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Merge pull request #1798 from ranaroussi/fix/price-repair-div-adjust

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Fix price repair div adjust
This commit is contained in:
ValueRaider
2023-12-31 21:43:44 +00:00
committed by GitHub
parent 7e6ad0834c d44eff4065
commit 477dc6e6c4
1 changed files with 11 additions and 7 deletions
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18
yfinance/base.py
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@@ -695,7 +695,7 @@ class TickerBase:
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.
@@ -707,26 +707,30 @@ class TickerBase:
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'