diff --git a/qlib/data/dataset/handler.py b/qlib/data/dataset/handler.py
index d1dbe1777..0a2fed637 100644
--- a/qlib/data/dataset/handler.py
+++ b/qlib/data/dataset/handler.py
@@ -51,7 +51,7 @@ class DataHandler(Serializable):
 
     def __init__(
         self,
-        instruments,
+        instruments=None,
         start_time=None,
         end_time=None,
         data_loader: Tuple[dict, str, DataLoader] = None,
@@ -242,7 +242,7 @@ class DataHandlerLP(DataHandler):
 
     def __init__(
         self,
-        instruments,
+        instruments=None,
         start_time=None,
         end_time=None,
         data_loader: Tuple[dict, str, DataLoader] = None,
diff --git a/qlib/data/dataset/loader.py b/qlib/data/dataset/loader.py
index 564a7e5d5..7e8dd507c 100644
--- a/qlib/data/dataset/loader.py
+++ b/qlib/data/dataset/loader.py
@@ -1,13 +1,16 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
+import os
 import abc
 import warnings
+import numpy as np
 import pandas as pd
 
-from typing import Tuple
+from typing import Tuple, Union
 
 from qlib.data import D
+from qlib.utils import load_dataset
 
 
 class DataLoader(abc.ABC):
@@ -139,6 +142,9 @@ class QlibDataLoader(DLWParser):
         super().__init__(config)
 
     def load_group_df(self, instruments, exprs: list, names: list, start_time=None, end_time=None) -> pd.DataFrame:
+        if instruments is None:
+            warnings.warn('`instruments` is not set, will load all stocks')
+            instruments = 'all'
         if isinstance(instruments, str):
             instruments = D.instruments(instruments, filter_pipe=self.filter_pipe)
         elif self.filter_pipe is not None:
@@ -148,3 +154,60 @@ class QlibDataLoader(DLWParser):
         df.columns = names
         df = df.swaplevel().sort_index()  # NOTE: always return <datetime, instrument>
         return df
+
+
+class StaticDataLoader(DataLoader):
+    """
+    DataLoader that supports loading data from file or as provided.
+    """
+
+    def __init__(self, feature_path_or_obj: Union[str, pd.DataFrame], label_path_or_obj: Union[str, pd.DataFrame] = None):
+        """
+        Parameters
+        ----------
+        feature_path_or_obj : str or pd.DataFrame
+            file path or pandas object for feature
+        label_path_or_obj : str or pd.DataFrame
+            file path or pandas object for label
+        """
+        if isinstance(feature_path_or_obj, str):
+            assert os.path.exists(feature_path_or_obj), f"cannot find feature `{feature_path_or_obj}"
+        else:
+            assert isinstance(feature_path_or_obj, pd.DataFrame), f"need to be dataframe"
+        self._feature_path_or_obj = feature_path_or_obj
+
+        if isinstance(label_path_or_obj, str):
+            assert os.path.exists(label_path_or_obj), f"cannot find label `{label_path_or_obj}"
+        elif label_path_or_obj is not None:
+            assert isinstance(label_path_or_obj, pd.DataFrame), f"need to be dataframe"
+        self._label_path_or_obj = label_path_or_obj
+
+        self._data = None
+
+    def load(self, instruments=None, start_time=None, end_time=None) -> pd.DataFrame:
+        self._maybe_load_raw_data()
+        if instruments is None:
+            df = self._data
+        else:
+            df = self._data.loc(axis=0)[:, instruments]
+        if start_time is None and end_time is None:
+            return df  # NOTE: avoid copy by loc
+        return df.loc[pd.Timestamp(start_time):pd.Timestamp(end_time)]
+
+    def _maybe_load_raw_data(self):
+        if self._data is not None:
+            return
+        self._data = load_dataset(self._feature_path_or_obj)
+        if self._label_path_or_obj is not None:
+            self._data = pd.concat(
+                {"feature": self._data, "label": load_dataset(self._label_path_or_obj)}, axis=1
+            )
+        if not isinstance(self._data.columns, pd.MultiIndex):
+            self._data.columns = pd.MultiIndex.from_arrays(
+                [
+                    np.array(["feature", "label"])[
+                        self._data.columns.str.contains("^LABEL").astype(int)
+                    ],
+                    self._data.columns,
+                ]
+            )
diff --git a/qlib/utils/__init__.py b/qlib/utils/__init__.py
index 79fd6fe5c..c77c67fa2 100644
--- a/qlib/utils/__init__.py
+++ b/qlib/utils/__init__.py
@@ -695,3 +695,17 @@ def register_wrapper(wrapper, cls_or_obj, module_path=None):
         cls_or_obj = getattr(module, cls_or_obj)
     obj = cls_or_obj() if isinstance(cls_or_obj, type) else cls_or_obj
     wrapper.register(obj)
+
+
+def load_dataset(path_or_obj):
+    """load dataset from multiple file formats"""
+    if isinstance(path_or_obj, pd.DataFrame):
+        return path_or_obj
+    _, extension = os.path.splitext(path_or_obj)
+    if extension == '.h5':
+        return pd.read_hdf(path_or_obj)
+    elif extension == '.pkl':
+        return pd.read_pickle(path_or_obj)
+    elif extension == '.csv':
+        return pd.read_csv(path_or_obj, parse_dates=True, index_col=[0, 1])
+    raise ValueError(f'unsupported file type `{extension}`')