Bump version to 0.2.12

Disable annoying 'backup decrypt' msg

.github/ISSUE_TEMPLATE/bug_report.md

@@ -7,7 +7,9 @@ assignees: ''
 
 ---
 
-# READ BEFORE POSTING
+# IMPORTANT
+
+If you want help, you got to read this first, follow the instructions.
 
 ### Are you up-to-date?
 
@@ -23,20 +25,19 @@ and comparing against [PIP](https://pypi.org/project/yfinance/#history).
 
 ### Does Yahoo actually have the data?
 
-Visit `finance.yahoo.com` and confim they have your data. Maybe your ticker was delisted.
+Are you spelling ticker *exactly* same as Yahoo?
 
-Then check that you are spelling ticker *exactly* same as Yahoo.
+Then visit `finance.yahoo.com` and confirm they have the data you want. Maybe your ticker was delisted, or your expectations of `yfinance` are wrong.
 
 ### Are you spamming Yahoo?
 
-Yahoo Finance free service has limit on query rate (roughly 100/s). Them delaying or blocking your spam is not a bug.
+Yahoo Finance free service has rate-limiting depending on request type - roughly 60/minute for prices, 10/minute for info. Once limit hit, Yahoo can delay, block, or return bad data. Not a `yfinance` bug.
 
 ### Still think it's a bug?
 
-Delete this default message and submit your bug report here, providing the following as best you can:
+Delete this default message (all of it) and submit your bug report here, providing the following as best you can:
 
-- Info about your system:
-  - yfinance version
-  - operating system
-- Simple code that reproduces your problem
-- The error message
+- Simple code that reproduces your problem, that we can copy-paste-run
+- Exception message with full traceback, or proof `yfinance` returning bad data
+- `yfinance` version and Python version
+- Operating system type

CHANGELOG.rst

@@ -1,6 +1,72 @@
 Change Log
 ===========
 
+0.2.12
+------
+Disable annoying 'backup decrypt' msg
+
+0.2.11
+------
+Fix history_metadata accesses for unusual symbols #1411
+
+0.2.10
+------
+General
+- allow using sqlite3 < 3.8.2 #1380
+- add another backup decrypt option #1379
+Prices
+- restore original download() timezone handling #1385
+- fix & improve price repair #1289 2a2928b 86d6acc
+- drop intraday intervals if in post-market but prepost=False #1311
+Info
+- fast_info improvements:
+  - add camelCase keys, add dict functions values() & items() #1368
+  - fix fast_info["previousClose"] #1383
+- catch TypeError Exception #1397
+
+0.2.9
+-----
+- Fix fast_info bugs #1362
+
+0.2.7
+-----
+- Fix Yahoo decryption, smarter this time #1353
+- Rename basic_info -> fast_info #1354
+
+0.2.6
+-----
+- Fix Ticker.basic_info lazy-loading #1342
+
+0.2.5
+-----
+- Fix Yahoo data decryption again #1336
+- New: Ticker.basic_info - faster Ticker.info #1317
+
+0.2.4
+-----
+- Fix Yahoo data decryption #1297
+- New feature: 'Ticker.get_shares_full()' #1301
+- Improve caching of financials data #1284
+- Restore download() original alignment behaviour #1283
+- Fix the database lock error in multithread download #1276
+
+0.2.3
+-----
+- Make financials API '_' use consistent
+
+0.2.2
+-----
+- Restore 'financials' attribute (map to 'income_stmt')
+
+0.2.1
+-----
+Release!
+
+0.2.0rc5
+--------
+- Improve financials error handling #1243
+- Fix '100x price' repair #1244
+
 0.2.0rc4
 --------
 - Access to old financials tables via `get_income_stmt(legacy=True)`

README.md

@@ -42,6 +42,11 @@ Yahoo! finance API is intended for personal use only.**
 
 ---
 
+## News [2023-01-27]
+Since December 2022 Yahoo has been encrypting the web data that `yfinance` scrapes for non-market data. Fortunately the decryption keys are available, although Yahoo moved/changed them several times hence `yfinance` breaking several times. `yfinance` is now better prepared for any future changes by Yahoo.
+
+Why is Yahoo doing this? We don't know. Is it to stop scrapers? Maybe, so we've implemented changes to reduce load on Yahoo. In December we rolled out version 0.2 with optimised scraping. Then in 0.2.6 introduced `Ticker.fast_info`, providing much faster access to some `info` elements wherever possible e.g. price stats and forcing users to switch (sorry but we think necessary). `info` will continue to exist for as long as there are elements without a fast alternative.
+
 ## Quick Start
 
 ### The Ticker module
@@ -53,30 +58,28 @@ import yfinance as yf
 
 msft = yf.Ticker("MSFT")
 
-# get stock info
+# get all stock info (slow)
 msft.info
+# fast access to subset of stock info (opportunistic)
+msft.fast_info
 
 # get historical market data
-hist = msft.history(period="max")
+hist = msft.history(period="1mo")
 
 # show meta information about the history (requires history() to be called first)
 msft.history_metadata
 
 # show actions (dividends, splits, capital gains)
 msft.actions
-
-# show dividends
 msft.dividends
-
-# show splits
 msft.splits
-
-
-# show capital gains (for mutual funds & etfs)
-msft.capital_gains
+msft.capital_gains  # only for mutual funds & etfs
 
 # show share count
+# - yearly summary:
 msft.shares
+# - accurate time-series count:
+msft.get_shares_full(start="2022-01-01", end=None)
 
 # show financials:
 # - income statement
@@ -90,13 +93,9 @@ msft.cashflow
 msft.quarterly_cashflow
 # see `Ticker.get_income_stmt()` for more options
 
-# show major holders
+# show holders
 msft.major_holders
-
-# show institutional holders
 msft.institutional_holders
-
-# show mutualfund holders
 msft.mutualfund_holders
 
 # show earnings
@@ -155,19 +154,6 @@ msft.option_chain(..., proxy="PROXY_SERVER")
 ...
 ```
 
-To use a custom `requests` session (for example to cache calls to the
-API or customize the `User-agent` header), pass a `session=` argument to
-the Ticker constructor.
-
-```python
-import requests_cache
-session = requests_cache.CachedSession('yfinance.cache')
-session.headers['User-agent'] = 'my-program/1.0'
-ticker = yf.Ticker('msft', session=session)
-# The scraped response will be stored in the cache
-ticker.actions
-```
-
 To initialize multiple `Ticker` objects, use
 
 ```python
@@ -188,63 +174,46 @@ import yfinance as yf
 data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
 ```
 
-I've also added some options to make life easier :)
+`yf.download()` and `Ticker.history()` have many options for configuring fetching and processing, e.g.:
 
 ```python
-data = yf.download(  # or pdr.get_data_yahoo(...
-        # tickers list or string as well
-        tickers = "SPY AAPL MSFT",
-
-        # use "period" instead of start/end
-        # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
-        # (optional, default is '1mo')
-        period = "ytd",
-
-        # fetch data by interval (including intraday if period < 60 days)
-        # valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
-        # (optional, default is '1d')
-        interval = "5d",
-
-        # Whether to ignore timezone when aligning ticker data from 
-        # different timezones. Default is True. False may be useful for 
-        # minute/hourly data.
-        ignore_tz = False,
-
-        # group by ticker (to access via data['SPY'])
-        # (optional, default is 'column')
-        group_by = 'ticker',
-
-        # adjust all OHLC automatically
-        # (optional, default is False)
-        auto_adjust = True,
-
-        # identify and attempt repair of currency unit mixups e.g. $/cents
-        repair = False,
-
-        # download pre/post regular market hours data
-        # (optional, default is False)
-        prepost = True,
-
-        # use threads for mass downloading? (True/False/Integer)
-        # (optional, default is True)
-        threads = True,
-
-        # proxy URL scheme use use when downloading?
-        # (optional, default is None)
-        proxy = None
-    )
+yf.download(tickers = "SPY AAPL",  # list of tickers
+            period = "1y",         # time period
+            interval = "1d",       # trading interval
+            ignore_tz = True,      # ignore timezone when aligning data from different exchanges?
+            prepost = False)       # download pre/post market hours data?
 ```
 
-### Timezone cache store
+Review the [Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
+
+### Smarter scraping
+
+To use a custom `requests` session (for example to cache calls to the
+API or customize the `User-agent` header), pass a `session=` argument to
+the Ticker constructor.
 
-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
-yf.set_tz_cache_location("custom/cache/location")
-...
+import requests_cache
+session = requests_cache.CachedSession('yfinance.cache')
+session.headers['User-agent'] = 'my-program/1.0'
+ticker = yf.Ticker('msft', session=session)
+# The scraped response will be stored in the cache
+ticker.actions
+```
+
+Combine a `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-limiter/blocker that can corrupt data.
+```python
+from requests import Session
+from requests_cache import CacheMixin, SQLiteCache
+from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
+class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
+    """ """
+
+session = CachedLimiterSession(
+    per_second=0.9,
+    bucket_class=MemoryQueueBucket,
+    backend=SQLiteCache("yfinance.cache"),
+)
 ```
 
 ### Managing Multi-Level Columns
@@ -262,6 +231,18 @@ yfinance?](https://stackoverflow.com/questions/63107801)
         -   How to download single or multiple tickers into a single
             dataframe with single level column names and a ticker column
 
+### Timezone cache store
+
+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
+yf.set_tz_cache_location("custom/cache/location")
+...
+```
+
 ---
 
 ## `pandas_datareader` override
@@ -306,6 +287,7 @@ To install `yfinance` using `conda`, see
 -   [frozendict](https://pypi.org/project/frozendict) \>= 2.3.4
 -   [beautifulsoup4](https://pypi.org/project/beautifulsoup4) \>= 4.11.1
 -   [html5lib](https://pypi.org/project/html5lib) \>= 1.1
+-   [cryptography](https://pypi.org/project/cryptography) \>= 3.3.2
 
 ### Optional (if you want to use `pandas_datareader`)
 

meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.2.0" %}
+{% set version = "0.2.12" %}
 
 package:
   name: "{{ name|lower }}"
@@ -26,6 +26,8 @@ requirements:
     - frozendict >=2.3.4
     - beautifulsoup4 >=4.11.1
     - html5lib >=1.1
+    # - pycryptodome >=3.6.6
+    - cryptography >=3.3.2
     - pip
     - python
 
@@ -40,6 +42,8 @@ requirements:
     - frozendict >=2.3.4
     - beautifulsoup4 >=4.11.1
     - html5lib >=1.1
+    # - pycryptodome >=3.6.6
+    - cryptography >=3.3.2
     - python
 
 test:

requirements.txt

@@ -8,3 +8,4 @@ pytz>=2022.5
 frozendict>=2.3.4
 beautifulsoup4>=4.11.1
 html5lib>=1.1
+cryptography>=3.3.2

setup.py

@@ -62,7 +62,9 @@ setup(
     install_requires=['pandas>=1.3.0', 'numpy>=1.16.5',
                       'requests>=2.26', 'multitasking>=0.0.7',
                       'lxml>=4.9.1', 'appdirs>=1.4.4', 'pytz>=2022.5',
-                      'frozendict>=2.3.4',
+                      'frozendict>=2.3.4', 
+                      # 'pycryptodome>=3.6.6',
+                      'cryptography>=3.3.2',
                       'beautifulsoup4>=4.11.1', 'html5lib>=1.1'],
     entry_points={
         'console_scripts': [

tests/prices.py

@@ -24,9 +24,7 @@ class TestPriceHistory(unittest.TestCase):
 
     def test_daily_index(self):
         tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
-
         intervals = ["1d", "1wk", "1mo"]
-
         for tkr in tkrs:
             dat = yf.Ticker(tkr, session=self.session)
 
@@ -44,8 +42,8 @@ class TestPriceHistory(unittest.TestCase):
 
             dt_utc = _tz.timezone("UTC").localize(_dt.datetime.utcnow())
             dt = dt_utc.astimezone(_tz.timezone(tz))
-
-            df = dat.history(start=dt.date() - _dt.timedelta(days=1), interval="1h")
+            start_d = dt.date() - _dt.timedelta(days=7)
+            df = dat.history(start=start_d, interval="1h")
 
             dt0 = df.index[-2]
             dt1 = df.index[-1]
@@ -55,7 +53,6 @@ class TestPriceHistory(unittest.TestCase):
                 print("Ticker = ", tkr)
                 raise
 
-
     def test_duplicatingDaily(self):
         tkrs = ["IMP.JO", "BHG.JO", "SSW.JO", "BP.L", "INTC"]
         test_run = False
@@ -110,22 +107,27 @@ class TestPriceHistory(unittest.TestCase):
     def test_intraDayWithEvents(self):
         # TASE dividend release pre-market, doesn't merge nicely with intra-day data so check still present
 
-        tkr = "ICL.TA"
-        # tkr = "ESLT.TA"
-        # tkr = "ONE.TA"
-        # tkr = "MGDL.TA"
-        start_d = _dt.date.today() - _dt.timedelta(days=60)
-        end_d = None
-        df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
-        df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
-        if df_daily_divs.shape[0] == 0:
-            self.skipTest("Skipping test_intraDayWithEvents() because 'ICL.TA' has no dividend in last 60 days")
+        tase_tkrs = ["ICL.TA", "ESLT.TA", "ONE.TA", "MGDL.TA"]
+        test_run = False
+        for tkr in tase_tkrs:
+            start_d = _dt.date.today() - _dt.timedelta(days=59)
+            end_d = None
+            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
+            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
+            if df_daily_divs.shape[0] == 0:
+                # self.skipTest("Skipping test_intraDayWithEvents() because 'ICL.TA' has no dividend in last 60 days")
+                continue
 
-        last_div_date = df_daily_divs.index[-1]
-        start_d = last_div_date.date()
-        end_d = last_div_date.date() + _dt.timedelta(days=1)
-        df = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
-        self.assertTrue((df["Dividends"] != 0.0).any())
+            last_div_date = df_daily_divs.index[-1]
+            start_d = last_div_date.date()
+            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            df = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df["Dividends"] != 0.0).any())
+            test_run = True
+            break
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
 
     def test_dailyWithEvents(self):
         # Reproduce issue #521
@@ -230,7 +232,6 @@ class TestPriceHistory(unittest.TestCase):
 
     def test_tz_dst_ambiguous(self):
         # Reproduce issue #1100
-
         try:
             yf.Ticker("ESLT.TA", session=self.session).history(start="2002-10-06", end="2002-10-09", interval="1d")
         except _tz.exceptions.AmbiguousTimeError:
@@ -261,6 +262,116 @@ class TestPriceHistory(unittest.TestCase):
             print("Weekly data not aligned to Monday")
             raise
 
+    def test_prune_post_intraday_us(self):
+        # Half-day before 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)
+        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)
+
+        # 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 not d 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)
+
+    def test_prune_post_intraday_asx(self):
+        # Setup
+        tkr = "BHP.AX"
+        interval = "1h"
+        interval_td = _dt.timedelta(hours=1)
+        time_open = _dt.time(10)
+        time_close = _dt.time(16,12)
+        # No early closes in 2022
+        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)
+        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)
+
     def test_weekly_2rows_fix(self):
         tkr = "AMZN"
         start = _dt.date.today() - _dt.timedelta(days=14)
@@ -270,11 +381,43 @@ class TestPriceHistory(unittest.TestCase):
         df = dat.history(start=start, interval="1wk")
         self.assertTrue((df.index.weekday == 0).all())
 
+class TestPriceRepair(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = requests_cache.CachedSession(backend='memory')
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def test_reconstruct_2m(self):
+        # 2m repair requires 1m data.
+        # Yahoo restricts 1m fetches to 7 days max within last 30 days.
+        # Need to test that '_reconstruct_intervals_batch()' can handle this.
+
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+
+        dt_now = _pd.Timestamp.utcnow()
+        td_7d = _dt.timedelta(days=7)
+        td_60d = _dt.timedelta(days=60)
+
+        # Round time for 'requests_cache' reuse
+        dt_now = dt_now.ceil("1h")
+
+        for tkr in tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+            end_dt = dt_now
+            start_dt = end_dt - td_60d
+            df = dat.history(start=start_dt, end=end_dt, interval="2m", repair=True)
+
     def test_repair_100x_weekly(self):
         # Setup:
         tkr = "PNL.L"
         dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
         data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
         df = _pd.DataFrame(data={"Open":      [470.5, 473.5, 474.5, 470],
@@ -283,22 +426,22 @@ class TestPriceHistory(unittest.TestCase):
                                  "Close":     [475,   473.5, 472,   473.5],
                                  "Adj Close": [475,   473.5, 472,   473.5],
                                  "Volume": [2295613, 2245604, 3000287, 2635611]},
-                                index=_pd.to_datetime([_dt.date(2022, 10, 23),
-                                                       _dt.date(2022, 10, 16),
-                                                       _dt.date(2022, 10, 9),
-                                                       _dt.date(2022, 10, 2)]))
+                                index=_pd.to_datetime([_dt.date(2022, 10, 24),
+                                                       _dt.date(2022, 10, 17),
+                                                       _dt.date(2022, 10, 10),
+                                                       _dt.date(2022, 10, 3)]))
         df = df.sort_index()
         df.index.name = "Date"
         df_bad = df.copy()
-        df_bad.loc["2022-10-23", "Close"] *= 100
-        df_bad.loc["2022-10-16", "Low"] *= 100
-        df_bad.loc["2022-10-2", "Open"] *= 100
+        df_bad.loc["2022-10-24", "Close"] *= 100
+        df_bad.loc["2022-10-17", "Low"] *= 100
+        df_bad.loc["2022-10-03", "Open"] *= 100
         df.index = df.index.tz_localize(tz_exchange)
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
 
         # Run test
 
-        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange)
+        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
@@ -326,7 +469,7 @@ class TestPriceHistory(unittest.TestCase):
 
         tkr = "PNL.L"
         dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
         data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
         df = _pd.DataFrame(data={"Open":      [400,   398,    392.5,   417],
@@ -353,7 +496,7 @@ class TestPriceHistory(unittest.TestCase):
         df.index = df.index.tz_localize(tz_exchange)
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
 
-        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange)
+        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
@@ -381,7 +524,7 @@ class TestPriceHistory(unittest.TestCase):
     def test_repair_100x_daily(self):
         tkr = "PNL.L"
         dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
         data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
         df = _pd.DataFrame(data={"Open":      [478,    476,   476,   472],
@@ -403,7 +546,7 @@ class TestPriceHistory(unittest.TestCase):
         df.index = df.index.tz_localize(tz_exchange)
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
 
-        df_repaired = dat._fix_unit_mixups(df_bad, "1d", tz_exchange)
+        df_repaired = dat._fix_unit_mixups(df_bad, "1d", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
@@ -423,7 +566,7 @@ class TestPriceHistory(unittest.TestCase):
     def test_repair_zeroes_daily(self):
         tkr = "BBIL.L"
         dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
         df_bad = _pd.DataFrame(data={"Open":      [0,      102.04, 102.04],
                                      "High":      [0,      102.1,  102.11],
@@ -438,7 +581,7 @@ class TestPriceHistory(unittest.TestCase):
         df_bad.index.name = "Date"
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
 
-        repaired_df = dat._fix_zeroes(df_bad, "1d", tz_exchange)
+        repaired_df = dat._fix_zeroes(df_bad, "1d", tz_exchange, prepost=False)
 
         correct_df = df_bad.copy()
         correct_df.loc["2022-11-01", "Open"] = 102.080002
@@ -450,40 +593,31 @@ class TestPriceHistory(unittest.TestCase):
     def test_repair_zeroes_hourly(self):
         tkr = "INTC"
         dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
-        df_bad = _pd.DataFrame(data={"Open":      [29.68, 29.49, 29.545, _np.nan, 29.485],
-                                     "High":      [29.68, 29.625, 29.58, _np.nan, 29.49],
-                                     "Low":       [29.46, 29.4, 29.45, _np.nan, 29.31],
-                                     "Close":     [29.485, 29.545, 29.485, _np.nan, 29.325],
-                                     "Adj Close": [29.485, 29.545, 29.485, _np.nan, 29.325],
-                                     "Volume": [3258528, 2140195, 1621010, 0, 0]},
-                                index=_pd.to_datetime([_dt.datetime(2022,11,25, 9,30),
-                                                       _dt.datetime(2022,11,25, 10,30),
-                                                       _dt.datetime(2022,11,25, 11,30),
-                                                       _dt.datetime(2022,11,25, 12,30),
-                                                       _dt.datetime(2022,11,25, 13,00)]))
-        df_bad = df_bad.sort_index()
-        df_bad.index.name = "Date"
-        df_bad.index = df_bad.index.tz_localize(tz_exchange)
+        correct_df = dat.history(period="1wk", interval="1h", auto_adjust=False, repair=True)
 
-        repaired_df = dat._fix_zeroes(df_bad, "1h", tz_exchange)
+        df_bad = correct_df.copy()
+        bad_idx = correct_df.index[10]
+        df_bad.loc[bad_idx, "Open"] = _np.nan
+        df_bad.loc[bad_idx, "High"] = _np.nan
+        df_bad.loc[bad_idx, "Low"] = _np.nan
+        df_bad.loc[bad_idx, "Close"] = _np.nan
+        df_bad.loc[bad_idx, "Adj Close"] = _np.nan
+        df_bad.loc[bad_idx, "Volume"] = 0
+
+        repaired_df = dat._fix_zeroes(df_bad, "1h", tz_exchange, prepost=False)
 
-        correct_df = df_bad.copy()
-        idx = _pd.Timestamp(2022,11,25, 12,30).tz_localize(tz_exchange)
-        correct_df.loc[idx, "Open"] = 29.485001
-        correct_df.loc[idx, "High"] = 29.49
-        correct_df.loc[idx, "Low"] = 29.43
-        correct_df.loc[idx, "Close"] = 29.455
-        correct_df.loc[idx, "Adj Close"] = 29.455
-        correct_df.loc[idx, "Volume"] = 609164
         for c in ["Open", "Low", "High", "Close"]:
             try:
                 self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-7).all())
             except:
                 print("COLUMN", c)
+                print("- repaired_df")
                 print(repaired_df)
+                print("- correct_df[c]:")
                 print(correct_df[c])
+                print("- diff:")
                 print(repaired_df[c] - correct_df[c])
                 raise
 

tests/ticker.py

@@ -9,6 +9,7 @@ Specific test class:
 
 """
 import pandas as pd
+import numpy as np
 
 from .context import yfinance as yf
 
@@ -51,12 +52,16 @@ class TestTicker(unittest.TestCase):
     def test_badTicker(self):
         # Check yfinance doesn't die when ticker delisted
 
-        tkr = "AM2Z.TA"
+        tkr = "DJI"  # typo of "^DJI"
         dat = yf.Ticker(tkr, session=self.session)
         dat.history(period="1wk")
         dat.history(start="2022-01-01")
         dat.history(start="2022-01-01", end="2022-03-01")
         yf.download([tkr], period="1wk")
+
+        for k in dat.fast_info:
+            dat.fast_info[k]
+
         dat.isin
         dat.major_holders
         dat.institutional_holders
@@ -65,6 +70,7 @@ class TestTicker(unittest.TestCase):
         dat.splits
         dat.actions
         dat.shares
+        dat.get_shares_full()
         dat.info
         dat.calendar
         dat.recommendations
@@ -89,48 +95,65 @@ class TestTicker(unittest.TestCase):
     def test_goodTicker(self):
         # that yfinance works when full api is called on same instance of ticker
 
-        tkr = "IBM"
-        dat = yf.Ticker(tkr, session=self.session)
+        tkrs = ["IBM"]
+        tkrs.append("QCSTIX")  # weird ticker, no price history but has previous close
+        for tkr in tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
 
-        dat.isin
-        dat.major_holders
-        dat.institutional_holders
-        dat.mutualfund_holders
-        dat.dividends
-        dat.splits
-        dat.actions
-        dat.shares
-        dat.info
-        dat.calendar
-        dat.recommendations
-        dat.earnings
-        dat.quarterly_earnings
-        dat.income_stmt
-        dat.quarterly_income_stmt
-        dat.balance_sheet
-        dat.quarterly_balance_sheet
-        dat.cashflow
-        dat.quarterly_cashflow
-        dat.recommendations_summary
-        dat.analyst_price_target
-        dat.revenue_forecasts
-        dat.sustainability
-        dat.options
-        dat.news
-        dat.earnings_trend
-        dat.earnings_dates
-        dat.earnings_forecasts
+            dat.history(period="1wk")
+            dat.history(start="2022-01-01")
+            dat.history(start="2022-01-01", end="2022-03-01")
+            yf.download([tkr], period="1wk")
 
-        dat.history(period="1wk")
-        dat.history(start="2022-01-01")
-        dat.history(start="2022-01-01", end="2022-03-01")
-        yf.download([tkr], period="1wk")
+            for k in dat.fast_info:
+                dat.fast_info[k]
+
+            dat.isin
+            dat.major_holders
+            dat.institutional_holders
+            dat.mutualfund_holders
+            dat.dividends
+            dat.splits
+            dat.actions
+            dat.shares
+            dat.get_shares_full()
+            dat.info
+            dat.calendar
+            dat.recommendations
+            dat.earnings
+            dat.quarterly_earnings
+            dat.income_stmt
+            dat.quarterly_income_stmt
+            dat.balance_sheet
+            dat.quarterly_balance_sheet
+            dat.cashflow
+            dat.quarterly_cashflow
+            dat.recommendations_summary
+            dat.analyst_price_target
+            dat.revenue_forecasts
+            dat.sustainability
+            dat.options
+            dat.news
+            dat.earnings_trend
+            dat.earnings_dates
+            dat.earnings_forecasts
 
 
 class TestTickerHistory(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = requests_cache.CachedSession(backend='memory')
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
     def setUp(self):
         # use a ticker that has dividends
-        self.ticker = yf.Ticker("IBM")
+        self.ticker = yf.Ticker("IBM", session=self.session)
 
     def tearDown(self):
         self.ticker = None
@@ -176,9 +199,19 @@ class TestTickerHistory(unittest.TestCase):
 
 
 class TestTickerEarnings(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = requests_cache.CachedSession(backend='memory')
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
 
     def setUp(self):
-        self.ticker = yf.Ticker("GOOGL")
+        self.ticker = yf.Ticker("GOOGL", session=self.session)
 
     def tearDown(self):
         self.ticker = None
@@ -237,9 +270,19 @@ class TestTickerEarnings(unittest.TestCase):
 
 
 class TestTickerHolders(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = requests_cache.CachedSession(backend='memory')
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
 
     def setUp(self):
-        self.ticker = yf.Ticker("GOOGL")
+        self.ticker = yf.Ticker("GOOGL", session=self.session)
 
     def tearDown(self):
         self.ticker = None
@@ -283,7 +326,7 @@ class TestTickerMiscFinancials(unittest.TestCase):
 
     def setUp(self):
         self.ticker = yf.Ticker("GOOGL", session=self.session)
-
+        
         # For ticker 'BSE.AX' (and others), Yahoo not returning 
         # full quarterly financials (usually cash-flow) with all entries, 
         # instead returns a smaller version in different data store.
@@ -497,6 +540,65 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data_cached = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
         self.assertIs(data, data_cached, "data not cached")
 
+    def test_income_alt_names(self):
+        i1 = self.ticker.income_stmt
+        i2 = self.ticker.incomestmt
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.financials
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.get_income_stmt()
+        i2 = self.ticker.get_incomestmt()
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.get_financials()
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.quarterly_income_stmt
+        i2 = self.ticker.quarterly_incomestmt
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.quarterly_financials
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.get_income_stmt(freq="quarterly")
+        i2 = self.ticker.get_incomestmt(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.get_financials(freq="quarterly")
+        self.assertTrue(i1.equals(i3))
+
+    def test_balance_sheet_alt_names(self):
+        i1 = self.ticker.balance_sheet
+        i2 = self.ticker.balancesheet
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_balance_sheet()
+        i2 = self.ticker.get_balancesheet()
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.quarterly_balance_sheet
+        i2 = self.ticker.quarterly_balancesheet
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_balance_sheet(freq="quarterly")
+        i2 = self.ticker.get_balancesheet(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+
+    def test_cash_flow_alt_names(self):
+        i1 = self.ticker.cash_flow
+        i2 = self.ticker.cashflow
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_cash_flow()
+        i2 = self.ticker.get_cashflow()
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.quarterly_cash_flow
+        i2 = self.ticker.quarterly_cashflow
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_cash_flow(freq="quarterly")
+        i2 = self.ticker.get_cashflow(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+
     def test_sustainability(self):
         data = self.ticker.sustainability
         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
@@ -563,16 +665,145 @@ class TestTickerMiscFinancials(unittest.TestCase):
         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
         self.assertFalse(data.empty, "data is empty")
 
-    def test_info(self):
-        data = self.ticker.info
-        self.assertIsInstance(data, dict, "data has wrong type")
-        self.assertIn("symbol", data.keys(), "Did not find expected key in info dict")
-        self.assertEqual("GOOGL", data["symbol"], "Wrong symbol value in info dict")
+    def test_shares_full(self):
+        data = self.ticker.get_shares_full()
+        self.assertIsInstance(data, pd.Series, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
 
     def test_bad_freq_value_raises_exception(self):
         self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))
 
 
+class TestTickerInfo(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = requests_cache.CachedSession(backend='memory')
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        self.symbols = []
+        self.symbols += ["ESLT.TA", "BP.L", "GOOGL"]
+        self.symbols.append("QCSTIX")  # good for testing, doesn't trade
+        self.symbols += ["BTC-USD", "IWO", "VFINX", "^GSPC"]
+        self.symbols += ["SOKE.IS", "ADS.DE"]  # detected bugs
+        self.tickers = [yf.Ticker(s, session=self.session) for s in self.symbols]
+
+    def tearDown(self):
+        self.ticker = None
+
+    def test_info(self):
+        data = self.tickers[0].info
+        self.assertIsInstance(data, dict, "data has wrong type")
+        self.assertIn("symbol", data.keys(), "Did not find expected key in info dict")
+        self.assertEqual(self.symbols[0], data["symbol"], "Wrong symbol value in info dict")
+
+    def test_fast_info(self):
+        yf.scrapers.quote.PRUNE_INFO = False
+
+        fast_info_keys = set()
+        for ticker in self.tickers:
+            fast_info_keys.update(set(ticker.fast_info.keys()))
+        fast_info_keys = sorted(list(fast_info_keys))
+
+        key_rename_map = {}
+        key_rename_map["currency"] = "currency"
+        key_rename_map["quote_type"] = "quoteType"
+        key_rename_map["timezone"] = "exchangeTimezoneName"
+
+        key_rename_map["last_price"] = ["currentPrice", "regularMarketPrice"]
+        key_rename_map["open"] = ["open", "regularMarketOpen"]
+        key_rename_map["day_high"] = ["dayHigh", "regularMarketDayHigh"]
+        key_rename_map["day_low"] = ["dayLow", "regularMarketDayLow"]
+        key_rename_map["previous_close"] = ["previousClose"]
+        key_rename_map["regular_market_previous_close"] = ["regularMarketPreviousClose"]
+
+        key_rename_map["fifty_day_average"] = "fiftyDayAverage"
+        key_rename_map["two_hundred_day_average"] = "twoHundredDayAverage"
+        key_rename_map["year_change"] = ["52WeekChange", "fiftyTwoWeekChange"]
+        key_rename_map["year_high"] = "fiftyTwoWeekHigh"
+        key_rename_map["year_low"] = "fiftyTwoWeekLow"
+
+        key_rename_map["last_volume"] = ["volume", "regularMarketVolume"]
+        key_rename_map["ten_day_average_volume"] = ["averageVolume10days", "averageDailyVolume10Day"]
+        key_rename_map["three_month_average_volume"] = "averageVolume"
+
+        key_rename_map["market_cap"] = "marketCap"
+        key_rename_map["shares"] = "sharesOutstanding"
+
+        for k in list(key_rename_map.keys()):
+            if '_' in k:
+                key_rename_map[yf.utils.snake_case_2_camelCase(k)] = key_rename_map[k]
+
+        # Note: share count items in info[] are bad. Sometimes the float > outstanding!
+        # So often fast_info["shares"] does not match. 
+        # Why isn't fast_info["shares"] wrong? Because using it to calculate market cap always correct.
+        bad_keys = {"shares"}
+
+        # Loose tolerance for averages, no idea why don't match info[]. Is info wrong?
+        custom_tolerances = {}
+        custom_tolerances["year_change"] = 1.0
+        # custom_tolerances["ten_day_average_volume"] = 1e-3
+        custom_tolerances["ten_day_average_volume"] = 1e-1
+        # custom_tolerances["three_month_average_volume"] = 1e-2
+        custom_tolerances["three_month_average_volume"] = 5e-1
+        custom_tolerances["fifty_day_average"] = 1e-2
+        custom_tolerances["two_hundred_day_average"] = 1e-2
+        for k in list(custom_tolerances.keys()):
+            if '_' in k:
+                custom_tolerances[yf.utils.snake_case_2_camelCase(k)] = custom_tolerances[k]
+
+        for k in fast_info_keys:
+            if k in key_rename_map:
+                k2 = key_rename_map[k]
+            else:
+                k2 = k
+
+            if not isinstance(k2, list):
+                k2 = [k2]
+
+            for m in k2:
+                for ticker in self.tickers:
+                    if not m in ticker.info:
+                        # print(f"symbol={ticker.ticker}: fast_info key '{k}' mapped to info key '{m}' but not present in info")
+                        continue
+
+                    if k in bad_keys:
+                        continue
+
+                    if k in custom_tolerances:
+                        rtol = custom_tolerances[k]
+                    else:
+                        rtol = 5e-3
+                        # rtol = 1e-4
+
+                    correct = ticker.info[m]
+                    test = ticker.fast_info[k]
+                    # print(f"Testing: symbol={ticker.ticker} m={m} k={k}: test={test} vs correct={correct}")
+                    if k in ["market_cap","marketCap"] and ticker.fast_info["currency"] in ["GBp", "ILA"]:
+                        # Adjust for currency to match Yahoo:
+                        test *= 0.01
+                    try:
+                        if correct is None:
+                            self.assertTrue(test is None or (not np.isnan(test)), f"{k}: {test} must be None or real value because correct={correct}")
+                        elif isinstance(test, float) or isinstance(correct, int):
+                            self.assertTrue(np.isclose(test, correct, rtol=rtol), f"{ticker.ticker} {k}: {test} != {correct}")
+                        else:
+                            self.assertEqual(test, correct, f"{k}: {test} != {correct}")
+                    except:
+                        if k in ["regularMarketPreviousClose"] and ticker.ticker in ["ADS.DE"]:
+                            # Yahoo is wrong, is returning post-market close not regular
+                            continue
+                        else:
+                            raise
+
+
+
 def suite():
     suite = unittest.TestSuite()
     suite.addTest(TestTicker('Test ticker'))
@@ -580,6 +811,7 @@ def suite():
     suite.addTest(TestTickerHolders('Test holders'))
     suite.addTest(TestTickerHistory('Test Ticker history'))
     suite.addTest(TestTickerMiscFinancials('Test misc financials'))
+    suite.addTest(TestTickerInfo('Test info & fast_info'))
     return suite
 
 

yfinance/data.py

@@ -1,8 +1,20 @@
 import functools
 from functools import lru_cache
 
+import hashlib
+from base64 import b64decode
+usePycryptodome = False  # slightly faster
+# usePycryptodome = True
+if usePycryptodome:
+    from Crypto.Cipher import AES
+    from Crypto.Util.Padding import unpad
+else:
+    from cryptography.hazmat.primitives import padding
+    from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+
 import requests as requests
 import re
+from bs4 import BeautifulSoup
 
 from frozendict import frozendict
 
@@ -35,6 +47,124 @@ def lru_cache_freezeargs(func):
     return wrapped
 
 
+def _extract_extra_keys_from_stores(data):
+    new_keys = [k for k in data.keys() if k not in ["context", "plugins"]]
+    new_keys_values = set([data[k] for k in new_keys])
+
+    # Maybe multiple keys have same value - keep one of each
+    new_keys_uniq = []
+    new_keys_uniq_values = set()
+    for k in new_keys:
+        v = data[k]
+        if not v in new_keys_uniq_values:
+            new_keys_uniq.append(k)
+            new_keys_uniq_values.add(v)
+
+    return [data[k] for k in new_keys_uniq]
+
+
+def decrypt_cryptojs_aes_stores(data, keys=None):
+    encrypted_stores = data['context']['dispatcher']['stores']
+
+    password = None
+    if keys is not None:
+        if not isinstance(keys, list):
+            raise TypeError("'keys' must be list")
+        candidate_passwords = keys
+    else:
+        candidate_passwords = []
+
+    if "_cs" in data and "_cr" in data:
+        _cs = data["_cs"]
+        _cr = data["_cr"]
+        _cr = b"".join(int.to_bytes(i, length=4, byteorder="big", signed=True) for i in json.loads(_cr)["words"])
+        password = hashlib.pbkdf2_hmac("sha1", _cs.encode("utf8"), _cr, 1, dklen=32).hex()
+
+    encrypted_stores = b64decode(encrypted_stores)
+    assert encrypted_stores[0:8] == b"Salted__"
+    salt = encrypted_stores[8:16]
+    encrypted_stores = encrypted_stores[16:]
+
+    def _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5") -> tuple:
+        """OpenSSL EVP Key Derivation Function
+        Args:
+            password (Union[str, bytes, bytearray]): Password to generate key from.
+            salt (Union[bytes, bytearray]): Salt to use.
+            keySize (int, optional): Output key length in bytes. Defaults to 32.
+            ivSize (int, optional): Output Initialization Vector (IV) length in bytes. Defaults to 16.
+            iterations (int, optional): Number of iterations to perform. Defaults to 1.
+            hashAlgorithm (str, optional): Hash algorithm to use for the KDF. Defaults to 'md5'.
+        Returns:
+            key, iv: Derived key and Initialization Vector (IV) bytes.
+
+        Taken from: https://gist.github.com/rafiibrahim8/0cd0f8c46896cafef6486cb1a50a16d3
+        OpenSSL original code: https://github.com/openssl/openssl/blob/master/crypto/evp/evp_key.c#L78
+        """
+
+        assert iterations > 0, "Iterations can not be less than 1."
+
+        if isinstance(password, str):
+            password = password.encode("utf-8")
+
+        final_length = keySize + ivSize
+        key_iv = b""
+        block = None
+
+        while len(key_iv) < final_length:
+            hasher = hashlib.new(hashAlgorithm)
+            if block:
+                hasher.update(block)
+            hasher.update(password)
+            hasher.update(salt)
+            block = hasher.digest()
+            for _ in range(1, iterations):
+                block = hashlib.new(hashAlgorithm, block).digest()
+            key_iv += block
+
+        key, iv = key_iv[:keySize], key_iv[keySize:final_length]
+        return key, iv
+
+    def _decrypt(encrypted_stores, password, key, iv):
+        if usePycryptodome:
+            cipher = AES.new(key, AES.MODE_CBC, iv=iv)
+            plaintext = cipher.decrypt(encrypted_stores)
+            plaintext = unpad(plaintext, 16, style="pkcs7")
+        else:
+            cipher = Cipher(algorithms.AES(key), modes.CBC(iv))
+            decryptor = cipher.decryptor()
+            plaintext = decryptor.update(encrypted_stores) + decryptor.finalize()
+            unpadder = padding.PKCS7(128).unpadder()
+            plaintext = unpadder.update(plaintext) + unpadder.finalize()
+            plaintext = plaintext.decode("utf-8")
+        return plaintext
+
+    if not password is None:
+        try:
+            key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
+        except:
+            raise Exception("yfinance failed to decrypt Yahoo data response")
+        plaintext = _decrypt(encrypted_stores, password, key, iv)
+    else:
+        success = False
+        for i in range(len(candidate_passwords)):
+            # print(f"Trying candiate pw {i+1}/{len(candidate_passwords)}")
+            password = candidate_passwords[i]
+            try:
+                key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
+
+                plaintext = _decrypt(encrypted_stores, password, key, iv)
+
+                success = True
+                break
+            except:
+                pass
+        if not success:
+            raise Exception("yfinance failed to decrypt Yahoo data response")
+
+    decoded_stores = json.loads(plaintext)
+    return decoded_stores
+
+
 _SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
 
 
@@ -72,6 +202,67 @@ class TickerData:
             proxy = {"https": proxy}
         return proxy
 
+    def _get_decryption_keys_from_yahoo_js(self, soup):
+        result = None
+
+        key_count = 4
+        re_script = soup.find("script", string=re.compile("root.App.main")).text
+        re_data = json.loads(re.search("root.App.main\s+=\s+(\{.*\})", re_script).group(1))
+        re_data.pop("context", None)
+        key_list = list(re_data.keys())
+        if re_data.get("plugins"):  # 1) attempt to get last 4 keys after plugins
+            ind = key_list.index("plugins")
+            if len(key_list) > ind+1:
+                sub_keys = key_list[ind+1:]
+                if len(sub_keys) == key_count:
+                    re_obj = {}
+                    missing_val = False
+                    for k in sub_keys:
+                        if not re_data.get(k):
+                            missing_val = True
+                            break
+                        re_obj.update({k: re_data.get(k)})
+                    if not missing_val:
+                        result = re_obj
+
+        if not result is None:
+            return [''.join(result.values())]
+
+        re_keys = []    # 2) attempt scan main.js file approach to get keys
+        prefix = "https://s.yimg.com/uc/finance/dd-site/js/main."
+        tags = [tag['src'] for tag in soup.find_all('script') if prefix in tag.get('src', '')]
+        for t in tags:
+            response_js = self.cache_get(t)
+            #
+            if response_js.status_code != 200:
+                time.sleep(random.randrange(10, 20))
+                response_js.close()
+            else:
+                r_data = response_js.content.decode("utf8")
+                re_list = [
+                    x.group() for x in re.finditer(r"context.dispatcher.stores=JSON.parse((?:.*?\r?\n?)*)toString", r_data)
+                ]
+                for rl in re_list:
+                    re_sublist = [x.group() for x in re.finditer(r"t\[\"((?:.*?\r?\n?)*)\"\]", rl)]
+                    if len(re_sublist) == key_count:
+                        re_keys = [sl.replace('t["', '').replace('"]', '') for sl in re_sublist]
+                        break
+                response_js.close()
+            if len(re_keys) == key_count:
+                break
+        if len(re_keys) > 0:
+            re_obj = {}
+            missing_val = False
+            for k in re_keys:
+                if not re_data.get(k):
+                    missing_val = True
+                    break
+                re_obj.update({k: re_data.get(k)})
+            if not missing_val:
+                return [''.join(re_obj.values())]
+
+        return []
+
     @lru_cache_freezeargs
     @lru_cache(maxsize=cache_maxsize)
     def get_json_data_stores(self, sub_page: str = None, proxy=None) -> dict:
@@ -83,15 +274,50 @@ class TickerData:
         else:
             ticker_url = "{}/{}".format(_SCRAPE_URL_, self.ticker)
 
-        html = self.get(url=ticker_url, proxy=proxy).text
+        response = self.get(url=ticker_url, proxy=proxy)
+        html = response.text
 
         # The actual json-data for stores is in a javascript assignment in the webpage
-        json_str = html.split('root.App.main =')[1].split(
-            '(this)')[0].split(';\n}')[0].strip()
-        data = json.loads(json_str)['context']['dispatcher']['stores']
+        try:
+            json_str = html.split('root.App.main =')[1].split(
+                '(this)')[0].split(';\n}')[0].strip()
+        except IndexError:
+            # Fetch failed, probably because Yahoo spam triggered
+            return {}
+
+        data = json.loads(json_str)
+
+        # Gather decryption keys:
+        soup = BeautifulSoup(response.content, "html.parser")
+        keys = self._get_decryption_keys_from_yahoo_js(soup)
+        # if len(keys) == 0:
+        #     msg = "No decryption keys could be extracted from JS file."
+        #     if "requests_cache" in str(type(response)):
+        #         msg += " Try flushing your 'requests_cache', probably parsing old JS."
+        #     print("WARNING: " + msg + " Falling back to backup decrypt methods.")
+        if len(keys) == 0:
+            keys = []
+            try:
+                extra_keys = _extract_extra_keys_from_stores(data)
+                keys = [''.join(extra_keys[-4:])]
+            except:
+                pass
+            #
+            keys_url = "https://github.com/ranaroussi/yfinance/raw/main/yfinance/scrapers/yahoo-keys.txt"
+            response_gh = self.cache_get(keys_url)
+            keys += response_gh.text.splitlines()
+
+        # Decrypt!
+        stores = decrypt_cryptojs_aes_stores(data, keys)
+        if stores is None:
+            # Maybe Yahoo returned old format, not encrypted
+            if "context" in data and "dispatcher" in data["context"]:
+                stores = data['context']['dispatcher']['stores']
+        if stores is None:
+            raise Exception(f"{self.ticker}: Failed to extract data stores from web request")
 
         # return data
-        new_data = json.dumps(data).replace('{}', 'null')
+        new_data = json.dumps(stores).replace('{}', 'null')
         new_data = re.sub(
             r'{[\'|\"]raw[\'|\"]:(.*?),(.*?)}', r'\1', new_data)
 

yfinance/exceptions.py

@@ -1,6 +1,6 @@
-class YFianceException(Exception):
+class YFinanceException(Exception):
     pass
 
 
-class YFianceDataException(YFianceException):
+class YFinanceDataException(YFinanceException):
     pass

yfinance/multi.py

@@ -29,7 +29,7 @@ from . import Ticker, utils
 from . import shared
 
 
-def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=True,
+def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=None,
              group_by='column', auto_adjust=False, back_adjust=False, repair=False, keepna=False,
              progress=True, period="max", show_errors=True, interval="1d", prepost=False,
              proxy=None, rounding=False, timeout=10):
@@ -68,7 +68,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             How many threads to use for mass downloading. Default is True
         ignore_tz: bool
             When combining from different timezones, ignore that part of datetime.
-            Default is True
+            Default depends on interval. Intraday = False. Day+ = True.
         proxy: str
             Optional. Proxy server URL scheme. Default is None
         rounding: bool
@@ -80,6 +80,14 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             seconds. (Can also be a fraction of a second e.g. 0.01)
     """
 
+    if ignore_tz is None:
+        # Set default value depending on interval
+        if interval[1:] in ['m', 'h']:
+            # Intraday
+            ignore_tz = False
+        else:
+            ignore_tz = True
+
     # create ticker list
     tickers = tickers if isinstance(
         tickers, (list, set, tuple)) else tickers.replace(',', ' ').split()

yfinance/scrapers/fundamentals.py

@@ -6,7 +6,7 @@ import numpy as np
 
 from yfinance import utils
 from yfinance.data import TickerData
-from yfinance.exceptions import YFianceDataException, YFianceException
+from yfinance.exceptions import YFinanceDataException, YFinanceException
 
 
 class Fundamentals:
@@ -22,10 +22,10 @@ class Fundamentals:
         self._financials_data = None
         self._fin_data_quote = None
         self._basics_already_scraped = False
-        self._financials = Fiancials(data)
+        self._financials = Financials(data)
 
     @property
-    def financials(self) -> "Fiancials":
+    def financials(self) -> "Financials":
         return self._financials
 
     @property
@@ -97,7 +97,7 @@ class Fundamentals:
             pass
 
 
-class Fiancials:
+class Financials:
     def __init__(self, data: TickerData):
         self._data = data
         self._income_time_series = {}
@@ -143,8 +143,8 @@ class Fiancials:
 
             if statement is not None:
                 return statement
-        except YFianceException as e:
-            print("Failed to create financials table for {} reason: {}".format(name, repr(e)))
+        except YFinanceException as e:
+            print(f"- {self._data.ticker}: Failed to create {name} financials table for reason: {repr(e)}")
         return pd.DataFrame()
 
     def _create_financials_table(self, name, timescale, proxy):
@@ -153,14 +153,8 @@ class Fiancials:
             name = "financials"
 
         keys = self._get_datastore_keys(name, proxy)
-
         try:
-            # Developers note: TTM and template stuff allows for reproducing the nested structure
-            # visible on Yahoo website. But more work needed to make it user-friendly! Ideally
-            # return a tree data structure instead of Pandas MultiIndex
-            # So until this is implemented, just return simple tables
             return self.get_financials_time_series(timescale, keys, proxy)
-
         except Exception as e:
             pass
 
@@ -183,10 +177,10 @@ class Fiancials:
         try:
             keys = _finditem1("key", data_stores['FinancialTemplateStore'])
         except KeyError as e:
-            raise YFianceDataException("Parsing FinancialTemplateStore failed, reason: {}".format(repr(e)))
+            raise YFinanceDataException("Parsing FinancialTemplateStore failed, reason: {}".format(repr(e)))
 
         if not keys:
-            raise YFianceDataException("No keys in FinancialTemplateStore")
+            raise YFinanceDataException("No keys in FinancialTemplateStore")
         return keys
 
     def get_financials_time_series(self, timescale, keys: list, proxy=None) -> pd.DataFrame:
@@ -201,7 +195,7 @@ class Fiancials:
         url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
         # Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
         start_dt = datetime.datetime(2016, 12, 31)
-        end = (datetime.datetime.now() + datetime.timedelta(days=366))
+        end = pd.Timestamp.utcnow().ceil("D")
         url += "&period1={}&period2={}".format(int(start_dt.timestamp()), int(end.timestamp()))
 
         # Step 3: fetch and reshape data
@@ -272,8 +266,8 @@ class Fiancials:
 
             if statement is not None:
                 return statement
-        except YFianceException as e:
-            print("Failed to create financials table for {} reason: {}".format(name, repr(e)))
+        except YFinanceException as e:
+            print(f"- {self._data.ticker}: Failed to create financials table for {name} reason: {repr(e)}")
         return pd.DataFrame()
 
     def _create_financials_table_old(self, name, timescale, proxy):
@@ -281,7 +275,7 @@ class Fiancials:
 
         # Fetch raw data
         if not "QuoteSummaryStore" in data_stores:
-            return pd.DataFrame()
+            raise YFinanceDataException(f"Yahoo not returning legacy financials data")
         data = data_stores["QuoteSummaryStore"]
 
         if name == "cash-flow":
@@ -296,12 +290,14 @@ class Fiancials:
         key1 += "History"
         if timescale == "quarterly":
             key1 += "Quarterly"
-        data = data.get(key1)[key2]
+        if key1 not in data or data[key1] is None or key2 not in data[key1]:
+            raise YFinanceDataException(f"Yahoo not returning legacy {name} financials data")
+        data = data[key1][key2]
 
         # Tabulate
         df = pd.DataFrame(data)
         if len(df) == 0:
-            return pd.DataFrame()
+            raise YFinanceDataException(f"Yahoo not returning legacy {name} financials data")
         df = df.drop(columns=['maxAge'])
         for col in df.columns:
             df[col] = df[col].replace('-', np.nan)

yfinance/scrapers/quote.py

@@ -7,6 +7,73 @@ from yfinance import utils
 from yfinance.data import TickerData
 
 
+info_retired_keys_price = {"currentPrice", "dayHigh", "dayLow", "open", "previousClose", "volume", "volume24Hr"}
+info_retired_keys_price.update({"regularMarket"+s for s in ["DayHigh", "DayLow", "Open", "PreviousClose", "Price", "Volume"]})
+info_retired_keys_price.update({"fiftyTwoWeekLow", "fiftyTwoWeekHigh", "fiftyTwoWeekChange", "52WeekChange", "fiftyDayAverage", "twoHundredDayAverage"})
+info_retired_keys_price.update({"averageDailyVolume10Day", "averageVolume10days", "averageVolume"})
+info_retired_keys_exchange = {"currency", "exchange", "exchangeTimezoneName", "exchangeTimezoneShortName", "quoteType"}
+info_retired_keys_marketCap = {"marketCap"}
+info_retired_keys_symbol = {"symbol"}
+info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_retired_keys_marketCap | info_retired_keys_symbol
+
+
+PRUNE_INFO = True
+# PRUNE_INFO = False
+
+
+from collections.abc import MutableMapping
+class InfoDictWrapper(MutableMapping):
+    """ Simple wrapper around info dict, intercepting 'gets' to 
+    print how-to-migrate messages for specific keys. Requires
+    override dict API"""
+
+    def __init__(self, info):
+        self.info = info
+
+    def keys(self):
+        return self.info.keys()
+
+    def __str__(self):
+        return self.info.__str__()
+
+    def __repr__(self):
+        return self.info.__repr__()
+
+    def __contains__(self, k):
+        return k in self.info.keys()
+
+    def __getitem__(self, k):
+        if k in info_retired_keys_price:
+            print(f"Price data removed from info (key='{k}'). Use Ticker.fast_info or history() instead")
+            return None
+        elif k in info_retired_keys_exchange:
+            print(f"Exchange data removed from info (key='{k}'). Use Ticker.fast_info or Ticker.get_history_metadata() instead")
+            return None
+        elif k in info_retired_keys_marketCap:
+            print(f"Market cap removed from info (key='{k}'). Use Ticker.fast_info instead")
+            return None
+        elif k in info_retired_keys_symbol:
+            print(f"Symbol removed from info (key='{k}'). You know this already")
+            return None
+        return self.info[self._keytransform(k)]
+
+    def __setitem__(self, k, value):
+        self.info[self._keytransform(k)] = value
+
+    def __delitem__(self, k):
+        del self.info[self._keytransform(k)]
+
+    def __iter__(self):
+        return iter(self.info)
+    
+    def __len__(self):
+        return len(self.info)
+
+    def _keytransform(self, k):
+        return k
+
+
+
 class Quote:
 
     def __init__(self, data: TickerData, proxy=None):
@@ -14,6 +81,7 @@ class Quote:
         self.proxy = proxy
 
         self._info = None
+        self._retired_info = None
         self._sustainability = None
         self._recommendations = None
         self._calendar = None
@@ -130,6 +198,19 @@ class Quote:
         except Exception:
             pass
 
+        # Delete redundant info[] keys, because values can be accessed faster
+        # elsewhere - e.g. price keys. Hope is reduces Yahoo spam effect.
+        # But record the dropped keys, because in rare cases they are needed.
+        self._retired_info = {}
+        for k in info_retired_keys:
+            if k in self._info:
+                self._retired_info[k] = self._info[k]
+                if PRUNE_INFO:
+                    del self._info[k]
+        if PRUNE_INFO:
+            # InfoDictWrapper will explain how to access above data elsewhere
+            self._info = InfoDictWrapper(self._info)
+
         # events
         try:
             cal = pd.DataFrame(quote_summary_store['calendarEvents']['earnings'])
@@ -194,17 +275,22 @@ class Quote:
             for k in keys:
                 url += "&type=" + k
             # Request 6 months of data
-            url += "&period1={}".format(
-                int((datetime.datetime.now() - datetime.timedelta(days=365 // 2)).timestamp()))
-            url += "&period2={}".format(int((datetime.datetime.now() + datetime.timedelta(days=1)).timestamp()))
+            start = pd.Timestamp.utcnow().floor("D") - datetime.timedelta(days=365 // 2)
+            start = int(start.timestamp())
+            end = pd.Timestamp.utcnow().ceil("D")
+            end = int(end.timestamp())
+            url += f"&period1={start}&period2={end}"
 
             json_str = self._data.cache_get(url=url, proxy=proxy).text
             json_data = json.loads(json_str)
-            key_stats = json_data["timeseries"]["result"][0]
-            if k not in key_stats:
-                # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+            try:
+                key_stats = json_data["timeseries"]["result"][0]
+                if k not in key_stats:
+                    # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+                    v = None
+                else:
+                    # Select most recent (last) raw value in list:
+                    v = key_stats[k][-1]["reportedValue"]["raw"]
+            except Exception:
                 v = None
-            else:
-                # Select most recent (last) raw value in list:
-                v = key_stats[k][-1]["reportedValue"]["raw"]
             self._info[k] = v

yfinance/scrapers/yahoo-keys.txt

@@ -0,0 +1,8 @@
+daf93e37cbf219cd4c1f3f74ec4551265ec5565b99e8c9322dccd6872941cf13c818cbb88cba6f530e643b4e2329b17ec7161f4502ce6a02bb0dbbe5fc0d0474
+ad4d90b3c9f2e1d156ef98eadfa0ff93e4042f6960e54aa2a13f06f528e6b50ba4265a26a1fd5b9cd3db0d268a9c34e1d080592424309429a58bce4adc893c87
+e9a8ab8e5620b712ebc2fb4f33d5c8b9c80c0d07e8c371911c785cf674789f1747d76a909510158a7b7419e86857f2d7abbd777813ff64840e4cbc514d12bcae
+6ae2523aeafa283dad746556540145bf603f44edbf37ad404d3766a8420bb5eb1d3738f52a227b88283cca9cae44060d5f0bba84b6a495082589f5fe7acbdc9e
+3365117c2a368ffa5df7313a4a84988f73926a86358e8eea9497c5ff799ce27d104b68e5f2fbffa6f8f92c1fef41765a7066fa6bcf050810a9c4c7872fd3ebf0
+15d8f57919857d5a5358d2082c7ef0f1129cfacd2a6480333dcfb954b7bb67d820abefebfdb0eaa6ef18a1c57f617b67d7e7b0ec040403b889630ae5db5a4dbb
+db9630d707a7d0953ac795cd8db1ca9ca6c9d8239197cdfda24b4e0ec9c37eaec4db82dab68b8f606ab7b5b4af3e65dab50606f8cf508269ec927e6ee605fb78
+3c895fb5ddcc37d20d3073ed74ee3efad59bcb147c8e80fd279f83701b74b092d503dcd399604c6d8be8f3013429d3c2c76ed5b31b80c9df92d5eab6d3339fce

yfinance/ticker.py

@@ -161,6 +161,22 @@ class Ticker(TickerBase):
     def quarterly_income_stmt(self) -> _pd.DataFrame:
         return self.get_income_stmt(pretty=True, freq='quarterly')
 
+    @property
+    def incomestmt(self) -> _pd.DataFrame:
+        return self.income_stmt
+
+    @property
+    def quarterly_incomestmt(self) -> _pd.DataFrame:
+        return self.quarterly_income_stmt
+
+    @property
+    def financials(self) -> _pd.DataFrame:
+        return self.income_stmt
+
+    @property
+    def quarterly_financials(self) -> _pd.DataFrame:
+        return self.quarterly_income_stmt
+
     @property
     def balance_sheet(self) -> _pd.DataFrame:
         return self.get_balance_sheet(pretty=True)
@@ -177,13 +193,21 @@ class Ticker(TickerBase):
     def quarterly_balancesheet(self) -> _pd.DataFrame:
         return self.quarterly_balance_sheet
 
+    @property
+    def cash_flow(self) -> _pd.DataFrame:
+        return self.get_cash_flow(pretty=True, freq="yearly")
+
+    @property
+    def quarterly_cash_flow(self) -> _pd.DataFrame:
+        return self.get_cash_flow(pretty=True, freq='quarterly')
+
     @property
     def cashflow(self) -> _pd.DataFrame:
-        return self.get_cashflow(pretty=True, freq="yearly")
+        return self.cash_flow
 
     @property
     def quarterly_cashflow(self) -> _pd.DataFrame:
-        return self.get_cashflow(pretty=True, freq='quarterly')
+        return self.quarterly_cash_flow
 
     @property
     def recommendations_summary(self):

yfinance/tickers.py

@@ -34,12 +34,8 @@ class Tickers:
         tickers = tickers if isinstance(
             tickers, list) else tickers.replace(',', ' ').split()
         self.symbols = [ticker.upper() for ticker in tickers]
-        ticker_objects = {}
+        self.tickers = {ticker:Ticker(ticker, session=session) for ticker in self.symbols}
 
-        for ticker in self.symbols:
-            ticker_objects[ticker] = Ticker(ticker, session=session)
-
-        self.tickers = ticker_objects
         # self.tickers = _namedtuple(
         #     "Tickers", ticker_objects.keys(), rename=True
         # )(*ticker_objects.values())

yfinance/utils.py

@@ -49,6 +49,18 @@ user_agent_headers = {
     'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
 
 
+# From https://stackoverflow.com/a/59128615
+from types import FunctionType
+from inspect import getmembers
+def attributes(obj):
+    disallowed_names = {
+      name for name, value in getmembers(type(obj)) 
+        if isinstance(value, FunctionType)}
+    return {
+      name: getattr(obj, name) for name in dir(obj) 
+        if name[0] != '_' and name not in disallowed_names and hasattr(obj, name)}
+
+
 def is_isin(string):
     return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9]{1})$", string))
 
@@ -288,6 +300,11 @@ def camel2title(strings: List[str], sep: str = ' ', acronyms: Optional[List[str]
     return strings
 
 
+def snake_case_2_camelCase(s):
+    sc = s.split('_')[0] + ''.join(x.title() for x in s.split('_')[1:])
+    return sc
+
+
 def _parse_user_dt(dt, exchange_tz):
     if isinstance(dt, int):
         # Should already be epoch, test with conversion:
@@ -307,7 +324,11 @@ def _parse_user_dt(dt, exchange_tz):
 
 def _interval_to_timedelta(interval):
     if interval == "1mo":
-        return _dateutil.relativedelta(months=1)
+        return _dateutil.relativedelta.relativedelta(months=1)
+    elif interval == "3mo":
+        return _dateutil.relativedelta.relativedelta(months=3)
+    elif interval == "1y":
+        return _dateutil.relativedelta.relativedelta(years=1)
     elif interval == "1wk":
         return _pd.Timedelta(days=7, unit='d')
     else: 
@@ -427,6 +448,35 @@ def set_df_tz(df, interval, tz):
     return df
 
 
+def fix_Yahoo_returning_prepost_unrequested(quotes, interval, metadata):
+    # Sometimes Yahoo returns post-market data despite not requesting it.
+    # Normally happens on half-day early closes.
+    #
+    # And sometimes returns pre-market data despite not requesting it.
+    # E.g. some London tickers.
+    tps_df = metadata["tradingPeriods"]
+    tps_df["_date"] = tps_df.index.date
+    quotes["_date"] = quotes.index.date
+    idx = quotes.index.copy()
+    quotes = quotes.merge(tps_df, how="left", validate="many_to_one")
+    quotes.index = idx
+    # "end" = end of regular trading hours (including any auction)
+    f_drop = quotes.index >= quotes["end"]
+    f_drop = f_drop | (quotes.index < quotes["start"])
+    if f_drop.any():
+        # When printing report, ignore rows that were already NaNs:
+        f_na = quotes[["Open","Close"]].isna().all(axis=1)
+        n_nna = quotes.shape[0] - _np.sum(f_na)
+        n_drop_nna = _np.sum(f_drop & ~f_na)
+        quotes_dropped = quotes[f_drop]
+        # if debug and n_drop_nna > 0:
+        #     print(f"Dropping {n_drop_nna}/{n_nna} intervals for falling outside regular trading hours")
+        quotes = quotes[~f_drop]
+    metadata["tradingPeriods"] = tps_df.drop(["_date"], axis=1)
+    quotes = quotes.drop(["_date", "start", "end"], axis=1)
+    return quotes
+
+
 def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
     # Yahoo bug fix. If market is open today then Yahoo normally returns 
     # todays data as a separate row from rest-of week/month interval in above row. 
@@ -607,7 +657,7 @@ def safe_merge_dfs(df_main, df_sub, interval):
         if interval.endswith('m') or interval.endswith('h') or interval == "1d":
             # Update: is possible with daily data when dividend very recent
             f_missing = ~df_sub.index.isin(df.index)
-            df_sub_missing = df_sub[f_missing]
+            df_sub_missing = df_sub[f_missing].copy()
             keys = {"Adj Open", "Open", "Adj High", "High", "Adj Low", "Low", "Adj Close",
                     "Close"}.intersection(df.columns)
             df_sub_missing[list(keys)] = _np.nan
@@ -640,6 +690,71 @@ def is_valid_timezone(tz: str) -> bool:
     return True
 
 
+def format_history_metadata(md):
+    if not isinstance(md, dict):
+        return md
+    if len(md) == 0:
+        return md
+
+    tz = md["exchangeTimezoneName"]
+
+    for k in ["firstTradeDate", "regularMarketTime"]:
+        if k in md and md[k] is not None:
+            md[k] = _pd.to_datetime(md[k], unit='s', utc=True).tz_convert(tz)
+
+    if "currentTradingPeriod" in md:
+        for m in ["regular", "pre", "post"]:
+            if m in md["currentTradingPeriod"]:
+                for t in ["start", "end"]:
+                    md["currentTradingPeriod"][m][t] = \
+                        _pd.to_datetime(md["currentTradingPeriod"][m][t], unit='s', utc=True).tz_convert(tz)
+                del md["currentTradingPeriod"][m]["gmtoffset"]
+                del md["currentTradingPeriod"][m]["timezone"]
+
+    if "tradingPeriods" in md:
+        if md["tradingPeriods"] == {"pre":[], "post":[]}:
+            del md["tradingPeriods"]
+
+    if "tradingPeriods" in md:
+        tps = md["tradingPeriods"]
+        if isinstance(tps, list):
+            # Only regular times
+            regs_dict = [tps[i][0] for i in range(len(tps))]
+            pres_dict = None
+            posts_dict = None
+        elif isinstance(tps, dict):
+            # Includes pre- and post-market
+            pres_dict = [tps["pre"][i][0] for i in range(len(tps["pre"]))]
+            posts_dict = [tps["post"][i][0] for i in range(len(tps["post"]))]
+            regs_dict = [tps["regular"][i][0] for i in range(len(tps["regular"]))]
+        else:
+            raise Exception()
+
+        def _dict_to_table(d):
+            df = _pd.DataFrame.from_dict(d).drop(["timezone", "gmtoffset"], axis=1)
+            df["end"] = _pd.to_datetime(df["end"], unit='s', utc=True).dt.tz_convert(tz)
+            df["start"] = _pd.to_datetime(df["start"], unit='s', utc=True).dt.tz_convert(tz)
+            df.index = _pd.to_datetime(df["start"].dt.date)
+            df.index = df.index.tz_localize(tz)
+            return df
+
+        df = _dict_to_table(regs_dict)
+        df_cols = ["start", "end"]
+        if pres_dict is not None:
+            pre_df = _dict_to_table(pres_dict)
+            df = df.merge(pre_df.rename(columns={"start":"pre_start", "end":"pre_end"}), left_index=True, right_index=True)
+            df_cols = ["pre_start", "pre_end"]+df_cols
+        if posts_dict is not None:
+            post_df = _dict_to_table(posts_dict)
+            df = df.merge(post_df.rename(columns={"start":"post_start", "end":"post_end"}), left_index=True, right_index=True)
+            df_cols = df_cols+["post_start", "post_end"]
+        df = df[df_cols]
+        df.index.name = "Date"
+
+        md["tradingPeriods"] = df
+
+    return md
+
 class ProgressBar:
     def __init__(self, iterations, text='completed'):
         self.text = text
@@ -702,7 +817,14 @@ class _KVStore:
         with self._cache_mutex:
             self.conn = _sqlite3.connect(filename, timeout=10, check_same_thread=False)
             self.conn.execute('pragma journal_mode=wal')
-            self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT) without rowid')
+            try:
+                self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT) without rowid')
+            except Exception as e:
+                if 'near "without": syntax error' in str(e):
+                    # "without rowid" requires sqlite 3.8.2. Older versions will raise exception
+                    self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT)')
+                else:
+                    raise
             self.conn.commit()
         _atexit.register(self.close)
 
@@ -743,8 +865,10 @@ class _TzCache:
     """Simple sqlite file cache of ticker->timezone"""
 
     def __init__(self):
-        self._tz_db = None
         self._setup_cache_folder()
+        # Must init db here, where is thread-safe
+        self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
+        self._migrate_cache_tkr_tz()
 
     def _setup_cache_folder(self):
         if not _os.path.isdir(self._db_dir):
@@ -776,11 +900,6 @@ class _TzCache:
 
     @property
     def tz_db(self):
-        # lazy init
-        if self._tz_db is None:
-            self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
-            self._migrate_cache_tkr_tz()
-
         return self._tz_db
 
     def _migrate_cache_tkr_tz(self):

yfinance/version.py

@@ -1 +1 @@
-version = "0.2.0rc4"
+version = "0.2.12"