diff --git a/vectorbt/indicators/__init__.py b/vectorbt/indicators/__init__.py
index 9f734b14..bb618f7b 100644
--- a/vectorbt/indicators/__init__.py
+++ b/vectorbt/indicators/__init__.py
@@ -24,6 +24,9 @@ def talib(*args, **kwargs) -> tp.Type[IndicatorBase]:
     """Shortcut for `vectorbt.indicators.factory.IndicatorFactory.from_talib`."""
     return IndicatorFactory.from_talib(*args, **kwargs)
 
+def mtalib(*args, **kwargs) -> tp.Type[IndicatorBase]:
+    """Shortcut for `vectorbt.indicators.factory.IndicatorFactory.from_talib`."""
+    return IndicatorFactory.from_mtalib(*args, **kwargs)
 
 def pandas_ta(*args, **kwargs) -> tp.Type[IndicatorBase]:
     """Shortcut for `vectorbt.indicators.factory.IndicatorFactory.from_pandas_ta`."""
@@ -38,6 +41,7 @@ def ta(*args, **kwargs) -> tp.Type[IndicatorBase]:
 __all__ = [
     'IndicatorFactory',
     'talib',
+    'mtalib',
     'pandas_ta',
     'ta',
     'MA',
@@ -50,6 +54,7 @@ def ta(*args, **kwargs) -> tp.Type[IndicatorBase]:
     'OBV'
 ]
 __whitelist__ = [
+    'mtalib',
     'talib',
     'pandas_ta',
     'ta'
diff --git a/vectorbt/indicators/factory.py b/vectorbt/indicators/factory.py
index 5948eef0..75451425 100644
--- a/vectorbt/indicators/factory.py
+++ b/vectorbt/indicators/factory.py
@@ -3490,6 +3490,136 @@ def apply_func(input_list: tp.List[tp.AnyArray],
         )
         return TALibIndicator
 
+
+    @classmethod
+    def from_mtalib(cls, func_name: str, timescale:int, init_kwargs: tp.KwargsLike = None, **kwargs) -> tp.Type[IndicatorBase]:
+        """Build an indicator class around a TA-Lib function.
+
+        Requires [TA-Lib](https://github.com/mrjbq7/ta-lib) installed.
+
+        For input, parameter and output names, see [docs](https://github.com/mrjbq7/ta-lib/blob/master/docs/index.md).
+
+        Args:
+            func_name (str): Function name.
+            timescale: 
+            init_kwargs (dict): Keyword arguments passed to `IndicatorFactory`.
+            **kwargs: Keyword arguments passed to `IndicatorFactory.from_custom_func`.
+
+        Returns:
+            Indicator
+
+        Usage:
+            ```pycon
+            >>> SMA = vbt.IndicatorFactory.from_talib('SMA')
+
+            >>> sma = SMA.run(price, timeperiod=[2, 3])
+            >>> sma.real
+            sma_timeperiod         2         3
+                              a    b    a    b
+            2020-01-01      NaN  NaN  NaN  NaN
+            2020-01-02      1.5  4.5  NaN  NaN
+            2020-01-03      2.5  3.5  2.0  4.0
+            2020-01-04      3.5  2.5  3.0  3.0
+            2020-01-05      4.5  1.5  4.0  2.0
+            ```
+
+            * To get help on running the indicator, use the `help` command:
+
+            ```pycon
+            >>> help(SMA.run)
+            Help on method run:
+
+            run(close, timeperiod=30, short_name='sma', hide_params=None, hide_default=True, **kwargs) method of builtins.type instance
+                Run `SMA` indicator.
+
+                * Inputs: `close`
+                * Parameters: `timeperiod`
+                * Outputs: `real`
+
+                Pass a list of parameter names as `hide_params` to hide their column levels.
+                Set `hide_default` to False to show the column levels of the parameters with a default value.
+
+                Other keyword arguments are passed to `vectorbt.indicators.factory.run_pipeline`.
+            ```
+        """
+        import talib
+        from talib import abstract
+
+        func_name = func_name.upper()
+        talib_func = getattr(talib, func_name)
+        info = abstract.Function(func_name).info
+        input_names = []
+        for in_names in info['input_names'].values():
+            if isinstance(in_names, (list, tuple)):
+                input_names.extend(list(in_names))
+            else:
+                input_names.append(in_names)
+        class_name = info['name']
+        class_docstring = "{}, {}".format(info['display_name'], info['group'])
+        param_names = list(info['parameters'].keys())
+        output_names = info['output_names']
+        output_flags = info['output_flags']
+
+        def apply_func(input_list: tp.List[tp.AnyArray],
+                       in_output_tuple: tp.Tuple[tp.AnyArray, ...],
+                       param_tuple: tp.Tuple[tp.Param, ...],
+                       **kwargs) -> tp.Union[tp.Array2d, tp.List[tp.Array2d]]:
+            
+            # TA-Lib functions can only process 1-dim arrays
+            n_input_cols = input_list[0].shape[1]
+            outputs = []
+            
+            # 增加 timescale 支持
+            # 保证 是 timescale的倍数
+            n = input_list[0].shape[0]
+            start = n % timescale
+            if start:
+                n = n - start + timescale
+            
+            for col in range(n_input_cols):
+                
+                s = np.full(n, fill_value=np.nan).reshape(-1, timescale)
+                for i in range(timescale):
+                    s[1 if start else 0:,i] = talib_func(
+                        *map(lambda x: x[start+i::timescale, col], input_list),
+                        *param_tuple,
+                        **kwargs
+                    )
+
+                if start:
+                    # 有start 会跳过第一个不完整的stride
+                    output = s.reshape(n,)[timescale-start:]
+                else:
+                    # 没有则不跳
+                    output = s.reshape(n, )
+
+                outputs.append(output)
+            if isinstance(outputs[0], tuple):  # multiple outputs
+                outputs = list(zip(*outputs))
+                return list(map(np.column_stack, outputs))
+            return np.column_stack(outputs)
+
+        TALibIndicator = cls(
+            **merge_dicts(
+                dict(
+                    class_name=class_name,
+                    class_docstring=class_docstring,
+                    input_names=input_names,
+                    param_names=param_names,  # 这里增加 timescale， 就得在apply_func 里剔除
+                    output_names=output_names,
+                    output_flags=output_flags
+                ),
+                init_kwargs
+            )
+        ).from_apply_func(
+            apply_func,
+            pass_packed=True,
+            **info['parameters'],  # 输入强制要求 kv 形式
+            **kwargs
+        )
+        return TALibIndicator
+    
+
     @classmethod
     def parse_pandas_ta_config(cls,
                                func: tp.Callable,