Bump version to 0.2.28

sync dev -> main

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,43 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-# IMPORTANT
-
-If you want help, you got to read this first, follow the instructions.
-
-### Are you up-to-date?
-
-Upgrade to the latest version and confirm the issue/bug is still there.
-
-`$ pip install yfinance --upgrade --no-cache-dir`
-
-Confirm by running:
-
-`import yfinance as yf ; print(yf.__version__)`
-
-and comparing against [PIP](https://pypi.org/project/yfinance/#history).
-
-### Does Yahoo actually have the data?
-
-Are you 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 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 (all of it) and submit your bug report here, providing the following as best you can:
-
-- 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

.github/ISSUE_TEMPLATE/bug_report.yaml

@@ -0,0 +1,88 @@
+name: Bug report
+description: Report a bug in our project
+labels: ["bug"]
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        # IMPORTANT - Read and follow these instructions carefully. Help us help you.
+
+        ### Does issue already exist?
+
+        Use the search tool. Don't annoy everyone by duplicating existing Issues.
+
+        ### Are you up-to-date?
+
+        Upgrade to the latest version and confirm the issue/bug is still there.
+
+        `$ pip install yfinance --upgrade --no-cache-dir`
+
+        Confirm by running:
+
+        `import yfinance as yf ; print(yf.__version__)`
+
+        and comparing against [PIP](https://pypi.org/project/yfinance/#history).
+
+        ### Does Yahoo actually have the data?
+
+        Are you spelling symbol *exactly* same as Yahoo?
+
+        Then visit `finance.yahoo.com` and confirm they have the data you want. Maybe your symbol was delisted, or your expectations of `yfinance` are wrong.
+
+        ### Are you spamming Yahoo?
+
+        Yahoo Finance free service has rate-limiting https://github.com/ranaroussi/yfinance/discussions/1513. Once limit hit, Yahoo can delay, block, or return bad data -> not a `yfinance` bug.
+        
+  - type: markdown
+    attributes:
+      value: |
+        ---
+        ## Still think it's a bug?
+
+        Provide the following as best you can:
+
+  - type: textarea
+    id: code
+    attributes:
+      label: "Simple code that reproduces your problem"
+      description: "Provide a snippet of code that we can copy-paste-run. Wrap code in Python Markdown code blocks for proper formatting (```` ```python ... ``` ````)."
+    validations:
+      required: true
+
+  - type: textarea
+    id: debug-log
+    attributes:
+      label: "Debug log"
+      description: "Run code with debug logging enabled and post the full output. Instructions: https://github.com/ranaroussi/yfinance/tree/main#logging"
+    validations:
+      required: true
+
+  - type: textarea
+    id: bad-data-proof
+    attributes:
+      label: "Bad data proof"
+      description: "If you think `yfinance` returning bad data, provide your proof here."
+    validations:
+      required: false
+
+  - type: input
+    id: version-yfinance
+    attributes:
+      label: "`yfinance` version"
+    validations:
+      required: true
+
+  - type: input
+    id: version-python
+    attributes:
+      label: "Python version"
+    validations:
+      required: false
+
+  - type: input
+    id: os
+    attributes:
+      label: "Operating system"
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,14 +0,0 @@
----
-name: Feature request
-about: Request a new feature
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Describe the problem**
-
-**Describe the solution**
-
-**Additional context**

.github/workflows/python-publish.yml

@@ -13,9 +13,9 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: '3.x'
     - name: Install dependencies

.gitignore

@@ -4,6 +4,7 @@ dist
 yfinance.egg-info
 *.pyc
 .coverage
+.idea/
 .vscode/
 build/
 *.html

CHANGELOG.rst

@@ -1,6 +1,61 @@
 Change Log
 ===========
 
+0.2.28
+------
+- Fix TypeError: 'FastInfo' object is not callable #1636
+- Improve & fix price repair #1633 #1660
+- option_chain() also return underlying data #1606
+
+0.2.27
+------
+Bug fixes:
+- fix merging 1d-prices with out-of-range divs/splits #1635
+- fix multithread error 'tz already in cache' #1648
+
+0.2.26
+------
+Proxy improvements
+- bug fixes #1371
+- security fix #1625
+
+0.2.25
+------
+Fix single ISIN as ticker #1611
+Fix 'Only 100 years allowed' error #1576
+
+0.2.24
+------
+Fix info[] missing values #1603
+
+0.2.23
+------
+Fix 'Unauthorized' error #1595
+
+0.2.22
+------
+Fix unhandled 'sqlite3.DatabaseError' #1574
+
+0.2.21
+------
+Fix financials tables #1568
+Price repair update: fix Yahoo messing up dividend and split adjustments #1543
+Fix logging behaviour #1562
+Fix merge future div/split into prices #1567
+
+0.2.20
+------
+Switch to `logging` module #1493 #1522 #1541
+Price history:
+- optimise #1514
+- fixes #1523
+- fix TZ-cache corruption #1528
+
+0.2.18
+------
+Fix 'fast_info' error '_np not found' #1496
+Fix bug in timezone cache #1498
+
 0.2.17
 ------
 Fix prices error with Pandas 2.0 #1488

README.md

@@ -42,11 +42,6 @@ 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.~ `info` now fixed and much faster than before.
-
 ## Quick Start
 
 ### The Ticker module
@@ -74,9 +69,6 @@ msft.splits
 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:
@@ -96,25 +88,6 @@ msft.major_holders
 msft.institutional_holders
 msft.mutualfund_holders
 
-# show earnings
-msft.earnings
-msft.quarterly_earnings
-
-# show sustainability
-msft.sustainability
-
-# show analysts recommendations
-msft.recommendations
-msft.recommendations_summary
-# show analysts other work
-msft.analyst_price_target
-msft.revenue_forecasts
-msft.earnings_forecasts
-msft.earnings_trend
-
-# show next event (earnings, etc)
-msft.calendar
-
 # 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
@@ -171,20 +144,14 @@ To download price history into one table:
 
 ```python
 import yfinance as yf
-data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
+data = yf.download("SPY AAPL", period="1mo")
 ```
 
-`yf.download()` and `Ticker.history()` have many options for configuring fetching and processing, e.g.:
+#### `yf.download()` and `Ticker.history()` have many options for configuring fetching and processing. [Review the Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
 
-```python
-yf.download(tickers = "SPY AAPL",  # list of tickers
-            period = "1y",         # time period
-            interval = "1d",       # trading interval
-            prepost = False,       # download pre/post market hours data?
-            repair = True)         # repair obvious price errors e.g. 100x?
-```
+### Logging
 
-Review the [Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
+`yfinance` now uses the `logging` module to handle messages, default behaviour is only print errors. If debugging, use `yf.enable_debug_mode()` to switch logging to debug with custom formatting.
 
 ### Smarter scraping
 
@@ -211,7 +178,7 @@ class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
     pass
 
 session = CachedLimiterSession(
-    limiter=Limiter(RequestRate(2, Duration.SECOND*5),  # max 2 requests per 5 seconds
+    limiter=Limiter(RequestRate(2, Duration.SECOND*5)),  # max 2 requests per 5 seconds
     bucket_class=MemoryQueueBucket,
     backend=SQLiteCache("yfinance.cache"),
 )
@@ -271,6 +238,11 @@ Install `yfinance` using `pip`:
 $ pip install yfinance --upgrade --no-cache-dir
 ```
 
+Test new features by installing betas, provide feedback in [corresponding Discussion](https://github.com/ranaroussi/yfinance/discussions):
+``` {.sourceCode .bash}
+$ pip install yfinance --upgrade --no-cache-dir --pre
+```
+
 To install `yfinance` using `conda`, see
 [this](https://anaconda.org/ranaroussi/yfinance).
 
@@ -279,14 +251,13 @@ To install `yfinance` using `conda`, see
 -   [Python](https://www.python.org) \>= 2.7, 3.4+
 -   [Pandas](https://github.com/pydata/pandas) \>= 1.3.0
 -   [Numpy](http://www.numpy.org) \>= 1.16.5
--   [requests](http://docs.python-requests.org/en/master) \>= 2.26
+-   [requests](http://docs.python-requests.org/en/master) \>= 2.31
 -   [lxml](https://pypi.org/project/lxml) \>= 4.9.1
 -   [appdirs](https://pypi.org/project/appdirs) \>= 1.4.4
 -   [pytz](https://pypi.org/project/pytz) \>=2022.5
 -   [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 = "0.2.17" %}
+{% set version = "0.2.28" %}
 
 package:
   name: "{{ name|lower }}"
@@ -18,7 +18,7 @@ requirements:
   host:
     - pandas >=1.3.0
     - numpy >=1.16.5
-    - requests >=2.26
+    - requests >=2.31
     - multitasking >=0.0.7
     - lxml >=4.9.1
     - appdirs >=1.4.4
@@ -27,14 +27,13 @@ requirements:
     - beautifulsoup4 >=4.11.1
     - html5lib >=1.1
     # - pycryptodome >=3.6.6
-    - cryptography >=3.3.2
     - pip
     - python
 
   run:
     - pandas >=1.3.0
     - numpy >=1.16.5
-    - requests >=2.26
+    - requests >=2.31
     - multitasking >=0.0.7
     - lxml >=4.9.1
     - appdirs >=1.4.4
@@ -43,7 +42,6 @@ requirements:
     - beautifulsoup4 >=4.11.1
     - html5lib >=1.1
     # - pycryptodome >=3.6.6
-    - cryptography >=3.3.2
     - python
 
 test:

requirements.txt

@@ -1,6 +1,6 @@
 pandas>=1.3.0
 numpy>=1.16.5
-requests>=2.26
+requests>=2.31
 multitasking>=0.0.7
 lxml>=4.9.1
 appdirs>=1.4.4
@@ -8,4 +8,3 @@ pytz>=2022.5
 frozendict>=2.3.4
 beautifulsoup4>=4.11.1
 html5lib>=1.1
-cryptography>=3.3.2

setup.py

@@ -39,7 +39,7 @@ setup(
         'License :: OSI Approved :: Apache Software License',
         # 'Development Status :: 3 - Alpha',
         'Development Status :: 4 - Beta',
-        #'Development Status :: 5 - Production/Stable',
+        # 'Development Status :: 5 - Production/Stable',
 
 
         'Operating System :: OS Independent',
@@ -60,12 +60,11 @@ setup(
     keywords='pandas, yahoo finance, pandas datareader',
     packages=find_packages(exclude=['contrib', 'docs', 'tests', 'examples']),
     install_requires=['pandas>=1.3.0', 'numpy>=1.16.5',
-                      'requests>=2.26', 'multitasking>=0.0.7',
+                      'requests>=2.31', 'multitasking>=0.0.7',
                       'lxml>=4.9.1', 'appdirs>=1.4.4', 'pytz>=2022.5',
                       'frozendict>=2.3.4', 
-                      # 'pycryptodome>=3.6.6',
-                      'cryptography>=3.3.2',
                       'beautifulsoup4>=4.11.1', 'html5lib>=1.1'],
+    # Note: Pandas.read_html() needs html5lib & beautifulsoup4
     entry_points={
         'console_scripts': [
             'sample=sample:main',

test_yfinance.py

@@ -15,6 +15,9 @@ Sanity check for most common library uses all working
 
 import yfinance as yf
 import unittest
+import logging
+
+logging.basicConfig(level=logging.DEBUG)
 
 symbols = ['MSFT', 'IWO', 'VFINX', '^GSPC', 'BTC-USD']
 tickers = [yf.Ticker(symbol) for symbol in symbols]

tests/context.py

@@ -7,3 +7,37 @@ _src_dp = _parent_dp
 sys.path.insert(0, _src_dp)
 
 import yfinance
+
+
+# Optional: see the exact requests that are made during tests:
+# import logging
+# logging.basicConfig(level=logging.DEBUG)
+
+
+# Setup a session to rate-limit and cache persistently:
+import datetime as _dt
+import os
+import appdirs as _ad
+from requests import Session
+from requests_cache import CacheMixin, SQLiteCache
+from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
+class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
+    pass
+from pyrate_limiter import Duration, RequestRate, Limiter
+history_rate = RequestRate(1, Duration.SECOND*2)
+limiter = Limiter(history_rate)
+cache_fp = os.path.join(_ad.user_cache_dir(), "py-yfinance", "unittests-cache")
+if os.path.isfile(cache_fp + '.sqlite'):
+    # Delete local cache if older than 1 day:
+    mod_dt = _dt.datetime.fromtimestamp(os.path.getmtime(cache_fp + '.sqlite'))
+    if mod_dt.date() < _dt.date.today():
+        os.remove(cache_fp + '.sqlite')
+session_gbl = CachedLimiterSession(
+    limiter=limiter,
+    bucket_class=MemoryQueueBucket,
+    backend=SQLiteCache(cache_fp, expire_after=_dt.timedelta(hours=1)),
+)
+# Use this instead if only want rate-limiting:
+# from requests_ratelimiter import LimiterSession
+# session_gbl = LimiterSession(limiter=limiter)
+

tests/data/4063-T-1d-bad-stock-split-fixed.csv

@@ -0,0 +1,23 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-04-14 00:00:00+09:00,4126,4130,4055,4129,4129,7459400,0,0
+2023-04-13 00:00:00+09:00,4064,4099,4026,4081,4081,5160200,0,0
+2023-04-12 00:00:00+09:00,3968,4084,3966,4064,4064,6372000,0,0
+2023-04-11 00:00:00+09:00,3990,4019,3954,3960,3960,6476500,0,0
+2023-04-10 00:00:00+09:00,3996,4009,3949,3964,3964,3485200,0,0
+2023-04-07 00:00:00+09:00,3897,3975,3892,3953,3953,4554700,0,0
+2023-04-06 00:00:00+09:00,4002,4004,3920,3942,3942,8615200,0,0
+2023-04-05 00:00:00+09:00,4150,4150,4080,4088,4088,6063700,0,0
+2023-04-04 00:00:00+09:00,4245,4245,4144,4155,4155,6780600,0,0
+2023-04-03 00:00:00+09:00,4250,4259,4162,4182,4182,7076800,0,0
+2023-03-31 00:00:00+09:00,4229,4299,4209,4275,4275,9608400,0,0
+2023-03-30 00:00:00+09:00,4257,4268,4119,4161,4161,5535200,55,5
+2023-03-29 00:00:00+09:00,4146,4211,4146,4206,4151,6514500,0,0
+2023-03-28 00:00:00+09:00,4200,4207,4124,4142,4087.837109375,4505500,0,0
+2023-03-27 00:00:00+09:00,4196,4204,4151,4192,4137.183203125,5959500,0,0
+2023-03-24 00:00:00+09:00,4130,4187,4123,4177,4122.379296875,8961500,0,0
+2023-03-23 00:00:00+09:00,4056,4106,4039,4086,4032.569140625,5480000,0,0
+2023-03-22 00:00:00+09:00,4066,4128,4057,4122,4068.0984375,8741500,0,0
+2023-03-20 00:00:00+09:00,4000,4027,3980,3980,3927.95546875,7006500,0,0
+2023-03-17 00:00:00+09:00,4018,4055,4016,4031,3978.28828125,6961500,0,0
+2023-03-16 00:00:00+09:00,3976,4045,3972,4035,3982.236328125,5019000,0,0
+2023-03-15 00:00:00+09:00,4034,4050,4003,4041,3988.1578125,6122000,0,0

tests/data/4063-T-1d-bad-stock-split.csv

@@ -0,0 +1,23 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-04-14 00:00:00+09:00,4126,4130,4055,4129,4129,7459400,0,0
+2023-04-13 00:00:00+09:00,4064,4099,4026,4081,4081,5160200,0,0
+2023-04-12 00:00:00+09:00,3968,4084,3966,4064,4064,6372000,0,0
+2023-04-11 00:00:00+09:00,3990,4019,3954,3960,3960,6476500,0,0
+2023-04-10 00:00:00+09:00,3996,4009,3949,3964,3964,3485200,0,0
+2023-04-07 00:00:00+09:00,3897,3975,3892,3953,3953,4554700,0,0
+2023-04-06 00:00:00+09:00,4002,4004,3920,3942,3942,8615200,0,0
+2023-04-05 00:00:00+09:00,4150,4150,4080,4088,4088,6063700,0,0
+2023-04-04 00:00:00+09:00,4245,4245,4144,4155,4155,6780600,0,0
+2023-04-03 00:00:00+09:00,4250,4259,4162,4182,4182,7076800,0,0
+2023-03-31 00:00:00+09:00,4229,4299,4209,4275,4275,9608400,0,0
+2023-03-30 00:00:00+09:00,4257,4268,4119,4161,4161,5535200,55,5
+2023-03-29 00:00:00+09:00,4146,4211,4146,4206,4151,6514500,0,0
+2023-03-28 00:00:00+09:00,21000,21035,20620,20710,20439.185546875,901100,0,0
+2023-03-27 00:00:00+09:00,20980,21020,20755,20960,20685.916015625,1191900,0,0
+2023-03-24 00:00:00+09:00,20650,20935,20615,20885,20611.896484375,1792300,0,0
+2023-03-23 00:00:00+09:00,20280,20530,20195,20430,20162.845703125,1096000,0,0
+2023-03-22 00:00:00+09:00,20330,20640,20285,20610,20340.4921875,1748300,0,0
+2023-03-20 00:00:00+09:00,20000,20135,19900,19900,19639.77734375,1401300,0,0
+2023-03-17 00:00:00+09:00,20090,20275,20080,20155,19891.44140625,1392300,0,0
+2023-03-16 00:00:00+09:00,19880,20225,19860,20175,19911.181640625,1003800,0,0
+2023-03-15 00:00:00+09:00,20170,20250,20015,20205,19940.7890625,1224400,0,0

tests/data/8TRA-DE-1d-missing-div-adjust-fixed.csv

@@ -0,0 +1,6 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-05-30 00:00:00+02:00,19.5900001525879,19.7999992370605,19.2700004577637,19.3500003814697,18.6291382416581,196309,0,0
+2023-05-31 00:00:00+02:00,19.1200008392334,19.1399993896484,18.7000007629395,18.7900009155273,18.0900009155273,156652,0,0
+2023-06-02 00:00:00+02:00,18.5499992370605,19,18.5100002288818,18.8999996185303,18.8999996185303,83439,0.7,0
+2023-06-05 00:00:00+02:00,18.9300003051758,19.0900001525879,18.8400001525879,19,19,153167,0,0
+2023-06-06 00:00:00+02:00,18.9099998474121,18.9500007629395,18.5100002288818,18.6599998474121,18.6599998474121,104352,0,0

tests/data/8TRA-DE-1d-missing-div-adjust.csv

@@ -0,0 +1,6 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-05-30 00:00:00+02:00,19.59000015258789,19.799999237060547,19.270000457763672,19.350000381469727,19.350000381469727,196309,0.0,0.0
+2023-05-31 00:00:00+02:00,19.1200008392334,19.139999389648438,18.700000762939453,18.790000915527344,18.790000915527344,156652,0.0,0.0
+2023-06-02 00:00:00+02:00,18.549999237060547,19.0,18.510000228881836,18.899999618530273,18.899999618530273,83439,0.7,0.0
+2023-06-05 00:00:00+02:00,18.93000030517578,19.09000015258789,18.84000015258789,19.0,19.0,153167,0.0,0.0
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tests/data/AET-L-1d-100x-error-fixed.csv

@@ -0,0 +1,24 @@
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tests/data/AET-L-1d-100x-error.csv

@@ -0,0 +1,24 @@
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tests/data/AET-L-1wk-100x-error-fixed.csv

@@ -0,0 +1,37 @@
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tests/data/AET-L-1wk-100x-error-fixed.csv.old

@@ -0,0 +1,25 @@
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tests/data/AET-L-1wk-100x-error.csv

@@ -0,0 +1,37 @@
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tests/data/AET-L-1wk-100x-error.csv.old

@@ -0,0 +1,25 @@
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tests/data/ALPHA-PA-1d-bad-stock-split-fixed.csv

@@ -0,0 +1,30 @@
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tests/data/ALPHA-PA-1d-bad-stock-split.csv

@@ -0,0 +1,30 @@
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tests/data/AV-L-1wk-bad-stock-split-fixed.csv

@@ -0,0 +1,85 @@
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tests/data/AV-L-1wk-bad-stock-split.csv

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tests/data/CNE-L-1d-bad-stock-split-fixed.csv

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tests/data/CNE-L-1d-bad-stock-split.csv

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tests/data/DEX-AX-1d-bad-stock-split-fixed.csv

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tests/data/DEX-AX-1d-bad-stock-split.csv

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tests/data/LA-V-1d-bad-stock-split-fixed.csv

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tests/data/LA-V-1d-bad-stock-split.csv

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tests/data/MOB-ST-1d-bad-stock-split-fixed.csv

@@ -0,0 +1,17 @@
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tests/data/MOB-ST-1d-bad-stock-split.csv

@@ -0,0 +1,17 @@
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tests/data/SPM-MI-1d-bad-stock-split-fixed.csv

@@ -0,0 +1,23 @@
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tests/data/SPM-MI-1d-bad-stock-split.csv

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+2022-06-23 00:00:00+02:00,1.6241190433502197,1.6852680444717407,1.3732000589370728,1.5977619886398315,1.5977619886398315,48720201,0.0,0.0
+2022-06-24 00:00:00+02:00,1.475993037223816,1.5461030006408691,1.1739410161972046,1.2493220567703247,1.2493220567703247,56877192,0.0,0.0
+2022-06-27 00:00:00+02:00,1.4989999532699585,1.7984999418258667,1.4989999532699585,1.7984999418258667,1.7984999418258667,460673,0.0,0.0
+2022-06-28 00:00:00+02:00,2.1579999923706055,3.0510001182556152,2.125999927520752,3.0510001182556152,3.0510001182556152,3058635,0.0,0.0
+2022-06-29 00:00:00+02:00,2.9000000953674316,3.73799991607666,2.8589999675750732,3.2639999389648438,3.2639999389648438,6516761,0.0,0.0
+2022-06-30 00:00:00+02:00,3.249000072479248,3.2809998989105225,2.5,2.555000066757202,2.555000066757202,4805984,0.0,0.0

tests/data/SSW-JO-1d-100x-error-fixed.csv

@@ -0,0 +1,30 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-06-09 00:00:00+02:00,34.7000,34.7100,33.2400,33.6200,33.6200,7148409,0,0
+2023-06-08 00:00:00+02:00,34.9000,34.9900,34.0400,34.3600,34.3600,10406999,0,0
+2023-06-07 00:00:00+02:00,34.5500,35.6400,34.3200,35.0900,35.0900,10118918,0,0
+2023-06-06 00:00:00+02:00,34.5000,34.8200,34.0500,34.4600,34.4600,9109709,0,0
+2023-06-05 00:00:00+02:00,35.0000,35.3000,34.2000,34.7000,34.7000,8791993,0,0
+2023-06-02 00:00:00+02:00,35.6900,36.1800,34.6000,34.9700,34.9700,8844549,0,0
+2023-06-01 00:00:00+02:00,35.2300,35.3800,34.2400,35.3500,35.3500,6721030,0,0
+2023-05-31 00:00:00+02:00,34.8,35.48,34.26,35.01,35.01,32605833,0,0
+2023-05-30 00:00:00+02:00,34.39,35.37,33.85,34.23,34.23,8970804,0,0
+2023-05-29 00:00:00+02:00,34.66,35.06,34.02,34.32,34.32,3912803,0,0
+2023-05-26 00:00:00+02:00,34.75,35.99,34.33,34.53,34.53,6744718,0,0
+2023-05-25 00:00:00+02:00,35.4,36.09,34.63,35.07,35.07,16900221,0,0
+2023-05-24 00:00:00+02:00,36.2,36.5,35.26,35.4,35.4,9049505,0,0
+2023-05-23 00:00:00+02:00,36.9,36.67,35.56,36.1,36.1,10797373,0,0
+2023-05-22 00:00:00+02:00,37.05,37.36,36.09,36.61,36.61,7132641,0,0
+2023-05-19 00:00:00+02:00,36.2,37.15,36.25,36.9,36.9,12648518,0,0
+2023-05-18 00:00:00+02:00,36.57,36.99,35.84,36.46,36.46,10674542,0,0
+2023-05-17 00:00:00+02:00,36.87,37.31,36.56,36.71,36.71,9892791,0,0
+2023-05-16 00:00:00+02:00,37.15,37.73,36.96,37.03,37.03,4706789,0,0
+2023-05-15 00:00:00+02:00,37.74,38.05,36.96,37.27,37.27,7890969,0,0
+2023-05-12 00:00:00+02:00,37.5,38.44,36.71,37.74,37.74,8724303,0,0
+2023-05-11 00:00:00+02:00,38.8,38.88,37.01,37.32,37.32,14371855,0,0
+2023-05-10 00:00:00+02:00,38.93,38.8,36.42,38.1,38.1,30393389,0,0
+2023-05-09 00:00:00+02:00,44.41,44.41,39.39,39.66,39.66,19833428,0,0
+2023-05-08 00:00:00+02:00,44.63,45.78,44.56,44.71,44.71,11092519,0,0
+2023-05-05 00:00:00+02:00,42.99,44.9,42.87,44.58,44.58,28539048,0,0
+2023-05-04 00:00:00+02:00,41.49,43.3,41.23,42.83,42.83,15506868,0,0
+2023-05-03 00:00:00+02:00,39.75,40.98,39.68,40.95,40.95,14657028,0,0
+2023-05-02 00:00:00+02:00,40.37,40.32,39.17,39.65,39.65,11818133,0,0

tests/data/SSW-JO-1d-100x-error.csv

@@ -0,0 +1,30 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-06-09 00:00:00+02:00,34.700001,34.709999,33.240002,33.619999,33.619999,7148409,0,0
+2023-06-08 00:00:00+02:00,34.900002,34.990002,34.040001,34.360001,34.360001,10406999,0,0
+2023-06-07 00:00:00+02:00,34.549999,35.639999,34.320000,35.090000,35.090000,10118918,0,0
+2023-06-06 00:00:00+02:00,34.500000,34.820000,34.049999,34.459999,34.459999,9109709,0,0
+2023-06-05 00:00:00+02:00,35.000000,35.299999,34.200001,34.700001,34.700001,8791993,0,0
+2023-06-02 00:00:00+02:00,35.689999,36.180000,34.599998,34.970001,34.970001,8844549,0,0
+2023-06-01 00:00:00+02:00,35.230000,35.380001,34.240002,35.349998,35.349998,6721030,0,0
+2023-05-31 00:00:00+02:00,3480,3548,3426,3501,3501,32605833,0,0
+2023-05-30 00:00:00+02:00,3439,3537,3385,3423,3423,8970804,0,0
+2023-05-29 00:00:00+02:00,3466,3506,3402,3432,3432,3912803,0,0
+2023-05-26 00:00:00+02:00,3475,3599,3433,3453,3453,6744718,0,0
+2023-05-25 00:00:00+02:00,3540,3609,3463,3507,3507,16900221,0,0
+2023-05-24 00:00:00+02:00,3620,3650,3526,3540,3540,9049505,0,0
+2023-05-23 00:00:00+02:00,3690,3667,3556,3610,3610,10797373,0,0
+2023-05-22 00:00:00+02:00,3705,3736,3609,3661,3661,7132641,0,0
+2023-05-19 00:00:00+02:00,3620,3715,3625,3690,3690,12648518,0,0
+2023-05-18 00:00:00+02:00,3657,3699,3584,3646,3646,10674542,0,0
+2023-05-17 00:00:00+02:00,3687,3731,3656,3671,3671,9892791,0,0
+2023-05-16 00:00:00+02:00,3715,3773,3696,3703,3703,4706789,0,0
+2023-05-15 00:00:00+02:00,3774,3805,3696,3727,3727,7890969,0,0
+2023-05-12 00:00:00+02:00,3750,3844,3671,3774,3774,8724303,0,0
+2023-05-11 00:00:00+02:00,3880,3888,3701,3732,3732,14371855,0,0
+2023-05-10 00:00:00+02:00,3893,3880,3642,3810,3810,30393389,0,0
+2023-05-09 00:00:00+02:00,4441,4441,3939,3966,3966,19833428,0,0
+2023-05-08 00:00:00+02:00,4463,4578,4456,4471,4471,11092519,0,0
+2023-05-05 00:00:00+02:00,4299,4490,4287,4458,4458,28539048,0,0
+2023-05-04 00:00:00+02:00,4149,4330,4123,4283,4283,15506868,0,0
+2023-05-03 00:00:00+02:00,3975,4098,3968,4095,4095,14657028,0,0
+2023-05-02 00:00:00+02:00,4037,4032,3917,3965,3965,11818133,0,0

tests/prices.py

@@ -1,21 +1,19 @@
 from .context import yfinance as yf
+from .context import session_gbl
 
 import unittest
 
+import os
 import datetime as _dt
 import pytz as _tz
 import numpy as _np
 import pandas as _pd
 
-import requests_cache
-
 
 class TestPriceHistory(unittest.TestCase):
-    session = None
-
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -34,11 +32,23 @@ class TestPriceHistory(unittest.TestCase):
                 f = df.index.time == _dt.time(0)
                 self.assertTrue(f.all())
 
+    def test_download(self):
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+        intervals = ["1d", "1wk", "1mo"]
+        for interval in intervals:
+            df = yf.download(tkrs, period="5y", interval=interval)
+
+            f = df.index.time == _dt.time(0)
+            self.assertTrue(f.all())
+
+            df_tkrs = df.columns.levels[1]
+            self.assertEqual(sorted(tkrs), sorted(df_tkrs))
+
     def test_duplicatingHourly(self):
         tkrs = ["IMP.JO", "BHG.JO", "SSW.JO", "BP.L", "INTC"]
         for tkr in tkrs:
             dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(debug_mode=False, proxy=None, timeout=None)
+            tz = dat._get_ticker_tz(proxy=None, timeout=None)
 
             dt_utc = _tz.timezone("UTC").localize(_dt.datetime.utcnow())
             dt = dt_utc.astimezone(_tz.timezone(tz))
@@ -49,7 +59,7 @@ class TestPriceHistory(unittest.TestCase):
             dt1 = df.index[-1]
             try:
                 self.assertNotEqual(dt0.hour, dt1.hour)
-            except:
+            except AssertionError:
                 print("Ticker = ", tkr)
                 raise
 
@@ -58,7 +68,7 @@ class TestPriceHistory(unittest.TestCase):
         test_run = False
         for tkr in tkrs:
             dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(debug_mode=False, proxy=None, timeout=None)
+            tz = dat._get_ticker_tz(proxy=None, timeout=None)
 
             dt_utc = _tz.timezone("UTC").localize(_dt.datetime.utcnow())
             dt = dt_utc.astimezone(_tz.timezone(tz))
@@ -72,7 +82,7 @@ class TestPriceHistory(unittest.TestCase):
             dt1 = df.index[-1]
             try:
                 self.assertNotEqual(dt0, dt1)
-            except:
+            except AssertionError:
                 print("Ticker = ", tkr)
                 raise
 
@@ -84,7 +94,7 @@ class TestPriceHistory(unittest.TestCase):
         test_run = False
         for tkr in tkrs:
             dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(debug_mode=False, proxy=None, timeout=None)
+            tz = dat._get_ticker_tz(proxy=None, timeout=None)
 
             dt = _tz.timezone(tz).localize(_dt.datetime.now())
             if dt.date().weekday() not in [1, 2, 3, 4]:
@@ -96,7 +106,7 @@ class TestPriceHistory(unittest.TestCase):
             dt1 = df.index[-1]
             try:
                 self.assertNotEqual(dt0.week, dt1.week)
-            except:
+            except AssertionError:
                 print("Ticker={}: Last two rows within same week:".format(tkr))
                 print(df.iloc[df.shape[0] - 2:])
                 raise
@@ -105,6 +115,32 @@ class TestPriceHistory(unittest.TestCase):
             self.skipTest("Skipping test_duplicatingWeekly() because not possible to fail Monday/weekend")
 
     def test_intraDayWithEvents(self):
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+        test_run = False
+        for tkr in 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:
+                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_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
+            test_run = True
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
+
+    def test_intraDayWithEvents_tase(self):
         # TASE dividend release pre-market, doesn't merge nicely with intra-day data so check still present
 
         tase_tkrs = ["ICL.TA", "ESLT.TA", "ONE.TA", "MGDL.TA"]
@@ -115,21 +151,46 @@ class TestPriceHistory(unittest.TestCase):
             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())
+            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
             test_run = True
-            break
 
         if not test_run:
-            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
+            self.skipTest("Skipping test_intraDayWithEvents_tase() because no tickers had a dividend in last 60 days")
 
     def test_dailyWithEvents(self):
+        start_d = _dt.date(2022, 1, 1)
+        end_d = _dt.date(2023, 1, 1)
+
+        tkr_div_dates = {'BHP.AX': [_dt.date(2022, 9, 1), _dt.date(2022, 2, 24)],  # Yahoo claims 23-Feb but wrong because DST
+                         'IMP.JO': [_dt.date(2022, 9, 21), _dt.date(2022, 3, 16)],
+                         'BP.L': [_dt.date(2022, 11, 10), _dt.date(2022, 8, 11), _dt.date(2022, 5, 12),
+                                  _dt.date(2022, 2, 17)],
+                         'INTC': [_dt.date(2022, 11, 4), _dt.date(2022, 8, 4), _dt.date(2022, 5, 5),
+                                  _dt.date(2022, 2, 4)]}
+
+        for tkr, dates in tkr_div_dates.items():
+            df = yf.Ticker(tkr, session=self.session).history(interval='1d', start=start_d, end=end_d)
+            df_divs = df[df['Dividends'] != 0].sort_index(ascending=False)
+            try:
+                self.assertTrue((df_divs.index.date == dates).all())
+            except AssertionError:
+                print(f'- ticker = {tkr}')
+                print('- response:') ; print(df_divs.index.date)
+                print('- answer:') ; print(dates)
+                raise
+
+    def test_dailyWithEvents_bugs(self):
         # Reproduce issue #521
         tkr1 = "QQQ"
         tkr2 = "GDX"
@@ -141,7 +202,7 @@ class TestPriceHistory(unittest.TestCase):
         self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
         try:
             self.assertTrue(df1.index.equals(df2.index))
-        except:
+        except AssertionError:
             missing_from_df1 = df2.index.difference(df1.index)
             missing_from_df2 = df1.index.difference(df2.index)
             print("{} missing these dates: {}".format(tkr1, missing_from_df1))
@@ -156,13 +217,76 @@ class TestPriceHistory(unittest.TestCase):
             self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
             try:
                 self.assertTrue(df1.index.equals(df2.index))
-            except:
+            except AssertionError:
                 missing_from_df1 = df2.index.difference(df1.index)
                 missing_from_df2 = df1.index.difference(df2.index)
                 print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
                 print("{}-without-events missing these dates: {}".format(tkr, missing_from_df2))
                 raise
 
+        # Reproduce issue #1634 - 1d dividend out-of-range, should be prepended to prices
+        div_dt = _pd.Timestamp(2022, 7, 21).tz_localize("America/New_York")
+        df_dividends = _pd.DataFrame(data={"Dividends":[1.0]}, index=[div_dt])
+        df_prices = _pd.DataFrame(data={c:[1.0] for c in yf.const.price_colnames}|{'Volume':0}, index=[div_dt+_dt.timedelta(days=1)])
+        df_merged = yf.utils.safe_merge_dfs(df_prices, df_dividends, '1d')
+        self.assertEqual(df_merged.shape[0], 2)
+        self.assertTrue(df_merged[df_prices.columns].iloc[1:].equals(df_prices))
+        self.assertEqual(df_merged.index[0], div_dt)
+
+    def test_intraDayWithEvents(self):
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+        test_run = False
+        for tkr in 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:
+                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_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
+            test_run = True
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
+
+    def test_intraDayWithEvents_tase(self):
+        # TASE dividend release pre-market, doesn't merge nicely with intra-day data so check still present
+
+        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:
+                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_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
+            test_run = True
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents_tase() because no tickers had a dividend in last 60 days")
+
     def test_weeklyWithEvents(self):
         # Reproduce issue #521
         tkr1 = "QQQ"
@@ -175,7 +299,7 @@ class TestPriceHistory(unittest.TestCase):
         self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
         try:
             self.assertTrue(df1.index.equals(df2.index))
-        except:
+        except AssertionError:
             missing_from_df1 = df2.index.difference(df1.index)
             missing_from_df2 = df1.index.difference(df2.index)
             print("{} missing these dates: {}".format(tkr1, missing_from_df1))
@@ -190,7 +314,7 @@ class TestPriceHistory(unittest.TestCase):
             self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
             try:
                 self.assertTrue(df1.index.equals(df2.index))
-            except:
+            except AssertionError:
                 missing_from_df1 = df2.index.difference(df1.index)
                 missing_from_df2 = df1.index.difference(df2.index)
                 print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
@@ -208,7 +332,7 @@ class TestPriceHistory(unittest.TestCase):
         self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
         try:
             self.assertTrue(df1.index.equals(df2.index))
-        except:
+        except AssertionError:
             missing_from_df1 = df2.index.difference(df1.index)
             missing_from_df2 = df1.index.difference(df2.index)
             print("{} missing these dates: {}".format(tkr1, missing_from_df1))
@@ -223,7 +347,7 @@ class TestPriceHistory(unittest.TestCase):
             self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
             try:
                 self.assertTrue(df1.index.equals(df2.index))
-            except:
+            except AssertionError:
                 missing_from_df1 = df2.index.difference(df1.index)
                 missing_from_df2 = df1.index.difference(df2.index)
                 print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
@@ -232,8 +356,19 @@ class TestPriceHistory(unittest.TestCase):
 
     def test_monthlyWithEvents2(self):
         # Simply check no exception from internal merge
-        tkr = "ABBV"
-        yf.Ticker("ABBV").history(period="max", interval="1mo")
+        dfm = yf.Ticker("ABBV").history(period="max", interval="1mo")
+        dfd = yf.Ticker("ABBV").history(period="max", interval="1d")
+        dfd = dfd[dfd.index > dfm.index[0]]
+        dfm_divs = dfm[dfm['Dividends'] != 0]
+        dfd_divs = dfd[dfd['Dividends'] != 0]
+        self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
+
+        dfm = yf.Ticker("F").history(period="50mo", interval="1mo")
+        dfd = yf.Ticker("F").history(period="50mo", interval="1d")
+        dfd = dfd[dfd.index > dfm.index[0]]
+        dfm_divs = dfm[dfm['Dividends'] != 0]
+        dfd_divs = dfd[dfd['Dividends'] != 0]
+        self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
 
     def test_tz_dst_ambiguous(self):
         # Reproduce issue #1100
@@ -263,7 +398,7 @@ class TestPriceHistory(unittest.TestCase):
         df = dat.history(start=start, end=end, interval=interval)
         try:
             self.assertTrue((df.index.weekday == 0).all())
-        except:
+        except AssertionError:
             print("Weekly data not aligned to Monday")
             raise
 
@@ -315,18 +450,18 @@ class TestPriceHistory(unittest.TestCase):
         interval = "1h"
         interval_td = _dt.timedelta(hours=1)
         time_open = _dt.time(9)
-        time_close = _dt.time(17,30)
+        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)]]
+        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)]
+        expected_incorrect_half_days = [_dt.date(2022, 4, 13)]
         half_days = sorted(half_days+expected_incorrect_half_days)
 
         # Run
@@ -343,7 +478,7 @@ class TestPriceHistory(unittest.TestCase):
         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]
+        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())
@@ -359,7 +494,7 @@ class TestPriceHistory(unittest.TestCase):
         interval = "1h"
         interval_td = _dt.timedelta(hours=1)
         time_open = _dt.time(10)
-        time_close = _dt.time(16,12)
+        time_close = _dt.time(16, 12)
         # No early closes in 2022
         dat = yf.Ticker(tkr, session=self.session)
 
@@ -396,12 +531,13 @@ class TestPriceHistory(unittest.TestCase):
 
         df = dat.history(start=start, end=end, interval=interval)
 
+
 class TestPriceRepair(unittest.TestCase):
     session = None
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -428,7 +564,7 @@ class TestPriceRepair(unittest.TestCase):
             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):
+    def test_repair_100x_random_weekly(self):
         # Setup:
         tkr = "PNL.L"
         dat = yf.Ticker(tkr, session=self.session)
@@ -439,12 +575,12 @@ class TestPriceRepair(unittest.TestCase):
                                  "High":      [476,   476.5, 477,   480],
                                  "Low":       [470.5, 470,   465.5, 468.26],
                                  "Close":     [475,   473.5, 472,   473.5],
-                                 "Adj Close": [475,   473.5, 472,   473.5],
+                                 "Adj Close": [470.1, 468.6, 467.1, 468.6],
                                  "Volume": [2295613, 2245604, 3000287, 2635611]},
-                                index=_pd.to_datetime([_dt.date(2022, 10, 24),
-                                                       _dt.date(2022, 10, 17),
-                                                       _dt.date(2022, 10, 10),
-                                                       _dt.date(2022, 10, 3)]))
+                           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()
@@ -456,18 +592,17 @@ class TestPriceRepair(unittest.TestCase):
 
         # Run test
 
-        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange, prepost=False)
+        df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
             try:
                 self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-            except:
+            except AssertionError:
                 print(df[c])
                 print(df_repaired[c])
                 raise
 
-
         # Second test - all differences should be either ~1x or ~100x
         ratio = df_bad[data_cols].values / df[data_cols].values
         ratio = ratio.round(2)
@@ -479,7 +614,10 @@ class TestPriceRepair(unittest.TestCase):
         f_1 = ratio == 1
         self.assertTrue((f_100 | f_1).all())
 
-    def test_repair_100x_weekly_preSplit(self):
+        self.assertTrue("Repaired?" in df_repaired.columns)
+        self.assertFalse(df_repaired["Repaired?"].isna().any())
+
+    def test_repair_100x_random_weekly_preSplit(self):
         # PNL.L has a stock-split in 2022. Sometimes requesting data before 2022 is not split-adjusted.
 
         tkr = "PNL.L"
@@ -487,16 +625,16 @@ class TestPriceRepair(unittest.TestCase):
         tz_exchange = dat.fast_info["timezone"]
 
         data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
-        df = _pd.DataFrame(data={"Open":      [400,   398,    392.5,   417],
-                                 "High":      [421,   425,    419,     420.5],
-                                 "Low":       [400,   380.5,  376.5,   396],
-                                 "Close":     [410,   409.5,  402,     399],
-                                 "Adj Close": [398.02, 397.53, 390.25, 387.34],
+        df = _pd.DataFrame(data={"Open":      [400,    398,    392.5,  417],
+                                 "High":      [421,    425,    419,    420.5],
+                                 "Low":       [400,    380.5,  376.5,  396],
+                                 "Close":     [410,    409.5,  402,    399],
+                                 "Adj Close": [393.91, 393.43, 386.22, 383.34],
                                  "Volume": [3232600, 3773900, 10835000, 4257900]},
-                                index=_pd.to_datetime([_dt.date(2020, 3, 30),
-                                                       _dt.date(2020, 3, 23),
-                                                       _dt.date(2020, 3, 16),
-                                                       _dt.date(2020, 3, 9)]))
+                           index=_pd.to_datetime([_dt.date(2020, 3, 30),
+                                                  _dt.date(2020, 3, 23),
+                                                  _dt.date(2020, 3, 16),
+                                                  _dt.date(2020, 3, 9)]))
         df = df.sort_index()
         # Simulate data missing split-adjustment:
         df[data_cols] *= 100.0
@@ -511,13 +649,13 @@ class TestPriceRepair(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, prepost=False)
+        df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
             try:
                 self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-            except:
+            except AssertionError:
                 print("Mismatch in column", c)
                 print("- df_repaired:")
                 print(df_repaired[c])
@@ -536,7 +674,10 @@ class TestPriceRepair(unittest.TestCase):
         f_1 = ratio == 1
         self.assertTrue((f_100 | f_1).all())
 
-    def test_repair_100x_daily(self):
+        self.assertTrue("Repaired?" in df_repaired.columns)
+        self.assertFalse(df_repaired["Repaired?"].isna().any())
+
+    def test_repair_100x_random_daily(self):
         tkr = "PNL.L"
         dat = yf.Ticker(tkr, session=self.session)
         tz_exchange = dat.fast_info["timezone"]
@@ -548,10 +689,10 @@ class TestPriceRepair(unittest.TestCase):
                                  "Close":     [475.5,  475.5, 474.5, 475],
                                  "Adj Close": [475.5,  475.5, 474.5, 475],
                                  "Volume": [436414, 485947, 358067, 287620]},
-                            index=_pd.to_datetime([_dt.date(2022, 11, 1),
-                                                   _dt.date(2022, 10, 31),
-                                                   _dt.date(2022, 10, 28),
-                                                   _dt.date(2022, 10, 27)]))
+                           index=_pd.to_datetime([_dt.date(2022, 11, 1),
+                                                  _dt.date(2022, 10, 31),
+                                                  _dt.date(2022, 10, 28),
+                                                  _dt.date(2022, 10, 27)]))
         df = df.sort_index()
         df.index.name = "Date"
         df_bad = df.copy()
@@ -561,7 +702,7 @@ class TestPriceRepair(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, prepost=False)
+        df_repaired = dat._fix_unit_random_mixups(df_bad, "1d", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
@@ -578,6 +719,63 @@ class TestPriceRepair(unittest.TestCase):
         f_1 = ratio == 1
         self.assertTrue((f_100 | f_1).all())
 
+        self.assertTrue("Repaired?" in df_repaired.columns)
+        self.assertFalse(df_repaired["Repaired?"].isna().any())
+
+    def test_repair_100x_block_daily(self):
+        # Some 100x errors are not sporadic.
+        # Sometimes Yahoo suddenly shifts from cents->$ from some recent date.
+
+        tkrs = ['AET.L', 'SSW.JO']
+        for tkr in tkrs:
+            for interval in ['1d', '1wk']:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+
+                data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
+                _dp = os.path.dirname(__file__)
+                fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error.csv")
+                if not os.path.isfile(fp):
+                    continue
+                df_bad = _pd.read_csv(fp, index_col="Date")
+                df_bad.index = _pd.to_datetime(df_bad.index, utc=True).tz_convert(tz_exchange)
+                df_bad = df_bad.sort_index()
+
+                df = df_bad.copy()
+                fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error-fixed.csv")
+                df = _pd.read_csv(fp, index_col="Date")
+                df.index = _pd.to_datetime(df.index, utc=True).tz_convert(tz_exchange)
+                df = df.sort_index()
+
+                df_repaired = dat._fix_unit_switch(df_bad, interval, tz_exchange)
+                df_repaired = df_repaired.sort_index()
+
+                # First test - no errors left
+                for c in data_cols:
+                    try:
+                        self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
+                    except:
+                        print("- repaired:")
+                        print(df_repaired[c])
+                        print("- correct:")
+                        print(df[c])
+                        print(f"TEST FAIL on column '{c}' (tkr={tkr} interval={interval})")
+                        raise
+
+                # Second test - all differences should be either ~1x or ~100x
+                ratio = df_bad[data_cols].values / df[data_cols].values
+                ratio = ratio.round(2)
+                # - round near-100 ratio to 100:
+                f = ratio > 90
+                ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
+                # - now test
+                f_100 = (ratio == 100) | (ratio == 0.01)
+                f_1 = ratio == 1
+                self.assertTrue((f_100 | f_1).all())
+
+                self.assertTrue("Repaired?" in df_repaired.columns)
+                self.assertFalse(df_repaired["Repaired?"].isna().any())
+
     def test_repair_zeroes_daily(self):
         tkr = "BBIL.L"
         dat = yf.Ticker(tkr, session=self.session)
@@ -589,9 +787,9 @@ class TestPriceRepair(unittest.TestCase):
                                      "Close":     [103.03, 102.05, 102.08],
                                      "Adj Close": [102.03, 102.05, 102.08],
                                      "Volume": [560, 137, 117]},
-                                index=_pd.to_datetime([_dt.datetime(2022, 11, 1),
-                                                       _dt.datetime(2022, 10, 31),
-                                                       _dt.datetime(2022, 10, 30)]))
+                               index=_pd.to_datetime([_dt.datetime(2022, 11, 1),
+                                                      _dt.datetime(2022, 10, 31),
+                                                      _dt.datetime(2022, 10, 30)]))
         df_bad = df_bad.sort_index()
         df_bad.index.name = "Date"
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
@@ -605,6 +803,45 @@ class TestPriceRepair(unittest.TestCase):
         for c in ["Open", "Low", "High", "Close"]:
             self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-8).all())
 
+        self.assertTrue("Repaired?" in repaired_df.columns)
+        self.assertFalse(repaired_df["Repaired?"].isna().any())
+
+    def test_repair_zeroes_daily_adjClose(self):
+        # Test that 'Adj Close' is reconstructed correctly, 
+        # particularly when a dividend occurred within 1 day.
+
+        tkr = "INTC"
+        df = _pd.DataFrame(data={"Open":      [28.95, 28.65, 29.55, 29.62, 29.25],
+                                 "High":      [29.12, 29.27, 29.65, 31.17, 30.30],
+                                 "Low":       [28.21, 28.43, 28.61, 29.53, 28.80],
+                                 "Close":     [28.24, 29.05, 28.69, 30.32, 30.19],
+                                 "Adj Close": [28.12, 28.93, 28.57, 29.83, 29.70],
+                                 "Volume":    [36e6, 51e6, 49e6, 58e6, 62e6],
+                                 "Dividends": [0, 0, 0.365, 0, 0]},
+                           index=_pd.to_datetime([_dt.datetime(2023, 2, 8),
+                                                  _dt.datetime(2023, 2, 7),
+                                                  _dt.datetime(2023, 2, 6),
+                                                  _dt.datetime(2023, 2, 3),
+                                                  _dt.datetime(2023, 2, 2)]))
+        df = df.sort_index()
+        df.index.name = "Date"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+        df.index = df.index.tz_localize(tz_exchange)
+
+        rtol = 5e-3
+        for i in [0, 1, 2]:
+            df_slice = df.iloc[i:i+3]
+            for j in range(3):
+                df_slice_bad = df_slice.copy()
+                df_slice_bad.loc[df_slice_bad.index[j], "Adj Close"] = 0.0
+
+                df_slice_bad_repaired = dat._fix_zeroes(df_slice_bad, "1d", tz_exchange, prepost=False)
+                for c in ["Close", "Adj Close"]:
+                    self.assertTrue(_np.isclose(df_slice_bad_repaired[c], df_slice[c], rtol=rtol).all())
+                self.assertTrue("Repaired?" in df_slice_bad_repaired.columns)
+                self.assertFalse(df_slice_bad_repaired["Repaired?"].isna().any())
+
     def test_repair_zeroes_hourly(self):
         tkr = "INTC"
         dat = yf.Ticker(tkr, session=self.session)
@@ -626,7 +863,7 @@ class TestPriceRepair(unittest.TestCase):
         for c in ["Open", "Low", "High", "Close"]:
             try:
                 self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-7).all())
-            except:
+            except AssertionError:
                 print("COLUMN", c)
                 print("- repaired_df")
                 print(repaired_df)
@@ -636,13 +873,133 @@ class TestPriceRepair(unittest.TestCase):
                 print(repaired_df[c] - correct_df[c])
                 raise
 
+        self.assertTrue("Repaired?" in repaired_df.columns)
+        self.assertFalse(repaired_df["Repaired?"].isna().any())
+
+    def test_repair_bad_stock_split(self):
+        # Stocks that split in 2022 but no problems in Yahoo data, 
+        # so repair should change nothing
+        good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
+        good_tkrs += ['AEI', 'CHRA', 'GHI', 'IRON', 'LXU', 'NUZE', 'RSLS', 'TISI']
+        good_tkrs += ['BOL.ST', 'TUI1.DE']
+        intervals = ['1d', '1wk', '1mo', '3mo']
+        for tkr in good_tkrs:
+            for interval in intervals:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+
+                _dp = os.path.dirname(__file__)
+                df_good = dat.history(start='2020-01-01', end=_dt.date.today(), interval=interval, auto_adjust=False)
+
+                repaired_df = dat._fix_bad_stock_split(df_good, interval, tz_exchange)
+
+                # Expect no change from repair
+                df_good = df_good.sort_index()
+                repaired_df = repaired_df.sort_index()
+                for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                    try:
+                        self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
+                    except:
+                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
+                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
+                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
+                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
+                        raise
+
+        bad_tkrs = ['4063.T', 'ALPHA.PA', 'AV.L', 'CNE.L', 'MOB.ST', 'SPM.MI']
+        bad_tkrs.append('LA.V')  # special case - stock split error is 3 years ago! why not fixed?
+        for tkr in bad_tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+            tz_exchange = dat.fast_info["timezone"]
+
+            _dp = os.path.dirname(__file__)
+            interval = '1d'
+            fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
+            if not os.path.isfile(fp):
+                interval = '1wk'
+                fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
+            df_bad = _pd.read_csv(fp, index_col="Date")
+            df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
+
+            repaired_df = dat._fix_bad_stock_split(df_bad, "1d", tz_exchange)
+
+            fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split-fixed.csv")
+            correct_df = _pd.read_csv(fp, index_col="Date")
+            correct_df.index = _pd.to_datetime(correct_df.index)
+
+            repaired_df = repaired_df.sort_index()
+            correct_df = correct_df.sort_index()
+            for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                try:
+                    self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
+                except AssertionError:
+                    print(f"tkr={tkr} 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
+
+        # Had very high price volatility in Jan-2021 around split date that could 
+        # be mistaken for missing stock split adjustment. And old logic did think 
+        # column 'High' required fixing - wrong!
+        sketchy_tkrs = ['FIZZ']
+        intervals = ['1wk']
+        for tkr in sketchy_tkrs:
+            for interval in intervals:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+
+                _dp = os.path.dirname(__file__)
+                df_good = dat.history(start='2020-11-30', end='2021-04-01', interval=interval, auto_adjust=False)
+
+                repaired_df = dat._fix_bad_stock_split(df_good, interval, tz_exchange)
+
+                # Expect no change from repair
+                df_good = df_good.sort_index()
+                repaired_df = repaired_df.sort_index()
+                for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                    try:
+                        self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
+                    except AssertionError:
+                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
+                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
+                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
+                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
+                        raise
+
+    def test_repair_missing_div_adjust(self):
+        tkr = '8TRA.DE'
+
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+
+        _dp = os.path.dirname(__file__)
+        df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust.csv"), index_col="Date")
+        df_bad.index = _pd.to_datetime(df_bad.index)
+
+        repaired_df = dat._fix_missing_div_adjust(df_bad, "1d", tz_exchange)
+
+        correct_df = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust-fixed.csv"), index_col="Date")
+        correct_df.index = _pd.to_datetime(correct_df.index)
+
+        repaired_df = repaired_df.sort_index()
+        correct_df = correct_df.sort_index()
+        for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+            try:
+                self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
+            except:
+                print(f"tkr={tkr} 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
+
+
 if __name__ == '__main__':
     unittest.main()
-
-# # Run tests sequentially:
-# import inspect
-# test_src = inspect.getsource(TestPriceHistory)
-# unittest.TestLoader.sortTestMethodsUsing = lambda _, x, y: (
-#     test_src.index(f"def {x}") - test_src.index(f"def {y}")
-# )
-# unittest.main(verbosity=2)

tests/ticker.py

@@ -12,25 +12,20 @@ import pandas as pd
 import numpy as np
 
 from .context import yfinance as yf
+from .context import session_gbl
 
 import unittest
 import requests_cache
 
-# Set this to see the exact requests that are made during tests
-DEBUG_LOG_REQUESTS = False
-
-if DEBUG_LOG_REQUESTS:
-    import logging
-
-    logging.basicConfig(level=logging.DEBUG)
-
 
 class TestTicker(unittest.TestCase):
     session = None
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
+
+        cls.proxy = None
 
     @classmethod
     def tearDownClass(cls):
@@ -45,7 +40,7 @@ class TestTicker(unittest.TestCase):
 
             # Test:
             dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(debug_mode=False, proxy=None, timeout=None)
+            tz = dat._get_ticker_tz(proxy=None, timeout=None)
 
             self.assertIsNotNone(tz)
 
@@ -54,10 +49,14 @@ class TestTicker(unittest.TestCase):
 
         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")
+        yf.download([tkr], period="1wk", threads=False, ignore_tz=False)
+        yf.download([tkr], period="1wk", threads=True, ignore_tz=False)
+        yf.download([tkr], period="1wk", threads=False, ignore_tz=True)
+        yf.download([tkr], period="1wk", threads=True, ignore_tz=True)
 
         for k in dat.fast_info:
             dat.fast_info[k]
@@ -69,28 +68,31 @@ class TestTicker(unittest.TestCase):
         dat.dividends
         dat.splits
         dat.actions
-        dat.shares
         dat.get_shares_full()
-        dat.info
-        dat.calendar
-        dat.recommendations
-        dat.earnings
-        dat.quarterly_earnings
+        dat.options
+        dat.news
+        dat.earnings_dates
+
         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
+
+        # These haven't been ported Yahoo API
+        # dat.shares
+        # dat.info
+        # dat.calendar
+        # dat.recommendations
+        # dat.earnings
+        # dat.quarterly_earnings
+        # dat.recommendations_summary
+        # dat.analyst_price_target
+        # dat.revenue_forecasts
+        # dat.sustainability
+        # dat.earnings_trend
+        # dat.earnings_forecasts
 
     def test_goodTicker(self):
         # that yfinance works when full api is called on same instance of ticker
@@ -103,7 +105,10 @@ class TestTicker(unittest.TestCase):
             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")
+            yf.download([tkr], period="1wk", threads=False, ignore_tz=False)
+            yf.download([tkr], period="1wk", threads=True, ignore_tz=False)
+            yf.download([tkr], period="1wk", threads=False, ignore_tz=True)
+            yf.download([tkr], period="1wk", threads=True, ignore_tz=True)
 
             for k in dat.fast_info:
                 dat.fast_info[k]
@@ -115,28 +120,154 @@ class TestTicker(unittest.TestCase):
             dat.dividends
             dat.splits
             dat.actions
-            dat.shares
             dat.get_shares_full()
-            dat.info
-            dat.calendar
-            dat.recommendations
-            dat.earnings
-            dat.quarterly_earnings
+            dat.options
+            dat.news
+            dat.earnings_dates
+
             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
+
+            # These require decryption which is broken:
+            # dat.shares
+            # dat.info
+            # dat.calendar
+            # dat.recommendations
+            # dat.earnings
+            # dat.quarterly_earnings
+            # dat.recommendations_summary
+            # dat.analyst_price_target
+            # dat.revenue_forecasts
+            # dat.sustainability
+            # dat.earnings_trend
+            # dat.earnings_forecasts
+
+    def test_goodTicker_withProxy(self):
+        # that yfinance works when full api is called on same instance of ticker
+
+        tkr = "IBM"
+        dat = yf.Ticker(tkr, session=self.session)
+
+        dat._fetch_ticker_tz(proxy=self.proxy, timeout=5, debug_mode=False, raise_errors=False)
+        dat._get_ticker_tz(proxy=self.proxy, timeout=5, debug_mode=False, raise_errors=False)
+        dat.history(period="1wk", proxy=self.proxy)
+
+        v = dat.stats(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(len(v) > 0)
+
+        v = dat.get_recommendations(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_calendar(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_major_holders(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_institutional_holders(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_mutualfund_holders(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_info(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(len(v) > 0)
+
+        v = dat.get_sustainability(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_recommendations_summary(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_analyst_price_target(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_rev_forecast(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_earnings_forecast(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_trend_details(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_earnings_trend(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_earnings(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_income_stmt(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_incomestmt(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_financials(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_balance_sheet(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_balancesheet(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_cash_flow(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_cashflow(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_shares(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_shares_full(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_isin(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(v != "")
+
+        v = dat.get_news(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(len(v) > 0)
+
+        v = dat.get_earnings_dates(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        # TODO: enable after merge
+        # dat.get_history_metadata(proxy=self.proxy)
+        # self.assertIsNotNone(v)
+        # self.assertTrue(len(v) > 0)
 
 
 class TestTickerHistory(unittest.TestCase):
@@ -144,7 +275,7 @@ class TestTickerHistory(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -153,19 +284,28 @@ class TestTickerHistory(unittest.TestCase):
 
     def setUp(self):
         # use a ticker that has dividends
-        self.ticker = yf.Ticker("IBM", session=self.session)
+        self.symbol = "IBM"
+        self.ticker = yf.Ticker(self.symbol, session=self.session)
+
+        self.symbols = ["AMZN", "MSFT", "NVDA"]
 
     def tearDown(self):
         self.ticker = None
 
     def test_history(self):
-        with self.assertRaises(RuntimeError):
-            self.ticker.history_metadata
+        md = self.ticker.history_metadata
+        self.assertIn("IBM", md.values(), "metadata missing")
         data = self.ticker.history("1y")
-        self.assertIn("IBM", self.ticker.history_metadata.values(), "metadata missing")
         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
         self.assertFalse(data.empty, "data is empty")
 
+    def test_download(self):
+        for t in [False, True]:
+            for i in [False, True]:
+                data = yf.download(self.symbols, threads=t, ignore_tz=i)
+                self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+                self.assertFalse(data.empty, "data is empty")
+
     def test_no_expensive_calls_introduced(self):
         """
         Make sure calling history to get price data has not introduced more calls to yahoo than absolutely necessary.
@@ -178,7 +318,7 @@ class TestTickerHistory(unittest.TestCase):
         actual_urls_called = tuple([r.url for r in session.cache.filter()])
         session.close()
         expected_urls = (
-            'https://query2.finance.yahoo.com/v8/finance/chart/GOOGL?range=1y&interval=1d&includePrePost=False&events=div%2Csplits%2CcapitalGains',
+            'https://query2.finance.yahoo.com/v8/finance/chart/GOOGL?events=div,splits,capitalGains&includePrePost=False&interval=1d&range=1y',
         )
         self.assertEqual(expected_urls, actual_urls_called, "Different than expected url used to fetch history.")
 
@@ -198,75 +338,76 @@ class TestTickerHistory(unittest.TestCase):
         self.assertFalse(data.empty, "data is empty")
 
 
-class TestTickerEarnings(unittest.TestCase):
-    session = None
+# Below will fail because not ported to Yahoo API
+# class TestTickerEarnings(unittest.TestCase):
+#     session = None
 
-    @classmethod
-    def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+#     @classmethod
+#     def setUpClass(cls):
+#         cls.session = session_gbl
 
-    @classmethod
-    def tearDownClass(cls):
-        if cls.session is not None:
-            cls.session.close()
+#     @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 setUp(self):
+#         self.ticker = yf.Ticker("GOOGL", session=self.session)
 
-    def tearDown(self):
-        self.ticker = None
+#     def tearDown(self):
+#         self.ticker = None
 
-    def test_earnings(self):
-        data = self.ticker.earnings
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
+#     def test_earnings(self):
+#         data = self.ticker.earnings
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
 
-        data_cached = self.ticker.earnings
-        self.assertIs(data, data_cached, "data not cached")
+#         data_cached = self.ticker.earnings
+#         self.assertIs(data, data_cached, "data not cached")
 
-    def test_quarterly_earnings(self):
-        data = self.ticker.quarterly_earnings
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
+#     def test_quarterly_earnings(self):
+#         data = self.ticker.quarterly_earnings
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
 
-        data_cached = self.ticker.quarterly_earnings
-        self.assertIs(data, data_cached, "data not cached")
+#         data_cached = self.ticker.quarterly_earnings
+#         self.assertIs(data, data_cached, "data not cached")
 
-    def test_earnings_forecasts(self):
-        data = self.ticker.earnings_forecasts
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
+#     def test_earnings_forecasts(self):
+#         data = self.ticker.earnings_forecasts
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
 
-        data_cached = self.ticker.earnings_forecasts
-        self.assertIs(data, data_cached, "data not cached")
+#         data_cached = self.ticker.earnings_forecasts
+#         self.assertIs(data, data_cached, "data not cached")
 
-    def test_earnings_dates(self):
-        data = self.ticker.earnings_dates
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
+#     def test_earnings_dates(self):
+#         data = self.ticker.earnings_dates
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
 
-        data_cached = self.ticker.earnings_dates
-        self.assertIs(data, data_cached, "data not cached")
+#         data_cached = self.ticker.earnings_dates
+#         self.assertIs(data, data_cached, "data not cached")
 
-    def test_earnings_trend(self):
-        data = self.ticker.earnings_trend
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
+#     def test_earnings_trend(self):
+#         data = self.ticker.earnings_trend
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
 
-        data_cached = self.ticker.earnings_trend
-        self.assertIs(data, data_cached, "data not cached")
+#         data_cached = self.ticker.earnings_trend
+#         self.assertIs(data, data_cached, "data not cached")
 
-    def test_earnings_dates_with_limit(self):
-        # use ticker with lots of historic earnings
-        ticker = yf.Ticker("IBM")
-        limit = 110
-        data = ticker.get_earnings_dates(limit=limit)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertEqual(len(data), limit, "Wrong number or rows")
+#     def test_earnings_dates_with_limit(self):
+#         # use ticker with lots of historic earnings
+#         ticker = yf.Ticker("IBM")
+#         limit = 110
+#         data = ticker.get_earnings_dates(limit=limit)
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+#         self.assertEqual(len(data), limit, "Wrong number or rows")
 
-        data_cached = ticker.get_earnings_dates(limit=limit)
-        self.assertIs(data, data_cached, "data not cached")
+#         data_cached = ticker.get_earnings_dates(limit=limit)
+#         self.assertIs(data, data_cached, "data not cached")
 
 
 class TestTickerHolders(unittest.TestCase):
@@ -274,7 +415,7 @@ class TestTickerHolders(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -317,7 +458,7 @@ class TestTickerMiscFinancials(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -335,6 +476,24 @@ class TestTickerMiscFinancials(unittest.TestCase):
     def tearDown(self):
         self.ticker = None
 
+    def test_isin(self):
+        data = self.ticker.isin
+        self.assertIsInstance(data, str, "data has wrong type")
+        self.assertEqual("ARDEUT116159", data, "data is empty")
+
+        data_cached = self.ticker.isin
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_options(self):
+        data = self.ticker.options
+        self.assertIsInstance(data, tuple, "data has wrong type")
+        self.assertTrue(len(data) > 1, "data is empty")
+
+    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_income_statement(self):
         expected_keys = ["Total Revenue", "Basic EPS"]
         expected_periods_days = 365
@@ -364,7 +523,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_income_stmt(as_dict=True)
         self.assertIsInstance(data, dict, "data has wrong type")
 
-
     def test_quarterly_income_statement(self):
         expected_keys = ["Total Revenue", "Basic EPS"]
         expected_periods_days = 365//4
@@ -394,16 +552,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_income_stmt(as_dict=True)
         self.assertIsInstance(data, dict, "data has wrong type")
 
-    def test_quarterly_income_statement_old_fmt(self):
-        expected_row = "TotalRevenue"
-        data = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertIn(expected_row, data.index, "Did not find expected row in index")
-
-        data_cached = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
-        self.assertIs(data, data_cached, "data not cached")
-
     def test_balance_sheet(self):
         expected_keys = ["Total Assets", "Net PPE"]
         expected_periods_days = 365
@@ -462,16 +610,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_balance_sheet(as_dict=True, freq="quarterly")
         self.assertIsInstance(data, dict, "data has wrong type")
 
-    def test_quarterly_balance_sheet_old_fmt(self):
-        expected_row = "TotalAssets"
-        data = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertIn(expected_row, data.index, "Did not find expected row in index")
-
-        data_cached = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
-        self.assertIs(data, data_cached, "data not cached")
-
     def test_cash_flow(self):
         expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
         expected_periods_days = 365
@@ -530,16 +668,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_cashflow(as_dict=True)
         self.assertIsInstance(data, dict, "data has wrong type")
 
-    def test_quarterly_cashflow_old_fmt(self):
-        expected_row = "NetIncome"
-        data = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertIn(expected_row, data.index, "Did not find expected row in index")
-
-        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
@@ -599,87 +727,71 @@ class TestTickerMiscFinancials(unittest.TestCase):
         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")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.sustainability
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_recommendations(self):
-        data = self.ticker.recommendations
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.recommendations
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_recommendations_summary(self):
-        data = self.ticker.recommendations_summary
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.recommendations_summary
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_analyst_price_target(self):
-        data = self.ticker.analyst_price_target
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.analyst_price_target
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_revenue_forecasts(self):
-        data = self.ticker.revenue_forecasts
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.revenue_forecasts
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_calendar(self):
-        data = self.ticker.calendar
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.calendar
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_isin(self):
-        data = self.ticker.isin
-        self.assertIsInstance(data, str, "data has wrong type")
-        self.assertEqual("ARDEUT116159", data, "data is empty")
-
-        data_cached = self.ticker.isin
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_options(self):
-        data = self.ticker.options
-        self.assertIsInstance(data, tuple, "data has wrong type")
-        self.assertTrue(len(data) > 1, "data is empty")
-
-    def test_shares(self):
-        data = self.ticker.shares
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-    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"))
 
+    # Below will fail because not ported to Yahoo API
+
+    # def test_sustainability(self):
+    #     data = self.ticker.sustainability
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.sustainability
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_recommendations(self):
+    #     data = self.ticker.recommendations
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.recommendations
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_recommendations_summary(self):
+    #     data = self.ticker.recommendations_summary
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.recommendations_summary
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_analyst_price_target(self):
+    #     data = self.ticker.analyst_price_target
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.analyst_price_target
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_revenue_forecasts(self):
+    #     data = self.ticker.revenue_forecasts
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.revenue_forecasts
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_calendar(self):
+    #     data = self.ticker.calendar
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     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):
     session = None
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -697,110 +809,116 @@ class TestTickerInfo(unittest.TestCase):
     def tearDown(self):
         self.ticker = None
 
+    def test_fast_info(self):
+        f = yf.Ticker("AAPL", session=self.session).fast_info
+        for k in f:
+            self.assertIsNotNone(f[k])
+
     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")
+        expected_keys = ['industry', 'currentPrice', 'exchange', 'floatShares', 'companyOfficers', 'bid']
+        for k in expected_keys:
+            print(k)
+            self.assertIn("symbol", data.keys(), f"Did not find expected key '{k}' 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
+    # def test_fast_info_matches_info(self):
+    #     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))
 
-        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 = {}
-        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["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["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["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"
 
-        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]
 
-        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"}
 
-        # 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]
 
-        # 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
 
-        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]
 
-            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
 
-            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 bad_keys:
-                        continue
+    #                 if k in custom_tolerances:
+    #                     rtol = custom_tolerances[k]
+    #                 else:
+    #                     rtol = 5e-3
+    #                     # rtol = 1e-4
 
-                    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
+    #                 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
 
 
 

tests/utils.py

@@ -0,0 +1,51 @@
+"""
+Tests for utils
+
+To run all tests in suite from commandline:
+   python -m unittest tests.utils
+
+Specific test class:
+   python -m unittest tests.utils.TestTicker
+
+"""
+# import pandas as pd
+# import numpy as np
+
+from .context import yfinance as yf
+from .context import session_gbl
+
+import unittest
+# import requests_cache
+import tempfile
+
+
+class TestUtils(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.tempCacheDir = tempfile.TemporaryDirectory()
+        yf.set_tz_cache_location(cls.tempCacheDir.name)
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.tempCacheDir.cleanup()
+
+    def test_storeTzNoRaise(self):
+        # storing TZ to cache should never raise exception
+        tkr = 'AMZN'
+        tz1 = "America/New_York"
+        tz2 = "London/Europe"
+        cache = yf.utils.get_tz_cache()
+        cache.store(tkr, tz1)
+        cache.store(tkr, tz2)
+
+
+def suite():
+    suite = unittest.TestSuite()
+    suite.addTest(TestUtils('Test utils'))
+    return suite
+
+
+if __name__ == '__main__':
+    unittest.main()

yfinance/__init__.py

@@ -23,7 +23,7 @@ from . import version
 from .ticker import Ticker
 from .tickers import Tickers
 from .multi import download
-from .utils import set_tz_cache_location
+from .utils import set_tz_cache_location, enable_debug_mode
 
 __version__ = version.version
 __author__ = "Ran Aroussi"
@@ -43,4 +43,4 @@ def pdr_override():
         pass
 
 
-__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override', 'set_tz_cache_location']
+__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override', 'enable_debug_mode', 'set_tz_cache_location']

yfinance/const.py

@@ -0,0 +1,118 @@
+_BASE_URL_ = 'https://query2.finance.yahoo.com'
+_ROOT_URL_ = 'https://finance.yahoo.com'
+
+fundamentals_keys = {
+    'financials': ["TaxEffectOfUnusualItems", "TaxRateForCalcs", "NormalizedEBITDA", "NormalizedDilutedEPS",
+                   "NormalizedBasicEPS", "TotalUnusualItems", "TotalUnusualItemsExcludingGoodwill",
+                   "NetIncomeFromContinuingOperationNetMinorityInterest", "ReconciledDepreciation",
+                   "ReconciledCostOfRevenue", "EBITDA", "EBIT", "NetInterestIncome", "InterestExpense",
+                   "InterestIncome", "ContinuingAndDiscontinuedDilutedEPS", "ContinuingAndDiscontinuedBasicEPS",
+                   "NormalizedIncome", "NetIncomeFromContinuingAndDiscontinuedOperation", "TotalExpenses",
+                   "RentExpenseSupplemental", "ReportedNormalizedDilutedEPS", "ReportedNormalizedBasicEPS",
+                   "TotalOperatingIncomeAsReported", "DividendPerShare", "DilutedAverageShares", "BasicAverageShares",
+                   "DilutedEPS", "DilutedEPSOtherGainsLosses", "TaxLossCarryforwardDilutedEPS",
+                   "DilutedAccountingChange", "DilutedExtraordinary", "DilutedDiscontinuousOperations",
+                   "DilutedContinuousOperations", "BasicEPS", "BasicEPSOtherGainsLosses", "TaxLossCarryforwardBasicEPS",
+                   "BasicAccountingChange", "BasicExtraordinary", "BasicDiscontinuousOperations",
+                   "BasicContinuousOperations", "DilutedNIAvailtoComStockholders", "AverageDilutionEarnings",
+                   "NetIncomeCommonStockholders", "OtherunderPreferredStockDividend", "PreferredStockDividends",
+                   "NetIncome", "MinorityInterests", "NetIncomeIncludingNoncontrollingInterests",
+                   "NetIncomeFromTaxLossCarryforward", "NetIncomeExtraordinary", "NetIncomeDiscontinuousOperations",
+                   "NetIncomeContinuousOperations", "EarningsFromEquityInterestNetOfTax", "TaxProvision",
+                   "PretaxIncome", "OtherIncomeExpense", "OtherNonOperatingIncomeExpenses", "SpecialIncomeCharges",
+                   "GainOnSaleOfPPE", "GainOnSaleOfBusiness", "OtherSpecialCharges", "WriteOff",
+                   "ImpairmentOfCapitalAssets", "RestructuringAndMergernAcquisition", "SecuritiesAmortization",
+                   "EarningsFromEquityInterest", "GainOnSaleOfSecurity", "NetNonOperatingInterestIncomeExpense",
+                   "TotalOtherFinanceCost", "InterestExpenseNonOperating", "InterestIncomeNonOperating",
+                   "OperatingIncome", "OperatingExpense", "OtherOperatingExpenses", "OtherTaxes",
+                   "ProvisionForDoubtfulAccounts", "DepreciationAmortizationDepletionIncomeStatement",
+                   "DepletionIncomeStatement", "DepreciationAndAmortizationInIncomeStatement", "Amortization",
+                   "AmortizationOfIntangiblesIncomeStatement", "DepreciationIncomeStatement", "ResearchAndDevelopment",
+                   "SellingGeneralAndAdministration", "SellingAndMarketingExpense", "GeneralAndAdministrativeExpense",
+                   "OtherGandA", "InsuranceAndClaims", "RentAndLandingFees", "SalariesAndWages", "GrossProfit",
+                   "CostOfRevenue", "TotalRevenue", "ExciseTaxes", "OperatingRevenue"],
+    'balance-sheet': ["TreasurySharesNumber", "PreferredSharesNumber", "OrdinarySharesNumber", "ShareIssued", "NetDebt",
+                      "TotalDebt", "TangibleBookValue", "InvestedCapital", "WorkingCapital", "NetTangibleAssets",
+                      "CapitalLeaseObligations", "CommonStockEquity", "PreferredStockEquity", "TotalCapitalization",
+                      "TotalEquityGrossMinorityInterest", "MinorityInterest", "StockholdersEquity",
+                      "OtherEquityInterest", "GainsLossesNotAffectingRetainedEarnings", "OtherEquityAdjustments",
+                      "FixedAssetsRevaluationReserve", "ForeignCurrencyTranslationAdjustments",
+                      "MinimumPensionLiabilities", "UnrealizedGainLoss", "TreasuryStock", "RetainedEarnings",
+                      "AdditionalPaidInCapital", "CapitalStock", "OtherCapitalStock", "CommonStock", "PreferredStock",
+                      "TotalPartnershipCapital", "GeneralPartnershipCapital", "LimitedPartnershipCapital",
+                      "TotalLiabilitiesNetMinorityInterest", "TotalNonCurrentLiabilitiesNetMinorityInterest",
+                      "OtherNonCurrentLiabilities", "LiabilitiesHeldforSaleNonCurrent", "RestrictedCommonStock",
+                      "PreferredSecuritiesOutsideStockEquity", "DerivativeProductLiabilities", "EmployeeBenefits",
+                      "NonCurrentPensionAndOtherPostretirementBenefitPlans", "NonCurrentAccruedExpenses",
+                      "DuetoRelatedPartiesNonCurrent", "TradeandOtherPayablesNonCurrent",
+                      "NonCurrentDeferredLiabilities", "NonCurrentDeferredRevenue",
+                      "NonCurrentDeferredTaxesLiabilities", "LongTermDebtAndCapitalLeaseObligation",
+                      "LongTermCapitalLeaseObligation", "LongTermDebt", "LongTermProvisions", "CurrentLiabilities",
+                      "OtherCurrentLiabilities", "CurrentDeferredLiabilities", "CurrentDeferredRevenue",
+                      "CurrentDeferredTaxesLiabilities", "CurrentDebtAndCapitalLeaseObligation",
+                      "CurrentCapitalLeaseObligation", "CurrentDebt", "OtherCurrentBorrowings", "LineOfCredit",
+                      "CommercialPaper", "CurrentNotesPayable", "PensionandOtherPostRetirementBenefitPlansCurrent",
+                      "CurrentProvisions", "PayablesAndAccruedExpenses", "CurrentAccruedExpenses", "InterestPayable",
+                      "Payables", "OtherPayable", "DuetoRelatedPartiesCurrent", "DividendsPayable", "TotalTaxPayable",
+                      "IncomeTaxPayable", "AccountsPayable", "TotalAssets", "TotalNonCurrentAssets",
+                      "OtherNonCurrentAssets", "DefinedPensionBenefit", "NonCurrentPrepaidAssets",
+                      "NonCurrentDeferredAssets", "NonCurrentDeferredTaxesAssets", "DuefromRelatedPartiesNonCurrent",
+                      "NonCurrentNoteReceivables", "NonCurrentAccountsReceivable", "FinancialAssets",
+                      "InvestmentsAndAdvances", "OtherInvestments", "InvestmentinFinancialAssets",
+                      "HeldToMaturitySecurities", "AvailableForSaleSecurities",
+                      "FinancialAssetsDesignatedasFairValueThroughProfitorLossTotal", "TradingSecurities",
+                      "LongTermEquityInvestment", "InvestmentsinJointVenturesatCost",
+                      "InvestmentsInOtherVenturesUnderEquityMethod", "InvestmentsinAssociatesatCost",
+                      "InvestmentsinSubsidiariesatCost", "InvestmentProperties", "GoodwillAndOtherIntangibleAssets",
+                      "OtherIntangibleAssets", "Goodwill", "NetPPE", "AccumulatedDepreciation", "GrossPPE", "Leases",
+                      "ConstructionInProgress", "OtherProperties", "MachineryFurnitureEquipment",
+                      "BuildingsAndImprovements", "LandAndImprovements", "Properties", "CurrentAssets",
+                      "OtherCurrentAssets", "HedgingAssetsCurrent", "AssetsHeldForSaleCurrent", "CurrentDeferredAssets",
+                      "CurrentDeferredTaxesAssets", "RestrictedCash", "PrepaidAssets", "Inventory",
+                      "InventoriesAdjustmentsAllowances", "OtherInventories", "FinishedGoods", "WorkInProcess",
+                      "RawMaterials", "Receivables", "ReceivablesAdjustmentsAllowances", "OtherReceivables",
+                      "DuefromRelatedPartiesCurrent", "TaxesReceivable", "AccruedInterestReceivable", "NotesReceivable",
+                      "LoansReceivable", "AccountsReceivable", "AllowanceForDoubtfulAccountsReceivable",
+                      "GrossAccountsReceivable", "CashCashEquivalentsAndShortTermInvestments",
+                      "OtherShortTermInvestments", "CashAndCashEquivalents", "CashEquivalents", "CashFinancial"],
+    'cash-flow': ["ForeignSales", "DomesticSales", "AdjustedGeographySegmentData", "FreeCashFlow",
+                  "RepurchaseOfCapitalStock", "RepaymentOfDebt", "IssuanceOfDebt", "IssuanceOfCapitalStock",
+                  "CapitalExpenditure", "InterestPaidSupplementalData", "IncomeTaxPaidSupplementalData",
+                  "EndCashPosition", "OtherCashAdjustmentOutsideChangeinCash", "BeginningCashPosition",
+                  "EffectOfExchangeRateChanges", "ChangesInCash", "OtherCashAdjustmentInsideChangeinCash",
+                  "CashFlowFromDiscontinuedOperation", "FinancingCashFlow", "CashFromDiscontinuedFinancingActivities",
+                  "CashFlowFromContinuingFinancingActivities", "NetOtherFinancingCharges", "InterestPaidCFF",
+                  "ProceedsFromStockOptionExercised", "CashDividendsPaid", "PreferredStockDividendPaid",
+                  "CommonStockDividendPaid", "NetPreferredStockIssuance", "PreferredStockPayments",
+                  "PreferredStockIssuance", "NetCommonStockIssuance", "CommonStockPayments", "CommonStockIssuance",
+                  "NetIssuancePaymentsOfDebt", "NetShortTermDebtIssuance", "ShortTermDebtPayments",
+                  "ShortTermDebtIssuance", "NetLongTermDebtIssuance", "LongTermDebtPayments", "LongTermDebtIssuance",
+                  "InvestingCashFlow", "CashFromDiscontinuedInvestingActivities",
+                  "CashFlowFromContinuingInvestingActivities", "NetOtherInvestingChanges", "InterestReceivedCFI",
+                  "DividendsReceivedCFI", "NetInvestmentPurchaseAndSale", "SaleOfInvestment", "PurchaseOfInvestment",
+                  "NetInvestmentPropertiesPurchaseAndSale", "SaleOfInvestmentProperties",
+                  "PurchaseOfInvestmentProperties", "NetBusinessPurchaseAndSale", "SaleOfBusiness",
+                  "PurchaseOfBusiness", "NetIntangiblesPurchaseAndSale", "SaleOfIntangibles", "PurchaseOfIntangibles",
+                  "NetPPEPurchaseAndSale", "SaleOfPPE", "PurchaseOfPPE", "CapitalExpenditureReported",
+                  "OperatingCashFlow", "CashFromDiscontinuedOperatingActivities",
+                  "CashFlowFromContinuingOperatingActivities", "TaxesRefundPaid", "InterestReceivedCFO",
+                  "InterestPaidCFO", "DividendReceivedCFO", "DividendPaidCFO", "ChangeInWorkingCapital",
+                  "ChangeInOtherWorkingCapital", "ChangeInOtherCurrentLiabilities", "ChangeInOtherCurrentAssets",
+                  "ChangeInPayablesAndAccruedExpense", "ChangeInAccruedExpense", "ChangeInInterestPayable",
+                  "ChangeInPayable", "ChangeInDividendPayable", "ChangeInAccountPayable", "ChangeInTaxPayable",
+                  "ChangeInIncomeTaxPayable", "ChangeInPrepaidAssets", "ChangeInInventory", "ChangeInReceivables",
+                  "ChangesInAccountReceivables", "OtherNonCashItems", "ExcessTaxBenefitFromStockBasedCompensation",
+                  "StockBasedCompensation", "UnrealizedGainLossOnInvestmentSecurities", "ProvisionandWriteOffofAssets",
+                  "AssetImpairmentCharge", "AmortizationOfSecurities", "DeferredTax", "DeferredIncomeTax",
+                  "DepreciationAmortizationDepletion", "Depletion", "DepreciationAndAmortization",
+                  "AmortizationCashFlow", "AmortizationOfIntangibles", "Depreciation", "OperatingGainsLosses",
+                  "PensionAndEmployeeBenefitExpense", "EarningsLossesFromEquityInvestments",
+                  "GainLossOnInvestmentSecurities", "NetForeignCurrencyExchangeGainLoss", "GainLossOnSaleOfPPE",
+                  "GainLossOnSaleOfBusiness", "NetIncomeFromContinuingOperations",
+                  "CashFlowsfromusedinOperatingActivitiesDirect", "TaxesRefundPaidDirect", "InterestReceivedDirect",
+                  "InterestPaidDirect", "DividendsReceivedDirect", "DividendsPaidDirect", "ClassesofCashPayments",
+                  "OtherCashPaymentsfromOperatingActivities", "PaymentsonBehalfofEmployees",
+                  "PaymentstoSuppliersforGoodsandServices", "ClassesofCashReceiptsfromOperatingActivities",
+                  "OtherCashReceiptsfromOperatingActivities", "ReceiptsfromGovernmentGrants", "ReceiptsfromCustomers"]}
+
+price_colnames = ['Open', 'High', 'Low', 'Close', 'Adj Close']

yfinance/data.py

@@ -1,29 +1,16 @@
 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 logging
 
 import requests as requests
 import re
-from bs4 import BeautifulSoup
 import random
 import time
 
 from frozendict import frozendict
 
-try:
-    import ujson as json
-except ImportError:
-    import json as json
+from . import utils
 
 cache_maxsize = 64
 
@@ -49,127 +36,6 @@ 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'
-
-
 class TickerData:
     """
     Have one place to retrieve data from Yahoo API in order to ease caching and speed up operations
@@ -199,7 +65,7 @@ class TickerData:
     def _get_proxy(self, proxy):
         # setup proxy in requests format
         if proxy is not None:
-            if isinstance(proxy, dict) and "https" in proxy:
+            if isinstance(proxy, (dict, frozendict)) and "https" in proxy:
                 proxy = proxy["https"]
             proxy = {"https": proxy}
         return proxy
@@ -208,124 +74,3 @@ class TickerData:
         response = self.get(url, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
         response.raise_for_status()
         return response.json()
-
-    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:
-        '''
-        get_json_data_stores returns a python dictionary of the data stores in yahoo finance web page.
-        '''
-        if sub_page:
-            ticker_url = "{}/{}/{}".format(_SCRAPE_URL_, self.ticker, sub_page)
-        else:
-            ticker_url = "{}/{}".format(_SCRAPE_URL_, self.ticker)
-
-        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
-        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(stores).replace('{}', 'null')
-        new_data = re.sub(
-            r'{[\'|\"]raw[\'|\"]:(.*?),(.*?)}', r'\1', new_data)
-
-        return json.loads(new_data)

yfinance/exceptions.py

@@ -4,3 +4,9 @@ class YFinanceException(Exception):
 
 class YFinanceDataException(YFinanceException):
     pass
+
+
+class YFNotImplementedError(NotImplementedError):
+    def __init__(self, method_name):
+        super().__init__(f"Have not implemented fetching '{method_name}' from Yahoo API")
+

yfinance/multi.py

@@ -21,7 +21,10 @@
 
 from __future__ import print_function
 
+import logging
 import time as _time
+import traceback
+
 import multitasking as _multitasking
 import pandas as _pd
 
@@ -29,10 +32,11 @@ from . import Ticker, utils
 from . import shared
 
 
+@utils.log_indent_decorator
 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):
+             progress=True, period="max", show_errors=None, interval="1d", prepost=False,
+             proxy=None, rounding=False, timeout=10, session=None):
     """Download yahoo tickers
     :Parameters:
         tickers : str, list
@@ -45,7 +49,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             Intraday data cannot extend last 60 days
         start: str
             Download start date string (YYYY-MM-DD) or _datetime, inclusive.
-            Default is 1900-01-01
+            Default is 99 years ago
             E.g. for start="2020-01-01", the first data point will be on "2020-01-01"
         end: str
             Download end date string (YYYY-MM-DD) or _datetime, exclusive.
@@ -77,10 +81,33 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             Optional. Round values to 2 decimal places?
         show_errors: bool
             Optional. Doesn't print errors if False
+            DEPRECATED, will be removed in future version
         timeout: None or float
             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)
+        session: None or Session
+            Optional. Pass your own session object to be used for all requests
     """
+    logger = utils.get_yf_logger()
+
+    if show_errors is not None:
+        if show_errors:
+            utils.print_once(f"yfinance: download(show_errors={show_errors}) 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: download(show_errors={show_errors}) 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)
+
+    if logger.isEnabledFor(logging.DEBUG):
+        if threads:
+            # With DEBUG, each thread generates a lot of log messages.
+            # And with multi-threading, these messages will be interleaved, bad!
+            # So disable multi-threading to make log readable.
+            logger.debug('Disabling multithreading because DEBUG logging enabled')
+            threads = False
+        if progress:
+            # Disable progress bar, interferes with display of log messages
+            progress = False
 
     if ignore_tz is None:
         # Set default value depending on interval
@@ -100,7 +127,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
     for ticker in tickers:
         if utils.is_isin(ticker):
             isin = ticker
-            ticker = utils.get_ticker_by_isin(ticker, proxy)
+            ticker = utils.get_ticker_by_isin(ticker, proxy, session=session)
             shared._ISINS[ticker] = isin
         _tickers_.append(ticker)
 
@@ -114,6 +141,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
     # reset shared._DFS
     shared._DFS = {}
     shared._ERRORS = {}
+    shared._TRACEBACKS = {}
 
     # download using threads
     if threads:
@@ -126,10 +154,9 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
                                    actions=actions, auto_adjust=auto_adjust,
                                    back_adjust=back_adjust, repair=repair, keepna=keepna,
                                    progress=(progress and i > 0), proxy=proxy,
-                                   rounding=rounding, timeout=timeout)
+                                   rounding=rounding, timeout=timeout, session=session)
         while len(shared._DFS) < len(tickers):
             _time.sleep(0.01)
-
     # download synchronously
     else:
         for i, ticker in enumerate(tickers):
@@ -138,20 +165,42 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
                                  actions=actions, auto_adjust=auto_adjust,
                                  back_adjust=back_adjust, repair=repair, keepna=keepna,
                                  proxy=proxy,
-                                 rounding=rounding, timeout=timeout)
-            shared._DFS[ticker.upper()] = data
+                                 rounding=rounding, timeout=timeout, session=session)
             if progress:
                 shared._PROGRESS_BAR.animate()
-
+    
     if progress:
         shared._PROGRESS_BAR.completed()
 
-    if shared._ERRORS and show_errors:
-        print('\n%.f Failed download%s:' % (
+    if shared._ERRORS:
+        # Send errors to logging module
+        logger = utils.get_yf_logger()
+        logger.error('\n%.f Failed download%s:' % (
             len(shared._ERRORS), 's' if len(shared._ERRORS) > 1 else ''))
-        # print(shared._ERRORS)
-        print("\n".join(['- %s: %s' %
-                         v for v in list(shared._ERRORS.items())]))
+
+        # Log each distinct error once, with list of symbols affected
+        errors = {}
+        for ticker in shared._ERRORS:
+            err = shared._ERRORS[ticker]
+            err = err.replace(f'{ticker}', '%ticker%')
+            if err not in errors:
+                errors[err] = [ticker]
+            else:
+                errors[err].append(ticker)
+        for err in errors.keys():
+            logger.error(f'{errors[err]}: ' + err)
+
+        # Log each distinct traceback once, with list of symbols affected
+        tbs = {}
+        for ticker in shared._TRACEBACKS:
+            tb = shared._TRACEBACKS[ticker]
+            tb = tb.replace(f'{ticker}', '%ticker%')
+            if tb not in tbs:
+                tbs[tb] = [ticker]
+            else:
+                tbs[tb].append(ticker)
+        for tb in tbs.keys():
+            logger.debug(f'{tbs[tb]}: ' + tb)
 
     if ignore_tz:
         for tkr in shared._DFS.keys():
@@ -160,7 +209,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
 
     if len(tickers) == 1:
         ticker = tickers[0]
-        return shared._DFS[shared._ISINS.get(ticker, ticker)]
+        return shared._DFS[ticker]
 
     try:
         data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
@@ -208,17 +257,10 @@ def _download_one_threaded(ticker, start=None, end=None,
                            auto_adjust=False, back_adjust=False, repair=False,
                            actions=False, progress=True, period="max",
                            interval="1d", prepost=False, proxy=None,
-                           keepna=False, rounding=False, timeout=10):
-    try:
-        data = _download_one(ticker, start, end, auto_adjust, back_adjust, repair,
-                             actions, period, interval, prepost, proxy, rounding,
-                             keepna, timeout)
-    except Exception as e:
-        # glob try/except needed as current thead implementation breaks if exception is raised.
-        shared._DFS[ticker] = utils.empty_df()
-        shared._ERRORS[ticker] = repr(e)
-    else:
-        shared._DFS[ticker.upper()] = data
+                           keepna=False, rounding=False, timeout=10, session=None):
+    data = _download_one(ticker, start, end, auto_adjust, back_adjust, repair,
+                         actions, period, interval, prepost, proxy, rounding,
+                         keepna, timeout, session)
     if progress:
         shared._PROGRESS_BAR.animate()
 
@@ -227,12 +269,23 @@ def _download_one(ticker, start=None, end=None,
                   auto_adjust=False, back_adjust=False, repair=False,
                   actions=False, period="max", interval="1d",
                   prepost=False, proxy=None, rounding=False,
-                  keepna=False, timeout=10):
-    return Ticker(ticker).history(
-        period=period, interval=interval,
-        start=start, end=end, prepost=prepost,
-        actions=actions, auto_adjust=auto_adjust,
-        back_adjust=back_adjust, repair=repair, proxy=proxy,
-        rounding=rounding, keepna=keepna, timeout=timeout,
-        debug=False, raise_errors=False  # debug and raise_errors false to not log and raise errors in threads
-    )
+                  keepna=False, timeout=10, session=None):
+    data = None
+    try:
+        data = Ticker(ticker, session=session).history(
+                period=period, interval=interval,
+                start=start, end=end, prepost=prepost,
+                actions=actions, auto_adjust=auto_adjust,
+                back_adjust=back_adjust, repair=repair, proxy=proxy,
+                rounding=rounding, keepna=keepna, timeout=timeout,
+                raise_errors=True
+        )
+    except Exception as e:
+        # glob try/except needed as current thead implementation breaks if exception is raised.
+        shared._DFS[ticker.upper()] = utils.empty_df()
+        shared._ERRORS[ticker.upper()] = repr(e)
+        shared._TRACEBACKS[ticker.upper()] = traceback.format_exc()
+    else:
+        shared._DFS[ticker.upper()] = data
+
+    return data

yfinance/scrapers/analysis.py

@@ -2,6 +2,7 @@ import pandas as pd
 
 from yfinance import utils
 from yfinance.data import TickerData
+from yfinance.exceptions import YFNotImplementedError
 
 
 class Analysis:
@@ -20,99 +21,29 @@ class Analysis:
     @property
     def earnings_trend(self) -> pd.DataFrame:
         if self._earnings_trend is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('earnings_trend')
         return self._earnings_trend
 
     @property
     def analyst_trend_details(self) -> pd.DataFrame:
         if self._analyst_trend_details is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('analyst_trend_details')
         return self._analyst_trend_details
 
     @property
     def analyst_price_target(self) -> pd.DataFrame:
         if self._analyst_price_target is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('analyst_price_target')
         return self._analyst_price_target
 
     @property
     def rev_est(self) -> pd.DataFrame:
         if self._rev_est is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('rev_est')
         return self._rev_est
 
     @property
     def eps_est(self) -> pd.DataFrame:
         if self._eps_est is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('eps_est')
         return self._eps_est
-
-    def _scrape(self, proxy):
-        if self._already_scraped:
-            return
-        self._already_scraped = True
-
-        # Analysis Data/Analyst Forecasts
-        analysis_data = self._data.get_json_data_stores("analysis", proxy=proxy)
-        try:
-            analysis_data = analysis_data['QuoteSummaryStore']
-        except KeyError as e:
-            err_msg = "No analysis data found, symbol may be delisted"
-            print('- %s: %s' % (self._data.ticker, err_msg))
-            return
-
-        if isinstance(analysis_data.get('earningsTrend'), dict):
-            try:
-                analysis = pd.DataFrame(analysis_data['earningsTrend']['trend'])
-                analysis['endDate'] = pd.to_datetime(analysis['endDate'])
-                analysis.set_index('period', inplace=True)
-                analysis.index = analysis.index.str.upper()
-                analysis.index.name = 'Period'
-                analysis.columns = utils.camel2title(analysis.columns)
-
-                dict_cols = []
-
-                for idx, row in analysis.iterrows():
-                    for colname, colval in row.items():
-                        if isinstance(colval, dict):
-                            dict_cols.append(colname)
-                            for k, v in colval.items():
-                                new_colname = colname + ' ' + \
-                                              utils.camel2title([k])[0]
-                                analysis.loc[idx, new_colname] = v
-
-                self._earnings_trend = analysis[[
-                    c for c in analysis.columns if c not in dict_cols]]
-            except Exception:
-                pass
-
-        try:
-            self._analyst_trend_details = pd.DataFrame(analysis_data['recommendationTrend']['trend'])
-        except Exception as e:
-            self._analyst_trend_details = None
-        try:
-            self._analyst_price_target = pd.DataFrame(analysis_data['financialData'], index=[0])[
-                ['targetLowPrice', 'currentPrice', 'targetMeanPrice', 'targetHighPrice', 'numberOfAnalystOpinions']].T
-        except Exception as e:
-            self._analyst_price_target = None
-        earnings_estimate = []
-        revenue_estimate = []
-        if self._analyst_trend_details is not None :
-            for key in analysis_data['earningsTrend']['trend']:
-                try:
-                    earnings_dict = key['earningsEstimate']
-                    earnings_dict['period'] = key['period']
-                    earnings_dict['endDate'] = key['endDate']
-                    earnings_estimate.append(earnings_dict)
-
-                    revenue_dict = key['revenueEstimate']
-                    revenue_dict['period'] = key['period']
-                    revenue_dict['endDate'] = key['endDate']
-                    revenue_estimate.append(revenue_dict)
-                except Exception as e:
-                    pass
-            self._rev_est = pd.DataFrame(revenue_estimate)
-            self._eps_est = pd.DataFrame(earnings_estimate)
-        else:
-            self._rev_est = pd.DataFrame()
-            self._eps_est = pd.DataFrame()

yfinance/scrapers/fundamentals.py

@@ -2,11 +2,10 @@ import datetime
 import json
 
 import pandas as pd
-import numpy as np
 
-from yfinance import utils
+from yfinance import utils, const
 from yfinance.data import TickerData
-from yfinance.exceptions import YFinanceDataException, YFinanceException
+from yfinance.exceptions import YFinanceException, YFNotImplementedError
 
 
 class Fundamentals:
@@ -31,71 +30,15 @@ class Fundamentals:
     @property
     def earnings(self) -> dict:
         if self._earnings is None:
-            self._scrape_earnings(self.proxy)
+            raise YFNotImplementedError('earnings')
         return self._earnings
 
     @property
     def shares(self) -> pd.DataFrame:
         if self._shares is None:
-            self._scrape_shares(self.proxy)
+            raise YFNotImplementedError('shares')
         return self._shares
 
-    def _scrape_basics(self, proxy):
-        if self._basics_already_scraped:
-            return
-        self._basics_already_scraped = True
-
-        self._financials_data = self._data.get_json_data_stores('financials', proxy)
-        try:
-            self._fin_data_quote = self._financials_data['QuoteSummaryStore']
-        except KeyError:
-            err_msg = "No financials data found, symbol may be delisted"
-            print('- %s: %s' % (self._data.ticker, err_msg))
-            return None
-
-    def _scrape_earnings(self, proxy):
-        self._scrape_basics(proxy)
-        # earnings
-        self._earnings = {"yearly": pd.DataFrame(), "quarterly": pd.DataFrame()}
-        if self._fin_data_quote is None:
-            return
-        if isinstance(self._fin_data_quote.get('earnings'), dict):
-            try:
-                earnings = self._fin_data_quote['earnings']['financialsChart']
-                earnings['financialCurrency'] = self._fin_data_quote['earnings'].get('financialCurrency', 'USD')
-                self._earnings['financialCurrency'] = earnings['financialCurrency']
-                df = pd.DataFrame(earnings['yearly']).set_index('date')
-                df.columns = utils.camel2title(df.columns)
-                df.index.name = 'Year'
-                self._earnings['yearly'] = df
-
-                df = pd.DataFrame(earnings['quarterly']).set_index('date')
-                df.columns = utils.camel2title(df.columns)
-                df.index.name = 'Quarter'
-                self._earnings['quarterly'] = df
-            except Exception:
-                pass
-
-    def _scrape_shares(self, proxy):
-        self._scrape_basics(proxy)
-        # shares outstanding
-        try:
-            # keep only years with non None data
-            available_shares = [shares_data for shares_data in
-                                self._financials_data['QuoteTimeSeriesStore']['timeSeries']['annualBasicAverageShares']
-                                if
-                                shares_data]
-            shares = pd.DataFrame(available_shares)
-            shares['Year'] = shares['asOfDate'].agg(lambda x: int(x[:4]))
-            shares.set_index('Year', inplace=True)
-            shares.drop(columns=['dataId', 'asOfDate',
-                                 'periodType', 'currencyCode'], inplace=True)
-            shares.rename(
-                columns={'reportedValue': "BasicShares"}, inplace=True)
-            self._shares = shares
-        except Exception:
-            pass
-
 
 class Financials:
     def __init__(self, data: TickerData):
@@ -103,28 +46,26 @@ class Financials:
         self._income_time_series = {}
         self._balance_sheet_time_series = {}
         self._cash_flow_time_series = {}
-        self._income_scraped = {}
-        self._balance_sheet_scraped = {}
-        self._cash_flow_scraped = {}
 
     def get_income_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
         res = self._income_time_series
         if freq not in res:
-            res[freq] = self._fetch_time_series("income", freq, proxy=None)
+            res[freq] = self._fetch_time_series("income", freq, proxy)
         return res[freq]
 
     def get_balance_sheet_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
         res = self._balance_sheet_time_series
         if freq not in res:
-            res[freq] = self._fetch_time_series("balance-sheet", freq, proxy=None)
+            res[freq] = self._fetch_time_series("balance-sheet", freq, proxy)
         return res[freq]
 
     def get_cash_flow_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
         res = self._cash_flow_time_series
         if freq not in res:
-            res[freq] = self._fetch_time_series("cash-flow", freq, proxy=None)
+            res[freq] = self._fetch_time_series("cash-flow", freq, proxy)
         return res[freq]
 
+    @utils.log_indent_decorator
     def _fetch_time_series(self, name, timescale, proxy=None):
         # Fetching time series preferred over scraping 'QuoteSummaryStore',
         # because it matches what Yahoo shows. But for some tickers returns nothing, 
@@ -134,9 +75,9 @@ class Financials:
         allowed_timescales = ["yearly", "quarterly"]
 
         if name not in allowed_names:
-            raise ValueError("Illegal argument: name must be one of: {}".format(allowed_names))
+            raise ValueError(f"Illegal argument: name must be one of: {allowed_names}")
         if timescale not in allowed_timescales:
-            raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
+            raise ValueError(f"Illegal argument: timescale must be one of: {allowed_names}")
 
         try:
             statement = self._create_financials_table(name, timescale, proxy)
@@ -144,7 +85,7 @@ class Financials:
             if statement is not None:
                 return statement
         except YFinanceException as e:
-            print(f"- {self._data.ticker}: Failed to create {name} financials table for reason: {repr(e)}")
+            utils.get_yf_logger().error(f"{self._data.ticker}: Failed to create {name} financials table for reason: {e}")
         return pd.DataFrame()
 
     def _create_financials_table(self, name, timescale, proxy):
@@ -152,51 +93,24 @@ class Financials:
             # Yahoo stores the 'income' table internally under 'financials' key
             name = "financials"
 
-        keys = self._get_datastore_keys(name, proxy)
+        keys = const.fundamentals_keys[name]
+
         try:
             return self.get_financials_time_series(timescale, keys, proxy)
         except Exception as e:
             pass
 
-    def _get_datastore_keys(self, sub_page, proxy) -> list:
-        data_stores = self._data.get_json_data_stores(sub_page, proxy)
-
-        # Step 1: get the keys:
-        def _finditem1(key, obj):
-            values = []
-            if isinstance(obj, dict):
-                if key in obj.keys():
-                    values.append(obj[key])
-                for k, v in obj.items():
-                    values += _finditem1(key, v)
-            elif isinstance(obj, list):
-                for v in obj:
-                    values += _finditem1(key, v)
-            return values
-
-        try:
-            keys = _finditem1("key", data_stores['FinancialTemplateStore'])
-        except KeyError as e:
-            raise YFinanceDataException("Parsing FinancialTemplateStore failed, reason: {}".format(repr(e)))
-
-        if not keys:
-            raise YFinanceDataException("No keys in FinancialTemplateStore")
-        return keys
-
     def get_financials_time_series(self, timescale, keys: list, proxy=None) -> pd.DataFrame:
         timescale_translation = {"yearly": "annual", "quarterly": "quarterly"}
         timescale = timescale_translation[timescale]
 
         # Step 2: construct url:
-        ts_url_base = \
-            "https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{0}?symbol={0}" \
-                .format(self._data.ticker)
-
+        ts_url_base = f"https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._data.ticker}?symbol={self._data.ticker}"
         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 = pd.Timestamp.utcnow().ceil("D")
-        url += "&period1={}&period2={}".format(int(start_dt.timestamp()), int(end.timestamp()))
+        url += f"&period1={int(start_dt.timestamp())}&period2={int(end.timestamp())}"
 
         # Step 3: fetch and reshape data
         json_str = self._data.cache_get(url=url, proxy=proxy).text
@@ -231,89 +145,3 @@ class Financials:
         df = df[sorted(df.columns, reverse=True)]
 
         return df
-
-    def get_income_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._income_scraped
-        if freq not in res:
-            res[freq] = self._scrape("income", freq, proxy=None)
-        return res[freq]
-
-    def get_balance_sheet_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._balance_sheet_scraped
-        if freq not in res:
-            res[freq] = self._scrape("balance-sheet", freq, proxy=None)
-        return res[freq]
-
-    def get_cash_flow_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._cash_flow_scraped
-        if freq not in res:
-            res[freq] = self._scrape("cash-flow", freq, proxy=None)
-        return res[freq]
-
-    def _scrape(self, name, timescale, proxy=None):
-        # Backup in case _fetch_time_series() fails to return data
-
-        allowed_names = ["income", "balance-sheet", "cash-flow"]
-        allowed_timescales = ["yearly", "quarterly"]
-
-        if name not in allowed_names:
-            raise ValueError("Illegal argument: name must be one of: {}".format(allowed_names))
-        if timescale not in allowed_timescales:
-            raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
-
-        try:
-            statement = self._create_financials_table_old(name, timescale, proxy)
-
-            if statement is not None:
-                return statement
-        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):
-        data_stores = self._data.get_json_data_stores("financials", proxy)
-
-        # Fetch raw data
-        if not "QuoteSummaryStore" in data_stores:
-            raise YFinanceDataException(f"Yahoo not returning legacy financials data")
-        data = data_stores["QuoteSummaryStore"]
-
-        if name == "cash-flow":
-            key1 = "cashflowStatement"
-            key2 = "cashflowStatements"
-        elif name == "balance-sheet":
-            key1 = "balanceSheet"
-            key2 = "balanceSheetStatements"
-        else:
-            key1 = "incomeStatement"
-            key2 = "incomeStatementHistory"
-        key1 += "History"
-        if timescale == "quarterly":
-            key1 += "Quarterly"
-        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:
-            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)
-        df.set_index('endDate', inplace=True)
-        try:
-            df.index = pd.to_datetime(df.index, unit='s')
-        except ValueError:
-            df.index = pd.to_datetime(df.index)
-        df = df.T
-        df.columns.name = ''
-        df.index.name = 'Breakdown'
-        # rename incorrect yahoo key
-        df.rename(index={'treasuryStock': 'gainsLossesNotAffectingRetainedEarnings'}, inplace=True)
-
-        # Upper-case first letter, leave rest unchanged:
-        s0 = df.index[0]
-        df.index = [s[0].upper()+s[1:] for s in df.index]
-
-        return df

yfinance/scrapers/holders.py

@@ -2,6 +2,7 @@ import pandas as pd
 
 from yfinance.data import TickerData
 
+
 class Holders:
     _SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
 
@@ -32,9 +33,9 @@ class Holders:
         return self._mutualfund
 
     def _scrape(self, proxy):
-        ticker_url = "{}/{}".format(self._SCRAPE_URL_, self._data.ticker)
+        ticker_url = f"{self._SCRAPE_URL_}/{self._data.ticker}"
         try:
-            resp = self._data.cache_get(ticker_url + '/holders', proxy)
+            resp = self._data.cache_get(ticker_url + '/holders', proxy=proxy)
             holders = pd.read_html(resp.text)
         except Exception:
             holders = []

yfinance/scrapers/quote.py

@@ -1,11 +1,15 @@
 import datetime
 import json
+import logging
+import warnings
+from collections.abc import MutableMapping
 
+import numpy as _np
 import pandas as pd
 
 from yfinance import utils
 from yfinance.data import TickerData
-
+from yfinance.exceptions import YFNotImplementedError
 
 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"]})
@@ -17,12 +21,9 @@ 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
-_BASIC_URL_ = "https://query2.finance.yahoo.com/v10/finance/quoteSummary"
+_BASIC_URL_ = "https://query2.finance.yahoo.com/v6/finance/quoteSummary"
 
 
-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
@@ -45,16 +46,16 @@ class InfoDictWrapper(MutableMapping):
 
     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")
+            warnings.warn(f"Price data removed from info (key='{k}'). Use Ticker.fast_info or history() instead", DeprecationWarning)
             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")
+            warnings.warn(f"Exchange data removed from info (key='{k}'). Use Ticker.fast_info or Ticker.get_history_metadata() instead", DeprecationWarning)
             return None
         elif k in info_retired_keys_marketCap:
-            print(f"Market cap removed from info (key='{k}'). Use Ticker.fast_info instead")
+            warnings.warn(f"Market cap removed from info (key='{k}'). Use Ticker.fast_info instead", DeprecationWarning)
             return None
         elif k in info_retired_keys_symbol:
-            print(f"Symbol removed from info (key='{k}'). You know this already")
+            warnings.warn(f"Symbol removed from info (key='{k}'). You know this already", DeprecationWarning)
             return None
         return self.info[self._keytransform(k)]
 
@@ -77,10 +78,9 @@ class InfoDictWrapper(MutableMapping):
 class FastInfo:
     # Contain small subset of info[] items that can be fetched faster elsewhere.
     # Imitates a dict.
-    def __init__(self, tickerBaseObject):
-        utils.print_once("Note: 'info' dict is now fixed & improved, 'fast_info' no longer faster")
-
+    def __init__(self, tickerBaseObject, proxy=None):
         self._tkr = tickerBaseObject
+        self.proxy = proxy
 
         self._prices_1y = None
         self._prices_1wk_1h_prepost = None
@@ -127,12 +127,12 @@ class FastInfo:
 
         # Because released before fixing key case, need to officially support 
         # camel-case but also secretly support snake-case
-        base_keys = [k for k in _properties if not '_' in k]
+        base_keys = [k for k in _properties if '_' not in k]
 
         sc_keys = [k for k in _properties if '_' in k]
 
-        self._sc_to_cc_key = {k:utils.snake_case_2_camelCase(k) for k in sc_keys}
-        self._cc_to_sc_key = {v:k for k,v in self._sc_to_cc_key.items()}
+        self._sc_to_cc_key = {k: utils.snake_case_2_camelCase(k) for k in sc_keys}
+        self._cc_to_sc_key = {v: k for k, v in self._sc_to_cc_key.items()}
  
         self._public_keys = sorted(base_keys + list(self._sc_to_cc_key.values()))
         self._keys = sorted(self._public_keys + sc_keys)
@@ -140,48 +140,58 @@ class FastInfo:
     # dict imitation:
     def keys(self):
         return self._public_keys
+
     def items(self):
-        return [(k,self[k]) for k in self._public_keys]
+        return [(k, self[k]) for k in self._public_keys]
+
     def values(self):
         return [self[k] for k in self._public_keys]
+
     def get(self, key, default=None):
         if key in self.keys():
             if key in self._cc_to_sc_key:
                 key = self._cc_to_sc_key[key]
             return self[key]
         return default
+
     def __getitem__(self, k):
         if not isinstance(k, str):
             raise KeyError(f"key must be a string")
-        if not k in self._keys:
+        if k not in self._keys:
             raise KeyError(f"'{k}' not valid key. Examine 'FastInfo.keys()'")
         if k in self._cc_to_sc_key:
             k = self._cc_to_sc_key[k]
         return getattr(self, k)
+
     def __contains__(self, k):
         return k in self.keys()
+
     def __iter__(self):
         return iter(self.keys())
 
     def __str__(self):
         return "lazy-loading dict with keys = " + str(self.keys())
+
     def __repr__(self):
         return self.__str__()
 
     def toJSON(self, indent=4):
-        d = {k:self[k] for k in self.keys()}
-        return _json.dumps({k:self[k] for k in self.keys()}, indent=indent)
+        d = {k: self[k] for k in self.keys()}
+        return json.dumps({k: self[k] for k in self.keys()}, indent=indent)
 
     def _get_1y_prices(self, fullDaysOnly=False):
         if self._prices_1y is None:
-            self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, debug=False, keepna=True)
-            self._md = self._tkr.get_history_metadata()
+            # Temporarily disable error printing
+            logging.disable(logging.CRITICAL)
+            self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, keepna=True, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
+            self._md = self._tkr.get_history_metadata(proxy=self.proxy)
             try:
                 ctp = self._md["currentTradingPeriod"]
                 self._today_open = pd.to_datetime(ctp["regular"]["start"], unit='s', utc=True).tz_convert(self.timezone)
                 self._today_close = pd.to_datetime(ctp["regular"]["end"], unit='s', utc=True).tz_convert(self.timezone)
                 self._today_midnight = self._today_close.ceil("D")
-            except:
+            except Exception:
                 self._today_open = None
                 self._today_close = None
                 self._today_midnight = None
@@ -200,12 +210,18 @@ class FastInfo:
 
     def _get_1wk_1h_prepost_prices(self):
         if self._prices_1wk_1h_prepost is None:
-            self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True, debug=False)
+            # Temporarily disable error printing
+            logging.disable(logging.CRITICAL)
+            self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
         return self._prices_1wk_1h_prepost
 
     def _get_1wk_1h_reg_prices(self):
         if self._prices_1wk_1h_reg is None:
-            self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False, debug=False)
+            # Temporarily disable error printing
+            logging.disable(logging.CRITICAL)
+            self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
         return self._prices_1wk_1h_reg
 
     def _get_exchange_metadata(self):
@@ -213,7 +229,7 @@ class FastInfo:
             return self._md
 
         self._get_1y_prices()
-        self._md = self._tkr.get_history_metadata()
+        self._md = self._tkr.get_history_metadata(proxy=self.proxy)
         return self._md
 
     def _exchange_open_now(self):
@@ -246,7 +262,7 @@ class FastInfo:
 
         if self._tkr._history_metadata is None:
             self._get_1y_prices()
-        md = self._tkr.get_history_metadata()
+        md = self._tkr.get_history_metadata(proxy=self.proxy)
         self._currency = md["currency"]
         return self._currency
 
@@ -257,7 +273,7 @@ class FastInfo:
 
         if self._tkr._history_metadata is None:
             self._get_1y_prices()
-        md = self._tkr.get_history_metadata()
+        md = self._tkr.get_history_metadata(proxy=self.proxy)
         self._quote_type = md["instrumentType"]
         return self._quote_type
 
@@ -282,10 +298,10 @@ class FastInfo:
         if self._shares is not None:
             return self._shares
 
-        shares = self._tkr.get_shares_full(start=pd.Timestamp.utcnow().date()-pd.Timedelta(days=548))
-        if shares is None:
-            # Requesting 18 months failed, so fallback to shares which should include last year
-            shares = self._tkr.get_shares()
+        shares = self._tkr.get_shares_full(start=pd.Timestamp.utcnow().date()-pd.Timedelta(days=548), proxy=self.proxy)
+        # if shares is None:
+        #     # Requesting 18 months failed, so fallback to shares which should include last year
+        #     shares = self._tkr.get_shares()
         if shares is not None:
             if isinstance(shares, pd.DataFrame):
                 shares = shares[shares.columns[0]]
@@ -515,6 +531,8 @@ class FastInfo:
         except Exception as e:
             if "Cannot retrieve share count" in str(e):
                 shares = None
+            elif "failed to decrypt Yahoo" in str(e):
+                shares = None
             else:
                 raise
 
@@ -550,9 +568,7 @@ class Quote:
     @property
     def info(self) -> dict:
         if self._info is None:
-            # self._scrape(self.proxy)  # decrypt broken
             self._fetch(self.proxy)
-
             self._fetch_complementary(self.proxy)
 
         return self._info
@@ -560,150 +576,29 @@ class Quote:
     @property
     def sustainability(self) -> pd.DataFrame:
         if self._sustainability is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('sustainability')
         return self._sustainability
 
     @property
     def recommendations(self) -> pd.DataFrame:
         if self._recommendations is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('recommendations')
         return self._recommendations
 
     @property
     def calendar(self) -> pd.DataFrame:
         if self._calendar is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('calendar')
         return self._calendar
 
-    def _scrape(self, proxy):
-        if self._already_scraped:
-            return
-        self._already_scraped = True
-
-        # get info and sustainability
-        json_data = self._data.get_json_data_stores(proxy=proxy)
-        try:
-            quote_summary_store = json_data['QuoteSummaryStore']
-        except KeyError:
-            err_msg = "No summary info found, symbol may be delisted"
-            print('- %s: %s' % (self._data.ticker, err_msg))
-            return None
-
-        # sustainability
-        d = {}
-        try:
-            if isinstance(quote_summary_store.get('esgScores'), dict):
-                for item in quote_summary_store['esgScores']:
-                    if not isinstance(quote_summary_store['esgScores'][item], (dict, list)):
-                        d[item] = quote_summary_store['esgScores'][item]
-
-                s = pd.DataFrame(index=[0], data=d)[-1:].T
-                s.columns = ['Value']
-                s.index.name = '%.f-%.f' % (
-                    s[s.index == 'ratingYear']['Value'].values[0],
-                    s[s.index == 'ratingMonth']['Value'].values[0])
-
-                self._sustainability = s[~s.index.isin(
-                    ['maxAge', 'ratingYear', 'ratingMonth'])]
-        except Exception:
-            pass
-
-        self._info = {}
-        try:
-            items = ['summaryProfile', 'financialData', 'quoteType',
-                     'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
-            for item in items:
-                if isinstance(quote_summary_store.get(item), dict):
-                    self._info.update(quote_summary_store[item])
-        except Exception:
-            pass
-
-        # For ETFs, provide this valuable data: the top holdings of the ETF
-        try:
-            if 'topHoldings' in quote_summary_store:
-                self._info.update(quote_summary_store['topHoldings'])
-        except Exception:
-            pass
-
-        try:
-            if not isinstance(quote_summary_store.get('summaryDetail'), dict):
-                # For some reason summaryDetail did not give any results. The price dict
-                # usually has most of the same info
-                self._info.update(quote_summary_store.get('price', {}))
-        except Exception:
-            pass
-
-        try:
-            # self._info['regularMarketPrice'] = self._info['regularMarketOpen']
-            self._info['regularMarketPrice'] = quote_summary_store.get('price', {}).get(
-                'regularMarketPrice', self._info.get('regularMarketOpen', None))
-        except Exception:
-            pass
-
-        try:
-            self._info['preMarketPrice'] = quote_summary_store.get('price', {}).get(
-                'preMarketPrice', self._info.get('preMarketPrice', None))
-        except Exception:
-            pass
-
-        self._info['logo_url'] = ""
-        try:
-            if not 'website' in self._info:
-                self._info['logo_url'] = 'https://logo.clearbit.com/%s.com' % \
-                                         self._info['shortName'].split(' ')[0].split(',')[0]
-            else:
-                domain = self._info['website'].split(
-                    '://')[1].split('/')[0].replace('www.', '')
-                self._info['logo_url'] = 'https://logo.clearbit.com/%s' % domain
-        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'])
-            cal['earningsDate'] = pd.to_datetime(
-                cal['earningsDate'], unit='s')
-            self._calendar = cal.T
-            self._calendar.index = utils.camel2title(self._calendar.index)
-            self._calendar.columns = ['Value']
-        except Exception as e:
-            pass
-
-        # analyst recommendations
-        try:
-            rec = pd.DataFrame(
-                quote_summary_store['upgradeDowngradeHistory']['history'])
-            rec['earningsDate'] = pd.to_datetime(
-                rec['epochGradeDate'], unit='s')
-            rec.set_index('earningsDate', inplace=True)
-            rec.index.name = 'Date'
-            rec.columns = utils.camel2title(rec.columns)
-            self._recommendations = rec[[
-                'Firm', 'To Grade', 'From Grade', 'Action']].sort_index()
-        except Exception:
-            pass
-
     def _fetch(self, proxy):
         if self._already_fetched:
             return
         self._already_fetched = True
-        modules = ['summaryProfile', 'financialData', 'quoteType',
-                     'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
+        modules = ['financialData', 'quoteType', 'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
+        params_dict = {"modules": modules, "ssl": "true"}
         result = self._data.get_raw_json(
-            _BASIC_URL_ + f"/{self._data.ticker}", params={"modules": ",".join(modules), "ssl": "true"}, proxy=proxy
+            _BASIC_URL_ + f"/{self._data.ticker}", params=params_dict, proxy=proxy
         )
         result["quoteSummary"]["result"][0]["symbol"] = self._data.ticker
         query1_info = next(
@@ -723,13 +618,14 @@ class Quote:
             if v1
         }
         # recursively format but only because of 'companyOfficers'
+
         def _format(k, v):
             if isinstance(v, dict) and "raw" in v and "fmt" in v:
                 v2 = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
             elif isinstance(v, list):
                 v2 = [_format(None, x) for x in v]
             elif isinstance(v, dict):
-                v2 = {k:_format(k, x) for k, x in v.items()}
+                v2 = {k: _format(k, x) for k, x in v.items()}
             elif isinstance(v, str):
                 v2 = v.replace("\xa0", " ")
             else:
@@ -774,8 +670,7 @@ class Quote:
             #     pass
             #
             # For just one/few variable is faster to query directly:
-            url = "https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{}?symbol={}".format(
-                self._data.ticker, self._data.ticker)
+            url = f"https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._data.ticker}?symbol={self._data.ticker}"
             for k in keys:
                 url += "&type=" + k
             # Request 6 months of data

yfinance/shared.py

@@ -22,4 +22,5 @@
 _DFS = {}
 _PROGRESS_BAR = None
 _ERRORS = {}
+_TRACEBACKS = {}
 _ISINS = {}

yfinance/ticker.py

@@ -22,10 +22,10 @@
 from __future__ import print_function
 
 import datetime as _datetime
-import pandas as _pd
-
 from collections import namedtuple as _namedtuple
 
+import pandas as _pd
+
 from .base import TickerBase
 
 
@@ -33,25 +33,29 @@ class Ticker(TickerBase):
     def __init__(self, ticker, session=None):
         super(Ticker, self).__init__(ticker, session=session)
         self._expirations = {}
+        self._underlying  = {}
 
     def __repr__(self):
-        return 'yfinance.Ticker object <%s>' % self.ticker
+        return f'yfinance.Ticker object <{self.ticker}>'
 
     def _download_options(self, date=None, proxy=None):
         if date is None:
-            url = "{}/v7/finance/options/{}".format(
-                self._base_url, self.ticker)
+            url = f"{self._base_url}/v7/finance/options/{self.ticker}"
         else:
-            url = "{}/v7/finance/options/{}?date={}".format(
-                self._base_url, self.ticker, date)
+            url = f"{self._base_url}/v7/finance/options/{self.ticker}?date={date}"
 
         r = self._data.get(url=url, proxy=proxy).json()
         if len(r.get('optionChain', {}).get('result', [])) > 0:
             for exp in r['optionChain']['result'][0]['expirationDates']:
                 self._expirations[_datetime.datetime.utcfromtimestamp(
                     exp).strftime('%Y-%m-%d')] = exp
+
+            self._underlying = r['optionChain']['result'][0].get('quote', {})
+
             opt = r['optionChain']['result'][0].get('options', [])
-            return opt[0] if len(opt) > 0 else []
+
+            return dict(**opt[0],underlying=self._underlying) if len(opt) > 0 else {}
+        return {}
 
     def _options2df(self, opt, tz=None):
         data = _pd.DataFrame(opt).reindex(columns=[
@@ -84,15 +88,15 @@ class Ticker(TickerBase):
                 self._download_options()
             if date not in self._expirations:
                 raise ValueError(
-                    "Expiration `%s` cannot be found. "
-                    "Available expiration are: [%s]" % (
-                        date, ', '.join(self._expirations)))
+                    f"Expiration `{date}` cannot be found. "
+                    f"Available expirations are: [{', '.join(self._expirations)}]")
             date = self._expirations[date]
             options = self._download_options(date, proxy=proxy)
 
-        return _namedtuple('Options', ['calls', 'puts'])(**{
+        return _namedtuple('Options', ['calls', 'puts', 'underlying'])(**{
             "calls": self._options2df(options['calls'], tz=tz),
-            "puts": self._options2df(options['puts'], tz=tz)
+            "puts": self._options2df(options['puts'], tz=tz),
+            "underlying": options['underlying']
         })
 
     # ------------------------
@@ -137,6 +141,10 @@ class Ticker(TickerBase):
     def info(self) -> dict:
         return self.get_info()
 
+    @property
+    def fast_info(self):
+        return self.get_fast_info()
+
     @property
     def calendar(self) -> _pd.DataFrame:
         return self.get_calendar()
@@ -235,6 +243,10 @@ class Ticker(TickerBase):
     def news(self):
         return self.get_news()
 
+    @property
+    def trend_details(self) -> _pd.DataFrame:
+        return self.get_trend_details()
+
     @property
     def earnings_trend(self) -> _pd.DataFrame:
         return self.get_earnings_trend()

yfinance/tickers.py

@@ -22,19 +22,21 @@
 from __future__ import print_function
 
 from . import Ticker, multi
+
+
 # from collections import namedtuple as _namedtuple
 
 
 class Tickers:
 
     def __repr__(self):
-        return 'yfinance.Tickers object <%s>' % ",".join(self.symbols)
+        return f"yfinance.Tickers object <{','.join(self.symbols)}>"
 
     def __init__(self, tickers, session=None):
         tickers = tickers if isinstance(
             tickers, list) else tickers.replace(',', ' ').split()
         self.symbols = [ticker.upper() for ticker in tickers]
-        self.tickers = {ticker:Ticker(ticker, session=session) for ticker in self.symbols}
+        self.tickers = {ticker: Ticker(ticker, session=session) for ticker in self.symbols}
 
         # self.tickers = _namedtuple(
         #     "Tickers", ticker_objects.keys(), rename=True
@@ -87,10 +89,4 @@ class Tickers:
         return data
 
     def news(self):
-        collection = {}
-        for ticker in self.symbols:
-            collection[ticker] = []
-            items = Ticker(ticker).news
-            for item in items:
-                collection[ticker].append(item)
-        return collection
+        return {ticker: [item for item in Ticker(ticker).news] for ticker in self.symbols}

yfinance/utils.py

@@ -21,26 +21,32 @@
 
 from __future__ import print_function
 
+import atexit as _atexit
+
 import datetime as _datetime
-import dateutil as _dateutil
+import logging
+import os as _os
+import re as _re
+import sqlite3 as _sqlite3
+import sys as _sys
+import threading
+from functools import lru_cache
+from inspect import getmembers
+from threading import Lock
+from types import FunctionType
 from typing import Dict, Union, List, Optional
 
+import appdirs as _ad
+import numpy as _np
+import pandas as _pd
 import pytz as _tz
 import requests as _requests
-import re as _re
-import pandas as _pd
-import numpy as _np
-import sys as _sys
-import os as _os
-import appdirs as _ad
-import sqlite3 as _sqlite3
-import atexit as _atexit
-from functools import lru_cache
-
-from threading import Lock
-
+from dateutil.relativedelta import relativedelta
 from pytz import UnknownTimeZoneError
 
+from yfinance import const
+from .const import _BASE_URL_
+
 try:
     import ujson as _json
 except ImportError:
@@ -51,14 +57,12 @@ user_agent_headers = {
 
 
 # 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)) 
+        name for name, value in getmembers(type(obj))
         if isinstance(value, FunctionType)}
     return {
-      name: getattr(obj, name) for name in dir(obj) 
+        name: getattr(obj, name) for name in dir(obj)
         if name[0] != '_' and name not in disallowed_names and hasattr(obj, name)}
 
 
@@ -69,17 +73,127 @@ def print_once(msg):
     print(msg)
 
 
+# Logging
+# Note: most of this logic is adding indentation with function depth,
+#       so that DEBUG log is readable.
+class IndentLoggerAdapter(logging.LoggerAdapter):
+    def process(self, msg, kwargs):
+        if get_yf_logger().isEnabledFor(logging.DEBUG):
+            i = ' ' * self.extra['indent']
+            if not isinstance(msg, str):
+                msg = str(msg)
+            msg = '\n'.join([i + m for m in msg.split('\n')])
+        return msg, kwargs
+
+
+_indentation_level = threading.local()
+
+
+class IndentationContext:
+    def __init__(self, increment=1):
+        self.increment = increment
+
+    def __enter__(self):
+        _indentation_level.indent = getattr(_indentation_level, 'indent', 0) + self.increment
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        _indentation_level.indent -= self.increment
+
+
+def get_indented_logger(name=None):
+    # Never cache the returned value! Will break indentation.
+    return IndentLoggerAdapter(logging.getLogger(name), {'indent': getattr(_indentation_level, 'indent', 0)})
+
+
+def log_indent_decorator(func):
+    def wrapper(*args, **kwargs):
+        logger = get_indented_logger('yfinance')
+        logger.debug(f'Entering {func.__name__}()')
+
+        with IndentationContext():
+            result = func(*args, **kwargs)
+
+        logger.debug(f'Exiting {func.__name__}()')
+        return result
+
+    return wrapper
+
+
+class MultiLineFormatter(logging.Formatter):
+    # The 'fmt' formatting further down is only applied to first line
+    # of log message, specifically the padding after %level%.
+    # For multi-line messages, need to manually copy over padding.
+    def __init__(self, fmt):
+        super().__init__(fmt)
+        # Extract amount of padding
+        match = _re.search(r'%\(levelname\)-(\d+)s', fmt)
+        self.level_length = int(match.group(1)) if match else 0
+
+    def format(self, record):
+        original = super().format(record)
+        lines = original.split('\n')
+        levelname = lines[0].split(' ')[0]
+        if len(lines) <= 1:
+            return original
+        else:
+            # Apply padding to all lines below first
+            formatted = [lines[0]]
+            if self.level_length == 0:
+                padding = ' ' * len(levelname)
+            else:
+                padding = ' ' * self.level_length
+            padding += ' '  # +1 for space between level and message
+            formatted.extend(padding + line for line in lines[1:])
+            return '\n'.join(formatted)
+
+
+yf_logger = None
+yf_log_indented = False
+
+
+def get_yf_logger():
+    global yf_logger
+    if yf_logger is None:
+        yf_logger = logging.getLogger('yfinance')
+    global yf_log_indented
+    if yf_log_indented:
+        yf_logger = get_indented_logger('yfinance')
+    return yf_logger
+
+
+def setup_debug_formatting():
+    global yf_logger
+    yf_logger = get_yf_logger()
+
+    if not yf_logger.isEnabledFor(logging.DEBUG):
+        yf_logger.warning("logging mode not set to 'DEBUG', so not setting up debug formatting")
+        return
+
+    if yf_logger.handlers is None or len(yf_logger.handlers) == 0:
+        h = logging.StreamHandler()
+        # Ensure different level strings don't interfere with indentation
+        formatter = MultiLineFormatter(fmt='%(levelname)-8s %(message)s')
+        h.setFormatter(formatter)
+        yf_logger.addHandler(h)
+
+    global yf_log_indented
+    yf_log_indented = True
+
+
+def enable_debug_mode():
+    get_yf_logger().setLevel(logging.DEBUG)
+    setup_debug_formatting()
+
+
 def is_isin(string):
-    return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9]{1})$", string))
+    return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9])$", string))
 
 
 def get_all_by_isin(isin, proxy=None, session=None):
     if not (is_isin(isin)):
         raise ValueError("Invalid ISIN number")
-
-    from .base import _BASE_URL_
     session = session or _requests
-    url = "{}/v1/finance/search?q={}".format(_BASE_URL_, isin)
+    url = f"{_BASE_URL_}/v1/finance/search?q={isin}"
     data = session.get(url=url, proxies=proxy, headers=user_agent_headers)
     try:
         data = data.json()
@@ -131,7 +245,7 @@ def empty_earnings_dates_df():
 
 
 def build_template(data):
-    '''
+    """
     build_template returns the details required to rebuild any of the yahoo finance financial statements in the same order as the yahoo finance webpage. The function is built to be used on the "FinancialTemplateStore" json which appears in any one of the three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
 
     Returns:
@@ -140,95 +254,80 @@ def build_template(data):
         - template_order: The order that quarterlies should be in (note that quarterlies have no pre-fix - hence why this is required).
         - level_detail: The level of each individual line item. E.g. for the "/financials" webpage, "Total Revenue" is a level 0 item and is the summation of "Operating Revenue" and "Excise Taxes" which are level 1 items.
 
-    '''
+    """
     template_ttm_order = []  # Save the TTM (Trailing Twelve Months) ordering to an object.
     template_annual_order = []  # Save the annual ordering to an object.
     template_order = []  # Save the ordering to an object (this can be utilized for quarterlies)
     level_detail = []  # Record the level of each line item of the income statement ("Operating Revenue" and "Excise Taxes" sum to return "Total Revenue" we need to keep track of this)
-    for key in data['template']:
-        # Loop through the json to retreive the exact financial order whilst appending to the objects
-        template_ttm_order.append('trailing{}'.format(key['key']))
-        template_annual_order.append('annual{}'.format(key['key']))
-        template_order.append('{}'.format(key['key']))
-        level_detail.append(0)
-        if 'children' in key:
-            for child1 in key['children']:  # Level 1
-                template_ttm_order.append('trailing{}'.format(child1['key']))
-                template_annual_order.append('annual{}'.format(child1['key']))
-                template_order.append('{}'.format(child1['key']))
-                level_detail.append(1)
-                if 'children' in child1:
-                    for child2 in child1['children']:  # Level 2
-                        template_ttm_order.append('trailing{}'.format(child2['key']))
-                        template_annual_order.append('annual{}'.format(child2['key']))
-                        template_order.append('{}'.format(child2['key']))
-                        level_detail.append(2)
-                        if 'children' in child2:
-                            for child3 in child2['children']:  # Level 3
-                                template_ttm_order.append('trailing{}'.format(child3['key']))
-                                template_annual_order.append('annual{}'.format(child3['key']))
-                                template_order.append('{}'.format(child3['key']))
-                                level_detail.append(3)
-                                if 'children' in child3:
-                                    for child4 in child3['children']:  # Level 4
-                                        template_ttm_order.append('trailing{}'.format(child4['key']))
-                                        template_annual_order.append('annual{}'.format(child4['key']))
-                                        template_order.append('{}'.format(child4['key']))
-                                        level_detail.append(4)
-                                        if 'children' in child4:
-                                            for child5 in child4['children']:  # Level 5
-                                                template_ttm_order.append('trailing{}'.format(child5['key']))
-                                                template_annual_order.append('annual{}'.format(child5['key']))
-                                                template_order.append('{}'.format(child5['key']))
-                                                level_detail.append(5)
+
+    def traverse(node, level):
+        """
+        A recursive function that visits a node and its children.
+
+        Args:
+            node: The current node in the data structure.
+            level: The depth of the current node in the data structure.
+        """
+        if level > 5:  # Stop when level is above 5
+            return
+        template_ttm_order.append(f"trailing{node['key']}")
+        template_annual_order.append(f"annual{node['key']}")
+        template_order.append(f"{node['key']}")
+        level_detail.append(level)
+        if 'children' in node:  # Check if the node has children
+            for child in node['children']:  # If yes, traverse each child
+                traverse(child, level + 1)  # Increment the level by 1 for each child
+
+    for key in data['template']:  # Loop through the data
+        traverse(key, 0)  # Call the traverse function with initial level being 0
+
     return template_ttm_order, template_annual_order, template_order, level_detail
 
 
-def retreive_financial_details(data):
-    '''
-    retreive_financial_details returns all of the available financial details under the "QuoteTimeSeriesStore" for any of the following three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
+def retrieve_financial_details(data):
+    """
+    retrieve_financial_details returns all of the available financial details under the
+    "QuoteTimeSeriesStore" for any of the following three yahoo finance webpages:
+    "/financials", "/cash-flow" and "/balance-sheet".
 
     Returns:
         - TTM_dicts: A dictionary full of all of the available Trailing Twelve Month figures, this can easily be converted to a pandas dataframe.
         - Annual_dicts: A dictionary full of all of the available Annual figures, this can easily be converted to a pandas dataframe.
-    '''
+    """
     TTM_dicts = []  # Save a dictionary object to store the TTM financials.
     Annual_dicts = []  # Save a dictionary object to store the Annual financials.
-    for key in data['timeSeries']:  # Loop through the time series data to grab the key financial figures.
+
+    for key, timeseries in data.get('timeSeries', {}).items():  # Loop through the time series data to grab the key financial figures.
         try:
-            if len(data['timeSeries'][key]) > 0:
-                time_series_dict = {}
-                time_series_dict['index'] = key
-                for each in data['timeSeries'][key]:  # Loop through the years
-                    if each == None:
+            if timeseries:
+                time_series_dict = {'index': key}
+                for each in timeseries:  # Loop through the years
+                    if not each:
                         continue
-                    else:
-                        time_series_dict[each['asOfDate']] = each['reportedValue']
-                    # time_series_dict["{}".format(each['asOfDate'])] = data['timeSeries'][key][each]['reportedValue']
+                    time_series_dict[each.get('asOfDate')] = each.get('reportedValue')
                 if 'trailing' in key:
                     TTM_dicts.append(time_series_dict)
                 elif 'annual' in key:
                     Annual_dicts.append(time_series_dict)
-        except Exception as e:
-            pass
+        except KeyError as e:
+            print(f"An error occurred while processing the key: {e}")
     return TTM_dicts, Annual_dicts
 
 
 def format_annual_financial_statement(level_detail, annual_dicts, annual_order, ttm_dicts=None, ttm_order=None):
-    '''
+    """
     format_annual_financial_statement formats any annual financial statement
 
     Returns:
         - _statement: A fully formatted annual financial statement in pandas dataframe.
-    '''
+    """
     Annual = _pd.DataFrame.from_dict(annual_dicts).set_index("index")
     Annual = Annual.reindex(annual_order)
     Annual.index = Annual.index.str.replace(r'annual', '')
 
     # Note: balance sheet is the only financial statement with no ttm detail
-    if (ttm_dicts not in [[], None]) and (ttm_order not in [[], None]):
-        TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index")
-        TTM = TTM.reindex(ttm_order)
+    if ttm_dicts and ttm_order:
+        TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index").reindex(ttm_order)
         # Add 'TTM' prefix to all column names, so if combined we can tell
         # the difference between actuals and TTM (similar to yahoo finance).
         TTM.columns = ['TTM ' + str(col) for col in TTM.columns]
@@ -246,12 +345,12 @@ def format_annual_financial_statement(level_detail, annual_dicts, annual_order,
 
 
 def format_quarterly_financial_statement(_statement, level_detail, order):
-    '''
+    """
     format_quarterly_financial_statements formats any quarterly financial statement
 
     Returns:
         - _statement: A fully formatted quarterly financial statement in pandas dataframe.
-    '''
+    """
     _statement = _statement.reindex(order)
     _statement.index = camel2title(_statement.T)
     _statement['level_detail'] = level_detail
@@ -302,7 +401,7 @@ def camel2title(strings: List[str], sep: str = ' ', acronyms: Optional[List[str]
 
     # Apply str.title() to non-acronym words
     strings = [s.split(sep) for s in strings]
-    strings = [[j.title() if not j in acronyms else j for j in s] for s in strings]
+    strings = [[j.title() if j not in acronyms else j for j in s] for s in strings]
     strings = [sep.join(s) for s in strings]
 
     return strings
@@ -332,24 +431,24 @@ def _parse_user_dt(dt, exchange_tz):
 
 def _interval_to_timedelta(interval):
     if interval == "1mo":
-        return _dateutil.relativedelta.relativedelta(months=1)
+        return relativedelta(months=1)
     elif interval == "3mo":
-        return _dateutil.relativedelta.relativedelta(months=3)
+        return relativedelta(months=3)
     elif interval == "1y":
-        return _dateutil.relativedelta.relativedelta(years=1)
+        return relativedelta(years=1)
     elif interval == "1wk":
-        return _pd.Timedelta(days=7, unit='d')
-    else: 
+        return _pd.Timedelta(days=7)
+    else:
         return _pd.Timedelta(interval)
 
 
 def auto_adjust(data):
     col_order = data.columns
     df = data.copy()
-    ratio = df["Close"] / df["Adj Close"]
-    df["Adj Open"] = df["Open"] / ratio
-    df["Adj High"] = df["High"] / ratio
-    df["Adj Low"] = df["Low"] / ratio
+    ratio = (df["Adj Close"] / df["Close"]).to_numpy()
+    df["Adj Open"] = df["Open"] * ratio
+    df["Adj High"] = df["High"] * ratio
+    df["Adj Low"] = df["Low"] * ratio
 
     df.drop(
         ["Open", "High", "Low", "Close"],
@@ -412,12 +511,9 @@ def parse_quotes(data):
 
 
 def parse_actions(data):
-    dividends = _pd.DataFrame(
-        columns=["Dividends"], index=_pd.DatetimeIndex([]))
-    capital_gains = _pd.DataFrame(
-        columns=["Capital Gains"], index=_pd.DatetimeIndex([]))
-    splits = _pd.DataFrame(
-        columns=["Stock Splits"], index=_pd.DatetimeIndex([]))
+    dividends = None
+    capital_gains = None
+    splits = None
 
     if "events" in data:
         if "dividends" in data["events"]:
@@ -442,10 +538,19 @@ def parse_actions(data):
             splits.set_index("date", inplace=True)
             splits.index = _pd.to_datetime(splits.index, unit="s")
             splits.sort_index(inplace=True)
-            splits["Stock Splits"] = splits["numerator"] / \
-                                     splits["denominator"]
+            splits["Stock Splits"] = splits["numerator"] / splits["denominator"]
             splits = splits[["Stock Splits"]]
 
+    if dividends is None:
+        dividends = _pd.DataFrame(
+            columns=["Dividends"], index=_pd.DatetimeIndex([]))
+    if capital_gains is None:
+        capital_gains = _pd.DataFrame(
+            columns=["Capital Gains"], index=_pd.DatetimeIndex([]))
+    if splits is None:
+        splits = _pd.DataFrame(
+            columns=["Stock Splits"], index=_pd.DatetimeIndex([]))
+
     return dividends, splits, capital_gains
 
 
@@ -456,31 +561,30 @@ def set_df_tz(df, interval, tz):
     return df
 
 
-def fix_Yahoo_returning_prepost_unrequested(quotes, interval, metadata):
+def fix_Yahoo_returning_prepost_unrequested(quotes, interval, tradingPeriods):
     # 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 = tradingPeriods.copy()
     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 = quotes.merge(tps_df, how="left")
     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]
+        # 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
 
@@ -513,22 +617,30 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
             elif interval == "3mo":
                 last_rows_same_interval = dt1.year == dt2.year and dt1.quarter == dt2.quarter
             else:
-                last_rows_same_interval = (dt1-dt2) < _pd.Timedelta(interval)
+                last_rows_same_interval = (dt1 - dt2) < _pd.Timedelta(interval)
 
             if last_rows_same_interval:
                 # Last two rows are within same interval
                 idx1 = quotes.index[n - 1]
                 idx2 = quotes.index[n - 2]
+                if idx1 == idx2:
+                    # Yahoo returning last interval duplicated, which means
+                    # Yahoo is not returning live data (phew!)
+                    return quotes
                 if _np.isnan(quotes.loc[idx2, "Open"]):
                     quotes.loc[idx2, "Open"] = quotes["Open"][n - 1]
-                # Note: nanmax() & nanmin() ignores NaNs
-                quotes.loc[idx2, "High"] = _np.nanmax([quotes["High"][n - 1], quotes["High"][n - 2]])
-                quotes.loc[idx2, "Low"] = _np.nanmin([quotes["Low"][n - 1], quotes["Low"][n - 2]])
+                # Note: nanmax() & nanmin() ignores NaNs, but still need to check not all are NaN to avoid warnings
+                if not _np.isnan(quotes["High"][n - 1]):
+                    quotes.loc[idx2, "High"] = _np.nanmax([quotes["High"][n - 1], quotes["High"][n - 2]])
+                    if "Adj High" in quotes.columns:
+                        quotes.loc[idx2, "Adj High"] = _np.nanmax([quotes["Adj High"][n - 1], quotes["Adj High"][n - 2]])
+
+                if not _np.isnan(quotes["Low"][n - 1]):
+                    quotes.loc[idx2, "Low"] = _np.nanmin([quotes["Low"][n - 1], quotes["Low"][n - 2]])
+                    if "Adj Low" in quotes.columns:
+                        quotes.loc[idx2, "Adj Low"] = _np.nanmin([quotes["Adj Low"][n - 1], quotes["Adj Low"][n - 2]])
+
                 quotes.loc[idx2, "Close"] = quotes["Close"][n - 1]
-                if "Adj High" in quotes.columns:
-                    quotes.loc[idx2, "Adj High"] = _np.nanmax([quotes["Adj High"][n - 1], quotes["Adj High"][n - 2]])
-                if "Adj Low" in quotes.columns:
-                    quotes.loc[idx2, "Adj Low"] = _np.nanmin([quotes["Adj Low"][n - 1], quotes["Adj Low"][n - 2]])
                 if "Adj Close" in quotes.columns:
                     quotes.loc[idx2, "Adj Close"] = quotes["Adj Close"][n - 1]
                 quotes.loc[idx2, "Volume"] += quotes["Volume"][n - 1]
@@ -538,11 +650,6 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
 
 
 def safe_merge_dfs(df_main, df_sub, interval):
-    # Carefully merge 'df_sub' onto 'df_main'
-    # If naive merge fails, try again with reindexing df_sub:
-    # 1) if interval is weekly or monthly, then try with index set to start of week/month
-    # 2) if still failing then manually search through df_main.index to reindex df_sub
-
     if df_sub.shape[0] == 0:
         raise Exception("No data to merge")
 
@@ -552,6 +659,72 @@ def safe_merge_dfs(df_main, df_sub, interval):
         raise Exception("Expected 1 data col")
     data_col = data_cols[0]
 
+    df_main = df_main.sort_index()
+    intraday = interval.endswith('m') or interval.endswith('s')
+
+    td = _interval_to_timedelta(interval)
+    if intraday:
+        # On some exchanges the event can occur before market open.
+        # Problem when combining with intraday data.
+        # Solution = use dates, not datetimes, to map/merge.
+        df_main['_date'] = df_main.index.date
+        df_sub['_date'] = df_sub.index.date
+        indices = _np.searchsorted(_np.append(df_main['_date'], [df_main['_date'].iloc[-1]+td]), df_sub['_date'], side='left')
+        df_main = df_main.drop('_date', axis=1)
+        df_sub = df_sub.drop('_date', axis=1)
+    else:
+        indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1] + td), df_sub.index, side='right')
+        indices -= 1  # Convert from [[i-1], [i]) to [[i], [i+1])
+        # Numpy.searchsorted does not handle out-of-range well, so handle manually:
+        for i in range(len(df_sub.index)):
+            dt = df_sub.index[i]
+            if dt < df_main.index[0] or dt >= df_main.index[-1] + td:
+                # Out-of-range
+                indices[i] = -1
+
+    f_outOfRange = indices == -1
+    if f_outOfRange.any() and not intraday:
+        empty_row_data = {c:[_np.nan] for c in const.price_colnames}|{'Volume':[0]}
+        if interval == '1d':
+            # For 1d, add all out-of-range event dates
+            for i in _np.where(f_outOfRange)[0]:
+                dt = df_sub.index[i]
+                get_yf_logger().debug(f"Adding out-of-range {data_col} @ {dt.date()} in new prices row of NaNs")
+                empty_row = _pd.DataFrame(data=empty_row_data, index=[dt])
+                df_main = _pd.concat([df_main, empty_row], sort=True)
+        else:
+            # Else, only add out-of-range event dates if occurring in interval 
+            # immediately after last pricfe row
+            last_dt = df_main.index[-1]
+            next_interval_start_dt = last_dt + td
+            next_interval_end_dt = next_interval_start_dt + td
+            for i in _np.where(f_outOfRange)[0]:
+                dt = df_sub.index[i]
+                if next_interval_start_dt <= dt < next_interval_end_dt:
+                    new_dt = next_interval_start_dt
+                    get_yf_logger().debug(f"Adding out-of-range {data_col} @ {dt.date()} in new prices row of NaNs")
+                    empty_row = _pd.DataFrame(data=empty_row_data, index=[dt])
+                    df_main = _pd.concat([df_main, empty_row], sort=True)
+        df_main = df_main.sort_index()
+
+        # Re-calculate indices
+        indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1] + td), df_sub.index, side='right')
+        indices -= 1  # Convert from [[i-1], [i]) to [[i], [i+1])
+        # Numpy.searchsorted does not handle out-of-range well, so handle manually:
+        for i in range(len(df_sub.index)):
+            dt = df_sub.index[i]
+            if dt < df_main.index[0] or dt >= df_main.index[-1] + td:
+                # Out-of-range
+                indices[i] = -1
+
+    f_outOfRange = indices == -1
+    if f_outOfRange.any():
+        if intraday or interval in ['1d', '1wk']:
+            raise Exception(f"The following '{data_col}' events are out-of-range, did not expect with interval {interval}: {df_sub.index[f_outOfRange]}")
+        get_yf_logger().debug(f'Discarding these {data_col} events:' + '\n' + str(df_sub[f_outOfRange]))
+        df_sub = df_sub[~f_outOfRange].copy()
+        indices = indices[~f_outOfRange]
+
     def _reindex_events(df, new_index, data_col_name):
         if len(new_index) == len(set(new_index)):
             # No duplicates, easy
@@ -569,110 +742,19 @@ def safe_merge_dfs(df_main, df_sub, interval):
             df = df.groupby("_NewIndex").prod()
             df.index.name = None
         else:
-            raise Exception("New index contains duplicates but unsure how to aggregate for '{}'".format(data_col_name))
+            raise Exception(f"New index contains duplicates but unsure how to aggregate for '{data_col_name}'")
         if "_NewIndex" in df.columns:
             df = df.drop("_NewIndex", axis=1)
         return df
 
-    df = df_main.join(df_sub)
-
-    f_na = df[data_col].isna()
-    data_lost = sum(~f_na) < df_sub.shape[0]
-    if not data_lost:
-        return df
-    # Lost data during join()
-    # Backdate all df_sub.index dates to start of week/month
-    if interval == "1wk":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='W').to_timestamp()
-    elif interval == "1mo":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='M').to_timestamp()
-    elif interval == "3mo":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='Q').to_timestamp()
-    else:
-        new_index = None
-
-    if new_index is not None:
-        new_index = new_index.tz_localize(df.index.tz, ambiguous=True, nonexistent='shift_forward')
-        df_sub = _reindex_events(df_sub, new_index, data_col)
-        df = df_main.join(df_sub)
-
-    f_na = df[data_col].isna()
-    data_lost = sum(~f_na) < df_sub.shape[0]
-    if not data_lost:
-        return df
-    # Lost data during join(). Manually check each df_sub.index date against df_main.index to
-    # find matching interval
-    df_sub = df_sub_backup.copy()
-    new_index = [-1] * df_sub.shape[0]
-    for i in range(df_sub.shape[0]):
-        dt_sub_i = df_sub.index[i]
-        if dt_sub_i in df_main.index:
-            new_index[i] = dt_sub_i
-            continue
-        # Found a bad index date, need to search for near-match in df_main (same week/month)
-        fixed = False
-        for j in range(df_main.shape[0] - 1):
-            dt_main_j0 = df_main.index[j]
-            dt_main_j1 = df_main.index[j + 1]
-            if (dt_main_j0 <= dt_sub_i) and (dt_sub_i < dt_main_j1):
-                fixed = True
-                if interval.endswith('h') or interval.endswith('m'):
-                    # Must also be same day
-                    fixed = (dt_main_j0.date() == dt_sub_i.date()) and (dt_sub_i.date() == dt_main_j1.date())
-                if fixed:
-                    dt_sub_i = dt_main_j0
-                    break
-        if not fixed:
-            last_main_dt = df_main.index[df_main.shape[0] - 1]
-            diff = dt_sub_i - last_main_dt
-            if interval == "1mo" and last_main_dt.month == dt_sub_i.month:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "3mo" and last_main_dt.year == dt_sub_i.year and last_main_dt.quarter == dt_sub_i.quarter:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "1wk":
-                if last_main_dt.week == dt_sub_i.week:
-                    dt_sub_i = last_main_dt
-                    fixed = True
-                elif (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < _datetime.timedelta(weeks=1)):
-                    # With some specific start dates (e.g. around early Jan), Yahoo
-                    # messes up start-of-week, is Saturday not Monday. So check
-                    # if same week another way
-                    dt_sub_i = last_main_dt
-                    fixed = True
-            elif interval == "1d" and last_main_dt.day == dt_sub_i.day:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "1h" and last_main_dt.hour == dt_sub_i.hour:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval.endswith('m') or interval.endswith('h'):
-                td = _pd.to_timedelta(interval)
-                if (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < td):
-                    dt_sub_i = last_main_dt
-                    fixed = True
-        new_index[i] = dt_sub_i
+    new_index = df_main.index[indices]
     df_sub = _reindex_events(df_sub, new_index, data_col)
-    df = df_main.join(df_sub)
 
+    df = df_main.join(df_sub)
     f_na = df[data_col].isna()
     data_lost = sum(~f_na) < df_sub.shape[0]
     if data_lost:
-        ## Not always possible to match events with trading, e.g. when released pre-market.
-        ## So have to append to bottom with nan prices.
-        ## But should only be impossible with intra-day price data.
-        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].copy()
-            keys = {"Adj Open", "Open", "Adj High", "High", "Adj Low", "Low", "Adj Close",
-                    "Close"}.intersection(df.columns)
-            df_sub_missing[list(keys)] = _np.nan
-            col_ordering = df.columns
-            df = _pd.concat([df, df_sub_missing], sort=True)[col_ordering]
-        else:
-            raise Exception("Lost data during merge despite all attempts to align data (see above)")
+        raise Exception('Data was lost in merge, investigate')
 
     return df
 
@@ -698,7 +780,7 @@ def is_valid_timezone(tz: str) -> bool:
     return True
 
 
-def format_history_metadata(md):
+def format_history_metadata(md, tradingPeriodsOnly=True):
     if not isinstance(md, dict):
         return md
     if len(md) == 0:
@@ -706,63 +788,58 @@ def format_history_metadata(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 not tradingPeriodsOnly:
+        for k in ["firstTradeDate", "regularMarketTime"]:
+            if k in md and md[k] is not None:
+                if isinstance(md[k], int):
+                    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 "currentTradingPeriod" in md:
+            for m in ["regular", "pre", "post"]:
+                if m in md["currentTradingPeriod"] and isinstance(md["currentTradingPeriod"][m]["start"], int):
+                    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:
         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()
+        if tps == {"pre": [], "post": []}:
+            # Ignore
+            pass
+        elif isinstance(tps, (list, dict)):
+            if isinstance(tps, list):
+                # Only regular times
+                df = _pd.DataFrame.from_records(_np.hstack(tps))
+                df = df.drop(["timezone", "gmtoffset"], axis=1)
+                df["start"] = _pd.to_datetime(df["start"], unit='s', utc=True).dt.tz_convert(tz)
+                df["end"] = _pd.to_datetime(df["end"], unit='s', utc=True).dt.tz_convert(tz)
+            elif isinstance(tps, dict):
+                # Includes pre- and post-market
+                pre_df = _pd.DataFrame.from_records(_np.hstack(tps["pre"]))
+                post_df = _pd.DataFrame.from_records(_np.hstack(tps["post"]))
+                regular_df = _pd.DataFrame.from_records(_np.hstack(tps["regular"]))
+
+                pre_df = pre_df.rename(columns={"start": "pre_start", "end": "pre_end"}).drop(["timezone", "gmtoffset"], axis=1)
+                post_df = post_df.rename(columns={"start": "post_start", "end": "post_end"}).drop(["timezone", "gmtoffset"], axis=1)
+                regular_df = regular_df.drop(["timezone", "gmtoffset"], axis=1)
+
+                cols = ["pre_start", "pre_end", "start", "end", "post_start", "post_end"]
+                df = regular_df.join(pre_df).join(post_df)
+                for c in cols:
+                    df[c] = _pd.to_datetime(df[c], unit='s', utc=True).dt.tz_convert(tz)
+                df = df[cols]
 
-        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.index.name = "Date"
 
-        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
+            md["tradingPeriods"] = df
 
     return md
 
+
 class ProgressBar:
     def __init__(self, iterations, text='completed'):
         self.text = text
@@ -795,19 +872,16 @@ class ProgressBar:
     def update_iteration(self, val=None):
         val = val if val is not None else self.elapsed / float(self.iterations)
         self.__update_amount(val * 100.0)
-        self.prog_bar += '  %s of %s %s' % (
-            self.elapsed, self.iterations, self.text)
+        self.prog_bar += f"  {self.elapsed} of {self.iterations} {self.text}"
 
     def __update_amount(self, new_amount):
         percent_done = int(round((new_amount / 100.0) * 100.0))
         all_full = self.width - 2
         num_hashes = int(round((percent_done / 100.0) * all_full))
-        self.prog_bar = '[' + self.fill_char * \
-                        num_hashes + ' ' * (all_full - num_hashes) + ']'
+        self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'
         pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
-        pct_string = '%d%%' % percent_done
-        self.prog_bar = self.prog_bar[0:pct_place] + \
-                        (pct_string + self.prog_bar[pct_place + len(pct_string):])
+        pct_string = f'{percent_done}%%'
+        self.prog_bar = self.prog_bar[0:pct_place] + (pct_string + self.prog_bar[pct_place + len(pct_string):])
 
     def __str__(self):
         return str(self.prog_bar)
@@ -818,7 +892,7 @@ class ProgressBar:
 # ---------------------------------
 
 class _KVStore:
-    """Simpel Sqlite backed key/value store, key and value are strings. Should be thread safe."""
+    """Simple Sqlite backed key/value store, key and value are strings. Should be thread safe."""
 
     def __init__(self, filename):
         self._cache_mutex = Lock()
@@ -844,14 +918,21 @@ class _KVStore:
 
     def get(self, key: str) -> Union[str, None]:
         """Get value for key if it exists else returns None"""
-        item = self.conn.execute('select value from "kv" where key=?', (key,))
+        try:
+            item = self.conn.execute('select value from "kv" where key=?', (key,))
+        except _sqlite3.IntegrityError as e:
+            self.delete(key)
+            return None
         if item:
             return next(item, (None,))[0]
 
     def set(self, key: str, value: str) -> None:
-        with self._cache_mutex:
-            self.conn.execute('replace into "kv" (key, value) values (?,?)', (key, value))
-            self.conn.commit()
+        if value is None:
+            self.delete(key)
+        else:
+            with self._cache_mutex:
+                self.conn.execute('replace into "kv" (key, value) values (?,?)', (key, value))
+                self.conn.commit()
 
     def bulk_set(self, kvdata: Dict[str, str]):
         records = tuple(i for i in kvdata.items())
@@ -875,7 +956,10 @@ class _TzCache:
     def __init__(self):
         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"))
+        try:
+            self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
+        except _sqlite3.DatabaseError as err:
+            raise _TzCacheException(f"Error creating TzCache folder: '{self._db_dir}' reason: {err}")
         self._migrate_cache_tkr_tz()
 
     def _setup_cache_folder(self):
@@ -883,12 +967,10 @@ class _TzCache:
             try:
                 _os.makedirs(self._db_dir)
             except OSError as err:
-                raise _TzCacheException("Error creating TzCache folder: '{}' reason: {}"
-                                        .format(self._db_dir, err))
+                raise _TzCacheException(f"Error creating TzCache folder: '{self._db_dir}' reason: {err}")
 
         elif not (_os.access(self._db_dir, _os.R_OK) and _os.access(self._db_dir, _os.W_OK)):
-            raise _TzCacheException("Cannot read and write in TzCache folder: '{}'"
-                                    .format(self._db_dir, ))
+            raise _TzCacheException(f"Cannot read and write in TzCache folder: '{self._db_dir}'")
 
     def lookup(self, tkr):
         return self.tz_db.get(tkr)
@@ -896,10 +978,14 @@ class _TzCache:
     def store(self, tkr, tz):
         if tz is None:
             self.tz_db.delete(tkr)
-        elif self.tz_db.get(tkr) is not None:
-            raise Exception("Tkr {} tz already in cache".format(tkr))
         else:
-            self.tz_db.set(tkr, tz)
+            tz_db = self.tz_db.get(tkr)
+            if tz_db is not None:
+                if tz != tz_db:
+                    get_yf_logger().debug(f'{tkr}: Overwriting cached TZ "{tz_db}" with different TZ "{tz}"')
+                    self.tz_db.set(tkr, tz)
+            else:
+                self.tz_db.set(tkr, tz)
 
     @property
     def _db_dir(self):
@@ -917,11 +1003,23 @@ class _TzCache:
         if not _os.path.isfile(old_cache_file_path):
             return None
         try:
-            df = _pd.read_csv(old_cache_file_path, index_col="Ticker")
+            df = _pd.read_csv(old_cache_file_path, index_col="Ticker", on_bad_lines="skip")
         except _pd.errors.EmptyDataError:
             _os.remove(old_cache_file_path)
+        except TypeError:
+            _os.remove(old_cache_file_path)
         else:
-            self.tz_db.bulk_set(df.to_dict()['Tz'])
+            # Discard corrupt data:
+            df = df[~df["Tz"].isna().to_numpy()]
+            df = df[~(df["Tz"] == '').to_numpy()]
+            df = df[~df.index.isna()]
+            if not df.empty:
+                try:
+                    self.tz_db.bulk_set(df.to_dict()['Tz'])
+                except Exception as e:
+                    # Ignore
+                    pass
+
             _os.remove(old_cache_file_path)
 
 
@@ -952,9 +1050,9 @@ def get_tz_cache():
             try:
                 _tz_cache = _TzCache()
             except _TzCacheException as err:
-                print("Failed to create TzCache, reason: {}".format(err))
-                print("TzCache will not be used.")
-                print("Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'")
+                get_yf_logger().info(f"Failed to create TzCache, reason: {err}. "
+                                     "TzCache will not be used. "
+                                     "Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'")
                 _tz_cache = _TzCacheDummy()
 
         return _tz_cache

yfinance/version.py

@@ -1 +1 @@
-version = "0.2.17"
+version = "0.2.28"