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Fix price repair div adjust #1798

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Dec 31, 2023
Merged

Fix price repair div adjust #1798

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18 changes: 11 additions & 7 deletions yfinance/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -694,7 +694,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
f_tag = df_block_calib['Adj Close'] == tag
if f_tag.any():
div_adjusts = df_block_calib['Adj Close'] / df_block_calib['Close']
# The loop below assumes each 1d repair is isoloated, i.e. surrounded by
# The loop below assumes each 1d repair is isolated, i.e. surrounded by
# good data. Which is case most of time.
# But in case are repairing a chunk of bad 1d data, back/forward-fill the
# good div-adjustments - not perfect, but a good backup.
Expand All @@ -706,26 +706,30 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
if df_new.loc[dt, "Dividends"] != 0:
if idx < n - 1:
# Easy, take div-adjustment from next-day
div_adjusts[idx] = div_adjusts.iloc[idx + 1]
div_adjusts.iloc[idx] = div_adjusts.iloc[idx + 1]
else:
# Take previous-day div-adjustment and reverse todays adjustment
div_adj = 1.0 - df_new_calib["Dividends"].iloc[idx] / df_new_calib['Close'].iloc[
idx - 1]
div_adjusts[idx] = div_adjusts.iloc[idx - 1] / div_adj
div_adjusts.iloc[idx] = div_adjusts.iloc[idx - 1] / div_adj
else:
if idx > 0:
# Easy, take div-adjustment from previous-day
div_adjusts[idx] = div_adjusts.iloc[idx - 1]
div_adjusts.iloc[idx] = div_adjusts.iloc[idx - 1]
else:
# Must take next-day div-adjustment
div_adjusts[idx] = div_adjusts.iloc[idx + 1]
div_adjusts.iloc[idx] = div_adjusts.iloc[idx + 1]
if df_new_calib["Dividends"].iloc[idx + 1] != 0:
div_adjusts[idx] *= 1.0 - df_new_calib["Dividends"].iloc[idx + 1] / \
div_adjusts.iloc[idx] *= 1.0 - df_new_calib["Dividends"].iloc[idx + 1] / \
df_new_calib['Close'].iloc[idx]
f_close_bad = df_block_calib['Close'] == tag
div_adjusts = div_adjusts.reindex(df_block.index, fill_value=np.nan).ffill().bfill()
df_new['Adj Close'] = df_block['Close'] * div_adjusts
if f_close_bad.any():
df_new.loc[f_close_bad, 'Adj Close'] = df_new['Close'][f_close_bad] * div_adjusts[f_close_bad]
f_close_bad_new = f_close_bad.reindex(df_new.index, fill_value=False)
div_adjusts_new = div_adjusts.reindex(df_new.index, fill_value=np.nan).ffill().bfill()
div_adjusts_new_np = f_close_bad_new.to_numpy()
df_new.loc[div_adjusts_new_np, 'Adj Close'] = df_new['Close'][div_adjusts_new_np] * div_adjusts_new[div_adjusts_new_np]

# Check whether 'df_fine' has different split-adjustment.
# If different, then adjust to match 'df'
Expand Down