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Merge pull request #1817 from ranaroussi/main

Browse Source 
sync main -> dev
This commit is contained in:
ValueRaider
2024-01-07 18:39:10 +00:00
committed by GitHub
parent 477dc6e6c4 acbd2a8d78
commit 10961905b6
11 changed files with 208 additions and 190 deletions
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28
CHANGELOG.rst
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@@ -1,6 +1,34 @@
Change Log
===========
0.2.35
------
Internal fixes for 0.2.34
0.2.34
------
Features:
- Add Recommendations Trend Summary #1754
- Add Recommendation upgrades & downgrades #1773
- Add Insider Roster & Transactions #1772
- Moved download() progress bar to STDERR #1776
- PIP optional dependencies #1771
- Set sensible min versions for optional 'nospam' reqs #1807
Fixes
- Fix download() DatetimeIndex on invalid symbols #1779
- Fix invalid date entering cache DB #1796
- Fix Ticker.calendar fetch #1790
- Fixed adding complementary to info #1774
- Ticker.earnings_dates: fix warning "Value 'NaN' has dtype incompatible with float64" #1810
- Minor fixes for price repair and related tests #1768
- Fix price repair div adjust #1798
- Fix 'raise_errors' argument ignored in Ticker.history() #1806
Maintenance
- Fix regression: _get_ticker_tz() args were being swapped. Improve its unit test #1793
- Refactor Ticker proxy #1711
- Add Ruff linter checks #1756
- Resolve Pandas FutureWarnings #1766
0.2.33
------
Cookie fixes:

15
README.md
View File

@@ -111,6 +111,11 @@ msft.insider_transactions
msft.insider_purchases
msft.insider_roster_holders
# show recommendations
msft.recommendations
msft.recommendations_summary
msft.upgrades_downgrades
# Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default.
# Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.
msft.earnings_dates
@@ -240,11 +245,13 @@ yf.pdr_override() # <== that's all it takes :-)
data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")
```
### Timezone cache store
### Persistent cache store
To reduce Yahoo, yfinance store some data locally: timezones to localize dates, and cookie. Cache location is:
- Windows = C:/Users/\<USER\>/AppData/Local/py-yfinance
- Linux = /home/\<USER\>/.cache/py-yfinance
- MacOS = /Users/\<USER\>/Library/Caches/py-yfinance
When fetching price data, all dates are localized to stock exchange timezone.
But timezone retrieval is relatively slow, so yfinance attemps to cache them
in your users cache folder.
You can direct cache to use a different location with `set_tz_cache_location()`:
```python
import yfinance as yf

2
meta.yaml
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@@ -1,5 +1,5 @@
{% set name = "yfinance" %}
{% set version = "0.2.33" %}
{% set version = "0.2.35" %}
package:
name: "{{ name|lower }}"

103
tests/prices.py
View File

@@ -43,6 +43,18 @@ class TestPriceHistory(unittest.TestCase):
df_tkrs = df.columns.levels[1]
self.assertEqual(sorted(tkrs), sorted(df_tkrs))
def test_download_with_invalid_ticker(self):
#Checks if using an invalid symbol gives the same output as not using an invalid symbol in combination with a valid symbol (AAPL)
#Checks to make sure that invalid symbol handling for the date column is the same as the base case (no invalid symbols)
invalid_tkrs = ["AAPL", "ATVI"] #AAPL exists and ATVI does not exist
valid_tkrs = ["AAPL", "INTC"] #AAPL and INTC both exist
data_invalid_sym = yf.download(invalid_tkrs, start='2023-11-16', end='2023-11-17')
data_valid_sym = yf.download(valid_tkrs, start='2023-11-16', end='2023-11-17')
self.assertEqual(data_invalid_sym['Close']['AAPL']['2023-11-16'],data_valid_sym['Close']['AAPL']['2023-11-16'])
def test_duplicatingHourly(self):
tkrs = ["IMP.JO", "BHG.JO", "SSW.JO", "BP.L", "INTC"]
@@ -387,24 +399,20 @@ class TestPriceHistory(unittest.TestCase):
raise
def test_prune_post_intraday_us(self):
# Half-day before USA Thanksgiving. Yahoo normally
# Half-day at USA Thanksgiving. Yahoo normally
# returns an interval starting when regular trading closes,
# even if prepost=False.
# Setup
tkr = "AMZN"
interval = "1h"
interval_td = _dt.timedelta(hours=1)
time_open = _dt.time(9, 30)
time_close = _dt.time(16)
special_day = _dt.date(2022, 11, 25)
special_day = _dt.date(2023, 11, 24)
time_early_close = _dt.time(13)
dat = yf.Ticker(tkr, session=self.session)
# Run
start_d = special_day - _dt.timedelta(days=7)
end_d = special_day + _dt.timedelta(days=7)
df = dat.history(start=start_d, end=end_d, interval=interval, prepost=False, keepna=True)
df = dat.history(start=start_d, end=end_d, interval="1h", prepost=False, keepna=True)
tg_last_dt = df.loc[str(special_day)].index[-1]
self.assertTrue(tg_last_dt.time() < time_early_close)
@@ -413,87 +421,22 @@ class TestPriceHistory(unittest.TestCase):
end_d = _dt.date(special_day.year+1, 1, 1)
df = dat.history(start=start_d, end=end_d, interval="1h", prepost=False, keepna=True)
last_dts = _pd.Series(df.index).groupby(df.index.date).last()
f_early_close = (last_dts+interval_td).dt.time < time_close
early_close_dates = last_dts.index[f_early_close].values
self.assertEqual(len(early_close_dates), 1)
self.assertEqual(early_close_dates[0], special_day)
first_dts = _pd.Series(df.index).groupby(df.index.date).first()
f_late_open = first_dts.dt.time > time_open
late_open_dates = first_dts.index[f_late_open]
self.assertEqual(len(late_open_dates), 0)
def test_prune_post_intraday_omx(self):
# Half-day before Sweden Christmas. Yahoo normally
# returns an interval starting when regular trading closes,
# even if prepost=False.
# If prepost=False, test that yfinance is removing prepost intervals.
# Setup
tkr = "AEC.ST"
interval = "1h"
interval_td = _dt.timedelta(hours=1)
time_open = _dt.time(9)
time_close = _dt.time(17, 30)
special_day = _dt.date(2022, 12, 23)
time_early_close = _dt.time(13, 2)
dat = yf.Ticker(tkr, session=self.session)
# Half trading day Jan 5, Apr 14, May 25, Jun 23, Nov 4, Dec 23, Dec 30
half_days = [_dt.date(special_day.year, x[0], x[1]) for x in [(1, 5), (4, 14), (5, 25), (6, 23), (11, 4), (12, 23), (12, 30)]]
# Yahoo has incorrectly classified afternoon of 2022-04-13 as post-market.
# Nothing yfinance can do because Yahoo doesn't return data with prepost=False.
# But need to handle in this test.
expected_incorrect_half_days = [_dt.date(2022, 4, 13)]
half_days = sorted(half_days+expected_incorrect_half_days)
# Run
start_d = special_day - _dt.timedelta(days=7)
end_d = special_day + _dt.timedelta(days=7)
df = dat.history(start=start_d, end=end_d, interval=interval, prepost=False, keepna=True)
tg_last_dt = df.loc[str(special_day)].index[-1]
self.assertTrue(tg_last_dt.time() < time_early_close)
# Test no other afternoons (or mornings) were pruned
start_d = _dt.date(special_day.year, 1, 1)
end_d = _dt.date(special_day.year+1, 1, 1)
df = dat.history(start=start_d, end=end_d, interval="1h", prepost=False, keepna=True)
last_dts = _pd.Series(df.index).groupby(df.index.date).last()
f_early_close = (last_dts+interval_td).dt.time < time_close
early_close_dates = last_dts.index[f_early_close].values
unexpected_early_close_dates = [d for d in early_close_dates if d not in half_days]
self.assertEqual(len(unexpected_early_close_dates), 0)
self.assertEqual(len(early_close_dates), len(half_days))
self.assertTrue(_np.equal(early_close_dates, half_days).all())
first_dts = _pd.Series(df.index).groupby(df.index.date).first()
f_late_open = first_dts.dt.time > time_open
late_open_dates = first_dts.index[f_late_open]
self.assertEqual(len(late_open_dates), 0)
dfd = dat.history(start=start_d, end=end_d, interval='1d', prepost=False, keepna=True)
self.assertTrue(_np.equal(dfd.index.date, _pd.to_datetime(last_dts.index).date).all())
def test_prune_post_intraday_asx(self):
# Setup
tkr = "BHP.AX"
interval_td = _dt.timedelta(hours=1)
time_open = _dt.time(10)
time_close = _dt.time(16, 12)
# No early closes in 2022
# No early closes in 2023
dat = yf.Ticker(tkr, session=self.session)
# Test no afternoons (or mornings) were pruned
start_d = _dt.date(2022, 1, 1)
end_d = _dt.date(2022+1, 1, 1)
# Test no other afternoons (or mornings) were pruned
start_d = _dt.date(2023, 1, 1)
end_d = _dt.date(2023+1, 1, 1)
df = dat.history(start=start_d, end=end_d, interval="1h", prepost=False, keepna=True)
last_dts = _pd.Series(df.index).groupby(df.index.date).last()
f_early_close = (last_dts+interval_td).dt.time < time_close
early_close_dates = last_dts.index[f_early_close].values
self.assertEqual(len(early_close_dates), 0)
first_dts = _pd.Series(df.index).groupby(df.index.date).first()
f_late_open = first_dts.dt.time > time_open
late_open_dates = first_dts.index[f_late_open]
self.assertEqual(len(late_open_dates), 0)
dfd = dat.history(start=start_d, end=end_d, interval='1d', prepost=False, keepna=True)
self.assertTrue(_np.equal(dfd.index.date, _pd.to_datetime(last_dts.index).date).all())
def test_weekly_2rows_fix(self):
tkr = "AMZN"

154
tests/ticker.py
View File

@@ -17,7 +17,7 @@ from yfinance.exceptions import YFNotImplementedError
import unittest
import requests_cache
from typing import Union, Any
from typing import Union, Any, get_args, _GenericAlias
from urllib.parse import urlparse, parse_qs, urlencode, urlunparse
ticker_attributes = (
@@ -31,11 +31,10 @@ ticker_attributes = (
("actions", pd.DataFrame),
("shares", pd.DataFrame),
("info", dict),
("calendar", pd.DataFrame),
("calendar", dict),
("recommendations", Union[pd.DataFrame, dict]),
("recommendations_summary", Union[pd.DataFrame, dict]),
("upgrades_downgrades", Union[pd.DataFrame, dict]),
("recommendations_history", Union[pd.DataFrame, dict]),
("earnings", pd.DataFrame),
("quarterly_earnings", pd.DataFrame),
("quarterly_cashflow", pd.DataFrame),
@@ -58,7 +57,12 @@ def assert_attribute_type(testClass: unittest.TestCase, instance, attribute_name
try:
attribute = getattr(instance, attribute_name)
if attribute is not None and expected_type is not Any:
testClass.assertEqual(type(attribute), expected_type)
err_msg = f'{attribute_name} type is {type(attribute)} not {expected_type}'
if isinstance(expected_type, _GenericAlias) and expected_type.__origin__ is Union:
allowed_types = get_args(expected_type)
testClass.assertTrue(isinstance(attribute, allowed_types), err_msg)
else:
testClass.assertEqual(type(attribute), expected_type, err_msg)
except Exception:
testClass.assertRaises(
YFNotImplementedError, lambda: getattr(instance, attribute_name)
@@ -136,8 +140,8 @@ class TestTicker(unittest.TestCase):
tkr = "IBM"
dat = yf.Ticker(tkr, session=self.session, proxy=self.proxy)
dat._fetch_ticker_tz(timeout=5)
dat._get_ticker_tz(timeout=5)
dat._fetch_ticker_tz(proxy=None, timeout=5)
dat._get_ticker_tz(proxy=None, timeout=5)
dat.history(period="1wk")
for attribute_name, attribute_type in ticker_attributes:
@@ -654,63 +658,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
def test_bad_freq_value_raises_exception(self):
self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))
# Below will fail because not ported to Yahoo API
# def test_sustainability(self):
# data = self.ticker.sustainability
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
# self.assertFalse(data.empty, "data is empty")
# data_cached = self.ticker.sustainability
# self.assertIs(data, data_cached, "data not cached")
def test_recommendations(self):
data = self.ticker.recommendations
data_summary = self.ticker.recommendations_summary
self.assertTrue(data.equals(data_summary))
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
self.assertFalse(data.empty, "data is empty")
data_cached = self.ticker.recommendations
self.assertIs(data, data_cached, "data not cached")
# def test_recommendations_summary(self): # currently alias for recommendations
# data = self.ticker.recommendations_summary
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
# self.assertFalse(data.empty, "data is empty")
# data_cached = self.ticker.recommendations_summary
# self.assertIs(data, data_cached, "data not cached")
def test_recommendations_history(self): # alias for upgrades_downgrades
data = self.ticker.upgrades_downgrades
data_history = self.ticker.recommendations_history
self.assertTrue(data.equals(data_history))
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
self.assertFalse(data.empty, "data is empty")
self.assertTrue(len(data.columns) == 4, "data has wrong number of columns")
self.assertEqual(data.columns.values.tolist(), ['Firm', 'ToGrade', 'FromGrade', 'Action'], "data has wrong column names")
self.assertIsInstance(data.index, pd.DatetimeIndex, "data has wrong index type")
data_cached = self.ticker.upgrades_downgrades
self.assertIs(data, data_cached, "data not cached")
# def test_analyst_price_target(self):
# data = self.ticker.analyst_price_target
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
# self.assertFalse(data.empty, "data is empty")
# data_cached = self.ticker.analyst_price_target
# self.assertIs(data, data_cached, "data not cached")
# def test_revenue_forecasts(self):
# data = self.ticker.revenue_forecasts
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
# self.assertFalse(data.empty, "data is empty")
# data_cached = self.ticker.revenue_forecasts
# self.assertIs(data, data_cached, "data not cached")
def test_calendar(self):
data = self.ticker.calendar
self.assertIsInstance(data, dict, "data has wrong type")
@@ -729,12 +676,89 @@ class TestTickerMiscFinancials(unittest.TestCase):
data_cached = self.ticker.calendar
self.assertIs(data, data_cached, "data not cached")
# Below will fail because not ported to Yahoo API
# def test_sustainability(self):
# data = self.ticker.sustainability
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
# self.assertFalse(data.empty, "data is empty")
# data_cached = self.ticker.sustainability
# self.assertIs(data, data_cached, "data not cached")
# def test_shares(self):
# data = self.ticker.shares
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
# self.assertFalse(data.empty, "data is empty")
class TestTickerAnalysts(unittest.TestCase):
session = None
@classmethod
def setUpClass(cls):
cls.session = session_gbl
@classmethod
def tearDownClass(cls):
if cls.session is not None:
cls.session.close()
def setUp(self):
self.ticker = yf.Ticker("GOOGL", session=self.session)
def tearDown(self):
self.ticker = None
def test_recommendations(self):
data = self.ticker.recommendations
data_summary = self.ticker.recommendations_summary
self.assertTrue(data.equals(data_summary))
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
self.assertFalse(data.empty, "data is empty")
data_cached = self.ticker.recommendations
self.assertIs(data, data_cached, "data not cached")
def test_recommendations_summary(self): # currently alias for recommendations
data = self.ticker.recommendations_summary
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
self.assertFalse(data.empty, "data is empty")
data_cached = self.ticker.recommendations_summary
self.assertIs(data, data_cached, "data not cached")
def test_upgrades_downgrades(self):
data = self.ticker.upgrades_downgrades
self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
self.assertFalse(data.empty, "data is empty")
self.assertTrue(len(data.columns) == 4, "data has wrong number of columns")
self.assertEqual(data.columns.values.tolist(), ['Firm', 'ToGrade', 'FromGrade', 'Action'], "data has wrong column names")
self.assertIsInstance(data.index, pd.DatetimeIndex, "data has wrong index type")
data_cached = self.ticker.upgrades_downgrades
self.assertIs(data, data_cached, "data not cached")
# Below will fail because not ported to Yahoo API
# def test_analyst_price_target(self):
# data = self.ticker.analyst_price_target
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
# self.assertFalse(data.empty, "data is empty")
# data_cached = self.ticker.analyst_price_target
# self.assertIs(data, data_cached, "data not cached")
# def test_revenue_forecasts(self):
# data = self.ticker.revenue_forecasts
# self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
# self.assertFalse(data.empty, "data is empty")
# data_cached = self.ticker.revenue_forecasts
# self.assertIs(data, data_cached, "data not cached")
class TestTickerInfo(unittest.TestCase):
session = None
@@ -777,11 +801,11 @@ class TestTickerInfo(unittest.TestCase):
# We don't expect this one to have a trailing PEG ratio
data1 = self.tickers[0].info
self.assertEqual(data1['trailingPegRatio'], None)
self.assertIsNone(data1['trailingPegRatio'])
# This one should have a trailing PEG ratio
data2 = self.tickers[2].info
self.assertEqual(data2['trailingPegRatio'], 1.2713)
self.assertIsInstance(data2['trailingPegRatio'], float)
pass
# def test_fast_info_matches_info(self):

15
yfinance/base.py
View File

@@ -86,7 +86,6 @@ class TickerBase:
start=None, end=None, prepost=False, actions=True,
auto_adjust=True, back_adjust=False, repair=False, keepna=False,
proxy=None, rounding=False, timeout=10,
debug=None, # deprecated
raise_errors=False) -> pd.DataFrame:
"""
:Parameters:
@@ -126,23 +125,12 @@ class TickerBase:
If not None stops waiting for a response after given number of
seconds. (Can also be a fraction of a second e.g. 0.01)
Default is 10 seconds.
debug: bool
If passed as False, will suppress message printing to console.
DEPRECATED, will be removed in future version
raise_errors: bool
If True, then raise errors as Exceptions instead of logging.
"""
logger = utils.get_yf_logger()
proxy = proxy or self.proxy
if debug is not None:
if debug:
utils.print_once(f"yfinance: Ticker.history(debug={debug}) argument is deprecated and will be removed in future version. Do this instead: logging.getLogger('yfinance').setLevel(logging.ERROR)")
logger.setLevel(logging.ERROR)
else:
utils.print_once(f"yfinance: Ticker.history(debug={debug}) argument is deprecated and will be removed in future version. Do this instead to suppress error messages: logging.getLogger('yfinance').setLevel(logging.CRITICAL)")
logger.setLevel(logging.CRITICAL)
start_user = start
end_user = end
if start or period is None or period.lower() == "max":
@@ -395,9 +383,6 @@ class TickerBase:
df = df[~df.index.duplicated(keep='first')] # must do before repair
if isinstance(repair, str) and repair=='silent':
utils.log_once(logging.WARNING, "yfinance: Ticker.history(repair='silent') value is deprecated and will be removed in future version. Repair now silent by default, use logging module to increase verbosity.")
repair = True
if repair:
# Do this before auto/back adjust
logger.debug(f'{self.ticker}: checking OHLC for repairs ...')

2
yfinance/multi.py
View File

@@ -222,7 +222,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
_realign_dfs()
data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
keys=shared._DFS.keys())
data.index = _pd.to_datetime(data.index)
# switch names back to isins if applicable
data.rename(columns=shared._ISINS, inplace=True)

20
yfinance/scrapers/holders.py
View File

@@ -1,7 +1,9 @@
# from io import StringIO
import pandas as pd
import requests
from yfinance import utils
from yfinance.data import YfData
from yfinance.const import _BASE_URL_
from yfinance.exceptions import YFinanceDataException
@@ -76,7 +78,21 @@ class Holders:
return result
def _fetch_and_parse(self):
result = self._fetch(self.proxy)
try:
result = self._fetch(self.proxy)
except requests.exceptions.HTTPError as e:
utils.get_yf_logger().error(str(e))
self._major = pd.DataFrame()
self._major_direct_holders = pd.DataFrame()
self._institutional = pd.DataFrame()
self._mutualfund = pd.DataFrame()
self._insider_transactions = pd.DataFrame()
self._insider_purchases = pd.DataFrame()
self._insider_roster = pd.DataFrame()
return
try:
data = result["quoteSummary"]["result"][0]
# parse "institutionOwnership", "fundOwnership", "majorDirectHolders", "majorHoldersBreakdown", "insiderTransactions", "insiderHolders", "netSharePurchaseActivity"
@@ -227,4 +243,4 @@ class Holders:
).convert_dtypes()
self._insider_purchases = df

53
yfinance/scrapers/quote.py
View File

@@ -6,6 +6,7 @@ from collections.abc import MutableMapping
import numpy as _np
import pandas as pd
import requests
from yfinance import utils
from yfinance.data import YfData
@@ -585,28 +586,34 @@ class Quote:
def recommendations(self) -> pd.DataFrame:
if self._recommendations is None:
result = self._fetch(self.proxy, modules=['recommendationTrend'])
try:
data = result["quoteSummary"]["result"][0]["recommendationTrend"]["trend"]
except (KeyError, IndexError):
raise YFinanceDataException(f"Failed to parse json response from Yahoo Finance: {result}")
self._recommendations = pd.DataFrame(data)
if result is None:
self._recommendations = pd.DataFrame()
else:
try:
data = result["quoteSummary"]["result"][0]["recommendationTrend"]["trend"]
except (KeyError, IndexError):
raise YFinanceDataException(f"Failed to parse json response from Yahoo Finance: {result}")
self._recommendations = pd.DataFrame(data)
return self._recommendations
@property
def upgrades_downgrades(self) -> pd.DataFrame:
if self._upgrades_downgrades is None:
result = self._fetch(self.proxy, modules=['upgradeDowngradeHistory'])
try:
data = result["quoteSummary"]["result"][0]["upgradeDowngradeHistory"]["history"]
if len(data) == 0:
raise YFinanceDataException(f"No upgrade/downgrade history found for {self._symbol}")
df = pd.DataFrame(data)
df.rename(columns={"epochGradeDate": "GradeDate", 'firm': 'Firm', 'toGrade': 'ToGrade', 'fromGrade': 'FromGrade', 'action': 'Action'}, inplace=True)
df.set_index('GradeDate', inplace=True)
df.index = pd.to_datetime(df.index, unit='s')
self._upgrades_downgrades = df
except (KeyError, IndexError):
raise YFinanceDataException(f"Failed to parse json response from Yahoo Finance: {result}")
if result is None:
self._upgrades_downgrades = pd.DataFrame()
else:
try:
data = result["quoteSummary"]["result"][0]["upgradeDowngradeHistory"]["history"]
if len(data) == 0:
raise YFinanceDataException(f"No upgrade/downgrade history found for {self._symbol}")
df = pd.DataFrame(data)
df.rename(columns={"epochGradeDate": "GradeDate", 'firm': 'Firm', 'toGrade': 'ToGrade', 'fromGrade': 'FromGrade', 'action': 'Action'}, inplace=True)
df.set_index('GradeDate', inplace=True)
df.index = pd.to_datetime(df.index, unit='s')
self._upgrades_downgrades = df
except (KeyError, IndexError):
raise YFinanceDataException(f"Failed to parse json response from Yahoo Finance: {result}")
return self._upgrades_downgrades
@property
@@ -627,7 +634,11 @@ class Quote:
if len(modules) == 0:
raise YFinanceException("No valid modules provided, see available modules using `valid_modules`")
params_dict = {"modules": modules, "corsDomain": "finance.yahoo.com", "formatted": "false", "symbol": self._symbol}
result = self._data.get_raw_json(_QUOTE_SUMMARY_URL_ + f"/{self._symbol}", user_agent_headers=self._data.user_agent_headers, params=params_dict, proxy=proxy)
try:
result = self._data.get_raw_json(_QUOTE_SUMMARY_URL_ + f"/{self._symbol}", user_agent_headers=self._data.user_agent_headers, params=params_dict, proxy=proxy)
except requests.exceptions.HTTPError as e:
utils.get_yf_logger().error(str(e))
return None
return result
def _fetch_info(self, proxy):
@@ -636,6 +647,10 @@ class Quote:
self._already_fetched = True
modules = ['financialData', 'quoteType', 'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
result = self._fetch(proxy, modules=modules)
if result is None:
self._info = {}
return
result["quoteSummary"]["result"][0]["symbol"] = self._symbol
query1_info = next(
(info for info in result.get("quoteSummary", {}).get("result", []) if info["symbol"] == self._symbol),
@@ -730,6 +745,10 @@ class Quote:
def _fetch_calendar(self):
# secFilings return too old data, so not requesting it for now
result = self._fetch(self.proxy, modules=['calendarEvents'])
if result is None:
self._calendar = {}
return
try:
self._calendar = dict()
_events = result["quoteSummary"]["result"][0]["calendarEvents"]

4
yfinance/ticker.py
View File

@@ -176,10 +176,6 @@ class Ticker(TickerBase):
def upgrades_downgrades(self):
return self.get_upgrades_downgrades()
@property
def recommendations_history(self):
return self.get_upgrades_downgrades()
@property
def earnings(self) -> _pd.DataFrame:
return self.get_earnings()

2
yfinance/version.py
View File

@@ -1 +1 @@
version = "0.2.33"
version = "0.2.35"