diff --git a/examples/nested_decision_execution/workflow.py b/examples/nested_decision_execution/workflow.py
index 3108960c8..b6c1362fd 100644
--- a/examples/nested_decision_execution/workflow.py
+++ b/examples/nested_decision_execution/workflow.py
@@ -124,14 +124,14 @@ class NestedDecisionExecutionWorkflow:
 
     def _init_qlib(self):
         """initialize qlib"""
-        provider_uri_day = "/data/stock_data/huaxia/qlib"
-        provider_uri_1min = "/data2/stock_data/huaxia_1min_qlib"
-        # provider_uri_day = "~/.qlib/qlib_data/cn_data"  # target_dir
-        # GetData().qlib_data(target_dir=provider_uri_day, region=REG_CN, version="v2", exists_skip=True)
-        # provider_uri_1min = HIGH_FREQ_CONFIG.get("provider_uri")
-        # GetData().qlib_data(
-        #    target_dir=provider_uri_1min, interval="1min", region=REG_CN, version="v2", exists_skip=True
-        # )
+        # provider_uri_day = "/data/stock_data/huaxia/qlib"
+        # provider_uri_1min = "/data2/stock_data/huaxia_1min_qlib"
+        provider_uri_day = "~/.qlib/qlib_data/cn_data"  # target_dir
+        GetData().qlib_data(target_dir=provider_uri_day, region=REG_CN, version="v2", exists_skip=True)
+        provider_uri_1min = HIGH_FREQ_CONFIG.get("provider_uri")
+        GetData().qlib_data(
+            target_dir=provider_uri_1min, interval="1min", region=REG_CN, version="v2", exists_skip=True
+        )
         provider_uri_map = {"1min": provider_uri_1min, "day": provider_uri_day}
         client_config = {
             "calendar_provider": {
diff --git a/qlib/data/dataset/storage.py b/qlib/data/dataset/storage.py
index cd38bbefa..9325807f9 100644
--- a/qlib/data/dataset/storage.py
+++ b/qlib/data/dataset/storage.py
@@ -37,8 +37,12 @@ class BaseHandlerStorage:
             Return the original data instead of copy if possible.
         proc_func: Callable
             please refer to the doc of DataHandler.fetch
-        """
 
+        Returns
+        -------
+        pd.DataFrame
+            the dataframe fetched
+        """
         raise NotImplementedError("fetch is method not implemented!")
 
     @staticmethod
@@ -46,6 +50,7 @@ class BaseHandlerStorage:
         raise NotImplementedError("from_df method is not implemented!")
 
     def is_proc_func_supported(self):
+        """whether the arg `proc_func` in `fetch` method is supported."""
         raise NotImplementedError("is_proc_func_supported method is not implemented!")
 
 
@@ -113,4 +118,5 @@ class HasingStockStorage(BaseHandlerStorage):
             return pd.concat(fetch_stock_df_list, sort=False, copy=~fetch_orig)
 
     def is_proc_func_supported(self):
+        """the arg `proc_func` in `fetch` method is not supported in HasingStockStorage"""
         return False
diff --git a/qlib/utils/resam.py b/qlib/utils/resam.py
index 7e0dc141c..9e9590e30 100644
--- a/qlib/utils/resam.py
+++ b/qlib/utils/resam.py
@@ -3,6 +3,8 @@ import datetime
 
 import numpy as np
 import pandas as pd
+
+from functools import partial
 from typing import Tuple, List, Union, Optional, Callable
 
 from . import lazy_sort_index
@@ -284,21 +286,15 @@ def get_valid_value(series, last=True):
     return series.fillna(method="ffill").iloc[-1] if last else series.fillna(method="bfill").iloc[0]
 
 
-def ts_data_last(ts_feature):
-    """get the last not nan value of pd.Series|DataFrame with single level index"""
+def _ts_data_valid(ts_feature, last=False):
+    """get the first/last not nan value of pd.Series|DataFrame with single level index"""
     if isinstance(ts_feature, pd.DataFrame):
-        return ts_feature.apply(lambda column: get_valid_value(column, last=True))
+        return ts_feature.apply(lambda column: get_valid_value(column, last=last))
     elif isinstance(ts_feature, pd.Series):
-        return get_valid_value(ts_feature, last=True)
+        return get_valid_value(ts_feature, last=last)
     else:
         raise TypeError(f"ts_feature should be pd.DataFrame/Series, not {type(ts_feature)}")
 
 
-def ts_data_first(ts_feature):
-    """get the first not nan value of pd.Series|DataFrame with single level index"""
-    if isinstance(ts_feature, pd.DataFrame):
-        return ts_feature.apply(lambda column: get_valid_value(column, last=False))
-    elif isinstance(ts_feature, pd.Series):
-        return get_valid_value(ts_feature, last=False)
-    else:
-        raise TypeError(f"ts_feature should be pd.DataFrame/Series, not {type(ts_feature)}")
+ts_data_last = partial(_ts_data_valid, last=False)
+ts_data_first = partial(_ts_data_valid, last=True)