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Improve bad-split-repair on multiday intervals 

Improve bad-split-repair on multiday intervals
Switch some repair log msgs from warning -> info
pull/1633/head
Value Raider 2023-07-31 18:19:42 +01:00
parent 4a1e1c4447
commit 93b6e024da
1 changed files with 33 additions and 31 deletions
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64
yfinance/base.py
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@ -619,7 +619,7 @@ class TickerBase:
else:
msg += f" {start_d}"
msg += ", too old, Yahoo will reject request for finer-grain data"
logger.warning(msg)
logger.info(msg)
continue
td_1d = _datetime.timedelta(days=1)
@ -703,7 +703,7 @@ class TickerBase:
common_index = _np.intersect1d(df_block.index, df_new.index)
if len(common_index) == 0:
# Can't calibrate so don't attempt repair
logger.warning(f"Can't calibrate {interval} block starting {start_d} so aborting repair")
logger.info(f"Can't calibrate {interval} block starting {start_d} so aborting repair")
continue
# First, attempt to calibrate the 'Adj Close' column. OK if cannot.
# Only necessary for 1d interval, because the 1h data is not div-adjusted.
@ -752,7 +752,7 @@ class TickerBase:
calib_filter = (df_block_calib != tag)
if not calib_filter.any():
# Can't calibrate so don't attempt repair
logger.warning(f"Can't calibrate {interval} block starting {start_d} so aborting repair")
logger.info(f"Can't calibrate {interval} block starting {start_d} so aborting repair")
continue
# Avoid divide-by-zero warnings:
for j in range(len(calib_cols)):
@ -858,7 +858,7 @@ class TickerBase:
return df
if df.shape[0] == 1:
# Need multiple rows to confidently identify outliers
logger.warning("price-repair-100x: Cannot check single-row table for 100x price errors")
logger.info("price-repair-100x: Cannot check single-row table for 100x price errors")
if not "Repaired?" in df.columns:
df["Repaired?"] = False
return df
@ -883,7 +883,7 @@ class TickerBase:
else:
df2_zeroes = None
if df2.shape[0] <= 1:
logger.warning("price-repair-100x: Insufficient good data for detecting 100x price errors")
logger.info("price-repair-100x: Insufficient good data for detecting 100x price errors")
if not "Repaired?" in df.columns:
df["Repaired?"] = False
return df
@ -1053,7 +1053,7 @@ class TickerBase:
return df
if f_prices_bad.sum() == len(price_cols)*len(df2):
# Need some good data to calibrate
logger.warning("price-repair-missing: No good data for calibration so cannot fix price=0 bad data")
logger.info("price-repair-missing: No good data for calibration so cannot fix price=0 bad data")
if not "Repaired?" in df.columns:
df["Repaired?"] = False
return df
@ -1240,8 +1240,6 @@ class TickerBase:
# If stock is currently suspended and not in USA, then usually Yahoo introduces
# 100x errors into suspended intervals. Clue is no price change and 0 volume.
# Better to use last active trading interval as baseline.
# But can't be smart
# But can only do with 1d:
f_no_activity = (df2['Low'] == df2['High']) & (df2['Volume']==0)
appears_suspended = f_no_activity.any() and _np.where(f_no_activity)[0][0]==0
f_active = ~f_no_activity
@ -1258,7 +1256,7 @@ class TickerBase:
debug_cols = ['Low', 'High']
# Calculate daily price % change. To reduce effect of price volatility,
# calculate change for each OHLC column and use average.
# calculate change for each OHLC column.
if interday and interval != '1d' and split not in [100.0, 100, 0.001]:
# Avoid using 'Low' and 'High'. For multiday intervals, these can be
# very volatile so reduce ability to detect genuine stock split errors
@ -1365,11 +1363,14 @@ class TickerBase:
logger.info(f'price-repair-split: No {fix_type}s detected')
return df
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f"price-repair-split: my workings:")
logger.debug('\n' + str(df_debug))
# if logger.isEnabledFor(logging.DEBUG):
# logger.debug(f"price-repair-split: my workings:")
# logger.debug('\n' + str(df_debug))
def map_signals_to_ranges(f, f_up, f_down):
if not f.any():
return []
true_indices = _np.where(f)[0]
ranges = []
@ -1382,7 +1383,7 @@ class TickerBase:
if idx_latest_active is None:
true_indices_old = []
else:
true_indices_old = [i for i in true_indices if i >= idx_latest_active]
true_indices_old = [i for i in true_indices if i > idx_latest_active]
if len(true_indices_old) > 0:
for i in range(len(true_indices_old) - 1):
if i % 2 == 0:
@ -1401,26 +1402,27 @@ class TickerBase:
# Next, process prices more recent than idx_latest_active:
true_indices_recent = [i for i in true_indices if i not in true_indices_old]
if split > 1.0:
adj = 'split' if f_up[true_indices_recent[0]] else '1.0/split'
else:
adj = '1.0/split' if f_up[true_indices_recent[0]] else 'split'
ranges.append((0, true_indices_recent[0], adj))
for i in range(1, len(true_indices_recent) - 1):
if i % 2 == 1:
if split > 1.0:
adj = '1.0/split' if f_up[true_indices_recent[i]] else 'split'
else:
adj = 'split' if f_up[true_indices_recent[i]] else '1.0/split'
ranges.append((true_indices_recent[i], true_indices_recent[i+1], adj))
if len(true_indices_recent) % 2 == 0:
if len(true_indices_recent) > 0:
if split > 1.0:
adj = 'split' if f_down[true_indices_recent[-1]] else '1.0/split'
adj = 'split' if f_up[true_indices_recent[0]] else '1.0/split'
else:
adj = '1.0/split' if f_down[true_indices_recent[-1]] else 'split'
ranges.append((true_indices_recent[-1], len(f), adj))
adj = '1.0/split' if f_up[true_indices_recent[0]] else 'split'
ranges.append((0, true_indices_recent[0], adj))
for i in range(1, len(true_indices_recent) - 1):
if i % 2 == 1:
if split > 1.0:
adj = '1.0/split' if f_up[true_indices_recent[i]] else 'split'
else:
adj = 'split' if f_up[true_indices_recent[i]] else '1.0/split'
ranges.append((true_indices_recent[i], true_indices_recent[i+1], adj))
if len(true_indices_recent) % 2 == 0:
if split > 1.0:
adj = 'split' if f_down[true_indices_recent[-1]] else '1.0/split'
else:
adj = '1.0/split' if f_down[true_indices_recent[-1]] else 'split'
ranges.append((true_indices_recent[-1], len(f), adj))
ranges = sorted(ranges, key=lambda x: x[0])