diff --git a/pandas_ta/core.py b/pandas_ta/core.py
index 8569ee6..d585291 100644
--- a/pandas_ta/core.py
+++ b/pandas_ta/core.py
@@ -717,43 +717,43 @@ class AnalysisIndicators(BasePandasObject):
 
         if use_multiprocessing:
             _total_ta = len(ta)
-            pool = Pool(self.cores)
-            # Some magic to optimize chunksize for speed based on total ta indicators
-            _chunksize = mp_chunksize - 1 if mp_chunksize > _total_ta else int(npLog10(_total_ta)) + 1
-            if verbose:
-                print(f"[i] Multiprocessing {_total_ta} indicators with {_chunksize} chunks and {self.cores}/{cpu_count()} cpus.")
+            with Pool(self.cores) as pool:
+                # Some magic to optimize chunksize for speed based on total ta indicators
+                _chunksize = mp_chunksize - 1 if mp_chunksize > _total_ta else int(npLog10(_total_ta)) + 1
+                if verbose:
+                    print(f"[i] Multiprocessing {_total_ta} indicators with {_chunksize} chunks and {self.cores}/{cpu_count()} cpus.")
 
-            results = None
-            if mode["custom"]:
-                # Create a list of all the custom indicators into a list
-                custom_ta = [(
-                    ind["kind"],
-                    ind["params"] if "params" in ind and isinstance(ind["params"], tuple) else (),
-                    {**ind, **kwargs},
-                ) for ind in ta]
-                # Custom multiprocessing pool. Must be ordered for Chained Strategies
-                # May fix this to cpus if Chaining/Composition if it remains
-                results = pool.imap(self._mp_worker, custom_ta, _chunksize)
-            else:
-                default_ta = [(ind, tuple(), kwargs) for ind in ta]
-                # All and Categorical multiprocessing pool.
-                if all_ordered:
-                    if Imports["tqdm"]:
-                        results = tqdm(pool.imap(self._mp_worker, default_ta, _chunksize)) # Order over Speed
-                    else:
-                        results = pool.imap(self._mp_worker, default_ta, _chunksize) # Order over Speed
+                results = None
+                if mode["custom"]:
+                    # Create a list of all the custom indicators into a list
+                    custom_ta = [(
+                        ind["kind"],
+                        ind["params"] if "params" in ind and isinstance(ind["params"], tuple) else (),
+                        {**ind, **kwargs},
+                    ) for ind in ta]
+                    # Custom multiprocessing pool. Must be ordered for Chained Strategies
+                    # May fix this to cpus if Chaining/Composition if it remains
+                    results = pool.imap(self._mp_worker, custom_ta, _chunksize)
                 else:
-                    if Imports["tqdm"]:
-                        results = tqdm(pool.imap_unordered(self._mp_worker, default_ta, _chunksize)) # Speed over Order
+                    default_ta = [(ind, tuple(), kwargs) for ind in ta]
+                    # All and Categorical multiprocessing pool.
+                    if all_ordered:
+                        if Imports["tqdm"]:
+                            results = tqdm(pool.imap(self._mp_worker, default_ta, _chunksize)) # Order over Speed
+                        else:
+                            results = pool.imap(self._mp_worker, default_ta, _chunksize) # Order over Speed
                     else:
-                        results = pool.imap_unordered(self._mp_worker, default_ta, _chunksize) # Speed over Order
-            if results is None:
-                print(f"[X] ta.strategy('{name}') has no results.")
-                return
+                        if Imports["tqdm"]:
+                            results = tqdm(pool.imap_unordered(self._mp_worker, default_ta, _chunksize)) # Speed over Order
+                        else:
+                            results = pool.imap_unordered(self._mp_worker, default_ta, _chunksize) # Speed over Order
+                if results is None:
+                    print(f"[X] ta.strategy('{name}') has no results.")
+                    return
 
-            pool.close()
-            pool.join()
-            self._last_run = get_time(self.exchange, to_string=True)
+                pool.close()
+                pool.join()
+                self._last_run = get_time(self.exchange, to_string=True)
 
         else:
             # Without multiprocessing: