Price-repair-split-errors: stop zeroes, big-divs, and 100x errors tri…

…ggering false split errors

yfinance/base.py

@@ -994,14 +994,6 @@ def _fix_unit_switch(self, df, interval, tz_exchange):
         # This function fixes the second.
         # Eventually Yahoo fixes but could take them 2 weeks.
 
-        # To detect, use 'bad split adjustment' algorithm. But only correct
-        # if no stock splits in data
-
-        f_splits = df['Stock Splits'].to_numpy() != 0.0
-        if f_splits.any():
-            utils.get_yf_logger().debug('price-repair-100x: Cannot check for chunked 100x errors because splits present')
-            return df
-
         return self._fix_prices_sudden_change(df, interval, tz_exchange, 100.0)
 
     @utils.log_indent_decorator
@@ -1291,11 +1283,25 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
             # Avoid using 'Low' and 'High'. For multiday intervals, these can be 
             # very volatile so reduce ability to detect genuine stock split errors
             _1d_change_x = np.full((n, 2), 1.0)
-            price_data = df2[['Open','Close']].replace(0.0, 1.0).to_numpy()
+            price_data = df2[['Open','Close']].to_numpy()
+            f_zero = price_data == 0.0
         else:
             _1d_change_x = np.full((n, 4), 1.0)
-            price_data = df2[OHLC].replace(0.0, 1.0).to_numpy()
+            price_data = df2[OHLC].to_numpy()
+            f_zero = price_data == 0.0
+        if f_zero.any():
+            price_data[f_zero] = 1.0
+
+        # Update: if a VERY large dividend is paid out, then can be mistaken for a 1:2 stock split.
+        # Fix = use adjusted prices
+        adj = df2['Adj Close'].to_numpy() / df2['Close'].to_numpy()
+        for j in range(price_data.shape[1]):
+            price_data[:,j] *= adj
+
         _1d_change_x[1:] = price_data[1:, ] / price_data[:-1, ]
+        f_zero_num_denom = f_zero | np.roll(f_zero, 1, axis=0)
+        if f_zero_num_denom.any():
+            _1d_change_x[f_zero_num_denom] = 1.0
         if interday and interval != '1d':
             # average change
             _1d_change_minx = np.average(_1d_change_x, axis=1)
@@ -1394,6 +1400,29 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
             logger.info(f'price-repair-split: No {fix_type}s detected')
             return df
 
+        # Update: if any 100x changes are soon after a stock split, so could be confused with split error, then abort
+        threshold_days = 30
+        f_splits = df['Stock Splits'].to_numpy() != 0.0
+        if change in [100.0, 0.01] and f_splits.any():
+            indices_A = np.where(f_splits)[0]
+            indices_B = np.where(f)[0]
+            if not len(indices_A) or not len(indices_B):
+                return None
+            gaps = indices_B[:, None] - indices_A
+            # Because data is sorted in DEscending order, need to flip gaps
+            gaps *= -1
+            f_pos = gaps > 0
+            if f_pos.any():
+                gap_min = gaps[f_pos].min()
+                gap_td = utils._interval_to_timedelta(interval) * gap_min
+                if isinstance(gap_td, _dateutil.relativedelta.relativedelta):
+                    threshold = _dateutil.relativedelta.relativedelta(days=threshold_days)
+                else:
+                    threshold = _datetime.timedelta(days=threshold_days)
+                if gap_td < threshold:
+                    logger.info(f'price-repair-split: 100x changes are too soon after stock split events, aborting')
+                    return df
+
         # if logger.isEnabledFor(logging.DEBUG):
         #     df_debug['i'] = list(range(0, df_debug.shape[0]))
         #     df_debug['i_rev'] = df_debug.shape[0]-1 - df_debug['i']