Price repair: reduce sensitivity to extreme-low Low prices

Switch to median from mean when scanning for sudden changes, reduces sensitivitity to extreme outliers

yfinance/scrapers/history.py

@@ -1317,10 +1317,12 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
             # average change
             _1d_change_minx = np.average(_1d_change_x, axis=1)
         else:
-            # change nearest to 1.0
-            diff = np.abs(_1d_change_x - 1.0)
-            j_indices = np.argmin(diff, axis=1)
-            _1d_change_minx = _1d_change_x[np.arange(n), j_indices]
+            # # change nearest to 1.0
+            # diff = np.abs(_1d_change_x - 1.0)
+            # j_indices = np.argmin(diff, axis=1)
+            # _1d_change_minx = _1d_change_x[np.arange(n), j_indices]
+            # Still sensitive to extreme-low low. Try median:
+            _1d_change_minx = np.median(_1d_change_x, axis=1)
         f_na = np.isnan(_1d_change_minx)
         if f_na.any():
             # Possible if data was too old for reconstruction.