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Fix tests ; Fine-tune split repair ; Fix UTC warning

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This commit is contained in:
ValueRaider
2024-05-19 14:57:05 +01:00
parent da1c466550
commit f3c9f9962d
7 changed files with 64 additions and 56 deletions
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tests/__init__.py
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tests/data/AV-L-1wk-bad-stock-split-fixed.csv
+23 -23
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@@ -1,27 +1,27 @@
Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
2021-12-13 00:00:00+00:00,393.999975585938,406.6,391.4,402.899916992188,291.232287597656,62714764.4736842,0,0
2021-12-20 00:00:00+00:00,393.999975585938,412.199990234375,392.502983398438,409.899997558594,296.292243652344,46596651.3157895,0,0
2021-12-27 00:00:00+00:00,409.899997558594,416.550971679688,408.387001953125,410.4,296.653642578125,10818482.8947368,0,0
2022-01-03 00:00:00+00:00,410.4,432.199995117188,410.4,432.099985351563,312.339265136719,44427327.6315789,0,0
2022-01-10 00:00:00+00:00,431.3,439.199982910156,429.099970703125,436.099912109375,315.230618896484,29091400,0,0
2022-01-17 00:00:00+00:00,437.999912109375,445.199965820313,426.999997558594,431.999975585938,312.267017822266,43787351.3157895,0,0
2022-01-24 00:00:00+00:00,430.099975585938,440.999973144531,420.999968261719,433.499982910156,313.351237792969,58487296.0526316,0,0
2022-01-31 00:00:00+00:00,436.199968261719,443.049987792969,432.099985351563,435.199916992188,314.580045166016,43335806.5789474,0,0
2022-02-07 00:00:00+00:00,437.899995117188,448.799992675781,436.051994628906,444.39998046875,321.230207519531,39644061.8421053,0,0
2022-02-14 00:00:00+00:00,437.699975585938,441.999978027344,426.699968261719,432.199995117188,312.411558837891,49972693.4210526,0,0
2022-02-21 00:00:00+00:00,435.499992675781,438.476999511719,408.29998046875,423.399970703125,306.050571289063,65719596.0526316,0,0
2022-02-28 00:00:00+00:00,415.099995117188,427.999909667969,386.199932861328,386.799945068359,279.594578857422,94057936.8421053,4.1875,0
2022-03-07 00:00:00+00:00,374.999952392578,417.299978027344,361.101981201172,409.599968261719,298.389248046875,71269101.3157895,0,0
2022-03-14 00:00:00+00:00,413.099985351563,426.699968261719,408.899992675781,422.399965820313,307.713929443359,55431927.6315789,0,0
2022-03-21 00:00:00+00:00,422.699995117188,442.7,422.399965820313,437.799985351563,318.932696533203,39896352.6315789,0,0
2022-03-28 00:00:00+01:00,442.49998046875,460.999978027344,440.097983398438,444.6,323.886403808594,56413515.7894737,0,0
2022-04-04 00:00:00+01:00,439.699985351563,445.399985351563,421.999973144531,425.799973144531,310.190817871094,49415836.8421053,19.342106,0
2022-04-11 00:00:00+01:00,425.39998046875,435.599909667969,420.799995117188,434.299968261719,327.211427001953,29875081.5789474,0,0
2022-04-18 00:00:00+01:00,434.299968261719,447.799987792969,433.599992675781,437.799985351563,329.848419189453,49288272.3684211,0,0
2022-04-25 00:00:00+01:00,430.699987792969,438.799990234375,423.999982910156,433.299916992188,326.457967529297,44656776.3157895,0,0
2022-05-02 00:00:00+01:00,433.299916992188,450.999975585938,414.499982910156,414.899975585938,312.595018310547,29538167.1052632,0,0
2022-05-09 00:00:00+01:00,413.199995117188,417.449992675781,368.282923583984,408.199970703125,307.547099609375,73989611.8421053,0,0
2022-05-16 00:00:00+01:00,384,423.600006103516,384,412.100006103516,310.485473632813,81938261,101.69,0.76
2021-12-13 00:00:00+00:00,518.421020507813,535,515,530.131469726563,383.200378417969,47663221,0,0
2021-12-20 00:00:00+00:00,518.421020507813,542.368408203125,516.451293945313,539.342102050781,389.858215332031,35413455,0,0
2021-12-27 00:00:00+00:00,539.342102050781,548.093383789063,537.351318359375,540,390.333740234375,8222047,0,0
2022-01-03 00:00:00+00:00,540,568.684204101563,540,568.552612304688,410.972717285156,33764769,0,0
2022-01-10 00:00:00+00:00,567.5,577.894714355469,564.605224609375,573.815673828125,414.777130126953,22109464,0,0
2022-01-17 00:00:00+00:00,576.315673828125,585.789428710938,561.842102050781,568.421020507813,410.877655029297,33278387,0,0
2022-01-24 00:00:00+00:00,565.921020507813,580.263122558594,553.947326660156,570.394714355469,412.304260253906,44450345,0,0
2022-01-31 00:00:00+00:00,573.947326660156,582.960510253906,568.552612304688,572.631469726563,413.921112060547,32935213,0,0
2022-02-07 00:00:00+00:00,576.184204101563,590.526306152344,573.752624511719,584.73681640625,422.671325683594,30129487,0,0
2022-02-14 00:00:00+00:00,575.921020507813,581.578918457031,561.447326660156,568.684204101563,411.067840576172,37979247,0,0
2022-02-21 00:00:00+00:00,573.026306152344,576.943420410156,537.23681640625,557.105224609375,402.698120117188,49946893,0,0
2022-02-28 00:00:00+00:00,546.184204101563,563.157775878906,508.157806396484,508.947296142578,367.887603759766,71484032,4.1875,0
2022-03-07 00:00:00+00:00,493.420989990234,549.078918457031,475.134185791016,538.947326660156,392.617431640625,54164517,0,0
2022-03-14 00:00:00+00:00,543.552612304688,561.447326660156,538.026306152344,555.789428710938,404.886749267578,42128265,0,0
2022-03-21 00:00:00+00:00,556.184204101563,582.5,555.789428710938,576.052612304688,419.648284912109,30321228,0,0
2022-03-28 00:00:00+01:00,582.23681640625,606.578918457031,579.076293945313,585,426.166320800781,42874272,0,0
2022-04-04 00:00:00+01:00,578.552612304688,586.052612304688,555.263122558594,560.263122558594,408.145812988281,37556036,19.342106,0
2022-04-11 00:00:00+01:00,559.73681640625,573.157775878906,553.684204101563,571.447326660156,430.541351318359,22705062,0,0
2022-04-18 00:00:00+01:00,571.447326660156,589.210510253906,570.526306152344,576.052612304688,434.011077880859,37459087,0,0
2022-04-25 00:00:00+01:00,566.710510253906,577.368408203125,557.894714355469,570.131469726563,429.549957275391,33939150,0,0
2022-05-02 00:00:00+01:00,570.131469726563,593.421020507813,545.394714355469,545.921020507813,411.309234619141,22449007,0,0
2022-05-09 00:00:00+01:00,543.684204101563,549.276306152344,484.582794189453,537.105224609375,404.667236328125,56232105,0,0
2022-05-16 00:00:00+01:00,505.263157894737,557.368429083573,505.263157894737,542.236850136205,408.533517937911,62273078.36,101.69,0.76
2022-05-23 00:00:00+01:00,416.100006103516,442.399993896484,341.915008544922,440.899993896484,409.764678955078,45432941,0,0
2022-05-30 00:00:00+01:00,442.700012207031,444.200012207031,426.600006103516,428.700012207031,398.426239013672,37906659,0,0
2022-06-06 00:00:00+01:00,425.299987792969,434.010009765625,405.200012207031,405.399993896484,376.771606445313,40648810,0,0
1 Date Open High Low Close Adj Close Volume Dividends Stock Splits
2 2021-12-13 00:00:00+00:00 393.999975585938 518.421020507813 406.6 535 391.4 515 402.899916992188 530.131469726563 291.232287597656 383.200378417969 62714764.4736842 47663221 0 0
3 2021-12-20 00:00:00+00:00 393.999975585938 518.421020507813 412.199990234375 542.368408203125 392.502983398438 516.451293945313 409.899997558594 539.342102050781 296.292243652344 389.858215332031 46596651.3157895 35413455 0 0
4 2021-12-27 00:00:00+00:00 409.899997558594 539.342102050781 416.550971679688 548.093383789063 408.387001953125 537.351318359375 410.4 540 296.653642578125 390.333740234375 10818482.8947368 8222047 0 0
5 2022-01-03 00:00:00+00:00 410.4 540 432.199995117188 568.684204101563 410.4 540 432.099985351563 568.552612304688 312.339265136719 410.972717285156 44427327.6315789 33764769 0 0
6 2022-01-10 00:00:00+00:00 431.3 567.5 439.199982910156 577.894714355469 429.099970703125 564.605224609375 436.099912109375 573.815673828125 315.230618896484 414.777130126953 29091400 22109464 0 0
7 2022-01-17 00:00:00+00:00 437.999912109375 576.315673828125 445.199965820313 585.789428710938 426.999997558594 561.842102050781 431.999975585938 568.421020507813 312.267017822266 410.877655029297 43787351.3157895 33278387 0 0
8 2022-01-24 00:00:00+00:00 430.099975585938 565.921020507813 440.999973144531 580.263122558594 420.999968261719 553.947326660156 433.499982910156 570.394714355469 313.351237792969 412.304260253906 58487296.0526316 44450345 0 0
9 2022-01-31 00:00:00+00:00 436.199968261719 573.947326660156 443.049987792969 582.960510253906 432.099985351563 568.552612304688 435.199916992188 572.631469726563 314.580045166016 413.921112060547 43335806.5789474 32935213 0 0
10 2022-02-07 00:00:00+00:00 437.899995117188 576.184204101563 448.799992675781 590.526306152344 436.051994628906 573.752624511719 444.39998046875 584.73681640625 321.230207519531 422.671325683594 39644061.8421053 30129487 0 0
11 2022-02-14 00:00:00+00:00 437.699975585938 575.921020507813 441.999978027344 581.578918457031 426.699968261719 561.447326660156 432.199995117188 568.684204101563 312.411558837891 411.067840576172 49972693.4210526 37979247 0 0
12 2022-02-21 00:00:00+00:00 435.499992675781 573.026306152344 438.476999511719 576.943420410156 408.29998046875 537.23681640625 423.399970703125 557.105224609375 306.050571289063 402.698120117188 65719596.0526316 49946893 0 0
13 2022-02-28 00:00:00+00:00 415.099995117188 546.184204101563 427.999909667969 563.157775878906 386.199932861328 508.157806396484 386.799945068359 508.947296142578 279.594578857422 367.887603759766 94057936.8421053 71484032 4.1875 0
14 2022-03-07 00:00:00+00:00 374.999952392578 493.420989990234 417.299978027344 549.078918457031 361.101981201172 475.134185791016 409.599968261719 538.947326660156 298.389248046875 392.617431640625 71269101.3157895 54164517 0 0
15 2022-03-14 00:00:00+00:00 413.099985351563 543.552612304688 426.699968261719 561.447326660156 408.899992675781 538.026306152344 422.399965820313 555.789428710938 307.713929443359 404.886749267578 55431927.6315789 42128265 0 0
16 2022-03-21 00:00:00+00:00 422.699995117188 556.184204101563 442.7 582.5 422.399965820313 555.789428710938 437.799985351563 576.052612304688 318.932696533203 419.648284912109 39896352.6315789 30321228 0 0
17 2022-03-28 00:00:00+01:00 442.49998046875 582.23681640625 460.999978027344 606.578918457031 440.097983398438 579.076293945313 444.6 585 323.886403808594 426.166320800781 56413515.7894737 42874272 0 0
18 2022-04-04 00:00:00+01:00 439.699985351563 578.552612304688 445.399985351563 586.052612304688 421.999973144531 555.263122558594 425.799973144531 560.263122558594 310.190817871094 408.145812988281 49415836.8421053 37556036 19.342106 0
19 2022-04-11 00:00:00+01:00 425.39998046875 559.73681640625 435.599909667969 573.157775878906 420.799995117188 553.684204101563 434.299968261719 571.447326660156 327.211427001953 430.541351318359 29875081.5789474 22705062 0 0
20 2022-04-18 00:00:00+01:00 434.299968261719 571.447326660156 447.799987792969 589.210510253906 433.599992675781 570.526306152344 437.799985351563 576.052612304688 329.848419189453 434.011077880859 49288272.3684211 37459087 0 0
21 2022-04-25 00:00:00+01:00 430.699987792969 566.710510253906 438.799990234375 577.368408203125 423.999982910156 557.894714355469 433.299916992188 570.131469726563 326.457967529297 429.549957275391 44656776.3157895 33939150 0 0
22 2022-05-02 00:00:00+01:00 433.299916992188 570.131469726563 450.999975585938 593.421020507813 414.499982910156 545.394714355469 414.899975585938 545.921020507813 312.595018310547 411.309234619141 29538167.1052632 22449007 0 0
23 2022-05-09 00:00:00+01:00 413.199995117188 543.684204101563 417.449992675781 549.276306152344 368.282923583984 484.582794189453 408.199970703125 537.105224609375 307.547099609375 404.667236328125 73989611.8421053 56232105 0 0
24 2022-05-16 00:00:00+01:00 384 505.263157894737 423.600006103516 557.368429083573 384 505.263157894737 412.100006103516 542.236850136205 310.485473632813 408.533517937911 81938261 62273078.36 101.69 0.76
25 2022-05-23 00:00:00+01:00 416.100006103516 442.399993896484 341.915008544922 440.899993896484 409.764678955078 45432941 0 0
26 2022-05-30 00:00:00+01:00 442.700012207031 444.200012207031 426.600006103516 428.700012207031 398.426239013672 37906659 0 0
27 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
+5 -12
View File
@@ -359,13 +359,6 @@ class TestPriceHistory(unittest.TestCase):
dfd_divs = dfd[dfd['Dividends'] != 0]
self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
dfm = yf.Ticker("F").history(period="50mo", interval="1mo")
dfd = yf.Ticker("F").history(period="50mo", interval="1d")
dfd = dfd[dfd.index > dfm.index[0]]
dfm_divs = dfm[dfm['Dividends'] != 0]
dfd_divs = dfd[dfd['Dividends'] != 0]
self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
def test_tz_dst_ambiguous(self):
# Reproduce issue #1100
try:
@@ -791,7 +784,7 @@ class TestPriceRepair(unittest.TestCase):
tz_exchange = dat.fast_info["timezone"]
hist = dat._lazy_load_price_history()
correct_df = hist.history(period="1wk", interval="1h", auto_adjust=False, repair=True)
correct_df = hist.history(period="5d", interval="1h", auto_adjust=False, repair=True)
df_bad = correct_df.copy()
bad_idx = correct_df.index[10]
@@ -820,7 +813,7 @@ class TestPriceRepair(unittest.TestCase):
self.assertTrue("Repaired?" in repaired_df.columns)
self.assertFalse(repaired_df["Repaired?"].isna().any())
def test_repair_bad_stock_split(self):
def test_repair_bad_stock_splits(self):
# Stocks that split in 2022 but no problems in Yahoo data,
# so repair should change nothing
good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
@@ -836,7 +829,7 @@ class TestPriceRepair(unittest.TestCase):
_dp = os.path.dirname(__file__)
df_good = dat.history(start='2020-01-01', end=_dt.date.today(), interval=interval, auto_adjust=False)
repaired_df = hist._fix_bad_stock_split(df_good, interval, tz_exchange)
repaired_df = hist._fix_bad_stock_splits(df_good, interval, tz_exchange)
# Expect no change from repair
df_good = df_good.sort_index()
@@ -867,7 +860,7 @@ class TestPriceRepair(unittest.TestCase):
df_bad = _pd.read_csv(fp, index_col="Date")
df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
repaired_df = hist._fix_bad_stock_split(df_bad, "1d", tz_exchange)
repaired_df = hist._fix_bad_stock_splits(df_bad, "1d", tz_exchange)
fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split-fixed.csv")
correct_df = _pd.read_csv(fp, index_col="Date")
@@ -902,7 +895,7 @@ class TestPriceRepair(unittest.TestCase):
_dp = os.path.dirname(__file__)
df_good = hist.history(start='2020-11-30', end='2021-04-01', interval=interval, auto_adjust=False)
repaired_df = hist._fix_bad_stock_split(df_good, interval, tz_exchange)
repaired_df = hist._fix_bad_stock_splits(df_good, interval, tz_exchange)
# Expect no change from repair
df_good = df_good.sort_index()
tests/test_ticker.py
+13 -13
View File
@@ -12,7 +12,7 @@ import pandas as pd
from .context import yfinance as yf
from .context import session_gbl
from yfinance.exceptions import YFChartError, YFInvalidPeriodError, YFNotImplementedError, YFPricesMissingError, YFTickerMissingError, YFTzMissingError
from yfinance.exceptions import YFChartError, YFInvalidPeriodError, YFNotImplementedError, YFTickerMissingError, YFTzMissingError
import unittest
@@ -100,13 +100,13 @@ class TestTicker(unittest.TestCase):
tkr = "DJI" # typo of "^DJI"
dat = yf.Ticker(tkr, session=self.session)
dat.history(period="1wk")
dat.history(period="5d")
dat.history(start="2022-01-01")
dat.history(start="2022-01-01", end="2022-03-01")
yf.download([tkr], period="1wk", threads=False, ignore_tz=False)
yf.download([tkr], period="1wk", threads=True, ignore_tz=False)
yf.download([tkr], period="1wk", threads=False, ignore_tz=True)
yf.download([tkr], period="1wk", threads=True, ignore_tz=True)
yf.download([tkr], period="5d", threads=False, ignore_tz=False)
yf.download([tkr], period="5d", threads=True, ignore_tz=False)
yf.download([tkr], period="5d", threads=False, ignore_tz=True)
yf.download([tkr], period="5d", threads=True, ignore_tz=True)
for k in dat.fast_info:
dat.fast_info[k]
@@ -144,7 +144,7 @@ class TestTicker(unittest.TestCase):
# META call option, 2024 April 26th @ strike of 180000
tkr = 'META240426C00180000'
dat = yf.Ticker(tkr, session=self.session)
with self.assertRaises(YFPricesMissingError):
with self.assertRaises(YFChartError):
dat.history(period="5d", interval="1m", raise_errors=True)
def test_ticker_missing(self):
@@ -162,13 +162,13 @@ class TestTicker(unittest.TestCase):
for tkr in tkrs:
dat = yf.Ticker(tkr, session=self.session)
dat.history(period="1wk")
dat.history(period="5d")
dat.history(start="2022-01-01")
dat.history(start="2022-01-01", end="2022-03-01")
yf.download([tkr], period="1wk", threads=False, ignore_tz=False)
yf.download([tkr], period="1wk", threads=True, ignore_tz=False)
yf.download([tkr], period="1wk", threads=False, ignore_tz=True)
yf.download([tkr], period="1wk", threads=True, ignore_tz=True)
yf.download([tkr], period="5d", threads=False, ignore_tz=False)
yf.download([tkr], period="5d", threads=True, ignore_tz=False)
yf.download([tkr], period="5d", threads=False, ignore_tz=True)
yf.download([tkr], period="5d", threads=True, ignore_tz=True)
for k in dat.fast_info:
dat.fast_info[k]
@@ -182,7 +182,7 @@ class TestTicker(unittest.TestCase):
dat._fetch_ticker_tz(proxy=None, timeout=5)
dat._get_ticker_tz(proxy=None, timeout=5)
dat.history(period="1wk")
dat.history(period="5d")
for attribute_name, attribute_type in ticker_attributes:
assert_attribute_type(self, dat, attribute_name, attribute_type)
yfinance/scrapers/history.py
+19 -3
View File
@@ -1204,14 +1204,25 @@ class PriceHistory:
logger.debug('price-repair-split: No splits in data')
return df
logger.debug(f'price-repair-split: Splits: {str(df['Stock Splits'][split_f].to_dict())}')
if not 'Repaired?' in df.columns:
df['Repaired?'] = False
for split_idx in np.where(split_f)[0]:
split_dt = df.index[split_idx]
split = df.loc[split_dt, 'Stock Splits']
if split_dt == df.index[0]:
continue
cutoff_idx = min(df.shape[0], split_idx+1) # add one row after to detect big change
# Add on a week:
if interval in ['1wk', '1mo', '3mo']:
split_idx += 1
else:
split_idx += 5
cutoff_idx = min(df.shape[0], split_idx) # add one row after to detect big change
df_pre_split = df.iloc[0:cutoff_idx+1]
logger.debug(f'price-repair-split: split_idx={split_idx} split_dt={split_dt}')
logger.debug(f'price-repair-split: df dt range: {df_pre_split.index[0].date()} -> {df_pre_split.index[-1].date()}')
df_pre_split_repaired = self._fix_prices_sudden_change(df_pre_split, interval, tz_exchange, split, correct_volume=True)
# Merge back in:
@@ -1240,7 +1251,7 @@ class PriceHistory:
# start_min = 1 year before oldest split
f = df['Stock Splits'].to_numpy() != 0.0
start_min = (df.index[f].min() - _dateutil.relativedelta.relativedelta(years=1)).date()
logger.debug(f'price-repair-split: start_min={start_min}')
logger.debug(f'price-repair-split: start_min={start_min} change={change}')
OHLC = ['Open', 'High', 'Low', 'Close']
@@ -1438,8 +1449,13 @@ class PriceHistory:
# 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']
# if correct_columns_individually:
# 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)
# else:
# f_change = df_debug['f_down'] | df_debug['f_up']
# f_change = f_change | np.roll(f_change, -1) | np.roll(f_change, 1) | np.roll(f_change, -2) | np.roll(f_change, 2)
# with pd.option_context('display.max_rows', None, 'display.max_columns', 10, 'display.width', 1000): # more options can be specified also
# logger.debug(f"price-repair-split: my workings:" + '\n' + str(df_debug))
# logger.debug(f"price-repair-split: my workings:" + '\n' + str(df_debug[f_change]))
def map_signals_to_ranges(f, f_up, f_down):
# Ensure 0th element is False, because True is nonsense
yfinance/scrapers/quote.py
+3 -3
View File
@@ -181,7 +181,7 @@ class FastInfo:
def _get_1y_prices(self, fullDaysOnly=False):
if self._prices_1y is None:
self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, keepna=True, proxy=self.proxy)
self._prices_1y = self._tkr.history(period="1y", auto_adjust=False, keepna=True, proxy=self.proxy)
self._md = self._tkr.get_history_metadata(proxy=self.proxy)
try:
ctp = self._md["currentTradingPeriod"]
@@ -207,12 +207,12 @@ class FastInfo:
def _get_1wk_1h_prepost_prices(self):
if self._prices_1wk_1h_prepost is None:
self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True, proxy=self.proxy)
self._prices_1wk_1h_prepost = self._tkr.history(period="5d", interval="1h", auto_adjust=False, prepost=True, proxy=self.proxy)
return self._prices_1wk_1h_prepost
def _get_1wk_1h_reg_prices(self):
if self._prices_1wk_1h_reg is None:
self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False, proxy=self.proxy)
self._prices_1wk_1h_reg = self._tkr.history(period="5d", interval="1h", auto_adjust=False, prepost=False, proxy=self.proxy)
return self._prices_1wk_1h_reg
def _get_exchange_metadata(self):
yfinance/ticker.py
+1 -2
View File
@@ -48,8 +48,7 @@ class Ticker(TickerBase):
r = self._data.get(url=url, proxy=self.proxy).json()
if len(r.get('optionChain', {}).get('result', [])) > 0:
for exp in r['optionChain']['result'][0]['expirationDates']:
self._expirations[_datetime.datetime.utcfromtimestamp(
exp).strftime('%Y-%m-%d')] = exp
self._expirations[_pd.Timestamp(exp, unit='s').strftime('%Y-%m-%d')] = exp
self._underlying = r['optionChain']['result'][0].get('quote', {})