Bump version to 0.2.31

sync dev -> main

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,17 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-*** READ BEFORE POSTING ***
-
-Before posting an issue - please upgrade to the latest version and confirm the issue/bug is still there.
-
-Upgrade using:
-`$ pip install yfinance --upgrade --no-cache-dir`
-
-Bug still there? Delete this content and submit your bug report here...

.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/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

@@ -4,8 +4,16 @@ dist
 yfinance.egg-info
 *.pyc
 .coverage
+.idea/
 .vscode/
 build/
 *.html
 *.css
 *.png
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/

CHANGELOG.rst

@@ -1,6 +1,248 @@
 Change Log
 ===========
 
+0.2.31
+------
+- Fix TZ cache exception blocking import #1705 #1709
+- Fix merging pre-market events with intraday prices #1703
+
+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
+
+0.2.10
+------
+General
+- allow using sqlite3 < 3.8.2 #1380
+- add another backup decrypt option #1379
+Prices
+- restore original download() timezone handling #1385
+- fix & improve price repair #1289 2a2928b 86d6acc
+- drop intraday intervals if in post-market but prepost=False #1311
+Info
+- fast_info improvements:
+  - add camelCase keys, add dict functions values() & items() #1368
+  - fix fast_info["previousClose"] #1383
+- catch TypeError Exception #1397
+
+0.2.9
+-----
+- Fix fast_info bugs #1362
+
+0.2.7
+-----
+- Fix Yahoo decryption, smarter this time #1353
+- Rename basic_info -> fast_info #1354
+
+0.2.6
+-----
+- Fix Ticker.basic_info lazy-loading #1342
+
+0.2.5
+-----
+- Fix Yahoo data decryption again #1336
+- New: Ticker.basic_info - faster Ticker.info #1317
+
+0.2.4
+-----
+- Fix Yahoo data decryption #1297
+- New feature: 'Ticker.get_shares_full()' #1301
+- Improve caching of financials data #1284
+- Restore download() original alignment behaviour #1283
+- Fix the database lock error in multithread download #1276
+
+0.2.3
+-----
+- Make financials API '_' use consistent
+
+0.2.2
+-----
+- Restore 'financials' attribute (map to 'income_stmt')
+
+0.2.1
+-----
+Release!
+
+0.2.0rc5
+--------
+- Improve financials error handling #1243
+- Fix '100x price' repair #1244
+
+0.2.0rc4
+--------
+- Access to old financials tables via `get_income_stmt(legacy=True)`
+- Optimise scraping financials & fundamentals, 2x faster
+- Add 'capital gains' alongside dividends & splits for ETFs, and metadata available via `history_metadata`, plus a bunch of price fixes
+For full list of changes see #1238
+
+0.2.0rc2
+--------
+Financials
+- fix financials tables to match website  #1128 #1157
+- lru_cache to optimise web requests  #1147
+Prices
+- improve price repair  #1148
+- fix merging dividends/splits with day/week/monthly prices  #1161
+- fix the Yahoo DST fixes  #1143
+- improve bad/delisted ticker handling  #1140
+Misc
+- fix 'trailingPegRatio'  #1138
+- improve error handling  #1118
+
+0.2.0rc1
+--------
+Jumping to 0.2 for this big update. 0.1.* will continue to receive bug-fixes
+- timezone cache performance massively improved. Thanks @fredrik-corneliusson #1113 #1112 #1109 #1105 #1099
+- price repair feature #1110
+- fix merging of dividends/splits with prices #1069 #1086 #1102
+- fix Yahoo returning latest price interval across 2 rows #1070
+- optional: raise errors as exceptions: raise_errors=True #1104
+- add proper unit tests #1069
+
+0.1.81
+------
+- Fix unhandled tz-cache exception #1107
+
+0.1.80
+------
+- Fix `download(ignore_tz=True)` for single ticker #1097
+- Fix rare case of error "Cannot infer DST time" #1100
+
+0.1.79
+------
+- Fix when Yahoo returns price=NaNs on dividend day
+
+0.1.78
+------
+- Fix download() when different timezones #1085
+
+0.1.77
+------
+- Fix user experience bug #1078
+
+0.1.75
+------
+- Fixed datetime-related issues: #1048
+- Add 'keepna' argument #1032
+- Speedup Ticker() creation #1042
+- Improve a bugfix #1033
+
+0.1.74
+------
+- Fixed bug introduced in 0.1.73 (sorry :/)
+
+0.1.73
+------
+- Merged several PR that fixed misc issues
+
+0.1.72
+------
+- Misc bugfixs
+
+0.1.71
+------
+- Added Tickers(…).news()
+- Return empty DF if YF missing earnings dates
+- Fix EPS % to 0->1
+- Fix timezone handling
+- Fix handling of missing data
+- Clean&format earnings_dates table
+- Add ``.get_earnings_dates()`` to retreive earnings calendar
+- Added ``.get_earnings_history()`` to fetch earnings data
+
 0.1.70
 ------
 - Bug fixed - Closes #937

README.md

@@ -48,58 +48,49 @@ Yahoo! finance API is intended for personal use only.**
 
 The `Ticker` module, which allows you to access ticker data in a more Pythonic way:
 
-Note: yahoo finance datetimes are received as UTC.
-
 ```python
 import yfinance as yf
 
 msft = yf.Ticker("MSFT")
 
-# get stock info
+# get all stock info
 msft.info
 
 # get historical market data
-hist = msft.history(period="max")
+hist = msft.history(period="1mo")
 
-# show actions (dividends, splits)
+# show meta information about the history (requires history() to be called first)
+msft.history_metadata
+
+# show actions (dividends, splits, capital gains)
 msft.actions
-
-# show dividends
 msft.dividends
-
-# show splits
 msft.splits
+msft.capital_gains  # only for mutual funds & etfs
 
-# show financials
-msft.financials
-msft.quarterly_financials
+# show share count
+msft.get_shares_full(start="2022-01-01", end=None)
 
-# show major holders
-msft.major_holders
-
-# show institutional holders
-msft.institutional_holders
-
-# show balance sheet
+# show financials:
+# - income statement
+msft.income_stmt
+msft.quarterly_income_stmt
+# - balance sheet
 msft.balance_sheet
 msft.quarterly_balance_sheet
-
-# show cashflow
+# - cash flow statement
 msft.cashflow
 msft.quarterly_cashflow
+# see `Ticker.get_income_stmt()` for more options
 
-# show earnings
-msft.earnings
-msft.quarterly_earnings
+# show holders
+msft.major_holders
+msft.institutional_holders
+msft.mutualfund_holders
 
-# show sustainability
-msft.sustainability
-
-# show analysts recommendations
-msft.recommendations
-
-# 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
 
 # show ISIN code - *experimental*
 # ISIN = International Securities Identification Number
@@ -127,12 +118,43 @@ msft.history(..., proxy="PROXY_SERVER")
 msft.get_actions(proxy="PROXY_SERVER")
 msft.get_dividends(proxy="PROXY_SERVER")
 msft.get_splits(proxy="PROXY_SERVER")
+msft.get_capital_gains(proxy="PROXY_SERVER")
 msft.get_balance_sheet(proxy="PROXY_SERVER")
 msft.get_cashflow(proxy="PROXY_SERVER")
 msft.option_chain(..., proxy="PROXY_SERVER")
 ...
 ```
 
+### Multiple tickers
+
+To initialize multiple `Ticker` objects, use
+
+```python
+import yfinance as yf
+
+tickers = yf.Tickers('msft aapl goog')
+
+# access each ticker using (example)
+tickers.tickers['MSFT'].info
+tickers.tickers['AAPL'].history(period="1mo")
+tickers.tickers['GOOG'].actions
+```
+
+To download price history into one table:
+
+```python
+import yfinance as yf
+data = yf.download("SPY AAPL", period="1mo")
+```
+
+#### `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.
+
+### Logging
+
+`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
+
 To use a custom `requests` session (for example to cache calls to the
 API or customize the `User-agent` header), pass a `session=` argument to
 the Ticker constructor.
@@ -141,69 +163,25 @@ the Ticker constructor.
 import requests_cache
 session = requests_cache.CachedSession('yfinance.cache')
 session.headers['User-agent'] = 'my-program/1.0'
-ticker = yf.Ticker('msft aapl goog', session=session)
+ticker = yf.Ticker('msft', session=session)
 # The scraped response will be stored in the cache
 ticker.actions
 ```
 
-To initialize multiple `Ticker` objects, use
-
+Combine a `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-limiter/blocker that can corrupt data.
 ```python
-import yfinance as yf
+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
 
-tickers = yf.Tickers('msft aapl goog')
-# ^ returns a named tuple of Ticker objects
-
-# access each ticker using (example)
-tickers.tickers.MSFT.info
-tickers.tickers.AAPL.history(period="1mo")
-tickers.tickers.GOOG.actions
-```
-
-### Fetching data for multiple tickers
-
-```python
-import yfinance as yf
-data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
-```
-
-I've also added some options to make life easier :)
-
-```python
-data = yf.download(  # or pdr.get_data_yahoo(...
-        # tickers list or string as well
-        tickers = "SPY AAPL MSFT",
-
-        # use "period" instead of start/end
-        # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
-        # (optional, default is '1mo')
-        period = "ytd",
-
-        # fetch data by interval (including intraday if period < 60 days)
-        # valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
-        # (optional, default is '1d')
-        interval = "1m",
-
-        # group by ticker (to access via data['SPY'])
-        # (optional, default is 'column')
-        group_by = 'ticker',
-
-        # adjust all OHLC automatically
-        # (optional, default is False)
-        auto_adjust = True,
-
-        # download pre/post regular market hours data
-        # (optional, default is False)
-        prepost = True,
-
-        # use threads for mass downloading? (True/False/Integer)
-        # (optional, default is True)
-        threads = True,
-
-        # proxy URL scheme use use when downloading?
-        # (optional, default is None)
-        proxy = None
-    )
+session = CachedLimiterSession(
+    limiter=Limiter(RequestRate(2, Duration.SECOND*5)),  # max 2 requests per 5 seconds
+    bucket_class=MemoryQueueBucket,
+    backend=SQLiteCache("yfinance.cache"),
+)
 ```
 
 ### Managing Multi-Level Columns
@@ -221,9 +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
 
----
-
-## `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()`
@@ -240,6 +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
@@ -250,23 +238,37 @@ 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).
 
 ### Requirements
 
 -   [Python](https://www.python.org) \>= 2.7, 3.4+
--   [Pandas](https://github.com/pydata/pandas) (tested to work with
-    \>=0.23.1)
--   [Numpy](http://www.numpy.org) \>= 1.11.1
--   [requests](http://docs.python-requests.org/en/master/) \>= 2.14.2
--   [lxml](https://pypi.org/project/lxml/) \>= 4.5.1
+-   [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) \>= 2.31
+-   [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
+-   [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 = "0.1.58" %}
+{% set version = "0.2.31" %}
 
 package:
   name: "{{ name|lower }}"
@@ -16,20 +16,34 @@ build:
 
 requirements:
   host:
-    - pandas >=0.24.0
+    - pandas >=1.3.0
     - numpy >=1.16.5
-    - requests >=2.21
+    - requests >=2.31
     - multitasking >=0.0.7
-    - lxml >=4.5.1
+    - lxml >=4.9.1
+    - appdirs >=1.4.4
+    - pytz >=2022.5
+    - frozendict >=2.3.4
+    - beautifulsoup4 >=4.11.1
+    - html5lib >=1.1
+    - peewee >=3.16.2
+    # - pycryptodome >=3.6.6
     - pip
     - python
 
   run:
-    - pandas >=0.24.0
+    - pandas >=1.3.0
     - numpy >=1.16.5
-    - requests >=2.21
+    - requests >=2.31
     - multitasking >=0.0.7
-    - lxml >=4.5.1
+    - lxml >=4.9.1
+    - appdirs >=1.4.4
+    - pytz >=2022.5
+    - frozendict >=2.3.4
+    - beautifulsoup4 >=4.11.1
+    - html5lib >=1.1
+    - peewee >=3.16.2
+    # - pycryptodome >=3.6.6
     - python
 
 test:

requirements.txt

@@ -1,5 +1,11 @@
-pandas>=0.24.0
+pandas>=1.3.0
 numpy>=1.16.5
-requests>=2.26
+requests>=2.31
 multitasking>=0.0.7
-lxml>=4.5.1
+lxml>=4.9.1
+appdirs>=1.4.4
+pytz>=2022.5
+frozendict>=2.3.4
+beautifulsoup4>=4.11.1
+html5lib>=1.1
+peewee>=3.16.2

setup.py

@@ -38,8 +38,8 @@ setup(
     classifiers=[
         'License :: OSI Approved :: Apache Software License',
         # 'Development Status :: 3 - Alpha',
-        # 'Development Status :: 4 - Beta',
-        'Development Status :: 5 - Production/Stable',
+        'Development Status :: 4 - Beta',
+        # 'Development Status :: 5 - Production/Stable',
 
 
         'Operating System :: OS Independent',
@@ -50,20 +50,21 @@ setup(
         'Topic :: Software Development :: Libraries',
         'Topic :: Software Development :: Libraries :: Python Modules',
 
-        'Programming Language :: Python :: 2.7',
-        'Programming Language :: Python :: 3.4',
-        'Programming Language :: Python :: 3.5',
-        # 'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
         'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
     ],
     platforms=['any'],
     keywords='pandas, yahoo finance, pandas datareader',
     packages=find_packages(exclude=['contrib', 'docs', 'tests', 'examples']),
-    install_requires=['pandas>=0.24.0', 'numpy>=1.15',
-                      'requests>=2.26', 'multitasking>=0.0.7',
-                      'lxml>=4.5.1'],
+    install_requires=['pandas>=1.3.0', 'numpy>=1.16.5',
+                      'requests>=2.31', 'multitasking>=0.0.7',
+                      'lxml>=4.9.1', 'appdirs>=1.4.4', 'pytz>=2022.5',
+                      '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

@@ -1,64 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: UTF-8 -*-
-#
-# yfinance - market data downloader
-# https://github.com/ranaroussi/yfinance
-
-"""
-Sanity check for most common library uses all working
-- Stock: Microsoft
-- ETF: Russell 2000 Growth
-- Mutual fund: Vanguard 500 Index fund
-- Index: S&P500
-- Currency BTC-USD
-"""
-
-import yfinance as yf
-import unittest
-
-symbols = ['MSFT', 'IWO', 'VFINX', '^GSPC', 'BTC-USD']
-tickers = [yf.Ticker(symbol) for symbol in symbols]
-
-
-class TestTicker(unittest.TestCase):
-    def test_info_history(self):
-        for ticker in tickers:
-            # always should have info and history for valid symbols
-            assert(ticker.info is not None and ticker.info != {})
-            history = ticker.history(period="max")
-            assert(history.empty is False and history is not None)
-
-    def test_attributes(self):
-        for ticker in tickers:
-            ticker.isin
-            ticker.major_holders
-            ticker.institutional_holders
-            ticker.mutualfund_holders
-            ticker.dividends
-            ticker.splits
-            ticker.actions
-            ticker.info
-            ticker.calendar
-            ticker.recommendations
-            ticker.earnings
-            ticker.quarterly_earnings
-            ticker.financials
-            ticker.quarterly_financials
-            ticker.balance_sheet
-            ticker.quarterly_balance_sheet
-            ticker.cashflow
-            ticker.quarterly_cashflow
-            ticker.sustainability
-            ticker.options
-            ticker.news
-            ticker.shares
-
-    def test_holders(self):
-        for ticker in tickers:
-            assert(ticker.info is not None and ticker.info != {})
-            assert(ticker.major_holders is not None)
-            assert(ticker.institutional_holders is not None)
-
-
-if __name__ == '__main__':
-    unittest.main()

tests/__init__.py

@@ -0,0 +1 @@
+#!/usr/bin/env python

tests/context.py

@@ -0,0 +1,47 @@
+# -*- 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__), '..'))
+_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
+2023-04-07 00:00:00+09:00,3897,3975,3892,3953,3953,4554700,0,0
+2023-04-06 00:00:00+09:00,4002,4004,3920,3942,3942,8615200,0,0
+2023-04-05 00:00:00+09:00,4150,4150,4080,4088,4088,6063700,0,0
+2023-04-04 00:00:00+09:00,4245,4245,4144,4155,4155,6780600,0,0
+2023-04-03 00:00:00+09:00,4250,4259,4162,4182,4182,7076800,0,0
+2023-03-31 00:00:00+09:00,4229,4299,4209,4275,4275,9608400,0,0
+2023-03-30 00:00:00+09:00,4257,4268,4119,4161,4161,5535200,55,5
+2023-03-29 00:00:00+09:00,4146,4211,4146,4206,4151,6514500,0,0
+2023-03-28 00:00:00+09:00,21000,21035,20620,20710,20439.185546875,901100,0,0
+2023-03-27 00:00:00+09:00,20980,21020,20755,20960,20685.916015625,1191900,0,0
+2023-03-24 00:00:00+09:00,20650,20935,20615,20885,20611.896484375,1792300,0,0
+2023-03-23 00:00:00+09:00,20280,20530,20195,20430,20162.845703125,1096000,0,0
+2023-03-22 00:00:00+09:00,20330,20640,20285,20610,20340.4921875,1748300,0,0
+2023-03-20 00:00:00+09:00,20000,20135,19900,19900,19639.77734375,1401300,0,0
+2023-03-17 00:00:00+09:00,20090,20275,20080,20155,19891.44140625,1392300,0,0
+2023-03-16 00:00:00+09:00,19880,20225,19860,20175,19911.181640625,1003800,0,0
+2023-03-15 00:00:00+09:00,20170,20250,20015,20205,19940.7890625,1224400,0,0

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

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

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

@@ -0,0 +1,6 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-05-30 00:00:00+02:00,19.59000015258789,19.799999237060547,19.270000457763672,19.350000381469727,19.350000381469727,196309,0.0,0.0
+2023-05-31 00:00:00+02:00,19.1200008392334,19.139999389648438,18.700000762939453,18.790000915527344,18.790000915527344,156652,0.0,0.0
+2023-06-02 00:00:00+02:00,18.549999237060547,19.0,18.510000228881836,18.899999618530273,18.899999618530273,83439,0.7,0.0
+2023-06-05 00:00:00+02:00,18.93000030517578,19.09000015258789,18.84000015258789,19.0,19.0,153167,0.0,0.0
+2023-06-06 00:00:00+02:00,18.90999984741211,18.950000762939453,18.510000228881836,18.65999984741211,18.65999984741211,104352,0.0,0.0

tests/data/AET-L-1d-100x-error-fixed.csv

@@ -0,0 +1,24 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2022-06-06 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-06-01 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-31 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-30 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-27 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-26 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-25 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-24 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-23 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-20 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-19 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
+2022-05-18 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,532454,0,0
+2022-05-17 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
+2022-05-16 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
+2022-05-13 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
+2022-05-12 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
+2022-05-11 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
+2022-05-10 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
+2022-05-09 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
+2022-05-06 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-05 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-04 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
+2022-05-03 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0

tests/data/AET-L-1d-100x-error.csv

@@ -0,0 +1,24 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2022-06-06 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-06-01 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-31 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-30 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-27 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-26 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-25 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-24 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-23 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-20 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-19 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-05-18 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,532454,0.0,0.0
+2022-05-17 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-05-16 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-05-13 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-05-12 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-05-11 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-05-10 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-05-09 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-05-06 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-05 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-04 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-03 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0

tests/data/AET-L-1wk-100x-error-fixed.csv

@@ -0,0 +1,37 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2022-05-30 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-05-23 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-05-16 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,532454,0,0
+2022-05-09 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-05-02 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-04-25 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-04-18 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-04-11 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-04-04 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-03-28 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-03-21 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-03-14 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-03-07 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-02-28 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-02-21 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-02-14 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-02-07 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-01-31 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-01-24 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-01-17 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-01-10 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-01-03 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-12-27 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-12-20 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-12-13 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-12-06 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-11-29 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-11-22 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-11-15 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-11-08 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-11-01 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-10-25 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-10-18 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-10-11 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2021-10-04 00:00:00+01:00,14.8000,15.3400,14.4000,14.5500,14.5500,2171373,0,0
+2021-09-27 00:00:00+01:00,15.6000,16.0000,14.9000,15.0500,15.0500,3860549,0,0

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
+2022-08-15 00:00:00+01:00,27.6000,28.2000,26.2000,27.6000,27.6000,3535668,0,0
+2022-08-12 00:00:00+01:00,27.3000,29.8000,26.4030,27.0000,27.0000,7223353,0,0
+2022-08-11 00:00:00+01:00,26.0000,29.8000,24.2000,27.1000,27.1000,12887933,0,0
+2022-08-10 00:00:00+01:00,25.0000,29.2000,22.5000,25.0000,25.0000,26572680,0,0
+2022-08-09 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-08 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-05 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-04 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-03 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-02 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-01 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-29 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-28 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-27 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-26 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-25 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-22 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-21 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-20 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-19 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-18 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-15 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-14 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-13 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0

tests/data/AET-L-1wk-100x-error.csv

@@ -0,0 +1,37 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2022-05-30 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-23 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-05-16 00:00:00+01:00,14.550000190734863,14.550000190734863,0.14550000429153442,0.14550000429153442,0.14550000429153442,532454,0.0,0.0
+2022-05-09 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-05-02 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-04-25 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-04-18 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-04-11 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-04-04 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-03-28 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-03-21 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-03-14 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-03-07 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-02-28 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-02-21 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-02-14 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-02-07 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-01-31 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-01-24 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-01-17 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-01-10 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-01-03 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-12-27 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-12-20 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-12-13 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-12-06 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-11-29 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-11-22 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-11-15 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-11-08 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-11-01 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-10-25 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-10-18 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-10-11 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2021-10-04 00:00:00+01:00,14.800000190734863,15.34000015258789,0.14399999380111694,0.14550000429153442,0.14550000429153442,2171373,0.0,0.0
+2021-09-27 00:00:00+01:00,15.600000381469727,16.0,14.899999618530273,15.050000190734863,15.050000190734863,3860549,0.0,0.0

tests/data/AET-L-1wk-100x-error.csv.old

@@ -0,0 +1,25 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2022-08-15 00:00:00+01:00,27.600000381469727,28.200000762939453,26.200000762939453,27.600000381469727,27.600000381469727,3535668,0.0,0.0
+2022-08-12 00:00:00+01:00,27.299999237060547,29.799999237060547,26.402999877929688,27.0,27.0,7223353,0.0,0.0
+2022-08-11 00:00:00+01:00,26.0,29.799999237060547,24.200000762939453,27.100000381469727,27.100000381469727,12887933,0.0,0.0
+2022-08-10 00:00:00+01:00,25.0,29.200000762939453,22.5,25.0,25.0,26572680,0.0,0.0
+2022-08-09 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-08-08 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-08-05 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-08-04 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-08-03 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-08-02 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-08-01 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
+2022-07-29 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-28 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-27 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-26 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-25 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-22 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-21 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-20 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-19 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-18 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-15 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-14 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
+2022-07-13 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0

tests/data/ALPHA-PA-1d-bad-stock-split-fixed.csv

@@ -0,0 +1,30 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-04-20 00:00:00+02:00,3,3,2,3,3,2076,0,0
+2023-04-21 00:00:00+02:00,3,3,2,3,3,2136,0,0
+2023-04-24 00:00:00+02:00,3,3,1,1,1,77147,0,0
+2023-04-25 00:00:00+02:00,1,2,1,2,2,9625,0,0
+2023-04-26 00:00:00+02:00,2,2,1,2,2,5028,0,0
+2023-04-27 00:00:00+02:00,2,2,1,1,1,3235,0,0
+2023-04-28 00:00:00+02:00,2,2,1,2,2,10944,0,0
+2023-05-02 00:00:00+02:00,2,2,2,2,2,12220,0,0
+2023-05-03 00:00:00+02:00,2,2,2,2,2,4683,0,0
+2023-05-04 00:00:00+02:00,2,2,1,2,2,3368,0,0
+2023-05-05 00:00:00+02:00,2,2,1,2,2,26069,0,0
+2023-05-08 00:00:00+02:00,1,2,1,1,1,70540,0,0
+2023-05-09 00:00:00+02:00,1,2,1,1,1,14228,0,0
+2023-05-10 00:00:00+02:00,1.08000004291534,1.39999997615814,0.879999995231628,1,1,81012,0,0.0001
+2023-05-11 00:00:00+02:00,1.03999996185303,1.03999996185303,0.850000023841858,1,1,40254,0,0
+2023-05-12 00:00:00+02:00,0.949999988079071,1.10000002384186,0.949999988079071,1.01999998092651,1.01999998092651,35026,0,0
+2023-05-15 00:00:00+02:00,0.949999988079071,1.01999998092651,0.860000014305115,0.939999997615814,0.939999997615814,41486,0,0
+2023-05-16 00:00:00+02:00,0.899999976158142,0.944000005722046,0.800000011920929,0.800000011920929,0.800000011920929,43583,0,0
+2023-05-17 00:00:00+02:00,0.850000023841858,0.850000023841858,0.779999971389771,0.810000002384186,0.810000002384186,29984,0,0
+2023-05-18 00:00:00+02:00,0.779999971389771,0.78600001335144,0.740000009536743,0.740000009536743,0.740000009536743,24679,0,0
+2023-05-19 00:00:00+02:00,0.78600001335144,0.78600001335144,0.649999976158142,0.65200001001358,0.65200001001358,26732,0,0
+2023-05-22 00:00:00+02:00,0.8299999833107,1.05999994277954,0.709999978542328,0.709999978542328,0.709999978542328,169538,0,0
+2023-05-23 00:00:00+02:00,0.899999976158142,1.60800004005432,0.860000014305115,1.22000002861023,1.22000002861023,858471,0,0
+2023-05-24 00:00:00+02:00,1.19400000572205,1.25999999046326,0.779999971389771,0.779999971389771,0.779999971389771,627823,0,0
+2023-05-25 00:00:00+02:00,0.980000019073486,1.22000002861023,0.702000021934509,0.732999980449677,0.732999980449677,1068939,0,0
+2023-05-26 00:00:00+02:00,0.660000026226044,0.72000002861023,0.602999985218048,0.611999988555908,0.611999988555908,631580,0,0
+2023-05-29 00:00:00+02:00,0.620000004768372,0.75,0.578999996185303,0.600000023841858,0.600000023841858,586150,0,0
+2023-05-30 00:00:00+02:00,0.610000014305115,0.634999990463257,0.497000008821487,0.497000008821487,0.497000008821487,552308,0,0
+2023-05-31 00:00:00+02:00,0.458999991416931,0.469999998807907,0.374000012874603,0.379999995231628,0.379999995231628,899067,0,0

tests/data/ALPHA-PA-1d-bad-stock-split.csv

@@ -0,0 +1,30 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-04-20 00:00:00+02:00,3.0,3.0,2.0,3.0,3.0,2076,0.0,0.0
+2023-04-21 00:00:00+02:00,3.0,3.0,2.0,3.0,3.0,2136,0.0,0.0
+2023-04-24 00:00:00+02:00,3.0,3.0,1.0,1.0,1.0,77147,0.0,0.0
+2023-04-25 00:00:00+02:00,1.0,2.0,1.0,2.0,2.0,9625,0.0,0.0
+2023-04-26 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,5028,0.0,0.0
+2023-04-27 00:00:00+02:00,2.0,2.0,1.0,1.0,1.0,3235,0.0,0.0
+2023-04-28 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,10944,0.0,0.0
+2023-05-02 00:00:00+02:00,2.0,2.0,2.0,2.0,2.0,12220,0.0,0.0
+2023-05-03 00:00:00+02:00,2.0,2.0,2.0,2.0,2.0,4683,0.0,0.0
+2023-05-04 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,3368,0.0,0.0
+2023-05-05 00:00:00+02:00,2.0,2.0,1.0,2.0,2.0,26069,0.0,0.0
+2023-05-08 00:00:00+02:00,9.999999747378752e-05,0.00019999999494757503,9.999999747378752e-05,9.999999747378752e-05,9.999999747378752e-05,705399568,0.0,0.0
+2023-05-09 00:00:00+02:00,1.0,2.0,1.0,1.0,1.0,14228,0.0,0.0
+2023-05-10 00:00:00+02:00,1.0800000429153442,1.399999976158142,0.8799999952316284,1.0,1.0,81012,0.0,0.0001
+2023-05-11 00:00:00+02:00,1.0399999618530273,1.0399999618530273,0.8500000238418579,1.0,1.0,40254,0.0,0.0
+2023-05-12 00:00:00+02:00,0.949999988079071,1.100000023841858,0.949999988079071,1.0199999809265137,1.0199999809265137,35026,0.0,0.0
+2023-05-15 00:00:00+02:00,0.949999988079071,1.0199999809265137,0.8600000143051147,0.9399999976158142,0.9399999976158142,41486,0.0,0.0
+2023-05-16 00:00:00+02:00,0.8999999761581421,0.9440000057220459,0.800000011920929,0.800000011920929,0.800000011920929,43583,0.0,0.0
+2023-05-17 00:00:00+02:00,0.8500000238418579,0.8500000238418579,0.7799999713897705,0.8100000023841858,0.8100000023841858,29984,0.0,0.0
+2023-05-18 00:00:00+02:00,0.7799999713897705,0.7860000133514404,0.7400000095367432,0.7400000095367432,0.7400000095367432,24679,0.0,0.0
+2023-05-19 00:00:00+02:00,0.7860000133514404,0.7860000133514404,0.6499999761581421,0.6520000100135803,0.6520000100135803,26732,0.0,0.0
+2023-05-22 00:00:00+02:00,0.8299999833106995,1.059999942779541,0.7099999785423279,0.7099999785423279,0.7099999785423279,169538,0.0,0.0
+2023-05-23 00:00:00+02:00,0.8999999761581421,1.6080000400543213,0.8600000143051147,1.2200000286102295,1.2200000286102295,858471,0.0,0.0
+2023-05-24 00:00:00+02:00,1.194000005722046,1.2599999904632568,0.7799999713897705,0.7799999713897705,0.7799999713897705,627823,0.0,0.0
+2023-05-25 00:00:00+02:00,0.9800000190734863,1.2200000286102295,0.7020000219345093,0.7329999804496765,0.7329999804496765,1068939,0.0,0.0
+2023-05-26 00:00:00+02:00,0.6600000262260437,0.7200000286102295,0.6029999852180481,0.6119999885559082,0.6119999885559082,631580,0.0,0.0
+2023-05-29 00:00:00+02:00,0.6200000047683716,0.75,0.5789999961853027,0.6000000238418579,0.6000000238418579,586150,0.0,0.0
+2023-05-30 00:00:00+02:00,0.6100000143051147,0.6349999904632568,0.4970000088214874,0.4970000088214874,0.4970000088214874,552308,0.0,0.0
+2023-05-31 00:00:00+02:00,0.45899999141693115,0.4699999988079071,0.37400001287460327,0.3799999952316284,0.3799999952316284,899067,0.0,0.0

tests/data/AV-L-1wk-bad-stock-split-fixed.csv

@@ -0,0 +1,85 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2021-12-13 00:00:00+00:00,393.999975585938,406.6,391.4,402.899916992188,291.232287597656,62714764.4736842,0,0
+2021-12-20 00:00:00+00:00,393.999975585938,412.199990234375,392.502983398438,409.899997558594,296.292243652344,46596651.3157895,0,0
+2021-12-27 00:00:00+00:00,409.899997558594,416.550971679688,408.387001953125,410.4,296.653642578125,10818482.8947368,0,0
+2022-01-03 00:00:00+00:00,410.4,432.199995117188,410.4,432.099985351563,312.339265136719,44427327.6315789,0,0
+2022-01-10 00:00:00+00:00,431.3,439.199982910156,429.099970703125,436.099912109375,315.230618896484,29091400,0,0
+2022-01-17 00:00:00+00:00,437.999912109375,445.199965820313,426.999997558594,431.999975585938,312.267017822266,43787351.3157895,0,0
+2022-01-24 00:00:00+00:00,430.099975585938,440.999973144531,420.999968261719,433.499982910156,313.351237792969,58487296.0526316,0,0
+2022-01-31 00:00:00+00:00,436.199968261719,443.049987792969,432.099985351563,435.199916992188,314.580045166016,43335806.5789474,0,0
+2022-02-07 00:00:00+00:00,437.899995117188,448.799992675781,436.051994628906,444.39998046875,321.230207519531,39644061.8421053,0,0
+2022-02-14 00:00:00+00:00,437.699975585938,441.999978027344,426.699968261719,432.199995117188,312.411558837891,49972693.4210526,0,0
+2022-02-21 00:00:00+00:00,435.499992675781,438.476999511719,408.29998046875,423.399970703125,306.050571289063,65719596.0526316,0,0
+2022-02-28 00:00:00+00:00,415.099995117188,427.999909667969,386.199932861328,386.799945068359,279.594578857422,94057936.8421053,4.1875,0
+2022-03-07 00:00:00+00:00,374.999952392578,417.299978027344,361.101981201172,409.599968261719,298.389248046875,71269101.3157895,0,0
+2022-03-14 00:00:00+00:00,413.099985351563,426.699968261719,408.899992675781,422.399965820313,307.713929443359,55431927.6315789,0,0
+2022-03-21 00:00:00+00:00,422.699995117188,442.7,422.399965820313,437.799985351563,318.932696533203,39896352.6315789,0,0
+2022-03-28 00:00:00+01:00,442.49998046875,460.999978027344,440.097983398438,444.6,323.886403808594,56413515.7894737,0,0
+2022-04-04 00:00:00+01:00,439.699985351563,445.399985351563,421.999973144531,425.799973144531,310.190817871094,49415836.8421053,19.342106,0
+2022-04-11 00:00:00+01:00,425.39998046875,435.599909667969,420.799995117188,434.299968261719,327.211427001953,29875081.5789474,0,0
+2022-04-18 00:00:00+01:00,434.299968261719,447.799987792969,433.599992675781,437.799985351563,329.848419189453,49288272.3684211,0,0
+2022-04-25 00:00:00+01:00,430.699987792969,438.799990234375,423.999982910156,433.299916992188,326.457967529297,44656776.3157895,0,0
+2022-05-02 00:00:00+01:00,433.299916992188,450.999975585938,414.499982910156,414.899975585938,312.595018310547,29538167.1052632,0,0
+2022-05-09 00:00:00+01:00,413.199995117188,417.449992675781,368.282923583984,408.199970703125,307.547099609375,73989611.8421053,0,0
+2022-05-16 00:00:00+01:00,384,423.600006103516,384,412.100006103516,310.485473632813,81938261,101.69,0.76
+2022-05-23 00:00:00+01:00,416.100006103516,442.399993896484,341.915008544922,440.899993896484,409.764678955078,45432941,0,0
+2022-05-30 00:00:00+01:00,442.700012207031,444.200012207031,426.600006103516,428.700012207031,398.426239013672,37906659,0,0
+2022-06-06 00:00:00+01:00,425.299987792969,434.010009765625,405.200012207031,405.399993896484,376.771606445313,40648810,0,0
+2022-06-13 00:00:00+01:00,402.5,420,399.799987792969,411.200012207031,382.162048339844,74196958,0,0
+2022-06-20 00:00:00+01:00,412.5,421.899993896484,398.399993896484,411.5,382.440826416016,28679717,0,0
+2022-06-27 00:00:00+01:00,413.100006103516,422.399993896484,397.399993896484,401.600006103516,373.239959716797,35468994,0,0
+2022-07-04 00:00:00+01:00,405.399993896484,406.600006103516,382.299987792969,401.299987792969,372.961120605469,35304748,0,0
+2022-07-11 00:00:00+01:00,394.799987792969,405.850006103516,383.399993896484,396.600006103516,368.593048095703,42308459,0,0
+2022-07-18 00:00:00+01:00,392.5,399.700012207031,384.799987792969,391.700012207031,364.039093017578,36656839,0,0
+2022-07-25 00:00:00+01:00,392.200012207031,400.799987792969,388.700012207031,396,368.035430908203,33124660,0,0
+2022-08-01 00:00:00+01:00,396.399993896484,405.5,390.415008544922,402,373.611724853516,21753121,0,0
+2022-08-08 00:00:00+01:00,406.600006103516,473.700012207031,403.299987792969,467.899993896484,434.858032226563,59155709,0,0
+2022-08-15 00:00:00+01:00,468.100006103516,470.5,434,437,406.140106201172,36989620,10.3,0
+2022-08-22 00:00:00+01:00,436.100006103516,436.869995117188,419.299987792969,420.5,399.780303955078,36492572,0,0
+2022-08-29 00:00:00+01:00,420.5,426.600006103516,408.600006103516,426.600006103516,405.579742431641,29573657,0,0
+2022-09-05 00:00:00+01:00,418.5,444.4169921875,416.100006103516,443.100006103516,421.266723632813,34375126,0,0
+2022-09-12 00:00:00+01:00,444.649993896484,448.899993896484,435.200012207031,440.100006103516,418.414520263672,39085960,0,0
+2022-09-19 00:00:00+01:00,440.100006103516,447.200012207031,419.299987792969,422.899993896484,402.062042236328,27982081,0,0
+2022-09-26 00:00:00+01:00,421.200012207031,421.200012207031,373.31201171875,388.200012207031,369.071868896484,70408935,0,0
+2022-10-03 00:00:00+01:00,382.899993896484,409.875,380.555999755859,400.700012207031,380.955932617188,37581751,0,0
+2022-10-10 00:00:00+01:00,395.799987792969,404.470001220703,366.700012207031,394.299987792969,374.871276855469,52952323,0,0
+2022-10-17 00:00:00+01:00,394.299987792969,414.799987792969,393,406.5,386.470123291016,26441475,0,0
+2022-10-24 00:00:00+01:00,407.100006103516,418.227996826172,407.100006103516,413.299987792969,392.93505859375,26239756,0,0
+2022-10-31 00:00:00+00:00,413.899993896484,430.200012207031,412,429.299987792969,408.146667480469,23168047,0,0
+2022-11-07 00:00:00+00:00,427.299987792969,445.899993896484,420.652008056641,438.399993896484,416.798278808594,36709117,0,0
+2022-11-14 00:00:00+00:00,438.299987792969,458.489990234375,435,455.100006103516,432.675415039063,29106506,0,0
+2022-11-21 00:00:00+00:00,454.399993896484,461,450,456.600006103516,434.101501464844,21667730,0,0
+2022-11-28 00:00:00+00:00,453.799987792969,456.899993896484,435.100006103516,444.799987792969,422.882934570313,33326204,0,0
+2022-12-05 00:00:00+00:00,442.899993896484,450.25,441.299987792969,448,425.925262451172,29147089,0,0
+2022-12-12 00:00:00+00:00,445.100006103516,451.299987792969,431.200012207031,436.100006103516,414.611633300781,46593233,0,0
+2022-12-19 00:00:00+00:00,436,452.600006103516,433.600006103516,444,422.122344970703,20982140,0,0
+2022-12-26 00:00:00+00:00,444,452.058013916016,442.399993896484,442.799987792969,420.981475830078,8249664,0,0
+2023-01-02 00:00:00+00:00,445.899993896484,458.149993896484,443.299987792969,456,433.531066894531,28687622,0,0
+2023-01-09 00:00:00+00:00,456,461.066009521484,435.799987792969,444.200012207031,422.3125,39237336,0,0
+2023-01-16 00:00:00+00:00,444.299987792969,447.200012207031,434.399993896484,439,417.368713378906,35267336,0,0
+2023-01-23 00:00:00+00:00,440,459.299987792969,439.5,457.399993896484,434.862091064453,37495012,0,0
+2023-01-30 00:00:00+00:00,454.399993896484,459.399993896484,447.799987792969,450.299987792969,428.111907958984,48879358,0,0
+2023-02-06 00:00:00+00:00,448,449.200012207031,436.299987792969,440,418.319458007813,38799772,0,0
+2023-02-13 00:00:00+00:00,441.200012207031,450.299987792969,440,447.600006103516,425.544982910156,30251441,0,0
+2023-02-20 00:00:00+00:00,448.5,450.799987792969,434.299987792969,440,418.319458007813,26764528,0,0
+2023-02-27 00:00:00+00:00,442.899993896484,450.5,441.608001708984,447.200012207031,425.164703369141,29895454,0,0
+2023-03-06 00:00:00+00:00,447.399993896484,467.299987792969,443.100006103516,449.700012207031,427.54150390625,82322819,0,0
+2023-03-13 00:00:00+00:00,450,451.417999267578,400.68701171875,402.200012207031,382.382019042969,85158023,0,0
+2023-03-20 00:00:00+00:00,396.200012207031,425.399993896484,383.496002197266,408.299987792969,388.181427001953,60152666,0,0
+2023-03-27 00:00:00+01:00,416,422.049987792969,399.549987792969,404.200012207031,384.283477783203,81534829,20.7,0
+2023-04-03 00:00:00+01:00,405,434.100006103516,404.399993896484,417.100006103516,417.100006103516,43217151,0,0
+2023-04-10 00:00:00+01:00,419.100006103516,426.700012207031,419.100006103516,421.700012207031,421.700012207031,32435695,0,0
+2023-04-17 00:00:00+01:00,423.700012207031,427.635009765625,415.399993896484,420.299987792969,420.299987792969,37715986,0,0
+2023-04-24 00:00:00+01:00,418.100006103516,423,415.299987792969,423,423,34331974,0,0
+2023-05-01 00:00:00+01:00,423.399993896484,426.100006103516,406.399993896484,414.600006103516,414.600006103516,40446519,0,0
+2023-05-08 00:00:00+01:00,414.600006103516,419.100006103516,408,412.700012207031,412.700012207031,36950836,0,0
+2023-05-15 00:00:00+01:00,414,418.399993896484,407.399993896484,413.5,413.5,53109487,0,0
+2023-05-22 00:00:00+01:00,413.600006103516,424,394.700012207031,401.299987792969,401.299987792969,64363368,0,0
+2023-05-29 00:00:00+01:00,401.299987792969,409.477996826172,392.700012207031,409.100006103516,409.100006103516,47587959,0,0
+2023-06-05 00:00:00+01:00,406.299987792969,410.700012207031,400.100006103516,400.899993896484,400.899993896484,22494985,0,0
+2023-06-12 00:00:00+01:00,404.100006103516,406,394.5,396,396,41531163,0,0
+2023-06-19 00:00:00+01:00,394,399.899993896484,380.720001220703,386.200012207031,386.200012207031,40439880,0,0
+2023-06-26 00:00:00+01:00,387.200012207031,397,382.899993896484,395.200012207031,395.200012207031,27701915,0,0
+2023-07-03 00:00:00+01:00,396.5,399.799987792969,380.100006103516,381.799987792969,381.799987792969,26005305,0,0
+2023-07-10 00:00:00+01:00,380,392.299987792969,379.403991699219,386,386,29789300,0,0
+2023-07-17 00:00:00+01:00,385,389.5,384.251007080078,387.100006103516,387.100006103516,0,0,0

tests/data/AV-L-1wk-bad-stock-split.csv

@@ -0,0 +1,85 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2021-12-13 00:00:00+00:00,518.4210205078125,535.0,515.0,530.1314697265625,383.20037841796875,47663221,0.0,0.0
+2021-12-20 00:00:00+00:00,518.4210205078125,542.368408203125,516.4512939453125,539.3421020507812,389.85821533203125,35413455,0.0,0.0
+2021-12-27 00:00:00+00:00,539.3421020507812,548.0933837890625,537.351318359375,540.0,390.333740234375,8222047,0.0,0.0
+2022-01-03 00:00:00+00:00,540.0,568.6842041015625,540.0,568.5526123046875,410.97271728515625,33764769,0.0,0.0
+2022-01-10 00:00:00+00:00,567.5,577.8947143554688,564.605224609375,573.815673828125,414.7771301269531,22109464,0.0,0.0
+2022-01-17 00:00:00+00:00,576.315673828125,585.7894287109375,561.8421020507812,568.4210205078125,410.8776550292969,33278387,0.0,0.0
+2022-01-24 00:00:00+00:00,565.9210205078125,580.2631225585938,553.9473266601562,570.3947143554688,412.30426025390625,44450345,0.0,0.0
+2022-01-31 00:00:00+00:00,573.9473266601562,582.9605102539062,568.5526123046875,572.6314697265625,413.9211120605469,32935213,0.0,0.0
+2022-02-07 00:00:00+00:00,576.1842041015625,590.5263061523438,573.7526245117188,584.73681640625,422.67132568359375,30129487,0.0,0.0
+2022-02-14 00:00:00+00:00,575.9210205078125,581.5789184570312,561.4473266601562,568.6842041015625,411.0678405761719,37979247,0.0,0.0
+2022-02-21 00:00:00+00:00,573.0263061523438,576.9434204101562,537.23681640625,557.105224609375,402.6981201171875,49946893,0.0,0.0
+2022-02-28 00:00:00+00:00,546.1842041015625,563.1577758789062,508.1578063964844,508.9472961425781,367.8876037597656,71484032,4.1875,0.0
+2022-03-07 00:00:00+00:00,493.4209899902344,549.0789184570312,475.1341857910156,538.9473266601562,392.617431640625,54164517,0.0,0.0
+2022-03-14 00:00:00+00:00,543.5526123046875,561.4473266601562,538.0263061523438,555.7894287109375,404.8867492675781,42128265,0.0,0.0
+2022-03-21 00:00:00+00:00,556.1842041015625,582.5,555.7894287109375,576.0526123046875,419.6482849121094,30321228,0.0,0.0
+2022-03-28 00:00:00+01:00,582.23681640625,606.5789184570312,579.0762939453125,585.0,426.16632080078125,42874272,0.0,0.0
+2022-04-04 00:00:00+01:00,578.5526123046875,586.0526123046875,555.2631225585938,560.2631225585938,408.14581298828125,37556036,19.342106,0.0
+2022-04-11 00:00:00+01:00,559.73681640625,573.1577758789062,553.6842041015625,571.4473266601562,430.5413513183594,22705062,0.0,0.0
+2022-04-18 00:00:00+01:00,571.4473266601562,589.2105102539062,570.5263061523438,576.0526123046875,434.0110778808594,37459087,0.0,0.0
+2022-04-25 00:00:00+01:00,566.7105102539062,577.368408203125,557.8947143554688,570.1314697265625,429.5499572753906,33939150,0.0,0.0
+2022-05-02 00:00:00+01:00,570.1314697265625,593.4210205078125,545.3947143554688,545.9210205078125,411.3092346191406,22449007,0.0,0.0
+2022-05-09 00:00:00+01:00,543.6842041015625,549.2763061523438,484.5827941894531,537.105224609375,404.667236328125,56232105,0.0,0.0
+2022-05-16 00:00:00+01:00,384.0,423.6000061035156,384.0,412.1000061035156,310.4854736328125,81938261,101.69,0.76
+2022-05-23 00:00:00+01:00,416.1000061035156,442.3999938964844,341.9150085449219,440.8999938964844,409.7646789550781,45432941,0.0,0.0
+2022-05-30 00:00:00+01:00,442.70001220703125,444.20001220703125,426.6000061035156,428.70001220703125,398.4262390136719,37906659,0.0,0.0
+2022-06-06 00:00:00+01:00,425.29998779296875,434.010009765625,405.20001220703125,405.3999938964844,376.7716064453125,40648810,0.0,0.0
+2022-06-13 00:00:00+01:00,402.5,420.0,399.79998779296875,411.20001220703125,382.16204833984375,74196958,0.0,0.0
+2022-06-20 00:00:00+01:00,412.5,421.8999938964844,398.3999938964844,411.5,382.4408264160156,28679717,0.0,0.0
+2022-06-27 00:00:00+01:00,413.1000061035156,422.3999938964844,397.3999938964844,401.6000061035156,373.2399597167969,35468994,0.0,0.0
+2022-07-04 00:00:00+01:00,405.3999938964844,406.6000061035156,382.29998779296875,401.29998779296875,372.96112060546875,35304748,0.0,0.0
+2022-07-11 00:00:00+01:00,394.79998779296875,405.8500061035156,383.3999938964844,396.6000061035156,368.5930480957031,42308459,0.0,0.0
+2022-07-18 00:00:00+01:00,392.5,399.70001220703125,384.79998779296875,391.70001220703125,364.0390930175781,36656839,0.0,0.0
+2022-07-25 00:00:00+01:00,392.20001220703125,400.79998779296875,388.70001220703125,396.0,368.0354309082031,33124660,0.0,0.0
+2022-08-01 00:00:00+01:00,396.3999938964844,405.5,390.4150085449219,402.0,373.6117248535156,21753121,0.0,0.0
+2022-08-08 00:00:00+01:00,406.6000061035156,473.70001220703125,403.29998779296875,467.8999938964844,434.8580322265625,59155709,0.0,0.0
+2022-08-15 00:00:00+01:00,468.1000061035156,470.5,434.0,437.0,406.1401062011719,36989620,10.3,0.0
+2022-08-22 00:00:00+01:00,436.1000061035156,436.8699951171875,419.29998779296875,420.5,399.7803039550781,36492572,0.0,0.0
+2022-08-29 00:00:00+01:00,420.5,426.6000061035156,408.6000061035156,426.6000061035156,405.5797424316406,29573657,0.0,0.0
+2022-09-05 00:00:00+01:00,418.5,444.4169921875,416.1000061035156,443.1000061035156,421.2667236328125,34375126,0.0,0.0
+2022-09-12 00:00:00+01:00,444.6499938964844,448.8999938964844,435.20001220703125,440.1000061035156,418.4145202636719,39085960,0.0,0.0
+2022-09-19 00:00:00+01:00,440.1000061035156,447.20001220703125,419.29998779296875,422.8999938964844,402.0620422363281,27982081,0.0,0.0
+2022-09-26 00:00:00+01:00,421.20001220703125,421.20001220703125,373.31201171875,388.20001220703125,369.0718688964844,70408935,0.0,0.0
+2022-10-03 00:00:00+01:00,382.8999938964844,409.875,380.5559997558594,400.70001220703125,380.9559326171875,37581751,0.0,0.0
+2022-10-10 00:00:00+01:00,395.79998779296875,404.4700012207031,366.70001220703125,394.29998779296875,374.87127685546875,52952323,0.0,0.0
+2022-10-17 00:00:00+01:00,394.29998779296875,414.79998779296875,393.0,406.5,386.4701232910156,26441475,0.0,0.0
+2022-10-24 00:00:00+01:00,407.1000061035156,418.2279968261719,407.1000061035156,413.29998779296875,392.93505859375,26239756,0.0,0.0
+2022-10-31 00:00:00+00:00,413.8999938964844,430.20001220703125,412.0,429.29998779296875,408.14666748046875,23168047,0.0,0.0
+2022-11-07 00:00:00+00:00,427.29998779296875,445.8999938964844,420.6520080566406,438.3999938964844,416.79827880859375,36709117,0.0,0.0
+2022-11-14 00:00:00+00:00,438.29998779296875,458.489990234375,435.0,455.1000061035156,432.6754150390625,29106506,0.0,0.0
+2022-11-21 00:00:00+00:00,454.3999938964844,461.0,450.0,456.6000061035156,434.10150146484375,21667730,0.0,0.0
+2022-11-28 00:00:00+00:00,453.79998779296875,456.8999938964844,435.1000061035156,444.79998779296875,422.8829345703125,33326204,0.0,0.0
+2022-12-05 00:00:00+00:00,442.8999938964844,450.25,441.29998779296875,448.0,425.9252624511719,29147089,0.0,0.0
+2022-12-12 00:00:00+00:00,445.1000061035156,451.29998779296875,431.20001220703125,436.1000061035156,414.61163330078125,46593233,0.0,0.0
+2022-12-19 00:00:00+00:00,436.0,452.6000061035156,433.6000061035156,444.0,422.1223449707031,20982140,0.0,0.0
+2022-12-26 00:00:00+00:00,444.0,452.0580139160156,442.3999938964844,442.79998779296875,420.9814758300781,8249664,0.0,0.0
+2023-01-02 00:00:00+00:00,445.8999938964844,458.1499938964844,443.29998779296875,456.0,433.53106689453125,28687622,0.0,0.0
+2023-01-09 00:00:00+00:00,456.0,461.0660095214844,435.79998779296875,444.20001220703125,422.3125,39237336,0.0,0.0
+2023-01-16 00:00:00+00:00,444.29998779296875,447.20001220703125,434.3999938964844,439.0,417.36871337890625,35267336,0.0,0.0
+2023-01-23 00:00:00+00:00,440.0,459.29998779296875,439.5,457.3999938964844,434.8620910644531,37495012,0.0,0.0
+2023-01-30 00:00:00+00:00,454.3999938964844,459.3999938964844,447.79998779296875,450.29998779296875,428.1119079589844,48879358,0.0,0.0
+2023-02-06 00:00:00+00:00,448.0,449.20001220703125,436.29998779296875,440.0,418.3194580078125,38799772,0.0,0.0
+2023-02-13 00:00:00+00:00,441.20001220703125,450.29998779296875,440.0,447.6000061035156,425.54498291015625,30251441,0.0,0.0
+2023-02-20 00:00:00+00:00,448.5,450.79998779296875,434.29998779296875,440.0,418.3194580078125,26764528,0.0,0.0
+2023-02-27 00:00:00+00:00,442.8999938964844,450.5,441.6080017089844,447.20001220703125,425.1647033691406,29895454,0.0,0.0
+2023-03-06 00:00:00+00:00,447.3999938964844,467.29998779296875,443.1000061035156,449.70001220703125,427.54150390625,82322819,0.0,0.0
+2023-03-13 00:00:00+00:00,450.0,451.4179992675781,400.68701171875,402.20001220703125,382.38201904296875,85158023,0.0,0.0
+2023-03-20 00:00:00+00:00,396.20001220703125,425.3999938964844,383.4960021972656,408.29998779296875,388.1814270019531,60152666,0.0,0.0
+2023-03-27 00:00:00+01:00,416.0,422.04998779296875,399.54998779296875,404.20001220703125,384.2834777832031,81534829,20.7,0.0
+2023-04-03 00:00:00+01:00,405.0,434.1000061035156,404.3999938964844,417.1000061035156,417.1000061035156,43217151,0.0,0.0
+2023-04-10 00:00:00+01:00,419.1000061035156,426.70001220703125,419.1000061035156,421.70001220703125,421.70001220703125,32435695,0.0,0.0
+2023-04-17 00:00:00+01:00,423.70001220703125,427.635009765625,415.3999938964844,420.29998779296875,420.29998779296875,37715986,0.0,0.0
+2023-04-24 00:00:00+01:00,418.1000061035156,423.0,415.29998779296875,423.0,423.0,34331974,0.0,0.0
+2023-05-01 00:00:00+01:00,423.3999938964844,426.1000061035156,406.3999938964844,414.6000061035156,414.6000061035156,40446519,0.0,0.0
+2023-05-08 00:00:00+01:00,414.6000061035156,419.1000061035156,408.0,412.70001220703125,412.70001220703125,36950836,0.0,0.0
+2023-05-15 00:00:00+01:00,414.0,418.3999938964844,407.3999938964844,413.5,413.5,53109487,0.0,0.0
+2023-05-22 00:00:00+01:00,413.6000061035156,424.0,394.70001220703125,401.29998779296875,401.29998779296875,64363368,0.0,0.0
+2023-05-29 00:00:00+01:00,401.29998779296875,409.4779968261719,392.70001220703125,409.1000061035156,409.1000061035156,47587959,0.0,0.0
+2023-06-05 00:00:00+01:00,406.29998779296875,410.70001220703125,400.1000061035156,400.8999938964844,400.8999938964844,22494985,0.0,0.0
+2023-06-12 00:00:00+01:00,404.1000061035156,406.0,394.5,396.0,396.0,41531163,0.0,0.0
+2023-06-19 00:00:00+01:00,394.0,399.8999938964844,380.7200012207031,386.20001220703125,386.20001220703125,40439880,0.0,0.0
+2023-06-26 00:00:00+01:00,387.20001220703125,397.0,382.8999938964844,395.20001220703125,395.20001220703125,27701915,0.0,0.0
+2023-07-03 00:00:00+01:00,396.5,399.79998779296875,380.1000061035156,381.79998779296875,381.79998779296875,26005305,0.0,0.0
+2023-07-10 00:00:00+01:00,380.0,392.29998779296875,379.40399169921875,386.0,386.0,29789300,0.0,0.0
+2023-07-17 00:00:00+01:00,385.0,389.5,384.2510070800781,387.1000061035156,387.1000061035156,0,0.0,0.0

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

@@ -0,0 +1,11 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-05-18 00:00:00+01:00,193.220001220703,200.839996337891,193.220001220703,196.839996337891,196.839996337891,653125,0,0
+2023-05-17 00:00:00+01:00,199.740005493164,207.738006591797,190.121994018555,197.860000610352,197.860000610352,822268,0,0
+2023-05-16 00:00:00+01:00,215.600006103516,215.600006103516,201.149993896484,205.100006103516,205.100006103516,451009,243.93939,0.471428571428571
+2023-05-15 00:00:00+01:00,215.399955531529,219.19995640346,210.599967302595,217.399987792969,102.39998147147,1761679.3939394,0,0
+2023-05-12 00:00:00+01:00,214.599988664899,216.199965558733,209.599965558733,211.399977329799,99.573855808803,1522298.48484849,0,0
+2023-05-11 00:00:00+01:00,219.999966430664,219.999966430664,212.199987357003,215.000000871931,101.269541277204,3568042.12121213,0,0
+2023-05-10 00:00:00+01:00,218.199954659598,223.000000435965,212.59995640346,215.399955531529,101.457929992676,5599908.78787879,0,0
+2023-05-09 00:00:00+01:00,224,227.688003540039,218.199996948242,218.399993896484,102.87100982666,1906090,0,0
+2023-05-05 00:00:00+01:00,220.999968174526,225.19996686663,220.799976457868,224.4,105.697140066964,964523.636363637,0,0
+2023-05-04 00:00:00+01:00,216.999989972796,222.799965558733,216.881988961356,221.399965994698,104.284055655343,880983.93939394,0,0

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

@@ -0,0 +1,11 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-05-18 00:00:00+01:00,193.220001220703,200.839996337891,193.220001220703,196.839996337891,196.839996337891,653125,0,0
+2023-05-17 00:00:00+01:00,199.740005493164,207.738006591797,190.121994018555,197.860000610352,197.860000610352,822268,0,0
+2023-05-16 00:00:00+01:00,215.600006103516,215.600006103516,201.149993896484,205.100006103516,205.100006103516,451009,243.93939,0.471428571428571
+2023-05-15 00:00:00+01:00,456.908996582031,464.969604492188,446.727203369141,461.151489257813,217.21208190918,830506,0,0
+2023-05-12 00:00:00+01:00,455.212097167969,458.605987548828,444.605987548828,448.424194335938,211.217269897461,717655,0,0
+2023-05-11 00:00:00+01:00,466.666595458984,466.666595458984,450.121185302734,456.060607910156,214.814178466797,1682077,0,0
+2023-05-10 00:00:00+01:00,462.848388671875,473.030303955078,450.969604492188,456.908996582031,215.213790893555,2639957,0,0
+2023-05-09 00:00:00+01:00,224,227.688003540039,218.199996948242,218.399993896484,102.87100982666,1906090,0,0
+2023-05-05 00:00:00+01:00,468.787811279297,477.696899414063,468.363586425781,476,224.2060546875,454704,0,0
+2023-05-04 00:00:00+01:00,460.303009033203,472.605987548828,460.052703857422,469.636291503906,221.208602905273,415321,0,0

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

@@ -0,0 +1,24 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-05-31 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-30 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0.4406
+2023-05-29 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-26 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-25 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-24 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-23 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-22 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-19 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-18 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-17 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-16 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-15 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-12 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-11 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-10 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-09 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-08 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-05 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-04 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-03 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-02 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-01 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0

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

@@ -0,0 +1,24 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-05-31 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-30 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0.4406
+2023-05-29 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-26 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-25 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-24 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-23 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-22 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
+2023-05-19 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-18 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-17 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-16 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-15 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-12 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-11 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-10 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-09 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-08 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-05 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-04 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-03 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-02 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0
+2023-05-01 00:00:00+10:00,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0.0529999993741512,0,0,0

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

@@ -0,0 +1,42 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2020-09-30 00:00:00-04:00,4.40000009536743,4.44999980926514,4.01999998092651,4.44999980926514,4.44999980926514,22600,0,0
+2020-09-29 00:00:00-04:00,4.3899998664856,4.40000009536743,4.13000011444092,4.30000019073486,4.30000019073486,10800,0,0
+2020-09-28 00:00:00-04:00,4.09000015258789,4.25,4.09000015258789,4.25,4.25,8000,0,0
+2020-09-25 00:00:00-04:00,3.95000004768372,4.09999990463257,3.95000004768372,4.05000019073486,4.05000019073486,13500,0,0
+2020-09-24 00:00:00-04:00,3.84999990463257,4,3.84999990463257,4,4,8800,0,0
+2020-09-23 00:00:00-04:00,3.99000000953674,4,3.99000000953674,4,4,5900,0,0
+2020-09-22 00:00:00-04:00,3.90000009536743,4.09999990463257,3.84999990463257,4.09999990463257,4.09999990463257,3100,0,0
+2020-09-21 00:00:00-04:00,4.09999990463257,4.09999990463257,4.09999990463257,4.09999990463257,4.09999990463257,1200,0,0
+2020-09-18 00:00:00-04:00,3.92000007629395,4.09999990463257,3.92000007629395,4.09999990463257,4.09999990463257,27200,0,0
+2020-09-17 00:00:00-04:00,3.90000009536743,3.99000000953674,3.8199999332428,3.99000000953674,3.99000000953674,3300,0,0
+2020-09-16 00:00:00-04:00,3.79999995231628,4,3.79999995231628,4,4,3300,0,0
+2020-09-15 00:00:00-04:00,3.95000004768372,4,3.95000004768372,4,4,2400,0,0
+2020-09-14 00:00:00-04:00,3.96000003814697,4,3.96000003814697,4,4,800,0,0
+2020-09-11 00:00:00-04:00,3.95000004768372,3.97000002861023,3.72000002861023,3.97000002861023,3.97000002861023,5700,0,0
+2020-09-10 00:00:00-04:00,4,4.09999990463257,4,4.09999990463257,4.09999990463257,7100,0,0
+2020-09-09 00:00:00-04:00,3.5699999332428,4,3.5699999332428,4,4,18100,0,0
+2020-09-08 00:00:00-04:00,3.40000009536743,3.59999990463257,3.40000009536743,3.59999990463257,3.59999990463257,19500,0,0
+2020-09-04 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,400,0,0
+2020-09-03 00:00:00-04:00,3.58999991416931,3.58999991416931,3.58999991416931,3.58999991416931,3.58999991416931,0,0,0
+2020-09-02 00:00:00-04:00,3.5,3.58999991416931,3.5,3.58999991416931,3.58999991416931,2000,0,0
+2020-09-01 00:00:00-04:00,3.5,3.59999990463257,3.5,3.59999990463257,3.59999990463257,1200,0,0
+2020-08-31 00:00:00-04:00,3.15000009536743,3.70000004768372,3.15000009536743,3.70000004768372,3.70000004768372,26500,0,0
+2020-08-28 00:00:00-04:00,3.76999998092651,3.76999998092651,3.70000004768372,3.70000004768372,3.70000004768372,1600,0,0
+2020-08-27 00:00:00-04:00,3.65000009536743,3.65000009536743,3.65000009536743,3.65000009536743,3.65000009536743,0,0,0
+2020-08-26 00:00:00-04:00,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,0,0,0.1
+2020-08-25 00:00:00-04:00,3.40000009536743,3.70000004768372,3.40000009536743,3.70000004768372,3.70000004768372,2900,0,0
+2020-08-24 00:00:00-04:00,3.29999995231628,3.5,3.29999995231628,3.5,3.5,10000,0,0
+2020-08-21 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,150,0,0
+2020-08-20 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
+2020-08-19 00:00:00-04:00,3.40000009536743,3.5,3.40000009536743,3.5,3.5,9050,0,0
+2020-08-18 00:00:00-04:00,3.5,3.79999995231628,3.5,3.5,3.5,2250,0,0
+2020-08-17 00:00:00-04:00,2.79999995231628,3.70000004768372,2.79999995231628,3.70000004768372,3.70000004768372,5050,0,0
+2020-08-14 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
+2020-08-13 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
+2020-08-12 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
+2020-08-11 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
+2020-08-10 00:00:00-04:00,3.5,3.70000004768372,3.5,3.5,3.5,3300,0,0
+2020-08-07 00:00:00-04:00,3.5,3.79999995231628,3.5,3.79999995231628,3.79999995231628,2500,0,0
+2020-08-06 00:00:00-04:00,3.5,3.70000004768372,3.40000009536743,3.70000004768372,3.70000004768372,3000,0,0
+2020-08-05 00:00:00-04:00,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,0,0,0
+2020-08-04 00:00:00-04:00,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,0,0,0

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

@@ -0,0 +1,42 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2020-09-30 00:00:00-04:00,4.40000009536743,4.44999980926514,4.01999998092651,4.44999980926514,4.44999980926514,22600,0,0
+2020-09-29 00:00:00-04:00,4.3899998664856,4.40000009536743,4.13000011444092,4.30000019073486,4.30000019073486,10800,0,0
+2020-09-28 00:00:00-04:00,4.09000015258789,4.25,4.09000015258789,4.25,4.25,8000,0,0
+2020-09-25 00:00:00-04:00,3.95000004768372,4.09999990463257,3.95000004768372,4.05000019073486,4.05000019073486,13500,0,0
+2020-09-24 00:00:00-04:00,3.84999990463257,4,3.84999990463257,4,4,8800,0,0
+2020-09-23 00:00:00-04:00,3.99000000953674,4,3.99000000953674,4,4,5900,0,0
+2020-09-22 00:00:00-04:00,3.90000009536743,4.09999990463257,3.84999990463257,4.09999990463257,4.09999990463257,3100,0,0
+2020-09-21 00:00:00-04:00,4.09999990463257,4.09999990463257,4.09999990463257,4.09999990463257,4.09999990463257,1200,0,0
+2020-09-18 00:00:00-04:00,3.92000007629395,4.09999990463257,3.92000007629395,4.09999990463257,4.09999990463257,27200,0,0
+2020-09-17 00:00:00-04:00,3.90000009536743,3.99000000953674,3.8199999332428,3.99000000953674,3.99000000953674,3300,0,0
+2020-09-16 00:00:00-04:00,3.79999995231628,4,3.79999995231628,4,4,3300,0,0
+2020-09-15 00:00:00-04:00,3.95000004768372,4,3.95000004768372,4,4,2400,0,0
+2020-09-14 00:00:00-04:00,3.96000003814697,4,3.96000003814697,4,4,800,0,0
+2020-09-11 00:00:00-04:00,3.95000004768372,3.97000002861023,3.72000002861023,3.97000002861023,3.97000002861023,5700,0,0
+2020-09-10 00:00:00-04:00,4,4.09999990463257,4,4.09999990463257,4.09999990463257,7100,0,0
+2020-09-09 00:00:00-04:00,3.5699999332428,4,3.5699999332428,4,4,18100,0,0
+2020-09-08 00:00:00-04:00,3.40000009536743,3.59999990463257,3.40000009536743,3.59999990463257,3.59999990463257,19500,0,0
+2020-09-04 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,400,0,0
+2020-09-03 00:00:00-04:00,3.58999991416931,3.58999991416931,3.58999991416931,3.58999991416931,3.58999991416931,0,0,0
+2020-09-02 00:00:00-04:00,3.5,3.58999991416931,3.5,3.58999991416931,3.58999991416931,2000,0,0
+2020-09-01 00:00:00-04:00,3.5,3.59999990463257,3.5,3.59999990463257,3.59999990463257,1200,0,0
+2020-08-31 00:00:00-04:00,3.15000009536743,3.70000004768372,3.15000009536743,3.70000004768372,3.70000004768372,26500,0,0
+2020-08-28 00:00:00-04:00,3.76999998092651,3.76999998092651,3.70000004768372,3.70000004768372,3.70000004768372,1600,0,0
+2020-08-27 00:00:00-04:00,3.65000009536743,3.65000009536743,3.65000009536743,3.65000009536743,3.65000009536743,0,0,0
+2020-08-26 00:00:00-04:00,0.370000004768372,0.370000004768372,0.370000004768372,0.370000004768372,0.370000004768372,0,0,0.1
+2020-08-25 00:00:00-04:00,3.40000009536743,3.70000004768372,3.40000009536743,3.70000004768372,3.70000004768372,2900,0,0
+2020-08-24 00:00:00-04:00,3.29999995231628,3.5,3.29999995231628,3.5,3.5,10000,0,0
+2020-08-21 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,150,0,0
+2020-08-20 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
+2020-08-19 00:00:00-04:00,3.40000009536743,3.5,3.40000009536743,3.5,3.5,9050,0,0
+2020-08-18 00:00:00-04:00,3.5,3.79999995231628,3.5,3.5,3.5,2250,0,0
+2020-08-17 00:00:00-04:00,2.79999995231628,3.70000004768372,2.79999995231628,3.70000004768372,3.70000004768372,5050,0,0
+2020-08-14 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
+2020-08-13 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
+2020-08-12 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
+2020-08-11 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,0,0,0
+2020-08-10 00:00:00-04:00,3.5,3.70000004768372,3.5,3.5,3.5,3300,0,0
+2020-08-07 00:00:00-04:00,3.5,3.79999995231628,3.5,3.79999995231628,3.79999995231628,2500,0,0
+2020-08-06 00:00:00-04:00,3.5,3.70000004768372,3.40000009536743,3.70000004768372,3.70000004768372,3000,0,0
+2020-08-05 00:00:00-04:00,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,0,0,0
+2020-08-04 00:00:00-04:00,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,3.70000004768372,0,0,0

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

@@ -0,0 +1,17 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-05-08 00:00:00+02:00,24.8999996185303,24.9500007629395,24.1000003814697,24.75,24.75,7187,0,0
+2023-05-09 00:00:00+02:00,25,25.5,23.1499996185303,24.1499996185303,24.1499996185303,22753,0,0
+2023-05-10 00:00:00+02:00,24.1499996185303,24.1499996185303,22,22.9500007629395,22.9500007629395,62727,0,0
+2023-05-11 00:00:00+02:00,22.9500007629395,25,22.9500007629395,23.3500003814697,23.3500003814697,19550,0,0
+2023-05-12 00:00:00+02:00,23.3500003814697,24,22.1000003814697,23.8500003814697,23.8500003814697,17143,0,0
+2023-05-15 00:00:00+02:00,23,25.7999992370605,22.5,23,23,43709,0,0
+2023-05-16 00:00:00+02:00,22.75,24.0499992370605,22.5,22.75,22.75,16068,0,0
+2023-05-17 00:00:00+02:00,23,23.8500003814697,22.1000003814697,23.6499996185303,23.6499996185303,19926,0,0
+2023-05-19 00:00:00+02:00,23.6499996185303,23.8500003814697,22.1000003814697,22.2999992370605,22.2999992370605,41050,0,0
+2023-05-22 00:00:00+02:00,22.0000004768372,24.1499996185303,21.5499997138977,22.7500009536743,22.7500009536743,34022,0,0
+2023-05-23 00:00:00+02:00,22.75,22.8999996185303,21.75,22.5,22.5,13992,0,0
+2023-05-24 00:00:00+02:00,21,24,21,22.0100002288818,22.0100002288818,18306,0,0.1
+2023-05-25 00:00:00+02:00,21.5699996948242,22.8899993896484,20,21.1599998474121,21.1599998474121,35398,0,0
+2023-05-26 00:00:00+02:00,21.1599998474121,22.4950008392334,20.5,21.0949993133545,21.0949993133545,8039,0,0
+2023-05-29 00:00:00+02:00,22.1000003814697,22.1000003814697,20.25,20.75,20.75,17786,0,0
+2023-05-30 00:00:00+02:00,20.75,21.6499996185303,20.1499996185303,20.4500007629395,20.4500007629395,10709,0,0

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

@@ -0,0 +1,17 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-05-08 00:00:00+02:00,24.899999618530273,24.950000762939453,24.100000381469727,24.75,24.75,7187,0.0,0.0
+2023-05-09 00:00:00+02:00,25.0,25.5,23.149999618530273,24.149999618530273,24.149999618530273,22753,0.0,0.0
+2023-05-10 00:00:00+02:00,24.149999618530273,24.149999618530273,22.0,22.950000762939453,22.950000762939453,62727,0.0,0.0
+2023-05-11 00:00:00+02:00,22.950000762939453,25.0,22.950000762939453,23.350000381469727,23.350000381469727,19550,0.0,0.0
+2023-05-12 00:00:00+02:00,23.350000381469727,24.0,22.100000381469727,23.850000381469727,23.850000381469727,17143,0.0,0.0
+2023-05-15 00:00:00+02:00,23.0,25.799999237060547,22.5,23.0,23.0,43709,0.0,0.0
+2023-05-16 00:00:00+02:00,22.75,24.049999237060547,22.5,22.75,22.75,16068,0.0,0.0
+2023-05-17 00:00:00+02:00,23.0,23.850000381469727,22.100000381469727,23.649999618530273,23.649999618530273,19926,0.0,0.0
+2023-05-19 00:00:00+02:00,23.649999618530273,23.850000381469727,22.100000381469727,22.299999237060547,22.299999237060547,41050,0.0,0.0
+2023-05-22 00:00:00+02:00,2.200000047683716,2.4149999618530273,2.1549999713897705,2.2750000953674316,2.2750000953674316,340215,0.0,0.0
+2023-05-23 00:00:00+02:00,22.75,22.899999618530273,21.75,22.5,22.5,13992,0.0,0.0
+2023-05-24 00:00:00+02:00,21.0,24.0,21.0,22.010000228881836,22.010000228881836,18306,0.0,0.1
+2023-05-25 00:00:00+02:00,21.56999969482422,22.889999389648438,20.0,21.15999984741211,21.15999984741211,35398,0.0,0.0
+2023-05-26 00:00:00+02:00,21.15999984741211,22.4950008392334,20.5,21.094999313354492,21.094999313354492,8039,0.0,0.0
+2023-05-29 00:00:00+02:00,22.100000381469727,22.100000381469727,20.25,20.75,20.75,17786,0.0,0.0
+2023-05-30 00:00:00+02:00,20.75,21.649999618530273,20.149999618530273,20.450000762939453,20.450000762939453,10709,0.0,0.0

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

@@ -0,0 +1,23 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2022-06-01 00:00:00+02:00,5.72999992370606,5.78199996948242,5.3939998626709,5.3939998626709,5.3939998626709,3095860,0,0
+2022-06-02 00:00:00+02:00,5.38600006103516,5.38600006103516,5.26800003051758,5.2939998626709,5.2939998626709,1662880,0,0
+2022-06-03 00:00:00+02:00,5.34599990844727,5.34599990844727,5.15800018310547,5.16800003051758,5.16800003051758,1698900,0,0
+2022-06-06 00:00:00+02:00,5.16800003051758,5.25200004577637,5.13800010681152,5.18800010681152,5.18800010681152,1074910,0,0
+2022-06-07 00:00:00+02:00,5.21800003051758,5.22200012207031,5.07400016784668,5.1560001373291,5.1560001373291,1850680,0,0
+2022-06-08 00:00:00+02:00,5.1560001373291,5.17599983215332,5.07200012207031,5.10200004577637,5.10200004577637,1140360,0,0
+2022-06-09 00:00:00+02:00,5.09799995422363,5.09799995422363,4.87599983215332,4.8939998626709,4.8939998626709,2025480,0,0
+2022-06-10 00:00:00+02:00,4.87999992370606,4.87999992370606,4.50400009155274,4.50400009155274,4.50400009155274,2982730,0,0
+2022-06-13 00:00:00+02:00,4.3,4.37599983215332,3.83600006103516,3.83600006103516,3.83600006103516,4568210,0,0.1
+2022-06-14 00:00:00+02:00,3.87750015258789,4.15999984741211,3.85200004577637,3.9439998626709,3.9439998626709,5354500,0,0
+2022-06-15 00:00:00+02:00,4.03400001525879,4.16450004577637,3.73050003051758,3.73050003051758,3.73050003051758,6662610,0,0
+2022-06-16 00:00:00+02:00,3.73050003051758,3.98499984741211,3.72400016784668,3.82550010681152,3.82550010681152,13379960,0,0
+2022-06-17 00:00:00+02:00,3.8,4.29949989318848,3.75,4.29949989318848,4.29949989318848,12844160,0,0
+2022-06-20 00:00:00+02:00,2.19422197341919,2.2295401096344,2.13992595672607,2.2295401096344,2.2295401096344,12364104,0,0
+2022-06-21 00:00:00+02:00,2.24719905853272,2.28515291213989,2.19712090492249,2.21557092666626,2.21557092666626,8434013,0,0
+2022-06-22 00:00:00+02:00,1.98679196834564,2.00365996360779,1.73798203468323,1.73798203468323,1.73798203468323,26496542,0,0
+2022-06-23 00:00:00+02:00,1.62411904335022,1.68526804447174,1.37320005893707,1.59776198863983,1.59776198863983,48720201,0,0
+2022-06-24 00:00:00+02:00,1.47599303722382,1.54610300064087,1.1739410161972,1.24932205677032,1.24932205677032,56877192,0,0
+2022-06-27 00:00:00+02:00,1.49899995326996,1.79849994182587,1.49899995326996,1.79849994182587,1.79849994182587,460673,0,0
+2022-06-28 00:00:00+02:00,2.15799999237061,3.05100011825562,2.12599992752075,3.05100011825562,3.05100011825562,3058635,0,0
+2022-06-29 00:00:00+02:00,2.90000009536743,3.73799991607666,2.85899996757507,3.26399993896484,3.26399993896484,6516761,0,0
+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/ticker.py

@@ -0,0 +1,924 @@
+"""
+Tests for Ticker
+
+To run all tests in suite from commandline:
+   python -m unittest tests.ticker
+
+Specific test class:
+   python -m unittest tests.ticker.TestTicker
+
+"""
+import pandas as pd
+
+from .context import yfinance as yf
+from .context import session_gbl
+from yfinance.exceptions import YFNotImplementedError
+
+
+import unittest
+import requests_cache
+from typing import Union, Any
+
+ticker_attributes = (
+    ("major_holders", pd.DataFrame),
+    ("institutional_holders", pd.DataFrame),
+    ("mutualfund_holders", pd.DataFrame),
+    ("splits", pd.Series),
+    ("actions", pd.DataFrame),
+    ("shares", pd.DataFrame),
+    ("info", dict),
+    ("calendar", pd.DataFrame),
+    ("recommendations", Union[pd.DataFrame, dict]),
+    ("earnings", pd.DataFrame),
+    ("quarterly_earnings", pd.DataFrame),
+    ("recommendations_summary", Union[pd.DataFrame, dict]),
+    ("quarterly_cashflow", pd.DataFrame),
+    ("cashflow", pd.DataFrame),
+    ("quarterly_balance_sheet", pd.DataFrame),
+    ("balance_sheet", pd.DataFrame),
+    ("quarterly_income_stmt", pd.DataFrame),
+    ("income_stmt", pd.DataFrame),
+    ("analyst_price_target", pd.DataFrame),
+    ("revenue_forecasts", pd.DataFrame),
+    ("sustainability", pd.DataFrame),
+    ("options", tuple),
+    ("news", Any),
+    ("earnings_trend", pd.DataFrame),
+    ("earnings_dates", pd.DataFrame),
+    ("earnings_forecasts", pd.DataFrame),
+)
+
+def assert_attribute_type(testClass: unittest.TestCase, instance, attribute_name, expected_type):
+    try:
+        attribute = getattr(instance, attribute_name)
+        if attribute is not None and expected_type is not Any:
+            testClass.assertEqual(type(attribute), expected_type)
+    except Exception:
+        testClass.assertRaises(
+            YFNotImplementedError, lambda: getattr(instance, attribute_name)
+        )
+
+class TestTicker(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+        cls.proxy = None
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def test_getTz(self):
+        tkrs = ["IMP.JO", "BHG.JO", "SSW.JO", "BP.L", "INTC"]
+        for tkr in tkrs:
+            # First step: remove ticker from tz-cache
+            yf.utils.get_tz_cache().store(tkr, None)
+
+            # Test:
+            dat = yf.Ticker(tkr, session=self.session)
+            tz = dat._get_ticker_tz(proxy=None, timeout=None)
+
+            self.assertIsNotNone(tz)
+
+    def test_badTicker(self):
+        # Check yfinance doesn't die when ticker delisted
+
+        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", 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]
+
+        for attribute_name, attribute_type in ticker_attributes:
+            assert_attribute_type(self, dat, attribute_name, attribute_type) 
+       
+    def test_goodTicker(self):
+        # that yfinance works when full api is called on same instance of ticker
+
+        tkrs = ["IBM"]
+        tkrs.append("QCSTIX")  # weird ticker, no price history but has previous close
+        for tkr in tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+
+            dat.history(period="1wk")
+            dat.history(start="2022-01-01")
+            dat.history(start="2022-01-01", end="2022-03-01")
+            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]
+
+            for attribute_name, attribute_type in ticker_attributes:
+                assert_attribute_type(self, dat, attribute_name, attribute_type) 
+            
+    #TODO:: Refactor with `assert_attribute` once proxy is accepted as a parameter of `Ticker`
+    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)
+        dat._get_ticker_tz(proxy=self.proxy, timeout=5)
+        dat.history(period="1wk", proxy=self.proxy)
+
+        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_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_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)
+
+        dat.get_history_metadata(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(len(v) > 0)
+
+        # Below will fail because not ported to Yahoo API
+
+        # 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_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_shares(proxy=self.proxy)
+        # self.assertIsNotNone(v)
+        # self.assertFalse(v.empty)
+
+
+class TestTickerHistory(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        # use a ticker that has dividends
+        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):
+        md = self.ticker.history_metadata
+        self.assertIn("IBM", md.values(), "metadata missing")
+        data = self.ticker.history("1y")
+        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.
+        As doing other type of scraping calls than "query2.finance.yahoo.com/v8/finance/chart" to yahoo website
+        will quickly trigger spam-block when doing bulk download of history data.
+        """
+        symbol = "GOOGL"
+        range = "1y"
+        with requests_cache.CachedSession(backend="memory") as session:
+            ticker = yf.Ticker(symbol, session=session)
+            ticker.history(range)
+            actual_urls_called = tuple([r.url for r in session.cache.filter()])
+
+        expected_urls = (
+            f"https://query2.finance.yahoo.com/v8/finance/chart/{symbol}?events=div%2Csplits%2CcapitalGains&includePrePost=False&interval=1d&range={range}",
+        )
+        self.assertEqual(
+            expected_urls,
+            actual_urls_called,
+            "Different than expected url used to fetch history."
+        )
+    def test_dividends(self):
+        data = self.ticker.dividends
+        self.assertIsInstance(data, pd.Series, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+    def test_splits(self):
+        data = self.ticker.splits
+        self.assertIsInstance(data, pd.Series, "data has wrong type")
+        # self.assertFalse(data.empty, "data is empty")
+
+    def test_actions(self):
+        data = self.ticker.actions
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+
+# Below will fail because not ported to Yahoo API
+# class TestTickerEarnings(unittest.TestCase):
+#     session = None
+
+#     @classmethod
+#     def setUpClass(cls):
+#         cls.session = session_gbl
+
+#     @classmethod
+#     def tearDownClass(cls):
+#         if cls.session is not None:
+#             cls.session.close()
+
+#     def setUp(self):
+#         self.ticker = yf.Ticker("GOOGL", session=self.session)
+
+#     def tearDown(self):
+#         self.ticker = None
+
+#     def test_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")
+
+#     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")
+
+#     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")
+
+#     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")
+
+#     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")
+
+#     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")
+
+
+class TestTickerHolders(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        self.ticker = yf.Ticker("GOOGL", session=self.session)
+
+    def tearDown(self):
+        self.ticker = None
+
+    def test_major_holders(self):
+        data = self.ticker.major_holders
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        data_cached = self.ticker.major_holders
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_institutional_holders(self):
+        data = self.ticker.institutional_holders
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        data_cached = self.ticker.institutional_holders
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_mutualfund_holders(self):
+        data = self.ticker.mutualfund_holders
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        data_cached = self.ticker.mutualfund_holders
+        self.assertIs(data, data_cached, "data not cached")
+
+
+class TestTickerMiscFinancials(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        self.ticker = yf.Ticker("GOOGL", session=self.session)
+        
+        # For ticker 'BSE.AX' (and others), Yahoo not returning 
+        # full quarterly financials (usually cash-flow) with all entries, 
+        # instead returns a smaller version in different data store.
+        self.ticker_old_fmt = yf.Ticker("BSE.AX", session=self.session)
+
+    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
+
+        # Test contents of table
+        data = self.ticker.get_income_stmt(pretty=True)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
+
+        # Test property defaults
+        data2 = self.ticker.income_stmt
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_income_stmt(pretty=False)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        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
+
+        # Test contents of table
+        data = self.ticker.get_income_stmt(pretty=True, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
+
+        # Test property defaults
+        data2 = self.ticker.quarterly_income_stmt
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_income_stmt(pretty=False, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        data = self.ticker.get_income_stmt(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")
+
+    def test_balance_sheet(self):
+        expected_keys = ["Total Assets", "Net PPE"]
+        expected_periods_days = 365
+
+        # Test contents of table
+        data = self.ticker.get_balance_sheet(pretty=True)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
+
+        # Test property defaults
+        data2 = self.ticker.balance_sheet
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_balance_sheet(pretty=False)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        data = self.ticker.get_balance_sheet(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")
+
+    def test_quarterly_balance_sheet(self):
+        expected_keys = ["Total Assets", "Net PPE"]
+        expected_periods_days = 365//4
+
+        # Test contents of table
+        data = self.ticker.get_balance_sheet(pretty=True, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
+
+        # Test property defaults
+        data2 = self.ticker.quarterly_balance_sheet
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_balance_sheet(pretty=False, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        data = self.ticker.get_balance_sheet(as_dict=True, freq="quarterly")
+        self.assertIsInstance(data, dict, "data has wrong type")
+
+    def test_cash_flow(self):
+        expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
+        expected_periods_days = 365
+
+        # Test contents of table
+        data = self.ticker.get_cashflow(pretty=True)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
+
+        # Test property defaults
+        data2 = self.ticker.cashflow
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_cashflow(pretty=False)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        data = self.ticker.get_cashflow(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")
+
+    def test_quarterly_cash_flow(self):
+        expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
+        expected_periods_days = 365//4
+
+        # Test contents of table
+        data = self.ticker.get_cashflow(pretty=True, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
+
+        # Test property defaults
+        data2 = self.ticker.quarterly_cashflow
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_cashflow(pretty=False, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        data = self.ticker.get_cashflow(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")
+
+    def test_income_alt_names(self):
+        i1 = self.ticker.income_stmt
+        i2 = self.ticker.incomestmt
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.financials
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.get_income_stmt()
+        i2 = self.ticker.get_incomestmt()
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.get_financials()
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.quarterly_income_stmt
+        i2 = self.ticker.quarterly_incomestmt
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.quarterly_financials
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.get_income_stmt(freq="quarterly")
+        i2 = self.ticker.get_incomestmt(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.get_financials(freq="quarterly")
+        self.assertTrue(i1.equals(i3))
+
+    def test_balance_sheet_alt_names(self):
+        i1 = self.ticker.balance_sheet
+        i2 = self.ticker.balancesheet
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_balance_sheet()
+        i2 = self.ticker.get_balancesheet()
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.quarterly_balance_sheet
+        i2 = self.ticker.quarterly_balancesheet
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_balance_sheet(freq="quarterly")
+        i2 = self.ticker.get_balancesheet(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+
+    def test_cash_flow_alt_names(self):
+        i1 = self.ticker.cash_flow
+        i2 = self.ticker.cashflow
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_cash_flow()
+        i2 = self.ticker.get_cashflow()
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.quarterly_cash_flow
+        i2 = self.ticker.quarterly_cashflow
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_cash_flow(freq="quarterly")
+        i2 = self.ticker.get_cashflow(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+
+    def test_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 = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        self.symbols = []
+        self.symbols += ["ESLT.TA", "BP.L", "GOOGL"]
+        self.symbols.append("QCSTIX")  # good for testing, doesn't trade
+        self.symbols += ["BTC-USD", "IWO", "VFINX", "^GSPC"]
+        self.symbols += ["SOKE.IS", "ADS.DE"]  # detected bugs
+        self.tickers = [yf.Ticker(s, session=self.session) for s in self.symbols]
+
+    def tearDown(self):
+        self.ticker = None
+
+    def test_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")
+        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_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))
+
+    #     key_rename_map = {}
+    #     key_rename_map["currency"] = "currency"
+    #     key_rename_map["quote_type"] = "quoteType"
+    #     key_rename_map["timezone"] = "exchangeTimezoneName"
+
+    #     key_rename_map["last_price"] = ["currentPrice", "regularMarketPrice"]
+    #     key_rename_map["open"] = ["open", "regularMarketOpen"]
+    #     key_rename_map["day_high"] = ["dayHigh", "regularMarketDayHigh"]
+    #     key_rename_map["day_low"] = ["dayLow", "regularMarketDayLow"]
+    #     key_rename_map["previous_close"] = ["previousClose"]
+    #     key_rename_map["regular_market_previous_close"] = ["regularMarketPreviousClose"]
+
+    #     key_rename_map["fifty_day_average"] = "fiftyDayAverage"
+    #     key_rename_map["two_hundred_day_average"] = "twoHundredDayAverage"
+    #     key_rename_map["year_change"] = ["52WeekChange", "fiftyTwoWeekChange"]
+    #     key_rename_map["year_high"] = "fiftyTwoWeekHigh"
+    #     key_rename_map["year_low"] = "fiftyTwoWeekLow"
+
+    #     key_rename_map["last_volume"] = ["volume", "regularMarketVolume"]
+    #     key_rename_map["ten_day_average_volume"] = ["averageVolume10days", "averageDailyVolume10Day"]
+    #     key_rename_map["three_month_average_volume"] = "averageVolume"
+
+    #     key_rename_map["market_cap"] = "marketCap"
+    #     key_rename_map["shares"] = "sharesOutstanding"
+
+    #     for k in list(key_rename_map.keys()):
+    #         if '_' in k:
+    #             key_rename_map[yf.utils.snake_case_2_camelCase(k)] = key_rename_map[k]
+
+    #     # Note: share count items in info[] are bad. Sometimes the float > outstanding!
+    #     # So often fast_info["shares"] does not match. 
+    #     # Why isn't fast_info["shares"] wrong? Because using it to calculate market cap always correct.
+    #     bad_keys = {"shares"}
+
+    #     # Loose tolerance for averages, no idea why don't match info[]. Is info wrong?
+    #     custom_tolerances = {}
+    #     custom_tolerances["year_change"] = 1.0
+    #     # custom_tolerances["ten_day_average_volume"] = 1e-3
+    #     custom_tolerances["ten_day_average_volume"] = 1e-1
+    #     # custom_tolerances["three_month_average_volume"] = 1e-2
+    #     custom_tolerances["three_month_average_volume"] = 5e-1
+    #     custom_tolerances["fifty_day_average"] = 1e-2
+    #     custom_tolerances["two_hundred_day_average"] = 1e-2
+    #     for k in list(custom_tolerances.keys()):
+    #         if '_' in k:
+    #             custom_tolerances[yf.utils.snake_case_2_camelCase(k)] = custom_tolerances[k]
+
+    #     for k in fast_info_keys:
+    #         if k in key_rename_map:
+    #             k2 = key_rename_map[k]
+    #         else:
+    #             k2 = k
+
+    #         if not isinstance(k2, list):
+    #             k2 = [k2]
+
+    #         for m in k2:
+    #             for ticker in self.tickers:
+    #                 if not m in ticker.info:
+    #                     # print(f"symbol={ticker.ticker}: fast_info key '{k}' mapped to info key '{m}' but not present in info")
+    #                     continue
+
+    #                 if k in bad_keys:
+    #                     continue
+
+    #                 if k in custom_tolerances:
+    #                     rtol = custom_tolerances[k]
+    #                 else:
+    #                     rtol = 5e-3
+    #                     # rtol = 1e-4
+
+    #                 correct = ticker.info[m]
+    #                 test = ticker.fast_info[k]
+    #                 # print(f"Testing: symbol={ticker.ticker} m={m} k={k}: test={test} vs correct={correct}")
+    #                 if k in ["market_cap","marketCap"] and ticker.fast_info["currency"] in ["GBp", "ILA"]:
+    #                     # Adjust for currency to match Yahoo:
+    #                     test *= 0.01
+    #                 try:
+    #                     if correct is None:
+    #                         self.assertTrue(test is None or (not np.isnan(test)), f"{k}: {test} must be None or real value because correct={correct}")
+    #                     elif isinstance(test, float) or isinstance(correct, int):
+    #                         self.assertTrue(np.isclose(test, correct, rtol=rtol), f"{ticker.ticker} {k}: {test} != {correct}")
+    #                     else:
+    #                         self.assertEqual(test, correct, f"{k}: {test} != {correct}")
+    #                 except:
+    #                     if k in ["regularMarketPreviousClose"] and ticker.ticker in ["ADS.DE"]:
+    #                         # Yahoo is wrong, is returning post-market close not regular
+    #                         continue
+    #                     else:
+    #                         raise
+
+
+
+def suite():
+    suite = unittest.TestSuite()
+    suite.addTest(TestTicker('Test ticker'))
+    suite.addTest(TestTickerEarnings('Test earnings'))
+    suite.addTest(TestTickerHolders('Test holders'))
+    suite.addTest(TestTickerHistory('Test Ticker history'))
+    suite.addTest(TestTickerMiscFinancials('Test misc financials'))
+    suite.addTest(TestTickerInfo('Test info & fast_info'))
+    return suite
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/utils.py

@@ -0,0 +1,91 @@
+"""
+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
+import os
+
+
+class TestCache(unittest.TestCase):
+    @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 test_setTzCacheLocation(self):
+        self.assertEqual(yf.utils._DBManager.get_location(), self.tempCacheDir.name)
+
+        tkr = 'AMZN'
+        tz1 = "America/New_York"
+        cache = yf.utils.get_tz_cache()
+        cache.store(tkr, tz1)
+
+        self.assertTrue(os.path.exists(os.path.join(self.tempCacheDir.name, "tkr-tz.db")))
+
+
+class TestCacheNoPermission(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        yf.set_tz_cache_location("/root/yf-cache")
+
+    def test_tzCacheRootStore(self):
+        # Test that if cache path in read-only filesystem, no exception.
+        tkr = 'AMZN'
+        tz1 = "America/New_York"
+
+        # During attempt to store, will discover cannot write
+        yf.utils.get_tz_cache().store(tkr, tz1)
+
+        # Handling the store failure replaces cache with a dummy
+        cache = yf.utils.get_tz_cache()
+        self.assertTrue(cache.dummy)
+        cache.store(tkr, tz1)
+
+    def test_tzCacheRootLookup(self):
+        # Test that if cache path in read-only filesystem, no exception.
+        tkr = 'AMZN'
+        # During attempt to lookup, will discover cannot write
+        yf.utils.get_tz_cache().lookup(tkr)
+
+        # Handling the lookup failure replaces cache with a dummy
+        cache = yf.utils.get_tz_cache()
+        self.assertTrue(cache.dummy)
+        cache.lookup(tkr)
+
+
+def suite():
+    suite = unittest.TestSuite()
+    suite.addTest(TestCache('Test cache'))
+    suite.addTest(TestCacheNoPermission('Test cache no permission'))
+    return suite
+
+
+if __name__ == '__main__':
+    unittest.main()

yfinance/__init__.py

@@ -23,6 +23,7 @@ from . import version
 from .ticker import Ticker
 from .tickers import Tickers
 from .multi import download
+from .utils import set_tz_cache_location, enable_debug_mode
 
 __version__ = version.version
 __author__ = "Ran Aroussi"
@@ -42,4 +43,4 @@ def pdr_override():
         pass
 
 
-__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override']
+__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

@@ -0,0 +1,76 @@
+import functools
+from functools import lru_cache
+
+import logging
+
+import requests as requests
+import re
+import random
+import time
+
+from frozendict import frozendict
+
+from . import utils
+
+cache_maxsize = 64
+
+
+def lru_cache_freezeargs(func):
+    """
+    Decorator transforms mutable dictionary and list arguments into immutable types
+    Needed so lru_cache can cache method calls what has dict or list arguments.
+    """
+
+    @functools.wraps(func)
+    def wrapped(*args, **kwargs):
+        args = tuple([frozendict(arg) if isinstance(arg, dict) else arg for arg in args])
+        kwargs = {k: frozendict(v) if isinstance(v, dict) else v for k, v in kwargs.items()}
+        args = tuple([tuple(arg) if isinstance(arg, list) else arg for arg in args])
+        kwargs = {k: tuple(v) if isinstance(v, list) else v for k, v in kwargs.items()}
+        return func(*args, **kwargs)
+
+    # copy over the lru_cache extra methods to this wrapper to be able to access them
+    # after this decorator has been applied
+    wrapped.cache_info = func.cache_info
+    wrapped.cache_clear = func.cache_clear
+    return wrapped
+
+
+class TickerData:
+    """
+    Have one place to retrieve data from Yahoo API in order to ease caching and speed up operations
+    """
+    user_agent_headers = {
+        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
+
+    def __init__(self, ticker: str, session=None):
+        self.ticker = ticker
+        self._session = session or requests
+
+    def get(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        proxy = self._get_proxy(proxy)
+        response = self._session.get(
+            url=url,
+            params=params,
+            proxies=proxy,
+            timeout=timeout,
+            headers=user_agent_headers or self.user_agent_headers)
+        return response
+
+    @lru_cache_freezeargs
+    @lru_cache(maxsize=cache_maxsize)
+    def cache_get(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        return self.get(url, user_agent_headers, params, proxy, timeout)
+
+    def _get_proxy(self, proxy):
+        # setup proxy in requests format
+        if proxy is not None:
+            if isinstance(proxy, (dict, frozendict)) and "https" in proxy:
+                proxy = proxy["https"]
+            proxy = {"https": proxy}
+        return proxy
+
+    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

@@ -0,0 +1,12 @@
+class YFinanceException(Exception):
+    pass
+
+
+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
 
 
-def download(tickers, start=None, end=None, actions=False, threads=True,
-             group_by='column', auto_adjust=False, back_adjust=False,
-             progress=True, period="max", show_errors=True, interval="1d", prepost=False,
-             proxy=None, rounding=False, timeout=None, **kwargs):
+@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=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,
             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
@@ -56,20 +62,60 @@ def download(tickers, start=None, end=None, actions=False, threads=True,
             Default is False
         auto_adjust: bool
             Adjust all OHLC automatically? Default is False
+        repair: bool
+            Detect currency unit 100x mixups and attempt repair
+            Default is False
+        keepna: bool
+            Keep NaN rows returned by Yahoo?
+            Default is False
         actions: bool
             Download dividend + stock splits data. Default is False
         threads: bool / int
             How many threads to use for mass downloading. Default is True
+        ignore_tz: bool
+            When combining from different timezones, ignore that part of datetime.
+            Default depends on interval. Intraday = False. Day+ = True.
         proxy: str
             Optional. Proxy server URL scheme. Default is None
         rounding: bool
             Optional. Round values to 2 decimal places?
         show_errors: bool
-            Optional. Doesn't print errors if True
+            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
+        if interval[1:] in ['m', 'h']:
+            # Intraday
+            ignore_tz = False
+        else:
+            ignore_tz = True
 
     # create ticker list
     tickers = tickers if isinstance(
@@ -81,7 +127,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True,
     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)
 
@@ -95,6 +141,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True,
     # reset shared._DFS
     shared._DFS = {}
     shared._ERRORS = {}
+    shared._TRACEBACKS = {}
 
     # download using threads
     if threads:
@@ -105,44 +152,71 @@ def download(tickers, start=None, end=None, actions=False, threads=True,
             _download_one_threaded(ticker, period=period, interval=interval,
                                    start=start, end=end, prepost=prepost,
                                    actions=actions, auto_adjust=auto_adjust,
-                                   back_adjust=back_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):
             data = _download_one(ticker, period=period, interval=interval,
                                  start=start, end=end, prepost=prepost,
                                  actions=actions, auto_adjust=auto_adjust,
-                                 back_adjust=back_adjust, proxy=proxy,
-                                 rounding=rounding, timeout=timeout)
-            shared._DFS[ticker.upper()] = data
+                                 back_adjust=back_adjust, repair=repair, keepna=keepna,
+                                 proxy=proxy,
+                                 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():
+            if (shared._DFS[tkr] is not None) and (shared._DFS[tkr].shape[0] > 0):
+                shared._DFS[tkr].index = shared._DFS[tkr].index.tz_localize(None)
 
     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,
+        data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
                           keys=shared._DFS.keys())
     except Exception:
         _realign_dfs()
-        data = _pd.concat(shared._DFS.values(), axis=1,
+        data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
                           keys=shared._DFS.keys())
 
     # switch names back to isins if applicable
@@ -180,28 +254,38 @@ def _realign_dfs():
 
 @_multitasking.task
 def _download_one_threaded(ticker, start=None, end=None,
-                           auto_adjust=False, back_adjust=False,
+                           auto_adjust=False, back_adjust=False, repair=False,
                            actions=False, progress=True, period="max",
                            interval="1d", prepost=False, proxy=None,
-                           rounding=False, timeout=None):
-
-    data = _download_one(ticker, start, end, auto_adjust, back_adjust,
+                           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,
-                         timeout)
-    shared._DFS[ticker.upper()] = data
+                         keepna, timeout, session)
     if progress:
         shared._PROGRESS_BAR.animate()
 
 
 def _download_one(ticker, start=None, end=None,
-                  auto_adjust=False, back_adjust=False,
+                  auto_adjust=False, back_adjust=False, repair=False,
                   actions=False, period="max", interval="1d",
                   prepost=False, proxy=None, rounding=False,
-                  timeout=None):
+                  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 Ticker(ticker).history(period=period, interval=interval,
-                                  start=start, end=end, prepost=prepost,
-                                  actions=actions, auto_adjust=auto_adjust,
-                                  back_adjust=back_adjust, proxy=proxy,
-                                  rounding=rounding, many=True,
-                                  timeout=timeout)
+    return data

yfinance/scrapers/analysis.py

@@ -0,0 +1,49 @@
+import pandas as pd
+
+from yfinance import utils
+from yfinance.data import TickerData
+from yfinance.exceptions import YFNotImplementedError
+
+
+class Analysis:
+
+    def __init__(self, data: TickerData, proxy=None):
+        self._data = data
+        self.proxy = proxy
+
+        self._earnings_trend = None
+        self._analyst_trend_details = None
+        self._analyst_price_target = None
+        self._rev_est = None
+        self._eps_est = None
+        self._already_scraped = False
+
+    @property
+    def earnings_trend(self) -> pd.DataFrame:
+        if self._earnings_trend is None:
+            raise YFNotImplementedError('earnings_trend')
+        return self._earnings_trend
+
+    @property
+    def analyst_trend_details(self) -> pd.DataFrame:
+        if self._analyst_trend_details is None:
+            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:
+            raise YFNotImplementedError('analyst_price_target')
+        return self._analyst_price_target
+
+    @property
+    def rev_est(self) -> pd.DataFrame:
+        if self._rev_est is None:
+            raise YFNotImplementedError('rev_est')
+        return self._rev_est
+
+    @property
+    def eps_est(self) -> pd.DataFrame:
+        if self._eps_est is None:
+            raise YFNotImplementedError('eps_est')
+        return self._eps_est

yfinance/scrapers/fundamentals.py

@@ -0,0 +1,147 @@
+import datetime
+import json
+
+import pandas as pd
+
+from yfinance import utils, const
+from yfinance.data import TickerData
+from yfinance.exceptions import YFinanceException, YFNotImplementedError
+
+
+class Fundamentals:
+
+    def __init__(self, data: TickerData, proxy=None):
+        self._data = data
+        self.proxy = proxy
+
+        self._earnings = None
+        self._financials = None
+        self._shares = None
+
+        self._financials_data = None
+        self._fin_data_quote = None
+        self._basics_already_scraped = False
+        self._financials = Financials(data)
+
+    @property
+    def financials(self) -> "Financials":
+        return self._financials
+
+    @property
+    def earnings(self) -> dict:
+        if self._earnings is None:
+            raise YFNotImplementedError('earnings')
+        return self._earnings
+
+    @property
+    def shares(self) -> pd.DataFrame:
+        if self._shares is None:
+            raise YFNotImplementedError('shares')
+        return self._shares
+
+
+class Financials:
+    def __init__(self, data: TickerData):
+        self._data = data
+        self._income_time_series = {}
+        self._balance_sheet_time_series = {}
+        self._cash_flow_time_series = {}
+
+    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)
+        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)
+        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)
+        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, 
+        # despite 'QuoteSummaryStore' containing valid data.
+
+        allowed_names = ["income", "balance-sheet", "cash-flow"]
+        allowed_timescales = ["yearly", "quarterly"]
+
+        if name not in allowed_names:
+            raise ValueError(f"Illegal argument: name must be one of: {allowed_names}")
+        if timescale not in allowed_timescales:
+            raise ValueError(f"Illegal argument: timescale must be one of: {allowed_names}")
+
+        try:
+            statement = self._create_financials_table(name, timescale, proxy)
+
+            if statement is not None:
+                return statement
+        except YFinanceException as 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):
+        if name == "income":
+            # Yahoo stores the 'income' table internally under 'financials' key
+            name = "financials"
+
+        keys = const.fundamentals_keys[name]
+
+        try:
+            return self.get_financials_time_series(timescale, keys, proxy)
+        except Exception as e:
+            pass
+
+    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 = 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 += 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
+        json_data = json.loads(json_str)
+        data_raw = json_data["timeseries"]["result"]
+        # data_raw = [v for v in data_raw if len(v) > 1] # Discard keys with no data
+        for d in data_raw:
+            del d["meta"]
+
+        # Now reshape data into a table:
+        # Step 1: get columns and index:
+        timestamps = set()
+        data_unpacked = {}
+        for x in data_raw:
+            for k in x.keys():
+                if k == "timestamp":
+                    timestamps.update(x[k])
+                else:
+                    data_unpacked[k] = x[k]
+        timestamps = sorted(list(timestamps))
+        dates = pd.to_datetime(timestamps, unit="s")
+        df = pd.DataFrame(columns=dates, index=list(data_unpacked.keys()))
+        for k, v in data_unpacked.items():
+            if df is None:
+                df = pd.DataFrame(columns=dates, index=[k])
+            df.loc[k] = {pd.Timestamp(x["asOfDate"]): x["reportedValue"]["raw"] for x in v}
+
+        df.index = df.index.str.replace("^" + timescale, "", regex=True)
+
+        # Reorder table to match order on Yahoo website
+        df = df.reindex([k for k in keys if k in df.index])
+        df = df[sorted(df.columns, reverse=True)]
+
+        return df

yfinance/scrapers/holders.py

@@ -0,0 +1,67 @@
+import pandas as pd
+
+from yfinance.data import TickerData
+
+
+class Holders:
+    _SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
+
+    def __init__(self, data: TickerData, proxy=None):
+        self._data = data
+        self.proxy = proxy
+
+        self._major = None
+        self._institutional = None
+        self._mutualfund = None
+
+    @property
+    def major(self) -> pd.DataFrame:
+        if self._major is None:
+            self._scrape(self.proxy)
+        return self._major
+
+    @property
+    def institutional(self) -> pd.DataFrame:
+        if self._institutional is None:
+            self._scrape(self.proxy)
+        return self._institutional
+
+    @property
+    def mutualfund(self) -> pd.DataFrame:
+        if self._mutualfund is None:
+            self._scrape(self.proxy)
+        return self._mutualfund
+
+    def _scrape(self, proxy):
+        ticker_url = f"{self._SCRAPE_URL_}/{self._data.ticker}"
+        try:
+            resp = self._data.cache_get(ticker_url + '/holders', proxy=proxy)
+            holders = pd.read_html(resp.text)
+        except Exception:
+            holders = []
+
+        if len(holders) >= 3:
+            self._major = holders[0]
+            self._institutional = holders[1]
+            self._mutualfund = holders[2]
+        elif len(holders) >= 2:
+            self._major = holders[0]
+            self._institutional = holders[1]
+        elif len(holders) >= 1:
+            self._major = holders[0]
+
+        if self._institutional is not None:
+            if 'Date Reported' in self._institutional:
+                self._institutional['Date Reported'] = pd.to_datetime(
+                    self._institutional['Date Reported'])
+            if '% Out' in self._institutional:
+                self._institutional['% Out'] = self._institutional[
+                                                   '% Out'].str.replace('%', '').astype(float) / 100
+
+        if self._mutualfund is not None:
+            if 'Date Reported' in self._mutualfund:
+                self._mutualfund['Date Reported'] = pd.to_datetime(
+                    self._mutualfund['Date Reported'])
+            if '% Out' in self._mutualfund:
+                self._mutualfund['% Out'] = self._mutualfund[
+                                                '% Out'].str.replace('%', '').astype(float) / 100

yfinance/scrapers/quote.py

@@ -0,0 +1,695 @@
+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"]})
+info_retired_keys_price.update({"fiftyTwoWeekLow", "fiftyTwoWeekHigh", "fiftyTwoWeekChange", "52WeekChange", "fiftyDayAverage", "twoHundredDayAverage"})
+info_retired_keys_price.update({"averageDailyVolume10Day", "averageVolume10days", "averageVolume"})
+info_retired_keys_exchange = {"currency", "exchange", "exchangeTimezoneName", "exchangeTimezoneShortName", "quoteType"}
+info_retired_keys_marketCap = {"marketCap"}
+info_retired_keys_symbol = {"symbol"}
+info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_retired_keys_marketCap | info_retired_keys_symbol
+
+
+_BASIC_URL_ = "https://query2.finance.yahoo.com/v6/finance/quoteSummary"
+
+
+class InfoDictWrapper(MutableMapping):
+    """ Simple wrapper around info dict, intercepting 'gets' to 
+    print how-to-migrate messages for specific keys. Requires
+    override dict API"""
+
+    def __init__(self, info):
+        self.info = info
+
+    def keys(self):
+        return self.info.keys()
+
+    def __str__(self):
+        return self.info.__str__()
+
+    def __repr__(self):
+        return self.info.__repr__()
+
+    def __contains__(self, k):
+        return k in self.info.keys()
+
+    def __getitem__(self, k):
+        if k in info_retired_keys_price:
+            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:
+            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:
+            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:
+            warnings.warn(f"Symbol removed from info (key='{k}'). You know this already", DeprecationWarning)
+            return None
+        return self.info[self._keytransform(k)]
+
+    def __setitem__(self, k, value):
+        self.info[self._keytransform(k)] = value
+
+    def __delitem__(self, k):
+        del self.info[self._keytransform(k)]
+
+    def __iter__(self):
+        return iter(self.info)
+    
+    def __len__(self):
+        return len(self.info)
+
+    def _keytransform(self, k):
+        return k
+
+
+class 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:
+
+    def __init__(self, data: TickerData, proxy=None):
+        self._data = data
+        self.proxy = proxy
+
+        self._info = None
+        self._retired_info = None
+        self._sustainability = None
+        self._recommendations = None
+        self._calendar = None
+
+        self._already_scraped = False
+        self._already_fetched = False
+        self._already_fetched_complementary = False
+
+    @property
+    def info(self) -> dict:
+        if self._info is None:
+            self._fetch(self.proxy)
+            self._fetch_complementary(self.proxy)
+
+        return self._info
+
+    @property
+    def sustainability(self) -> pd.DataFrame:
+        if self._sustainability is None:
+            raise YFNotImplementedError('sustainability')
+        return self._sustainability
+
+    @property
+    def recommendations(self) -> pd.DataFrame:
+        if self._recommendations is None:
+            raise YFNotImplementedError('recommendations')
+        return self._recommendations
+
+    @property
+    def calendar(self) -> pd.DataFrame:
+        if self._calendar is None:
+            raise YFNotImplementedError('calendar')
+        return self._calendar
+
+    def _fetch(self, proxy):
+        if self._already_fetched:
+            return
+        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'
+
+        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:
+                v2 = v
+            return v2
+        for k, v in query1_info.items():
+            query1_info[k] = _format(k, v)
+        self._info = query1_info
+
+    def _fetch_complementary(self, proxy):
+        if self._already_fetched_complementary:
+            return
+        self._already_fetched_complementary = True
+
+        # self._scrape(proxy)  # decrypt broken
+        self._fetch(proxy)
+        if self._info is None:
+            return
+
+        # Complementary key-statistics. For now just want 'trailing PEG ratio'
+        keys = {"trailingPegRatio"}
+        if keys:
+            # Simplified the original scrape code for key-statistics. Very expensive for fetching
+            # just one value, best if scraping most/all:
+            #
+            # p = _re.compile(r'root\.App\.main = (.*);')
+            # url = 'https://finance.yahoo.com/quote/{}/key-statistics?p={}'.format(self._ticker.ticker, self._ticker.ticker)
+            # try:
+            #     r = session.get(url, headers=utils.user_agent_headers)
+            #     data = _json.loads(p.findall(r.text)[0])
+            #     key_stats = data['context']['dispatcher']['stores']['QuoteTimeSeriesStore']["timeSeries"]
+            #     for k in keys:
+            #         if k not in key_stats or len(key_stats[k])==0:
+            #             # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+            #             v = None
+            #         else:
+            #             # Select most recent (last) raw value in list:
+            #             v = key_stats[k][-1]["reportedValue"]["raw"]
+            #         self._info[k] = v
+            # except Exception:
+            #     raise
+            #     pass
+            #
+            # For just one/few variable is faster to query directly:
+            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
+            start = pd.Timestamp.utcnow().floor("D") - datetime.timedelta(days=365 // 2)
+            start = int(start.timestamp())
+            end = pd.Timestamp.utcnow().ceil("D")
+            end = int(end.timestamp())
+            url += f"&period1={start}&period2={end}"
+
+            json_str = self._data.cache_get(url=url, proxy=proxy).text
+            json_data = json.loads(json_str)
+            try:
+                key_stats = json_data["timeseries"]["result"][0]
+                if k not in key_stats:
+                    # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+                    v = None
+                else:
+                    # Select most recent (last) raw value in list:
+                    v = key_stats[k][-1]["reportedValue"]["raw"]
+            except Exception:
+                v = None
+            self._info[k] = v

yfinance/scrapers/yahoo-keys.txt

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

yfinance/shared.py

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

yfinance/ticker.py

@@ -21,50 +21,41 @@
 
 from __future__ import print_function
 
-# import time as _time
 import datetime as _datetime
-import requests as _requests
-import pandas as _pd
-# import numpy as _np
-
-# import json as _json
-# import re as _re
 from collections import namedtuple as _namedtuple
 
-from . import utils
+import pandas as _pd
+
 from .base import TickerBase
 
 
 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}"
 
-        # setup proxy in requests format
-        if proxy is not None:
-            if isinstance(proxy, dict) and "https" in proxy:
-                proxy = proxy["https"]
-            proxy = {"https": proxy}
-
-        r = _requests.get(
-            url=url,
-            proxies=proxy,
-            headers=utils.user_agent_headers
-        ).json()
+        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=[
@@ -97,15 +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']
         })
 
     # ------------------------
@@ -115,39 +106,47 @@ class Ticker(TickerBase):
         return self.get_isin()
 
     @property
-    def major_holders(self):
+    def major_holders(self) -> _pd.DataFrame:
         return self.get_major_holders()
 
     @property
-    def institutional_holders(self):
+    def institutional_holders(self) -> _pd.DataFrame:
         return self.get_institutional_holders()
 
     @property
-    def mutualfund_holders(self):
+    def mutualfund_holders(self) -> _pd.DataFrame:
         return self.get_mutualfund_holders()
 
     @property
-    def dividends(self):
+    def dividends(self) -> _pd.Series:
         return self.get_dividends()
 
     @property
-    def splits(self):
+    def capital_gains(self):
+        return self.get_capital_gains()
+
+    @property
+    def splits(self) -> _pd.Series:
         return self.get_splits()
 
     @property
-    def actions(self):
+    def actions(self) -> _pd.DataFrame:
         return self.get_actions()
 
     @property
-    def shares(self):
+    def shares(self) -> _pd.DataFrame :
         return self.get_shares()
 
     @property
-    def info(self):
+    def info(self) -> dict:
         return self.get_info()
 
     @property
-    def calendar(self):
+    def fast_info(self):
+        return self.get_fast_info()
+
+    @property
+    def calendar(self) -> _pd.DataFrame:
         return self.get_calendar()
 
     @property
@@ -155,51 +154,87 @@ class Ticker(TickerBase):
         return self.get_recommendations()
 
     @property
-    def earnings(self):
+    def earnings(self) -> _pd.DataFrame:
         return self.get_earnings()
 
     @property
-    def quarterly_earnings(self):
+    def quarterly_earnings(self) -> _pd.DataFrame:
         return self.get_earnings(freq='quarterly')
 
     @property
-    def financials(self):
-        return self.get_financials()
+    def income_stmt(self) -> _pd.DataFrame:
+        return self.get_income_stmt(pretty=True)
 
     @property
-    def quarterly_financials(self):
-        return self.get_financials(freq='quarterly')
+    def quarterly_income_stmt(self) -> _pd.DataFrame:
+        return self.get_income_stmt(pretty=True, freq='quarterly')
 
     @property
-    def balance_sheet(self):
-        return self.get_balancesheet()
+    def incomestmt(self) -> _pd.DataFrame:
+        return self.income_stmt
 
     @property
-    def quarterly_balance_sheet(self):
-        return self.get_balancesheet(freq='quarterly')
+    def quarterly_incomestmt(self) -> _pd.DataFrame:
+        return self.quarterly_income_stmt
 
     @property
-    def balancesheet(self):
-        return self.get_balancesheet()
+    def financials(self) -> _pd.DataFrame:
+        return self.income_stmt
 
     @property
-    def quarterly_balancesheet(self):
-        return self.get_balancesheet(freq='quarterly')
+    def quarterly_financials(self) -> _pd.DataFrame:
+        return self.quarterly_income_stmt
 
     @property
-    def cashflow(self):
-        return self.get_cashflow()
+    def balance_sheet(self) -> _pd.DataFrame:
+        return self.get_balance_sheet(pretty=True)
 
     @property
-    def quarterly_cashflow(self):
-        return self.get_cashflow(freq='quarterly')
+    def quarterly_balance_sheet(self) -> _pd.DataFrame:
+        return self.get_balance_sheet(pretty=True, freq='quarterly')
 
     @property
-    def sustainability(self):
+    def balancesheet(self) -> _pd.DataFrame:
+        return self.balance_sheet
+
+    @property
+    def quarterly_balancesheet(self) -> _pd.DataFrame:
+        return self.quarterly_balance_sheet
+
+    @property
+    def cash_flow(self) -> _pd.DataFrame:
+        return self.get_cash_flow(pretty=True, freq="yearly")
+
+    @property
+    def quarterly_cash_flow(self) -> _pd.DataFrame:
+        return self.get_cash_flow(pretty=True, freq='quarterly')
+
+    @property
+    def cashflow(self) -> _pd.DataFrame:
+        return self.cash_flow
+
+    @property
+    def quarterly_cashflow(self) -> _pd.DataFrame:
+        return self.quarterly_cash_flow
+
+    @property
+    def recommendations_summary(self):
+        return self.get_recommendations_summary()
+
+    @property
+    def analyst_price_target(self) -> _pd.DataFrame:
+        return self.get_analyst_price_target()
+
+    @property
+    def revenue_forecasts(self) -> _pd.DataFrame:
+        return self.get_rev_forecast()
+
+    @property
+    def sustainability(self) -> _pd.DataFrame:
         return self.get_sustainability()
 
     @property
-    def options(self):
+    def options(self) -> tuple:
         if not self._expirations:
             self._download_options()
         return tuple(self._expirations.keys())
@@ -209,5 +244,21 @@ class Ticker(TickerBase):
         return self.get_news()
 
     @property
-    def analysis(self):
-        return self.get_analysis()
+    def trend_details(self) -> _pd.DataFrame:
+        return self.get_trend_details()
+
+    @property
+    def earnings_trend(self) -> _pd.DataFrame:
+        return self.get_earnings_trend()
+
+    @property
+    def earnings_dates(self) -> _pd.DataFrame:
+        return self.get_earnings_dates()
+
+    @property
+    def earnings_forecasts(self) -> _pd.DataFrame:
+        return self.get_earnings_forecast()
+
+    @property
+    def history_metadata(self) -> dict:
+        return self.get_history_metadata()

yfinance/tickers.py

@@ -22,51 +22,53 @@
 from __future__ import print_function
 
 from . import Ticker, multi
+
+
 # from collections import namedtuple as _namedtuple
 
 
-class Tickers():
+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):
+    def __init__(self, tickers, session=None):
         tickers = tickers if isinstance(
             tickers, list) else tickers.replace(',', ' ').split()
         self.symbols = [ticker.upper() for ticker in tickers]
-        ticker_objects = {}
+        self.tickers = {ticker: Ticker(ticker, session=session) for ticker in self.symbols}
 
-        for ticker in self.symbols:
-            ticker_objects[ticker] = Ticker(ticker)
-
-        self.tickers = ticker_objects
         # self.tickers = _namedtuple(
         #     "Tickers", ticker_objects.keys(), rename=True
         # )(*ticker_objects.values())
 
     def history(self, period="1mo", interval="1d",
                 start=None, end=None, prepost=False,
-                actions=True, auto_adjust=True, proxy=None,
+                actions=True, auto_adjust=True, repair=False,
+                proxy=None,
                 threads=True, group_by='column', progress=True,
-                timeout=None, **kwargs):
+                timeout=10, **kwargs):
 
         return self.download(
-                period, interval,
-                start, end, prepost,
-                actions, auto_adjust, proxy,
-                threads, group_by, progress,
-                timeout, **kwargs)
+            period, interval,
+            start, end, prepost,
+            actions, auto_adjust, repair, 
+            proxy,
+            threads, group_by, progress,
+            timeout, **kwargs)
 
     def download(self, period="1mo", interval="1d",
                  start=None, end=None, prepost=False,
-                 actions=True, auto_adjust=True, proxy=None,
+                 actions=True, auto_adjust=True, repair=False, 
+                 proxy=None,
                  threads=True, group_by='column', progress=True,
-                 timeout=None, **kwargs):
+                 timeout=10, **kwargs):
 
         data = multi.download(self.symbols,
                               start=start, end=end,
                               actions=actions,
                               auto_adjust=auto_adjust,
+                              repair=repair,
                               period=period,
                               interval=interval,
                               prepost=prepost,
@@ -85,3 +87,6 @@ class Tickers():
             data.sort_index(level=0, axis=1, inplace=True)
 
         return data
+
+    def news(self):
+        return {ticker: [item for item in Ticker(ticker).news] for ticker in self.symbols}

yfinance/version.py

@@ -1 +1 @@
-version = "0.1.70"
+version = "0.2.31"