Fix '_set_cookie_strategy', was double-toggling. + more logging

Add cookie & crumb to requests

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,42 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-# READ BEFORE POSTING
-
-### 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 spelling ticker *exactly* same as Yahoo?
-
-Visit `finance.yahoo.com` and confim they have your data. Maybe your ticker was delisted.
-
-### Are you spamming Yahoo?
-
-Yahoo Finance free service has limit on query rate dependent on request - roughly 500/minute for prices, 10/minute for info. Them delaying or blocking your spam is not a bug.
-
-### Still think it's a bug?
-
-Delete this default message and submit your bug report here, providing the following as best you can:
-
-- Simple code that reproduces your problem
-- Error message, with traceback if shown
-- Info about your system:
-  - yfinance version
-  - operating system

.github/ISSUE_TEMPLATE/bug_report.yaml

@@ -0,0 +1,95 @@
+name: Bug report
+description: Report a bug in our project
+labels: ["bug"]
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        # IMPORTANT - Read and follow these instructions carefully. Help us help you.
+
+        ### Does issue already exist?
+
+        Use the search tool. Don't annoy everyone by duplicating existing Issues.
+
+        ### Are you up-to-date?
+
+        Upgrade to the latest version and confirm the issue/bug is still there.
+
+        `$ pip install yfinance --upgrade --no-cache-dir`
+
+        Confirm by running:
+
+        `import yfinance as yf ; print(yf.__version__)`
+
+        and comparing against [PIP](https://pypi.org/project/yfinance/#history).
+
+        ### Does Yahoo actually have the data?
+
+        Are you spelling symbol *exactly* same as Yahoo?
+
+        Then visit `finance.yahoo.com` and confirm they have the data you want. Maybe your symbol was delisted, or your expectations of `yfinance` are wrong.
+
+        ### Are you spamming Yahoo?
+
+        Yahoo Finance free service has rate-limiting https://github.com/ranaroussi/yfinance/discussions/1513. Once limit hit, Yahoo can delay, block, or return bad data -> not a `yfinance` bug.
+        
+  - type: markdown
+    attributes:
+      value: |
+        ---
+        ## Still think it's a bug?
+
+        Provide the following as best you can:
+
+  - type: textarea
+    id: summary
+    attributes:
+      label: "Describe bug"
+    validations:
+      required: true
+
+  - type: textarea
+    id: code
+    attributes:
+      label: "Simple code that reproduces your problem"
+      description: "Provide a snippet of code that we can copy-paste-run. Wrap code in Python Markdown code blocks for proper formatting (```` ```python ... ``` ````)."
+    validations:
+      required: true
+
+  - type: textarea
+    id: debug-log
+    attributes:
+      label: "Debug log"
+      description: "Run code with debug logging enabled and post the full output. Instructions: https://github.com/ranaroussi/yfinance/tree/main#logging"
+    validations:
+      required: true
+
+  - type: textarea
+    id: bad-data-proof
+    attributes:
+      label: "Bad data proof"
+      description: "If you think `yfinance` returning bad data, provide your proof here."
+    validations:
+      required: false
+
+  - type: input
+    id: version-yfinance
+    attributes:
+      label: "`yfinance` version"
+    validations:
+      required: true
+
+  - type: input
+    id: version-python
+    attributes:
+      label: "Python version"
+    validations:
+      required: false
+
+  - type: input
+    id: os
+    attributes:
+      label: "Operating system"
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/feature_request.md

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

.github/workflows/python-publish.yml

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

.gitignore

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

CHANGELOG.rst

@@ -1,6 +1,127 @@
 Change Log
 ===========
 
+0.2.32
+------
+Add cookie & crumb to requests #1657
+
+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

CODE_OF_CONDUCT.md

@@ -0,0 +1,15 @@
+# Code of Conduct
+
+## Submitting a new issue
+
+* Search through existing Issues and Discussions, in case your issue already exists and a solution is being developed.
+* Ensure you read & follow the template form.
+* Consider you may be the best person to investigate and fix.
+
+## Contributing to an existing Issue
+
+* Read the entire thread.
+* Ensure your comment is contributing something new/useful. Remember you can simply react to other comments.
+* Be concise:
+  - use the formatting options
+  - if replying to a big comment, instead of quoting it, link to it

README.md

@@ -42,13 +42,6 @@ Yahoo! finance API is intended for personal use only.**
 
 ---
 
-## What's new in version 0.2
-
-- Optimised web scraping
-- All 3 financials tables now match website so expect keys to change. If you really want old tables, use [`Ticker.get_[income_stmt|balance_sheet|cashflow](legacy=True, ...)`](https://github.com/ranaroussi/yfinance/blob/85783da515761a145411d742c2a8a3c1517264b0/yfinance/base.py#L968)
-- price data improvements: fix bug NaN rows with dividend; new repair feature for missing or 100x prices `download(repair=True)`; new attribute `Ticker.history_metadata`
-[See release notes for full list of changes](https://github.com/ranaroussi/yfinance/releases/tag/0.2.1)
-
 ## Quick Start
 
 ### The Ticker module
@@ -60,33 +53,23 @@ import yfinance as yf
 
 msft = yf.Ticker("MSFT")
 
-# fast access to subset of stock info
-msft.basic_info
-# slow access to all stock info
+# get all stock info
 msft.info
 
 # get historical market data
-hist = msft.history(period="max")
+hist = msft.history(period="1mo")
 
 # show meta information about the history (requires history() to be called first)
 msft.history_metadata
 
 # show actions (dividends, splits, capital gains)
 msft.actions
-
-# show dividends
 msft.dividends
-
-# show splits
 msft.splits
-
-
-# show capital gains (for mutual funds & etfs)
-msft.capital_gains
+msft.capital_gains  # only for mutual funds & etfs
 
 # show share count
-msft.shares
-msft.get_shares_full()
+msft.get_shares_full(start="2022-01-01", end=None)
 
 # show financials:
 # - income statement
@@ -100,34 +83,11 @@ msft.cashflow
 msft.quarterly_cashflow
 # see `Ticker.get_income_stmt()` for more options
 
-# show major holders
+# show holders
 msft.major_holders
-
-# show institutional holders
 msft.institutional_holders
-
-# show mutualfund holders
 msft.mutualfund_holders
 
-# show earnings
-msft.earnings
-msft.quarterly_earnings
-
-# show sustainability
-msft.sustainability
-
-# show analysts recommendations
-msft.recommendations
-msft.recommendations_summary
-# show analysts other work
-msft.analyst_price_target
-msft.revenue_forecasts
-msft.earnings_forecasts
-msft.earnings_trend
-
-# show next event (earnings, etc)
-msft.calendar
-
 # Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default. 
 # Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.
 msft.earnings_dates
@@ -165,18 +125,7 @@ msft.option_chain(..., proxy="PROXY_SERVER")
 ...
 ```
 
-To use a custom `requests` session (for example to cache calls to the
-API or customize the `User-agent` header), pass a `session=` argument to
-the Ticker constructor.
-
-```python
-import requests_cache
-session = requests_cache.CachedSession('yfinance.cache')
-session.headers['User-agent'] = 'my-program/1.0'
-ticker = yf.Ticker('msft', session=session)
-# The scraped response will be stored in the cache
-ticker.actions
-```
+### Multiple tickers
 
 To initialize multiple `Ticker` objects, use
 
@@ -191,69 +140,48 @@ tickers.tickers['AAPL'].history(period="1mo")
 tickers.tickers['GOOG'].actions
 ```
 
-### Fetching data for multiple tickers
+To download price history into one table:
 
 ```python
 import yfinance as yf
-data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
+data = yf.download("SPY AAPL", period="1mo")
 ```
 
-I've also added some options to make life easier :)
+#### `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.
 
 ```python
-data = yf.download(  # or pdr.get_data_yahoo(...
-        # tickers list or string as well
-        tickers = "SPY AAPL MSFT",
-
-        # use "period" instead of start/end
-        # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
-        # (optional, default is '1mo')
-        period = "ytd",
-
-        # fetch data by interval (including intraday if period < 60 days)
-        # valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
-        # (optional, default is '1d')
-        interval = "5d",
-
-        # Whether to ignore timezone when aligning ticker data from 
-        # different timezones. Default is False.
-        ignore_tz = False,
-
-        # group by ticker (to access via data['SPY'])
-        # (optional, default is 'column')
-        group_by = 'ticker',
-
-        # adjust all OHLC automatically
-        # (optional, default is False)
-        auto_adjust = True,
-
-        # attempt repair of missing data or currency mixups e.g. $/cents
-        repair = False,
-
-        # download pre/post regular market hours data
-        # (optional, default is False)
-        prepost = True,
-
-        # use threads for mass downloading? (True/False/Integer)
-        # (optional, default is True)
-        threads = True,
-
-        # proxy URL scheme use use when downloading?
-        # (optional, default is None)
-        proxy = None
-    )
+import requests_cache
+session = requests_cache.CachedSession('yfinance.cache')
+session.headers['User-agent'] = 'my-program/1.0'
+ticker = yf.Ticker('msft', session=session)
+# The scraped response will be stored in the cache
+ticker.actions
 ```
 
-### 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()`:
+Combine a `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-limiter/blocker that can corrupt data.
 ```python
-import yfinance as yf
-yf.set_tz_cache_location("custom/cache/location")
-...
+from requests import Session
+from requests_cache import CacheMixin, SQLiteCache
+from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
+from pyrate_limiter import Duration, RequestRate, Limiter
+class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
+    pass
+
+session = CachedLimiterSession(
+    limiter=Limiter(RequestRate(2, Duration.SECOND*5)),  # max 2 requests per 5 seconds
+    bucket_class=MemoryQueueBucket,
+    backend=SQLiteCache("yfinance.cache"),
+)
 ```
 
 ### Managing Multi-Level Columns
@@ -271,9 +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()`
@@ -290,6 +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
@@ -300,6 +238,11 @@ Install `yfinance` using `pip`:
 $ pip install yfinance --upgrade --no-cache-dir
 ```
 
+Test new features by installing betas, provide feedback in [corresponding Discussion](https://github.com/ranaroussi/yfinance/discussions):
+``` {.sourceCode .bash}
+$ pip install yfinance --upgrade --no-cache-dir --pre
+```
+
 To install `yfinance` using `conda`, see
 [this](https://anaconda.org/ranaroussi/yfinance).
 
@@ -308,20 +251,24 @@ To install `yfinance` using `conda`, see
 -   [Python](https://www.python.org) \>= 2.7, 3.4+
 -   [Pandas](https://github.com/pydata/pandas) \>= 1.3.0
 -   [Numpy](http://www.numpy.org) \>= 1.16.5
--   [requests](http://docs.python-requests.org/en/master) \>= 2.26
+-   [requests](http://docs.python-requests.org/en/master) \>= 2.31
 -   [lxml](https://pypi.org/project/lxml) \>= 4.9.1
 -   [appdirs](https://pypi.org/project/appdirs) \>= 1.4.4
 -   [pytz](https://pypi.org/project/pytz) \>=2022.5
 -   [frozendict](https://pypi.org/project/frozendict) \>= 2.3.4
 -   [beautifulsoup4](https://pypi.org/project/beautifulsoup4) \>= 4.11.1
 -   [html5lib](https://pypi.org/project/html5lib) \>= 1.1
--   [cryptography](https://pypi.org/project/cryptography) \>= 3.3.2
+-   [peewee](https://pypi.org/project/peewee)  \>= 3.16.2
 
-### Optional (if you want to use `pandas_datareader`)
+#### Optional (if you want to use `pandas_datareader`)
 
 -   [pandas\_datareader](https://github.com/pydata/pandas-datareader)
     \>= 0.4.0
 
+## Developers: want to contribute?
+
+`yfinance` relies on community to investigate bugs and contribute code. Developer guide: https://github.com/ranaroussi/yfinance/discussions/1084
+
 ---
 
 ### Legal Stuff

meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.2.9" %}
+{% set version = "0.2.32" %}
 
 package:
   name: "{{ name|lower }}"
@@ -18,7 +18,7 @@ requirements:
   host:
     - pandas >=1.3.0
     - numpy >=1.16.5
-    - requests >=2.26
+    - requests >=2.31
     - multitasking >=0.0.7
     - lxml >=4.9.1
     - appdirs >=1.4.4
@@ -26,15 +26,15 @@ requirements:
     - frozendict >=2.3.4
     - beautifulsoup4 >=4.11.1
     - html5lib >=1.1
+    - peewee >=3.16.2
     # - pycryptodome >=3.6.6
-    - cryptography >=3.3.2
     - pip
     - python
 
   run:
     - pandas >=1.3.0
     - numpy >=1.16.5
-    - requests >=2.26
+    - requests >=2.31
     - multitasking >=0.0.7
     - lxml >=4.9.1
     - appdirs >=1.4.4
@@ -42,8 +42,8 @@ requirements:
     - frozendict >=2.3.4
     - beautifulsoup4 >=4.11.1
     - html5lib >=1.1
+    - peewee >=3.16.2
     # - pycryptodome >=3.6.6
-    - cryptography >=3.3.2
     - python
 
 test:

requirements.txt

@@ -1,6 +1,6 @@
 pandas>=1.3.0
 numpy>=1.16.5
-requests>=2.26
+requests>=2.31
 multitasking>=0.0.7
 lxml>=4.9.1
 appdirs>=1.4.4
@@ -8,4 +8,4 @@ pytz>=2022.5
 frozendict>=2.3.4
 beautifulsoup4>=4.11.1
 html5lib>=1.1
-cryptography>=3.3.2
+peewee>=3.16.2

setup.py

@@ -39,7 +39,7 @@ setup(
         'License :: OSI Approved :: Apache Software License',
         # 'Development Status :: 3 - Alpha',
         'Development Status :: 4 - Beta',
-        #'Development Status :: 5 - Production/Stable',
+        # 'Development Status :: 5 - Production/Stable',
 
 
         'Operating System :: OS Independent',
@@ -60,12 +60,11 @@ setup(
     keywords='pandas, yahoo finance, pandas datareader',
     packages=find_packages(exclude=['contrib', 'docs', 'tests', 'examples']),
     install_requires=['pandas>=1.3.0', 'numpy>=1.16.5',
-                      'requests>=2.26', 'multitasking>=0.0.7',
+                      'requests>=2.31', 'multitasking>=0.0.7',
                       'lxml>=4.9.1', 'appdirs>=1.4.4', 'pytz>=2022.5',
-                      'frozendict>=2.3.4', 
-                      # 'pycryptodome>=3.6.6',
-                      'cryptography>=3.3.2',
+                      'frozendict>=2.3.4', 'peewee>=3.16.2',
                       'beautifulsoup4>=4.11.1', 'html5lib>=1.1'],
+    # Note: Pandas.read_html() needs html5lib & beautifulsoup4
     entry_points={
         'console_scripts': [
             'sample=sample:main',

test_yfinance.py

@@ -1,70 +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.shares
-            ticker.info
-            ticker.calendar
-            ticker.recommendations
-            ticker.earnings
-            ticker.quarterly_earnings
-            ticker.income_stmt
-            ticker.quarterly_income_stmt
-            ticker.balance_sheet
-            ticker.quarterly_balance_sheet
-            ticker.cashflow
-            ticker.quarterly_cashflow
-            ticker.recommendations_summary
-            ticker.analyst_price_target
-            ticker.revenue_forecasts
-            ticker.sustainability
-            ticker.options
-            ticker.news
-            ticker.earnings_trend
-            ticker.earnings_dates
-            ticker.earnings_forecasts
-
-    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/context.py

@@ -1,5 +1,7 @@
 # -*- coding: utf-8 -*-
 
+import appdirs as _ad
+import datetime as _dt
 import sys
 import os
 _parent_dp = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
@@ -7,3 +9,39 @@ _src_dp = _parent_dp
 sys.path.insert(0, _src_dp)
 
 import yfinance
+
+
+# Optional: see the exact requests that are made during tests:
+# import logging
+# logging.basicConfig(level=logging.DEBUG)
+
+
+# Use adjacent cache folder for testing, delete if already exists and older than today
+testing_cache_dirpath = os.path.join(_ad.user_cache_dir(), "py-yfinance-testing")
+yfinance.set_tz_cache_location(testing_cache_dirpath)
+if os.path.isdir(testing_cache_dirpath):
+    mtime = _dt.datetime.fromtimestamp(os.path.getmtime(testing_cache_dirpath))
+    if mtime.date() < _dt.date.today():
+        import shutil
+        shutil.rmtree(testing_cache_dirpath)
+
+
+# Setup a session to rate-limit and cache persistently:
+from requests import Session
+from requests_cache import CacheMixin, SQLiteCache
+from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
+class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
+    pass
+from pyrate_limiter import Duration, RequestRate, Limiter
+history_rate = RequestRate(1, Duration.SECOND*2)
+limiter = Limiter(history_rate)
+cache_fp = os.path.join(testing_cache_dirpath, "unittests-cache")
+session_gbl = CachedLimiterSession(
+    limiter=limiter,
+    bucket_class=MemoryQueueBucket,
+    backend=SQLiteCache(cache_fp, expire_after=_dt.timedelta(hours=1)),
+)
+# Use this instead if only want rate-limiting:
+# from requests_ratelimiter import LimiterSession
+# session_gbl = LimiterSession(limiter=limiter)
+

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

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

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

@@ -0,0 +1,23 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-04-14 00:00:00+09:00,4126,4130,4055,4129,4129,7459400,0,0
+2023-04-13 00:00:00+09:00,4064,4099,4026,4081,4081,5160200,0,0
+2023-04-12 00:00:00+09:00,3968,4084,3966,4064,4064,6372000,0,0
+2023-04-11 00:00:00+09:00,3990,4019,3954,3960,3960,6476500,0,0
+2023-04-10 00:00:00+09:00,3996,4009,3949,3964,3964,3485200,0,0
+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

@@ -9,28 +9,65 @@ Specific test class:
 
 """
 import pandas as pd
-import numpy as np
 
 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
+import re
+from urllib.parse import urlparse, parse_qs, urlencode, urlunparse
 
-# Set this to see the exact requests that are made during tests
-DEBUG_LOG_REQUESTS = False
-
-if DEBUG_LOG_REQUESTS:
-    import logging
-
-    logging.basicConfig(level=logging.DEBUG)
+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 = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
+
+        cls.proxy = None
 
     @classmethod
     def tearDownClass(cls):
@@ -41,93 +78,180 @@ class TestTicker(unittest.TestCase):
         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)
+            yf.cache.get_tz_cache().store(tkr, None)
 
             # Test:
             dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(debug_mode=False, proxy=None, timeout=None)
+            tz = dat._get_ticker_tz(proxy=None, timeout=None)
 
             self.assertIsNotNone(tz)
 
     def test_badTicker(self):
         # Check yfinance doesn't die when ticker delisted
 
-        tkr = "AM2Z.TA"
+        tkr = "DJI"  # typo of "^DJI"
         dat = yf.Ticker(tkr, session=self.session)
+
         dat.history(period="1wk")
         dat.history(start="2022-01-01")
         dat.history(start="2022-01-01", end="2022-03-01")
-        yf.download([tkr], period="1wk")
-        dat.isin
-        dat.major_holders
-        dat.institutional_holders
-        dat.mutualfund_holders
-        dat.dividends
-        dat.splits
-        dat.actions
-        dat.shares
-        dat.get_shares_full()
-        dat.info
-        dat.calendar
-        dat.recommendations
-        dat.earnings
-        dat.quarterly_earnings
-        dat.income_stmt
-        dat.quarterly_income_stmt
-        dat.balance_sheet
-        dat.quarterly_balance_sheet
-        dat.cashflow
-        dat.quarterly_cashflow
-        dat.recommendations_summary
-        dat.analyst_price_target
-        dat.revenue_forecasts
-        dat.sustainability
-        dat.options
-        dat.news
-        dat.earnings_trend
-        dat.earnings_dates
-        dat.earnings_forecasts
+        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.isin
-        dat.major_holders
-        dat.institutional_holders
-        dat.mutualfund_holders
-        dat.dividends
-        dat.splits
-        dat.actions
-        dat.shares
-        dat.get_shares_full()
-        dat.info
-        dat.calendar
-        dat.recommendations
-        dat.earnings
-        dat.quarterly_earnings
-        dat.income_stmt
-        dat.quarterly_income_stmt
-        dat.balance_sheet
-        dat.quarterly_balance_sheet
-        dat.cashflow
-        dat.quarterly_cashflow
-        dat.recommendations_summary
-        dat.analyst_price_target
-        dat.revenue_forecasts
-        dat.sustainability
-        dat.options
-        dat.news
-        dat.earnings_trend
-        dat.earnings_dates
-        dat.earnings_forecasts
+        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)
 
-        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")
+        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):
@@ -135,7 +259,7 @@ class TestTickerHistory(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -144,35 +268,60 @@ class TestTickerHistory(unittest.TestCase):
 
     def setUp(self):
         # use a ticker that has dividends
-        self.ticker = yf.Ticker("IBM", session=self.session)
+        self.symbol = "IBM"
+        self.ticker = yf.Ticker(self.symbol, session=self.session)
+
+        self.symbols = ["AMZN", "MSFT", "NVDA"]
 
     def tearDown(self):
         self.ticker = None
 
     def test_history(self):
-        with self.assertRaises(RuntimeError):
-            self.ticker.history_metadata
+        md = self.ticker.history_metadata
+        self.assertIn("IBM", md.values(), "metadata missing")
         data = self.ticker.history("1y")
-        self.assertIn("IBM", self.ticker.history_metadata.values(), "metadata missing")
         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
         self.assertFalse(data.empty, "data is empty")
 
+    def test_download(self):
+        for t in [False, True]:
+            for i in [False, True]:
+                data = yf.download(self.symbols, threads=t, ignore_tz=i)
+                self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+                self.assertFalse(data.empty, "data is empty")
+
     def test_no_expensive_calls_introduced(self):
         """
         Make sure calling history to get price data has not introduced more calls to yahoo than absolutely necessary.
         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.
         """
-        session = requests_cache.CachedSession(backend='memory')
-        ticker = yf.Ticker("GOOGL", session=session)
-        ticker.history("1y")
-        actual_urls_called = tuple([r.url for r in session.cache.filter()])
-        session.close()
-        expected_urls = (
-            'https://query2.finance.yahoo.com/v8/finance/chart/GOOGL?range=1y&interval=1d&includePrePost=False&events=div%2Csplits%2CcapitalGains',
-        )
-        self.assertEqual(expected_urls, actual_urls_called, "Different than expected url used to fetch history.")
+        symbol = "GOOGL"
+        period = "1y"
+        with requests_cache.CachedSession(backend="memory") as session:
+            ticker = yf.Ticker(symbol, session=session)
+            ticker.history(period=period)
+            actual_urls_called = [r.url for r in session.cache.filter()]
 
+        # Remove 'crumb' argument
+        for i in range(len(actual_urls_called)):
+            u = actual_urls_called[i]
+            parsed_url = urlparse(u)
+            query_params = parse_qs(parsed_url.query)
+            query_params.pop('crumb', None)
+            query_params.pop('cookie', None)
+            u = urlunparse(parsed_url._replace(query=urlencode(query_params, doseq=True)))
+            actual_urls_called[i] = u
+        actual_urls_called = tuple(actual_urls_called)
+
+        expected_urls = (
+            f"https://query2.finance.yahoo.com/v8/finance/chart/{symbol}?events=div%2Csplits%2CcapitalGains&includePrePost=False&interval=1d&range={period}",
+        )
+        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")
@@ -194,7 +343,7 @@ class TestTickerEarnings(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -207,46 +356,11 @@ class TestTickerEarnings(unittest.TestCase):
     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")
@@ -259,13 +373,50 @@ class TestTickerEarnings(unittest.TestCase):
         data_cached = ticker.get_earnings_dates(limit=limit)
         self.assertIs(data, data_cached, "data not cached")
 
+    # Below will fail because not ported to Yahoo API
+
+    # 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")
+
+    #     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")
+
 
 class TestTickerHolders(unittest.TestCase):
     session = None
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -308,7 +459,7 @@ class TestTickerMiscFinancials(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -326,6 +477,24 @@ class TestTickerMiscFinancials(unittest.TestCase):
     def tearDown(self):
         self.ticker = None
 
+    def test_isin(self):
+        data = self.ticker.isin
+        self.assertIsInstance(data, str, "data has wrong type")
+        self.assertEqual("ARDEUT116159", data, "data is empty")
+
+        data_cached = self.ticker.isin
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_options(self):
+        data = self.ticker.options
+        self.assertIsInstance(data, tuple, "data has wrong type")
+        self.assertTrue(len(data) > 1, "data is empty")
+
+    def test_shares_full(self):
+        data = self.ticker.get_shares_full()
+        self.assertIsInstance(data, pd.Series, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
     def test_income_statement(self):
         expected_keys = ["Total Revenue", "Basic EPS"]
         expected_periods_days = 365
@@ -355,7 +524,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_income_stmt(as_dict=True)
         self.assertIsInstance(data, dict, "data has wrong type")
 
-
     def test_quarterly_income_statement(self):
         expected_keys = ["Total Revenue", "Basic EPS"]
         expected_periods_days = 365//4
@@ -385,16 +553,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_income_stmt(as_dict=True)
         self.assertIsInstance(data, dict, "data has wrong type")
 
-    def test_quarterly_income_statement_old_fmt(self):
-        expected_row = "TotalRevenue"
-        data = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertIn(expected_row, data.index, "Did not find expected row in index")
-
-        data_cached = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
-        self.assertIs(data, data_cached, "data not cached")
-
     def test_balance_sheet(self):
         expected_keys = ["Total Assets", "Net PPE"]
         expected_periods_days = 365
@@ -453,16 +611,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_balance_sheet(as_dict=True, freq="quarterly")
         self.assertIsInstance(data, dict, "data has wrong type")
 
-    def test_quarterly_balance_sheet_old_fmt(self):
-        expected_row = "TotalAssets"
-        data = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertIn(expected_row, data.index, "Did not find expected row in index")
-
-        data_cached = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
-        self.assertIs(data, data_cached, "data not cached")
-
     def test_cash_flow(self):
         expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
         expected_periods_days = 365
@@ -521,16 +669,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_cashflow(as_dict=True)
         self.assertIsInstance(data, dict, "data has wrong type")
 
-    def test_quarterly_cashflow_old_fmt(self):
-        expected_row = "NetIncome"
-        data = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertIn(expected_row, data.index, "Did not find expected row in index")
-
-        data_cached = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
-        self.assertIs(data, data_cached, "data not cached")
-
     def test_income_alt_names(self):
         i1 = self.ticker.income_stmt
         i2 = self.ticker.incomestmt
@@ -590,87 +728,71 @@ class TestTickerMiscFinancials(unittest.TestCase):
         i2 = self.ticker.get_cashflow(freq="quarterly")
         self.assertTrue(i1.equals(i2))
 
-    def test_sustainability(self):
-        data = self.ticker.sustainability
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.sustainability
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_recommendations(self):
-        data = self.ticker.recommendations
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.recommendations
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_recommendations_summary(self):
-        data = self.ticker.recommendations_summary
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.recommendations_summary
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_analyst_price_target(self):
-        data = self.ticker.analyst_price_target
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.analyst_price_target
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_revenue_forecasts(self):
-        data = self.ticker.revenue_forecasts
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.revenue_forecasts
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_calendar(self):
-        data = self.ticker.calendar
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.calendar
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_isin(self):
-        data = self.ticker.isin
-        self.assertIsInstance(data, str, "data has wrong type")
-        self.assertEqual("ARDEUT116159", data, "data is empty")
-
-        data_cached = self.ticker.isin
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_options(self):
-        data = self.ticker.options
-        self.assertIsInstance(data, tuple, "data has wrong type")
-        self.assertTrue(len(data) > 1, "data is empty")
-
-    def test_shares(self):
-        data = self.ticker.shares
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-    def test_shares_full(self):
-        data = self.ticker.get_shares_full()
-        self.assertIsInstance(data, pd.Series, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
     def test_bad_freq_value_raises_exception(self):
         self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))
 
+    # Below will fail because not ported to Yahoo API
+
+    # def test_sustainability(self):
+    #     data = self.ticker.sustainability
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.sustainability
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_recommendations(self):
+    #     data = self.ticker.recommendations
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.recommendations
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_recommendations_summary(self):
+    #     data = self.ticker.recommendations_summary
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.recommendations_summary
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_analyst_price_target(self):
+    #     data = self.ticker.analyst_price_target
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.analyst_price_target
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_revenue_forecasts(self):
+    #     data = self.ticker.revenue_forecasts
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.revenue_forecasts
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_calendar(self):
+    #     data = self.ticker.calendar
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.calendar
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_shares(self):
+    #     data = self.ticker.shares
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
 
 class TestTickerInfo(unittest.TestCase):
     session = None
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -682,101 +804,122 @@ class TestTickerInfo(unittest.TestCase):
         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")
-        self.assertIn("symbol", data.keys(), "Did not find expected key in info dict")
+        expected_keys = ['industry', 'currentPrice', 'exchange', 'floatShares', 'companyOfficers', 'bid']
+        for k in expected_keys:
+            print(k)
+            self.assertIn("symbol", data.keys(), f"Did not find expected key '{k}' in info dict")
         self.assertEqual(self.symbols[0], data["symbol"], "Wrong symbol value in info dict")
 
-    def test_fast_info(self):
-        yf.scrapers.quote.PRUNE_INFO = False
+    # def test_fast_info_matches_info(self):
+    #     fast_info_keys = set()
+    #     for ticker in self.tickers:
+    #         fast_info_keys.update(set(ticker.fast_info.keys()))
+    #     fast_info_keys = sorted(list(fast_info_keys))
 
-        fast_info_keys = set()
-        for ticker in self.tickers:
-            fast_info_keys.update(set(ticker.fast_info.keys()))
-        fast_info_keys = sorted(list(fast_info_keys))
+    #     key_rename_map = {}
+    #     key_rename_map["currency"] = "currency"
+    #     key_rename_map["quote_type"] = "quoteType"
+    #     key_rename_map["timezone"] = "exchangeTimezoneName"
 
-        key_rename_map = {}
-        key_rename_map["last_price"] = ["currentPrice", "regularMarketPrice"]
-        key_rename_map["open"] = ["open", "regularMarketOpen"]
-        key_rename_map["day_high"] = ["dayHigh", "regularMarketDayHigh"]
-        key_rename_map["day_low"] = ["dayLow", "regularMarketDayLow"]
-        key_rename_map["previous_close"] = ["previousClose"]
-        key_rename_map["regular_market_previous_close"] = ["regularMarketPreviousClose"]
+    #     key_rename_map["last_price"] = ["currentPrice", "regularMarketPrice"]
+    #     key_rename_map["open"] = ["open", "regularMarketOpen"]
+    #     key_rename_map["day_high"] = ["dayHigh", "regularMarketDayHigh"]
+    #     key_rename_map["day_low"] = ["dayLow", "regularMarketDayLow"]
+    #     key_rename_map["previous_close"] = ["previousClose"]
+    #     key_rename_map["regular_market_previous_close"] = ["regularMarketPreviousClose"]
 
-        # preMarketPrice
+    #     key_rename_map["fifty_day_average"] = "fiftyDayAverage"
+    #     key_rename_map["two_hundred_day_average"] = "twoHundredDayAverage"
+    #     key_rename_map["year_change"] = ["52WeekChange", "fiftyTwoWeekChange"]
+    #     key_rename_map["year_high"] = "fiftyTwoWeekHigh"
+    #     key_rename_map["year_low"] = "fiftyTwoWeekLow"
 
-        key_rename_map["fifty_day_average"] = "fiftyDayAverage"
-        key_rename_map["two_hundred_day_average"] = "twoHundredDayAverage"
-        key_rename_map["year_change"] = "52WeekChange"
-        key_rename_map["year_high"] = "fiftyTwoWeekHigh"
-        key_rename_map["year_low"] = "fiftyTwoWeekLow"
+    #     key_rename_map["last_volume"] = ["volume", "regularMarketVolume"]
+    #     key_rename_map["ten_day_average_volume"] = ["averageVolume10days", "averageDailyVolume10Day"]
+    #     key_rename_map["three_month_average_volume"] = "averageVolume"
 
-        key_rename_map["last_volume"] = ["volume", "regularMarketVolume"]
-        key_rename_map["ten_day_average_volume"] = ["averageVolume10days", "averageDailyVolume10Day"]
-        key_rename_map["three_month_average_volume"] = "averageVolume"
+    #     key_rename_map["market_cap"] = "marketCap"
+    #     key_rename_map["shares"] = "sharesOutstanding"
 
-        key_rename_map["market_cap"] = "marketCap"
-        key_rename_map["shares"] = "floatShares"
-        key_rename_map["timezone"] = "exchangeTimezoneName"
+    #     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]
 
-        approximate_keys = {"fifty_day_average", "ten_day_average_volume"}
-        approximate_keys.update({"market_cap"})
+    #     # 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"}
 
-        # bad_keys = []
-        bad_keys = {"shares"}
+    #     # Loose tolerance for averages, no idea why don't match info[]. Is info wrong?
+    #     custom_tolerances = {}
+    #     custom_tolerances["year_change"] = 1.0
+    #     # custom_tolerances["ten_day_average_volume"] = 1e-3
+    #     custom_tolerances["ten_day_average_volume"] = 1e-1
+    #     # custom_tolerances["three_month_average_volume"] = 1e-2
+    #     custom_tolerances["three_month_average_volume"] = 5e-1
+    #     custom_tolerances["fifty_day_average"] = 1e-2
+    #     custom_tolerances["two_hundred_day_average"] = 1e-2
+    #     for k in list(custom_tolerances.keys()):
+    #         if '_' in k:
+    #             custom_tolerances[yf.utils.snake_case_2_camelCase(k)] = custom_tolerances[k]
 
-        # Loose tolerance for averages, no idea why don't match info[]. Is info wrong?
-        custom_tolerances = {}
-        # custom_tolerances["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 fast_info_keys:
+    #         if k in key_rename_map:
+    #             k2 = key_rename_map[k]
+    #         else:
+    #             k2 = k
 
-        for k in fast_info_keys:
-            if k in key_rename_map:
-                k2 = key_rename_map[k]
-            else:
-                k2 = k
+    #         if not isinstance(k2, list):
+    #             k2 = [k2]
 
-            if not isinstance(k2, list):
-                k2 = [k2]
+    #         for m in k2:
+    #             for ticker in self.tickers:
+    #                 if not m in ticker.info:
+    #                     # print(f"symbol={ticker.ticker}: fast_info key '{k}' mapped to info key '{m}' but not present in info")
+    #                     continue
 
-            for m in k2:
-                for ticker in self.tickers:
-                    if not m in ticker.info:
-                        # print(f"symbol={ticker.ticker}: fast_info key '{k}' mapped to info key '{m}' but not present in info")
-                        continue
+    #                 if k in bad_keys:
+    #                     continue
 
-                    if k in bad_keys:
-                        # Doesn't match, investigate why
-                        continue
+    #                 if k in custom_tolerances:
+    #                     rtol = custom_tolerances[k]
+    #                 else:
+    #                     rtol = 5e-3
+    #                     # rtol = 1e-4
 
-                    if k in custom_tolerances:
-                        rtol = custom_tolerances[k]
-                    else:
-                        rtol = 5e-3
-                        # rtol = 1e-4
-
-                    correct = ticker.info[m]
-                    test = ticker.fast_info[k]
-                    # print(f"Testing: symbol={ticker.ticker} m={m} k={k}: test={test} vs correct={correct}")
-                    if k == "market_cap" and ticker.fast_info["currency"] in ["GBp", "ILA"]:
-                        # Adjust for currency to match Yahoo:
-                        test *= 0.01
-                    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"{k}: {test} != {correct}")
-                    else:
-                        self.assertEqual(test, correct, f"{k}: {test} != {correct}")
+    #                 correct = ticker.info[m]
+    #                 test = ticker.fast_info[k]
+    #                 # print(f"Testing: symbol={ticker.ticker} m={m} k={k}: test={test} vs correct={correct}")
+    #                 if k in ["market_cap","marketCap"] and ticker.fast_info["currency"] in ["GBp", "ILA"]:
+    #                     # Adjust for currency to match Yahoo:
+    #                     test *= 0.01
+    #                 try:
+    #                     if correct is None:
+    #                         self.assertTrue(test is None or (not np.isnan(test)), f"{k}: {test} must be None or real value because correct={correct}")
+    #                     elif isinstance(test, float) or isinstance(correct, int):
+    #                         self.assertTrue(np.isclose(test, correct, rtol=rtol), f"{ticker.ticker} {k}: {test} != {correct}")
+    #                     else:
+    #                         self.assertEqual(test, correct, f"{k}: {test} != {correct}")
+    #                 except:
+    #                     if k in ["regularMarketPreviousClose"] and ticker.ticker in ["ADS.DE"]:
+    #                         # Yahoo is wrong, is returning post-market close not regular
+    #                         continue
+    #                     else:
+    #                         raise
 
 
 

tests/utils.py

@@ -0,0 +1,91 @@
+"""
+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

@@ -23,7 +23,8 @@ from . import version
 from .ticker import Ticker
 from .tickers import Tickers
 from .multi import download
-from .utils import set_tz_cache_location
+from .utils import enable_debug_mode
+from .cache import set_tz_cache_location
 
 __version__ = version.version
 __author__ = "Ran Aroussi"
@@ -43,4 +44,4 @@ def pdr_override():
         pass
 
 
-__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override', 'set_tz_cache_location']
+__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override', 'enable_debug_mode', 'set_tz_cache_location']

yfinance/cache.py

@@ -0,0 +1,400 @@
+import peewee as _peewee
+from threading import Lock
+import os as _os
+import appdirs as _ad
+import atexit as _atexit
+import datetime as _datetime
+import pickle as _pkl
+
+from .utils import get_yf_logger
+
+_cache_init_lock = Lock()
+
+# --------------
+# TimeZone cache
+# --------------
+
+class _TzCacheException(Exception):
+    pass
+
+
+class _TzCacheDummy:
+    """Dummy cache to use if tz cache is disabled"""
+
+    def lookup(self, tkr):
+        return None
+
+    def store(self, tkr, tz):
+        pass
+
+    @property
+    def tz_db(self):
+        return None
+
+
+class _TzCacheManager:
+    _tz_cache = None
+
+    @classmethod
+    def get_tz_cache(cls):
+        if cls._tz_cache is None:
+            with _cache_init_lock:
+                cls._initialise()
+        return cls._tz_cache
+
+    @classmethod
+    def _initialise(cls, cache_dir=None):
+        cls._tz_cache = _TzCache()
+
+
+class _TzDBManager:
+    _db = None
+    _cache_dir = _os.path.join(_ad.user_cache_dir(), "py-yfinance")
+
+    @classmethod
+    def get_database(cls):
+        if cls._db is None:
+            cls._initialise()
+        return cls._db
+
+    @classmethod
+    def close_db(cls):
+        if cls._db is not None:
+            try:
+                cls._db.close()
+            except Exception:
+                # Must discard exceptions because Python trying to quit.
+                pass
+
+
+    @classmethod
+    def _initialise(cls, cache_dir=None):
+        if cache_dir is not None:
+            cls._cache_dir = cache_dir
+
+        if not _os.path.isdir(cls._cache_dir):
+            try:
+                _os.makedirs(cls._cache_dir)
+            except OSError as err:
+                raise _TzCacheException(f"Error creating TzCache folder: '{cls._cache_dir}' reason: {err}")
+        elif not (_os.access(cls._cache_dir, _os.R_OK) and _os.access(cls._cache_dir, _os.W_OK)):
+            raise _TzCacheException(f"Cannot read and write in TzCache folder: '{cls._cache_dir}'")
+
+        cls._db = _peewee.SqliteDatabase(
+            _os.path.join(cls._cache_dir, 'tkr-tz.db'),
+            pragmas={'journal_mode': 'wal', 'cache_size': -64}
+        )
+
+        old_cache_file_path = _os.path.join(cls._cache_dir, "tkr-tz.csv")
+        if _os.path.isfile(old_cache_file_path):
+            _os.remove(old_cache_file_path)
+
+    @classmethod
+    def set_location(cls, new_cache_dir):
+        if cls._db is not None:
+            cls._db.close()
+            cls._db = None
+        cls._cache_dir = new_cache_dir
+
+    @classmethod
+    def get_location(cls):
+        return cls._cache_dir
+
+# close DB when Python exists
+_atexit.register(_TzDBManager.close_db)
+
+
+tz_db_proxy = _peewee.Proxy()
+class _KV(_peewee.Model):
+    key = _peewee.CharField(primary_key=True)
+    value = _peewee.CharField(null=True)
+    
+    class Meta:
+        database = tz_db_proxy
+        without_rowid = True
+
+
+class _TzCache:
+    def __init__(self):
+        self.initialised = -1
+        self.db = None
+        self.dummy = False
+
+    def get_db(self):
+        if self.db is not None:
+            return self.db
+
+        try:
+            self.db = _TzDBManager.get_database()
+        except _TzCacheException as err:
+            get_yf_logger().info(f"Failed to create TzCache, reason: {err}. "
+                                 "TzCache will not be used. "
+                                 "Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'")
+            self.dummy = True
+            return None
+        return self.db
+
+    def initialise(self):
+        if self.initialised != -1:
+            return
+
+        db = self.get_db()
+        if db is None:
+            self.initialised = 0  # failure
+            return
+
+        db.connect()
+        tz_db_proxy.initialize(db)
+        db.create_tables([_KV])
+        self.initialised = 1  # success
+
+    def lookup(self, key):
+        if self.dummy:
+            return None
+
+        if self.initialised == -1:
+            self.initialise()
+
+        if self.initialised == 0:  # failure
+            return None
+
+        try:
+            return _KV.get(_KV.key == key).value
+        except _KV.DoesNotExist:
+            return None
+
+    def store(self, key, value):
+        if self.dummy:
+            return
+
+        if self.initialised == -1:
+            self.initialise()
+
+        if self.initialised == 0:  # failure
+            return
+
+        db = self.get_db()
+        if db is None:
+            return
+        try:
+            if value is None:
+                q = _KV.delete().where(_KV.key == key)
+                q.execute()
+                return
+            with db.atomic():
+                _KV.insert(key=key, value=value).execute()
+        except _peewee.IntegrityError:
+            # Integrity error means the key already exists. Try updating the key.
+            old_value = self.lookup(key)
+            if old_value != value:
+                get_yf_logger().debug(f"Value for key {key} changed from {old_value} to {value}.")
+                with db.atomic():
+                    q = _KV.update(value=value).where(_KV.key == key)
+                    q.execute()
+
+
+def get_tz_cache():
+    return _TzCacheManager.get_tz_cache()
+
+
+def set_tz_cache_location(cache_dir: str):
+    """
+    Sets the path to create the "py-yfinance" cache folder in.
+    Useful if the default folder returned by "appdir.user_cache_dir()" is not writable.
+    Must be called before cache is used (that is, before fetching tickers).
+    :param cache_dir: Path to use for caches
+    :return: None
+    """
+    _TzDBManager.set_location(cache_dir)
+
+
+
+# --------------
+# Cookie cache
+# --------------
+
+class _CookieCacheException(Exception):
+    pass
+
+
+class _CookieCacheDummy:
+    """Dummy cache to use if Cookie cache is disabled"""
+
+    def lookup(self, tkr):
+        return None
+
+    def store(self, tkr, Cookie):
+        pass
+
+    @property
+    def Cookie_db(self):
+        return None
+
+
+class _CookieCacheManager:
+    _Cookie_cache = None
+
+    @classmethod
+    def get_cookie_cache(cls):
+        if cls._Cookie_cache is None:
+            with _cache_init_lock:
+                cls._initialise()
+        return cls._Cookie_cache
+
+    @classmethod
+    def _initialise(cls, cache_dir=None):
+        cls._Cookie_cache = _CookieCache()
+
+
+class _CookieDBManager:
+    _db = None
+    _cache_dir = _os.path.join(_ad.user_cache_dir(), "py-yfinance")
+
+    @classmethod
+    def get_database(cls):
+        if cls._db is None:
+            cls._initialise()
+        return cls._db
+
+    @classmethod
+    def close_db(cls):
+        if cls._db is not None:
+            try:
+                cls._db.close()
+            except Exception:
+                # Must discard exceptions because Python trying to quit.
+                pass
+
+
+    @classmethod
+    def _initialise(cls, cache_dir=None):
+        if cache_dir is not None:
+            cls._cache_dir = cache_dir
+
+        if not _os.path.isdir(cls._cache_dir):
+            try:
+                _os.makedirs(cls._cache_dir)
+            except OSError as err:
+                raise _CookieCacheException(f"Error creating CookieCache folder: '{cls._cache_dir}' reason: {err}")
+        elif not (_os.access(cls._cache_dir, _os.R_OK) and _os.access(cls._cache_dir, _os.W_OK)):
+            raise _CookieCacheException(f"Cannot read and write in CookieCache folder: '{cls._cache_dir}'")
+
+        cls._db = _peewee.SqliteDatabase(
+            _os.path.join(cls._cache_dir, 'cookies.db'),
+            pragmas={'journal_mode': 'wal', 'cache_size': -64}
+        )
+
+    @classmethod
+    def set_location(cls, new_cache_dir):
+        if cls._db is not None:
+            cls._db.close()
+            cls._db = None
+        cls._cache_dir = new_cache_dir
+
+    @classmethod
+    def get_location(cls):
+        return cls._cache_dir
+
+# close DB when Python exists
+_atexit.register(_CookieDBManager.close_db)
+
+
+Cookie_db_proxy = _peewee.Proxy()
+class _CookieSchema(_peewee.Model):
+    strategy = _peewee.CharField(primary_key=True)
+    fetch_date = _peewee.DateTimeField(default=_datetime.datetime.now)
+    
+    # Which cookie type depends on strategy
+    cookie_bytes = _peewee.BlobField()
+
+    class Meta:
+        database = Cookie_db_proxy
+        without_rowid = True
+
+
+class _CookieCache:
+    def __init__(self):
+        self.initialised = -1
+        self.db = None
+        self.dummy = False
+
+    def get_db(self):
+        if self.db is not None:
+            return self.db
+
+        try:
+            self.db = _CookieDBManager.get_database()
+        except _CookieCacheException as err:
+            get_yf_logger().info(f"Failed to create CookieCache, reason: {err}. "
+                                 "CookieCache will not be used. "
+                                 "Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'")
+            self.dummy = True
+            return None
+        return self.db
+
+    def initialise(self):
+        if self.initialised != -1:
+            return
+
+        db = self.get_db()
+        if db is None:
+            self.initialised = 0  # failure
+            return
+
+        db.connect()
+        Cookie_db_proxy.initialize(db)
+        db.create_tables([_CookieSchema])
+        self.initialised = 1  # success
+
+    def lookup(self, strategy):
+        if self.dummy:
+            return None
+
+        if self.initialised == -1:
+            self.initialise()
+
+        if self.initialised == 0:  # failure
+            return None
+
+        try:
+            data =  _CookieSchema.get(_CookieSchema.strategy == strategy)
+            cookie = _pkl.loads(data.cookie_bytes)
+            return {'cookie':cookie, 'age':_datetime.datetime.now()-data.fetch_date}
+        except _CookieSchema.DoesNotExist:
+            return None
+
+    def store(self, strategy, cookie):
+        if self.dummy:
+            return
+
+        if self.initialised == -1:
+            self.initialise()
+
+        if self.initialised == 0:  # failure
+            return
+
+        db = self.get_db()
+        if db is None:
+            return
+        try:
+            q = _CookieSchema.delete().where(_CookieSchema.strategy == strategy)
+            q.execute()
+            if cookie is None:
+                return
+            with db.atomic():
+                cookie_pkl = _pkl.dumps(cookie, _pkl.HIGHEST_PROTOCOL)
+                _CookieSchema.insert(strategy=strategy, cookie_bytes=cookie_pkl).execute()
+        except _peewee.IntegrityError:
+            raise
+            # # Integrity error means the strategy already exists. Try updating the strategy.
+            # old_value = self.lookup(strategy)
+            # if old_value != cookie:
+            #     get_yf_logger().debug(f"cookie for strategy {strategy} changed from {old_value} to {cookie}.")
+            #     with db.atomic():
+            #         q = _CookieSchema.update(cookie=cookie).where(_CookieSchema.strategy == strategy)
+            #         q.execute()
+
+
+def get_cookie_cache():
+    return _CookieCacheManager.get_cookie_cache()
+

yfinance/const.py

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

yfinance/data.py

@@ -1,27 +1,13 @@
 import functools
 from functools import lru_cache
 
-import hashlib
-from base64 import b64decode
-usePycryptodome = False  # slightly faster
-# usePycryptodome = True
-if usePycryptodome:
-    from Crypto.Cipher import AES
-    from Crypto.Util.Padding import unpad
-else:
-    from cryptography.hazmat.primitives import padding
-    from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
-
 import requests as requests
-import re
 from bs4 import BeautifulSoup
+import datetime
 
 from frozendict import frozendict
 
-try:
-    import ujson as json
-except ImportError:
-    import json as json
+from . import utils, cache
 
 cache_maxsize = 64
 
@@ -47,146 +33,352 @@ def lru_cache_freezeargs(func):
     return wrapped
 
 
-def _extract_extra_keys_from_stores(data):
-    new_keys = [k for k in data.keys() if k not in ["context", "plugins"]]
-    new_keys_values = set([data[k] for k in new_keys])
-
-    # Maybe multiple keys have same value - keep one of each
-    new_keys_uniq = []
-    new_keys_uniq_values = set()
-    for k in new_keys:
-        v = data[k]
-        if not v in new_keys_uniq_values:
-            new_keys_uniq.append(k)
-            new_keys_uniq_values.add(v)
-
-    return new_keys_uniq
-
-
-def decrypt_cryptojs_aes_stores(data, keys=None):
-    encrypted_stores = data['context']['dispatcher']['stores']
-
-    password = None
-    if keys is not None:
-        if not isinstance(keys, list):
-            raise TypeError("'keys' must be list")
-        candidate_passwords = keys
-    else:
-        candidate_passwords = []
-
-    if "_cs" in data and "_cr" in data:
-        _cs = data["_cs"]
-        _cr = data["_cr"]
-        _cr = b"".join(int.to_bytes(i, length=4, byteorder="big", signed=True) for i in json.loads(_cr)["words"])
-        password = hashlib.pbkdf2_hmac("sha1", _cs.encode("utf8"), _cr, 1, dklen=32).hex()
-
-    encrypted_stores = b64decode(encrypted_stores)
-    assert encrypted_stores[0:8] == b"Salted__"
-    salt = encrypted_stores[8:16]
-    encrypted_stores = encrypted_stores[16:]
-
-    def _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5") -> tuple:
-        """OpenSSL EVP Key Derivation Function
-        Args:
-            password (Union[str, bytes, bytearray]): Password to generate key from.
-            salt (Union[bytes, bytearray]): Salt to use.
-            keySize (int, optional): Output key length in bytes. Defaults to 32.
-            ivSize (int, optional): Output Initialization Vector (IV) length in bytes. Defaults to 16.
-            iterations (int, optional): Number of iterations to perform. Defaults to 1.
-            hashAlgorithm (str, optional): Hash algorithm to use for the KDF. Defaults to 'md5'.
-        Returns:
-            key, iv: Derived key and Initialization Vector (IV) bytes.
-
-        Taken from: https://gist.github.com/rafiibrahim8/0cd0f8c46896cafef6486cb1a50a16d3
-        OpenSSL original code: https://github.com/openssl/openssl/blob/master/crypto/evp/evp_key.c#L78
-        """
-
-        assert iterations > 0, "Iterations can not be less than 1."
-
-        if isinstance(password, str):
-            password = password.encode("utf-8")
-
-        final_length = keySize + ivSize
-        key_iv = b""
-        block = None
-
-        while len(key_iv) < final_length:
-            hasher = hashlib.new(hashAlgorithm)
-            if block:
-                hasher.update(block)
-            hasher.update(password)
-            hasher.update(salt)
-            block = hasher.digest()
-            for _ in range(1, iterations):
-                block = hashlib.new(hashAlgorithm, block).digest()
-            key_iv += block
-
-        key, iv = key_iv[:keySize], key_iv[keySize:final_length]
-        return key, iv
-
-    def _decrypt(encrypted_stores, password, key, iv):
-        if usePycryptodome:
-            cipher = AES.new(key, AES.MODE_CBC, iv=iv)
-            plaintext = cipher.decrypt(encrypted_stores)
-            plaintext = unpad(plaintext, 16, style="pkcs7")
-        else:
-            cipher = Cipher(algorithms.AES(key), modes.CBC(iv))
-            decryptor = cipher.decryptor()
-            plaintext = decryptor.update(encrypted_stores) + decryptor.finalize()
-            unpadder = padding.PKCS7(128).unpadder()
-            plaintext = unpadder.update(plaintext) + unpadder.finalize()
-            plaintext = plaintext.decode("utf-8")
-        return plaintext
-
-    if not password is None:
-        try:
-            key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
-        except:
-            raise Exception("yfinance failed to decrypt Yahoo data response")
-        plaintext = _decrypt(encrypted_stores, password, key, iv)
-    else:
-        success = False
-        for i in range(len(candidate_passwords)):
-            # print(f"Trying candiate pw {i+1}/{len(candidate_passwords)}")
-            password = candidate_passwords[i]
-            try:
-                key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
-
-                plaintext = _decrypt(encrypted_stores, password, key, iv)
-
-                success = True
-                break
-            except:
-                pass
-        if not success:
-            raise Exception("yfinance failed to decrypt Yahoo data response")
-
-    decoded_stores = json.loads(plaintext)
-    return decoded_stores
-
-
-_SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
-
-
-class TickerData:
+import threading
+class SingletonMeta(type):
     """
-    Have one place to retrieve data from Yahoo API in order to ease caching and speed up operations
+    Metaclass that creates a Singleton instance.
+    """
+    _instances = {}
+    _lock = threading.Lock()
+
+    def __call__(cls, *args, **kwargs):
+        with cls._lock:
+            if cls not in cls._instances:
+                instance = super().__call__(*args, **kwargs)
+                cls._instances[cls] = instance
+            else:
+                cls._instances[cls]._set_session(*args, **kwargs)
+            return cls._instances[cls]
+
+
+class YfData(metaclass=SingletonMeta):
+    """
+    Have one place to retrieve data from Yahoo API in order to ease caching and speed up operations.
+    Singleton means one session one cookie shared by all threads.
     """
     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 __init__(self, session=None):
+        self._session = session or requests.Session()
 
-    def get(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
-        proxy = self._get_proxy(proxy)
+        try:
+            self._session.cache
+        except AttributeError:
+            # Not caching
+            self._session_is_caching = False
+        else:
+            # Is caching. This is annoying. 
+            # Can't simply use a non-caching session to fetch cookie & crumb, 
+            # because then the caching-session won't have cookie.
+            self._session_is_caching = True
+            from requests_cache import DO_NOT_CACHE
+            self._expire_after = DO_NOT_CACHE
+        self._crumb = None
+        self._cookie = None
+        if self._session_is_caching and self._cookie is None:
+            utils.print_once("WARNING: cookie & crumb does not work well with requests_cache. Am experimenting with 'expire_after=DO_NOT_CACHE', but you need to help stress-test.")
+
+        # Default to using 'basic' strategy
+        self._cookie_strategy = 'basic'
+        # If it fails, then fallback method is 'csrf'
+        # self._cookie_strategy = 'csrf'
+
+        self._cookie_lock = threading.Lock()
+
+    def _set_session(self, session):
+        if session is None:
+            return
+        with self._cookie_lock:
+            self._session = session
+
+    def _set_cookie_strategy(self, strategy, have_lock=False):
+        if strategy == self._cookie_strategy:
+            return
+        if not have_lock:
+            self._cookie_lock.acquire()
+
+        try:
+            if self._cookie_strategy == 'csrf':
+                utils.get_yf_logger().debug(f'toggling cookie strategy {self._cookie_strategy} -> basic')
+                self._session.cookies.clear()
+                self._cookie_strategy = 'basic'
+            else:
+                utils.get_yf_logger().debug(f'toggling cookie strategy {self._cookie_strategy} -> csrf')
+                self._cookie_strategy = 'csrf'
+            self._cookie = None
+            self._crumb = None
+        except Exception:
+            self._cookie_lock.release()
+            raise
+
+        if not have_lock:
+            self._cookie_lock.release()
+
+    def _save_session_cookies(self):
+        try:
+            cache.get_cookie_cache().store('csrf', self._session.cookies)
+        except Exception:
+            return False
+        return True
+
+    def _load_session_cookies(self):
+        cookie_dict = cache.get_cookie_cache().lookup('csrf')
+        if cookie_dict is None:
+            return False
+        # Periodically refresh, 24 hours seems fair.
+        if cookie_dict['age'] > datetime.timedelta(days=1):
+            return False
+        self._session.cookies.update(cookie_dict['cookie'])
+        utils.get_yf_logger().debug('loaded persistent cookie')
+
+    def _save_cookie_basic(self, cookie):
+        try:
+            cache.get_cookie_cache().store('basic', cookie)
+        except Exception:
+            return False
+        return True
+    def _load_cookie_basic(self):
+        cookie_dict = cache.get_cookie_cache().lookup('basic')
+        if cookie_dict is None:
+            return None
+        # Periodically refresh, 24 hours seems fair.
+        if cookie_dict['age'] > datetime.timedelta(days=1):
+            return None
+        utils.get_yf_logger().debug('loaded persistent cookie')
+        return cookie_dict['cookie']
+
+    def _get_cookie_basic(self, proxy=None, timeout=30):
+        if self._cookie is not None:
+            utils.get_yf_logger().debug('reusing cookie')
+            return self._cookie
+
+        self._cookie = self._load_cookie_basic()
+        if self._cookie is not None:
+            return self._cookie
+
+        # To avoid infinite recursion, do NOT use self.get()
+        # - 'allow_redirects' copied from @psychoz971 solution - does it help USA?
         response = self._session.get(
-            url=url,
-            params=params,
+            url='https://fc.yahoo.com',
+            headers=self.user_agent_headers,
             proxies=proxy,
             timeout=timeout,
-            headers=user_agent_headers or self.user_agent_headers)
+            allow_redirects=True)
+
+        if not response.cookies:
+            utils.get_yf_logger().debug("response.cookies = None")
+            return None
+        self._cookie = list(response.cookies)[0]
+        if self._cookie == '':
+            utils.get_yf_logger().debug("list(response.cookies)[0] = ''")
+            return None
+        self._save_cookie_basic(self._cookie)
+        utils.get_yf_logger().debug(f"fetched basic cookie = {self._cookie}")
+        return self._cookie
+
+    def _get_crumb_basic(self, proxy=None, timeout=30):
+        if self._crumb is not None:
+            utils.get_yf_logger().debug('reusing crumb')
+            return self._crumb
+
+        cookie = self._get_cookie_basic()
+        if cookie is None:
+            return None
+
+        # - 'allow_redirects' copied from @psychoz971 solution - does it help USA?
+        get_args = {
+            'url': "https://query1.finance.yahoo.com/v1/test/getcrumb",
+            'headers': self.user_agent_headers,
+            'cookies': {cookie.name: cookie.value},
+            'proxies': proxy,
+            'timeout': timeout,
+            'allow_redirects': True
+        }
+        if self._session_is_caching:
+            get_args['expire_after'] = self._expire_after
+            crumb_response = self._session.get(**get_args)
+        else:
+            crumb_response = self._session.get(**get_args)
+        self._crumb = crumb_response.text
+        if self._crumb is None or '<html>' in self._crumb:
+            utils.get_yf_logger().debug("Didn't receive crumb")
+            return None
+
+        utils.get_yf_logger().debug(f"crumb = '{self._crumb}'")
+        return self._crumb
+    
+    @utils.log_indent_decorator
+    def _get_cookie_and_crumb_basic(self, proxy, timeout):
+        cookie = self._get_cookie_basic(proxy, timeout)
+        crumb = self._get_crumb_basic(proxy, timeout)
+        return cookie, crumb
+
+    def _get_cookie_csrf(self, proxy, timeout):
+        if self._cookie is not None:
+            utils.get_yf_logger().debug('reusing cookie')
+            return True
+
+        elif self._load_session_cookies():
+            utils.get_yf_logger().debug('reusing persistent cookie')
+            self._cookie = True
+            return True
+
+        base_args = {
+            'headers': self.user_agent_headers,
+            'proxies': proxy,
+            'timeout': timeout}
+
+        get_args = {**base_args, 'url': 'https://guce.yahoo.com/consent'}
+        if self._session_is_caching:
+            get_args['expire_after'] = self._expire_after
+            response = self._session.get(**get_args)
+        else:
+            response = self._session.get(**get_args)
+
+        soup = BeautifulSoup(response.content, 'html.parser')
+        csrfTokenInput = soup.find('input', attrs={'name': 'csrfToken'})
+        if csrfTokenInput is None:
+            utils.get_yf_logger().debug('Failed to find "csrfToken" in response')
+            return False
+        csrfToken = csrfTokenInput['value']
+        utils.get_yf_logger().debug(f'csrfToken = {csrfToken}')
+        sessionIdInput = soup.find('input', attrs={'name': 'sessionId'})
+        sessionId = sessionIdInput['value']
+        utils.get_yf_logger().debug(f"sessionId='{sessionId}")
+
+        originalDoneUrl = 'https://finance.yahoo.com/'
+        namespace = 'yahoo'
+        data = {
+            'agree': ['agree', 'agree'],
+            'consentUUID': 'default',
+            'sessionId': sessionId,
+            'csrfToken': csrfToken,
+            'originalDoneUrl': originalDoneUrl,
+            'namespace': namespace,
+        }
+        post_args = {**base_args, 
+            'url': f'https://consent.yahoo.com/v2/collectConsent?sessionId={sessionId}',
+            'data': data}
+        get_args = {**base_args, 
+            'url': f'https://guce.yahoo.com/copyConsent?sessionId={sessionId}',
+            'data': data}
+        if self._session_is_caching:
+            post_args['expire_after'] = self._expire_after
+            get_args['expire_after'] = self._expire_after
+            self._session.post(**post_args)
+            self._session.get(**get_args)
+        else:
+            self._session.post(**post_args)
+            self._session.get(**get_args)
+        self._cookie = True
+        self._save_session_cookies()
+        return True
+
+    @utils.log_indent_decorator
+    def _get_crumb_csrf(self, proxy=None, timeout=30):
+        # Credit goes to @bot-unit #1729
+
+        if self._crumb is not None:
+            utils.get_yf_logger().debug('reusing crumb')
+            return self._crumb
+
+        if not self._get_cookie_csrf(proxy, timeout):
+            # This cookie stored in session
+            return None
+
+        get_args = {
+            'url': 'https://query2.finance.yahoo.com/v1/test/getcrumb', 
+            'headers': self.user_agent_headers,
+            'proxies': proxy,
+            'timeout': timeout}
+        if self._session_is_caching:
+            get_args['expire_after'] = self._expire_after
+            r = self._session.get(**get_args)
+        else:
+            r = self._session.get(**get_args)
+        self._crumb = r.text
+
+        if self._crumb is None or '<html>' in self._crumb or self._crumb == '':
+            utils.get_yf_logger().debug("Didn't receive crumb")
+            return None
+
+        utils.get_yf_logger().debug(f"crumb = '{self._crumb}'")
+        return self._crumb
+
+    @utils.log_indent_decorator
+    def _get_cookie_and_crumb(self, proxy=None, timeout=30):
+        cookie, crumb, strategy = None, None, None
+
+        utils.get_yf_logger().debug(f"cookie_mode = '{self._cookie_strategy}'")
+
+        with self._cookie_lock:
+            if self._cookie_strategy == 'csrf':
+                crumb = self._get_crumb_csrf()
+                if crumb is None:
+                    # Fail
+                    self._set_cookie_strategy('basic', have_lock=True)
+                    cookie, crumb = self._get_cookie_and_crumb_basic(proxy, timeout)
+            else:
+                # Fallback strategy
+                cookie, crumb = self._get_cookie_and_crumb_basic(proxy, timeout)
+                if cookie is None or crumb is None:
+                    # Fail
+                    self._set_cookie_strategy('csrf', have_lock=True)
+                    crumb = self._get_crumb_csrf()
+            strategy = self._cookie_strategy
+        return cookie, crumb, strategy
+
+    @utils.log_indent_decorator
+    def get(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        # Important: treat input arguments as immutable.
+
+        if len(url) > 200:
+            utils.get_yf_logger().debug(f'url={url[:200]}...')
+        else:
+            utils.get_yf_logger().debug(f'url={url}')
+        utils.get_yf_logger().debug(f'params={params}')
+        proxy = self._get_proxy(proxy)
+
+        if params is None:
+            params = {}
+        if 'crumb' in params:
+            raise Exception("Don't manually add 'crumb' to params dict, let data.py handle it")
+
+        cookie, crumb, strategy = self._get_cookie_and_crumb()
+        if crumb is not None:
+            crumbs = {'crumb': crumb}
+        else:
+            crumbs = {}
+        if strategy == 'basic' and cookie is not None:
+            # Basic cookie strategy adds cookie to GET parameters
+            cookies = {cookie.name: cookie.value}
+        else:
+            cookies = None
+
+        request_args = {
+            'url': url,
+            'params': {**params, **crumbs},
+            'cookies': cookies,
+            'proxies': proxy,
+            'timeout': timeout,
+            'headers': user_agent_headers or self.user_agent_headers
+        }
+        response = self._session.get(**request_args)
+        utils.get_yf_logger().debug(f'response code={response.status_code}')
+        if response.status_code >= 400:
+            # Retry with other cookie strategy
+            if strategy == 'basic':
+                self._set_cookie_strategy('csrf')
+            else:
+                self._set_cookie_strategy('basic')
+            cookie, crumb, strategy = self._get_cookie_and_crumb(proxy, timeout)
+            request_args['params']['crumb'] = crumb
+            if strategy == 'basic':
+                request_args['cookies'] = {cookie.name: cookie.value}
+            response = self._session.get(**request_args)
+            utils.get_yf_logger().debug(f'response code={response.status_code}')
+
         return response
 
     @lru_cache_freezeargs
@@ -197,124 +389,13 @@ class TickerData:
     def _get_proxy(self, proxy):
         # setup proxy in requests format
         if proxy is not None:
-            if isinstance(proxy, dict) and "https" in proxy:
+            if isinstance(proxy, (dict, frozendict)) and "https" in proxy:
                 proxy = proxy["https"]
             proxy = {"https": proxy}
         return proxy
 
-    def _get_decryption_keys_from_yahoo_js(self, soup):
-        result = None
-
-        key_count = 4
-        re_script = soup.find("script", string=re.compile("root.App.main")).text
-        re_data = json.loads(re.search("root.App.main\s+=\s+(\{.*\})", re_script).group(1))
-        re_data.pop("context", None)
-        key_list = list(re_data.keys())
-        if re_data.get("plugins"):  # 1) attempt to get last 4 keys after plugins
-            ind = key_list.index("plugins")
-            if len(key_list) > ind+1:
-                sub_keys = key_list[ind+1:]
-                if len(sub_keys) == key_count:
-                    re_obj = {}
-                    missing_val = False
-                    for k in sub_keys:
-                        if not re_data.get(k):
-                            missing_val = True
-                            break
-                        re_obj.update({k: re_data.get(k)})
-                    if not missing_val:
-                        result = re_obj
-
-        if not result is None:
-            return [''.join(result.values())]
-
-        re_keys = []    # 2) attempt scan main.js file approach to get keys
-        prefix = "https://s.yimg.com/uc/finance/dd-site/js/main."
-        tags = [tag['src'] for tag in soup.find_all('script') if prefix in tag.get('src', '')]
-        for t in tags:
-            response_js = self.cache_get(t)
-            #
-            if response_js.status_code != 200:
-                time.sleep(random.randrange(10, 20))
-                response_js.close()
-            else:
-                r_data = response_js.content.decode("utf8")
-                re_list = [
-                    x.group() for x in re.finditer(r"context.dispatcher.stores=JSON.parse((?:.*?\r?\n?)*)toString", r_data)
-                ]
-                for rl in re_list:
-                    re_sublist = [x.group() for x in re.finditer(r"t\[\"((?:.*?\r?\n?)*)\"\]", rl)]
-                    if len(re_sublist) == key_count:
-                        re_keys = [sl.replace('t["', '').replace('"]', '') for sl in re_sublist]
-                        break
-                response_js.close()
-            if len(re_keys) == key_count:
-                break
-        re_obj = {}
-        missing_val = False
-        for k in re_keys:
-            if not re_data.get(k):
-                missing_val = True
-                break
-            re_obj.update({k: re_data.get(k)})
-        if not missing_val:
-            return [''.join(re_obj.values())]
-
-        return []
-
-    @lru_cache_freezeargs
-    @lru_cache(maxsize=cache_maxsize)
-    def get_json_data_stores(self, sub_page: str = None, proxy=None) -> dict:
-        '''
-        get_json_data_stores returns a python dictionary of the data stores in yahoo finance web page.
-        '''
-        if sub_page:
-            ticker_url = "{}/{}/{}".format(_SCRAPE_URL_, self.ticker, sub_page)
-        else:
-            ticker_url = "{}/{}".format(_SCRAPE_URL_, self.ticker)
-
-        response = self.get(url=ticker_url, proxy=proxy)
-        html = response.text
-
-        # The actual json-data for stores is in a javascript assignment in the webpage
-        try:
-            json_str = html.split('root.App.main =')[1].split(
-                '(this)')[0].split(';\n}')[0].strip()
-        except IndexError:
-            # Fetch failed, probably because Yahoo spam triggered
-            return {}
-
-        data = json.loads(json_str)
-
-        # Gather decryption keys:
-        soup = BeautifulSoup(response.content, "html.parser")
-        keys = self._get_decryption_keys_from_yahoo_js(soup)
-        if len(keys) == 0:
-            msg = "No decryption keys could be extracted from JS file."
-            if "requests_cache" in str(type(response)):
-                msg += " Try flushing your 'requests_cache', probably parsing old JS."
-            print("WARNING: " + msg + " Falling back to backup decrypt methods.")
-        if len(keys) == 0:
-            keys_url = "https://github.com/ranaroussi/yfinance/raw/main/yfinance/scrapers/yahoo-keys.txt"
-            response_gh = self.cache_get(keys_url)
-            keys = response_gh.text.splitlines()
-            extra_keys = _extract_extra_keys_from_stores(data)
-            if len(extra_keys) < 10:
-                # Only brute-force with these extra keys if few
-                keys += extra_keys
-
-        # Decrypt!
-        stores = decrypt_cryptojs_aes_stores(data, keys)
-        if stores is None:
-            # Maybe Yahoo returned old format, not encrypted
-            if "context" in data and "dispatcher" in data["context"]:
-                stores = data['context']['dispatcher']['stores']
-        if stores is None:
-            raise Exception(f"{self.ticker}: Failed to extract data stores from web request")
-
-        # return data
-        new_data = json.dumps(stores).replace('{}', 'null')
-        new_data = re.sub(
-            r'{[\'|\"]raw[\'|\"]:(.*?),(.*?)}', r'\1', new_data)
-
-        return json.loads(new_data)
+    def get_raw_json(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        utils.get_yf_logger().debug(f'get_raw_json(): {url}')
+        response = self.get(url, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
+        response.raise_for_status()
+        return response.json()

yfinance/exceptions.py

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

yfinance/multi.py

@@ -21,18 +21,23 @@
 
 from __future__ import print_function
 
+import logging
 import time as _time
+import traceback
+
 import multitasking as _multitasking
 import pandas as _pd
 
 from . import Ticker, utils
+from .data import YfData
 from . import shared
 
 
-def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=False,
+@utils.log_indent_decorator
+def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=None,
              group_by='column', auto_adjust=False, back_adjust=False, repair=False, keepna=False,
-             progress=True, period="max", show_errors=True, interval="1d", prepost=False,
-             proxy=None, rounding=False, timeout=10):
+             progress=True, period="max", show_errors=None, interval="1d", prepost=False,
+             proxy=None, rounding=False, timeout=10, session=None):
     """Download yahoo tickers
     :Parameters:
         tickers : str, list
@@ -44,11 +49,13 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
             Intraday data cannot extend last 60 days
         start: str
-            Download start date string (YYYY-MM-DD) or _datetime.
-            Default is 1900-01-01
+            Download start date string (YYYY-MM-DD) or _datetime, inclusive.
+            Default is 99 years ago
+            E.g. for start="2020-01-01", the first data point will be on "2020-01-01"
         end: str
-            Download end date string (YYYY-MM-DD) or _datetime.
+            Download end date string (YYYY-MM-DD) or _datetime, exclusive.
             Default is now
+            E.g. for end="2023-01-01", the last data point will be on "2022-12-31"
         group_by : str
             Group by 'ticker' or 'column' (default)
         prepost : bool
@@ -68,17 +75,48 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             How many threads to use for mass downloading. Default is True
         ignore_tz: bool
             When combining from different timezones, ignore that part of datetime.
-            Default is False
+            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 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(
@@ -90,7 +128,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
     for ticker in tickers:
         if utils.is_isin(ticker):
             isin = ticker
-            ticker = utils.get_ticker_by_isin(ticker, proxy)
+            ticker = utils.get_ticker_by_isin(ticker, proxy, session=session)
             shared._ISINS[ticker] = isin
         _tickers_.append(ticker)
 
@@ -104,6 +142,10 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
     # reset shared._DFS
     shared._DFS = {}
     shared._ERRORS = {}
+    shared._TRACEBACKS = {}
+
+    # Ensure data initialised with session.
+    YfData(session=session)
 
     # download using threads
     if threads:
@@ -119,7 +161,6 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
                                    rounding=rounding, timeout=timeout)
         while len(shared._DFS) < len(tickers):
             _time.sleep(0.01)
-
     # download synchronously
     else:
         for i, ticker in enumerate(tickers):
@@ -129,19 +170,41 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
                                  back_adjust=back_adjust, repair=repair, keepna=keepna,
                                  proxy=proxy,
                                  rounding=rounding, timeout=timeout)
-            shared._DFS[ticker.upper()] = data
             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():
@@ -150,7 +213,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
 
     if len(tickers) == 1:
         ticker = tickers[0]
-        return shared._DFS[shared._ISINS.get(ticker, ticker)]
+        return shared._DFS[ticker]
 
     try:
         data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
@@ -199,16 +262,9 @@ def _download_one_threaded(ticker, start=None, end=None,
                            actions=False, progress=True, period="max",
                            interval="1d", prepost=False, proxy=None,
                            keepna=False, rounding=False, timeout=10):
-    try:
-        data = _download_one(ticker, start, end, auto_adjust, back_adjust, repair,
-                             actions, period, interval, prepost, proxy, rounding,
-                             keepna, timeout)
-    except Exception as e:
-        # glob try/except needed as current thead implementation breaks if exception is raised.
-        shared._DFS[ticker] = utils.empty_df()
-        shared._ERRORS[ticker] = repr(e)
-    else:
-        shared._DFS[ticker.upper()] = data
+    data = _download_one(ticker, start, end, auto_adjust, back_adjust, repair,
+                         actions, period, interval, prepost, proxy, rounding,
+                         keepna, timeout)
     if progress:
         shared._PROGRESS_BAR.animate()
 
@@ -218,11 +274,22 @@ def _download_one(ticker, start=None, end=None,
                   actions=False, period="max", interval="1d",
                   prepost=False, proxy=None, rounding=False,
                   keepna=False, timeout=10):
-    return Ticker(ticker).history(
-        period=period, interval=interval,
-        start=start, end=end, prepost=prepost,
-        actions=actions, auto_adjust=auto_adjust,
-        back_adjust=back_adjust, repair=repair, proxy=proxy,
-        rounding=rounding, keepna=keepna, timeout=timeout,
-        debug=False, raise_errors=False  # debug and raise_errors false to not log and raise errors in threads
-    )
+    data = None
+    try:
+        data = Ticker(ticker).history(
+                period=period, interval=interval,
+                start=start, end=end, prepost=prepost,
+                actions=actions, auto_adjust=auto_adjust,
+                back_adjust=back_adjust, repair=repair, proxy=proxy,
+                rounding=rounding, keepna=keepna, timeout=timeout,
+                raise_errors=True
+        )
+    except Exception as e:
+        # glob try/except needed as current thead implementation breaks if exception is raised.
+        shared._DFS[ticker.upper()] = utils.empty_df()
+        shared._ERRORS[ticker.upper()] = repr(e)
+        shared._TRACEBACKS[ticker.upper()] = traceback.format_exc()
+    else:
+        shared._DFS[ticker.upper()] = data
+
+    return data

yfinance/scrapers/analysis.py

@@ -1,13 +1,15 @@
 import pandas as pd
 
 from yfinance import utils
-from yfinance.data import TickerData
+from yfinance.data import YfData
+from yfinance.exceptions import YFNotImplementedError
 
 
 class Analysis:
 
-    def __init__(self, data: TickerData, proxy=None):
+    def __init__(self, data: YfData, symbol: str, proxy=None):
         self._data = data
+        self._symbol = symbol
         self.proxy = proxy
 
         self._earnings_trend = None
@@ -20,99 +22,29 @@ class Analysis:
     @property
     def earnings_trend(self) -> pd.DataFrame:
         if self._earnings_trend is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('earnings_trend')
         return self._earnings_trend
 
     @property
     def analyst_trend_details(self) -> pd.DataFrame:
         if self._analyst_trend_details is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('analyst_trend_details')
         return self._analyst_trend_details
 
     @property
     def analyst_price_target(self) -> pd.DataFrame:
         if self._analyst_price_target is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('analyst_price_target')
         return self._analyst_price_target
 
     @property
     def rev_est(self) -> pd.DataFrame:
         if self._rev_est is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('rev_est')
         return self._rev_est
 
     @property
     def eps_est(self) -> pd.DataFrame:
         if self._eps_est is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('eps_est')
         return self._eps_est
-
-    def _scrape(self, proxy):
-        if self._already_scraped:
-            return
-        self._already_scraped = True
-
-        # Analysis Data/Analyst Forecasts
-        analysis_data = self._data.get_json_data_stores("analysis", proxy=proxy)
-        try:
-            analysis_data = analysis_data['QuoteSummaryStore']
-        except KeyError as e:
-            err_msg = "No analysis data found, symbol may be delisted"
-            print('- %s: %s' % (self._data.ticker, err_msg))
-            return
-
-        if isinstance(analysis_data.get('earningsTrend'), dict):
-            try:
-                analysis = pd.DataFrame(analysis_data['earningsTrend']['trend'])
-                analysis['endDate'] = pd.to_datetime(analysis['endDate'])
-                analysis.set_index('period', inplace=True)
-                analysis.index = analysis.index.str.upper()
-                analysis.index.name = 'Period'
-                analysis.columns = utils.camel2title(analysis.columns)
-
-                dict_cols = []
-
-                for idx, row in analysis.iterrows():
-                    for colname, colval in row.items():
-                        if isinstance(colval, dict):
-                            dict_cols.append(colname)
-                            for k, v in colval.items():
-                                new_colname = colname + ' ' + \
-                                              utils.camel2title([k])[0]
-                                analysis.loc[idx, new_colname] = v
-
-                self._earnings_trend = analysis[[
-                    c for c in analysis.columns if c not in dict_cols]]
-            except Exception:
-                pass
-
-        try:
-            self._analyst_trend_details = pd.DataFrame(analysis_data['recommendationTrend']['trend'])
-        except Exception as e:
-            self._analyst_trend_details = None
-        try:
-            self._analyst_price_target = pd.DataFrame(analysis_data['financialData'], index=[0])[
-                ['targetLowPrice', 'currentPrice', 'targetMeanPrice', 'targetHighPrice', 'numberOfAnalystOpinions']].T
-        except Exception as e:
-            self._analyst_price_target = None
-        earnings_estimate = []
-        revenue_estimate = []
-        if self._analyst_trend_details is not None :
-            for key in analysis_data['earningsTrend']['trend']:
-                try:
-                    earnings_dict = key['earningsEstimate']
-                    earnings_dict['period'] = key['period']
-                    earnings_dict['endDate'] = key['endDate']
-                    earnings_estimate.append(earnings_dict)
-
-                    revenue_dict = key['revenueEstimate']
-                    revenue_dict['period'] = key['period']
-                    revenue_dict['endDate'] = key['endDate']
-                    revenue_estimate.append(revenue_dict)
-                except Exception as e:
-                    pass
-            self._rev_est = pd.DataFrame(revenue_estimate)
-            self._eps_est = pd.DataFrame(earnings_estimate)
-        else:
-            self._rev_est = pd.DataFrame()
-            self._eps_est = pd.DataFrame()

yfinance/scrapers/fundamentals.py

@@ -2,17 +2,17 @@ import datetime
 import json
 
 import pandas as pd
-import numpy as np
 
-from yfinance import utils
-from yfinance.data import TickerData
-from yfinance.exceptions import YFinanceDataException, YFinanceException
+from yfinance import utils, const
+from yfinance.data import YfData
+from yfinance.exceptions import YFinanceException, YFNotImplementedError
 
 
 class Fundamentals:
 
-    def __init__(self, data: TickerData, proxy=None):
+    def __init__(self, data: YfData, symbol: str, proxy=None):
         self._data = data
+        self._symbol = symbol
         self.proxy = proxy
 
         self._earnings = None
@@ -22,7 +22,7 @@ class Fundamentals:
         self._financials_data = None
         self._fin_data_quote = None
         self._basics_already_scraped = False
-        self._financials = Financials(data)
+        self._financials = Financials(data, symbol)
 
     @property
     def financials(self) -> "Financials":
@@ -31,100 +31,43 @@ class Fundamentals:
     @property
     def earnings(self) -> dict:
         if self._earnings is None:
-            self._scrape_earnings(self.proxy)
+            raise YFNotImplementedError('earnings')
         return self._earnings
 
     @property
     def shares(self) -> pd.DataFrame:
         if self._shares is None:
-            self._scrape_shares(self.proxy)
+            raise YFNotImplementedError('shares')
         return self._shares
 
-    def _scrape_basics(self, proxy):
-        if self._basics_already_scraped:
-            return
-        self._basics_already_scraped = True
-
-        self._financials_data = self._data.get_json_data_stores('financials', proxy)
-        try:
-            self._fin_data_quote = self._financials_data['QuoteSummaryStore']
-        except KeyError:
-            err_msg = "No financials data found, symbol may be delisted"
-            print('- %s: %s' % (self._data.ticker, err_msg))
-            return None
-
-    def _scrape_earnings(self, proxy):
-        self._scrape_basics(proxy)
-        # earnings
-        self._earnings = {"yearly": pd.DataFrame(), "quarterly": pd.DataFrame()}
-        if self._fin_data_quote is None:
-            return
-        if isinstance(self._fin_data_quote.get('earnings'), dict):
-            try:
-                earnings = self._fin_data_quote['earnings']['financialsChart']
-                earnings['financialCurrency'] = self._fin_data_quote['earnings'].get('financialCurrency', 'USD')
-                self._earnings['financialCurrency'] = earnings['financialCurrency']
-                df = pd.DataFrame(earnings['yearly']).set_index('date')
-                df.columns = utils.camel2title(df.columns)
-                df.index.name = 'Year'
-                self._earnings['yearly'] = df
-
-                df = pd.DataFrame(earnings['quarterly']).set_index('date')
-                df.columns = utils.camel2title(df.columns)
-                df.index.name = 'Quarter'
-                self._earnings['quarterly'] = df
-            except Exception:
-                pass
-
-    def _scrape_shares(self, proxy):
-        self._scrape_basics(proxy)
-        # shares outstanding
-        try:
-            # keep only years with non None data
-            available_shares = [shares_data for shares_data in
-                                self._financials_data['QuoteTimeSeriesStore']['timeSeries']['annualBasicAverageShares']
-                                if
-                                shares_data]
-            shares = pd.DataFrame(available_shares)
-            shares['Year'] = shares['asOfDate'].agg(lambda x: int(x[:4]))
-            shares.set_index('Year', inplace=True)
-            shares.drop(columns=['dataId', 'asOfDate',
-                                 'periodType', 'currencyCode'], inplace=True)
-            shares.rename(
-                columns={'reportedValue': "BasicShares"}, inplace=True)
-            self._shares = shares
-        except Exception:
-            pass
-
 
 class Financials:
-    def __init__(self, data: TickerData):
+    def __init__(self, data: YfData, symbol: str):
         self._data = data
+        self._symbol = symbol
         self._income_time_series = {}
         self._balance_sheet_time_series = {}
         self._cash_flow_time_series = {}
-        self._income_scraped = {}
-        self._balance_sheet_scraped = {}
-        self._cash_flow_scraped = {}
 
     def get_income_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
         res = self._income_time_series
         if freq not in res:
-            res[freq] = self._fetch_time_series("income", freq, proxy=None)
+            res[freq] = self._fetch_time_series("income", freq, proxy)
         return res[freq]
 
     def get_balance_sheet_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
         res = self._balance_sheet_time_series
         if freq not in res:
-            res[freq] = self._fetch_time_series("balance-sheet", freq, proxy=None)
+            res[freq] = self._fetch_time_series("balance-sheet", freq, proxy)
         return res[freq]
 
     def get_cash_flow_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
         res = self._cash_flow_time_series
         if freq not in res:
-            res[freq] = self._fetch_time_series("cash-flow", freq, proxy=None)
+            res[freq] = self._fetch_time_series("cash-flow", freq, proxy)
         return res[freq]
 
+    @utils.log_indent_decorator
     def _fetch_time_series(self, name, timescale, proxy=None):
         # Fetching time series preferred over scraping 'QuoteSummaryStore',
         # because it matches what Yahoo shows. But for some tickers returns nothing, 
@@ -134,9 +77,9 @@ class Financials:
         allowed_timescales = ["yearly", "quarterly"]
 
         if name not in allowed_names:
-            raise ValueError("Illegal argument: name must be one of: {}".format(allowed_names))
+            raise ValueError(f"Illegal argument: name must be one of: {allowed_names}")
         if timescale not in allowed_timescales:
-            raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
+            raise ValueError(f"Illegal argument: timescale must be one of: {allowed_names}")
 
         try:
             statement = self._create_financials_table(name, timescale, proxy)
@@ -144,7 +87,7 @@ class Financials:
             if statement is not None:
                 return statement
         except YFinanceException as e:
-            print(f"- {self._data.ticker}: Failed to create {name} financials table for reason: {repr(e)}")
+            utils.get_yf_logger().error(f"{self._symbol}: Failed to create {name} financials table for reason: {e}")
         return pd.DataFrame()
 
     def _create_financials_table(self, name, timescale, proxy):
@@ -152,51 +95,24 @@ class Financials:
             # Yahoo stores the 'income' table internally under 'financials' key
             name = "financials"
 
-        keys = self._get_datastore_keys(name, proxy)
+        keys = const.fundamentals_keys[name]
+
         try:
             return self.get_financials_time_series(timescale, keys, proxy)
         except Exception as e:
             pass
 
-    def _get_datastore_keys(self, sub_page, proxy) -> list:
-        data_stores = self._data.get_json_data_stores(sub_page, proxy)
-
-        # Step 1: get the keys:
-        def _finditem1(key, obj):
-            values = []
-            if isinstance(obj, dict):
-                if key in obj.keys():
-                    values.append(obj[key])
-                for k, v in obj.items():
-                    values += _finditem1(key, v)
-            elif isinstance(obj, list):
-                for v in obj:
-                    values += _finditem1(key, v)
-            return values
-
-        try:
-            keys = _finditem1("key", data_stores['FinancialTemplateStore'])
-        except KeyError as e:
-            raise YFinanceDataException("Parsing FinancialTemplateStore failed, reason: {}".format(repr(e)))
-
-        if not keys:
-            raise YFinanceDataException("No keys in FinancialTemplateStore")
-        return keys
-
     def get_financials_time_series(self, timescale, keys: list, proxy=None) -> pd.DataFrame:
         timescale_translation = {"yearly": "annual", "quarterly": "quarterly"}
         timescale = timescale_translation[timescale]
 
         # Step 2: construct url:
-        ts_url_base = \
-            "https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{0}?symbol={0}" \
-                .format(self._data.ticker)
-
+        ts_url_base = f"https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._symbol}?symbol={self._symbol}"
         url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
         # Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
         start_dt = datetime.datetime(2016, 12, 31)
         end = pd.Timestamp.utcnow().ceil("D")
-        url += "&period1={}&period2={}".format(int(start_dt.timestamp()), int(end.timestamp()))
+        url += f"&period1={int(start_dt.timestamp())}&period2={int(end.timestamp())}"
 
         # Step 3: fetch and reshape data
         json_str = self._data.cache_get(url=url, proxy=proxy).text
@@ -231,89 +147,3 @@ class Financials:
         df = df[sorted(df.columns, reverse=True)]
 
         return df
-
-    def get_income_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._income_scraped
-        if freq not in res:
-            res[freq] = self._scrape("income", freq, proxy=None)
-        return res[freq]
-
-    def get_balance_sheet_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._balance_sheet_scraped
-        if freq not in res:
-            res[freq] = self._scrape("balance-sheet", freq, proxy=None)
-        return res[freq]
-
-    def get_cash_flow_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._cash_flow_scraped
-        if freq not in res:
-            res[freq] = self._scrape("cash-flow", freq, proxy=None)
-        return res[freq]
-
-    def _scrape(self, name, timescale, proxy=None):
-        # Backup in case _fetch_time_series() fails to return data
-
-        allowed_names = ["income", "balance-sheet", "cash-flow"]
-        allowed_timescales = ["yearly", "quarterly"]
-
-        if name not in allowed_names:
-            raise ValueError("Illegal argument: name must be one of: {}".format(allowed_names))
-        if timescale not in allowed_timescales:
-            raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
-
-        try:
-            statement = self._create_financials_table_old(name, timescale, proxy)
-
-            if statement is not None:
-                return statement
-        except YFinanceException as e:
-            print(f"- {self._data.ticker}: Failed to create financials table for {name} reason: {repr(e)}")
-        return pd.DataFrame()
-
-    def _create_financials_table_old(self, name, timescale, proxy):
-        data_stores = self._data.get_json_data_stores("financials", proxy)
-
-        # Fetch raw data
-        if not "QuoteSummaryStore" in data_stores:
-            raise YFinanceDataException(f"Yahoo not returning legacy financials data")
-        data = data_stores["QuoteSummaryStore"]
-
-        if name == "cash-flow":
-            key1 = "cashflowStatement"
-            key2 = "cashflowStatements"
-        elif name == "balance-sheet":
-            key1 = "balanceSheet"
-            key2 = "balanceSheetStatements"
-        else:
-            key1 = "incomeStatement"
-            key2 = "incomeStatementHistory"
-        key1 += "History"
-        if timescale == "quarterly":
-            key1 += "Quarterly"
-        if key1 not in data or data[key1] is None or key2 not in data[key1]:
-            raise YFinanceDataException(f"Yahoo not returning legacy {name} financials data")
-        data = data[key1][key2]
-
-        # Tabulate
-        df = pd.DataFrame(data)
-        if len(df) == 0:
-            raise YFinanceDataException(f"Yahoo not returning legacy {name} financials data")
-        df = df.drop(columns=['maxAge'])
-        for col in df.columns:
-            df[col] = df[col].replace('-', np.nan)
-        df.set_index('endDate', inplace=True)
-        try:
-            df.index = pd.to_datetime(df.index, unit='s')
-        except ValueError:
-            df.index = pd.to_datetime(df.index)
-        df = df.T
-        df.columns.name = ''
-        df.index.name = 'Breakdown'
-        # rename incorrect yahoo key
-        df.rename(index={'treasuryStock': 'gainsLossesNotAffectingRetainedEarnings'}, inplace=True)
-
-        # Upper-case first letter, leave rest unchanged:
-        s0 = df.index[0]
-        df.index = [s[0].upper()+s[1:] for s in df.index]
-
-        return df

yfinance/scrapers/holders.py

@@ -1,12 +1,16 @@
+from io import StringIO
+
 import pandas as pd
 
-from yfinance.data import TickerData
+from yfinance.data import YfData
+
 
 class Holders:
     _SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
 
-    def __init__(self, data: TickerData, proxy=None):
+    def __init__(self, data: YfData, symbol: str, proxy=None):
         self._data = data
+        self._symbol = symbol
         self.proxy = proxy
 
         self._major = None
@@ -32,10 +36,10 @@ class Holders:
         return self._mutualfund
 
     def _scrape(self, proxy):
-        ticker_url = "{}/{}".format(self._SCRAPE_URL_, self._data.ticker)
+        ticker_url = f"{self._SCRAPE_URL_}/{self._symbol}"
         try:
-            resp = self._data.cache_get(ticker_url + '/holders', proxy)
-            holders = pd.read_html(resp.text)
+            resp = self._data.cache_get(ticker_url + '/holders', proxy=proxy)
+            holders = pd.read_html(StringIO(resp.text))
         except Exception:
             holders = []
 

yfinance/scrapers/quote.py

@@ -1,27 +1,29 @@
 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.data import YfData
+from yfinance.exceptions import YFNotImplementedError
 
-
-info_retired_keys_price = {"currentPrice", "dayHigh", "dayLow", "open", "previousClose", "volume"}
+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", "fiftyDayAverage", "twoHundredDayAverage"})
+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"}
+info_retired_keys_exchange = {"currency", "exchange", "exchangeTimezoneName", "exchangeTimezoneShortName", "quoteType"}
 info_retired_keys_marketCap = {"marketCap"}
 info_retired_keys_symbol = {"symbol"}
 info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_retired_keys_marketCap | info_retired_keys_symbol
 
 
-PRUNE_INFO = True
-# PRUNE_INFO = False
+_BASIC_URL_ = "https://query2.finance.yahoo.com/v10/finance/quoteSummary"
 
 
-from collections.abc import MutableMapping
 class InfoDictWrapper(MutableMapping):
     """ Simple wrapper around info dict, intercepting 'gets' to 
     print how-to-migrate messages for specific keys. Requires
@@ -44,16 +46,16 @@ class InfoDictWrapper(MutableMapping):
 
     def __getitem__(self, k):
         if k in info_retired_keys_price:
-            print(f"Price data removed from info (key='{k}'). Use Ticker.fast_info or history() instead")
+            warnings.warn(f"Price data removed from info (key='{k}'). Use Ticker.fast_info or history() instead", DeprecationWarning)
             return None
         elif k in info_retired_keys_exchange:
-            print(f"Exchange data removed from info (key='{k}'). Use Ticker.fast_info or Ticker.get_history_metadata() instead")
+            warnings.warn(f"Exchange data removed from info (key='{k}'). Use Ticker.fast_info or Ticker.get_history_metadata() instead", DeprecationWarning)
             return None
         elif k in info_retired_keys_marketCap:
-            print(f"Market cap removed from info (key='{k}'). Use Ticker.fast_info instead")
+            warnings.warn(f"Market cap removed from info (key='{k}'). Use Ticker.fast_info instead", DeprecationWarning)
             return None
         elif k in info_retired_keys_symbol:
-            print(f"Symbol removed from info (key='{k}'). You know this already")
+            warnings.warn(f"Symbol removed from info (key='{k}'). You know this already", DeprecationWarning)
             return None
         return self.info[self._keytransform(k)]
 
@@ -73,11 +75,485 @@ class InfoDictWrapper(MutableMapping):
         return k
 
 
+class FastInfo:
+    # Contain small subset of info[] items that can be fetched faster elsewhere.
+    # Imitates a dict.
+    def __init__(self, tickerBaseObject, proxy=None):
+        self._tkr = tickerBaseObject
+        self.proxy = proxy
+
+        self._prices_1y = None
+        self._prices_1wk_1h_prepost = None
+        self._prices_1wk_1h_reg = None
+        self._md = None
+
+        self._currency = None
+        self._quote_type = None
+        self._exchange = None
+        self._timezone = None
+
+        self._shares = None
+        self._mcap = None
+
+        self._open = None
+        self._day_high = None
+        self._day_low = None
+        self._last_price = None
+        self._last_volume = None
+
+        self._prev_close = None
+
+        self._reg_prev_close = None
+
+        self._50d_day_average = None
+        self._200d_day_average = None
+        self._year_high = None
+        self._year_low = None
+        self._year_change = None
+
+        self._10d_avg_vol = None
+        self._3mo_avg_vol = None
+
+        # attrs = utils.attributes(self)
+        # self.keys = attrs.keys()
+        # utils.attributes is calling each method, bad! Have to hardcode
+        _properties = ["currency", "quote_type", "exchange", "timezone"]
+        _properties += ["shares", "market_cap"]
+        _properties += ["last_price", "previous_close", "open", "day_high", "day_low"]
+        _properties += ["regular_market_previous_close"]
+        _properties += ["last_volume"]
+        _properties += ["fifty_day_average", "two_hundred_day_average", "ten_day_average_volume", "three_month_average_volume"]
+        _properties += ["year_high", "year_low", "year_change"]
+
+        # Because released before fixing key case, need to officially support 
+        # camel-case but also secretly support snake-case
+        base_keys = [k for k in _properties if '_' not in k]
+
+        sc_keys = [k for k in _properties if '_' in k]
+
+        self._sc_to_cc_key = {k: utils.snake_case_2_camelCase(k) for k in sc_keys}
+        self._cc_to_sc_key = {v: k for k, v in self._sc_to_cc_key.items()}
+ 
+        self._public_keys = sorted(base_keys + list(self._sc_to_cc_key.values()))
+        self._keys = sorted(self._public_keys + sc_keys)
+
+    # dict imitation:
+    def keys(self):
+        return self._public_keys
+
+    def items(self):
+        return [(k, self[k]) for k in self._public_keys]
+
+    def values(self):
+        return [self[k] for k in self._public_keys]
+
+    def get(self, key, default=None):
+        if key in self.keys():
+            if key in self._cc_to_sc_key:
+                key = self._cc_to_sc_key[key]
+            return self[key]
+        return default
+
+    def __getitem__(self, k):
+        if not isinstance(k, str):
+            raise KeyError(f"key must be a string")
+        if k not in self._keys:
+            raise KeyError(f"'{k}' not valid key. Examine 'FastInfo.keys()'")
+        if k in self._cc_to_sc_key:
+            k = self._cc_to_sc_key[k]
+        return getattr(self, k)
+
+    def __contains__(self, k):
+        return k in self.keys()
+
+    def __iter__(self):
+        return iter(self.keys())
+
+    def __str__(self):
+        return "lazy-loading dict with keys = " + str(self.keys())
+
+    def __repr__(self):
+        return self.__str__()
+
+    def toJSON(self, indent=4):
+        d = {k: self[k] for k in self.keys()}
+        return json.dumps({k: self[k] for k in self.keys()}, indent=indent)
+
+    def _get_1y_prices(self, fullDaysOnly=False):
+        if self._prices_1y is None:
+            # Temporarily disable error printing
+            logging.disable(logging.CRITICAL)
+            self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, keepna=True, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
+            self._md = self._tkr.get_history_metadata(proxy=self.proxy)
+            try:
+                ctp = self._md["currentTradingPeriod"]
+                self._today_open = pd.to_datetime(ctp["regular"]["start"], unit='s', utc=True).tz_convert(self.timezone)
+                self._today_close = pd.to_datetime(ctp["regular"]["end"], unit='s', utc=True).tz_convert(self.timezone)
+                self._today_midnight = self._today_close.ceil("D")
+            except Exception:
+                self._today_open = None
+                self._today_close = None
+                self._today_midnight = None
+                raise
+
+        if self._prices_1y.empty:
+            return self._prices_1y
+
+        dnow = pd.Timestamp.utcnow().tz_convert(self.timezone).date()
+        d1 = dnow
+        d0 = (d1 + datetime.timedelta(days=1)) - utils._interval_to_timedelta("1y")
+        if fullDaysOnly and self._exchange_open_now():
+            # Exclude today
+            d1 -= utils._interval_to_timedelta("1d")
+        return self._prices_1y.loc[str(d0):str(d1)]
+
+    def _get_1wk_1h_prepost_prices(self):
+        if self._prices_1wk_1h_prepost is None:
+            # Temporarily disable error printing
+            logging.disable(logging.CRITICAL)
+            self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
+        return self._prices_1wk_1h_prepost
+
+    def _get_1wk_1h_reg_prices(self):
+        if self._prices_1wk_1h_reg is None:
+            # Temporarily disable error printing
+            logging.disable(logging.CRITICAL)
+            self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
+        return self._prices_1wk_1h_reg
+
+    def _get_exchange_metadata(self):
+        if self._md is not None:
+            return self._md
+
+        self._get_1y_prices()
+        self._md = self._tkr.get_history_metadata(proxy=self.proxy)
+        return self._md
+
+    def _exchange_open_now(self):
+        t = pd.Timestamp.utcnow()
+        self._get_exchange_metadata()
+
+        # if self._today_open is None and self._today_close is None:
+        #     r = False
+        # else:
+        #     r = self._today_open <= t and t < self._today_close
+
+        # if self._today_midnight is None:
+        #     r = False
+        # elif self._today_midnight.date() > t.tz_convert(self.timezone).date():
+        #     r = False
+        # else:
+        #     r = t < self._today_midnight
+
+        last_day_cutoff = self._get_1y_prices().index[-1] + datetime.timedelta(days=1)
+        last_day_cutoff += datetime.timedelta(minutes=20)
+        r = t < last_day_cutoff
+
+        # print("_exchange_open_now() returning", r)
+        return r
+
+    @property
+    def currency(self):
+        if self._currency is not None:
+            return self._currency
+
+        if self._tkr._history_metadata is None:
+            self._get_1y_prices()
+        md = self._tkr.get_history_metadata(proxy=self.proxy)
+        self._currency = md["currency"]
+        return self._currency
+
+    @property
+    def quote_type(self):
+        if self._quote_type is not None:
+            return self._quote_type
+
+        if self._tkr._history_metadata is None:
+            self._get_1y_prices()
+        md = self._tkr.get_history_metadata(proxy=self.proxy)
+        self._quote_type = md["instrumentType"]
+        return self._quote_type
+
+    @property
+    def exchange(self):
+        if self._exchange is not None:
+            return self._exchange
+
+        self._exchange = self._get_exchange_metadata()["exchangeName"]
+        return self._exchange
+
+    @property
+    def timezone(self):
+        if self._timezone is not None:
+            return self._timezone
+
+        self._timezone = self._get_exchange_metadata()["exchangeTimezoneName"]
+        return self._timezone
+
+    @property
+    def shares(self):
+        if self._shares is not None:
+            return self._shares
+
+        shares = self._tkr.get_shares_full(start=pd.Timestamp.utcnow().date()-pd.Timedelta(days=548), proxy=self.proxy)
+        # if shares is None:
+        #     # Requesting 18 months failed, so fallback to shares which should include last year
+        #     shares = self._tkr.get_shares()
+        if shares is not None:
+            if isinstance(shares, pd.DataFrame):
+                shares = shares[shares.columns[0]]
+            self._shares = int(shares.iloc[-1])
+        return self._shares
+
+    @property
+    def last_price(self):
+        if self._last_price is not None:
+            return self._last_price
+        prices = self._get_1y_prices()
+        if prices.empty:
+            md = self._get_exchange_metadata()
+            if "regularMarketPrice" in md:
+                self._last_price = md["regularMarketPrice"]
+        else:
+            self._last_price = float(prices["Close"].iloc[-1])
+            if _np.isnan(self._last_price):
+                md = self._get_exchange_metadata()
+                if "regularMarketPrice" in md:
+                    self._last_price = md["regularMarketPrice"]
+        return self._last_price
+
+    @property
+    def previous_close(self):
+        if self._prev_close is not None:
+            return self._prev_close
+        prices = self._get_1wk_1h_prepost_prices()
+        fail = False
+        if prices.empty:
+            fail = True
+        else:
+            prices = prices[["Close"]].groupby(prices.index.date).last()
+            if prices.shape[0] < 2:
+                # Very few symbols have previousClose despite no 
+                # no trading data e.g. 'QCSTIX'.
+                fail = True
+            else:
+                self._prev_close = float(prices["Close"].iloc[-2])
+        if fail:
+            # Fallback to original info[] if available.
+            self._tkr.info  # trigger fetch
+            k = "previousClose"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._prev_close = self._tkr._quote._retired_info[k]
+        return self._prev_close
+
+    @property
+    def regular_market_previous_close(self):
+        if self._reg_prev_close is not None:
+            return self._reg_prev_close
+        prices = self._get_1y_prices()
+        if prices.shape[0] == 1:
+            # Tiny % of tickers don't return daily history before last trading day, 
+            # so backup option is hourly history:
+            prices = self._get_1wk_1h_reg_prices()
+            prices = prices[["Close"]].groupby(prices.index.date).last()
+        if prices.shape[0] < 2:
+            # Very few symbols have regularMarketPreviousClose despite no 
+            # no trading data. E.g. 'QCSTIX'.
+            # So fallback to original info[] if available.
+            self._tkr.info  # trigger fetch
+            k = "regularMarketPreviousClose"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._reg_prev_close = self._tkr._quote._retired_info[k]
+        else:
+            self._reg_prev_close = float(prices["Close"].iloc[-2])
+        return self._reg_prev_close
+
+    @property
+    def open(self):
+        if self._open is not None:
+            return self._open
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._open = None
+        else:
+            self._open = float(prices["Open"].iloc[-1])
+            if _np.isnan(self._open):
+                self._open = None
+        return self._open
+
+    @property
+    def day_high(self):
+        if self._day_high is not None:
+            return self._day_high
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._day_high = None
+        else:
+            self._day_high = float(prices["High"].iloc[-1])
+            if _np.isnan(self._day_high):
+                self._day_high = None
+        return self._day_high
+
+    @property
+    def day_low(self):
+        if self._day_low is not None:
+            return self._day_low
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._day_low = None
+        else:
+            self._day_low = float(prices["Low"].iloc[-1])
+            if _np.isnan(self._day_low):
+                self._day_low = None
+        return self._day_low
+
+    @property
+    def last_volume(self):
+        if self._last_volume is not None:
+            return self._last_volume
+        prices = self._get_1y_prices()
+        self._last_volume = None if prices.empty else int(prices["Volume"].iloc[-1])
+        return self._last_volume
+
+    @property
+    def fifty_day_average(self):
+        if self._50d_day_average is not None:
+            return self._50d_day_average
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._50d_day_average = None
+        else:
+            n = prices.shape[0]
+            a = n-50
+            b = n
+            if a < 0:
+                a = 0
+            self._50d_day_average = float(prices["Close"].iloc[a:b].mean())
+
+        return self._50d_day_average
+
+    @property
+    def two_hundred_day_average(self):
+        if self._200d_day_average is not None:
+            return self._200d_day_average
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._200d_day_average = None
+        else:
+            n = prices.shape[0]
+            a = n-200
+            b = n
+            if a < 0:
+                a = 0
+
+            self._200d_day_average = float(prices["Close"].iloc[a:b].mean())
+
+        return self._200d_day_average
+
+    @property
+    def ten_day_average_volume(self):
+        if self._10d_avg_vol is not None:
+            return self._10d_avg_vol
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._10d_avg_vol = None
+        else:
+            n = prices.shape[0]
+            a = n-10
+            b = n
+            if a < 0:
+                a = 0
+            self._10d_avg_vol = int(prices["Volume"].iloc[a:b].mean())
+
+        return self._10d_avg_vol
+
+    @property
+    def three_month_average_volume(self):
+        if self._3mo_avg_vol is not None:
+            return self._3mo_avg_vol
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._3mo_avg_vol = None
+        else:
+            dt1 = prices.index[-1]
+            dt0 = dt1 - utils._interval_to_timedelta("3mo") + utils._interval_to_timedelta("1d")
+            self._3mo_avg_vol = int(prices.loc[dt0:dt1, "Volume"].mean())
+
+        return self._3mo_avg_vol
+
+    @property
+    def year_high(self):
+        if self._year_high is not None:
+            return self._year_high
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            prices = self._get_1y_prices(fullDaysOnly=False)
+        self._year_high = float(prices["High"].max())
+        return self._year_high
+
+    @property
+    def year_low(self):
+        if self._year_low is not None:
+            return self._year_low
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            prices = self._get_1y_prices(fullDaysOnly=False)
+        self._year_low = float(prices["Low"].min())
+        return self._year_low
+
+    @property
+    def year_change(self):
+        if self._year_change is not None:
+            return self._year_change
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.shape[0] >= 2:
+            self._year_change = (prices["Close"].iloc[-1] - prices["Close"].iloc[0]) / prices["Close"].iloc[0]
+            self._year_change = float(self._year_change)
+        return self._year_change
+
+    @property
+    def market_cap(self):
+        if self._mcap is not None:
+            return self._mcap
+
+        try:
+            shares = self.shares
+        except Exception as e:
+            if "Cannot retrieve share count" in str(e):
+                shares = None
+            elif "failed to decrypt Yahoo" in str(e):
+                shares = None
+            else:
+                raise
+
+        if shares is None:
+            # Very few symbols have marketCap despite no share count.
+            # E.g. 'BTC-USD'
+            # So fallback to original info[] if available.
+            self._tkr.info
+            k = "marketCap"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._mcap = self._tkr._quote._retired_info[k]
+        else:
+            self._mcap = float(shares * self.last_price)
+        return self._mcap
+
 
 class Quote:
 
-    def __init__(self, data: TickerData, proxy=None):
+    def __init__(self, data: YfData, symbol: str, proxy=None):
         self._data = data
+        self._symbol = symbol
         self.proxy = proxy
 
         self._info = None
@@ -87,161 +563,87 @@ class Quote:
         self._calendar = None
 
         self._already_scraped = False
-        self._already_scraped_complementary = False
+        self._already_fetched = False
+        self._already_fetched_complementary = False
 
     @property
     def info(self) -> dict:
         if self._info is None:
-            self._scrape(self.proxy)
-            self._scrape_complementary(self.proxy)
+            self._fetch(self.proxy)
+            self._fetch_complementary(self.proxy)
 
         return self._info
 
     @property
     def sustainability(self) -> pd.DataFrame:
         if self._sustainability is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('sustainability')
         return self._sustainability
 
     @property
     def recommendations(self) -> pd.DataFrame:
         if self._recommendations is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('recommendations')
         return self._recommendations
 
     @property
     def calendar(self) -> pd.DataFrame:
         if self._calendar is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('calendar')
         return self._calendar
 
-    def _scrape(self, proxy):
-        if self._already_scraped:
+    def _fetch(self, proxy):
+        if self._already_fetched:
             return
-        self._already_scraped = True
+        self._already_fetched = True
+        modules = ['financialData', 'quoteType', 'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
+        modules = ','.join(modules)
+        params_dict = {"modules": modules, "ssl": "true"}
+        result = self._data.get_raw_json(
+            _BASIC_URL_ + f"/{self._symbol}", params=params_dict, proxy=proxy
+        )
+        result["quoteSummary"]["result"][0]["symbol"] = self._symbol
+        query1_info = next(
+            (info for info in result.get("quoteSummary", {}).get("result", []) if info["symbol"] == self._symbol),
+            None,
+        )
+        # Most keys that appear in multiple dicts have same value. Except 'maxAge' because
+        # Yahoo not consistent with days vs seconds. Fix it here:
+        for k in query1_info:
+            if "maxAge" in query1_info[k] and query1_info[k]["maxAge"] == 1:
+                query1_info[k]["maxAge"] = 86400
+        query1_info = {
+            k1: v1 
+            for k, v in query1_info.items() 
+            if isinstance(v, dict) 
+            for k1, v1 in v.items() 
+            if v1
+        }
+        # recursively format but only because of 'companyOfficers'
 
-        # get info and sustainability
-        json_data = self._data.get_json_data_stores(proxy=proxy)
-        try:
-            quote_summary_store = json_data['QuoteSummaryStore']
-        except KeyError:
-            err_msg = "No summary info found, symbol may be delisted"
-            print('- %s: %s' % (self._data.ticker, err_msg))
-            return None
-
-        # sustainability
-        d = {}
-        try:
-            if isinstance(quote_summary_store.get('esgScores'), dict):
-                for item in quote_summary_store['esgScores']:
-                    if not isinstance(quote_summary_store['esgScores'][item], (dict, list)):
-                        d[item] = quote_summary_store['esgScores'][item]
-
-                s = pd.DataFrame(index=[0], data=d)[-1:].T
-                s.columns = ['Value']
-                s.index.name = '%.f-%.f' % (
-                    s[s.index == 'ratingYear']['Value'].values[0],
-                    s[s.index == 'ratingMonth']['Value'].values[0])
-
-                self._sustainability = s[~s.index.isin(
-                    ['maxAge', 'ratingYear', 'ratingMonth'])]
-        except Exception:
-            pass
-
-        self._info = {}
-        try:
-            items = ['summaryProfile', 'financialData', 'quoteType',
-                     'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
-            for item in items:
-                if isinstance(quote_summary_store.get(item), dict):
-                    self._info.update(quote_summary_store[item])
-        except Exception:
-            pass
-
-        # For ETFs, provide this valuable data: the top holdings of the ETF
-        try:
-            if 'topHoldings' in quote_summary_store:
-                self._info.update(quote_summary_store['topHoldings'])
-        except Exception:
-            pass
-
-        try:
-            if not isinstance(quote_summary_store.get('summaryDetail'), dict):
-                # For some reason summaryDetail did not give any results. The price dict
-                # usually has most of the same info
-                self._info.update(quote_summary_store.get('price', {}))
-        except Exception:
-            pass
-
-        try:
-            # self._info['regularMarketPrice'] = self._info['regularMarketOpen']
-            self._info['regularMarketPrice'] = quote_summary_store.get('price', {}).get(
-                'regularMarketPrice', self._info.get('regularMarketOpen', None))
-        except Exception:
-            pass
-
-        try:
-            self._info['preMarketPrice'] = quote_summary_store.get('price', {}).get(
-                'preMarketPrice', self._info.get('preMarketPrice', None))
-        except Exception:
-            pass
-
-        self._info['logo_url'] = ""
-        try:
-            if not 'website' in self._info:
-                self._info['logo_url'] = 'https://logo.clearbit.com/%s.com' % \
-                                         self._info['shortName'].split(' ')[0].split(',')[0]
+        def _format(k, v):
+            if isinstance(v, dict) and "raw" in v and "fmt" in v:
+                v2 = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
+            elif isinstance(v, list):
+                v2 = [_format(None, x) for x in v]
+            elif isinstance(v, dict):
+                v2 = {k: _format(k, x) for k, x in v.items()}
+            elif isinstance(v, str):
+                v2 = v.replace("\xa0", " ")
             else:
-                domain = self._info['website'].split(
-                    '://')[1].split('/')[0].replace('www.', '')
-                self._info['logo_url'] = 'https://logo.clearbit.com/%s' % domain
-        except Exception:
-            pass
+                v2 = v
+            return v2
+        for k, v in query1_info.items():
+            query1_info[k] = _format(k, v)
+        self._info = query1_info
 
-        # Delete redundant info[] keys, because values can be accessed faster
-        # elsewhere - e.g. price keys. Hope is reduces Yahoo spam effect.
-        # But record the dropped keys, because in rare cases they are needed.
-        self._retired_info = {}
-        for k in info_retired_keys:
-            if k in self._info:
-                self._retired_info[k] = self._info[k]
-                if PRUNE_INFO:
-                    del self._info[k]
-        if PRUNE_INFO:
-            # InfoDictWrapper will explain how to access above data elsewhere
-            self._info = InfoDictWrapper(self._info)
-
-        # events
-        try:
-            cal = pd.DataFrame(quote_summary_store['calendarEvents']['earnings'])
-            cal['earningsDate'] = pd.to_datetime(
-                cal['earningsDate'], unit='s')
-            self._calendar = cal.T
-            self._calendar.index = utils.camel2title(self._calendar.index)
-            self._calendar.columns = ['Value']
-        except Exception as e:
-            pass
-
-        # analyst recommendations
-        try:
-            rec = pd.DataFrame(
-                quote_summary_store['upgradeDowngradeHistory']['history'])
-            rec['earningsDate'] = pd.to_datetime(
-                rec['epochGradeDate'], unit='s')
-            rec.set_index('earningsDate', inplace=True)
-            rec.index.name = 'Date'
-            rec.columns = utils.camel2title(rec.columns)
-            self._recommendations = rec[[
-                'Firm', 'To Grade', 'From Grade', 'Action']].sort_index()
-        except Exception:
-            pass
-
-    def _scrape_complementary(self, proxy):
-        if self._already_scraped_complementary:
+    def _fetch_complementary(self, proxy):
+        if self._already_fetched_complementary:
             return
-        self._already_scraped_complementary = True
+        self._already_fetched_complementary = True
 
-        self._scrape(proxy)
+        # self._scrape(proxy)  # decrypt broken
+        self._fetch(proxy)
         if self._info is None:
             return
 
@@ -270,8 +672,7 @@ class Quote:
             #     pass
             #
             # For just one/few variable is faster to query directly:
-            url = "https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{}?symbol={}".format(
-                self._data.ticker, self._data.ticker)
+            url = f"https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._symbol}?symbol={self._symbol}"
             for k in keys:
                 url += "&type=" + k
             # Request 6 months of data
@@ -283,11 +684,14 @@ class Quote:
 
             json_str = self._data.cache_get(url=url, proxy=proxy).text
             json_data = json.loads(json_str)
-            key_stats = json_data["timeseries"]["result"][0]
-            if k not in key_stats:
-                # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+            try:
+                key_stats = json_data["timeseries"]["result"][0]
+                if k not in key_stats:
+                    # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+                    v = None
+                else:
+                    # Select most recent (last) raw value in list:
+                    v = key_stats[k][-1]["reportedValue"]["raw"]
+            except Exception:
                 v = None
-            else:
-                # Select most recent (last) raw value in list:
-                v = key_stats[k][-1]["reportedValue"]["raw"]
             self._info[k] = v

yfinance/scrapers/yahoo-keys.txt

@@ -3,3 +3,6 @@ ad4d90b3c9f2e1d156ef98eadfa0ff93e4042f6960e54aa2a13f06f528e6b50ba4265a26a1fd5b9c
 e9a8ab8e5620b712ebc2fb4f33d5c8b9c80c0d07e8c371911c785cf674789f1747d76a909510158a7b7419e86857f2d7abbd777813ff64840e4cbc514d12bcae
 6ae2523aeafa283dad746556540145bf603f44edbf37ad404d3766a8420bb5eb1d3738f52a227b88283cca9cae44060d5f0bba84b6a495082589f5fe7acbdc9e
 3365117c2a368ffa5df7313a4a84988f73926a86358e8eea9497c5ff799ce27d104b68e5f2fbffa6f8f92c1fef41765a7066fa6bcf050810a9c4c7872fd3ebf0
+15d8f57919857d5a5358d2082c7ef0f1129cfacd2a6480333dcfb954b7bb67d820abefebfdb0eaa6ef18a1c57f617b67d7e7b0ec040403b889630ae5db5a4dbb
+db9630d707a7d0953ac795cd8db1ca9ca6c9d8239197cdfda24b4e0ec9c37eaec4db82dab68b8f606ab7b5b4af3e65dab50606f8cf508269ec927e6ee605fb78
+3c895fb5ddcc37d20d3073ed74ee3efad59bcb147c8e80fd279f83701b74b092d503dcd399604c6d8be8f3013429d3c2c76ed5b31b80c9df92d5eab6d3339fce

yfinance/shared.py

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

yfinance/ticker.py

@@ -22,10 +22,10 @@
 from __future__ import print_function
 
 import datetime as _datetime
-import pandas as _pd
-
 from collections import namedtuple as _namedtuple
 
+import pandas as _pd
+
 from .base import TickerBase
 
 
@@ -33,25 +33,29 @@ class Ticker(TickerBase):
     def __init__(self, ticker, session=None):
         super(Ticker, self).__init__(ticker, session=session)
         self._expirations = {}
+        self._underlying  = {}
 
     def __repr__(self):
-        return 'yfinance.Ticker object <%s>' % self.ticker
+        return f'yfinance.Ticker object <{self.ticker}>'
 
     def _download_options(self, date=None, proxy=None):
         if date is None:
-            url = "{}/v7/finance/options/{}".format(
-                self._base_url, self.ticker)
+            url = f"{self._base_url}/v7/finance/options/{self.ticker}"
         else:
-            url = "{}/v7/finance/options/{}?date={}".format(
-                self._base_url, self.ticker, date)
+            url = f"{self._base_url}/v7/finance/options/{self.ticker}?date={date}"
 
         r = self._data.get(url=url, proxy=proxy).json()
         if len(r.get('optionChain', {}).get('result', [])) > 0:
             for exp in r['optionChain']['result'][0]['expirationDates']:
                 self._expirations[_datetime.datetime.utcfromtimestamp(
                     exp).strftime('%Y-%m-%d')] = exp
+
+            self._underlying = r['optionChain']['result'][0].get('quote', {})
+
             opt = r['optionChain']['result'][0].get('options', [])
-            return opt[0] if len(opt) > 0 else []
+
+            return dict(**opt[0],underlying=self._underlying) if len(opt) > 0 else {}
+        return {}
 
     def _options2df(self, opt, tz=None):
         data = _pd.DataFrame(opt).reindex(columns=[
@@ -84,15 +88,15 @@ class Ticker(TickerBase):
                 self._download_options()
             if date not in self._expirations:
                 raise ValueError(
-                    "Expiration `%s` cannot be found. "
-                    "Available expiration are: [%s]" % (
-                        date, ', '.join(self._expirations)))
+                    f"Expiration `{date}` cannot be found. "
+                    f"Available expirations are: [{', '.join(self._expirations)}]")
             date = self._expirations[date]
             options = self._download_options(date, proxy=proxy)
 
-        return _namedtuple('Options', ['calls', 'puts'])(**{
+        return _namedtuple('Options', ['calls', 'puts', 'underlying'])(**{
             "calls": self._options2df(options['calls'], tz=tz),
-            "puts": self._options2df(options['puts'], tz=tz)
+            "puts": self._options2df(options['puts'], tz=tz),
+            "underlying": options['underlying']
         })
 
     # ------------------------
@@ -133,14 +137,14 @@ class Ticker(TickerBase):
     def shares(self) -> _pd.DataFrame :
         return self.get_shares()
 
-    @property
-    def market_cap(self) -> float:
-        return self.calc_market_cap()
-
     @property
     def info(self) -> dict:
         return self.get_info()
 
+    @property
+    def fast_info(self):
+        return self.get_fast_info()
+
     @property
     def calendar(self) -> _pd.DataFrame:
         return self.get_calendar()
@@ -239,6 +243,10 @@ class Ticker(TickerBase):
     def news(self):
         return self.get_news()
 
+    @property
+    def trend_details(self) -> _pd.DataFrame:
+        return self.get_trend_details()
+
     @property
     def earnings_trend(self) -> _pd.DataFrame:
         return self.get_earnings_trend()

yfinance/tickers.py

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

yfinance/utils.py

@@ -22,24 +22,25 @@
 from __future__ import print_function
 
 import datetime as _datetime
-import dateutil as _dateutil
-from typing import Dict, Union, List, Optional
+import logging
+import re as _re
+import sys as _sys
+import threading
+from functools import lru_cache
+from inspect import getmembers
+from types import FunctionType
+from typing import Dict, List, Optional
 
+import numpy as _np
+import pandas as _pd
 import pytz as _tz
 import requests as _requests
-import re as _re
-import pandas as _pd
-import numpy as _np
-import sys as _sys
-import os as _os
-import appdirs as _ad
-import sqlite3 as _sqlite3
-import atexit as _atexit
-
-from threading import Lock
-
+from dateutil.relativedelta import relativedelta
 from pytz import UnknownTimeZoneError
 
+from yfinance import const
+from .const import _BASE_URL_
+
 try:
     import ujson as _json
 except ImportError:
@@ -50,28 +51,144 @@ user_agent_headers = {
 
 
 # From https://stackoverflow.com/a/59128615
-from types import FunctionType
-from inspect import getmembers
 def attributes(obj):
     disallowed_names = {
-      name for name, value in getmembers(type(obj)) 
+        name for name, value in getmembers(type(obj))
         if isinstance(value, FunctionType)}
     return {
-      name: getattr(obj, name) for name in dir(obj) 
+        name: getattr(obj, name) for name in dir(obj)
         if name[0] != '_' and name not in disallowed_names and hasattr(obj, name)}
 
 
+@lru_cache(maxsize=20)
+def print_once(msg):
+    # 'warnings' module suppression of repeat messages does not work. 
+    # This function replicates correct behaviour
+    print(msg)
+
+
+# Logging
+# Note: most of this logic is adding indentation with function depth,
+#       so that DEBUG log is readable.
+class IndentLoggerAdapter(logging.LoggerAdapter):
+    def process(self, msg, kwargs):
+        if get_yf_logger().isEnabledFor(logging.DEBUG):
+            i = ' ' * self.extra['indent']
+            if not isinstance(msg, str):
+                msg = str(msg)
+            msg = '\n'.join([i + m for m in msg.split('\n')])
+        return msg, kwargs
+
+
+_indentation_level = threading.local()
+
+
+class IndentationContext:
+    def __init__(self, increment=1):
+        self.increment = increment
+
+    def __enter__(self):
+        _indentation_level.indent = getattr(_indentation_level, 'indent', 0) + self.increment
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        _indentation_level.indent -= self.increment
+
+
+def get_indented_logger(name=None):
+    # Never cache the returned value! Will break indentation.
+    return IndentLoggerAdapter(logging.getLogger(name), {'indent': getattr(_indentation_level, 'indent', 0)})
+
+
+def log_indent_decorator(func):
+    def wrapper(*args, **kwargs):
+        logger = get_indented_logger('yfinance')
+        logger.debug(f'Entering {func.__name__}()')
+
+        with IndentationContext():
+            result = func(*args, **kwargs)
+
+        logger.debug(f'Exiting {func.__name__}()')
+        return result
+
+    return wrapper
+
+
+class MultiLineFormatter(logging.Formatter):
+    # The 'fmt' formatting further down is only applied to first line
+    # of log message, specifically the padding after %level%.
+    # For multi-line messages, need to manually copy over padding.
+    def __init__(self, fmt):
+        super().__init__(fmt)
+        # Extract amount of padding
+        match = _re.search(r'%\(levelname\)-(\d+)s', fmt)
+        self.level_length = int(match.group(1)) if match else 0
+
+    def format(self, record):
+        original = super().format(record)
+        lines = original.split('\n')
+        levelname = lines[0].split(' ')[0]
+        if len(lines) <= 1:
+            return original
+        else:
+            # Apply padding to all lines below first
+            formatted = [lines[0]]
+            if self.level_length == 0:
+                padding = ' ' * len(levelname)
+            else:
+                padding = ' ' * self.level_length
+            padding += ' '  # +1 for space between level and message
+            formatted.extend(padding + line for line in lines[1:])
+            return '\n'.join(formatted)
+
+
+yf_logger = None
+yf_log_indented = False
+
+
+def get_yf_logger():
+    global yf_logger
+    if yf_logger is None:
+        yf_logger = logging.getLogger('yfinance')
+    global yf_log_indented
+    if yf_log_indented:
+        yf_logger = get_indented_logger('yfinance')
+    return yf_logger
+
+
+def setup_debug_formatting():
+    global yf_logger
+    yf_logger = get_yf_logger()
+
+    if not yf_logger.isEnabledFor(logging.DEBUG):
+        yf_logger.warning("logging mode not set to 'DEBUG', so not setting up debug formatting")
+        return
+
+    global yf_log_indented
+    if not yf_log_indented:
+        if yf_logger.handlers is None or len(yf_logger.handlers) == 0:
+            h = logging.StreamHandler()
+            # Ensure different level strings don't interfere with indentation
+            formatter = MultiLineFormatter(fmt='%(levelname)-8s %(message)s')
+            h.setFormatter(formatter)
+            yf_logger.addHandler(h)
+
+    yf_log_indented = True
+
+
+def enable_debug_mode():
+    get_yf_logger().setLevel(logging.DEBUG)
+    setup_debug_formatting()
+
+
 def is_isin(string):
-    return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9]{1})$", string))
+    return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9])$", string))
 
 
 def get_all_by_isin(isin, proxy=None, session=None):
     if not (is_isin(isin)):
         raise ValueError("Invalid ISIN number")
-
-    from .base import _BASE_URL_
     session = session or _requests
-    url = "{}/v1/finance/search?q={}".format(_BASE_URL_, isin)
+    url = f"{_BASE_URL_}/v1/finance/search?q={isin}"
     data = session.get(url=url, proxies=proxy, headers=user_agent_headers)
     try:
         data = data.json()
@@ -123,7 +240,7 @@ def empty_earnings_dates_df():
 
 
 def build_template(data):
-    '''
+    """
     build_template returns the details required to rebuild any of the yahoo finance financial statements in the same order as the yahoo finance webpage. The function is built to be used on the "FinancialTemplateStore" json which appears in any one of the three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
 
     Returns:
@@ -132,95 +249,80 @@ def build_template(data):
         - template_order: The order that quarterlies should be in (note that quarterlies have no pre-fix - hence why this is required).
         - level_detail: The level of each individual line item. E.g. for the "/financials" webpage, "Total Revenue" is a level 0 item and is the summation of "Operating Revenue" and "Excise Taxes" which are level 1 items.
 
-    '''
+    """
     template_ttm_order = []  # Save the TTM (Trailing Twelve Months) ordering to an object.
     template_annual_order = []  # Save the annual ordering to an object.
     template_order = []  # Save the ordering to an object (this can be utilized for quarterlies)
     level_detail = []  # Record the level of each line item of the income statement ("Operating Revenue" and "Excise Taxes" sum to return "Total Revenue" we need to keep track of this)
-    for key in data['template']:
-        # Loop through the json to retreive the exact financial order whilst appending to the objects
-        template_ttm_order.append('trailing{}'.format(key['key']))
-        template_annual_order.append('annual{}'.format(key['key']))
-        template_order.append('{}'.format(key['key']))
-        level_detail.append(0)
-        if 'children' in key:
-            for child1 in key['children']:  # Level 1
-                template_ttm_order.append('trailing{}'.format(child1['key']))
-                template_annual_order.append('annual{}'.format(child1['key']))
-                template_order.append('{}'.format(child1['key']))
-                level_detail.append(1)
-                if 'children' in child1:
-                    for child2 in child1['children']:  # Level 2
-                        template_ttm_order.append('trailing{}'.format(child2['key']))
-                        template_annual_order.append('annual{}'.format(child2['key']))
-                        template_order.append('{}'.format(child2['key']))
-                        level_detail.append(2)
-                        if 'children' in child2:
-                            for child3 in child2['children']:  # Level 3
-                                template_ttm_order.append('trailing{}'.format(child3['key']))
-                                template_annual_order.append('annual{}'.format(child3['key']))
-                                template_order.append('{}'.format(child3['key']))
-                                level_detail.append(3)
-                                if 'children' in child3:
-                                    for child4 in child3['children']:  # Level 4
-                                        template_ttm_order.append('trailing{}'.format(child4['key']))
-                                        template_annual_order.append('annual{}'.format(child4['key']))
-                                        template_order.append('{}'.format(child4['key']))
-                                        level_detail.append(4)
-                                        if 'children' in child4:
-                                            for child5 in child4['children']:  # Level 5
-                                                template_ttm_order.append('trailing{}'.format(child5['key']))
-                                                template_annual_order.append('annual{}'.format(child5['key']))
-                                                template_order.append('{}'.format(child5['key']))
-                                                level_detail.append(5)
+
+    def traverse(node, level):
+        """
+        A recursive function that visits a node and its children.
+
+        Args:
+            node: The current node in the data structure.
+            level: The depth of the current node in the data structure.
+        """
+        if level > 5:  # Stop when level is above 5
+            return
+        template_ttm_order.append(f"trailing{node['key']}")
+        template_annual_order.append(f"annual{node['key']}")
+        template_order.append(f"{node['key']}")
+        level_detail.append(level)
+        if 'children' in node:  # Check if the node has children
+            for child in node['children']:  # If yes, traverse each child
+                traverse(child, level + 1)  # Increment the level by 1 for each child
+
+    for key in data['template']:  # Loop through the data
+        traverse(key, 0)  # Call the traverse function with initial level being 0
+
     return template_ttm_order, template_annual_order, template_order, level_detail
 
 
-def retreive_financial_details(data):
-    '''
-    retreive_financial_details returns all of the available financial details under the "QuoteTimeSeriesStore" for any of the following three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
+def retrieve_financial_details(data):
+    """
+    retrieve_financial_details returns all of the available financial details under the
+    "QuoteTimeSeriesStore" for any of the following three yahoo finance webpages:
+    "/financials", "/cash-flow" and "/balance-sheet".
 
     Returns:
         - TTM_dicts: A dictionary full of all of the available Trailing Twelve Month figures, this can easily be converted to a pandas dataframe.
         - Annual_dicts: A dictionary full of all of the available Annual figures, this can easily be converted to a pandas dataframe.
-    '''
+    """
     TTM_dicts = []  # Save a dictionary object to store the TTM financials.
     Annual_dicts = []  # Save a dictionary object to store the Annual financials.
-    for key in data['timeSeries']:  # Loop through the time series data to grab the key financial figures.
+
+    for key, timeseries in data.get('timeSeries', {}).items():  # Loop through the time series data to grab the key financial figures.
         try:
-            if len(data['timeSeries'][key]) > 0:
-                time_series_dict = {}
-                time_series_dict['index'] = key
-                for each in data['timeSeries'][key]:  # Loop through the years
-                    if each == None:
+            if timeseries:
+                time_series_dict = {'index': key}
+                for each in timeseries:  # Loop through the years
+                    if not each:
                         continue
-                    else:
-                        time_series_dict[each['asOfDate']] = each['reportedValue']
-                    # time_series_dict["{}".format(each['asOfDate'])] = data['timeSeries'][key][each]['reportedValue']
+                    time_series_dict[each.get('asOfDate')] = each.get('reportedValue')
                 if 'trailing' in key:
                     TTM_dicts.append(time_series_dict)
                 elif 'annual' in key:
                     Annual_dicts.append(time_series_dict)
-        except Exception as e:
-            pass
+        except KeyError as e:
+            print(f"An error occurred while processing the key: {e}")
     return TTM_dicts, Annual_dicts
 
 
 def format_annual_financial_statement(level_detail, annual_dicts, annual_order, ttm_dicts=None, ttm_order=None):
-    '''
+    """
     format_annual_financial_statement formats any annual financial statement
 
     Returns:
         - _statement: A fully formatted annual financial statement in pandas dataframe.
-    '''
+    """
     Annual = _pd.DataFrame.from_dict(annual_dicts).set_index("index")
     Annual = Annual.reindex(annual_order)
     Annual.index = Annual.index.str.replace(r'annual', '')
 
     # Note: balance sheet is the only financial statement with no ttm detail
-    if (ttm_dicts not in [[], None]) and (ttm_order not in [[], None]):
-        TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index")
-        TTM = TTM.reindex(ttm_order)
+    if ttm_dicts and ttm_order:
+        TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index").reindex(ttm_order)
         # Add 'TTM' prefix to all column names, so if combined we can tell
         # the difference between actuals and TTM (similar to yahoo finance).
         TTM.columns = ['TTM ' + str(col) for col in TTM.columns]
@@ -238,12 +340,12 @@ def format_annual_financial_statement(level_detail, annual_dicts, annual_order,
 
 
 def format_quarterly_financial_statement(_statement, level_detail, order):
-    '''
+    """
     format_quarterly_financial_statements formats any quarterly financial statement
 
     Returns:
         - _statement: A fully formatted quarterly financial statement in pandas dataframe.
-    '''
+    """
     _statement = _statement.reindex(order)
     _statement.index = camel2title(_statement.T)
     _statement['level_detail'] = level_detail
@@ -294,12 +396,17 @@ def camel2title(strings: List[str], sep: str = ' ', acronyms: Optional[List[str]
 
     # Apply str.title() to non-acronym words
     strings = [s.split(sep) for s in strings]
-    strings = [[j.title() if not j in acronyms else j for j in s] for s in strings]
+    strings = [[j.title() if j not in acronyms else j for j in s] for s in strings]
     strings = [sep.join(s) for s in strings]
 
     return strings
 
 
+def snake_case_2_camelCase(s):
+    sc = s.split('_')[0] + ''.join(x.title() for x in s.split('_')[1:])
+    return sc
+
+
 def _parse_user_dt(dt, exchange_tz):
     if isinstance(dt, int):
         # Should already be epoch, test with conversion:
@@ -319,24 +426,24 @@ def _parse_user_dt(dt, exchange_tz):
 
 def _interval_to_timedelta(interval):
     if interval == "1mo":
-        return _dateutil.relativedelta.relativedelta(months=1)
+        return relativedelta(months=1)
     elif interval == "3mo":
-        return _dateutil.relativedelta.relativedelta(months=3)
+        return relativedelta(months=3)
     elif interval == "1y":
-        return _dateutil.relativedelta.relativedelta(years=1)
+        return relativedelta(years=1)
     elif interval == "1wk":
-        return _pd.Timedelta(days=7, unit='d')
-    else: 
+        return _pd.Timedelta(days=7)
+    else:
         return _pd.Timedelta(interval)
 
 
 def auto_adjust(data):
     col_order = data.columns
     df = data.copy()
-    ratio = df["Close"] / df["Adj Close"]
-    df["Adj Open"] = df["Open"] / ratio
-    df["Adj High"] = df["High"] / ratio
-    df["Adj Low"] = df["Low"] / ratio
+    ratio = (df["Adj Close"] / df["Close"]).to_numpy()
+    df["Adj Open"] = df["Open"] * ratio
+    df["Adj High"] = df["High"] * ratio
+    df["Adj Low"] = df["Low"] * ratio
 
     df.drop(
         ["Open", "High", "Low", "Close"],
@@ -399,12 +506,9 @@ def parse_quotes(data):
 
 
 def parse_actions(data):
-    dividends = _pd.DataFrame(
-        columns=["Dividends"], index=_pd.DatetimeIndex([]))
-    capital_gains = _pd.DataFrame(
-        columns=["Capital Gains"], index=_pd.DatetimeIndex([]))
-    splits = _pd.DataFrame(
-        columns=["Stock Splits"], index=_pd.DatetimeIndex([]))
+    dividends = None
+    capital_gains = None
+    splits = None
 
     if "events" in data:
         if "dividends" in data["events"]:
@@ -429,10 +533,19 @@ def parse_actions(data):
             splits.set_index("date", inplace=True)
             splits.index = _pd.to_datetime(splits.index, unit="s")
             splits.sort_index(inplace=True)
-            splits["Stock Splits"] = splits["numerator"] / \
-                                     splits["denominator"]
+            splits["Stock Splits"] = splits["numerator"] / splits["denominator"]
             splits = splits[["Stock Splits"]]
 
+    if dividends is None:
+        dividends = _pd.DataFrame(
+            columns=["Dividends"], index=_pd.DatetimeIndex([]))
+    if capital_gains is None:
+        capital_gains = _pd.DataFrame(
+            columns=["Capital Gains"], index=_pd.DatetimeIndex([]))
+    if splits is None:
+        splits = _pd.DataFrame(
+            columns=["Stock Splits"], index=_pd.DatetimeIndex([]))
+
     return dividends, splits, capital_gains
 
 
@@ -443,6 +556,34 @@ def set_df_tz(df, interval, tz):
     return df
 
 
+def fix_Yahoo_returning_prepost_unrequested(quotes, interval, tradingPeriods):
+    # Sometimes Yahoo returns post-market data despite not requesting it.
+    # Normally happens on half-day early closes.
+    #
+    # And sometimes returns pre-market data despite not requesting it.
+    # E.g. some London tickers.
+    tps_df = tradingPeriods.copy()
+    tps_df["_date"] = tps_df.index.date
+    quotes["_date"] = quotes.index.date
+    idx = quotes.index.copy()
+    quotes = quotes.merge(tps_df, how="left")
+    quotes.index = idx
+    # "end" = end of regular trading hours (including any auction)
+    f_drop = quotes.index >= quotes["end"]
+    f_drop = f_drop | (quotes.index < quotes["start"])
+    if f_drop.any():
+        # When printing report, ignore rows that were already NaNs:
+        # f_na = quotes[["Open","Close"]].isna().all(axis=1)
+        # n_nna = quotes.shape[0] - _np.sum(f_na)
+        # n_drop_nna = _np.sum(f_drop & ~f_na)
+        # quotes_dropped = quotes[f_drop]
+        # if debug and n_drop_nna > 0:
+        #     print(f"Dropping {n_drop_nna}/{n_nna} intervals for falling outside regular trading hours")
+        quotes = quotes[~f_drop]
+    quotes = quotes.drop(["_date", "start", "end"], axis=1)
+    return quotes
+
+
 def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
     # Yahoo bug fix. If market is open today then Yahoo normally returns 
     # todays data as a separate row from rest-of week/month interval in above row. 
@@ -471,38 +612,43 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
             elif interval == "3mo":
                 last_rows_same_interval = dt1.year == dt2.year and dt1.quarter == dt2.quarter
             else:
-                last_rows_same_interval = (dt1-dt2) < _pd.Timedelta(interval)
+                last_rows_same_interval = (dt1 - dt2) < _pd.Timedelta(interval)
 
             if last_rows_same_interval:
                 # Last two rows are within same interval
                 idx1 = quotes.index[n - 1]
                 idx2 = quotes.index[n - 2]
+                if idx1 == idx2:
+                    # Yahoo returning last interval duplicated, which means
+                    # Yahoo is not returning live data (phew!)
+                    return quotes
                 if _np.isnan(quotes.loc[idx2, "Open"]):
-                    quotes.loc[idx2, "Open"] = quotes["Open"][n - 1]
-                # Note: nanmax() & nanmin() ignores NaNs
-                quotes.loc[idx2, "High"] = _np.nanmax([quotes["High"][n - 1], quotes["High"][n - 2]])
-                quotes.loc[idx2, "Low"] = _np.nanmin([quotes["Low"][n - 1], quotes["Low"][n - 2]])
-                quotes.loc[idx2, "Close"] = quotes["Close"][n - 1]
-                if "Adj High" in quotes.columns:
-                    quotes.loc[idx2, "Adj High"] = _np.nanmax([quotes["Adj High"][n - 1], quotes["Adj High"][n - 2]])
-                if "Adj Low" in quotes.columns:
-                    quotes.loc[idx2, "Adj Low"] = _np.nanmin([quotes["Adj Low"][n - 1], quotes["Adj Low"][n - 2]])
+                    quotes.loc[idx2, "Open"] = quotes["Open"].iloc[n - 1]
+                # Note: nanmax() & nanmin() ignores NaNs, but still need to check not all are NaN to avoid warnings
+                if not _np.isnan(quotes["High"].iloc[n - 1]):
+                    quotes.loc[idx2, "High"] = _np.nanmax([quotes["High"].iloc[n - 1], quotes["High"].iloc[n - 2]])
+                    if "Adj High" in quotes.columns:
+                        quotes.loc[idx2, "Adj High"] = _np.nanmax([quotes["Adj High"].iloc[n - 1], quotes["Adj High"].iloc[n - 2]])
+
+                if not _np.isnan(quotes["Low"].iloc[n - 1]):
+                    quotes.loc[idx2, "Low"] = _np.nanmin([quotes["Low"].iloc[n - 1], quotes["Low"].iloc[n - 2]])
+                    if "Adj Low" in quotes.columns:
+                        quotes.loc[idx2, "Adj Low"] = _np.nanmin([quotes["Adj Low"].iloc[n - 1], quotes["Adj Low"].iloc[n - 2]])
+
+                quotes.loc[idx2, "Close"] = quotes["Close"].iloc[n - 1]
                 if "Adj Close" in quotes.columns:
-                    quotes.loc[idx2, "Adj Close"] = quotes["Adj Close"][n - 1]
-                quotes.loc[idx2, "Volume"] += quotes["Volume"][n - 1]
+                    quotes.loc[idx2, "Adj Close"] = quotes["Adj Close"].iloc[n - 1]
+                quotes.loc[idx2, "Volume"] += quotes["Volume"].iloc[n - 1]
                 quotes = quotes.drop(quotes.index[n - 1])
 
     return quotes
 
 
 def safe_merge_dfs(df_main, df_sub, interval):
-    # Carefully merge 'df_sub' onto 'df_main'
-    # If naive merge fails, try again with reindexing df_sub:
-    # 1) if interval is weekly or monthly, then try with index set to start of week/month
-    # 2) if still failing then manually search through df_main.index to reindex df_sub
-
-    if df_sub.shape[0] == 0:
+    if df_sub.empty:
         raise Exception("No data to merge")
+    if df_main.empty:
+        return df_main
 
     df_sub_backup = df_sub.copy()
     data_cols = [c for c in df_sub.columns if c not in df_main]
@@ -510,6 +656,86 @@ def safe_merge_dfs(df_main, df_sub, interval):
         raise Exception("Expected 1 data col")
     data_col = data_cols[0]
 
+    df_main = df_main.sort_index()
+    intraday = interval.endswith('m') or interval.endswith('s')
+
+    td = _interval_to_timedelta(interval)
+    if intraday:
+        # On some exchanges the event can occur before market open.
+        # Problem when combining with intraday data.
+        # Solution = use dates, not datetimes, to map/merge.
+        df_main['_date'] = df_main.index.date
+        df_sub['_date'] = df_sub.index.date
+        indices = _np.searchsorted(_np.append(df_main['_date'], [df_main['_date'].iloc[-1]+td]), df_sub['_date'], side='left')
+        df_main = df_main.drop('_date', axis=1)
+        df_sub = df_sub.drop('_date', axis=1)
+    else:
+        indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1] + td), df_sub.index, side='right')
+        indices -= 1  # Convert from [[i-1], [i]) to [[i], [i+1])
+    # Numpy.searchsorted does not handle out-of-range well, so handle manually:
+    if intraday:
+        for i in range(len(df_sub.index)):
+            dt = df_sub.index[i].date()
+            if dt < df_main.index[0].date() or dt >= df_main.index[-1].date() + _datetime.timedelta(days=1):
+                # Out-of-range
+                indices[i] = -1
+    else:
+        for i in range(len(df_sub.index)):
+            dt = df_sub.index[i]
+            if dt < df_main.index[0] or dt >= df_main.index[-1] + td:
+                # Out-of-range
+                indices[i] = -1
+
+    f_outOfRange = indices == -1
+    if f_outOfRange.any():
+        if intraday:
+            # Discard out-of-range dividends in intraday data, assume user not interested
+            df_sub = df_sub[~f_outOfRange]
+            if df_sub.empty:
+                df_main['Dividends'] = 0.0
+                return df_main
+        else:
+            empty_row_data = {**{c:[_np.nan] for c in const.price_colnames}, 'Volume':[0]}
+            if interval == '1d':
+                # For 1d, add all out-of-range event dates
+                for i in _np.where(f_outOfRange)[0]:
+                    dt = df_sub.index[i]
+                    get_yf_logger().debug(f"Adding out-of-range {data_col} @ {dt.date()} in new prices row of NaNs")
+                    empty_row = _pd.DataFrame(data=empty_row_data, index=[dt])
+                    df_main = _pd.concat([df_main, empty_row], sort=True)
+            else:
+                # Else, only add out-of-range event dates if occurring in interval 
+                # immediately after last price row
+                last_dt = df_main.index[-1]
+                next_interval_start_dt = last_dt + td
+                next_interval_end_dt = next_interval_start_dt + td
+                for i in _np.where(f_outOfRange)[0]:
+                    dt = df_sub.index[i]
+                    if next_interval_start_dt <= dt < next_interval_end_dt:
+                        new_dt = next_interval_start_dt
+                        get_yf_logger().debug(f"Adding out-of-range {data_col} @ {dt.date()} in new prices row of NaNs")
+                        empty_row = _pd.DataFrame(data=empty_row_data, index=[dt])
+                        df_main = _pd.concat([df_main, empty_row], sort=True)
+            df_main = df_main.sort_index()
+
+            # Re-calculate indices
+            indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1] + td), df_sub.index, side='right')
+            indices -= 1  # Convert from [[i-1], [i]) to [[i], [i+1])
+            # Numpy.searchsorted does not handle out-of-range well, so handle manually:
+            for i in range(len(df_sub.index)):
+                dt = df_sub.index[i]
+                if dt < df_main.index[0] or dt >= df_main.index[-1] + td:
+                    # Out-of-range
+                    indices[i] = -1
+
+    f_outOfRange = indices == -1
+    if f_outOfRange.any():
+        if intraday or interval in ['1d', '1wk']:
+            raise Exception(f"The following '{data_col}' events are out-of-range, did not expect with interval {interval}: {df_sub.index[f_outOfRange]}")
+        get_yf_logger().debug(f'Discarding these {data_col} events:' + '\n' + str(df_sub[f_outOfRange]))
+        df_sub = df_sub[~f_outOfRange].copy()
+        indices = indices[~f_outOfRange]
+
     def _reindex_events(df, new_index, data_col_name):
         if len(new_index) == len(set(new_index)):
             # No duplicates, easy
@@ -518,7 +744,7 @@ def safe_merge_dfs(df_main, df_sub, interval):
 
         df["_NewIndex"] = new_index
         # Duplicates present within periods but can aggregate
-        if data_col_name == "Dividends":
+        if data_col_name in ["Dividends", "Capital Gains"]:
             # Add
             df = df.groupby("_NewIndex").sum()
             df.index.name = None
@@ -527,110 +753,19 @@ def safe_merge_dfs(df_main, df_sub, interval):
             df = df.groupby("_NewIndex").prod()
             df.index.name = None
         else:
-            raise Exception("New index contains duplicates but unsure how to aggregate for '{}'".format(data_col_name))
+            raise Exception(f"New index contains duplicates but unsure how to aggregate for '{data_col_name}'")
         if "_NewIndex" in df.columns:
             df = df.drop("_NewIndex", axis=1)
         return df
 
-    df = df_main.join(df_sub)
-
-    f_na = df[data_col].isna()
-    data_lost = sum(~f_na) < df_sub.shape[0]
-    if not data_lost:
-        return df
-    # Lost data during join()
-    # Backdate all df_sub.index dates to start of week/month
-    if interval == "1wk":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='W').to_timestamp()
-    elif interval == "1mo":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='M').to_timestamp()
-    elif interval == "3mo":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='Q').to_timestamp()
-    else:
-        new_index = None
-
-    if new_index is not None:
-        new_index = new_index.tz_localize(df.index.tz, ambiguous=True, nonexistent='shift_forward')
-        df_sub = _reindex_events(df_sub, new_index, data_col)
-        df = df_main.join(df_sub)
-
-    f_na = df[data_col].isna()
-    data_lost = sum(~f_na) < df_sub.shape[0]
-    if not data_lost:
-        return df
-    # Lost data during join(). Manually check each df_sub.index date against df_main.index to
-    # find matching interval
-    df_sub = df_sub_backup.copy()
-    new_index = [-1] * df_sub.shape[0]
-    for i in range(df_sub.shape[0]):
-        dt_sub_i = df_sub.index[i]
-        if dt_sub_i in df_main.index:
-            new_index[i] = dt_sub_i
-            continue
-        # Found a bad index date, need to search for near-match in df_main (same week/month)
-        fixed = False
-        for j in range(df_main.shape[0] - 1):
-            dt_main_j0 = df_main.index[j]
-            dt_main_j1 = df_main.index[j + 1]
-            if (dt_main_j0 <= dt_sub_i) and (dt_sub_i < dt_main_j1):
-                fixed = True
-                if interval.endswith('h') or interval.endswith('m'):
-                    # Must also be same day
-                    fixed = (dt_main_j0.date() == dt_sub_i.date()) and (dt_sub_i.date() == dt_main_j1.date())
-                if fixed:
-                    dt_sub_i = dt_main_j0
-                    break
-        if not fixed:
-            last_main_dt = df_main.index[df_main.shape[0] - 1]
-            diff = dt_sub_i - last_main_dt
-            if interval == "1mo" and last_main_dt.month == dt_sub_i.month:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "3mo" and last_main_dt.year == dt_sub_i.year and last_main_dt.quarter == dt_sub_i.quarter:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "1wk":
-                if last_main_dt.week == dt_sub_i.week:
-                    dt_sub_i = last_main_dt
-                    fixed = True
-                elif (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < _datetime.timedelta(weeks=1)):
-                    # With some specific start dates (e.g. around early Jan), Yahoo
-                    # messes up start-of-week, is Saturday not Monday. So check
-                    # if same week another way
-                    dt_sub_i = last_main_dt
-                    fixed = True
-            elif interval == "1d" and last_main_dt.day == dt_sub_i.day:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "1h" and last_main_dt.hour == dt_sub_i.hour:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval.endswith('m') or interval.endswith('h'):
-                td = _pd.to_timedelta(interval)
-                if (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < td):
-                    dt_sub_i = last_main_dt
-                    fixed = True
-        new_index[i] = dt_sub_i
+    new_index = df_main.index[indices]
     df_sub = _reindex_events(df_sub, new_index, data_col)
-    df = df_main.join(df_sub)
 
+    df = df_main.join(df_sub)
     f_na = df[data_col].isna()
     data_lost = sum(~f_na) < df_sub.shape[0]
     if data_lost:
-        ## Not always possible to match events with trading, e.g. when released pre-market.
-        ## So have to append to bottom with nan prices.
-        ## But should only be impossible with intra-day price data.
-        if interval.endswith('m') or interval.endswith('h') or interval == "1d":
-            # Update: is possible with daily data when dividend very recent
-            f_missing = ~df_sub.index.isin(df.index)
-            df_sub_missing = df_sub[f_missing].copy()
-            keys = {"Adj Open", "Open", "Adj High", "High", "Adj Low", "Low", "Adj Close",
-                    "Close"}.intersection(df.columns)
-            df_sub_missing[list(keys)] = _np.nan
-            col_ordering = df.columns
-            df = _pd.concat([df, df_sub_missing], sort=True)[col_ordering]
-        else:
-            raise Exception("Lost data during merge despite all attempts to align data (see above)")
+        raise Exception('Data was lost in merge, investigate')
 
     return df
 
@@ -656,6 +791,66 @@ def is_valid_timezone(tz: str) -> bool:
     return True
 
 
+def format_history_metadata(md, tradingPeriodsOnly=True):
+    if not isinstance(md, dict):
+        return md
+    if len(md) == 0:
+        return md
+
+    tz = md["exchangeTimezoneName"]
+
+    if not tradingPeriodsOnly:
+        for k in ["firstTradeDate", "regularMarketTime"]:
+            if k in md and md[k] is not None:
+                if isinstance(md[k], int):
+                    md[k] = _pd.to_datetime(md[k], unit='s', utc=True).tz_convert(tz)
+
+        if "currentTradingPeriod" in md:
+            for m in ["regular", "pre", "post"]:
+                if m in md["currentTradingPeriod"] and isinstance(md["currentTradingPeriod"][m]["start"], int):
+                    for t in ["start", "end"]:
+                        md["currentTradingPeriod"][m][t] = \
+                            _pd.to_datetime(md["currentTradingPeriod"][m][t], unit='s', utc=True).tz_convert(tz)
+                    del md["currentTradingPeriod"][m]["gmtoffset"]
+                    del md["currentTradingPeriod"][m]["timezone"]
+
+    if "tradingPeriods" in md:
+        tps = md["tradingPeriods"]
+        if tps == {"pre": [], "post": []}:
+            # Ignore
+            pass
+        elif isinstance(tps, (list, dict)):
+            if isinstance(tps, list):
+                # Only regular times
+                df = _pd.DataFrame.from_records(_np.hstack(tps))
+                df = df.drop(["timezone", "gmtoffset"], axis=1)
+                df["start"] = _pd.to_datetime(df["start"], unit='s', utc=True).dt.tz_convert(tz)
+                df["end"] = _pd.to_datetime(df["end"], unit='s', utc=True).dt.tz_convert(tz)
+            elif isinstance(tps, dict):
+                # Includes pre- and post-market
+                pre_df = _pd.DataFrame.from_records(_np.hstack(tps["pre"]))
+                post_df = _pd.DataFrame.from_records(_np.hstack(tps["post"]))
+                regular_df = _pd.DataFrame.from_records(_np.hstack(tps["regular"]))
+
+                pre_df = pre_df.rename(columns={"start": "pre_start", "end": "pre_end"}).drop(["timezone", "gmtoffset"], axis=1)
+                post_df = post_df.rename(columns={"start": "post_start", "end": "post_end"}).drop(["timezone", "gmtoffset"], axis=1)
+                regular_df = regular_df.drop(["timezone", "gmtoffset"], axis=1)
+
+                cols = ["pre_start", "pre_end", "start", "end", "post_start", "post_end"]
+                df = regular_df.join(pre_df).join(post_df)
+                for c in cols:
+                    df[c] = _pd.to_datetime(df[c], unit='s', utc=True).dt.tz_convert(tz)
+                df = df[cols]
+
+            df.index = _pd.to_datetime(df["start"].dt.date)
+            df.index = df.index.tz_localize(tz)
+            df.index.name = "Date"
+
+            md["tradingPeriods"] = df
+
+    return md
+
+
 class ProgressBar:
     def __init__(self, iterations, text='completed'):
         self.text = text
@@ -688,177 +883,17 @@ class ProgressBar:
     def update_iteration(self, val=None):
         val = val if val is not None else self.elapsed / float(self.iterations)
         self.__update_amount(val * 100.0)
-        self.prog_bar += '  %s of %s %s' % (
-            self.elapsed, self.iterations, self.text)
+        self.prog_bar += f"  {self.elapsed} of {self.iterations} {self.text}"
 
     def __update_amount(self, new_amount):
         percent_done = int(round((new_amount / 100.0) * 100.0))
         all_full = self.width - 2
         num_hashes = int(round((percent_done / 100.0) * all_full))
-        self.prog_bar = '[' + self.fill_char * \
-                        num_hashes + ' ' * (all_full - num_hashes) + ']'
+        self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'
         pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
-        pct_string = '%d%%' % percent_done
-        self.prog_bar = self.prog_bar[0:pct_place] + \
-                        (pct_string + self.prog_bar[pct_place + len(pct_string):])
+        pct_string = f'{percent_done}%%'
+        self.prog_bar = self.prog_bar[0:pct_place] + (pct_string + self.prog_bar[pct_place + len(pct_string):])
 
     def __str__(self):
         return str(self.prog_bar)
 
-
-# ---------------------------------
-# TimeZone cache related code
-# ---------------------------------
-
-class _KVStore:
-    """Simpel Sqlite backed key/value store, key and value are strings. Should be thread safe."""
-
-    def __init__(self, filename):
-        self._cache_mutex = Lock()
-        with self._cache_mutex:
-            self.conn = _sqlite3.connect(filename, timeout=10, check_same_thread=False)
-            self.conn.execute('pragma journal_mode=wal')
-            self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT) without rowid')
-            self.conn.commit()
-        _atexit.register(self.close)
-
-    def close(self):
-        if self.conn is not None:
-            with self._cache_mutex:
-                self.conn.close()
-                self.conn = None
-
-    def get(self, key: str) -> Union[str, None]:
-        """Get value for key if it exists else returns None"""
-        item = self.conn.execute('select value from "kv" where key=?', (key,))
-        if item:
-            return next(item, (None,))[0]
-
-    def set(self, key: str, value: str) -> None:
-        with self._cache_mutex:
-            self.conn.execute('replace into "kv" (key, value) values (?,?)', (key, value))
-            self.conn.commit()
-
-    def bulk_set(self, kvdata: Dict[str, str]):
-        records = tuple(i for i in kvdata.items())
-        with self._cache_mutex:
-            self.conn.executemany('replace into "kv" (key, value) values (?,?)', records)
-            self.conn.commit()
-
-    def delete(self, key: str):
-        with self._cache_mutex:
-            self.conn.execute('delete from "kv" where key=?', (key,))
-            self.conn.commit()
-
-
-class _TzCacheException(Exception):
-    pass
-
-
-class _TzCache:
-    """Simple sqlite file cache of ticker->timezone"""
-
-    def __init__(self):
-        self._setup_cache_folder()
-        # Must init db here, where is thread-safe
-        self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
-        self._migrate_cache_tkr_tz()
-
-    def _setup_cache_folder(self):
-        if not _os.path.isdir(self._db_dir):
-            try:
-                _os.makedirs(self._db_dir)
-            except OSError as err:
-                raise _TzCacheException("Error creating TzCache folder: '{}' reason: {}"
-                                        .format(self._db_dir, err))
-
-        elif not (_os.access(self._db_dir, _os.R_OK) and _os.access(self._db_dir, _os.W_OK)):
-            raise _TzCacheException("Cannot read and write in TzCache folder: '{}'"
-                                    .format(self._db_dir, ))
-
-    def lookup(self, tkr):
-        return self.tz_db.get(tkr)
-
-    def store(self, tkr, tz):
-        if tz is None:
-            self.tz_db.delete(tkr)
-        elif self.tz_db.get(tkr) is not None:
-            raise Exception("Tkr {} tz already in cache".format(tkr))
-        else:
-            self.tz_db.set(tkr, tz)
-
-    @property
-    def _db_dir(self):
-        global _cache_dir
-        return _os.path.join(_cache_dir, "py-yfinance")
-
-    @property
-    def tz_db(self):
-        return self._tz_db
-
-    def _migrate_cache_tkr_tz(self):
-        """Migrate contents from old ticker CSV-cache to SQLite db"""
-        old_cache_file_path = _os.path.join(self._db_dir, "tkr-tz.csv")
-
-        if not _os.path.isfile(old_cache_file_path):
-            return None
-        try:
-            df = _pd.read_csv(old_cache_file_path, index_col="Ticker")
-        except _pd.errors.EmptyDataError:
-            _os.remove(old_cache_file_path)
-        else:
-            self.tz_db.bulk_set(df.to_dict()['Tz'])
-            _os.remove(old_cache_file_path)
-
-
-class _TzCacheDummy:
-    """Dummy cache to use if tz cache is disabled"""
-
-    def lookup(self, tkr):
-        return None
-
-    def store(self, tkr, tz):
-        pass
-
-    @property
-    def tz_db(self):
-        return None
-
-
-def get_tz_cache():
-    """
-    Get the timezone cache, initializes it and creates cache folder if needed on first call.
-    If folder cannot be created for some reason it will fall back to initialize a
-    dummy cache with same interface as real cash.
-    """
-    # as this can be called from multiple threads, protect it.
-    with _cache_init_lock:
-        global _tz_cache
-        if _tz_cache is None:
-            try:
-                _tz_cache = _TzCache()
-            except _TzCacheException as err:
-                print("Failed to create TzCache, reason: {}".format(err))
-                print("TzCache will not be used.")
-                print("Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'")
-                _tz_cache = _TzCacheDummy()
-
-        return _tz_cache
-
-
-_cache_dir = _ad.user_cache_dir()
-_cache_init_lock = Lock()
-_tz_cache = None
-
-
-def set_tz_cache_location(cache_dir: str):
-    """
-    Sets the path to create the "py-yfinance" cache folder in.
-    Useful if the default folder returned by "appdir.user_cache_dir()" is not writable.
-    Must be called before cache is used (that is, before fetching tickers).
-    :param cache_dir: Path to use for caches
-    :return: None
-    """
-    global _cache_dir, _tz_cache
-    assert _tz_cache is None, "Time Zone cache already initialized, setting path must be done before cache is created"
-    _cache_dir = cache_dir

yfinance/version.py

@@ -1 +1 @@
-version = "0.2.9"
+version = "0.2.32"