kernc
diff --git a/Collapse file
‎backtesting/backtesting.py‎
Copy file name to clipboardExpand all lines: backtesting/backtesting.py
+4-2Lines changed: 4 additions & 2 deletions b/Collapse file
‎backtesting/backtesting.py‎
Copy file name to clipboardExpand all lines: backtesting/backtesting.py
+4-2Lines changed: 4 additions & 2 deletions
diff --git a/Collapse file
‎backtesting/lib.py‎
Copy file name to clipboardExpand all lines: backtesting/lib.py
+3-2Lines changed: 3 additions & 2 deletions b/Collapse file
‎backtesting/lib.py‎
Copy file name to clipboardExpand all lines: backtesting/lib.py
+3-2Lines changed: 3 additions & 2 deletions
@@ -1308,7 +1308,8 @@ def run(self, **kwargs) -> pd.Series:
         # np.nan >= 3 is not invalid; it's False.
         with np.errstate(invalid='ignore'):
 
-            for i in _tqdm(range(start, len(self._data)), desc=self.run.__qualname__):
+            for i in _tqdm(range(start, len(self._data)), desc=self.run.__qualname__,
+                           unit='bar', mininterval=2, miniters=100):
                 # Prepare data and indicators for `next` call
                 data._set_length(i + 1)
                 for attr, indicator in indicator_attrs:
@@ -1564,7 +1565,8 @@ def memoized_run(tup):
                 stats = self.run(**dict(tup))
                 return -maximize(stats)
 
-            progress = iter(_tqdm(repeat(None), total=max_tries, leave=False, desc='Backtest.optimize'))
+            progress = iter(_tqdm(repeat(None), total=max_tries, leave=False,
+                                  desc=self.optimize.__qualname__, mininterval=2))
             _names = tuple(kwargs.keys())
 
             def objective_function(x):
 
@@ -577,7 +577,8 @@ def run(self, **kwargs):
                           ((df_batch, self._strategy, self._bt_kwargs, kwargs)
                            for df_batch in _batch(shm))),
                 total=len(shm),
-                desc=self.__class__.__name__,
+                desc=self.run.__qualname__,
+                mininterval=2
             )
             df = pd.DataFrame(list(chain(*results))).transpose()
         return df
@@ -605,7 +606,7 @@ def optimize(self, **kwargs) -> pd.DataFrame:
         """
         heatmaps = []
         # Simple loop since bt.optimize already does its own multiprocessing
-        for df in _tqdm(self._dfs, desc=self.__class__.__name__):
+        for df in _tqdm(self._dfs, desc=self.__class__.__name__, mininterval=2):
             bt = Backtest(df, self._strategy, **self._bt_kwargs)
             _best_stats, heatmap = bt.optimize(  # type: ignore
                 return_heatmap=True, return_optimization=False, **kwargs)