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Add new data to README, remove deprecated stuff, fix tests, v0.2.35

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Ticker.recommendations*:
- add to README
- organise their unit tests
- remove redundant recommendations_history

Remove deprecated arguments from Ticker.history

Fix 'bad symbol' behaviour & tests
Fix some prices tests

Bump version 0.2.35
This commit is contained in:
Value Raider
2024-01-06 23:41:50 +00:00
parent a7c41afa52
commit 61c4696c65
10 changed files with 165 additions and 185 deletions
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4
CHANGELOG.rst
View File

@@ -1,6 +1,10 @@
Change Log
===========
0.2.35
------
Internal fixes for 0.2.34
0.2.34
------
Features:

5
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

2
meta.yaml
View File

@@ -1,5 +1,5 @@
{% set name = "yfinance" %}
{% set version = "0.2.34" %}
{% set version = "0.2.35" %}
package:
name: "{{ name|lower }}"

91
tests/prices.py
View File

@@ -399,24 +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)
@@ -425,87 +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 ...')

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 = "0.2.34"
version = "0.2.35"