx = self.ta.align_idx(segments[self.test_key][0])
-                segments[self.test_key] = (self.ta.get(test_start_idx), self.ta.get(test_start_idx + self.step - 1))
-            else:
-                segments = {}
-                try:
-                    for k, seg in prev_seg.items():
-                        # decide how to shift
-                        # expanding only for train data, the segments size of test data and valid data won't change
-                        if k == self.train_key and self.rtype == self.ROLL_EX:
-                            rtype = self.ta.SHIFT_EX
-                        else:
-                            rtype = self.ta.SHIFT_SD
-                        # shift the segments data
-                        segments[k] = self.ta.shift(seg, step=self.step, rtype=rtype)
-                    if segments[self.test_key][0] > test_end:
-                        break
-                except KeyError:
-                    # We reach the end of tasks
-                    # No more rolling
-                    break
+        t = copy.deepcopy(task)
 
-            # update segments of this task
-            t["dataset"]["kwargs"]["segments"] = copy.deepcopy(segments)
-            prev_seg = segments
-            if self.ds_extra_mod_func is not None:
-                self.ds_extra_mod_func(t, self)
-            res.append(t)
+        # calculate segments
+
+        # First rolling
+        # 1) prepare the end point
+        segments: dict = copy.deepcopy(self.ta.align_seg(t["dataset"]["kwargs"]["segments"]))
+        test_end = transform_end_date(segments[self.test_key][1])
+        # 2) and init test segments
+        test_start_idx = self.ta.align_idx(segments[self.test_key][0])
+        segments[self.test_key] = (self.ta.get(test_start_idx), self.ta.get(test_start_idx + self.step - 1))
+
+        # update segments of this task
+        self._update_task_segs(t, segments)
+
+        res.append(t)
+
+        # Update the following rolling
+        res.extend(self.gen_following_tasks(t, test_end))
         return res