Merge pull request #1816 from ranaroussi/fix/ticker-api

0.2.34 fixes: Add new data to README, remove deprecated stuff, fix tests

CHANGELOG.rst

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

README.md

@@ -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

meta.yaml

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

tests/prices.py

@@ -399,71 +399,20 @@ def test_dst_fix(self):
             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)
-        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
-        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)
 
@@ -472,40 +421,22 @@ def test_prune_post_intraday_omx(self):
         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"

tests/ticker.py

@@ -17,7 +17,7 @@
 
 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 @@
     ("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 @@ def test_goodTicker_withProxy(self):
         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:
@@ -654,6 +658,24 @@ def test_cash_flow_alt_names(self):
     def test_bad_freq_value_raises_exception(self):
         self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))
 
+    def test_calendar(self):
+        data = self.ticker.calendar
+        self.assertIsInstance(data, dict, "data has wrong type")
+        self.assertTrue(len(data) > 0, "data is empty")
+        self.assertIn("Earnings Date", data.keys(), "data missing expected key")
+        self.assertIn("Earnings Average", data.keys(), "data missing expected key")
+        self.assertIn("Earnings Low", data.keys(), "data missing expected key")
+        self.assertIn("Earnings High", data.keys(), "data missing expected key")
+        self.assertIn("Revenue Average", data.keys(), "data missing expected key")
+        self.assertIn("Revenue Low", data.keys(), "data missing expected key")
+        self.assertIn("Revenue High", data.keys(), "data missing expected key")
+        # dividend date is not available for tested ticker GOOGL
+        if self.ticker.ticker != "GOOGL":
+            self.assertIn("Dividend Date", data.keys(), "data missing expected key")
+        # ex-dividend date is not always available
+        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):
@@ -664,6 +686,30 @@ def test_bad_freq_value_raises_exception(self):
     #     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
@@ -674,18 +720,16 @@ def test_recommendations(self):
         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")
+    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")
+        data_cached = self.ticker.recommendations_summary
+        self.assertIs(data, data_cached, "data not cached")
 
-    def test_recommendations_history(self):  # alias for upgrades_downgrades
+    def test_upgrades_downgrades(self):
         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")
@@ -695,6 +739,8 @@ def test_recommendations_history(self):  # alias for upgrades_downgrades
         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")
@@ -711,28 +757,6 @@ def test_recommendations_history(self):  # alias for upgrades_downgrades
     #     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")
-        self.assertTrue(len(data) > 0, "data is empty")
-        self.assertIn("Earnings Date", data.keys(), "data missing expected key")
-        self.assertIn("Earnings Average", data.keys(), "data missing expected key")
-        self.assertIn("Earnings Low", data.keys(), "data missing expected key")
-        self.assertIn("Earnings High", data.keys(), "data missing expected key")
-        self.assertIn("Revenue Average", data.keys(), "data missing expected key")
-        self.assertIn("Revenue Low", data.keys(), "data missing expected key")
-        self.assertIn("Revenue High", data.keys(), "data missing expected key")
-        # dividend date is not available for tested ticker GOOGL
-        if self.ticker.ticker != "GOOGL":
-            self.assertIn("Dividend Date", data.keys(), "data missing expected key")
-        # ex-dividend date is not always available
-        data_cached = self.ticker.calendar
-        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 TestTickerInfo(unittest.TestCase):
@@ -777,11 +801,11 @@ def test_complementary_info(self):
 
         # 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):

yfinance/base.py

@@ -86,7 +86,6 @@ def history(self, period="1mo", interval="1d",
                 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 @@ def history(self, period="1mo", interval="1d",
                 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 @@ def history(self, period="1mo", interval="1d",
 
         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 ...')