diff --git a/examples/highfreq/highfreq_processor.py b/examples/highfreq/highfreq_processor.py
index 4ec8f3dd2..6ed68ff38 100644
--- a/examples/highfreq/highfreq_processor.py
+++ b/examples/highfreq/highfreq_processor.py
@@ -71,7 +71,7 @@ class HighFreqNorm(Processor):
         ).sort_index()
         return df_new_features
     
-    def config(fit_start_time=None, fit_end_time=None, **kwargs):
+    def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
         if fit_start_time:
             self.fit_start_time = fit_start_time
         if fit_end_time:
diff --git a/examples/highfreq/workflow.py b/examples/highfreq/workflow.py
index 0b48b971f..97762f182 100644
--- a/examples/highfreq/workflow.py
+++ b/examples/highfreq/workflow.py
@@ -31,7 +31,7 @@ class HighfreqWorkflow(object):
 
     SPEC_CONF = {"custom_ops": [DayLast, FFillNan, BFillNan, Date, Select, IsNull, Cut], "expression_cache": None}
 
-    MARKET = "csi300"
+    MARKET = "all"
 
     start_time = "2020-09-15 00:00:00"
     end_time = "2021-01-18 16:00:00"
diff --git a/examples/rolling_process_data/workflow.py b/examples/rolling_process_data/workflow.py
index 0be88dddc..ffdd8329a 100644
--- a/examples/rolling_process_data/workflow.py
+++ b/examples/rolling_process_data/workflow.py
@@ -101,15 +101,16 @@ class RollingDataWorkflow(object):
 
             print(f"===========rolling{rolling_offset} start===========")
             if rolling_offset:
-                dataset.init(
+                dataset.config(
                     handler_kwargs={
-                        "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:]),
+                        "processor_kwargs":{
+                            "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={
+                    segments={
                         "train": (
                             datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]),
                             datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]),
@@ -124,6 +125,9 @@ class RollingDataWorkflow(object):
                         ),
                     },
                 )
+                dataset.setup_data(
+                    handler_kwargs={"init_type": DataHandlerLP.IT_FIT_SEQ,}
+                )
 
             dtrain, dvalid, dtest = dataset.prepare(["train", "valid", "test"])
             print(dtrain, dvalid, dtest)
diff --git a/qlib/data/dataset/handler.py b/qlib/data/dataset/handler.py
index 712cd6232..4adef23a0 100644
--- a/qlib/data/dataset/handler.py
+++ b/qlib/data/dataset/handler.py
@@ -407,7 +407,7 @@ class DataHandlerLP(DataHandler):
         if self.drop_raw:
             del self._data
 
-    def config(self, processors_kwargs:dict = None, **kwargs):
+    def config(self, processor_kwargs:dict = None, **kwargs):
         """
         configuration of data.
         # what data to be loaded from data source
@@ -417,7 +417,7 @@ class DataHandlerLP(DataHandler):
 
         """
         super().config(**kwargs)
-        if processors_kwargs is not None:
+        if processor_kwargs is not None:
             for processor in self.get_all_processors():
                 processor.config(**processor_kwargs)
 
diff --git a/qlib/data/dataset/processor.py b/qlib/data/dataset/processor.py
index e14e85831..5be178c5c 100755
--- a/qlib/data/dataset/processor.py
+++ b/qlib/data/dataset/processor.py
@@ -72,7 +72,7 @@ class Processor(Serializable):
         """
         return True
 
-    def config(**kwargs):
+    def config(self, **kwargs):
         super().config(kwargs.get("dump_all", None), kwargs.get("exclude", None))
 
 
@@ -195,7 +195,7 @@ class MinMaxNorm(Processor):
         df.loc(axis=1)[self.cols] = normalize(df[self.cols].values)
         return df
 
-    def config(fit_start_time=None, fit_end_time=None, **kwargs):
+    def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
         if fit_start_time:
             self.fit_start_time = fit_start_time
         if fit_end_time:
@@ -230,7 +230,7 @@ class ZScoreNorm(Processor):
         df.loc(axis=1)[self.cols] = normalize(df[self.cols].values)
         return df
     
-    def config(fit_start_time=None, fit_end_time=None, **kwargs):
+    def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
         if fit_start_time:
             self.fit_start_time = fit_start_time
         if fit_end_time:
@@ -273,7 +273,7 @@ class RobustZScoreNorm(Processor):
             df.clip(-3, 3, inplace=True)
         return df
 
-    def config(fit_start_time=None, fit_end_time=None, **kwargs):
+    def config(self, fit_start_time=None, fit_end_time=None, **kwargs):
         if fit_start_time:
             self.fit_start_time = fit_start_time
         if fit_end_time: