diff --git a/examples/rolling_process_data/workflow.py b/examples/rolling_process_data/workflow.py
index 719d93a1b..0be88dddc 100644
--- a/examples/rolling_process_data/workflow.py
+++ b/examples/rolling_process_data/workflow.py
@@ -98,6 +98,7 @@ class RollingDataWorkflow(object):
         dataset = init_instance_by_config(dataset_config)
 
         for rolling_offset in range(self.rolling_cnt):
+
             print(f"===========rolling{rolling_offset} start===========")
             if rolling_offset:
                 dataset.init(
@@ -105,6 +106,8 @@ class RollingDataWorkflow(object):
                         "init_type": DataHandlerLP.IT_FIT_SEQ,
                         "start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
                         "end_time": datetime(test_end_time[0] + rolling_offset, *test_end_time[1:]),
+                        "fit_start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
+                        "fit_end_time": datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]),
                     },
                     segment_kwargs={
                         "train": (
@@ -123,6 +126,7 @@ class RollingDataWorkflow(object):
                 )
 
             dtrain, dvalid, dtest = dataset.prepare(["train", "valid", "test"])
+            print(dtrain, dvalid, dtest)
             ## print or dump data
             print(f"===========rolling{rolling_offset} end===========")
 
diff --git a/qlib/data/dataset/__init__.py b/qlib/data/dataset/__init__.py
index 0f5d2baba..518b8eecd 100644
--- a/qlib/data/dataset/__init__.py
+++ b/qlib/data/dataset/__init__.py
@@ -98,7 +98,7 @@ class DatasetH(Dataset):
                 raise TypeError(f"param handler_kwargs must be type dict, not {type(handler_kwargs)}")
             kwargs_init = {}
             kwargs_conf_data = {}
-            conf_data_arg = {"instruments", "start_time", "end_time"}
+            conf_data_arg = {"instruments", "start_time", "end_time", "fit_start_time", "fit_end_time"}
             for k, v in handler_kwargs.items():
                 if k in conf_data_arg:
                     kwargs_conf_data.update({k: v})
diff --git a/qlib/data/dataset/handler.py b/qlib/data/dataset/handler.py
index f4795c566..40db5e4f3 100644
--- a/qlib/data/dataset/handler.py
+++ b/qlib/data/dataset/handler.py
@@ -115,8 +115,7 @@ class DataHandler(Serializable):
         for k, v in kwargs.items():
             if k in attr_list:
                 setattr(self, k, v)
-            else:
-                raise KeyError("Such config is not supported.")
+
 
     def init(self, enable_cache: bool = False):
         """
@@ -405,11 +404,34 @@ class DataHandlerLP(DataHandler):
         if self.drop_raw:
             del self._data
 
+
+    def conf_data(self, **kwargs):
+        """
+        configuration of data.
+        # what data to be loaded from data source
+
+        This method will be used when loading pickled handler from dataset.
+        The data will be initialized with different time range.
+
+        """
+        attr_list = {"fit_start_time", "fit_end_time"}
+        for k, v in kwargs.items():
+            if k in attr_list:
+                for infer_processor in self.infer_processors:
+                    if getattr(infer_processor, k, None):
+                        setattr(infer_processor, k, v)
+
+                for learn_processor in self.learn_processors:
+                    if getattr(learn_processor, k, None):
+                        setattr(learn_processor, k, v)
+
+        super().conf_data(**kwargs)
+
     # init type
     IT_FIT_SEQ = "fit_seq"  # the input of `fit` will be the output of the previous processor
     IT_FIT_IND = "fit_ind"  # the input of `fit` will be the original df
     IT_LS = "load_state"  # The state of the object has been load by pickle
-
+    
     def init(self, init_type: str = IT_FIT_SEQ, enable_cache: bool = False):
         """
         Initialize the data of Qlib