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Fix tests ; Fine-tune split repair ; Fix UTC warning
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+64
-56
lines changed

7 files changed

+64
-56
lines changed

‎tests/__init__.py

Whitespace-only changes.

‎tests/data/AV-L-1wk-bad-stock-split-fixed.csv

Lines changed: 23 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -1,27 +1,27 @@
11
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
2-
2021-12-13 00:00:00+00:00,393.999975585938,406.6,391.4,402.899916992188,291.232287597656,62714764.4736842,0,0
3-
2021-12-20 00:00:00+00:00,393.999975585938,412.199990234375,392.502983398438,409.899997558594,296.292243652344,46596651.3157895,0,0
4-
2021-12-27 00:00:00+00:00,409.899997558594,416.550971679688,408.387001953125,410.4,296.653642578125,10818482.8947368,0,0
5-
2022-01-03 00:00:00+00:00,410.4,432.199995117188,410.4,432.099985351563,312.339265136719,44427327.6315789,0,0
6-
2022-01-10 00:00:00+00:00,431.3,439.199982910156,429.099970703125,436.099912109375,315.230618896484,29091400,0,0
7-
2022-01-17 00:00:00+00:00,437.999912109375,445.199965820313,426.999997558594,431.999975585938,312.267017822266,43787351.3157895,0,0
8-
2022-01-24 00:00:00+00:00,430.099975585938,440.999973144531,420.999968261719,433.499982910156,313.351237792969,58487296.0526316,0,0
9-
2022-01-31 00:00:00+00:00,436.199968261719,443.049987792969,432.099985351563,435.199916992188,314.580045166016,43335806.5789474,0,0
10-
2022-02-07 00:00:00+00:00,437.899995117188,448.799992675781,436.051994628906,444.39998046875,321.230207519531,39644061.8421053,0,0
11-
2022-02-14 00:00:00+00:00,437.699975585938,441.999978027344,426.699968261719,432.199995117188,312.411558837891,49972693.4210526,0,0
12-
2022-02-21 00:00:00+00:00,435.499992675781,438.476999511719,408.29998046875,423.399970703125,306.050571289063,65719596.0526316,0,0
13-
2022-02-28 00:00:00+00:00,415.099995117188,427.999909667969,386.199932861328,386.799945068359,279.594578857422,94057936.8421053,4.1875,0
14-
2022-03-07 00:00:00+00:00,374.999952392578,417.299978027344,361.101981201172,409.599968261719,298.389248046875,71269101.3157895,0,0
15-
2022-03-14 00:00:00+00:00,413.099985351563,426.699968261719,408.899992675781,422.399965820313,307.713929443359,55431927.6315789,0,0
16-
2022-03-21 00:00:00+00:00,422.699995117188,442.7,422.399965820313,437.799985351563,318.932696533203,39896352.6315789,0,0
17-
2022-03-28 00:00:00+01:00,442.49998046875,460.999978027344,440.097983398438,444.6,323.886403808594,56413515.7894737,0,0
18-
2022-04-04 00:00:00+01:00,439.699985351563,445.399985351563,421.999973144531,425.799973144531,310.190817871094,49415836.8421053,19.342106,0
19-
2022-04-11 00:00:00+01:00,425.39998046875,435.599909667969,420.799995117188,434.299968261719,327.211427001953,29875081.5789474,0,0
20-
2022-04-18 00:00:00+01:00,434.299968261719,447.799987792969,433.599992675781,437.799985351563,329.848419189453,49288272.3684211,0,0
21-
2022-04-25 00:00:00+01:00,430.699987792969,438.799990234375,423.999982910156,433.299916992188,326.457967529297,44656776.3157895,0,0
22-
2022-05-02 00:00:00+01:00,433.299916992188,450.999975585938,414.499982910156,414.899975585938,312.595018310547,29538167.1052632,0,0
23-
2022-05-09 00:00:00+01:00,413.199995117188,417.449992675781,368.282923583984,408.199970703125,307.547099609375,73989611.8421053,0,0
24-
2022-05-16 00:00:00+01:00,384,423.600006103516,384,412.100006103516,310.485473632813,81938261,101.69,0.76
2+
2021-12-13 00:00:00+00:00,518.421020507813,535,515,530.131469726563,383.200378417969,47663221,0,0
3+
2021-12-20 00:00:00+00:00,518.421020507813,542.368408203125,516.451293945313,539.342102050781,389.858215332031,35413455,0,0
4+
2021-12-27 00:00:00+00:00,539.342102050781,548.093383789063,537.351318359375,540,390.333740234375,8222047,0,0
5+
2022-01-03 00:00:00+00:00,540,568.684204101563,540,568.552612304688,410.972717285156,33764769,0,0
6+
2022-01-10 00:00:00+00:00,567.5,577.894714355469,564.605224609375,573.815673828125,414.777130126953,22109464,0,0
7+
2022-01-17 00:00:00+00:00,576.315673828125,585.789428710938,561.842102050781,568.421020507813,410.877655029297,33278387,0,0
8+
2022-01-24 00:00:00+00:00,565.921020507813,580.263122558594,553.947326660156,570.394714355469,412.304260253906,44450345,0,0
9+
2022-01-31 00:00:00+00:00,573.947326660156,582.960510253906,568.552612304688,572.631469726563,413.921112060547,32935213,0,0
10+
2022-02-07 00:00:00+00:00,576.184204101563,590.526306152344,573.752624511719,584.73681640625,422.671325683594,30129487,0,0
11+
2022-02-14 00:00:00+00:00,575.921020507813,581.578918457031,561.447326660156,568.684204101563,411.067840576172,37979247,0,0
12+
2022-02-21 00:00:00+00:00,573.026306152344,576.943420410156,537.23681640625,557.105224609375,402.698120117188,49946893,0,0
13+
2022-02-28 00:00:00+00:00,546.184204101563,563.157775878906,508.157806396484,508.947296142578,367.887603759766,71484032,4.1875,0
14+
2022-03-07 00:00:00+00:00,493.420989990234,549.078918457031,475.134185791016,538.947326660156,392.617431640625,54164517,0,0
15+
2022-03-14 00:00:00+00:00,543.552612304688,561.447326660156,538.026306152344,555.789428710938,404.886749267578,42128265,0,0
16+
2022-03-21 00:00:00+00:00,556.184204101563,582.5,555.789428710938,576.052612304688,419.648284912109,30321228,0,0
17+
2022-03-28 00:00:00+01:00,582.23681640625,606.578918457031,579.076293945313,585,426.166320800781,42874272,0,0
18+
2022-04-04 00:00:00+01:00,578.552612304688,586.052612304688,555.263122558594,560.263122558594,408.145812988281,37556036,19.342106,0
19+
2022-04-11 00:00:00+01:00,559.73681640625,573.157775878906,553.684204101563,571.447326660156,430.541351318359,22705062,0,0
20+
2022-04-18 00:00:00+01:00,571.447326660156,589.210510253906,570.526306152344,576.052612304688,434.011077880859,37459087,0,0
21+
2022-04-25 00:00:00+01:00,566.710510253906,577.368408203125,557.894714355469,570.131469726563,429.549957275391,33939150,0,0
22+
2022-05-02 00:00:00+01:00,570.131469726563,593.421020507813,545.394714355469,545.921020507813,411.309234619141,22449007,0,0
23+
2022-05-09 00:00:00+01:00,543.684204101563,549.276306152344,484.582794189453,537.105224609375,404.667236328125,56232105,0,0
24+
2022-05-16 00:00:00+01:00,505.263157894737,557.368429083573,505.263157894737,542.236850136205,408.533517937911,62273078.36,101.69,0.76
2525
2022-05-23 00:00:00+01:00,416.100006103516,442.399993896484,341.915008544922,440.899993896484,409.764678955078,45432941,0,0
2626
2022-05-30 00:00:00+01:00,442.700012207031,444.200012207031,426.600006103516,428.700012207031,398.426239013672,37906659,0,0
2727
2022-06-06 00:00:00+01:00,425.299987792969,434.010009765625,405.200012207031,405.399993896484,376.771606445313,40648810,0,0

‎tests/test_prices.py

Lines changed: 5 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -359,13 +359,6 @@ def test_monthlyWithEvents2(self):
359359
dfd_divs = dfd[dfd['Dividends'] != 0]
360360
self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
361361

362-
dfm = yf.Ticker("F").history(period="50mo", interval="1mo")
363-
dfd = yf.Ticker("F").history(period="50mo", interval="1d")
364-
dfd = dfd[dfd.index > dfm.index[0]]
365-
dfm_divs = dfm[dfm['Dividends'] != 0]
366-
dfd_divs = dfd[dfd['Dividends'] != 0]
367-
self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
368-
369362
def test_tz_dst_ambiguous(self):
370363
# Reproduce issue #1100
371364
try:
@@ -791,7 +784,7 @@ def test_repair_zeroes_hourly(self):
791784
tz_exchange = dat.fast_info["timezone"]
792785
hist = dat._lazy_load_price_history()
793786

794-
correct_df = hist.history(period="1wk", interval="1h", auto_adjust=False, repair=True)
787+
correct_df = hist.history(period="5d", interval="1h", auto_adjust=False, repair=True)
795788

796789
df_bad = correct_df.copy()
797790
bad_idx = correct_df.index[10]
@@ -820,7 +813,7 @@ def test_repair_zeroes_hourly(self):
820813
self.assertTrue("Repaired?" in repaired_df.columns)
821814
self.assertFalse(repaired_df["Repaired?"].isna().any())
822815

823-
def test_repair_bad_stock_split(self):
816+
def test_repair_bad_stock_splits(self):
824817
# Stocks that split in 2022 but no problems in Yahoo data,
825818
# so repair should change nothing
826819
good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
@@ -836,7 +829,7 @@ def test_repair_bad_stock_split(self):
836829
_dp = os.path.dirname(__file__)
837830
df_good = dat.history(start='2020-01-01', end=_dt.date.today(), interval=interval, auto_adjust=False)
838831

839-
repaired_df = hist._fix_bad_stock_split(df_good, interval, tz_exchange)
832+
repaired_df = hist._fix_bad_stock_splits(df_good, interval, tz_exchange)
840833

841834
# Expect no change from repair
842835
df_good = df_good.sort_index()
@@ -867,7 +860,7 @@ def test_repair_bad_stock_split(self):
867860
df_bad = _pd.read_csv(fp, index_col="Date")
868861
df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
869862

870-
repaired_df = hist._fix_bad_stock_split(df_bad, "1d", tz_exchange)
863+
repaired_df = hist._fix_bad_stock_splits(df_bad, "1d", tz_exchange)
871864

872865
fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split-fixed.csv")
873866
correct_df = _pd.read_csv(fp, index_col="Date")
@@ -902,7 +895,7 @@ def test_repair_bad_stock_split(self):
902895
_dp = os.path.dirname(__file__)
903896
df_good = hist.history(start='2020-11-30', end='2021-04-01', interval=interval, auto_adjust=False)
904897

905-
repaired_df = hist._fix_bad_stock_split(df_good, interval, tz_exchange)
898+
repaired_df = hist._fix_bad_stock_splits(df_good, interval, tz_exchange)
906899

907900
# Expect no change from repair
908901
df_good = df_good.sort_index()

‎tests/test_ticker.py

Lines changed: 13 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -12,7 +12,7 @@
1212

1313
from .context import yfinance as yf
1414
from .context import session_gbl
15-
from yfinance.exceptions import YFChartError, YFInvalidPeriodError, YFNotImplementedError, YFPricesMissingError, YFTickerMissingError, YFTzMissingError
15+
from yfinance.exceptions import YFChartError, YFInvalidPeriodError, YFNotImplementedError, YFTickerMissingError, YFTzMissingError
1616

1717

1818
import unittest
@@ -100,13 +100,13 @@ def test_badTicker(self):
100100
tkr = "DJI" # typo of "^DJI"
101101
dat = yf.Ticker(tkr, session=self.session)
102102

103-
dat.history(period="1wk")
103+
dat.history(period="5d")
104104
dat.history(start="2022-01-01")
105105
dat.history(start="2022-01-01", end="2022-03-01")
106-
yf.download([tkr], period="1wk", threads=False, ignore_tz=False)
107-
yf.download([tkr], period="1wk", threads=True, ignore_tz=False)
108-
yf.download([tkr], period="1wk", threads=False, ignore_tz=True)
109-
yf.download([tkr], period="1wk", threads=True, ignore_tz=True)
106+
yf.download([tkr], period="5d", threads=False, ignore_tz=False)
107+
yf.download([tkr], period="5d", threads=True, ignore_tz=False)
108+
yf.download([tkr], period="5d", threads=False, ignore_tz=True)
109+
yf.download([tkr], period="5d", threads=True, ignore_tz=True)
110110

111111
for k in dat.fast_info:
112112
dat.fast_info[k]
@@ -144,7 +144,7 @@ def test_prices_missing(self):
144144
# META call option, 2024 April 26th @ strike of 180000
145145
tkr = 'META240426C00180000'
146146
dat = yf.Ticker(tkr, session=self.session)
147-
with self.assertRaises(YFPricesMissingError):
147+
with self.assertRaises(YFChartError):
148148
dat.history(period="5d", interval="1m", raise_errors=True)
149149

150150
def test_ticker_missing(self):
@@ -162,13 +162,13 @@ def test_goodTicker(self):
162162
for tkr in tkrs:
163163
dat = yf.Ticker(tkr, session=self.session)
164164

165-
dat.history(period="1wk")
165+
dat.history(period="5d")
166166
dat.history(start="2022-01-01")
167167
dat.history(start="2022-01-01", end="2022-03-01")
168-
yf.download([tkr], period="1wk", threads=False, ignore_tz=False)
169-
yf.download([tkr], period="1wk", threads=True, ignore_tz=False)
170-
yf.download([tkr], period="1wk", threads=False, ignore_tz=True)
171-
yf.download([tkr], period="1wk", threads=True, ignore_tz=True)
168+
yf.download([tkr], period="5d", threads=False, ignore_tz=False)
169+
yf.download([tkr], period="5d", threads=True, ignore_tz=False)
170+
yf.download([tkr], period="5d", threads=False, ignore_tz=True)
171+
yf.download([tkr], period="5d", threads=True, ignore_tz=True)
172172

173173
for k in dat.fast_info:
174174
dat.fast_info[k]
@@ -182,7 +182,7 @@ def test_goodTicker_withProxy(self):
182182

183183
dat._fetch_ticker_tz(proxy=None, timeout=5)
184184
dat._get_ticker_tz(proxy=None, timeout=5)
185-
dat.history(period="1wk")
185+
dat.history(period="5d")
186186

187187
for attribute_name, attribute_type in ticker_attributes:
188188
assert_attribute_type(self, dat, attribute_name, attribute_type)

‎yfinance/scrapers/history.py

Lines changed: 19 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -1204,14 +1204,25 @@ def _fix_bad_stock_splits(self, df, interval, tz_exchange):
12041204
logger.debug('price-repair-split: No splits in data')
12051205
return df
12061206

1207+
logger.debug(f'price-repair-split: Splits: {str(df['Stock Splits'][split_f].to_dict())}')
1208+
1209+
if not 'Repaired?' in df.columns:
1210+
df['Repaired?'] = False
12071211
for split_idx in np.where(split_f)[0]:
12081212
split_dt = df.index[split_idx]
12091213
split = df.loc[split_dt, 'Stock Splits']
12101214
if split_dt == df.index[0]:
12111215
continue
12121216

1213-
cutoff_idx = min(df.shape[0], split_idx+1) # add one row after to detect big change
1217+
# Add on a week:
1218+
if interval in ['1wk', '1mo', '3mo']:
1219+
split_idx += 1
1220+
else:
1221+
split_idx += 5
1222+
cutoff_idx = min(df.shape[0], split_idx) # add one row after to detect big change
12141223
df_pre_split = df.iloc[0:cutoff_idx+1]
1224+
logger.debug(f'price-repair-split: split_idx={split_idx} split_dt={split_dt}')
1225+
logger.debug(f'price-repair-split: df dt range: {df_pre_split.index[0].date()} -> {df_pre_split.index[-1].date()}')
12151226

12161227
df_pre_split_repaired = self._fix_prices_sudden_change(df_pre_split, interval, tz_exchange, split, correct_volume=True)
12171228
# Merge back in:
@@ -1240,7 +1251,7 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
12401251
# start_min = 1 year before oldest split
12411252
f = df['Stock Splits'].to_numpy() != 0.0
12421253
start_min = (df.index[f].min() - _dateutil.relativedelta.relativedelta(years=1)).date()
1243-
logger.debug(f'price-repair-split: start_min={start_min}')
1254+
logger.debug(f'price-repair-split: start_min={start_min} change={change}')
12441255

12451256
OHLC = ['Open', 'High', 'Low', 'Close']
12461257

@@ -1438,8 +1449,13 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
14381449
# if logger.isEnabledFor(logging.DEBUG):
14391450
# df_debug['i'] = list(range(0, df_debug.shape[0]))
14401451
# df_debug['i_rev'] = df_debug.shape[0]-1 - df_debug['i']
1452+
# if correct_columns_individually:
1453+
# f_change = df_debug[[c+'_f_down' for c in debug_cols]].any(axis=1) | df_debug[[c+'_f_up' for c in debug_cols]].any(axis=1)
1454+
# else:
1455+
# f_change = df_debug['f_down'] | df_debug['f_up']
1456+
# f_change = f_change | np.roll(f_change, -1) | np.roll(f_change, 1) | np.roll(f_change, -2) | np.roll(f_change, 2)
14411457
# with pd.option_context('display.max_rows', None, 'display.max_columns', 10, 'display.width', 1000): # more options can be specified also
1442-
# logger.debug(f"price-repair-split: my workings:" + '\n' + str(df_debug))
1458+
# logger.debug(f"price-repair-split: my workings:" + '\n' + str(df_debug[f_change]))
14431459

14441460
def map_signals_to_ranges(f, f_up, f_down):
14451461
# Ensure 0th element is False, because True is nonsense

‎yfinance/scrapers/quote.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -181,7 +181,7 @@ def toJSON(self, indent=4):
181181

182182
def _get_1y_prices(self, fullDaysOnly=False):
183183
if self._prices_1y is None:
184-
self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, keepna=True, proxy=self.proxy)
184+
self._prices_1y = self._tkr.history(period="1y", auto_adjust=False, keepna=True, proxy=self.proxy)
185185
self._md = self._tkr.get_history_metadata(proxy=self.proxy)
186186
try:
187187
ctp = self._md["currentTradingPeriod"]
@@ -207,12 +207,12 @@ def _get_1y_prices(self, fullDaysOnly=False):
207207

208208
def _get_1wk_1h_prepost_prices(self):
209209
if self._prices_1wk_1h_prepost is None:
210-
self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True, proxy=self.proxy)
210+
self._prices_1wk_1h_prepost = self._tkr.history(period="5d", interval="1h", auto_adjust=False, prepost=True, proxy=self.proxy)
211211
return self._prices_1wk_1h_prepost
212212

213213
def _get_1wk_1h_reg_prices(self):
214214
if self._prices_1wk_1h_reg is None:
215-
self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False, proxy=self.proxy)
215+
self._prices_1wk_1h_reg = self._tkr.history(period="5d", interval="1h", auto_adjust=False, prepost=False, proxy=self.proxy)
216216
return self._prices_1wk_1h_reg
217217

218218
def _get_exchange_metadata(self):

‎yfinance/ticker.py

Lines changed: 1 addition & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -48,8 +48,7 @@ def _download_options(self, date=None):
4848
r = self._data.get(url=url, proxy=self.proxy).json()
4949
if len(r.get('optionChain', {}).get('result', [])) > 0:
5050
for exp in r['optionChain']['result'][0]['expirationDates']:
51-
self._expirations[_datetime.datetime.utcfromtimestamp(
52-
exp).strftime('%Y-%m-%d')] = exp
51+
self._expirations[_pd.Timestamp(exp, unit='s').strftime('%Y-%m-%d')] = exp
5352

5453
self._underlying = r['optionChain']['result'][0].get('quote', {})
5554

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