Bump version to 0.1.85

Merge pull request #1135 from ranaroussi/patch/unknown-ticker-timezone
Backport ticker tz verification for nice error
2022-11-03 19:04:45 +00:00 · 2022-11-02 15:18:26 +00:00 · 2022-10-31 21:14:50 +00:00 · 2022-10-27 22:34:18 +01:00 · 2022-10-26 00:12:29 +01:00 · 2022-10-26 00:09:23 +01:00
63 changed files with 1346 additions and 8538 deletions
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,20 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+*** READ BEFORE POSTING ***
+
+Before posting an issue - please upgrade to the latest version and confirm the issue/bug is still there.
+
+Upgrade using:
+`$ pip install yfinance --upgrade --no-cache-dir`
+
+Bug still there? Delete this content and submit your bug report here and provide the following, as best you can:
+
+- Simple code that reproduces your problem
+- The error message
--- a/.github/ISSUE_TEMPLATE/bug_report.yaml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yaml
@@ -1,89 +0,0 @@
-name: Bug report
-description: Report a bug in our project
-labels: ["bug"]
-
-body:
-  - type: markdown
-    attributes:
-      value: |
-        # !!! IMPORTANT !!! FOLLOW THESE INSTRUCTIONS CAREFULLY !!!
-
-        ### Are you up-to-date?
-
-        Upgrade to the latest version: `$ pip install yfinance --upgrade --no-cache-dir`
-
-        Confirm latest version by running: `import yfinance as yf ; print(yf.__version__)` and comparing against [PyPI](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.
-        
-        ### Does issue already exist?
-
-        Use the search tool. Don't duplicate existing issues.
-        
-  - 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. IMPORTANT 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
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -8,11 +8,11 @@ jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
        with:
          python-version: 3.x
      - run: pip install -r requirements.txt
      - run: pip install mkdocstrings==0.14.0
      - run: pip install mkdocs-material
-      - run: mkdocs gh-deploy --force
+      - run: mkdocs gh-deploy --force
--- a/.github/workflows/python-publish.yml
+++ b/.github/workflows/python-publish.yml
@@ -13,9 +13,9 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
    - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v2
      with:
        python-version: '3.x'
    - name: Install dependencies
--- a/.github/workflows/ruff.yml
+++ b/.github/workflows/ruff.yml
@@ -1,13 +0,0 @@
-name: Ruff
-on:
-  pull_request:
-    branches:
-      - master
-      - main
-      - dev
-jobs:
-  ruff:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - uses: chartboost/ruff-action@v1
--- a/.gitignore
+++ b/.gitignore
@@ -4,17 +4,8 @@ dist
 yfinance.egg-info
 *.pyc
 .coverage
-.idea/
 .vscode/
 build/
 *.html
 *.css
 *.png
-test.ipynb
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,289 +1,18 @@
 Change Log
 ===========

-0.2.41
+0.1.85
 ------
-Improvements:
- add keys to financials #1965 #1985
- fetch Sustainability #1959
- improve price-repair-zeroes #1990
-Fixes (prices):
- fetching when period='max' #1967
- metadata: Fix '1wk is invalid' & repeated calls #1970
- Pandas warnings #1955 #1981
- price repair syntax errors #1989
-Maintenance:
- deprecate Ticker.earnings #1977
+- Fix info['log_url'] #1062
+- Fix handling delisted ticker #1137

-0.2.40
+0.1.84
 ------
-Fix typo in 0.2.39 c7af213
+- Make tz-cache thread-safe

-0.2.39
+0.1.83
 ------
-Fixes:
- Fix switching session from/to requests_cache #1930
-Price repair:
- Fix potential for price repair to discard price=0 rows #1874
- Don't price-repair FX volume=0, is normal #1920
- Improve 'sudden change' repair for splits & currency  #1931
-Information:
- Fix help(yf.download) not showing the information about the function #1913 @vittoboa
- Add more specific error throwing based on PR 1918 #1928 @elibroftw @marcofognog
-Maintenance:
- Replace dead 'appdirs' package with 'platformdirs' #1896
- Deprecate 'pandas_datareader', remove a deprecated argument #1897
- Fix: datetime.datetime.utcnow() is deprecated ... #1922
-
-0.2.38
------
-Fix holders & insiders #1908
-
-0.2.37
------
-Small fixes:
- Fix Pandas warnings #1838 #1844
- Fix price repair bug, typos, refactor #1866 #1865 #1849
- Stop disabling logging #1841
-
-0.2.36
------
-Small fixes:
- Update README.md for better copy-ability  #1823 
- Name download() column levels  #1795 
- Fix history(keepna=False) when repair=True  #1824 
- Replace empty list with empty pd.Series  #1724 
- Handle peewee with old sqlite  #1827 
- Fix JSON error handling  #1830 #1833
-
-0.2.35
------
-Internal fixes for 0.2.34
-
-0.2.34
------
-Features:
- Add Recommendations Trend Summary #1754
- Add Recommendation upgrades & downgrades #1773
- Add Insider Roster & Transactions #1772
- Moved download() progress bar to STDERR #1776
- PIP optional dependencies #1771
- Set sensible min versions for optional 'nospam' reqs #1807
-Fixes
- Fix download() DatetimeIndex on invalid symbols #1779
- Fix invalid date entering cache DB #1796
- Fix Ticker.calendar fetch #1790
- Fixed adding complementary to info #1774
- Ticker.earnings_dates: fix warning "Value 'NaN' has dtype incompatible with float64" #1810
- Minor fixes for price repair and related tests #1768
- Fix price repair div adjust #1798
- Fix 'raise_errors' argument ignored in Ticker.history() #1806
-Maintenance
- Fix regression: _get_ticker_tz() args were being swapped. Improve its unit test #1793
- Refactor Ticker proxy #1711
- Add Ruff linter checks #1756
- Resolve Pandas FutureWarnings #1766
-
-0.2.33
------
-Cookie fixes:
- fix backup strategy #1759
- fix Ticker(ISIN) #1760
-
-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
-
-0.2.7
-----
- Fix Yahoo decryption, smarter this time #1353
- Rename basic_info -> fast_info #1354
-
-0.2.6
-----
- Fix Ticker.basic_info lazy-loading #1342
-
-0.2.5
-----
- Fix Yahoo data decryption again #1336
- New: Ticker.basic_info - faster Ticker.info #1317
-
-0.2.4
-----
- Fix Yahoo data decryption #1297
- New feature: 'Ticker.get_shares_full()' #1301
- Improve caching of financials data #1284
- Restore download() original alignment behaviour #1283
- Fix the database lock error in multithread download #1276
-
-0.2.3
-----
- Make financials API '_' use consistent
-
-0.2.2
-----
- Restore 'financials' attribute (map to 'income_stmt')
-
-0.2.1
-----
-Release!
-
-0.2.0rc5
--------
- Improve financials error handling #1243
- Fix '100x price' repair #1244
-
-0.2.0rc4
--------
- Access to old financials tables via `get_income_stmt(legacy=True)`
- Optimise scraping financials & fundamentals, 2x faster
- Add 'capital gains' alongside dividends & splits for ETFs, and metadata available via `history_metadata`, plus a bunch of price fixes
-For full list of changes see #1238
-
-0.2.0rc2
--------
-Financials
- fix financials tables to match website  #1128 #1157
- lru_cache to optimise web requests  #1147
-Prices
- improve price repair  #1148
- fix merging dividends/splits with day/week/monthly prices  #1161
- fix the Yahoo DST fixes  #1143
- improve bad/delisted ticker handling  #1140
-Misc
- fix 'trailingPegRatio'  #1138
- improve error handling  #1118
-
-0.2.0rc1
--------
-Jumping to 0.2 for this big update. 0.1.* will continue to receive bug-fixes
- timezone cache performance massively improved. Thanks @fredrik-corneliusson #1113 #1112 #1109 #1105 #1099
- price repair feature #1110
- fix merging of dividends/splits with prices #1069 #1086 #1102
- fix Yahoo returning latest price interval across 2 rows #1070
- optional: raise errors as exceptions: raise_errors=True #1104
- add proper unit tests #1069
+- Reduce spam-effect of tz-fetch

 0.1.81
 ------
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -1,15 +0,0 @@
-# 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
--- a/README.md
+++ b/README.md
@@ -42,34 +42,6 @@ Yahoo! finance API is intended for personal use only.**

 ---

- [Installation](#installation)
- [Quick start](#quick-start)
- [Advanced](#logging)
- [Wiki](https://github.com/ranaroussi/yfinance/wiki)
- [Contribute](#developers-want-to-contribute)
-
---
-
-## Installation
-
-Install `yfinance` using `pip`:
-
-``` {.sourceCode .bash}
-$ pip install yfinance --upgrade --no-cache-dir
-```
-
-[With Conda](https://anaconda.org/ranaroussi/yfinance).
-
-To install with optional dependencies, replace `optional` with: `nospam` for [caching-requests](#smarter-scraping), `repair` for [price repair](https://github.com/ranaroussi/yfinance/wiki/Price-repair), or `nospam,repair` for both:
-
-``` {.sourceCode .bash}
-$ pip install "yfinance[optional]"
-```
-
-[Required dependencies](./requirements.txt) , [all dependencies](./setup.py#L62).
-
---
-
 ## Quick Start

 ### The Ticker module
@@ -81,53 +53,53 @@ import yfinance as yf

 msft = yf.Ticker("MSFT")

-# get all stock info
+# get stock info
 msft.info

 # get historical market data
-hist = msft.history(period="1mo")
+hist = msft.history(period="max")

-# show meta information about the history (requires history() to be called first)
-msft.history_metadata
-
-# show actions (dividends, splits, capital gains)
+# show actions (dividends, splits)
 msft.actions
+
+# show dividends
 msft.dividends
+
+# show splits
 msft.splits
-msft.capital_gains  # only for mutual funds & etfs

-# show share count
-msft.get_shares_full(start="2022-01-01", end=None)
+# show financials
+msft.financials
+msft.quarterly_financials

-# show financials:
-# - income statement
-msft.income_stmt
-msft.quarterly_income_stmt
-# - balance sheet
+# show major holders
+msft.major_holders
+
+# show institutional holders
+msft.institutional_holders
+
+# show balance sheet
 msft.balance_sheet
 msft.quarterly_balance_sheet
-# - cash flow statement
+
+# show cashflow
 msft.cashflow
 msft.quarterly_cashflow
-# see `Ticker.get_income_stmt()` for more options

-# show holders
-msft.major_holders
-msft.institutional_holders
-msft.mutualfund_holders
-msft.insider_transactions
-msft.insider_purchases
-msft.insider_roster_holders
+# show earnings
+msft.earnings
+msft.quarterly_earnings

-msfs.sustainability
+# show sustainability
+msft.sustainability

-# show recommendations
+# show analysts recommendations
 msft.recommendations
-msft.recommendations_summary
-msft.upgrades_downgrades

-# 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.
+# show next event (earnings, etc)
+msft.calendar
+
+# show all earnings dates
 msft.earnings_dates

 # show ISIN code - *experimental*
@@ -156,14 +128,24 @@ msft.history(..., proxy="PROXY_SERVER")
 msft.get_actions(proxy="PROXY_SERVER")
 msft.get_dividends(proxy="PROXY_SERVER")
 msft.get_splits(proxy="PROXY_SERVER")
-msft.get_capital_gains(proxy="PROXY_SERVER")
 msft.get_balance_sheet(proxy="PROXY_SERVER")
 msft.get_cashflow(proxy="PROXY_SERVER")
 msft.option_chain(..., proxy="PROXY_SERVER")
 ...
 ```

-### Multiple tickers
+To 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 aapl goog', session=session)
+# The scraped response will be stored in the cache
+ticker.actions
+```

 To initialize multiple `Ticker` objects, use

@@ -171,56 +153,75 @@ To initialize multiple `Ticker` objects, use
 import yfinance as yf

 tickers = yf.Tickers('msft aapl goog')
+# ^ returns a named tuple of Ticker objects

 # access each ticker using (example)
-tickers.tickers['MSFT'].info
-tickers.tickers['AAPL'].history(period="1mo")
-tickers.tickers['GOOG'].actions
+tickers.tickers.MSFT.info
+tickers.tickers.AAPL.history(period="1mo")
+tickers.tickers.GOOG.actions
 ```

-To download price history into one table:
+### Fetching data for multiple tickers

 ```python
 import yfinance as yf
-data = yf.download("SPY AAPL", period="1mo")
+data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
 ```

-#### `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
-
-Install the `nospam` packages for smarter scraping using `pip` (see [Installation](#installation)). These packages help cache calls such that Yahoo is not spammed with requests.
-
-To use a custom `requests` session, pass a `session=` argument to
-the Ticker constructor. This allows for caching calls to the API as well as a custom way to modify requests via  the `User-agent` header.
+I've also added some options to make life easier :)

 ```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
+data = yf.download(  # or pdr.get_data_yahoo(...
+        # tickers list or string as well
+        tickers = "SPY AAPL MSFT",
+
+        # use "period" instead of start/end
+        # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
+        # (optional, default is '1mo')
+        period = "ytd",
+
+        # fetch data by interval (including intraday if period < 60 days)
+        # valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
+        # (optional, default is '1d')
+        interval = "1m",
+
+        # Whether to ignore timezone when aligning ticker data from 
+        # different timezones. Default is True. False may be useful for 
+        # minute/hourly data.
+        ignore_tz = False,
+
+        # group by ticker (to access via data['SPY'])
+        # (optional, default is 'column')
+        group_by = 'ticker',
+
+        # adjust all OHLC automatically
+        # (optional, default is False)
+        auto_adjust = True,
+
+        # 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
+    )
 ```

-Combine `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-limiter/blocker that can corrupt data.
-```python
-from requests import Session
-from requests_cache import CacheMixin, SQLiteCache
-from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
-from pyrate_limiter import Duration, RequestRate, Limiter
-class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
-    pass
+### Timezone cache store

-session = CachedLimiterSession(
-    limiter=Limiter(RequestRate(2, Duration.SECOND*5)),  # max 2 requests per 5 seconds
-    bucket_class=MemoryQueueBucket,
-    backend=SQLiteCache("yfinance.cache"),
-)
+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")
+...
 ```

 ### Managing Multi-Level Columns
@@ -238,27 +239,52 @@ 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

-### Persistent cache store
+---

-To reduce Yahoo, yfinance store some data locally: timezones to localize dates, and cookie. Cache location is:
+## `pandas_datareader` override

- Windows = C:/Users/\<USER\>/AppData/Local/py-yfinance
- Linux = /home/\<USER\>/.cache/py-yfinance
- MacOS = /Users/\<USER\>/Library/Caches/py-yfinance
-
-You can direct cache to use a different location with `set_tz_cache_location()`:
+If your code uses `pandas_datareader` and you want to download data
+faster, you can "hijack" `pandas_datareader.data.get_data_yahoo()`
+method to use **yfinance** while making sure the returned data is in the
+same format as **pandas\_datareader**'s `get_data_yahoo()`.

 ```python
+from pandas_datareader import data as pdr
+
 import yfinance as yf
-yf.set_tz_cache_location("custom/cache/location")
-...
+yf.pdr_override() # <== that's all it takes :-)
+
+# download dataframe
+data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")
 ```

 ---

-## Developers: want to contribute?
+## Installation

-`yfinance` relies on community to investigate bugs and contribute code. Developer guide: https://github.com/ranaroussi/yfinance/discussions/1084
+Install `yfinance` using `pip`:
+
+``` {.sourceCode .bash}
+$ pip install yfinance --upgrade --no-cache-dir
+```
+
+To install `yfinance` using `conda`, see
+[this](https://anaconda.org/ranaroussi/yfinance).
+
+### Requirements
+
+-   [Python](https://www.python.org) \>= 2.7, 3.4+
+-   [Pandas](https://github.com/pydata/pandas) (tested to work with
+    \>=0.23.1)
+-   [Numpy](http://www.numpy.org) \>= 1.11.1
+-   [requests](http://docs.python-requests.org/en/master/) \>= 2.14.2
+-   [lxml](https://pypi.org/project/lxml/) \>= 4.5.1
+-   [appdirs](https://pypi.org/project/appdirs) \>=1.4.4
+
+### Optional (if you want to use `pandas_datareader`)
+
+-   [pandas\_datareader](https://github.com/pydata/pandas-datareader)
+    \>= 0.4.0

 ---

@@ -274,7 +300,7 @@ intended for research and educational purposes. You should refer to Yahoo!'s ter
 ([here](https://policies.yahoo.com/us/en/yahoo/terms/product-atos/apiforydn/index.htm),
 [here](https://legal.yahoo.com/us/en/yahoo/terms/otos/index.html), and
 [here](https://policies.yahoo.com/us/en/yahoo/terms/index.htm)) for
-details on your rights to use the actual data downloaded.
+detailes on your rights to use the actual data downloaded.

 ---

--- a/meta.yaml
+++ b/meta.yaml
@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.2.41" %}
+{% set version = "0.1.58" %}

 package:
  name: "{{ name|lower }}"
@@ -16,34 +16,22 @@ build:

 requirements:
  host:
-    - pandas >=1.3.0
+    - pandas >=0.24.0
    - numpy >=1.16.5
-    - requests >=2.31
+    - requests >=2.21
    - multitasking >=0.0.7
-    - lxml >=4.9.1
-    - platformdirs >=2.0.0
-    - pytz >=2022.5
-    - frozendict >=2.3.4
-    - beautifulsoup4 >=4.11.1
-    - html5lib >=1.1
-    - peewee >=3.16.2
-    # - pycryptodome >=3.6.6
+    - lxml >=4.5.1
+    - appdirs >= 1.4.4
    - pip
    - python

  run:
-    - pandas >=1.3.0
+    - pandas >=0.24.0
    - numpy >=1.16.5
-    - requests >=2.31
+    - requests >=2.21
    - multitasking >=0.0.7
-    - lxml >=4.9.1
-    - platformdirs >=2.0.0
-    - pytz >=2022.5
-    - frozendict >=2.3.4
-    - beautifulsoup4 >=4.11.1
-    - html5lib >=1.1
-    - peewee >=3.16.2
-    # - pycryptodome >=3.6.6
+    - lxml >=4.5.1
+    - appdirs >= 1.4.4
    - python

 test:
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,11 +1,6 @@
-pandas>=1.3.0
+pandas>=0.24.0
 numpy>=1.16.5
-requests>=2.31
+requests>=2.26
 multitasking>=0.0.7
-lxml>=4.9.1
-platformdirs>=2.0.0
-pytz>=2022.5
-frozendict>=2.3.4
-beautifulsoup4>=4.11.1
-html5lib>=1.1
-peewee>=3.16.2
+lxml>=4.5.1
+appdirs>=1.4.4
--- a/setup.py
+++ b/setup.py
@@ -38,8 +38,8 @@ setup(
    classifiers=[
        'License :: OSI Approved :: Apache Software License',
        # 'Development Status :: 3 - Alpha',
-        'Development Status :: 4 - Beta',
-        # 'Development Status :: 5 - Production/Stable',
+        # 'Development Status :: 4 - Beta',
+        'Development Status :: 5 - Production/Stable',


        'Operating System :: OS Independent',
@@ -50,25 +50,20 @@ setup(
        'Topic :: Software Development :: Libraries',
        'Topic :: Software Development :: Libraries :: Python Modules',

-        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 2.7',
+        'Programming Language :: Python :: 3.4',
+        'Programming Language :: Python :: 3.5',
+        # 'Programming Language :: Python :: 3.6',
        'Programming Language :: Python :: 3.7',
        'Programming Language :: Python :: 3.8',
        'Programming Language :: Python :: 3.9',
-        'Programming Language :: Python :: 3.10',
    ],
    platforms=['any'],
    keywords='pandas, yahoo finance, pandas datareader',
    packages=find_packages(exclude=['contrib', 'docs', 'tests', 'examples']),
-    install_requires=['pandas>=1.3.0', 'numpy>=1.16.5',
-                      'requests>=2.31', 'multitasking>=0.0.7',
-                      'lxml>=4.9.1', 'platformdirs>=2.0.0', 'pytz>=2022.5',
-                      'frozendict>=2.3.4', 'peewee>=3.16.2',
-                      'beautifulsoup4>=4.11.1', 'html5lib>=1.1'],
-    extras_require={
-        'nospam': ['requests_cache>=1.0', 'requests_ratelimiter>=0.3.1'],
-        'repair': ['scipy>=1.6.3'],
-    },
-    # Note: Pandas.read_html() needs html5lib & beautifulsoup4
+    install_requires=['pandas>=0.24.0', 'numpy>=1.15',
+                      'requests>=2.26', 'multitasking>=0.0.7',
+                      'lxml>=4.5.1', 'appdirs>=1.4.4'],
    entry_points={
        'console_scripts': [
            'sample=sample:main',
--- a/test_yfinance.py
+++ b/test_yfinance.py
@@ -0,0 +1,69 @@
+#!/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)
+
+        histories = yf.download(symbols, period="1yr")
+        assert(histories.empty is False and histories is not None)
+
+    def test_attributes(self):
+        for ticker in tickers:
+            ticker.isin
+            ticker.major_holders
+            ticker.institutional_holders
+            ticker.mutualfund_holders
+            ticker.dividends
+            ticker.splits
+            ticker.actions
+            ticker.info
+            ticker.calendar
+            ticker.recommendations
+            ticker.earnings
+            ticker.quarterly_earnings
+            ticker.financials
+            ticker.quarterly_financials
+            ticker.balance_sheet
+            ticker.quarterly_balance_sheet
+            ticker.cashflow
+            ticker.quarterly_cashflow
+            ticker.sustainability
+            ticker.options
+            ticker.news
+            ticker.shares
+            ticker.earnings_history
+            ticker.earnings_dates
+
+    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()
--- a/tests/init.py
+++ b/tests/init.py
--- a/tests/context.py
+++ b/tests/context.py
@@ -1,45 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import platformdirs as _ad
-import datetime as _dt
-import sys
-import os
-import yfinance
-from requests import Session
-from requests_cache import CacheMixin, SQLiteCache
-from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
-from pyrate_limiter import Duration, RequestRate, Limiter
-
-_parent_dp = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
-_src_dp = _parent_dp
-sys.path.insert(0, _src_dp)
-
-# 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:
-class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
-    pass
-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)
-
--- a/tests/data/4063-T-1d-bad-stock-split-fixed.csv
+++ b/tests/data/4063-T-1d-bad-stock-split-fixed.csv
@@ -1,23 +0,0 @@
-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
--- a/tests/data/4063-T-1d-bad-stock-split.csv
+++ b/tests/data/4063-T-1d-bad-stock-split.csv
@@ -1,23 +0,0 @@
-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
--- a/tests/data/8TRA-DE-1d-missing-div-adjust-fixed.csv
+++ b/tests/data/8TRA-DE-1d-missing-div-adjust-fixed.csv
@@ -1,6 +0,0 @@
-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
--- a/tests/data/8TRA-DE-1d-missing-div-adjust.csv
+++ b/tests/data/8TRA-DE-1d-missing-div-adjust.csv
@@ -1,6 +0,0 @@
-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
--- a/tests/data/AET-L-1d-100x-error-fixed.csv
+++ b/tests/data/AET-L-1d-100x-error-fixed.csv
@@ -1,24 +0,0 @@
-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
--- a/tests/data/AET-L-1d-100x-error.csv
+++ b/tests/data/AET-L-1d-100x-error.csv
@@ -1,24 +0,0 @@
-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
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-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
--- a/tests/data/AET-L-1wk-100x-error-fixed.csv
+++ b/tests/data/AET-L-1wk-100x-error-fixed.csv
@@ -1,37 +0,0 @@
-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
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-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
--- a/tests/data/AET-L-1wk-100x-error-fixed.csv.old
+++ b/tests/data/AET-L-1wk-100x-error-fixed.csv.old
@@ -1,25 +0,0 @@
-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
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-2022-07-20 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
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-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
--- a/tests/data/AET-L-1wk-100x-error.csv
+++ b/tests/data/AET-L-1wk-100x-error.csv
@@ -1,37 +0,0 @@
-Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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-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
--- a/tests/data/AET-L-1wk-100x-error.csv.old
+++ b/tests/data/AET-L-1wk-100x-error.csv.old
@@ -1,25 +0,0 @@
-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
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-2022-08-10 00:00:00+01:00,25.0,29.200000762939453,22.5,25.0,25.0,26572680,0.0,0.0
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-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
--- a/tests/data/ALPHA-PA-1d-bad-stock-split-fixed.csv
+++ b/tests/data/ALPHA-PA-1d-bad-stock-split-fixed.csv
@@ -1,30 +0,0 @@
-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
--- a/tests/data/ALPHA-PA-1d-bad-stock-split.csv
+++ b/tests/data/ALPHA-PA-1d-bad-stock-split.csv
@@ -1,30 +0,0 @@
-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
--- a/tests/data/AV-L-1wk-bad-stock-split-fixed.csv
+++ b/tests/data/AV-L-1wk-bad-stock-split-fixed.csv
@@ -1,85 +0,0 @@
-Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
-2021-12-13 00:00:00+00:00,518.421020507813,535,515,530.131469726563,383.200378417969,47663221,0,0
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-2021-12-27 00:00:00+00:00,539.342102050781,548.093383789063,537.351318359375,540,390.333740234375,8222047,0,0
-2022-01-03 00:00:00+00:00,540,568.684204101563,540,568.552612304688,410.972717285156,33764769,0,0
-2022-01-10 00:00:00+00:00,567.5,577.894714355469,564.605224609375,573.815673828125,414.777130126953,22109464,0,0
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-2022-01-24 00:00:00+00:00,565.921020507813,580.263122558594,553.947326660156,570.394714355469,412.304260253906,44450345,0,0
-2022-01-31 00:00:00+00:00,573.947326660156,582.960510253906,568.552612304688,572.631469726563,413.921112060547,32935213,0,0
-2022-02-07 00:00:00+00:00,576.184204101563,590.526306152344,573.752624511719,584.73681640625,422.671325683594,30129487,0,0
-2022-02-14 00:00:00+00:00,575.921020507813,581.578918457031,561.447326660156,568.684204101563,411.067840576172,37979247,0,0
-2022-02-21 00:00:00+00:00,573.026306152344,576.943420410156,537.23681640625,557.105224609375,402.698120117188,49946893,0,0
-2022-02-28 00:00:00+00:00,546.184204101563,563.157775878906,508.157806396484,508.947296142578,367.887603759766,71484032,4.1875,0
-2022-03-07 00:00:00+00:00,493.420989990234,549.078918457031,475.134185791016,538.947326660156,392.617431640625,54164517,0,0
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--- a/tests/data/AV-L-1wk-bad-stock-split.csv
+++ b/tests/data/AV-L-1wk-bad-stock-split.csv
@@ -1,85 +0,0 @@
-Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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-2023-07-17 00:00:00+01:00,385.0,389.5,384.2510070800781,387.1000061035156,387.1000061035156,0,0.0,0.0
--- a/tests/data/CNE-L-1d-bad-stock-split-fixed.csv
+++ b/tests/data/CNE-L-1d-bad-stock-split-fixed.csv
@@ -1,11 +0,0 @@
-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+01:00,456.9090,464.9696,446.7272,461.1515,217.2121,830506.0000,0,0
-2023-05-12 00:00:00+01:00,455.2121,458.6060,444.6060,448.4242,211.2173,717655.0000,0,0
-2023-05-11 00:00:00+01:00,466.6666,466.6666,450.1212,456.0606,214.8142,1682077.0000,0,0
-2023-05-10 00:00:00+01:00,462.8484,473.0303,450.9696,456.9090,215.2138,2639957.0000,0,0
-2023-05-09 00:00:00+01:00,475.1515,482.9746,462.8485,463.2727,218.2112,898585.2857,0,0
-2023-05-05 00:00:00+01:00,468.7878,477.6969,468.3636,476.0000,224.2061,454704.0000,0,0
-2023-05-04 00:00:00+01:00,460.3030,472.6060,460.0527,469.6363,221.2086,415321.0000,0,0
--- a/tests/data/CNE-L-1d-bad-stock-split.csv
+++ b/tests/data/CNE-L-1d-bad-stock-split.csv
@@ -1,11 +0,0 @@
-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
--- a/tests/data/DEX-AX-1d-bad-stock-split-fixed.csv
+++ b/tests/data/DEX-AX-1d-bad-stock-split-fixed.csv
@@ -1,24 +0,0 @@
-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
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-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
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-2023-05-18 00:00:00+10:00,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0.120290003716946,0,0,0
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-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
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-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
--- a/tests/data/DEX-AX-1d-bad-stock-split.csv
+++ b/tests/data/DEX-AX-1d-bad-stock-split.csv
@@ -1,24 +0,0 @@
-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
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-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
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-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
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-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
--- a/tests/data/LA-V-1d-bad-stock-split-fixed.csv
+++ b/tests/data/LA-V-1d-bad-stock-split-fixed.csv
@@ -1,42 +0,0 @@
-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
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-2020-09-04 00:00:00-04:00,3.5,3.5,3.5,3.5,3.5,400,0,0
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-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
--- a/tests/data/LA-V-1d-bad-stock-split.csv
+++ b/tests/data/LA-V-1d-bad-stock-split.csv
@@ -1,42 +0,0 @@
-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
--- a/tests/data/MOB-ST-1d-bad-stock-split-fixed.csv
+++ b/tests/data/MOB-ST-1d-bad-stock-split-fixed.csv
@@ -1,17 +0,0 @@
-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
--- a/tests/data/MOB-ST-1d-bad-stock-split.csv
+++ b/tests/data/MOB-ST-1d-bad-stock-split.csv
@@ -1,17 +0,0 @@
-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
--- a/tests/data/SPM-MI-1d-bad-stock-split-fixed.csv
+++ b/tests/data/SPM-MI-1d-bad-stock-split-fixed.csv
@@ -1,23 +0,0 @@
-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
--- a/tests/data/SPM-MI-1d-bad-stock-split.csv
+++ b/tests/data/SPM-MI-1d-bad-stock-split.csv
@@ -1,23 +0,0 @@
-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
--- a/tests/data/SSW-JO-1d-100x-error-fixed.csv
+++ b/tests/data/SSW-JO-1d-100x-error-fixed.csv
@@ -1,30 +0,0 @@
-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
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-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
--- a/tests/data/SSW-JO-1d-100x-error.csv
+++ b/tests/data/SSW-JO-1d-100x-error.csv
@@ -1,30 +0,0 @@
-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
--- a/tests/test_prices.py
+++ b/tests/test_prices.py
@@ -1,946 +0,0 @@
-from .context import yfinance as yf
-from .context import session_gbl
-
-import unittest
-
-import os
-import datetime as _dt
-import pytz as _tz
-import numpy as _np
-import pandas as _pd
-
-
-class TestPriceHistory(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        cls.session = session_gbl
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.session is not None:
-            cls.session.close()
-
-    def test_daily_index(self):
-        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
-        intervals = ["1d", "1wk", "1mo"]
-        for tkr in tkrs:
-            dat = yf.Ticker(tkr, session=self.session)
-
-            for interval in intervals:
-                df = dat.history(period="5y", interval=interval)
-
-                f = df.index.time == _dt.time(0)
-                self.assertTrue(f.all())
-
-    def test_download(self):
-        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
-        intervals = ["1d", "1wk", "1mo"]
-        for interval in intervals:
-            df = yf.download(tkrs, period="5y", interval=interval)
-
-            f = df.index.time == _dt.time(0)
-            self.assertTrue(f.all())
-
-            df_tkrs = df.columns.levels[1]
-            self.assertEqual(sorted(tkrs), sorted(df_tkrs))
-
-    def test_download_with_invalid_ticker(self):
-        #Checks if using an invalid symbol gives the same output as not using an invalid symbol in combination with a valid symbol (AAPL)
-        #Checks to make sure that invalid symbol handling for the date column is the same as the base case (no invalid symbols)
-
-        invalid_tkrs = ["AAPL", "ATVI"] #AAPL exists and ATVI does not exist
-        valid_tkrs = ["AAPL", "INTC"] #AAPL and INTC both exist
-
-        data_invalid_sym = yf.download(invalid_tkrs, start='2023-11-16', end='2023-11-17')
-        data_valid_sym = yf.download(valid_tkrs, start='2023-11-16', end='2023-11-17')
-
-        self.assertEqual(data_invalid_sym['Close']['AAPL']['2023-11-16'],data_valid_sym['Close']['AAPL']['2023-11-16'])
-
-    def test_duplicatingHourly(self):
-        tkrs = ["IMP.JO", "BHG.JO", "SSW.JO", "BP.L", "INTC"]
-        for tkr in tkrs:
-            dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(proxy=None, timeout=None)
-
-            dt_utc = _pd.Timestamp.utcnow()
-            dt = dt_utc.astimezone(_tz.timezone(tz))
-            start_d = dt.date() - _dt.timedelta(days=7)
-            df = dat.history(start=start_d, interval="1h")
-
-            dt0 = df.index[-2]
-            dt1 = df.index[-1]
-            try:
-                self.assertNotEqual(dt0.hour, dt1.hour)
-            except AssertionError:
-                print("Ticker = ", tkr)
-                raise
-
-    def test_duplicatingDaily(self):
-        tkrs = ["IMP.JO", "BHG.JO", "SSW.JO", "BP.L", "INTC"]
-        test_run = False
-        for tkr in tkrs:
-            dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(proxy=None, timeout=None)
-
-            dt_utc = _pd.Timestamp.utcnow()
-            dt = dt_utc.astimezone(_tz.timezone(tz))
-            if dt.time() < _dt.time(17, 0):
-                continue
-            test_run = True
-
-            df = dat.history(start=dt.date() - _dt.timedelta(days=7), interval="1d")
-
-            dt0 = df.index[-2]
-            dt1 = df.index[-1]
-            try:
-                self.assertNotEqual(dt0, dt1)
-            except AssertionError:
-                print("Ticker = ", tkr)
-                raise
-
-        if not test_run:
-            self.skipTest("Skipping test_duplicatingDaily() because only expected to fail just after market close")
-
-    def test_duplicatingWeekly(self):
-        tkrs = ['MSFT', 'IWO', 'VFINX', '^GSPC', 'BTC-USD']
-        test_run = False
-        for tkr in tkrs:
-            dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(proxy=None, timeout=None)
-
-            dt = _tz.timezone(tz).localize(_dt.datetime.now())
-            if dt.date().weekday() not in [1, 2, 3, 4]:
-                continue
-            test_run = True
-
-            df = dat.history(start=dt.date() - _dt.timedelta(days=7), interval="1wk")
-            dt0 = df.index[-2]
-            dt1 = df.index[-1]
-            try:
-                self.assertNotEqual(dt0.week, dt1.week)
-            except AssertionError:
-                print("Ticker={}: Last two rows within same week:".format(tkr))
-                print(df.iloc[df.shape[0] - 2:])
-                raise
-
-        if not test_run:
-            self.skipTest("Skipping test_duplicatingWeekly() because not possible to fail Monday/weekend")
-
-    def test_pricesEventsMerge(self):
-        # Test case: dividend occurs after last row in price data
-        tkr = 'INTC'
-        start_d = _dt.date(2022, 1, 1)
-        end_d = _dt.date(2023, 1, 1)
-        df = yf.Ticker(tkr, session=self.session).history(interval='1d', start=start_d, end=end_d)
-        div = 1.0
-        future_div_dt = df.index[-1] + _dt.timedelta(days=1)
-        if future_div_dt.weekday() in [5, 6]:
-            future_div_dt += _dt.timedelta(days=1) * (7 - future_div_dt.weekday())
-        divs = _pd.DataFrame(data={"Dividends":[div]}, index=[future_div_dt])
-        df2 = yf.utils.safe_merge_dfs(df.drop(['Dividends', 'Stock Splits'], axis=1), divs, '1d')
-        self.assertIn(future_div_dt, df2.index)
-        self.assertIn("Dividends", df2.columns)
-        self.assertEqual(df2['Dividends'].iloc[-1], div)
-
-    def test_pricesEventsMerge_bug(self):
-        # Reproduce exception when merging intraday prices with future dividend
-        interval = '30m'
-        df_index = []
-        d = 13
-        for h in range(0, 16):
-            for m in [0, 30]:
-                df_index.append(_dt.datetime(2023, 9, d, h, m))
-        df_index.append(_dt.datetime(2023, 9, d, 16))
-        df = _pd.DataFrame(index=df_index)
-        df.index = _pd.to_datetime(df.index)
-        df['Close'] = 1.0
-
-        div = 1.0
-        future_div_dt = _dt.datetime(2023, 9, 14, 10)
-        divs = _pd.DataFrame(data={"Dividends":[div]}, index=[future_div_dt])
-
-        yf.utils.safe_merge_dfs(df, divs, interval)
-        # No exception = test pass
-
-    def test_intraDayWithEvents(self):
-        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
-        test_run = False
-        for tkr in tkrs:
-            start_d = _dt.date.today() - _dt.timedelta(days=59)
-            end_d = None
-            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
-            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
-            if df_daily_divs.shape[0] == 0:
-                continue
-
-            last_div_date = df_daily_divs.index[-1]
-            start_d = last_div_date.date()
-            end_d = last_div_date.date() + _dt.timedelta(days=1)
-            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
-            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
-
-            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
-            df_intraday_divs.index = df_intraday_divs.index.floor('D')
-            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
-
-            test_run = True
-
-        if not test_run:
-            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
-
-    def test_intraDayWithEvents_tase(self):
-        # TASE dividend release pre-market, doesn't merge nicely with intra-day data so check still present
-
-        tase_tkrs = ["ICL.TA", "ESLT.TA", "ONE.TA", "MGDL.TA"]
-        test_run = False
-        for tkr in tase_tkrs:
-            start_d = _dt.date.today() - _dt.timedelta(days=59)
-            end_d = None
-            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
-            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
-            if df_daily_divs.shape[0] == 0:
-                continue
-
-            last_div_date = df_daily_divs.index[-1]
-            start_d = last_div_date.date()
-            end_d = last_div_date.date() + _dt.timedelta(days=1)
-            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
-            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
-
-            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
-            df_intraday_divs.index = df_intraday_divs.index.floor('D')
-            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
-
-            test_run = True
-
-        if not test_run:
-            self.skipTest("Skipping test_intraDayWithEvents_tase() because no tickers had a dividend in last 60 days")
-
-    def test_dailyWithEvents(self):
-        start_d = _dt.date(2022, 1, 1)
-        end_d = _dt.date(2023, 1, 1)
-
-        tkr_div_dates = {'BHP.AX': [_dt.date(2022, 9, 1), _dt.date(2022, 2, 24)],  # Yahoo claims 23-Feb but wrong because DST
-                         'IMP.JO': [_dt.date(2022, 9, 21), _dt.date(2022, 3, 16)],
-                         'BP.L': [_dt.date(2022, 11, 10), _dt.date(2022, 8, 11), _dt.date(2022, 5, 12),
-                                  _dt.date(2022, 2, 17)],
-                         'INTC': [_dt.date(2022, 11, 4), _dt.date(2022, 8, 4), _dt.date(2022, 5, 5),
-                                  _dt.date(2022, 2, 4)]}
-
-        for tkr, dates in tkr_div_dates.items():
-            df = yf.Ticker(tkr, session=self.session).history(interval='1d', start=start_d, end=end_d)
-            df_divs = df[df['Dividends'] != 0].sort_index(ascending=False)
-            try:
-                self.assertTrue((df_divs.index.date == dates).all())
-            except AssertionError:
-                print(f'- ticker = {tkr}')
-                print('- response:')
-                print(df_divs.index.date)
-                print('- answer:')
-                print(dates)
-                raise
-
-    def test_dailyWithEvents_bugs(self):
-        # Reproduce issue #521
-        tkr1 = "QQQ"
-        tkr2 = "GDX"
-        start_d = "2014-12-29"
-        end_d = "2020-11-29"
-        df1 = yf.Ticker(tkr1).history(start=start_d, end=end_d, interval="1d", actions=True)
-        df2 = yf.Ticker(tkr2).history(start=start_d, end=end_d, interval="1d", actions=True)
-        self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
-        self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
-        try:
-            self.assertTrue(df1.index.equals(df2.index))
-        except AssertionError:
-            missing_from_df1 = df2.index.difference(df1.index)
-            missing_from_df2 = df1.index.difference(df2.index)
-            print("{} missing these dates: {}".format(tkr1, missing_from_df1))
-            print("{} missing these dates: {}".format(tkr2, missing_from_df2))
-            raise
-
-        # Test that index same with and without events:
-        tkrs = [tkr1, tkr2]
-        for tkr in tkrs:
-            df1 = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
-            df2 = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=False)
-            self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
-            try:
-                self.assertTrue(df1.index.equals(df2.index))
-            except AssertionError:
-                missing_from_df1 = df2.index.difference(df1.index)
-                missing_from_df2 = df1.index.difference(df2.index)
-                print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
-                print("{}-without-events missing these dates: {}".format(tkr, missing_from_df2))
-                raise
-
-        # Reproduce issue #1634 - 1d dividend out-of-range, should be prepended to prices
-        div_dt = _pd.Timestamp(2022, 7, 21).tz_localize("America/New_York")
-        df_dividends = _pd.DataFrame(data={"Dividends":[1.0]}, index=[div_dt])
-        df_prices = _pd.DataFrame(data={c:[1.0] for c in yf.const._PRICE_COLNAMES_}|{'Volume':0}, index=[div_dt+_dt.timedelta(days=1)])
-        df_merged = yf.utils.safe_merge_dfs(df_prices, df_dividends, '1d')
-        self.assertEqual(df_merged.shape[0], 2)
-        self.assertTrue(df_merged[df_prices.columns].iloc[1:].equals(df_prices))
-        self.assertEqual(df_merged.index[0], div_dt)
-
-    def test_weeklyWithEvents(self):
-        # Reproduce issue #521
-        tkr1 = "QQQ"
-        tkr2 = "GDX"
-        start_d = "2014-12-29"
-        end_d = "2020-11-29"
-        df1 = yf.Ticker(tkr1).history(start=start_d, end=end_d, interval="1wk", actions=True)
-        df2 = yf.Ticker(tkr2).history(start=start_d, end=end_d, interval="1wk", actions=True)
-        self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
-        self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
-        try:
-            self.assertTrue(df1.index.equals(df2.index))
-        except AssertionError:
-            missing_from_df1 = df2.index.difference(df1.index)
-            missing_from_df2 = df1.index.difference(df2.index)
-            print("{} missing these dates: {}".format(tkr1, missing_from_df1))
-            print("{} missing these dates: {}".format(tkr2, missing_from_df2))
-            raise
-
-        # Test that index same with and without events:
-        tkrs = [tkr1, tkr2]
-        for tkr in tkrs:
-            df1 = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1wk", actions=True)
-            df2 = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1wk", actions=False)
-            self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
-            try:
-                self.assertTrue(df1.index.equals(df2.index))
-            except AssertionError:
-                missing_from_df1 = df2.index.difference(df1.index)
-                missing_from_df2 = df1.index.difference(df2.index)
-                print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
-                print("{}-without-events missing these dates: {}".format(tkr, missing_from_df2))
-                raise
-
-    def test_monthlyWithEvents(self):
-        tkr1 = "QQQ"
-        tkr2 = "GDX"
-        start_d = "2014-12-29"
-        end_d = "2020-11-29"
-        df1 = yf.Ticker(tkr1).history(start=start_d, end=end_d, interval="1mo", actions=True)
-        df2 = yf.Ticker(tkr2).history(start=start_d, end=end_d, interval="1mo", actions=True)
-        self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
-        self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
-        try:
-            self.assertTrue(df1.index.equals(df2.index))
-        except AssertionError:
-            missing_from_df1 = df2.index.difference(df1.index)
-            missing_from_df2 = df1.index.difference(df2.index)
-            print("{} missing these dates: {}".format(tkr1, missing_from_df1))
-            print("{} missing these dates: {}".format(tkr2, missing_from_df2))
-            raise
-
-        # Test that index same with and without events:
-        tkrs = [tkr1, tkr2]
-        for tkr in tkrs:
-            df1 = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1mo", actions=True)
-            df2 = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1mo", actions=False)
-            self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
-            try:
-                self.assertTrue(df1.index.equals(df2.index))
-            except AssertionError:
-                missing_from_df1 = df2.index.difference(df1.index)
-                missing_from_df2 = df1.index.difference(df2.index)
-                print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
-                print("{}-without-events missing these dates: {}".format(tkr, missing_from_df2))
-                raise
-
-    def test_monthlyWithEvents2(self):
-        # Simply check no exception from internal merge
-        dfm = yf.Ticker("ABBV").history(period="max", interval="1mo")
-        dfd = yf.Ticker("ABBV").history(period="max", interval="1d")
-        dfd = dfd[dfd.index > dfm.index[0]]
-        dfm_divs = dfm[dfm['Dividends'] != 0]
-        dfd_divs = dfd[dfd['Dividends'] != 0]
-        self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
-
-    def test_tz_dst_ambiguous(self):
-        # Reproduce issue #1100
-        try:
-            yf.Ticker("ESLT.TA", session=self.session).history(start="2002-10-06", end="2002-10-09", interval="1d")
-        except _tz.exceptions.AmbiguousTimeError:
-            raise Exception("Ambiguous DST issue not resolved")
-
-    def test_dst_fix(self):
-        # Daily intervals should start at time 00:00. But for some combinations of date and timezone,
-        # Yahoo has time off by few hours (e.g. Brazil 23:00 around Jan-2022). Suspect DST problem.
-        # The clue is (a) minutes=0 and (b) hour near 0.
-        # Obviously Yahoo meant 00:00, so ensure this doesn't affect date conversion.
-
-        # The correction is successful if no days are weekend, and weekly data begins Monday
-
-        tkr = "AGRO3.SA"
-        dat = yf.Ticker(tkr, session=self.session)
-        start = "2021-01-11"
-        end = "2022-11-05"
-
-        interval = "1d"
-        df = dat.history(start=start, end=end, interval=interval)
-        self.assertTrue(((df.index.weekday >= 0) & (df.index.weekday <= 4)).all())
-
-        interval = "1wk"
-        df = dat.history(start=start, end=end, interval=interval)
-        try:
-            self.assertTrue((df.index.weekday == 0).all())
-        except AssertionError:
-            print("Weekly data not aligned to Monday")
-            raise
-
-    def test_prune_post_intraday_us(self):
-        # Half-day at USA Thanksgiving. Yahoo normally
-        # returns an interval starting when regular trading closes,
-        # even if prepost=False.
-
-        # Setup
-        tkr = "AMZN"
-        special_day = _dt.date(2023, 11, 24)
-        time_early_close = _dt.time(13)
-        dat = yf.Ticker(tkr, session=self.session)
-
-        # Run
-        start_d = special_day - _dt.timedelta(days=7)
-        end_d = special_day + _dt.timedelta(days=7)
-        df = dat.history(start=start_d, end=end_d, interval="1h", prepost=False, keepna=True)
-        tg_last_dt = df.loc[str(special_day)].index[-1]
-        self.assertTrue(tg_last_dt.time() < time_early_close)
-
-        # Test no other afternoons (or mornings) were pruned
-        start_d = _dt.date(special_day.year, 1, 1)
-        end_d = _dt.date(special_day.year+1, 1, 1)
-        df = dat.history(start=start_d, end=end_d, interval="1h", prepost=False, keepna=True)
-        last_dts = _pd.Series(df.index).groupby(df.index.date).last()
-        dfd = dat.history(start=start_d, end=end_d, interval='1d', prepost=False, keepna=True)
-        self.assertTrue(_np.equal(dfd.index.date, _pd.to_datetime(last_dts.index).date).all())
-
-    def test_prune_post_intraday_asx(self):
-        # Setup
-        tkr = "BHP.AX"
-        # No early closes in 2023
-        dat = yf.Ticker(tkr, session=self.session)
-
-        # Test no other afternoons (or mornings) were pruned
-        start_d = _dt.date(2023, 1, 1)
-        end_d = _dt.date(2023+1, 1, 1)
-        df = dat.history(start=start_d, end=end_d, interval="1h", prepost=False, keepna=True)
-        last_dts = _pd.Series(df.index).groupby(df.index.date).last()
-        dfd = dat.history(start=start_d, end=end_d, interval='1d', prepost=False, keepna=True)
-        self.assertTrue(_np.equal(dfd.index.date, _pd.to_datetime(last_dts.index).date).all())
-
-    def test_weekly_2rows_fix(self):
-        tkr = "AMZN"
-        start = _dt.date.today() - _dt.timedelta(days=14)
-        start -= _dt.timedelta(days=start.weekday())
-
-        dat = yf.Ticker(tkr)
-        df = dat.history(start=start, interval="1wk")
-        self.assertTrue((df.index.weekday == 0).all())
-
-    def test_aggregate_capital_gains(self):
-        # Setup
-        tkr = "FXAIX"
-        dat = yf.Ticker(tkr, session=self.session)
-        start = "2017-12-31"
-        end = "2019-12-31"
-        interval = "3mo"
-
-        dat.history(start=start, end=end, interval=interval)
-
-
-class TestPriceRepair(unittest.TestCase):
-    session = None
-
-    @classmethod
-    def setUpClass(cls):
-        cls.session = session_gbl
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.session is not None:
-            cls.session.close()
-
-    def test_types(self):
-        tkr = 'INTC'
-        dat = yf.Ticker(tkr, session=self.session)
-
-        data = dat.history(period="3mo", interval="1d", prepost=True, repair=True)
-        self.assertIsInstance(data, _pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        reconstructed = dat._lazy_load_price_history()._reconstruct_intervals_batch(data, "1wk", True)
-        self.assertIsInstance(reconstructed, _pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-    def test_reconstruct_2m(self):
-        # 2m repair requires 1m data.
-        # Yahoo restricts 1m fetches to 7 days max within last 30 days.
-        # Need to test that '_reconstruct_intervals_batch()' can handle this.
-
-        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
-
-        dt_now = _pd.Timestamp.utcnow()
-        td_60d = _dt.timedelta(days=60)
-
-        # Round time for 'requests_cache' reuse
-        dt_now = dt_now.ceil("1h")
-
-        for tkr in tkrs:
-            dat = yf.Ticker(tkr, session=self.session)
-            end_dt = dt_now
-            start_dt = end_dt - td_60d
-            dat.history(start=start_dt, end=end_dt, interval="2m", repair=True)
-
-    def test_repair_100x_random_weekly(self):
-        # Setup:
-        tkr = "PNL.L"
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
-        df = _pd.DataFrame(data={"Open":      [470.5, 473.5, 474.5, 470],
-                                 "High":      [476,   476.5, 477,   480],
-                                 "Low":       [470.5, 470,   465.5, 468.26],
-                                 "Close":     [475,   473.5, 472,   473.5],
-                                 "Adj Close": [470.1, 468.6, 467.1, 468.6],
-                                 "Volume": [2295613, 2245604, 3000287, 2635611]},
-                           index=_pd.to_datetime([_dt.date(2022, 10, 24),
-                                                  _dt.date(2022, 10, 17),
-                                                  _dt.date(2022, 10, 10),
-                                                  _dt.date(2022, 10, 3)]))
-        df = df.sort_index()
-        df.index.name = "Date"
-        df_bad = df.copy()
-        df_bad.loc["2022-10-24", "Close"] *= 100
-        df_bad.loc["2022-10-17", "Low"] *= 100
-        df_bad.loc["2022-10-03", "Open"] *= 100
-        df.index = df.index.tz_localize(tz_exchange)
-        df_bad.index = df_bad.index.tz_localize(tz_exchange)
-
-        # Run test
-
-        df_repaired = hist._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
-
-        # First test - no errors left
-        for c in data_cols:
-            try:
-                self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-            except AssertionError:
-                print(df[c])
-                print(df_repaired[c])
-                raise
-
-        # Second test - all differences should be either ~1x or ~100x
-        ratio = df_bad[data_cols].values / df[data_cols].values
-        ratio = ratio.round(2)
-        # - round near-100 ratio to 100:
-        f = ratio > 90
-        ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
-        # - now test
-        f_100 = ratio == 100
-        f_1 = ratio == 1
-        self.assertTrue((f_100 | f_1).all())
-
-        self.assertTrue("Repaired?" in df_repaired.columns)
-        self.assertFalse(df_repaired["Repaired?"].isna().any())
-
-    def test_repair_100x_random_weekly_preSplit(self):
-        # PNL.L has a stock-split in 2022. Sometimes requesting data before 2022 is not split-adjusted.
-
-        tkr = "PNL.L"
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
-        df = _pd.DataFrame(data={"Open":      [400,    398,    392.5,  417],
-                                 "High":      [421,    425,    419,    420.5],
-                                 "Low":       [400,    380.5,  376.5,  396],
-                                 "Close":     [410,    409.5,  402,    399],
-                                 "Adj Close": [393.91, 393.43, 386.22, 383.34],
-                                 "Volume": [3232600, 3773900, 10835000, 4257900]},
-                           index=_pd.to_datetime([_dt.date(2020, 3, 30),
-                                                  _dt.date(2020, 3, 23),
-                                                  _dt.date(2020, 3, 16),
-                                                  _dt.date(2020, 3, 9)]))
-        df = df.sort_index()
-        # Simulate data missing split-adjustment:
-        df[data_cols] *= 100.0
-        df["Volume"] *= 0.01
-        #
-        df.index.name = "Date"
-        # Create 100x errors:
-        df_bad = df.copy()
-        df_bad.loc["2020-03-30", "Close"] *= 100
-        df_bad.loc["2020-03-23", "Low"] *= 100
-        df_bad.loc["2020-03-09", "Open"] *= 100
-        df.index = df.index.tz_localize(tz_exchange)
-        df_bad.index = df_bad.index.tz_localize(tz_exchange)
-
-        df_repaired = hist._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
-
-        # First test - no errors left
-        for c in data_cols:
-            try:
-                self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-            except AssertionError:
-                print("Mismatch in column", c)
-                print("- df_repaired:")
-                print(df_repaired[c])
-                print("- answer:")
-                print(df[c])
-                raise
-
-        # Second test - all differences should be either ~1x or ~100x
-        ratio = df_bad[data_cols].values / df[data_cols].values
-        ratio = ratio.round(2)
-        # - round near-100 ratio to 100:
-        f = ratio > 90
-        ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
-        # - now test
-        f_100 = ratio == 100
-        f_1 = ratio == 1
-        self.assertTrue((f_100 | f_1).all())
-
-        self.assertTrue("Repaired?" in df_repaired.columns)
-        self.assertFalse(df_repaired["Repaired?"].isna().any())
-
-    def test_repair_100x_random_daily(self):
-        tkr = "PNL.L"
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
-        df = _pd.DataFrame(data={"Open":      [478,    476,   476,   472],
-                                 "High":      [478,    477.5, 477,   475],
-                                 "Low":       [474.02, 474,   473,   470.75],
-                                 "Close":     [475.5,  475.5, 474.5, 475],
-                                 "Adj Close": [475.5,  475.5, 474.5, 475],
-                                 "Volume": [436414, 485947, 358067, 287620]},
-                           index=_pd.to_datetime([_dt.date(2022, 11, 1),
-                                                  _dt.date(2022, 10, 31),
-                                                  _dt.date(2022, 10, 28),
-                                                  _dt.date(2022, 10, 27)]))
-        df = df.sort_index()
-        df.index.name = "Date"
-        df_bad = df.copy()
-        df_bad.loc["2022-11-01", "Close"] *= 100
-        df_bad.loc["2022-10-31", "Low"] *= 100
-        df_bad.loc["2022-10-27", "Open"] *= 100
-        df.index = df.index.tz_localize(tz_exchange)
-        df_bad.index = df_bad.index.tz_localize(tz_exchange)
-
-        df_repaired = hist._fix_unit_random_mixups(df_bad, "1d", tz_exchange, prepost=False)
-
-        # First test - no errors left
-        for c in data_cols:
-            self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-
-        # Second test - all differences should be either ~1x or ~100x
-        ratio = df_bad[data_cols].values / df[data_cols].values
-        ratio = ratio.round(2)
-        # - round near-100 ratio to 100:
-        f = ratio > 90
-        ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
-        # - now test
-        f_100 = ratio == 100
-        f_1 = ratio == 1
-        self.assertTrue((f_100 | f_1).all())
-
-        self.assertTrue("Repaired?" in df_repaired.columns)
-        self.assertFalse(df_repaired["Repaired?"].isna().any())
-
-    def test_repair_100x_block_daily(self):
-        # Some 100x errors are not sporadic.
-        # Sometimes Yahoo suddenly shifts from cents->$ from some recent date.
-
-        tkrs = ['AET.L', 'SSW.JO']
-        for tkr in tkrs:
-            for interval in ['1d', '1wk']:
-                dat = yf.Ticker(tkr, session=self.session)
-                tz_exchange = dat.fast_info["timezone"]
-                hist = dat._lazy_load_price_history()
-
-                data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
-                _dp = os.path.dirname(__file__)
-                fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error.csv")
-                if not os.path.isfile(fp):
-                    continue
-                df_bad = _pd.read_csv(fp, index_col="Date")
-                df_bad.index = _pd.to_datetime(df_bad.index, utc=True).tz_convert(tz_exchange)
-                df_bad = df_bad.sort_index()
-
-                df = df_bad.copy()
-                fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error-fixed.csv")
-                df = _pd.read_csv(fp, index_col="Date")
-                df.index = _pd.to_datetime(df.index, utc=True).tz_convert(tz_exchange)
-                df = df.sort_index()
-
-                df_repaired = hist._fix_unit_switch(df_bad, interval, tz_exchange)
-                df_repaired = df_repaired.sort_index()
-
-                # First test - no errors left
-                for c in data_cols:
-                    try:
-                        self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-                    except:
-                        print("- repaired:")
-                        print(df_repaired[c])
-                        print("- correct:")
-                        print(df[c])
-                        print(f"TEST FAIL on column '{c}' (tkr={tkr} interval={interval})")
-                        raise
-
-                # Second test - all differences should be either ~1x or ~100x
-                ratio = df_bad[data_cols].values / df[data_cols].values
-                ratio = ratio.round(2)
-                # - round near-100 ratio to 100:
-                f = ratio > 90
-                ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
-                # - now test
-                f_100 = (ratio == 100) | (ratio == 0.01)
-                f_1 = ratio == 1
-                self.assertTrue((f_100 | f_1).all())
-
-                self.assertTrue("Repaired?" in df_repaired.columns)
-                self.assertFalse(df_repaired["Repaired?"].isna().any())
-
-    def test_repair_zeroes_daily(self):
-        tkr = "BBIL.L"
-        dat = yf.Ticker(tkr, session=self.session)
-        hist = dat._lazy_load_price_history()
-        tz_exchange = dat.fast_info["timezone"]
-
-        df_bad = _pd.DataFrame(data={"Open":      [0,      102.04, 102.04],
-                                     "High":      [0,      102.1,  102.11],
-                                     "Low":       [0,      102.04, 102.04],
-                                     "Close":     [103.03, 102.05, 102.08],
-                                     "Adj Close": [102.03, 102.05, 102.08],
-                                     "Volume": [560, 137, 117]},
-                               index=_pd.to_datetime([_dt.datetime(2022, 11, 1),
-                                                      _dt.datetime(2022, 10, 31),
-                                                      _dt.datetime(2022, 10, 30)]))
-        df_bad = df_bad.sort_index()
-        df_bad.index.name = "Date"
-        df_bad.index = df_bad.index.tz_localize(tz_exchange)
-
-        repaired_df = hist._fix_zeroes(df_bad, "1d", tz_exchange, prepost=False)
-
-        correct_df = df_bad.copy()
-        correct_df.loc["2022-11-01", "Open"] = 102.080002
-        correct_df.loc["2022-11-01", "Low"] = 102.032501
-        correct_df.loc["2022-11-01", "High"] = 102.080002
-        for c in ["Open", "Low", "High", "Close"]:
-            self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-8).all())
-
-        self.assertTrue("Repaired?" in repaired_df.columns)
-        self.assertFalse(repaired_df["Repaired?"].isna().any())
-
-    def test_repair_zeroes_daily_adjClose(self):
-        # Test that 'Adj Close' is reconstructed correctly,
-        # particularly when a dividend occurred within 1 day.
-
-        tkr = "INTC"
-        df = _pd.DataFrame(data={"Open":      [28.95, 28.65, 29.55, 29.62, 29.25],
-                                 "High":      [29.12, 29.27, 29.65, 31.17, 30.30],
-                                 "Low":       [28.21, 28.43, 28.61, 29.53, 28.80],
-                                 "Close":     [28.24, 29.05, 28.69, 30.32, 30.19],
-                                 "Adj Close": [28.12, 28.93, 28.57, 29.83, 29.70],
-                                 "Volume":    [36e6, 51e6, 49e6, 58e6, 62e6],
-                                 "Dividends": [0, 0, 0.365, 0, 0]},
-                           index=_pd.to_datetime([_dt.datetime(2023, 2, 8),
-                                                  _dt.datetime(2023, 2, 7),
-                                                  _dt.datetime(2023, 2, 6),
-                                                  _dt.datetime(2023, 2, 3),
-                                                  _dt.datetime(2023, 2, 2)]))
-        df = df.sort_index()
-        df.index.name = "Date"
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        df.index = df.index.tz_localize(tz_exchange)
-        hist = dat._lazy_load_price_history()
-
-        rtol = 5e-3
-        for i in [0, 1, 2]:
-            df_slice = df.iloc[i:i+3]
-            for j in range(3):
-                df_slice_bad = df_slice.copy()
-                df_slice_bad.loc[df_slice_bad.index[j], "Adj Close"] = 0.0
-
-                df_slice_bad_repaired = hist._fix_zeroes(df_slice_bad, "1d", tz_exchange, prepost=False)
-                for c in ["Close", "Adj Close"]:
-                    self.assertTrue(_np.isclose(df_slice_bad_repaired[c], df_slice[c], rtol=rtol).all())
-                self.assertTrue("Repaired?" in df_slice_bad_repaired.columns)
-                self.assertFalse(df_slice_bad_repaired["Repaired?"].isna().any())
-
-    def test_repair_zeroes_hourly(self):
-        tkr = "INTC"
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        correct_df = hist.history(period="5d", interval="1h", auto_adjust=False, repair=True)
-
-        df_bad = correct_df.copy()
-        bad_idx = correct_df.index[10]
-        df_bad.loc[bad_idx, "Open"] = _np.nan
-        df_bad.loc[bad_idx, "High"] = _np.nan
-        df_bad.loc[bad_idx, "Low"] = _np.nan
-        df_bad.loc[bad_idx, "Close"] = _np.nan
-        df_bad.loc[bad_idx, "Adj Close"] = _np.nan
-        df_bad.loc[bad_idx, "Volume"] = 0
-
-        repaired_df = hist._fix_zeroes(df_bad, "1h", tz_exchange, prepost=False)
-
-        for c in ["Open", "Low", "High", "Close"]:
-            try:
-                self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-7).all())
-            except AssertionError:
-                print("COLUMN", c)
-                print("- repaired_df")
-                print(repaired_df)
-                print("- correct_df[c]:")
-                print(correct_df[c])
-                print("- diff:")
-                print(repaired_df[c] - correct_df[c])
-                raise
-
-        self.assertTrue("Repaired?" in repaired_df.columns)
-        self.assertFalse(repaired_df["Repaired?"].isna().any())
-
-    def test_repair_bad_stock_splits(self):
-        # Stocks that split in 2022 but no problems in Yahoo data,
-        # so repair should change nothing
-        good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
-        good_tkrs += ['AEI', 'GHI', 'IRON', 'LXU', 'NUZE', 'RSLS', 'TISI']
-        good_tkrs += ['BOL.ST', 'TUI1.DE']
-        intervals = ['1d', '1wk', '1mo', '3mo']
-        for tkr in good_tkrs:
-            for interval in intervals:
-                dat = yf.Ticker(tkr, session=self.session)
-                tz_exchange = dat.fast_info["timezone"]
-                hist = dat._lazy_load_price_history()
-
-                _dp = os.path.dirname(__file__)
-                df_good = dat.history(start='2020-01-01', end=_dt.date.today(), interval=interval, auto_adjust=False)
-
-                repaired_df = hist._fix_bad_stock_splits(df_good, interval, tz_exchange)
-
-                # Expect no change from repair
-                df_good = df_good.sort_index()
-                repaired_df = repaired_df.sort_index()
-                for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
-                    try:
-                        self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
-                    except:
-                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
-                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
-                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
-                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
-                        raise
-
-        bad_tkrs = ['4063.T', 'ALPHA.PA', 'AV.L', 'CNE.L', 'MOB.ST', 'SPM.MI']
-        bad_tkrs.append('LA.V')  # special case - stock split error is 3 years ago! why not fixed?
-        for tkr in bad_tkrs:
-            dat = yf.Ticker(tkr, session=self.session)
-            tz_exchange = dat.fast_info["timezone"]
-            hist = dat._lazy_load_price_history()
-
-            _dp = os.path.dirname(__file__)
-            interval = '1d'
-            fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
-            if not os.path.isfile(fp):
-                interval = '1wk'
-                fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
-            df_bad = _pd.read_csv(fp, index_col="Date")
-            df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
-
-            repaired_df = hist._fix_bad_stock_splits(df_bad, "1d", tz_exchange)
-
-            fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split-fixed.csv")
-            correct_df = _pd.read_csv(fp, index_col="Date")
-            correct_df.index = _pd.to_datetime(correct_df.index, utc=True)
-
-            repaired_df = repaired_df.sort_index()
-            correct_df = correct_df.sort_index()
-            for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
-                try:
-                    self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
-                except AssertionError:
-                    print(f"tkr={tkr} COLUMN={c}")
-                    # print("- repaired_df")
-                    # print(repaired_df)
-                    # print("- correct_df[c]:")
-                    # print(correct_df[c])
-                    # print("- diff:")
-                    # print(repaired_df[c] - correct_df[c])
-                    raise
-
-        # Had very high price volatility in Jan-2021 around split date that could
-        # be mistaken for missing stock split adjustment. And old logic did think
-        # column 'High' required fixing - wrong!
-        sketchy_tkrs = ['FIZZ']
-        intervals = ['1wk']
-        for tkr in sketchy_tkrs:
-            for interval in intervals:
-                dat = yf.Ticker(tkr, session=self.session)
-                tz_exchange = dat.fast_info["timezone"]
-                hist = dat._lazy_load_price_history()
-
-                _dp = os.path.dirname(__file__)
-                df_good = hist.history(start='2020-11-30', end='2021-04-01', interval=interval, auto_adjust=False)
-
-                repaired_df = hist._fix_bad_stock_splits(df_good, interval, tz_exchange)
-
-                # Expect no change from repair
-                df_good = df_good.sort_index()
-                repaired_df = repaired_df.sort_index()
-                for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
-                    try:
-                        self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
-                    except AssertionError:
-                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
-                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
-                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
-                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
-                        raise
-
-    def test_repair_missing_div_adjust(self):
-        tkr = '8TRA.DE'
-
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        _dp = os.path.dirname(__file__)
-        df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust.csv"), index_col="Date")
-        df_bad.index = _pd.to_datetime(df_bad.index)
-
-        repaired_df = hist._fix_missing_div_adjust(df_bad, "1d", tz_exchange)
-
-        correct_df = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust-fixed.csv"), index_col="Date")
-        correct_df.index = _pd.to_datetime(correct_df.index)
-
-        repaired_df = repaired_df.sort_index()
-        correct_df = correct_df.sort_index()
-        for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
-            try:
-                self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
-            except:
-                print(f"tkr={tkr} COLUMN={c}")
-                print("- repaired_df")
-                print(repaired_df)
-                print("- correct_df[c]:")
-                print(correct_df[c])
-                print("- diff:")
-                print(repaired_df[c] - correct_df[c])
-                raise
-
-
-if __name__ == '__main__':
-    unittest.main()
--- a/tests/test_ticker.py
+++ b/tests/test_ticker.py
@@ -1,930 +0,0 @@
-"""
-Tests for Ticker
-
-To run all tests in suite from commandline:
-   python -m unittest tests.ticker
-
-Specific test class:
-   python -m unittest tests.ticker.TestTicker
-
-"""
-import pandas as pd
-
-from .context import yfinance as yf
-from .context import session_gbl
-from yfinance.exceptions import YFChartError, YFInvalidPeriodError, YFNotImplementedError, YFTickerMissingError, YFTzMissingError
-
-
-import unittest
-import requests_cache
-from typing import Union, Any, get_args, _GenericAlias
-from urllib.parse import urlparse, parse_qs, urlencode, urlunparse
-
-ticker_attributes = (
-    ("major_holders", pd.DataFrame),
-    ("institutional_holders", pd.DataFrame),
-    ("mutualfund_holders", pd.DataFrame),
-    ("insider_transactions", pd.DataFrame),
-    ("insider_purchases", pd.DataFrame),
-    ("insider_roster_holders", pd.DataFrame),
-    ("splits", pd.Series),
-    ("actions", pd.DataFrame),
-    ("shares", pd.DataFrame),
-    ("info", dict),
-    ("calendar", dict),
-    ("recommendations", Union[pd.DataFrame, dict]),
-    ("recommendations_summary", Union[pd.DataFrame, dict]),
-    ("upgrades_downgrades", 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:
-            err_msg = f'{attribute_name} type is {type(attribute)} not {expected_type}'
-            if isinstance(expected_type, _GenericAlias) and expected_type.__origin__ is Union:
-                allowed_types = get_args(expected_type)
-                testClass.assertTrue(isinstance(attribute, allowed_types), err_msg)
-            else:
-                testClass.assertEqual(type(attribute), expected_type, err_msg)
-    except Exception:
-        testClass.assertRaises(
-            YFNotImplementedError, lambda: getattr(instance, attribute_name)
-        )
-
-class TestTicker(unittest.TestCase):
-    session = None
-
-    @classmethod
-    def setUpClass(cls):
-        cls.session = session_gbl
-
-        cls.proxy = None
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.session is not None:
-            cls.session.close()
-
-    def test_getTz(self):
-        tkrs = ["IMP.JO", "BHG.JO", "SSW.JO", "BP.L", "INTC"]
-        for tkr in tkrs:
-            # First step: remove ticker from tz-cache
-            yf.cache.get_tz_cache().store(tkr, None)
-
-            # Test:
-            dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(proxy=None, timeout=5)
-
-            self.assertIsNotNone(tz)
-
-    def test_badTicker(self):
-        # Check yfinance doesn't die when ticker delisted
-
-        tkr = "DJI"  # typo of "^DJI"
-        dat = yf.Ticker(tkr, session=self.session)
-
-        dat.history(period="5d")
-        dat.history(start="2022-01-01")
-        dat.history(start="2022-01-01", end="2022-03-01")
-        yf.download([tkr], period="5d", threads=False, ignore_tz=False)
-        yf.download([tkr], period="5d", threads=True, ignore_tz=False)
-        yf.download([tkr], period="5d", threads=False, ignore_tz=True)
-        yf.download([tkr], period="5d", 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)
-
-        assert isinstance(dat.dividends, pd.Series)
-        assert dat.dividends.empty
-        assert isinstance(dat.splits, pd.Series)
-        assert dat.splits.empty
-        assert isinstance(dat.capital_gains, pd.Series)
-        assert dat.capital_gains.empty
-        with self.assertRaises(YFNotImplementedError):
-            assert isinstance(dat.shares, pd.DataFrame)
-            assert dat.shares.empty
-        assert isinstance(dat.actions, pd.DataFrame)
-        assert dat.actions.empty
-
-    def test_invalid_period(self):
-        tkr = 'VALE'
-        dat = yf.Ticker(tkr, session=self.session)
-        with self.assertRaises(YFInvalidPeriodError):
-            dat.history(period="2wks", interval="1d", raise_errors=True)
-        with self.assertRaises(YFInvalidPeriodError):
-            dat.history(period="2mo", interval="1d", raise_errors=True)
-
-
-    def test_prices_missing(self):
-        # this test will need to be updated every time someone wants to run a test
-        # hard to find a ticker that matches this error other than options
-        # META call option, 2024 April 26th @ strike of 180000
-        tkr = 'META240426C00180000'
-        dat = yf.Ticker(tkr, session=self.session)
-        with self.assertRaises(YFChartError):
-            dat.history(period="5d", interval="1m", raise_errors=True)
-
-    def test_ticker_missing(self):
-        tkr = 'ATVI'
-        dat = yf.Ticker(tkr, session=self.session)
-        # A missing ticker can trigger either a niche error or the generalized error
-        with self.assertRaises((YFTickerMissingError, YFTzMissingError, YFChartError)):
-            dat.history(period="3mo", interval="1d", raise_errors=True)
-
-    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="5d")
-            dat.history(start="2022-01-01")
-            dat.history(start="2022-01-01", end="2022-03-01")
-            yf.download([tkr], period="5d", threads=False, ignore_tz=False)
-            yf.download([tkr], period="5d", threads=True, ignore_tz=False)
-            yf.download([tkr], period="5d", threads=False, ignore_tz=True)
-            yf.download([tkr], period="5d", 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_withProxy(self):
-        tkr = "IBM"
-        dat = yf.Ticker(tkr, session=self.session, proxy=self.proxy)
-
-        dat._fetch_ticker_tz(proxy=None, timeout=5)
-        dat._get_ticker_tz(proxy=None, timeout=5)
-        dat.history(period="5d")
-
-        for attribute_name, attribute_type in ticker_attributes:
-            assert_attribute_type(self, dat, attribute_name, attribute_type)
-
-
-class TestTickerHistory(unittest.TestCase):
-    session = None
-
-    @classmethod
-    def setUpClass(cls):
-        cls.session = session_gbl
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.session is not None:
-            cls.session.close()
-
-    def setUp(self):
-        # use a ticker that has dividends
-        self.symbol = "IBM"
-        self.ticker = yf.Ticker(self.symbol, session=self.session)
-
-        self.symbols = ["AMZN", "MSFT", "NVDA"]
-
-    def tearDown(self):
-        self.ticker = None
-
-    def test_history(self):
-        md = self.ticker.history_metadata
-        self.assertIn("IBM", md.values(), "metadata missing")
-        data = self.ticker.history("1y")
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-    def test_download(self):
-        for t in [False, True]:
-            for i in [False, True]:
-                data = yf.download(self.symbols, threads=t, ignore_tz=i)
-                self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-                self.assertFalse(data.empty, "data is empty")
-
-    def test_no_expensive_calls_introduced(self):
-        """
-        Make sure calling history to get price data has not introduced more calls to yahoo than absolutely necessary.
-        As doing other type of scraping calls than "query2.finance.yahoo.com/v8/finance/chart" to yahoo website
-        will quickly trigger spam-block when doing bulk download of history data.
-        """
-        symbol = "GOOGL"
-        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")
-        self.assertFalse(data.empty, "data is empty")
-
-    def test_splits(self):
-        data = self.ticker.splits
-        self.assertIsInstance(data, pd.Series, "data has wrong type")
-        # self.assertFalse(data.empty, "data is empty")
-
-    def test_actions(self):
-        data = self.ticker.actions
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-
-class TestTickerEarnings(unittest.TestCase):
-    session = None
-
-    @classmethod
-    def setUpClass(cls):
-        cls.session = session_gbl
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.session is not None:
-            cls.session.close()
-
-    def setUp(self):
-        self.ticker = yf.Ticker("GOOGL", session=self.session)
-
-    def tearDown(self):
-        self.ticker = None
-
-    def test_earnings_dates(self):
-        data = self.ticker.earnings_dates
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-    def test_earnings_dates_with_limit(self):
-        # use ticker with lots of historic earnings
-        ticker = yf.Ticker("IBM")
-        limit = 110
-        data = ticker.get_earnings_dates(limit=limit)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertEqual(len(data), limit, "Wrong number or rows")
-
-        data_cached = ticker.get_earnings_dates(limit=limit)
-        self.assertIs(data, data_cached, "data not cached")
-
-    # Below will fail because not ported to Yahoo API
-
-    # 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 = session_gbl
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.session is not None:
-            cls.session.close()
-
-    def setUp(self):
-        self.ticker = yf.Ticker("GOOGL", session=self.session)
-
-    def tearDown(self):
-        self.ticker = None
-
-    def test_major_holders(self):
-        data = self.ticker.major_holders
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.major_holders
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_institutional_holders(self):
-        data = self.ticker.institutional_holders
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.institutional_holders
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_mutualfund_holders(self):
-        data = self.ticker.mutualfund_holders
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.mutualfund_holders
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_insider_transactions(self):
-        data = self.ticker.insider_transactions
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.insider_transactions
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_insider_purchases(self):
-        data = self.ticker.insider_purchases
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.insider_purchases
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_insider_roster_holders(self):
-        data = self.ticker.insider_roster_holders
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.insider_roster_holders
-        self.assertIs(data, data_cached, "data not cached")
-
-
-class TestTickerMiscFinancials(unittest.TestCase):
-    session = None
-
-    @classmethod
-    def setUpClass(cls):
-        cls.session = session_gbl
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.session is not None:
-            cls.session.close()
-
-    def setUp(self):
-        self.ticker = yf.Ticker("GOOGL", session=self.session)
-
-        # For ticker 'BSE.AX' (and others), Yahoo not returning
-        # full quarterly financials (usually cash-flow) with all entries,
-        # instead returns a smaller version in different data store.
-        self.ticker_old_fmt = yf.Ticker("BSE.AX", session=self.session)
-
-    def tearDown(self):
-        self.ticker = None
-
-    def test_isin(self):
-        data = self.ticker.isin
-        self.assertIsInstance(data, str, "data has wrong type")
-        self.assertEqual("ARDEUT116159", data, "data is empty")
-
-        data_cached = self.ticker.isin
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_options(self):
-        data = self.ticker.options
-        self.assertIsInstance(data, tuple, "data has wrong type")
-        self.assertTrue(len(data) > 1, "data is empty")
-
-    def test_shares_full(self):
-        data = self.ticker.get_shares_full()
-        self.assertIsInstance(data, pd.Series, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-    def test_income_statement(self):
-        expected_keys = ["Total Revenue", "Basic EPS"]
-        expected_periods_days = 365
-
-        # Test contents of table
-        data = self.ticker.get_income_stmt(pretty=True)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-        period = abs((data.columns[0]-data.columns[1]).days)
-        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
-
-        # Test property defaults
-        data2 = self.ticker.income_stmt
-        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
-
-        # Test pretty=False
-        expected_keys = [k.replace(' ', '') for k in expected_keys]
-        data = self.ticker.get_income_stmt(pretty=False)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-
-        # Test to_dict
-        data = self.ticker.get_income_stmt(as_dict=True)
-        self.assertIsInstance(data, dict, "data has wrong type")
-
-    def test_quarterly_income_statement(self):
-        expected_keys = ["Total Revenue", "Basic EPS"]
-        expected_periods_days = 365//4
-
-        # Test contents of table
-        data = self.ticker.get_income_stmt(pretty=True, freq="quarterly")
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-        period = abs((data.columns[0]-data.columns[1]).days)
-        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
-
-        # Test property defaults
-        data2 = self.ticker.quarterly_income_stmt
-        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
-
-        # Test pretty=False
-        expected_keys = [k.replace(' ', '') for k in expected_keys]
-        data = self.ticker.get_income_stmt(pretty=False, freq="quarterly")
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-
-        # Test to_dict
-        data = self.ticker.get_income_stmt(as_dict=True)
-        self.assertIsInstance(data, dict, "data has wrong type")
-
-    def test_balance_sheet(self):
-        expected_keys = ["Total Assets", "Net PPE"]
-        expected_periods_days = 365
-
-        # Test contents of table
-        data = self.ticker.get_balance_sheet(pretty=True)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-        period = abs((data.columns[0]-data.columns[1]).days)
-        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
-
-        # Test property defaults
-        data2 = self.ticker.balance_sheet
-        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
-
-        # Test pretty=False
-        expected_keys = [k.replace(' ', '') for k in expected_keys]
-        data = self.ticker.get_balance_sheet(pretty=False)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-
-        # Test to_dict
-        data = self.ticker.get_balance_sheet(as_dict=True)
-        self.assertIsInstance(data, dict, "data has wrong type")
-
-    def test_quarterly_balance_sheet(self):
-        expected_keys = ["Total Assets", "Net PPE"]
-        expected_periods_days = 365//4
-
-        # Test contents of table
-        data = self.ticker.get_balance_sheet(pretty=True, freq="quarterly")
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-        period = abs((data.columns[0]-data.columns[1]).days)
-        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
-
-        # Test property defaults
-        data2 = self.ticker.quarterly_balance_sheet
-        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
-
-        # Test pretty=False
-        expected_keys = [k.replace(' ', '') for k in expected_keys]
-        data = self.ticker.get_balance_sheet(pretty=False, freq="quarterly")
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-
-        # Test to_dict
-        data = self.ticker.get_balance_sheet(as_dict=True, freq="quarterly")
-        self.assertIsInstance(data, dict, "data has wrong type")
-
-    def test_cash_flow(self):
-        expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
-        expected_periods_days = 365
-
-        # Test contents of table
-        data = self.ticker.get_cashflow(pretty=True)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-        period = abs((data.columns[0]-data.columns[1]).days)
-        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
-
-        # Test property defaults
-        data2 = self.ticker.cashflow
-        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
-
-        # Test pretty=False
-        expected_keys = [k.replace(' ', '') for k in expected_keys]
-        data = self.ticker.get_cashflow(pretty=False)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-
-        # Test to_dict
-        data = self.ticker.get_cashflow(as_dict=True)
-        self.assertIsInstance(data, dict, "data has wrong type")
-
-    def test_quarterly_cash_flow(self):
-        expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
-        expected_periods_days = 365//4
-
-        # Test contents of table
-        data = self.ticker.get_cashflow(pretty=True, freq="quarterly")
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-        period = abs((data.columns[0]-data.columns[1]).days)
-        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
-
-        # Test property defaults
-        data2 = self.ticker.quarterly_cashflow
-        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
-
-        # Test pretty=False
-        expected_keys = [k.replace(' ', '') for k in expected_keys]
-        data = self.ticker.get_cashflow(pretty=False, freq="quarterly")
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        for k in expected_keys:
-            self.assertIn(k, data.index, "Did not find expected row in index")
-
-        # Test to_dict
-        data = self.ticker.get_cashflow(as_dict=True)
-        self.assertIsInstance(data, dict, "data has wrong type")
-
-    def test_income_alt_names(self):
-        i1 = self.ticker.income_stmt
-        i2 = self.ticker.incomestmt
-        self.assertTrue(i1.equals(i2))
-        i3 = self.ticker.financials
-        self.assertTrue(i1.equals(i3))
-
-        i1 = self.ticker.get_income_stmt()
-        i2 = self.ticker.get_incomestmt()
-        self.assertTrue(i1.equals(i2))
-        i3 = self.ticker.get_financials()
-        self.assertTrue(i1.equals(i3))
-
-        i1 = self.ticker.quarterly_income_stmt
-        i2 = self.ticker.quarterly_incomestmt
-        self.assertTrue(i1.equals(i2))
-        i3 = self.ticker.quarterly_financials
-        self.assertTrue(i1.equals(i3))
-
-        i1 = self.ticker.get_income_stmt(freq="quarterly")
-        i2 = self.ticker.get_incomestmt(freq="quarterly")
-        self.assertTrue(i1.equals(i2))
-        i3 = self.ticker.get_financials(freq="quarterly")
-        self.assertTrue(i1.equals(i3))
-
-    def test_balance_sheet_alt_names(self):
-        i1 = self.ticker.balance_sheet
-        i2 = self.ticker.balancesheet
-        self.assertTrue(i1.equals(i2))
-
-        i1 = self.ticker.get_balance_sheet()
-        i2 = self.ticker.get_balancesheet()
-        self.assertTrue(i1.equals(i2))
-
-        i1 = self.ticker.quarterly_balance_sheet
-        i2 = self.ticker.quarterly_balancesheet
-        self.assertTrue(i1.equals(i2))
-
-        i1 = self.ticker.get_balance_sheet(freq="quarterly")
-        i2 = self.ticker.get_balancesheet(freq="quarterly")
-        self.assertTrue(i1.equals(i2))
-
-    def test_cash_flow_alt_names(self):
-        i1 = self.ticker.cash_flow
-        i2 = self.ticker.cashflow
-        self.assertTrue(i1.equals(i2))
-
-        i1 = self.ticker.get_cash_flow()
-        i2 = self.ticker.get_cashflow()
-        self.assertTrue(i1.equals(i2))
-
-        i1 = self.ticker.quarterly_cash_flow
-        i2 = self.ticker.quarterly_cashflow
-        self.assertTrue(i1.equals(i2))
-
-        i1 = self.ticker.get_cash_flow(freq="quarterly")
-        i2 = self.ticker.get_cashflow(freq="quarterly")
-        self.assertTrue(i1.equals(i2))
-
-    def test_bad_freq_value_raises_exception(self):
-        self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))
-
-    def test_calendar(self):
-        data = self.ticker.calendar
-        self.assertIsInstance(data, dict, "data has wrong type")
-        self.assertTrue(len(data) > 0, "data is empty")
-        self.assertIn("Earnings Date", data.keys(), "data missing expected key")
-        self.assertIn("Earnings Average", data.keys(), "data missing expected key")
-        self.assertIn("Earnings Low", data.keys(), "data missing expected key")
-        self.assertIn("Earnings High", data.keys(), "data missing expected key")
-        self.assertIn("Revenue Average", data.keys(), "data missing expected key")
-        self.assertIn("Revenue Low", data.keys(), "data missing expected key")
-        self.assertIn("Revenue High", data.keys(), "data missing expected key")
-        # dividend date is not available for tested ticker GOOGL
-        if self.ticker.ticker != "GOOGL":
-            self.assertIn("Dividend Date", data.keys(), "data missing expected key")
-        # ex-dividend date is not always available
-        data_cached = self.ticker.calendar
-        self.assertIs(data, data_cached, "data not cached")
-
-
-    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_shares(self):
-    #     data = self.ticker.shares
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
-
-
-class TestTickerAnalysts(unittest.TestCase):
-    session = None
-
-    @classmethod
-    def setUpClass(cls):
-        cls.session = session_gbl
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.session is not None:
-            cls.session.close()
-
-    def setUp(self):
-        self.ticker = yf.Ticker("GOOGL", session=self.session)
-
-    def tearDown(self):
-        self.ticker = None
-
-    def test_recommendations(self):
-        data = self.ticker.recommendations
-        data_summary = self.ticker.recommendations_summary
-        self.assertTrue(data.equals(data_summary))
-        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):  # currently alias for recommendations
-        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_upgrades_downgrades(self):
-        data = self.ticker.upgrades_downgrades
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertTrue(len(data.columns) == 4, "data has wrong number of columns")
-        self.assertEqual(data.columns.values.tolist(), ['Firm', 'ToGrade', 'FromGrade', 'Action'], "data has wrong column names")
-        self.assertIsInstance(data.index, pd.DatetimeIndex, "data has wrong index type")
-
-        data_cached = self.ticker.upgrades_downgrades
-        self.assertIs(data, data_cached, "data not cached")
-
-    # Below will fail because not ported to Yahoo API
-
-    # 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")
-
-
-
-class TestTickerInfo(unittest.TestCase):
-    session = None
-
-    @classmethod
-    def setUpClass(cls):
-        cls.session = session_gbl
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.session is not None:
-            cls.session.close()
-
-    def setUp(self):
-        self.symbols = []
-        self.symbols += ["ESLT.TA", "BP.L", "GOOGL"]
-        self.symbols.append("QCSTIX")  # good for testing, doesn't trade
-        self.symbols += ["BTC-USD", "IWO", "VFINX", "^GSPC"]
-        self.symbols += ["SOKE.IS", "ADS.DE"]  # detected bugs
-        self.tickers = [yf.Ticker(s, session=self.session) for s in self.symbols]
-
-    def tearDown(self):
-        self.ticker = None
-
-    def test_fast_info(self):
-        f = yf.Ticker("AAPL", session=self.session).fast_info
-        for k in f:
-            self.assertIsNotNone(f[k])
-
-    def test_info(self):
-        data = self.tickers[0].info
-        self.assertIsInstance(data, dict, "data has wrong type")
-        expected_keys = ['industry', 'currentPrice', 'exchange', 'floatShares', 'companyOfficers', 'bid']
-        for k in expected_keys:
-            print(k)
-            self.assertIn("symbol", data.keys(), f"Did not find expected key '{k}' in info dict")
-        self.assertEqual(self.symbols[0], data["symbol"], "Wrong symbol value in info dict")
-
-    def test_complementary_info(self):
-        # This test is to check that we can successfully retrieve the trailing PEG ratio
-
-        # We don't expect this one to have a trailing PEG ratio
-        data1 = self.tickers[0].info
-        self.assertIsNone(data1['trailingPegRatio'])
-
-        # This one should have a trailing PEG ratio
-        data2 = self.tickers[2].info
-        self.assertIsInstance(data2['trailingPegRatio'], float)
-
-    # def test_fast_info_matches_info(self):
-    #     fast_info_keys = set()
-    #     for ticker in self.tickers:
-    #         fast_info_keys.update(set(ticker.fast_info.keys()))
-    #     fast_info_keys = sorted(list(fast_info_keys))
-
-    #     key_rename_map = {}
-    #     key_rename_map["currency"] = "currency"
-    #     key_rename_map["quote_type"] = "quoteType"
-    #     key_rename_map["timezone"] = "exchangeTimezoneName"
-
-    #     key_rename_map["last_price"] = ["currentPrice", "regularMarketPrice"]
-    #     key_rename_map["open"] = ["open", "regularMarketOpen"]
-    #     key_rename_map["day_high"] = ["dayHigh", "regularMarketDayHigh"]
-    #     key_rename_map["day_low"] = ["dayLow", "regularMarketDayLow"]
-    #     key_rename_map["previous_close"] = ["previousClose"]
-    #     key_rename_map["regular_market_previous_close"] = ["regularMarketPreviousClose"]
-
-    #     key_rename_map["fifty_day_average"] = "fiftyDayAverage"
-    #     key_rename_map["two_hundred_day_average"] = "twoHundredDayAverage"
-    #     key_rename_map["year_change"] = ["52WeekChange", "fiftyTwoWeekChange"]
-    #     key_rename_map["year_high"] = "fiftyTwoWeekHigh"
-    #     key_rename_map["year_low"] = "fiftyTwoWeekLow"
-
-    #     key_rename_map["last_volume"] = ["volume", "regularMarketVolume"]
-    #     key_rename_map["ten_day_average_volume"] = ["averageVolume10days", "averageDailyVolume10Day"]
-    #     key_rename_map["three_month_average_volume"] = "averageVolume"
-
-    #     key_rename_map["market_cap"] = "marketCap"
-    #     key_rename_map["shares"] = "sharesOutstanding"
-
-    #     for k in list(key_rename_map.keys()):
-    #         if '_' in k:
-    #             key_rename_map[yf.utils.snake_case_2_camelCase(k)] = key_rename_map[k]
-
-    #     # Note: share count items in info[] are bad. Sometimes the float > outstanding!
-    #     # So often fast_info["shares"] does not match.
-    #     # Why isn't fast_info["shares"] wrong? Because using it to calculate market cap always correct.
-    #     bad_keys = {"shares"}
-
-    #     # Loose tolerance for averages, no idea why don't match info[]. Is info wrong?
-    #     custom_tolerances = {}
-    #     custom_tolerances["year_change"] = 1.0
-    #     # custom_tolerances["ten_day_average_volume"] = 1e-3
-    #     custom_tolerances["ten_day_average_volume"] = 1e-1
-    #     # custom_tolerances["three_month_average_volume"] = 1e-2
-    #     custom_tolerances["three_month_average_volume"] = 5e-1
-    #     custom_tolerances["fifty_day_average"] = 1e-2
-    #     custom_tolerances["two_hundred_day_average"] = 1e-2
-    #     for k in list(custom_tolerances.keys()):
-    #         if '_' in k:
-    #             custom_tolerances[yf.utils.snake_case_2_camelCase(k)] = custom_tolerances[k]
-
-    #     for k in fast_info_keys:
-    #         if k in key_rename_map:
-    #             k2 = key_rename_map[k]
-    #         else:
-    #             k2 = k
-
-    #         if not isinstance(k2, list):
-    #             k2 = [k2]
-
-    #         for m in k2:
-    #             for ticker in self.tickers:
-    #                 if not m in ticker.info:
-    #                     # print(f"symbol={ticker.ticker}: fast_info key '{k}' mapped to info key '{m}' but not present in info")
-    #                     continue
-
-    #                 if k in bad_keys:
-    #                     continue
-
-    #                 if k in custom_tolerances:
-    #                     rtol = custom_tolerances[k]
-    #                 else:
-    #                     rtol = 5e-3
-    #                     # rtol = 1e-4
-
-    #                 correct = ticker.info[m]
-    #                 test = ticker.fast_info[k]
-    #                 # print(f"Testing: symbol={ticker.ticker} m={m} k={k}: test={test} vs correct={correct}")
-    #                 if k in ["market_cap","marketCap"] and ticker.fast_info["currency"] in ["GBp", "ILA"]:
-    #                     # Adjust for currency to match Yahoo:
-    #                     test *= 0.01
-    #                 try:
-    #                     if correct is None:
-    #                         self.assertTrue(test is None or (not np.isnan(test)), f"{k}: {test} must be None or real value because correct={correct}")
-    #                     elif isinstance(test, float) or isinstance(correct, int):
-    #                         self.assertTrue(np.isclose(test, correct, rtol=rtol), f"{ticker.ticker} {k}: {test} != {correct}")
-    #                     else:
-    #                         self.assertEqual(test, correct, f"{k}: {test} != {correct}")
-    #                 except:
-    #                     if k in ["regularMarketPreviousClose"] and ticker.ticker in ["ADS.DE"]:
-    #                         # Yahoo is wrong, is returning post-market close not regular
-    #                         continue
-    #                     else:
-    #                         raise
-
-
-
-def suite():
-    suite = unittest.TestSuite()
-    suite.addTest(TestTicker('Test ticker'))
-    suite.addTest(TestTickerEarnings('Test earnings'))
-    suite.addTest(TestTickerHolders('Test holders'))
-    suite.addTest(TestTickerHistory('Test Ticker history'))
-    suite.addTest(TestTickerMiscFinancials('Test misc financials'))
-    suite.addTest(TestTickerInfo('Test info & fast_info'))
-    return suite
-
-
-if __name__ == '__main__':
-    unittest.main()
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,92 +0,0 @@
-"""
-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
-
-"""
-from unittest import TestSuite
-
-# import pandas as pd
-# import numpy as np
-
-from .context import yfinance as yf
-
-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.cache.get_tz_cache()
-        cache.store(tkr, tz1)
-        cache.store(tkr, tz2)
-
-    def test_setTzCacheLocation(self):
-        self.assertEqual(yf.cache._TzDBManager.get_location(), self.tempCacheDir.name)
-
-        tkr = 'AMZN'
-        tz1 = "America/New_York"
-        cache = yf.cache.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.cache.get_tz_cache().store(tkr, tz1)
-
-        # Handling the store failure replaces cache with a dummy
-        cache = yf.cache.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.cache.get_tz_cache().lookup(tkr)
-
-        # Handling the lookup failure replaces cache with a dummy
-        cache = yf.cache.get_tz_cache()
-        self.assertTrue(cache.dummy)
-        cache.lookup(tkr)
-
-
-def suite():
-    ts: TestSuite = unittest.TestSuite()
-    ts.addTest(TestCache('Test cache'))
-    ts.addTest(TestCacheNoPermission('Test cache no permission'))
-    return ts
-
-
-if __name__ == '__main__':
-    unittest.main()
--- a/yfinance/init.py
+++ b/yfinance/init.py
@@ -23,13 +23,24 @@ from . import version
 from .ticker import Ticker
 from .tickers import Tickers
 from .multi import download
-from .utils import enable_debug_mode
-from .cache import set_tz_cache_location
+from .utils import set_tz_cache_location

 __version__ = version.version
 __author__ = "Ran Aroussi"

-import warnings
-warnings.filterwarnings('default', category=DeprecationWarning, module='^yfinance')

-__all__ = ['download', 'Ticker', 'Tickers', 'enable_debug_mode', 'set_tz_cache_location']
+def pdr_override():
+    """
+    make pandas datareader optional
+    otherwise can be called via fix_yahoo_finance.download(...)
+    """
+    try:
+        import pandas_datareader
+        pandas_datareader.data.get_data_yahoo = download
+        pandas_datareader.data.get_data_yahoo_actions = download
+        pandas_datareader.data.DataReader = download
+    except Exception:
+        pass
+
+
+__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override', 'set_tz_cache_location']
--- a/yfinance/base.py
+++ b/yfinance/base.py
--- a/yfinance/cache.py
+++ b/yfinance/cache.py
@@ -1,431 +0,0 @@
-import peewee as _peewee
-from threading import Lock
-import os as _os
-import platformdirs 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)
-        try:
-            db.create_tables([_KV])
-        except _peewee.OperationalError as e:
-            if 'WITHOUT' in str(e):
-                _KV._meta.without_rowid = False
-                db.create_tables([_KV])
-            else:
-                raise
-        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()
-
-
-
-# --------------
-# 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 ISODateTimeField(_peewee.DateTimeField):
-    # Ensure Python datetime is read & written correctly for sqlite, 
-    # because user discovered peewee allowed an invalid datetime
-    # to get written.
-    def db_value(self, value):
-        if value and isinstance(value, _datetime.datetime):
-            return value.isoformat()
-        return super().db_value(value)
-    def python_value(self, value):
-        if value and isinstance(value, str) and 'T' in value:
-            return _datetime.datetime.fromisoformat(value)
-        return super().python_value(value)
-class _CookieSchema(_peewee.Model):
-    strategy = _peewee.CharField(primary_key=True)
-    fetch_date = ISODateTimeField(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)
-        try:
-            db.create_tables([_CookieSchema])
-        except _peewee.OperationalError as e:
-            if 'WITHOUT' in str(e):
-                _CookieSchema._meta.without_rowid = False
-                db.create_tables([_CookieSchema])
-            else:
-                raise
-        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()
-
-
-
-def set_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)
-    _CookieDBManager.set_location(cache_dir)
-
-def set_tz_cache_location(cache_dir: str):
-    set_cache_location(cache_dir)
-
--- a/yfinance/const.py
+++ b/yfinance/const.py
@@ -1,157 +0,0 @@
-_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", "LossAdjustmentExpense",
-                   "NetPolicyholderBenefitsAndClaims", "PolicyholderBenefitsGross", "PolicyholderBenefitsCeded",
-                   "OccupancyAndEquipment", "ProfessionalExpenseAndContractServicesExpense", "OtherNonInterestExpense"],
-    '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",
-                      "CashCashEquivalentsAndFederalFundsSold"],
-    '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']
-
-quote_summary_valid_modules = (
-    "summaryProfile",  # contains general information about the company
-    "summaryDetail",  # prices + volume + market cap + etc
-    "assetProfile",  # summaryProfile + company officers
-    "fundProfile",
-    "price",  # current prices
-    "quoteType",  # quoteType
-    "esgScores",  # Environmental, social, and governance (ESG) scores, sustainability and ethical performance of companies
-    "incomeStatementHistory",
-    "incomeStatementHistoryQuarterly",
-    "balanceSheetHistory",
-    "balanceSheetHistoryQuarterly",
-    "cashFlowStatementHistory",
-    "cashFlowStatementHistoryQuarterly",
-    "defaultKeyStatistics",  # KPIs (PE, enterprise value, EPS, EBITA, and more)
-    "financialData",  # Financial KPIs (revenue, gross margins, operating cash flow, free cash flow, and more)
-    "calendarEvents",  # future earnings date
-    "secFilings",  # SEC filings, such as 10K and 10Q reports
-    "upgradeDowngradeHistory",  # upgrades and downgrades that analysts have given a company's stock
-    "institutionOwnership",  # institutional ownership, holders and shares outstanding
-    "fundOwnership",  # mutual fund ownership, holders and shares outstanding
-    "majorDirectHolders",
-    "majorHoldersBreakdown",
-    "insiderTransactions",  # insider transactions, such as the number of shares bought and sold by company executives
-    "insiderHolders",  # insider holders, such as the number of shares held by company executives
-    "netSharePurchaseActivity",  # net share purchase activity, such as the number of shares bought and sold by company executives
-    "earnings",  # earnings history
-    "earningsHistory",
-    "earningsTrend",  # earnings trend
-    "industryTrend",
-    "indexTrend",
-    "sectorTrend",
-    "recommendationTrend",
-    "futuresChain",
-)
--- a/yfinance/data.py
+++ b/yfinance/data.py
@@ -1,400 +0,0 @@
-import functools
-from functools import lru_cache
-
-import requests as requests
-from bs4 import BeautifulSoup
-import datetime
-
-from frozendict import frozendict
-
-from . import utils, cache
-import threading
-
-cache_maxsize = 64
-
-
-def lru_cache_freezeargs(func):
-    """
-    Decorator transforms mutable dictionary and list arguments into immutable types
-    Needed so lru_cache can cache method calls what has dict or list arguments.
-    """
-
-    @functools.wraps(func)
-    def wrapped(*args, **kwargs):
-        args = tuple([frozendict(arg) if isinstance(arg, dict) else arg for arg in args])
-        kwargs = {k: frozendict(v) if isinstance(v, dict) else v for k, v in kwargs.items()}
-        args = tuple([tuple(arg) if isinstance(arg, list) else arg for arg in args])
-        kwargs = {k: tuple(v) if isinstance(v, list) else v for k, v in kwargs.items()}
-        return func(*args, **kwargs)
-
-    # copy over the lru_cache extra methods to this wrapper to be able to access them
-    # after this decorator has been applied
-    wrapped.cache_info = func.cache_info
-    wrapped.cache_clear = func.cache_clear
-    return wrapped
-
-
-class SingletonMeta(type):
-    """
-    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, session=None):
-        self._crumb = None
-        self._cookie = None
-
-        # 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()
-
-        self._set_session(session or requests.Session())
-
-    def _set_session(self, session):
-        if session is None:
-            return
-        with self._cookie_lock:
-            self._session = session
-
-        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
-
-    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='https://fc.yahoo.com',
-            headers=self.user_agent_headers,
-            proxies=proxy,
-            timeout=timeout,
-            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
-    @lru_cache(maxsize=cache_maxsize)
-    def cache_get(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
-        return self.get(url, user_agent_headers, params, proxy, timeout)
-
-    def _get_proxy(self, proxy):
-        # setup proxy in requests format
-        if proxy is not None:
-            if isinstance(proxy, (dict, frozendict)) and "https" in proxy:
-                proxy = proxy["https"]
-            proxy = {"https": proxy}
-        return proxy
-
-    def get_raw_json(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
-        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()
--- a/yfinance/exceptions.py
+++ b/yfinance/exceptions.py
@@ -1,50 +0,0 @@
-class YFException(Exception):
-    def __init__(self, description=""):
-        super().__init__(description)
-
-
-class YFDataException(YFException):
-    pass
-
-
-class YFChartError(YFException):
-    def __init__(self, ticker, description):
-        self.ticker = ticker
-        super().__init__(f"{self.ticker}: {description}")
-
-
-class YFNotImplementedError(NotImplementedError):
-    def __init__(self, method_name):
-        super().__init__(f"Have not implemented fetching '{method_name}' from Yahoo API")
-
-
-class YFTickerMissingError(YFException):
-    def __init__(self, ticker, rationale):
-        super().__init__(f"${ticker}: possibly delisted; {rationale}")
-        self.rationale = rationale
-        self.ticker = ticker
-
-
-class YFTzMissingError(YFTickerMissingError):
-    def __init__(self, ticker):
-        super().__init__(ticker, "No timezone found")
-
-
-class YFPricesMissingError(YFTickerMissingError):
-    def __init__(self, ticker, debug_info):
-        self.debug_info = debug_info
-        super().__init__(ticker, f"No price data found {debug_info}")
-
-
-class YFEarningsDateMissing(YFTickerMissingError):
-    # note that this does not get raised. Added in case of raising it in the future
-    def __init__(self, ticker):
-        super().__init__(ticker, "No earnings dates found")
-
-
-class YFInvalidPeriodError(YFException):
-    def __init__(self, ticker, invalid_period, valid_ranges):
-        self.ticker = ticker
-        self.invalid_period = invalid_period
-        self.valid_ranges = valid_ranges
-        super().__init__(f"{self.ticker}: Period '{invalid_period}' is invalid, must be one of {valid_ranges}")
--- a/yfinance/multi.py
+++ b/yfinance/multi.py
@@ -21,23 +21,18 @@

 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


-@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", interval="1d", prepost=False,
-             proxy=None, rounding=False, timeout=10, session=None):
+def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=True, 
+             group_by='column', auto_adjust=False, back_adjust=False, keepna=False,
+             progress=True, period="max", show_errors=True, interval="1d", prepost=False,
+             proxy=None, rounding=False, timeout=None, **kwargs):
    """Download yahoo tickers
    :Parameters:
        tickers : str, list
@@ -49,13 +44,11 @@ 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, inclusive.
-            Default is 99 years ago
-            E.g. for start="2020-01-01", the first data point will be on "2020-01-01"
+            Download start date string (YYYY-MM-DD) or _datetime.
+            Default is 1900-01-01
        end: str
-            Download end date string (YYYY-MM-DD) or _datetime, exclusive.
+            Download end date string (YYYY-MM-DD) or _datetime.
            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
@@ -63,9 +56,6 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
            Default is False
        auto_adjust: bool
            Adjust all OHLC automatically? Default is False
-        repair: bool
-            Detect currency unit 100x mixups and attempt repair
-            Default is False
        keepna: bool
            Keep NaN rows returned by Yahoo?
            Default is False
@@ -75,37 +65,17 @@ 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 depends on interval. Intraday = False. Day+ = True.
+            Default is 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
        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 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(
@@ -117,7 +87,7 @@ 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, session=session)
+            ticker = utils.get_ticker_by_isin(ticker, proxy)
            shared._ISINS[ticker] = isin
        _tickers_.append(ticker)

@@ -131,10 +101,6 @@ 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:
@@ -145,73 +111,51 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
            _download_one_threaded(ticker, period=period, interval=interval,
                                   start=start, end=end, prepost=prepost,
                                   actions=actions, auto_adjust=auto_adjust,
-                                   back_adjust=back_adjust, repair=repair, keepna=keepna,
+                                   back_adjust=back_adjust, keepna=keepna,
                                   progress=(progress and i > 0), proxy=proxy,
                                   rounding=rounding, timeout=timeout)
        while len(shared._DFS) < len(tickers):
            _time.sleep(0.01)
+
    # download synchronously
    else:
        for i, ticker in enumerate(tickers):
            data = _download_one(ticker, period=period, interval=interval,
                                 start=start, end=end, prepost=prepost,
                                 actions=actions, auto_adjust=auto_adjust,
-                                 back_adjust=back_adjust, repair=repair, keepna=keepna,
-                                 proxy=proxy,
+                                 back_adjust=back_adjust, 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:
-        # Send errors to logging module
-        logger = utils.get_yf_logger()
-        logger.error('\n%.f Failed download%s:' % (
+    if shared._ERRORS and show_errors:
+        print('\n%.f Failed download%s:' % (
            len(shared._ERRORS), 's' if len(shared._ERRORS) > 1 else ''))
-
-        # 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)
+        # print(shared._ERRORS)
+        print("\n".join(['- %s: %s' %
+                         v for v in list(shared._ERRORS.items())]))

    if ignore_tz:
        for tkr in shared._DFS.keys():
-            if (shared._DFS[tkr] is not None) and (shared._DFS[tkr].shape[0] > 0):
+            if (shared._DFS[tkr] is not None) and (shared._DFS[tkr].shape[0]>0):
                shared._DFS[tkr].index = shared._DFS[tkr].index.tz_localize(None)

    if len(tickers) == 1:
        ticker = tickers[0]
-        return shared._DFS[ticker]
+        return shared._DFS[shared._ISINS.get(ticker, ticker)]

    try:
        data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
-                          keys=shared._DFS.keys(), names=['Ticker', 'Price'])
+                          keys=shared._DFS.keys())
    except Exception:
        _realign_dfs()
        data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
-                          keys=shared._DFS.keys(), names=['Ticker', 'Price'])
-    data.index = _pd.to_datetime(data.index)
+                          keys=shared._DFS.keys())
+
    # switch names back to isins if applicable
    data.rename(columns=shared._ISINS, inplace=True)

@@ -247,38 +191,28 @@ def _realign_dfs():

@_multitasking.task
 def _download_one_threaded(ticker, start=None, end=None,
-                           auto_adjust=False, back_adjust=False, repair=False,
+                           auto_adjust=False, back_adjust=False,
                           actions=False, progress=True, period="max",
                           interval="1d", prepost=False, proxy=None,
-                           keepna=False, rounding=False, timeout=10):
-    _download_one(ticker, start, end, auto_adjust, back_adjust, repair,
+                           keepna=False, rounding=False, timeout=None):
+
+    data = _download_one(ticker, start, end, auto_adjust, back_adjust,
                         actions, period, interval, prepost, proxy, rounding,
                         keepna, timeout)
+    shared._DFS[ticker.upper()] = data
    if progress:
        shared._PROGRESS_BAR.animate()


 def _download_one(ticker, start=None, end=None,
-                  auto_adjust=False, back_adjust=False, repair=False,
+                  auto_adjust=False, back_adjust=False,
                  actions=False, period="max", interval="1d",
                  prepost=False, proxy=None, rounding=False,
-                  keepna=False, timeout=10):
-    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
+                  keepna=False, timeout=None):

-    return data
+    return Ticker(ticker).history(period=period, interval=interval,
+                                  start=start, end=end, prepost=prepost,
+                                  actions=actions, auto_adjust=auto_adjust,
+                                  back_adjust=back_adjust, proxy=proxy,
+                                  rounding=rounding, keepna=keepna, many=True,
+                                  timeout=timeout)
--- a/yfinance/scrapers/init.py
+++ b/yfinance/scrapers/init.py
--- a/yfinance/scrapers/analysis.py
+++ b/yfinance/scrapers/analysis.py
@@ -1,49 +0,0 @@
-import pandas as pd
-
-from yfinance.data import YfData
-from yfinance.exceptions import YFNotImplementedError
-
-
-class Analysis:
-
-    def __init__(self, data: YfData, symbol: str, proxy=None):
-        self._data = data
-        self._symbol = symbol
-        self.proxy = proxy
-
-        self._earnings_trend = None
-        self._analyst_trend_details = None
-        self._analyst_price_target = None
-        self._rev_est = None
-        self._eps_est = None
-        self._already_scraped = False
-
-    @property
-    def earnings_trend(self) -> pd.DataFrame:
-        if self._earnings_trend is None:
-            raise YFNotImplementedError('earnings_trend')
-        return self._earnings_trend
-
-    @property
-    def analyst_trend_details(self) -> pd.DataFrame:
-        if self._analyst_trend_details is None:
-            raise YFNotImplementedError('analyst_trend_details')
-        return self._analyst_trend_details
-
-    @property
-    def analyst_price_target(self) -> pd.DataFrame:
-        if self._analyst_price_target is None:
-            raise YFNotImplementedError('analyst_price_target')
-        return self._analyst_price_target
-
-    @property
-    def rev_est(self) -> pd.DataFrame:
-        if self._rev_est is None:
-            raise YFNotImplementedError('rev_est')
-        return self._rev_est
-
-    @property
-    def eps_est(self) -> pd.DataFrame:
-        if self._eps_est is None:
-            raise YFNotImplementedError('eps_est')
-        return self._eps_est
--- a/yfinance/scrapers/fundamentals.py
+++ b/yfinance/scrapers/fundamentals.py
@@ -1,148 +0,0 @@
-import datetime
-import json
-import warnings
-
-import pandas as pd
-
-from yfinance import utils, const
-from yfinance.data import YfData
-from yfinance.exceptions import YFException, YFNotImplementedError
-
-class Fundamentals:
-
-    def __init__(self, data: YfData, symbol: str, proxy=None):
-        self._data = data
-        self._symbol = symbol
-        self.proxy = proxy
-
-        self._earnings = None
-        self._financials = None
-        self._shares = None
-
-        self._financials_data = None
-        self._fin_data_quote = None
-        self._basics_already_scraped = False
-        self._financials = Financials(data, symbol)
-
-    @property
-    def financials(self) -> "Financials":
-        return self._financials
-
-    @property
-    def earnings(self) -> dict:
-        warnings.warn("'Ticker.earnings' is deprecated as not available via API. Look for \"Net Income\" in Ticker.income_stmt.", DeprecationWarning)
-        return None
-
-    @property
-    def shares(self) -> pd.DataFrame:
-        if self._shares is None:
-            raise YFNotImplementedError('shares')
-        return self._shares
-
-
-class Financials:
-    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 = {}
-
-    def get_income_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._income_time_series
-        if freq not in res:
-            res[freq] = self._fetch_time_series("income", freq, proxy)
-        return res[freq]
-
-    def get_balance_sheet_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._balance_sheet_time_series
-        if freq not in res:
-            res[freq] = self._fetch_time_series("balance-sheet", freq, proxy)
-        return res[freq]
-
-    def get_cash_flow_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._cash_flow_time_series
-        if freq not in res:
-            res[freq] = self._fetch_time_series("cash-flow", freq, proxy)
-        return res[freq]
-
-    @utils.log_indent_decorator
-    def _fetch_time_series(self, name, timescale, proxy=None):
-        # Fetching time series preferred over scraping 'QuoteSummaryStore',
-        # because it matches what Yahoo shows. But for some tickers returns nothing,
-        # despite 'QuoteSummaryStore' containing valid data.
-
-        allowed_names = ["income", "balance-sheet", "cash-flow"]
-        allowed_timescales = ["yearly", "quarterly"]
-
-        if name not in allowed_names:
-            raise ValueError(f"Illegal argument: name must be one of: {allowed_names}")
-        if timescale not in allowed_timescales:
-            raise ValueError(f"Illegal argument: timescale must be one of: {allowed_timescales}")
-
-        try:
-            statement = self._create_financials_table(name, timescale, proxy)
-
-            if statement is not None:
-                return statement
-        except YFException as 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):
-        if name == "income":
-            # Yahoo stores the 'income' table internally under 'financials' key
-            name = "financials"
-
-        keys = const.fundamentals_keys[name]
-
-        try:
-            return self.get_financials_time_series(timescale, keys, proxy)
-        except Exception:
-            pass
-
-    def get_financials_time_series(self, timescale, keys: list, proxy=None) -> pd.DataFrame:
-        timescale_translation = {"yearly": "annual", "quarterly": "quarterly"}
-        timescale = timescale_translation[timescale]
-
-        # Step 2: construct url:
-        ts_url_base = f"https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._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 += f"&period1={int(start_dt.timestamp())}&period2={int(end.timestamp())}"
-
-        # Step 3: fetch and reshape data
-        json_str = self._data.cache_get(url=url, proxy=proxy).text
-        json_data = json.loads(json_str)
-        data_raw = json_data["timeseries"]["result"]
-        # data_raw = [v for v in data_raw if len(v) > 1] # Discard keys with no data
-        for d in data_raw:
-            del d["meta"]
-
-        # Now reshape data into a table:
-        # Step 1: get columns and index:
-        timestamps = set()
-        data_unpacked = {}
-        for x in data_raw:
-            for k in x.keys():
-                if k == "timestamp":
-                    timestamps.update(x[k])
-                else:
-                    data_unpacked[k] = x[k]
-        timestamps = sorted(list(timestamps))
-        dates = pd.to_datetime(timestamps, unit="s")
-        df = pd.DataFrame(columns=dates, index=list(data_unpacked.keys()))
-        for k, v in data_unpacked.items():
-            if df is None:
-                df = pd.DataFrame(columns=dates, index=[k])
-            df.loc[k] = {pd.Timestamp(x["asOfDate"]): x["reportedValue"]["raw"] for x in v}
-
-        df.index = df.index.str.replace("^" + timescale, "", regex=True)
-
-        # Reorder table to match order on Yahoo website
-        df = df.reindex([k for k in keys if k in df.index])
-        df = df[sorted(df.columns, reverse=True)]
-
-        return df
--- a/yfinance/scrapers/history.py
+++ b/yfinance/scrapers/history.py
--- a/yfinance/scrapers/holders.py
+++ b/yfinance/scrapers/holders.py
@@ -1,244 +0,0 @@
-# from io import StringIO
-
-import pandas as pd
-import requests
-
-from yfinance import utils
-from yfinance.data import YfData
-from yfinance.const import _BASE_URL_
-from yfinance.exceptions import YFDataException
-
-_QUOTE_SUMMARY_URL_ = f"{_BASE_URL_}/v10/finance/quoteSummary/"
-
-
-class Holders:
-    _SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
-
-    def __init__(self, data: YfData, symbol: str, proxy=None):
-        self._data = data
-        self._symbol = symbol
-        self.proxy = proxy
-
-        self._major = None
-        self._major_direct_holders = None
-        self._institutional = None
-        self._mutualfund = None
-
-        self._insider_transactions = None
-        self._insider_purchases = None
-        self._insider_roster = None
-
-    @property
-    def major(self) -> pd.DataFrame:
-        if self._major is None:
-            # self._scrape(self.proxy)
-            self._fetch_and_parse()
-        return self._major
-
-    @property
-    def institutional(self) -> pd.DataFrame:
-        if self._institutional is None:
-            # self._scrape(self.proxy)
-            self._fetch_and_parse()
-        return self._institutional
-
-    @property
-    def mutualfund(self) -> pd.DataFrame:
-        if self._mutualfund is None:
-            # self._scrape(self.proxy)
-            self._fetch_and_parse()
-        return self._mutualfund
-
-    @property
-    def insider_transactions(self) -> pd.DataFrame:
-        if self._insider_transactions is None:
-            # self._scrape_insider_transactions(self.proxy)
-            self._fetch_and_parse()
-        return self._insider_transactions
-
-    @property
-    def insider_purchases(self) -> pd.DataFrame:
-        if self._insider_purchases is None:
-            # self._scrape_insider_transactions(self.proxy)
-            self._fetch_and_parse()
-        return self._insider_purchases
-
-    @property
-    def insider_roster(self) -> pd.DataFrame:
-        if self._insider_roster is None:
-            # self._scrape_insider_ros(self.proxy)
-            self._fetch_and_parse()
-        return self._insider_roster
-
-    def _fetch(self, proxy):
-        modules = ','.join(
-            ["institutionOwnership", "fundOwnership", "majorDirectHolders", "majorHoldersBreakdown", "insiderTransactions", "insiderHolders", "netSharePurchaseActivity"])
-        params_dict = {"modules": modules, "corsDomain": "finance.yahoo.com", "formatted": "false"}
-        result = self._data.get_raw_json(f"{_QUOTE_SUMMARY_URL_}/{self._symbol}", user_agent_headers=self._data.user_agent_headers, params=params_dict, proxy=proxy)
-        return result
-
-    def _fetch_and_parse(self):
-        try:
-            result = self._fetch(self.proxy)
-        except requests.exceptions.HTTPError as e:
-            utils.get_yf_logger().error(str(e))
-
-            self._major = pd.DataFrame()
-            self._major_direct_holders = pd.DataFrame()
-            self._institutional = pd.DataFrame()
-            self._mutualfund = pd.DataFrame()
-            self._insider_transactions = pd.DataFrame()
-            self._insider_purchases = pd.DataFrame()
-            self._insider_roster = pd.DataFrame()
-
-            return
-
-        try:
-            data = result["quoteSummary"]["result"][0]
-            # parse "institutionOwnership", "fundOwnership", "majorDirectHolders", "majorHoldersBreakdown", "insiderTransactions", "insiderHolders", "netSharePurchaseActivity"
-            self._parse_institution_ownership(data["institutionOwnership"])
-            self._parse_fund_ownership(data["fundOwnership"])
-            # self._parse_major_direct_holders(data["majorDirectHolders"])  # need more data to investigate
-            self._parse_major_holders_breakdown(data["majorHoldersBreakdown"])
-            self._parse_insider_transactions(data["insiderTransactions"])
-            self._parse_insider_holders(data["insiderHolders"])
-            self._parse_net_share_purchase_activity(data["netSharePurchaseActivity"])
-        except (KeyError, IndexError):
-            raise YFDataException("Failed to parse holders json data.")
-
-    @staticmethod
-    def _parse_raw_values(data):
-        if isinstance(data, dict) and "raw" in data:
-            return data["raw"]
-        return data
-
-    def _parse_institution_ownership(self, data):
-        holders = data["ownershipList"]
-        for owner in holders:
-            for k, v in owner.items():
-                owner[k] = self._parse_raw_values(v)
-            del owner["maxAge"]
-        df = pd.DataFrame(holders)
-        if not df.empty:
-            df["reportDate"] = pd.to_datetime(df["reportDate"], unit="s")
-            df.rename(columns={"reportDate": "Date Reported", "organization": "Holder", "position": "Shares", "value": "Value"}, inplace=True)  # "pctHeld": "% Out"
-        self._institutional = df
-
-    def _parse_fund_ownership(self, data):
-        holders = data["ownershipList"]
-        for owner in holders:
-            for k, v in owner.items():
-                owner[k] = self._parse_raw_values(v)
-            del owner["maxAge"]
-        df = pd.DataFrame(holders)
-        if not df.empty:
-            df["reportDate"] = pd.to_datetime(df["reportDate"], unit="s")
-            df.rename(columns={"reportDate": "Date Reported", "organization": "Holder", "position": "Shares", "value": "Value"}, inplace=True)
-        self._mutualfund = df
-
-    def _parse_major_direct_holders(self, data):
-        holders = data["holders"]
-        for owner in holders:
-            for k, v in owner.items():
-                owner[k] = self._parse_raw_values(v)
-            del owner["maxAge"]
-        df = pd.DataFrame(holders)
-        if not df.empty:
-            df["reportDate"] = pd.to_datetime(df["reportDate"], unit="s")
-            df.rename(columns={"reportDate": "Date Reported", "organization": "Holder", "positionDirect": "Shares", "valueDirect": "Value"}, inplace=True)
-        self._major_direct_holders = df
-
-    def _parse_major_holders_breakdown(self, data):
-        if "maxAge" in data:
-            del data["maxAge"]
-        df = pd.DataFrame.from_dict(data, orient="index")
-        if not df.empty:
-            df.columns.name = "Breakdown"
-            df.rename(columns={df.columns[0]: 'Value'}, inplace=True)
-        self._major = df
-
-    def _parse_insider_transactions(self, data):
-        holders = data["transactions"]
-        for owner in holders:
-            for k, v in owner.items():
-                owner[k] = self._parse_raw_values(v)
-            del owner["maxAge"]
-        df = pd.DataFrame(holders)
-        if not df.empty:
-            df["startDate"] = pd.to_datetime(df["startDate"], unit="s")
-            df.rename(columns={
-                "startDate": "Start Date",
-                "filerName": "Insider",
-                "filerRelation": "Position",
-                "filerUrl": "URL",
-                "moneyText": "Transaction",
-                "transactionText": "Text",
-                "shares": "Shares",
-                "value": "Value",
-                "ownership": "Ownership"  # ownership flag, direct or institutional
-            }, inplace=True)
-        self._insider_transactions = df
-
-    def _parse_insider_holders(self, data):
-        holders = data["holders"]
-        for owner in holders:
-            for k, v in owner.items():
-                owner[k] = self._parse_raw_values(v)
-            del owner["maxAge"]
-        df = pd.DataFrame(holders)
-        if not df.empty:
-            df["positionDirectDate"] = pd.to_datetime(df["positionDirectDate"], unit="s")
-            df["latestTransDate"] = pd.to_datetime(df["latestTransDate"], unit="s")
-
-            df.rename(columns={
-                "name": "Name",
-                "relation": "Position",
-                "url": "URL",
-                "transactionDescription": "Most Recent Transaction",
-                "latestTransDate": "Latest Transaction Date",
-                "positionDirectDate": "Position Direct Date",
-                "positionDirect": "Shares Owned Directly",
-                "positionIndirectDate": "Position Indirect Date",
-                "positionIndirect": "Shares Owned Indirectly"
-            }, inplace=True)
-
-            df["Name"] = df["Name"].astype(str)
-            df["Position"] = df["Position"].astype(str)
-            df["URL"] = df["URL"].astype(str)
-            df["Most Recent Transaction"] = df["Most Recent Transaction"].astype(str)
-
-        self._insider_roster = df
-
-    def _parse_net_share_purchase_activity(self, data):
-        df = pd.DataFrame(
-            {
-                "Insider Purchases Last " + data.get("period", ""): [
-                    "Purchases",
-                    "Sales",
-                    "Net Shares Purchased (Sold)",
-                    "Total Insider Shares Held",
-                    "% Net Shares Purchased (Sold)",
-                    "% Buy Shares",
-                    "% Sell Shares"
-                ],
-                "Shares": [
-                    data.get('buyInfoShares'),
-                    data.get('sellInfoShares'),
-                    data.get('netInfoShares'),
-                    data.get('totalInsiderShares'),
-                    data.get('netPercentInsiderShares'),
-                    data.get('buyPercentInsiderShares'),
-                    data.get('sellPercentInsiderShares')
-                ],
-                "Trans": [
-                    data.get('buyInfoCount'),
-                    data.get('sellInfoCount'),
-                    data.get('netInfoCount'),
-                    pd.NA,
-                    pd.NA,
-                    pd.NA,
-                    pd.NA
-                ]
-            }
-        ).convert_dtypes()
-        self._insider_purchases = df
--- a/yfinance/scrapers/quote.py
+++ b/yfinance/scrapers/quote.py
@@ -1,712 +0,0 @@
-import datetime
-import json
-
-import numpy as _np
-import pandas as pd
-import requests
-
-from yfinance import utils
-from yfinance.data import YfData
-from yfinance.const import quote_summary_valid_modules, _BASE_URL_
-from yfinance.exceptions import YFDataException, YFException
-
-info_retired_keys_price = {"currentPrice", "dayHigh", "dayLow", "open", "previousClose", "volume", "volume24Hr"}
-info_retired_keys_price.update({"regularMarket"+s for s in ["DayHigh", "DayLow", "Open", "PreviousClose", "Price", "Volume"]})
-info_retired_keys_price.update({"fiftyTwoWeekLow", "fiftyTwoWeekHigh", "fiftyTwoWeekChange", "52WeekChange", "fiftyDayAverage", "twoHundredDayAverage"})
-info_retired_keys_price.update({"averageDailyVolume10Day", "averageVolume10days", "averageVolume"})
-info_retired_keys_exchange = {"currency", "exchange", "exchangeTimezoneName", "exchangeTimezoneShortName", "quoteType"}
-info_retired_keys_marketCap = {"marketCap"}
-info_retired_keys_symbol = {"symbol"}
-info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_retired_keys_marketCap | info_retired_keys_symbol
-
-
-_QUOTE_SUMMARY_URL_ = f"{_BASE_URL_}/v10/finance/quoteSummary"
-
-
-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("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):
-        return json.dumps({k: self[k] for k in self.keys()}, indent=indent)
-
-    def _get_1y_prices(self, fullDaysOnly=False):
-        if self._prices_1y is None:
-            self._prices_1y = self._tkr.history(period="1y", auto_adjust=False, keepna=True, proxy=self.proxy)
-            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:
-            self._prices_1wk_1h_prepost = self._tkr.history(period="5d", interval="1h", auto_adjust=False, prepost=True, proxy=self.proxy)
-        return self._prices_1wk_1h_prepost
-
-    def _get_1wk_1h_reg_prices(self):
-        if self._prices_1wk_1h_reg is None:
-            self._prices_1wk_1h_reg = self._tkr.history(period="5d", interval="1h", auto_adjust=False, prepost=False, proxy=self.proxy)
-        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
-
-        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
-
-        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: YfData, symbol: str, proxy=None):
-        self._data = data
-        self._symbol = symbol
-        self.proxy = proxy
-
-        self._info = None
-        self._retired_info = None
-        self._sustainability = None
-        self._recommendations = None
-        self._upgrades_downgrades = None
-        self._calendar = None
-
-        self._already_scraped = False
-        self._already_fetched = False
-        self._already_fetched_complementary = False
-
-    @property
-    def info(self) -> dict:
-        if self._info is None:
-            self._fetch_info(self.proxy)
-            self._fetch_complementary(self.proxy)
-
-        return self._info
-
-    @property
-    def sustainability(self) -> pd.DataFrame:
-        if self._sustainability is None:
-            result = self._fetch(self.proxy, modules=['esgScores'])
-            if result is None:
-                self._sustainability = pd.DataFrame()
-            else:
-                try:
-                    data = result["quoteSummary"]["result"][0]
-                except (KeyError, IndexError):
-                    raise YFDataException(f"Failed to parse json response from Yahoo Finance: {result}")
-                self._sustainability = pd.DataFrame(data)
-        return self._sustainability
-
-    @property
-    def recommendations(self) -> pd.DataFrame:
-        if self._recommendations is None:
-            result = self._fetch(self.proxy, modules=['recommendationTrend'])
-            if result is None:
-                self._recommendations = pd.DataFrame()
-            else:
-                try:
-                    data = result["quoteSummary"]["result"][0]["recommendationTrend"]["trend"]
-                except (KeyError, IndexError):
-                    raise YFDataException(f"Failed to parse json response from Yahoo Finance: {result}")
-                self._recommendations = pd.DataFrame(data)
-        return self._recommendations
-
-    @property
-    def upgrades_downgrades(self) -> pd.DataFrame:
-        if self._upgrades_downgrades is None:
-            result = self._fetch(self.proxy, modules=['upgradeDowngradeHistory'])
-            if result is None:
-                self._upgrades_downgrades = pd.DataFrame()
-            else:
-                try:
-                    data = result["quoteSummary"]["result"][0]["upgradeDowngradeHistory"]["history"]
-                    if len(data) == 0:
-                        raise YFDataException(f"No upgrade/downgrade history found for {self._symbol}")
-                    df = pd.DataFrame(data)
-                    df.rename(columns={"epochGradeDate": "GradeDate", 'firm': 'Firm', 'toGrade': 'ToGrade', 'fromGrade': 'FromGrade', 'action': 'Action'}, inplace=True)
-                    df.set_index('GradeDate', inplace=True)
-                    df.index = pd.to_datetime(df.index, unit='s')
-                    self._upgrades_downgrades = df
-                except (KeyError, IndexError):
-                    raise YFDataException(f"Failed to parse json response from Yahoo Finance: {result}")
-        return self._upgrades_downgrades
-
-    @property
-    def calendar(self) -> dict:
-        if self._calendar is None:
-            self._fetch_calendar()
-        return self._calendar
-
-    @staticmethod
-    def valid_modules():
-        return quote_summary_valid_modules
-
-    def _fetch(self, proxy, modules: list):
-        if not isinstance(modules, list):
-            raise YFException("Should provide a list of modules, see available modules using `valid_modules`")
-
-        modules = ','.join([m for m in modules if m in quote_summary_valid_modules])
-        if len(modules) == 0:
-            raise YFException("No valid modules provided, see available modules using `valid_modules`")
-        params_dict = {"modules": modules, "corsDomain": "finance.yahoo.com", "formatted": "false", "symbol": self._symbol}
-        try:
-            result = self._data.get_raw_json(_QUOTE_SUMMARY_URL_ + f"/{self._symbol}", user_agent_headers=self._data.user_agent_headers, params=params_dict, proxy=proxy)
-        except requests.exceptions.HTTPError as e:
-            utils.get_yf_logger().error(str(e))
-            return None
-        return result
-
-    def _fetch_info(self, proxy):
-        if self._already_fetched:
-            return
-        self._already_fetched = True
-        modules = ['financialData', 'quoteType', 'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
-        result = self._fetch(proxy, modules=modules)
-        if result is None:
-            self._info = {}
-            return
-
-        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'
-
-        def _format(k, v):
-            if isinstance(v, dict) and "raw" in v and "fmt" in v:
-                v2 = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
-            elif isinstance(v, list):
-                v2 = [_format(None, x) for x in v]
-            elif isinstance(v, dict):
-                v2 = {k: _format(k, x) for k, x in v.items()}
-            elif isinstance(v, str):
-                v2 = v.replace("\xa0", " ")
-            else:
-                v2 = v
-            return v2
-        for k, v in query1_info.items():
-            query1_info[k] = _format(k, v)
-        self._info = query1_info
-
-    def _fetch_complementary(self, proxy):
-        if self._already_fetched_complementary:
-            return
-        self._already_fetched_complementary = True
-
-        # self._scrape(proxy)  # decrypt broken
-        self._fetch_info(proxy)
-        if self._info is None:
-            return
-
-        # Complementary key-statistics. For now just want 'trailing PEG ratio'
-        keys = {"trailingPegRatio"}
-        if keys:
-            # Simplified the original scrape code for key-statistics. Very expensive for fetching
-            # just one value, best if scraping most/all:
-            #
-            # p = _re.compile(r'root\.App\.main = (.*);')
-            # url = 'https://finance.yahoo.com/quote/{}/key-statistics?p={}'.format(self._ticker.ticker, self._ticker.ticker)
-            # try:
-            #     r = session.get(url, headers=utils.user_agent_headers)
-            #     data = _json.loads(p.findall(r.text)[0])
-            #     key_stats = data['context']['dispatcher']['stores']['QuoteTimeSeriesStore']["timeSeries"]
-            #     for k in keys:
-            #         if k not in key_stats or len(key_stats[k])==0:
-            #             # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
-            #             v = None
-            #         else:
-            #             # Select most recent (last) raw value in list:
-            #             v = key_stats[k][-1]["reportedValue"]["raw"]
-            #         self._info[k] = v
-            # except Exception:
-            #     raise
-            #     pass
-            #
-            # For just one/few variable is faster to query directly:
-            url = f"https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._symbol}?symbol={self._symbol}"
-            for k in keys:
-                url += "&type=" + k
-            # Request 6 months of data
-            start = pd.Timestamp.utcnow().floor("D") - datetime.timedelta(days=365 // 2)
-            start = int(start.timestamp())
-            end = pd.Timestamp.utcnow().ceil("D")
-            end = int(end.timestamp())
-            url += f"&period1={start}&period2={end}"
-
-            json_str = self._data.cache_get(url=url, proxy=proxy).text
-            json_data = json.loads(json_str)
-            json_result = json_data.get("timeseries") or json_data.get("finance")
-            if json_result["error"] is not None:
-                raise YFException("Failed to parse json response from Yahoo Finance: " + str(json_result["error"]))
-            for k in keys:
-                keydict = json_result["result"][0]
-                if k in keydict:
-                    self._info[k] = keydict[k][-1]["reportedValue"]["raw"]
-                else:
-                    self.info[k] = None
-
-    def _fetch_calendar(self):
-        # secFilings return too old data, so not requesting it for now
-        result = self._fetch(self.proxy, modules=['calendarEvents'])
-        if result is None:
-            self._calendar = {}
-            return
-
-        try:
-            self._calendar = dict()
-            _events = result["quoteSummary"]["result"][0]["calendarEvents"]
-            if 'dividendDate' in _events:
-                self._calendar['Dividend Date'] = datetime.datetime.fromtimestamp(_events['dividendDate']).date()
-            if 'exDividendDate' in _events:
-                self._calendar['Ex-Dividend Date'] = datetime.datetime.fromtimestamp(_events['exDividendDate']).date()
-            # splits = _events.get('splitDate')  # need to check later, i will add code for this if found data
-            earnings = _events.get('earnings')
-            if earnings is not None:
-                self._calendar['Earnings Date'] = [datetime.datetime.fromtimestamp(d).date() for d in earnings.get('earningsDate', [])]
-                self._calendar['Earnings High'] = earnings.get('earningsHigh', None)
-                self._calendar['Earnings Low'] = earnings.get('earningsLow', None)
-                self._calendar['Earnings Average'] = earnings.get('earningsAverage', None)
-                self._calendar['Revenue High'] = earnings.get('revenueHigh', None)
-                self._calendar['Revenue Low'] = earnings.get('revenueLow', None)
-                self._calendar['Revenue Average'] = earnings.get('revenueAverage', None)
-        except (KeyError, IndexError):
-            raise YFDataException(f"Failed to parse json response from Yahoo Finance: {result}")
--- a/yfinance/scrapers/yahoo-keys.txt
+++ b/yfinance/scrapers/yahoo-keys.txt
@@ -1,8 +0,0 @@
-daf93e37cbf219cd4c1f3f74ec4551265ec5565b99e8c9322dccd6872941cf13c818cbb88cba6f530e643b4e2329b17ec7161f4502ce6a02bb0dbbe5fc0d0474
-ad4d90b3c9f2e1d156ef98eadfa0ff93e4042f6960e54aa2a13f06f528e6b50ba4265a26a1fd5b9cd3db0d268a9c34e1d080592424309429a58bce4adc893c87
-e9a8ab8e5620b712ebc2fb4f33d5c8b9c80c0d07e8c371911c785cf674789f1747d76a909510158a7b7419e86857f2d7abbd777813ff64840e4cbc514d12bcae
-6ae2523aeafa283dad746556540145bf603f44edbf37ad404d3766a8420bb5eb1d3738f52a227b88283cca9cae44060d5f0bba84b6a495082589f5fe7acbdc9e
-3365117c2a368ffa5df7313a4a84988f73926a86358e8eea9497c5ff799ce27d104b68e5f2fbffa6f8f92c1fef41765a7066fa6bcf050810a9c4c7872fd3ebf0
-15d8f57919857d5a5358d2082c7ef0f1129cfacd2a6480333dcfb954b7bb67d820abefebfdb0eaa6ef18a1c57f617b67d7e7b0ec040403b889630ae5db5a4dbb
-db9630d707a7d0953ac795cd8db1ca9ca6c9d8239197cdfda24b4e0ec9c37eaec4db82dab68b8f606ab7b5b4af3e65dab50606f8cf508269ec927e6ee605fb78
-3c895fb5ddcc37d20d3073ed74ee3efad59bcb147c8e80fd279f83701b74b092d503dcd399604c6d8be8f3013429d3c2c76ed5b31b80c9df92d5eab6d3339fce
--- a/yfinance/shared.py
+++ b/yfinance/shared.py
@@ -22,5 +22,4 @@
 _DFS = {}
 _PROGRESS_BAR = None
 _ERRORS = {}
-_TRACEBACKS = {}
 _ISINS = {}
--- a/yfinance/ticker.py
+++ b/yfinance/ticker.py
@@ -21,40 +21,50 @@

 from __future__ import print_function

+# import time as _time
+import datetime as _datetime
+import requests as _requests
+import pandas as _pd
+# import numpy as _np
+
+# import json as _json
+# import re as _re
 from collections import namedtuple as _namedtuple

-import pandas as _pd
-
+from . import utils
 from .base import TickerBase
-from .const import _BASE_URL_


 class Ticker(TickerBase):
-    def __init__(self, ticker, session=None, proxy=None):
-        super(Ticker, self).__init__(ticker, session=session, proxy=proxy)
-        self._expirations = {}
-        self._underlying  = {}

    def __repr__(self):
-        return f'yfinance.Ticker object <{self.ticker}>'
+        return 'yfinance.Ticker object <%s>' % self.ticker

-    def _download_options(self, date=None):
+    def _download_options(self, date=None, proxy=None):
        if date is None:
-            url = f"{_BASE_URL_}/v7/finance/options/{self.ticker}"
+            url = "{}/v7/finance/options/{}".format(
+                self._base_url, self.ticker)
        else:
-            url = f"{_BASE_URL_}/v7/finance/options/{self.ticker}?date={date}"
+            url = "{}/v7/finance/options/{}?date={}".format(
+                self._base_url, self.ticker, date)

-        r = self._data.get(url=url, proxy=self.proxy).json()
+        # setup proxy in requests format
+        if proxy is not None:
+            if isinstance(proxy, dict) and "https" in proxy:
+                proxy = proxy["https"]
+            proxy = {"https": proxy}
+
+        r = _requests.get(
+            url=url,
+            proxies=proxy,
+            headers=utils.user_agent_headers
+        ).json()
        if len(r.get('optionChain', {}).get('result', [])) > 0:
            for exp in r['optionChain']['result'][0]['expirationDates']:
-                self._expirations[_pd.Timestamp(exp, unit='s').strftime('%Y-%m-%d')] = exp
-
-            self._underlying = r['optionChain']['result'][0].get('quote', {})
-
+                self._expirations[_datetime.datetime.utcfromtimestamp(
+                    exp).strftime('%Y-%m-%d')] = exp
            opt = r['optionChain']['result'][0].get('options', [])
-
-            return dict(**opt[0],underlying=self._underlying) if len(opt) > 0 else {}
-        return {}
+            return opt[0] if len(opt) > 0 else []

    def _options2df(self, opt, tz=None):
        data = _pd.DataFrame(opt).reindex(columns=[
@@ -79,23 +89,23 @@ class Ticker(TickerBase):
            data['lastTradeDate'] = data['lastTradeDate'].dt.tz_convert(tz)
        return data

-    def option_chain(self, date=None, tz=None):
+    def option_chain(self, date=None, proxy=None, tz=None):
        if date is None:
-            options = self._download_options()
+            options = self._download_options(proxy=proxy)
        else:
            if not self._expirations:
                self._download_options()
            if date not in self._expirations:
                raise ValueError(
-                    f"Expiration `{date}` cannot be found. "
-                    f"Available expirations are: [{', '.join(self._expirations)}]")
+                    "Expiration `%s` cannot be found. "
+                    "Available expiration are: [%s]" % (
+                        date, ', '.join(self._expirations)))
            date = self._expirations[date]
-            options = self._download_options(date)
+            options = self._download_options(date, proxy=proxy)

-        return _namedtuple('Options', ['calls', 'puts', 'underlying'])(**{
+        return _namedtuple('Options', ['calls', 'puts'])(**{
            "calls": self._options2df(options['calls'], tz=tz),
-            "puts": self._options2df(options['puts'], tz=tz),
-            "underlying": options['underlying']
+            "puts": self._options2df(options['puts'], tz=tz)
        })

    # ------------------------
@@ -105,62 +115,39 @@ class Ticker(TickerBase):
        return self.get_isin()

    @property
-    def major_holders(self) -> _pd.DataFrame:
+    def major_holders(self):
        return self.get_major_holders()

    @property
-    def institutional_holders(self) -> _pd.DataFrame:
+    def institutional_holders(self):
        return self.get_institutional_holders()

    @property
-    def mutualfund_holders(self) -> _pd.DataFrame:
+    def mutualfund_holders(self):
        return self.get_mutualfund_holders()

    @property
-    def insider_purchases(self) -> _pd.DataFrame:
-        return self.get_insider_purchases()
-
-    @property
-    def insider_transactions(self) -> _pd.DataFrame:
-        return self.get_insider_transactions()
-
-    @property
-    def insider_roster_holders(self) -> _pd.DataFrame:
-        return self.get_insider_roster_holders()
-
-    @property
-    def dividends(self) -> _pd.Series:
+    def dividends(self):
        return self.get_dividends()

    @property
-    def capital_gains(self) -> _pd.Series:
-        return self.get_capital_gains()
-
-    @property
-    def splits(self) -> _pd.Series:
+    def splits(self):
        return self.get_splits()

    @property
-    def actions(self) -> _pd.DataFrame:
+    def actions(self):
        return self.get_actions()

    @property
-    def shares(self) -> _pd.DataFrame:
+    def shares(self):
        return self.get_shares()

    @property
-    def info(self) -> dict:
+    def info(self):
        return self.get_info()

    @property
-    def fast_info(self):
-        return self.get_fast_info()
-
-    @property
-    def calendar(self) -> dict:
-        """
-        Returns a dictionary of events, earnings, and dividends for the ticker
-        """
+    def calendar(self):
        return self.get_calendar()

    @property
@@ -168,115 +155,67 @@ class Ticker(TickerBase):
        return self.get_recommendations()

    @property
-    def recommendations_summary(self):
-        return self.get_recommendations_summary()
-
-    @property
-    def upgrades_downgrades(self):
-        return self.get_upgrades_downgrades()
-
-    @property
-    def earnings(self) -> _pd.DataFrame:
+    def earnings(self):
        return self.get_earnings()

    @property
-    def quarterly_earnings(self) -> _pd.DataFrame:
+    def quarterly_earnings(self):
        return self.get_earnings(freq='quarterly')

    @property
-    def income_stmt(self) -> _pd.DataFrame:
-        return self.get_income_stmt(pretty=True)
+    def financials(self):
+        return self.get_financials()

    @property
-    def quarterly_income_stmt(self) -> _pd.DataFrame:
-        return self.get_income_stmt(pretty=True, freq='quarterly')
+    def quarterly_financials(self):
+        return self.get_financials(freq='quarterly')

    @property
-    def incomestmt(self) -> _pd.DataFrame:
-        return self.income_stmt
+    def balance_sheet(self):
+        return self.get_balancesheet()

    @property
-    def quarterly_incomestmt(self) -> _pd.DataFrame:
-        return self.quarterly_income_stmt
+    def quarterly_balance_sheet(self):
+        return self.get_balancesheet(freq='quarterly')

    @property
-    def financials(self) -> _pd.DataFrame:
-        return self.income_stmt
+    def balancesheet(self):
+        return self.get_balancesheet()

    @property
-    def quarterly_financials(self) -> _pd.DataFrame:
-        return self.quarterly_income_stmt
+    def quarterly_balancesheet(self):
+        return self.get_balancesheet(freq='quarterly')

    @property
-    def balance_sheet(self) -> _pd.DataFrame:
-        return self.get_balance_sheet(pretty=True)
+    def cashflow(self):
+        return self.get_cashflow()

    @property
-    def quarterly_balance_sheet(self) -> _pd.DataFrame:
-        return self.get_balance_sheet(pretty=True, freq='quarterly')
+    def quarterly_cashflow(self):
+        return self.get_cashflow(freq='quarterly')

    @property
-    def balancesheet(self) -> _pd.DataFrame:
-        return self.balance_sheet
-
-    @property
-    def quarterly_balancesheet(self) -> _pd.DataFrame:
-        return self.quarterly_balance_sheet
-
-    @property
-    def cash_flow(self) -> _pd.DataFrame:
-        return self.get_cash_flow(pretty=True, freq="yearly")
-
-    @property
-    def quarterly_cash_flow(self) -> _pd.DataFrame:
-        return self.get_cash_flow(pretty=True, freq='quarterly')
-
-    @property
-    def cashflow(self) -> _pd.DataFrame:
-        return self.cash_flow
-
-    @property
-    def quarterly_cashflow(self) -> _pd.DataFrame:
-        return self.quarterly_cash_flow
-
-    @property
-    def analyst_price_target(self) -> _pd.DataFrame:
-        return self.get_analyst_price_target()
-
-    @property
-    def revenue_forecasts(self) -> _pd.DataFrame:
-        return self.get_rev_forecast()
-
-    @property
-    def sustainability(self) -> _pd.DataFrame:
+    def sustainability(self):
        return self.get_sustainability()

    @property
-    def options(self) -> tuple:
+    def options(self):
        if not self._expirations:
            self._download_options()
        return tuple(self._expirations.keys())

    @property
-    def news(self) -> list:
+    def news(self):
        return self.get_news()

    @property
-    def trend_details(self) -> _pd.DataFrame:
-        return self.get_trend_details()
+    def analysis(self):
+        return self.get_analysis()

    @property
-    def earnings_trend(self) -> _pd.DataFrame:
-        return self.get_earnings_trend()
+    def earnings_history(self):
+        return self.get_earnings_history()

    @property
-    def earnings_dates(self) -> _pd.DataFrame:
+    def earnings_dates(self):
        return self.get_earnings_dates()
-
-    @property
-    def earnings_forecasts(self) -> _pd.DataFrame:
-        return self.get_earnings_forecast()
-
-    @property
-    def history_metadata(self) -> dict:
-        return self.get_history_metadata()
--- a/yfinance/tickers.py
+++ b/yfinance/tickers.py
@@ -22,53 +22,51 @@
 from __future__ import print_function

 from . import Ticker, multi
-
-
 # from collections import namedtuple as _namedtuple


-class Tickers:
+class Tickers():

    def __repr__(self):
-        return f"yfinance.Tickers object <{','.join(self.symbols)}>"
+        return 'yfinance.Tickers object <%s>' % ",".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}
+        ticker_objects = {}

+        for ticker in self.symbols:
+            ticker_objects[ticker] = Ticker(ticker, session=session)
+
+        self.tickers = ticker_objects
        # self.tickers = _namedtuple(
        #     "Tickers", ticker_objects.keys(), rename=True
        # )(*ticker_objects.values())

    def history(self, period="1mo", interval="1d",
                start=None, end=None, prepost=False,
-                actions=True, auto_adjust=True, repair=False,
-                proxy=None,
+                actions=True, auto_adjust=True, proxy=None,
                threads=True, group_by='column', progress=True,
-                timeout=10, **kwargs):
+                timeout=None, **kwargs):

        return self.download(
            period, interval,
            start, end, prepost,
-            actions, auto_adjust, repair, 
-            proxy,
+            actions, auto_adjust, proxy,
            threads, group_by, progress,
            timeout, **kwargs)

    def download(self, period="1mo", interval="1d",
                 start=None, end=None, prepost=False,
-                 actions=True, auto_adjust=True, repair=False, 
-                 proxy=None,
+                 actions=True, auto_adjust=True, proxy=None,
                 threads=True, group_by='column', progress=True,
-                 timeout=10, **kwargs):
+                 timeout=None, **kwargs):

        data = multi.download(self.symbols,
                              start=start, end=end,
                              actions=actions,
                              auto_adjust=auto_adjust,
-                              repair=repair,
                              period=period,
                              interval=interval,
                              prepost=prepost,
@@ -89,4 +87,10 @@ class Tickers:
        return data

    def news(self):
-        return {ticker: [item for item in Ticker(ticker).news] for ticker in self.symbols}
+        collection = {}
+        for ticker in self.symbols:
+            collection[ticker] = []
+            items = Ticker(ticker).news
+            for item in items:
+                collection[ticker].append(item)
+        return collection
--- a/yfinance/utils.py
+++ b/yfinance/utils.py
@@ -22,169 +22,39 @@
 from __future__ import print_function

 import datetime as _datetime
-import logging
-import re as _re
-import sys as _sys
-import threading
-from functools import lru_cache, wraps
-from inspect import getmembers
-from types import FunctionType
-from typing import List, Optional
-
-import numpy as _np
-import pandas as _pd
 import pytz as _tz
 import requests as _requests
-from dateutil.relativedelta import relativedelta
-from pytz import UnknownTimeZoneError
+import re as _re
+import pandas as _pd
+import numpy as _np
+import sys as _sys
+import os as _os
+import appdirs as _ad
+
+from threading import Lock
+mutex = Lock()
+
+try:
+    import ujson as _json
+except ImportError:
+    import json as _json

-from yfinance import const
-from .const import _BASE_URL_

 user_agent_headers = {
    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}


-# From https://stackoverflow.com/a/59128615
-def attributes(obj):
-    disallowed_names = {
-        name for name, value in getmembers(type(obj))
-        if isinstance(value, FunctionType)}
-    return {
-        name: getattr(obj, name) for name in dir(obj)
-        if name[0] != '_' and name not in disallowed_names and hasattr(obj, name)}
-
-
-@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):
-    @wraps(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])$", string))
+    return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9]{1})$", string))


 def get_all_by_isin(isin, proxy=None, session=None):
-    if not (is_isin(isin)):
+    if not(is_isin(isin)):
        raise ValueError("Invalid ISIN number")
+
+    from .base import _BASE_URL_
    session = session or _requests
-    url = f"{_BASE_URL_}/v1/finance/search?q={isin}"
+    url = "{}/v1/finance/search?q={}".format(_BASE_URL_, isin)
    data = session.get(url=url, proxies=proxy, headers=user_agent_headers)
    try:
        data = data.json()
@@ -218,9 +88,7 @@ def get_news_by_isin(isin, proxy=None, session=None):
    return data.get('news', {})


-def empty_df(index=None):
-    if index is None:
-        index = []
+def empty_df(index=[]):
    empty = _pd.DataFrame(index=index, data={
        'Open': _np.nan, 'High': _np.nan, 'Low': _np.nan,
        'Close': _np.nan, 'Adj Close': _np.nan, 'Volume': _np.nan})
@@ -235,177 +103,48 @@ def empty_earnings_dates_df():
    return empty


-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:
-        - template_annual_order: The order that annual figures should be listed in.
-        - template_ttm_order: The order that TTM (Trailing Twelve Month) figures should be listed in.
-        - 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)
-
-    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 get_html(url, proxy=None, session=None):
+    session = session or _requests
+    html = session.get(url=url, proxies=proxy, headers=user_agent_headers).text
+    return html


-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".
+def get_json(url, proxy=None, session=None):
+    session = session or _requests
+    html = session.get(url=url, proxies=proxy, headers=user_agent_headers).text

-    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.
+    if "QuoteSummaryStore" not in html:
+        html = session.get(url=url, proxies=proxy).text
+        if "QuoteSummaryStore" not in html:
+            return {}

-    for key, timeseries in data.get('timeSeries', {}).items():  # Loop through the time series data to grab the key financial figures.
-        try:
-            if timeseries:
-                time_series_dict = {'index': key}
-                for each in timeseries:  # Loop through the years
-                    if not each:
-                        continue
-                    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 KeyError as e:
-            print(f"An error occurred while processing the key: {e}")
-    return TTM_dicts, Annual_dicts
+    json_str = html.split('root.App.main =')[1].split(
+        '(this)')[0].split(';\n}')[0].strip()
+    data = _json.loads(json_str)[
+        'context']['dispatcher']['stores']['QuoteSummaryStore']
+    # add data about Shares Outstanding for companies' tickers if they are available
+    try:
+        data['annualBasicAverageShares'] = _json.loads(
+            json_str)['context']['dispatcher']['stores'][
+                'QuoteTimeSeriesStore']['timeSeries']['annualBasicAverageShares']
+    except Exception:
+        pass
+
+    # return data
+    new_data = _json.dumps(data).replace('{}', 'null')
+    new_data = _re.sub(
+        r'\{[\'|\"]raw[\'|\"]:(.*?),(.*?)\}', r'\1', new_data)
+
+    return _json.loads(new_data)


-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 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]
-        TTM.index = TTM.index.str.replace(r'trailing', '')
-        _statement = Annual.merge(TTM, left_index=True, right_index=True)
-    else:
-        _statement = Annual
-
-    _statement.index = camel2title(_statement.T.index)
-    _statement['level_detail'] = level_detail
-    _statement = _statement.set_index([_statement.index, 'level_detail'])
-    _statement = _statement[sorted(_statement.columns, reverse=True)]
-    _statement = _statement.dropna(how='all')
-    return _statement
-
-
-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
-    _statement = _statement.set_index([_statement.index, 'level_detail'])
-    _statement = _statement[sorted(_statement.columns, reverse=True)]
-    _statement = _statement.dropna(how='all')
-    _statement.columns = _pd.to_datetime(_statement.columns).date
-    return _statement
-
-
-def camel2title(strings: List[str], sep: str = ' ', acronyms: Optional[List[str]] = None) -> List[str]:
-    if isinstance(strings, str) or not hasattr(strings, '__iter__'):
-        raise TypeError("camel2title() 'strings' argument must be iterable of strings")
-    if len(strings) == 0:
-        return strings
-    if not isinstance(strings[0], str):
-        raise TypeError("camel2title() 'strings' argument must be iterable of strings")
-    if not isinstance(sep, str) or len(sep) != 1:
-        raise ValueError(f"camel2title() 'sep' argument = '{sep}' must be single character")
-    if _re.match("[a-zA-Z0-9]", sep):
-        raise ValueError(f"camel2title() 'sep' argument = '{sep}' cannot be alpha-numeric")
-    if _re.escape(sep) != sep and sep not in {' ', '-'}:
-        # Permit some exceptions, I don't understand why they get escaped
-        raise ValueError(f"camel2title() 'sep' argument = '{sep}' cannot be special character")
-
-    if acronyms is None:
-        pat = "([a-z])([A-Z])"
-        rep = rf"\g<1>{sep}\g<2>"
-        return [_re.sub(pat, rep, s).title() for s in strings]
-
-    # Handling acronyms requires more care. Assumes Yahoo returns acronym strings upper-case
-    if isinstance(acronyms, str) or not hasattr(acronyms, '__iter__') or not isinstance(acronyms[0], str):
-        raise TypeError("camel2title() 'acronyms' argument must be iterable of strings")
-    for a in acronyms:
-        if not _re.match("^[A-Z]+$", a):
-            raise ValueError(f"camel2title() 'acronyms' argument must only contain upper-case, but '{a}' detected")
-
-    # Insert 'sep' between lower-then-upper-case
-    pat = "([a-z])([A-Z])"
-    rep = rf"\g<1>{sep}\g<2>"
-    strings = [_re.sub(pat, rep, s) for s in strings]
-
-    # Insert 'sep' after acronyms
-    for a in acronyms:
-        pat = f"({a})([A-Z][a-z])"
-        rep = rf"\g<1>{sep}\g<2>"
-        strings = [_re.sub(pat, rep, s) for s in strings]
-
-    # Apply str.title() to non-acronym words
-    strings = [s.split(sep) 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 camel2title(o):
+    return [_re.sub("([a-z])([A-Z])", r"\g<1> \g<2>", i).title() for i in o]


 def _parse_user_dt(dt, exchange_tz):
    if isinstance(dt, int):
-        # Should already be epoch, test with conversion:
+        ## Should already be epoch, test with conversion:
        _datetime.datetime.fromtimestamp(dt)
    else:
        # Convert str/date -> datetime, set tzinfo=exchange, get timestamp:
@@ -420,26 +159,12 @@ def _parse_user_dt(dt, exchange_tz):
    return dt


-def _interval_to_timedelta(interval):
-    if interval == "1mo":
-        return relativedelta(months=1)
-    elif interval == "3mo":
-        return relativedelta(months=3)
-    elif interval == "1y":
-        return relativedelta(years=1)
-    elif interval == "1wk":
-        return _pd.Timedelta(days=7)
-    else:
-        return _pd.Timedelta(interval)
-
-
 def auto_adjust(data):
-    col_order = data.columns
    df = data.copy()
-    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
+    ratio = df["Close"] / df["Adj Close"]
+    df["Adj Open"] = df["Open"] / ratio
+    df["Adj High"] = df["High"] / ratio
+    df["Adj Low"] = df["Low"] / ratio

    df.drop(
        ["Open", "High", "Low", "Close"],
@@ -450,13 +175,13 @@ def auto_adjust(data):
        "Adj Low": "Low", "Adj Close": "Close"
    }, inplace=True)

-    return df[[c for c in col_order if c in df.columns]]
+    df = df[["Open", "High", "Low", "Close", "Volume"]]
+    return df[["Open", "High", "Low", "Close", "Volume"]]


 def back_adjust(data):
    """ back-adjusted data to mimic true historical prices """

-    col_order = data.columns
    df = data.copy()
    ratio = df["Adj Close"] / df["Close"]
    df["Adj Open"] = df["Open"] * ratio
@@ -472,7 +197,7 @@ def back_adjust(data):
        "Adj Low": "Low"
    }, inplace=True)

-    return df[[c for c in col_order if c in df.columns]]
+    return df[["Open", "High", "Low", "Close", "Volume"]]


 def parse_quotes(data):
@@ -502,9 +227,10 @@ def parse_quotes(data):


 def parse_actions(data):
-    dividends = None
-    capital_gains = None
-    splits = None
+    dividends = _pd.DataFrame(
+        columns=["Dividends"], index=_pd.DatetimeIndex([]))
+    splits = _pd.DataFrame(
+        columns=["Stock Splits"], index=_pd.DatetimeIndex([]))

    if "events" in data:
        if "dividends" in data["events"]:
@@ -513,15 +239,8 @@ def parse_actions(data):
            dividends.set_index("date", inplace=True)
            dividends.index = _pd.to_datetime(dividends.index, unit="s")
            dividends.sort_index(inplace=True)
-            dividends.columns = ["Dividends"]

-        if "capitalGains" in data["events"]:
-            capital_gains = _pd.DataFrame(
-                data=list(data["events"]["capitalGains"].values()))
-            capital_gains.set_index("date", inplace=True)
-            capital_gains.index = _pd.to_datetime(capital_gains.index, unit="s")
-            capital_gains.sort_index(inplace=True)
-            capital_gains.columns = ["Capital Gains"]
+            dividends.columns = ["Dividends"]

        if "splits" in data["events"]:
            splits = _pd.DataFrame(
@@ -529,322 +248,26 @@ 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 = splits[["Stock Splits"]]
+            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
-
-
-def set_df_tz(df, interval, tz):
-    if df.index.tz is None:
-        df.index = df.index.tz_localize("UTC")
-    df.index = df.index.tz_convert(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.
-    # Seems to depend on what exchange e.g. crypto OK.
-    # Fix = merge them together
-    n = quotes.shape[0]
-    if n > 1:
-        dt1 = quotes.index[n - 1]
-        dt2 = quotes.index[n - 2]
-        if quotes.index.tz is None:
-            dt1 = dt1.tz_localize("UTC")
-            dt2 = dt2.tz_localize("UTC")
-        dt1 = dt1.tz_convert(tz_exchange)
-        dt2 = dt2.tz_convert(tz_exchange)
-        if interval == "1d":
-            # Similar bug in daily data except most data is simply duplicated
-            # - exception is volume, *slightly* greater on final row (and matches website)
-            if dt1.date() == dt2.date():
-                # Last two rows are on same day. Drop second-to-last row
-                quotes = quotes.drop(quotes.index[n - 2])
-        else:
-            if interval == "1wk":
-                last_rows_same_interval = dt1.year == dt2.year and dt1.week == dt2.week
-            elif interval == "1mo":
-                last_rows_same_interval = dt1.month == dt2.month
-            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)
-
-            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"].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"].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):
-    if df_sub.empty:
-        raise Exception("No data to merge")
-    if df_main.empty:
-        return df_main
-
-    data_cols = [c for c in df_sub.columns if c not in df_main]
-    if len(data_cols) > 1:
-        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:
-                        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
-            df.index = new_index
-            return df
-
-        df["_NewIndex"] = new_index
-        # Duplicates present within periods but can aggregate
-        if data_col_name in ["Dividends", "Capital Gains"]:
-            # Add
-            df = df.groupby("_NewIndex").sum()
-            df.index.name = None
-        elif data_col_name == "Stock Splits":
-            # Product
-            df = df.groupby("_NewIndex").prod()
-            df.index.name = None
-        else:
-            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
-
-    new_index = df_main.index[indices]
-    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 data_lost:
-        raise Exception('Data was lost in merge, investigate')
-
-    return df
+    return dividends, splits


 def fix_Yahoo_dst_issue(df, interval):
-    if interval in ["1d", "1w", "1wk"]:
-        # These intervals should start at time 00:00. But for some combinations of date and timezone,
+    if interval in ["1d","1w","1wk"]:
+        # These intervals should start at time 00:00. But for some combinations of date and timezone, 
        # Yahoo has time off by few hours (e.g. Brazil 23:00 around Jan-2022). Suspect DST problem.
-        # The clue is (a) minutes=0 and (b) hour near 0.
+        # The clue is (a) minutes=0 and (b) hour near 0. 
        # Obviously Yahoo meant 00:00, so ensure this doesn't affect date conversion:
-        f_pre_midnight = (df.index.minute == 0) & (df.index.hour.isin([22, 23]))
-        dst_error_hours = _np.array([0] * df.shape[0])
-        dst_error_hours[f_pre_midnight] = 24 - df.index[f_pre_midnight].hour
-        df.index += _pd.to_timedelta(dst_error_hours, 'h')
+        f_pre_midnight = (df.index.minute == 0) & (df.index.hour.isin([22,23]))
+        dst_error_hours = _np.array([0]*df.shape[0])
+        dst_error_hours[f_pre_midnight] = 24-df.index[f_pre_midnight].hour
+        df.index += _pd.TimedeltaIndex(dst_error_hours, 'h')
    return df


-def is_valid_timezone(tz: str) -> bool:
-    try:
-        _tz.timezone(tz)
-    except UnknownTimeZoneError:
-        return False
-    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
@@ -859,9 +282,9 @@ class ProgressBar:
        if self.elapsed > self.iterations:
            self.elapsed = self.iterations
        self.update_iteration(1)
-        print('\r' + str(self), end='', file=_sys.stderr)
-        _sys.stderr.flush()
-        print("", file=_sys.stderr)
+        print('\r' + str(self), end='')
+        _sys.stdout.flush()
+        print()

    def animate(self, iteration=None):
        if iteration is None:
@@ -870,24 +293,78 @@ class ProgressBar:
        else:
            self.elapsed += iteration

-        print('\r' + str(self), end='', file=_sys.stderr)
-        _sys.stderr.flush()
+        print('\r' + str(self), end='')
+        _sys.stdout.flush()
        self.update_iteration()

    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 += f"  {self.elapsed} of {self.iterations} {self.text}"
+        self.prog_bar += '  %s of %s %s' % (
+            self.elapsed, 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 = f'{percent_done}%%'
-        self.prog_bar = self.prog_bar[0:pct_place] + (pct_string + self.prog_bar[pct_place + len(pct_string):])
+        pct_string = '%d%%' % 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)

+
+# Simple file cache of ticker->timezone:
+_cache_dp = None
+def get_cache_dirpath():
+    if _cache_dp is None:
+        dp = _os.path.join(_ad.user_cache_dir(), "py-yfinance")
+    else:
+        dp = _os.path.join(_cache_dp, "py-yfinance")
+    return dp
+def set_tz_cache_location(dp):
+    global _cache_dp
+    _cache_dp = dp
+
+def cache_lookup_tkr_tz(tkr):
+    fp = _os.path.join(get_cache_dirpath(), "tkr-tz.csv")
+    if not _os.path.isfile(fp):
+        return None
+
+    mutex.acquire()
+    df = _pd.read_csv(fp, index_col="Ticker")
+    mutex.release()
+    if tkr in df.index:
+        return df.loc[tkr,"Tz"]
+    else:
+        return None
+def cache_store_tkr_tz(tkr,tz):
+
+    dp = get_cache_dirpath()
+    fp = _os.path.join(dp, "tkr-tz.csv")
+    mutex.acquire()
+    if not _os.path.isdir(dp):
+        _os.makedirs(dp)
+    if (not _os.path.isfile(fp)) and (tz is not None):
+        df = _pd.DataFrame({"Tz":[tz]}, index=[tkr])
+        df.index.name = "Ticker"
+        df.to_csv(fp)
+
+    else:
+        df = _pd.read_csv(fp, index_col="Ticker")
+        if tz is None:
+            # Delete if in cache:
+            if tkr in df.index:
+                df.drop(tkr).to_csv(fp)
+        else:
+            if tkr in df.index:
+                raise Exception("Tkr {} tz already in cache".format(tkr))
+            df.loc[tkr,"Tz"] = tz
+            df.to_csv(fp)
+    
+    mutex.release()
+
--- a/yfinance/version.py
+++ b/yfinance/version.py
@@ -1 +1 @@
-version = "0.2.41"
+version = "0.1.85"
Author	SHA1	Message	Date
ValueRaider	66af3080dd	Bump version to 0.1.85	2022-11-03 19:04:45 +00:00
ValueRaider	9d396b9559	Merge pull request #1135 from ranaroussi/patch/unknown-ticker-timezone Backport ticker tz verification for nice error	2022-11-02 15:18:26 +00:00
ValueRaider	23b6ad12c1	Backport ticker tz verification for nice error	2022-10-31 21:14:50 +00:00
ValueRaider	22131e9fc7	Merge pull request #1124 from Jossan84/main Bugfix: Get logo url when no website exists	2022-10-27 22:34:18 +01:00
ValueRaider	e99e61f95a	Bump version to 0.1.84	2022-10-26 00:12:29 +01:00
ValueRaider	a3fe95ea27	Make tz-cache thread-safe	2022-10-26 00:09:23 +01:00
ValueRaider	000cb70bcb	Bump version to 0.1.83	2022-10-25 23:23:32 +01:00
ValueRaider	c8d9d06e75	Expose _fetch_ticker_tz() arguments	2022-10-25 23:21:56 +01:00
ValueRaider	a5e07a0375	Bump version to 0.1.82	2022-10-25 23:15:48 +01:00
ValueRaider	a0a12bcf4c	Backport _fetch_ticker_tz()	2022-10-25 23:07:48 +01:00