Bump version to 0.2.30

Fix: OperationalError('unable to open database file')

.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 +1,95 @@
+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: summary
+    attributes:
+      label: "Describe bug"
+    validations:
+      required: true
+
+  - 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 +1,99 @@
 Change Log
 ===========
 
+0.2.30
+------
+- Fix OperationalError #1698
+
+0.2.29
+------
+- Fix pandas warning when retrieving quotes. #1672
+- Replace sqlite3 with peewee for 100% thread-safety #1675
+- Fix merging events with intraday prices #1684
+- Fix error when calling enable_debug_mode twice #1687
+- Price repair fixes #1688
+
+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
+
+0.2.16
+------
+Fix 'fast_info deprecated' msg appearing at Ticker() init
+
+0.2.15
+------
+Restore missing Ticker.info keys #1480
+
+0.2.14
+------
+Fix Ticker.info dict by fetching from API #1461
+
+0.2.13
+------
+Price bug fixes:
+- fetch big-interval with Capital Gains #1455
+- merging dividends & splits with prices #1452
+
+0.2.12
+------
+Disable annoying 'backup decrypt' msg
+
 0.2.11
 ------
 Fix history_metadata accesses for unusual symbols #1411

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.
-
 ## Quick Start
 
 ### The Ticker module
@@ -58,10 +53,8 @@ import yfinance as yf
 
 msft = yf.Ticker("MSFT")
 
-# get all stock info (slow)
+# get all stock info
 msft.info
-# fast access to subset of stock info (opportunistic)
-msft.fast_info
 
 # get historical market data
 hist = msft.history(period="1mo")
@@ -76,9 +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:
@@ -98,25 +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
@@ -154,6 +125,8 @@ msft.option_chain(..., proxy="PROXY_SERVER")
 ...
 ```
 
+### Multiple tickers
+
 To initialize multiple `Ticker` objects, use
 
 ```python
@@ -167,24 +140,18 @@ tickers.tickers['AAPL'].history(period="1mo")
 tickers.tickers['GOOG'].actions
 ```
 
-### Fetching data for multiple tickers
+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
-            ignore_tz = True,      # ignore timezone when aligning data from different exchanges?
-            prepost = False)       # download pre/post market hours data?
-```
+### 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
 
@@ -206,11 +173,12 @@ Combine a `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-l
 from requests import Session
 from requests_cache import CacheMixin, SQLiteCache
 from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
+from pyrate_limiter import Duration, RequestRate, Limiter
 class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
-    """ """
+    pass
 
 session = CachedLimiterSession(
-    per_second=0.9,
+    limiter=Limiter(RequestRate(2, Duration.SECOND*5)),  # max 2 requests per 5 seconds
     bucket_class=MemoryQueueBucket,
     backend=SQLiteCache("yfinance.cache"),
 )
@@ -231,21 +199,7 @@ yfinance?](https://stackoverflow.com/questions/63107801)
         -   How to download single or multiple tickers into a single
             dataframe with single level column names and a ticker column
 
-### Timezone cache store
-
-When fetching price data, all dates are localized to stock exchange timezone. 
-But timezone retrieval is relatively slow, so yfinance attemps to cache them 
-in your users cache folder. 
-You can direct cache to use a different location with `set_tz_cache_location()`:
-```python
-import yfinance as yf
-yf.set_tz_cache_location("custom/cache/location")
-...
-```
-
----
-
-## `pandas_datareader` override
+### `pandas_datareader` override
 
 If your code uses `pandas_datareader` and you want to download data
 faster, you can "hijack" `pandas_datareader.data.get_data_yahoo()`
@@ -262,6 +216,18 @@ yf.pdr_override() # <== that's all it takes :-)
 data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")
 ```
 
+### Timezone cache store
+
+When fetching price data, all dates are localized to stock exchange timezone. 
+But timezone retrieval is relatively slow, so yfinance attemps to cache them 
+in your users cache folder. 
+You can direct cache to use a different location with `set_tz_cache_location()`:
+```python
+import yfinance as yf
+yf.set_tz_cache_location("custom/cache/location")
+...
+```
+
 ---
 
 ## Installation
@@ -272,6 +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).
 
@@ -280,20 +251,24 @@ 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
+-   [peewee](https://pypi.org/project/peewee)  \>= 3.16.2
 
-### Optional (if you want to use `pandas_datareader`)
+#### Optional (if you want to use `pandas_datareader`)
 
 -   [pandas\_datareader](https://github.com/pydata/pandas-datareader)
     \>= 0.4.0
 
+## Developers: want to contribute?
+
+`yfinance` relies on community to investigate bugs and contribute code. Developer guide: https://github.com/ranaroussi/yfinance/discussions/1084
+
 ---
 
 ### Legal Stuff

meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.2.11" %}
+{% set version = "0.2.30" %}
 
 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
@@ -26,15 +26,15 @@ requirements:
     - frozendict >=2.3.4
     - beautifulsoup4 >=4.11.1
     - html5lib >=1.1
+    - peewee >=3.16.2
     # - 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
@@ -42,8 +42,8 @@ requirements:
     - frozendict >=2.3.4
     - beautifulsoup4 >=4.11.1
     - html5lib >=1.1
+    - peewee >=3.16.2
     # - 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 +8,4 @@ pytz>=2022.5
 frozendict>=2.3.4
 beautifulsoup4>=4.11.1
 html5lib>=1.1
-cryptography>=3.3.2
+peewee>=3.16.2

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',
+                      'frozendict>=2.3.4', 'peewee>=3.16.2',
                       '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

@@ -1,5 +1,7 @@
 # -*- coding: utf-8 -*-
 
+import appdirs as _ad
+import datetime as _dt
 import sys
 import os
 _parent_dp = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
@@ -7,3 +9,39 @@ _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)
+
+
+# Use adjacent cache folder for testing, delete if already exists and older than today
+testing_cache_dirpath = os.path.join(_ad.user_cache_dir(), "py-yfinance-testing")
+yfinance.set_tz_cache_location(testing_cache_dirpath)
+if os.path.isdir(testing_cache_dirpath):
+    mtime = _dt.datetime.fromtimestamp(os.path.getmtime(testing_cache_dirpath))
+    if mtime.date() < _dt.date.today():
+        import shutil
+        shutil.rmtree(testing_cache_dirpath)
+
+
+# Setup a session to rate-limit and cache persistently:
+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(testing_cache_dirpath, "unittests-cache")
+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
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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
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tests/data/8TRA-DE-1d-missing-div-adjust.csv

@@ -0,0 +1,6 @@
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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 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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tests/data/AET-L-1wk-100x-error.csv

@@ -0,0 +1,37 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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tests/data/AET-L-1wk-100x-error.csv.old

@@ -0,0 +1,25 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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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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+2022-06-30 00:00:00+02:00,3.24900007247925,3.28099989891052,2.5,2.5550000667572,2.5550000667572,4805984,0,0

tests/data/SPM-MI-1d-bad-stock-split.csv

@@ -0,0 +1,23 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2022-06-01 00:00:00+02:00,57.29999923706055,57.81999969482422,53.939998626708984,53.939998626708984,53.939998626708984,309586,0.0,0.0
+2022-06-02 00:00:00+02:00,53.86000061035156,53.86000061035156,52.68000030517578,52.939998626708984,52.939998626708984,166288,0.0,0.0
+2022-06-03 00:00:00+02:00,53.459999084472656,53.459999084472656,51.58000183105469,51.68000030517578,51.68000030517578,169890,0.0,0.0
+2022-06-06 00:00:00+02:00,51.68000030517578,52.52000045776367,51.380001068115234,51.880001068115234,51.880001068115234,107491,0.0,0.0
+2022-06-07 00:00:00+02:00,52.18000030517578,52.220001220703125,50.7400016784668,51.560001373291016,51.560001373291016,185068,0.0,0.0
+2022-06-08 00:00:00+02:00,51.560001373291016,51.7599983215332,50.720001220703125,51.02000045776367,51.02000045776367,114036,0.0,0.0
+2022-06-09 00:00:00+02:00,50.97999954223633,50.97999954223633,48.7599983215332,48.939998626708984,48.939998626708984,202548,0.0,0.0
+2022-06-10 00:00:00+02:00,48.79999923706055,48.79999923706055,45.040000915527344,45.040000915527344,45.040000915527344,298273,0.0,0.0
+2022-06-13 00:00:00+02:00,43.0,43.7599983215332,38.36000061035156,38.36000061035156,38.36000061035156,456821,0.0,0.1
+2022-06-14 00:00:00+02:00,38.775001525878906,41.599998474121094,38.52000045776367,39.439998626708984,39.439998626708984,535450,0.0,0.0
+2022-06-15 00:00:00+02:00,40.34000015258789,41.64500045776367,37.30500030517578,37.30500030517578,37.30500030517578,666261,0.0,0.0
+2022-06-16 00:00:00+02:00,37.30500030517578,39.849998474121094,37.2400016784668,38.255001068115234,38.255001068115234,1337996,0.0,0.0
+2022-06-17 00:00:00+02:00,38.0,42.994998931884766,37.5,42.994998931884766,42.994998931884766,1284416,0.0,0.0
+2022-06-20 00:00:00+02:00,2.1942219734191895,2.2295401096343994,2.139925956726074,2.2295401096343994,2.2295401096343994,12364104,0.0,0.0
+2022-06-21 00:00:00+02:00,2.247199058532715,2.2851529121398926,2.1971209049224854,2.2155709266662598,2.2155709266662598,8434013,0.0,0.0
+2022-06-22 00:00:00+02:00,1.986791968345642,2.003659963607788,1.7379820346832275,1.7379820346832275,1.7379820346832275,26496542,0.0,0.0
+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
@@ -104,7 +114,70 @@ class TestPriceHistory(unittest.TestCase):
         if not test_run:
             self.skipTest("Skipping test_duplicatingWeekly() because not possible to fail Monday/weekend")
 
+    def test_pricesEventsMerge(self):
+        # Test case: dividend occurs after last row in price data
+        tkr = 'INTC'
+        start_d = _dt.date(2022, 1, 1)
+        end_d = _dt.date(2023, 1, 1)
+        df = yf.Ticker(tkr, session=self.session).history(interval='1d', start=start_d, end=end_d)
+        div = 1.0
+        future_div_dt = df.index[-1] + _dt.timedelta(days=1)
+        if future_div_dt.weekday() in [5, 6]:
+            future_div_dt += _dt.timedelta(days=1) * (7 - future_div_dt.weekday())
+        divs = _pd.DataFrame(data={"Dividends":[div]}, index=[future_div_dt])
+        df2 = yf.utils.safe_merge_dfs(df.drop(['Dividends', 'Stock Splits'], axis=1), divs, '1d')
+        self.assertIn(future_div_dt, df2.index)
+        self.assertIn("Dividends", df2.columns)
+        self.assertEqual(df2['Dividends'].iloc[-1], div)
+
+    def test_pricesEventsMerge_bug(self):
+        # Reproduce exception when merging intraday prices with future dividend
+        tkr = 'S32.AX'
+        interval = '30m'
+        df_index = []
+        d = 13
+        for h in range(0, 16):
+            for m in [0, 30]:
+                df_index.append(_dt.datetime(2023, 9, d, h, m))
+        df_index.append(_dt.datetime(2023, 9, d, 16))
+        df = _pd.DataFrame(index=df_index)
+        df.index = _pd.to_datetime(df.index)
+        df['Close'] = 1.0
+
+        div = 1.0
+        future_div_dt = _dt.datetime(2023, 9, 14, 10)
+        divs = _pd.DataFrame(data={"Dividends":[div]}, index=[future_div_dt])
+
+        df2 = yf.utils.safe_merge_dfs(df, divs, interval)
+        # No exception = test pass
+
     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 +188,46 @@ 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 +239,7 @@ 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 +254,76 @@ 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 +336,7 @@ 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 +351,7 @@ 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 +369,7 @@ 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))
@@ -223,13 +384,29 @@ 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
 
+    def test_monthlyWithEvents2(self):
+        # Simply check no exception from internal merge
+        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
         try:
@@ -258,7 +435,7 @@ 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
 
@@ -310,18 +487,18 @@ 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
@@ -338,7 +515,7 @@ 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())
@@ -354,7 +531,7 @@ 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)
 
@@ -381,12 +558,23 @@ class TestPriceHistory(unittest.TestCase):
         df = dat.history(start=start, interval="1wk")
         self.assertTrue((df.index.weekday == 0).all())
 
+    def test_aggregate_capital_gains(self):
+        # Setup
+        tkr = "FXAIX"
+        dat = yf.Ticker(tkr, session=self.session)
+        start = "2017-12-31"
+        end = "2019-12-31"
+        interval = "3mo"
+
+        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):
@@ -413,7 +601,7 @@ 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)
@@ -424,12 +612,12 @@ 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()
@@ -441,18 +629,17 @@ 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)
@@ -464,7 +651,10 @@ 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"
@@ -472,16 +662,16 @@ 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
@@ -496,13 +686,13 @@ 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])
@@ -521,7 +711,10 @@ 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"]
@@ -533,10 +726,10 @@ 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()
@@ -546,7 +739,7 @@ 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:
@@ -563,6 +756,63 @@ 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)
@@ -574,9 +824,9 @@ 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)
@@ -590,6 +840,45 @@ 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)
@@ -611,7 +900,7 @@ 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)
@@ -621,13 +910,133 @@ 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

@@ -1,27 +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
 
@@ -47,127 +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
@@ -197,128 +65,12 @@ 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
 
-    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)
+    def get_raw_json(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        response = self.get(url, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
+        response.raise_for_status()
+        return response.json()

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
@@ -44,11 +48,13 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
             Intraday data cannot extend last 60 days
         start: str
-            Download start date string (YYYY-MM-DD) or _datetime.
-            Default is 1900-01-01
+            Download start date string (YYYY-MM-DD) or _datetime, inclusive.
+            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.
+            Download end date string (YYYY-MM-DD) or _datetime, exclusive.
             Default is now
+            E.g. for end="2023-01-01", the last data point will be on "2022-12-31"
         group_by : str
             Group by 'ticker' or 'column' (default)
         prepost : bool
@@ -75,10 +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
@@ -98,7 +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)
 
@@ -112,6 +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:
@@ -124,10 +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):
@@ -136,20 +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():
@@ -158,7 +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,
@@ -206,17 +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()
 
@@ -225,12 +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"]})
@@ -17,11 +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/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
@@ -44,16 +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)]
 
@@ -73,6 +75,479 @@ class InfoDictWrapper(MutableMapping):
         return k
 
 
+class FastInfo:
+    # Contain small subset of info[] items that can be fetched faster elsewhere.
+    # Imitates a dict.
+    def __init__(self, tickerBaseObject, proxy=None):
+        self._tkr = tickerBaseObject
+        self.proxy = proxy
+
+        self._prices_1y = None
+        self._prices_1wk_1h_prepost = None
+        self._prices_1wk_1h_reg = None
+        self._md = None
+
+        self._currency = None
+        self._quote_type = None
+        self._exchange = None
+        self._timezone = None
+
+        self._shares = None
+        self._mcap = None
+
+        self._open = None
+        self._day_high = None
+        self._day_low = None
+        self._last_price = None
+        self._last_volume = None
+
+        self._prev_close = None
+
+        self._reg_prev_close = None
+
+        self._50d_day_average = None
+        self._200d_day_average = None
+        self._year_high = None
+        self._year_low = None
+        self._year_change = None
+
+        self._10d_avg_vol = None
+        self._3mo_avg_vol = None
+
+        # attrs = utils.attributes(self)
+        # self.keys = attrs.keys()
+        # utils.attributes is calling each method, bad! Have to hardcode
+        _properties = ["currency", "quote_type", "exchange", "timezone"]
+        _properties += ["shares", "market_cap"]
+        _properties += ["last_price", "previous_close", "open", "day_high", "day_low"]
+        _properties += ["regular_market_previous_close"]
+        _properties += ["last_volume"]
+        _properties += ["fifty_day_average", "two_hundred_day_average", "ten_day_average_volume", "three_month_average_volume"]
+        _properties += ["year_high", "year_low", "year_change"]
+
+        # 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]
+
+        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._public_keys = sorted(base_keys + list(self._sc_to_cc_key.values()))
+        self._keys = sorted(self._public_keys + sc_keys)
+
+    # dict imitation:
+    def keys(self):
+        return self._public_keys
+
+    def items(self):
+        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 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)
+
+    def _get_1y_prices(self, fullDaysOnly=False):
+        if self._prices_1y is None:
+            # 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 Exception:
+                self._today_open = None
+                self._today_close = None
+                self._today_midnight = None
+                raise
+
+        if self._prices_1y.empty:
+            return self._prices_1y
+
+        dnow = pd.Timestamp.utcnow().tz_convert(self.timezone).date()
+        d1 = dnow
+        d0 = (d1 + datetime.timedelta(days=1)) - utils._interval_to_timedelta("1y")
+        if fullDaysOnly and self._exchange_open_now():
+            # Exclude today
+            d1 -= utils._interval_to_timedelta("1d")
+        return self._prices_1y.loc[str(d0):str(d1)]
+
+    def _get_1wk_1h_prepost_prices(self):
+        if self._prices_1wk_1h_prepost is None:
+            # 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:
+            # 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):
+        if self._md is not None:
+            return self._md
+
+        self._get_1y_prices()
+        self._md = self._tkr.get_history_metadata(proxy=self.proxy)
+        return self._md
+
+    def _exchange_open_now(self):
+        t = pd.Timestamp.utcnow()
+        self._get_exchange_metadata()
+
+        # if self._today_open is None and self._today_close is None:
+        #     r = False
+        # else:
+        #     r = self._today_open <= t and t < self._today_close
+
+        # if self._today_midnight is None:
+        #     r = False
+        # elif self._today_midnight.date() > t.tz_convert(self.timezone).date():
+        #     r = False
+        # else:
+        #     r = t < self._today_midnight
+
+        last_day_cutoff = self._get_1y_prices().index[-1] + datetime.timedelta(days=1)
+        last_day_cutoff += datetime.timedelta(minutes=20)
+        r = t < last_day_cutoff
+
+        # print("_exchange_open_now() returning", r)
+        return r
+
+    @property
+    def currency(self):
+        if self._currency is not None:
+            return self._currency
+
+        if self._tkr._history_metadata is None:
+            self._get_1y_prices()
+        md = self._tkr.get_history_metadata(proxy=self.proxy)
+        self._currency = md["currency"]
+        return self._currency
+
+    @property
+    def quote_type(self):
+        if self._quote_type is not None:
+            return self._quote_type
+
+        if self._tkr._history_metadata is None:
+            self._get_1y_prices()
+        md = self._tkr.get_history_metadata(proxy=self.proxy)
+        self._quote_type = md["instrumentType"]
+        return self._quote_type
+
+    @property
+    def exchange(self):
+        if self._exchange is not None:
+            return self._exchange
+
+        self._exchange = self._get_exchange_metadata()["exchangeName"]
+        return self._exchange
+
+    @property
+    def timezone(self):
+        if self._timezone is not None:
+            return self._timezone
+
+        self._timezone = self._get_exchange_metadata()["exchangeTimezoneName"]
+        return self._timezone
+
+    @property
+    def shares(self):
+        if self._shares is not None:
+            return self._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]]
+            self._shares = int(shares.iloc[-1])
+        return self._shares
+
+    @property
+    def last_price(self):
+        if self._last_price is not None:
+            return self._last_price
+        prices = self._get_1y_prices()
+        if prices.empty:
+            md = self._get_exchange_metadata()
+            if "regularMarketPrice" in md:
+                self._last_price = md["regularMarketPrice"]
+        else:
+            self._last_price = float(prices["Close"].iloc[-1])
+            if _np.isnan(self._last_price):
+                md = self._get_exchange_metadata()
+                if "regularMarketPrice" in md:
+                    self._last_price = md["regularMarketPrice"]
+        return self._last_price
+
+    @property
+    def previous_close(self):
+        if self._prev_close is not None:
+            return self._prev_close
+        prices = self._get_1wk_1h_prepost_prices()
+        fail = False
+        if prices.empty:
+            fail = True
+        else:
+            prices = prices[["Close"]].groupby(prices.index.date).last()
+            if prices.shape[0] < 2:
+                # Very few symbols have previousClose despite no 
+                # no trading data e.g. 'QCSTIX'.
+                fail = True
+            else:
+                self._prev_close = float(prices["Close"].iloc[-2])
+        if fail:
+            # Fallback to original info[] if available.
+            self._tkr.info  # trigger fetch
+            k = "previousClose"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._prev_close = self._tkr._quote._retired_info[k]
+        return self._prev_close
+
+    @property
+    def regular_market_previous_close(self):
+        if self._reg_prev_close is not None:
+            return self._reg_prev_close
+        prices = self._get_1y_prices()
+        if prices.shape[0] == 1:
+            # Tiny % of tickers don't return daily history before last trading day, 
+            # so backup option is hourly history:
+            prices = self._get_1wk_1h_reg_prices()
+            prices = prices[["Close"]].groupby(prices.index.date).last()
+        if prices.shape[0] < 2:
+            # Very few symbols have regularMarketPreviousClose despite no 
+            # no trading data. E.g. 'QCSTIX'.
+            # So fallback to original info[] if available.
+            self._tkr.info  # trigger fetch
+            k = "regularMarketPreviousClose"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._reg_prev_close = self._tkr._quote._retired_info[k]
+        else:
+            self._reg_prev_close = float(prices["Close"].iloc[-2])
+        return self._reg_prev_close
+
+    @property
+    def open(self):
+        if self._open is not None:
+            return self._open
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._open = None
+        else:
+            self._open = float(prices["Open"].iloc[-1])
+            if _np.isnan(self._open):
+                self._open = None
+        return self._open
+
+    @property
+    def day_high(self):
+        if self._day_high is not None:
+            return self._day_high
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._day_high = None
+        else:
+            self._day_high = float(prices["High"].iloc[-1])
+            if _np.isnan(self._day_high):
+                self._day_high = None
+        return self._day_high
+
+    @property
+    def day_low(self):
+        if self._day_low is not None:
+            return self._day_low
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._day_low = None
+        else:
+            self._day_low = float(prices["Low"].iloc[-1])
+            if _np.isnan(self._day_low):
+                self._day_low = None
+        return self._day_low
+
+    @property
+    def last_volume(self):
+        if self._last_volume is not None:
+            return self._last_volume
+        prices = self._get_1y_prices()
+        self._last_volume = None if prices.empty else int(prices["Volume"].iloc[-1])
+        return self._last_volume
+
+    @property
+    def fifty_day_average(self):
+        if self._50d_day_average is not None:
+            return self._50d_day_average
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._50d_day_average = None
+        else:
+            n = prices.shape[0]
+            a = n-50
+            b = n
+            if a < 0:
+                a = 0
+            self._50d_day_average = float(prices["Close"].iloc[a:b].mean())
+
+        return self._50d_day_average
+
+    @property
+    def two_hundred_day_average(self):
+        if self._200d_day_average is not None:
+            return self._200d_day_average
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._200d_day_average = None
+        else:
+            n = prices.shape[0]
+            a = n-200
+            b = n
+            if a < 0:
+                a = 0
+
+            self._200d_day_average = float(prices["Close"].iloc[a:b].mean())
+
+        return self._200d_day_average
+
+    @property
+    def ten_day_average_volume(self):
+        if self._10d_avg_vol is not None:
+            return self._10d_avg_vol
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._10d_avg_vol = None
+        else:
+            n = prices.shape[0]
+            a = n-10
+            b = n
+            if a < 0:
+                a = 0
+            self._10d_avg_vol = int(prices["Volume"].iloc[a:b].mean())
+
+        return self._10d_avg_vol
+
+    @property
+    def three_month_average_volume(self):
+        if self._3mo_avg_vol is not None:
+            return self._3mo_avg_vol
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._3mo_avg_vol = None
+        else:
+            dt1 = prices.index[-1]
+            dt0 = dt1 - utils._interval_to_timedelta("3mo") + utils._interval_to_timedelta("1d")
+            self._3mo_avg_vol = int(prices.loc[dt0:dt1, "Volume"].mean())
+
+        return self._3mo_avg_vol
+
+    @property
+    def year_high(self):
+        if self._year_high is not None:
+            return self._year_high
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            prices = self._get_1y_prices(fullDaysOnly=False)
+        self._year_high = float(prices["High"].max())
+        return self._year_high
+
+    @property
+    def year_low(self):
+        if self._year_low is not None:
+            return self._year_low
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            prices = self._get_1y_prices(fullDaysOnly=False)
+        self._year_low = float(prices["Low"].min())
+        return self._year_low
+
+    @property
+    def year_change(self):
+        if self._year_change is not None:
+            return self._year_change
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.shape[0] >= 2:
+            self._year_change = (prices["Close"].iloc[-1] - prices["Close"].iloc[0]) / prices["Close"].iloc[0]
+            self._year_change = float(self._year_change)
+        return self._year_change
+
+    @property
+    def market_cap(self):
+        if self._mcap is not None:
+            return self._mcap
+
+        try:
+            shares = self.shares
+        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
+
+        if shares is None:
+            # Very few symbols have marketCap despite no share count.
+            # E.g. 'BTC-USD'
+            # So fallback to original info[] if available.
+            self._tkr.info
+            k = "marketCap"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._mcap = self._tkr._quote._retired_info[k]
+        else:
+            self._mcap = float(shares * self.last_price)
+        return self._mcap
+
 
 class Quote:
 
@@ -87,161 +562,86 @@ class Quote:
         self._calendar = None
 
         self._already_scraped = False
-        self._already_scraped_complementary = False
+        self._already_fetched = False
+        self._already_fetched_complementary = False
 
     @property
     def info(self) -> dict:
         if self._info is None:
-            self._scrape(self.proxy)
-            self._scrape_complementary(self.proxy)
+            self._fetch(self.proxy)
+            self._fetch_complementary(self.proxy)
 
         return self._info
 
     @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:
+    def _fetch(self, proxy):
+        if self._already_fetched:
             return
-        self._already_scraped = True
+        self._already_fetched = True
+        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=params_dict, proxy=proxy
+        )
+        result["quoteSummary"]["result"][0]["symbol"] = self._data.ticker
+        query1_info = next(
+            (info for info in result.get("quoteSummary", {}).get("result", []) if info["symbol"] == self._data.ticker),
+            None,
+        )
+        # Most keys that appear in multiple dicts have same value. Except 'maxAge' because
+        # Yahoo not consistent with days vs seconds. Fix it here:
+        for k in query1_info:
+            if "maxAge" in query1_info[k] and query1_info[k]["maxAge"] == 1:
+                query1_info[k]["maxAge"] = 86400
+        query1_info = {
+            k1: v1 
+            for k, v in query1_info.items() 
+            if isinstance(v, dict) 
+            for k1, v1 in v.items() 
+            if v1
+        }
+        # recursively format but only because of 'companyOfficers'
 
-        # 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]
+        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()}
+            elif isinstance(v, str):
+                v2 = v.replace("\xa0", " ")
             else:
-                domain = self._info['website'].split(
-                    '://')[1].split('/')[0].replace('www.', '')
-                self._info['logo_url'] = 'https://logo.clearbit.com/%s' % domain
-        except Exception:
-            pass
+                v2 = v
+            return v2
+        for k, v in query1_info.items():
+            query1_info[k] = _format(k, v)
+        self._info = query1_info
 
-        # 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 _scrape_complementary(self, proxy):
-        if self._already_scraped_complementary:
+    def _fetch_complementary(self, proxy):
+        if self._already_fetched_complementary:
             return
-        self._already_scraped_complementary = True
+        self._already_fetched_complementary = True
 
-        self._scrape(proxy)
+        # self._scrape(proxy)  # decrypt broken
+        self._fetch(proxy)
         if self._info is None:
             return
 
@@ -270,8 +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/scrapers/yahoo-keys.txt

@@ -4,3 +4,5 @@ e9a8ab8e5620b712ebc2fb4f33d5c8b9c80c0d07e8c371911c785cf674789f1747d76a909510158a
 6ae2523aeafa283dad746556540145bf603f44edbf37ad404d3766a8420bb5eb1d3738f52a227b88283cca9cae44060d5f0bba84b6a495082589f5fe7acbdc9e
 3365117c2a368ffa5df7313a4a84988f73926a86358e8eea9497c5ff799ce27d104b68e5f2fbffa6f8f92c1fef41765a7066fa6bcf050810a9c4c7872fd3ebf0
 15d8f57919857d5a5358d2082c7ef0f1129cfacd2a6480333dcfb954b7bb67d820abefebfdb0eaa6ef18a1c57f617b67d7e7b0ec040403b889630ae5db5a4dbb
+db9630d707a7d0953ac795cd8db1ca9ca6c9d8239197cdfda24b4e0ec9c37eaec4db82dab68b8f606ab7b5b4af3e65dab50606f8cf508269ec927e6ee605fb78
+3c895fb5ddcc37d20d3073ed74ee3efad59bcb147c8e80fd279f83701b74b092d503dcd399604c6d8be8f3013429d3c2c76ed5b31b80c9df92d5eab6d3339fce

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/version.py

@@ -1 +1 @@
-version = "0.2.11"
+version = "0.2.30"