diff --git a/qlib/data/dataset/handler.py b/qlib/data/dataset/handler.py
index 2fe8f8a63..9b2a6fa32 100644
--- a/qlib/data/dataset/handler.py
+++ b/qlib/data/dataset/handler.py
@@ -7,6 +7,7 @@ from typing import Callable, Union, Tuple, List, Iterator, Optional
 
 import pandas as pd
 
+from qlib.typehint import Literal
 from ...log import get_module_logger, TimeInspector
 from ...utils import init_instance_by_config
 from ...utils.serial import Serializable
@@ -49,6 +50,8 @@ class DataHandler(Serializable):
     - Fetching data with `col_set=CS_RAW` will return the raw data and may avoid pandas from copying the data when calling `loc`
     """
 
+    _data: pd.DataFrame  # underlying data.
+
     def __init__(
         self,
         instruments=None,
@@ -155,6 +158,11 @@ class DataHandler(Serializable):
         """
         fetch data from underlying data source
 
+        Design motivation:
+        - providing a unified interface for underlying data.
+        - Potential to make the interface more friendly.
+        - User can improve performance when fetching data in this extra layer
+
         Parameters
         ----------
         selector : Union[pd.Timestamp, slice, str]
@@ -328,6 +336,9 @@ class DataHandler(Serializable):
             yield cur_date, self.fetch(selector, **kwargs)
 
 
+DATA_KEY_TYPE = Literal["raw", "infer", "learn"]
+
+
 class DataHandlerLP(DataHandler):
     """
     DataHandler with **(L)earnable (P)rocessor**
@@ -353,10 +364,15 @@ class DataHandlerLP(DataHandler):
         - `drop_raw=True`: this will modify the data inplace on raw data;
     """
 
+    # based on `self._data`, _infer and _learn are genrated after processors
+    _infer: pd.DataFrame  # data for inference
+    _learn: pd.DataFrame  # data for learning models
+
     # data key
-    DK_R = "raw"
-    DK_I = "infer"
-    DK_L = "learn"
+    DK_R: DATA_KEY_TYPE = "raw"
+    DK_I: DATA_KEY_TYPE = "infer"
+    DK_L: DATA_KEY_TYPE = "learn"
+    # map data_key to attribute name
     ATTR_MAP = {DK_R: "_data", DK_I: "_infer", DK_L: "_learn"}
 
     # process type
@@ -600,7 +616,7 @@ class DataHandlerLP(DataHandler):
 
         # TODO: Be able to cache handler data. Save the memory for data processing
 
-    def _get_df_by_key(self, data_key: str = DK_I) -> pd.DataFrame:
+    def _get_df_by_key(self, data_key: DATA_KEY_TYPE = DK_I) -> pd.DataFrame:
         if data_key == self.DK_R and self.drop_raw:
             raise AttributeError(
                 "DataHandlerLP has not attribute _data, please set drop_raw = False if you want to use raw data"
@@ -613,7 +629,7 @@ class DataHandlerLP(DataHandler):
         selector: Union[pd.Timestamp, slice, str] = slice(None, None),
         level: Union[str, int] = "datetime",
         col_set=DataHandler.CS_ALL,
-        data_key: str = DK_I,
+        data_key: DATA_KEY_TYPE = DK_I,
         squeeze: bool = False,
         proc_func: Callable = None,
     ) -> pd.DataFrame:
@@ -647,7 +663,7 @@ class DataHandlerLP(DataHandler):
             proc_func=proc_func,
         )
 
-    def get_cols(self, col_set=DataHandler.CS_ALL, data_key: str = DK_I) -> list:
+    def get_cols(self, col_set=DataHandler.CS_ALL, data_key: DATA_KEY_TYPE = DK_I) -> list:
         """
         get the column names
 
@@ -655,7 +671,7 @@ class DataHandlerLP(DataHandler):
         ----------
         col_set : str
             select a set of meaningful columns.(e.g. features, columns).
-        data_key : str
+        data_key : DATA_KEY_TYPE
             the data to fetch:  DK_*.
 
         Returns