Dev version 0.2.19b4

Bugfix in `download` logging tracebacks & boost tests

.github/ISSUE_TEMPLATE/bug_report.md

@@ -7,11 +7,38 @@ assignees: ''
 
 ---
 
-*** READ BEFORE POSTING ***
+# IMPORTANT
 
-Before posting an issue - please upgrade to the latest version and confirm the issue/bug is still there.
+# Read and follow these instructions carefully. Help us help you.
+
+### Are you up-to-date?
+
+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...
+Confirm by running:
+
+`import yfinance as yf ; print(yf.__version__)`
+
+and comparing against [PIP](https://pypi.org/project/yfinance/#history).
+
+### Does Yahoo actually have the data?
+
+Are you spelling symbol *exactly* same as Yahoo?
+
+Then visit `finance.yahoo.com` and confirm they have the data you want. Maybe your symbol was delisted, or your expectations of `yfinance` are wrong.
+
+### Are you spamming Yahoo?
+
+Yahoo Finance free service has rate-limiting depending on request type - roughly 60/minute for prices, 10/minute for info. Once limit hit, Yahoo can delay, block, or return bad data -> not a `yfinance` bug.
+
+### Still think it's a bug?
+
+**Delete these instructions** and replace with your bug report, providing the following as best you can:
+
+- Simple code that reproduces your problem, that we can copy-paste-run.
+- Run code with [debug logging enabled](https://github.com/ranaroussi/yfinance/tree/dev#logging) and post the full output.
+- If you think `yfinance` returning bad data, give us proof.
+- `yfinance` version and Python version.
+- Operating system type.

.github/ISSUE_TEMPLATE/feature_request.md

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

.gitignore

@@ -9,3 +9,10 @@ build/
 *.html
 *.css
 *.png
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/

.travis.yml

@@ -6,7 +6,6 @@ fast_finish: true
 matrix:
   include:
     - python: 2.7
-    - python: 3.5
     - python: 3.6
     - python: 3.7
     - python: 3.8

CHANGELOG.rst

@@ -1,6 +1,213 @@
 Change Log
 ===========
 
+0.2.19b4
+--------
+Fix `download` logging #1541
+Fix corrupt tkr-tz-csv halting code #1528
+
+0.2.19b3
+-------
+Improve logging messages #1522
+Price fixes #1523
+
+0.2.19b1 - beta
+-------
+Optimise Ticker.history #1514
+Logging module #1493
+
+0.2.18
+------
+Fix 'fast_info' error '_np not found' #1496
+Fix bug in timezone cache #1498
+
+0.2.17
+------
+Fix prices error with Pandas 2.0 #1488
+
+0.2.16
+------
+Fix 'fast_info deprecated' msg appearing at Ticker() init
+
+0.2.15
+------
+Restore missing Ticker.info keys #1480
+
+0.2.14
+------
+Fix Ticker.info dict by fetching from API #1461
+
+0.2.13
+------
+Price bug fixes:
+- fetch big-interval with Capital Gains #1455
+- merging dividends & splits with prices #1452
+
+0.2.12
+------
+Disable annoying 'backup decrypt' msg
+
+0.2.11
+------
+Fix history_metadata accesses for unusual symbols #1411
+
+0.2.10
+------
+General
+- allow using sqlite3 < 3.8.2 #1380
+- add another backup decrypt option #1379
+Prices
+- restore original download() timezone handling #1385
+- fix & improve price repair #1289 2a2928b 86d6acc
+- drop intraday intervals if in post-market but prepost=False #1311
+Info
+- fast_info improvements:
+  - add camelCase keys, add dict functions values() & items() #1368
+  - fix fast_info["previousClose"] #1383
+- catch TypeError Exception #1397
+
+0.2.9
+-----
+- Fix fast_info bugs #1362
+
+0.2.7
+-----
+- Fix Yahoo decryption, smarter this time #1353
+- Rename basic_info -> fast_info #1354
+
+0.2.6
+-----
+- Fix Ticker.basic_info lazy-loading #1342
+
+0.2.5
+-----
+- Fix Yahoo data decryption again #1336
+- New: Ticker.basic_info - faster Ticker.info #1317
+
+0.2.4
+-----
+- Fix Yahoo data decryption #1297
+- New feature: 'Ticker.get_shares_full()' #1301
+- Improve caching of financials data #1284
+- Restore download() original alignment behaviour #1283
+- Fix the database lock error in multithread download #1276
+
+0.2.3
+-----
+- Make financials API '_' use consistent
+
+0.2.2
+-----
+- Restore 'financials' attribute (map to 'income_stmt')
+
+0.2.1
+-----
+Release!
+
+0.2.0rc5
+--------
+- Improve financials error handling #1243
+- Fix '100x price' repair #1244
+
+0.2.0rc4
+--------
+- Access to old financials tables via `get_income_stmt(legacy=True)`
+- Optimise scraping financials & fundamentals, 2x faster
+- Add 'capital gains' alongside dividends & splits for ETFs, and metadata available via `history_metadata`, plus a bunch of price fixes
+For full list of changes see #1238
+
+0.2.0rc2
+--------
+Financials
+- fix financials tables to match website  #1128 #1157
+- lru_cache to optimise web requests  #1147
+Prices
+- improve price repair  #1148
+- fix merging dividends/splits with day/week/monthly prices  #1161
+- fix the Yahoo DST fixes  #1143
+- improve bad/delisted ticker handling  #1140
+Misc
+- fix 'trailingPegRatio'  #1138
+- improve error handling  #1118
+
+0.2.0rc1
+--------
+Jumping to 0.2 for this big update. 0.1.* will continue to receive bug-fixes
+- timezone cache performance massively improved. Thanks @fredrik-corneliusson #1113 #1112 #1109 #1105 #1099
+- price repair feature #1110
+- fix merging of dividends/splits with prices #1069 #1086 #1102
+- fix Yahoo returning latest price interval across 2 rows #1070
+- optional: raise errors as exceptions: raise_errors=True #1104
+- add proper unit tests #1069
+
+0.1.81
+------
+- Fix unhandled tz-cache exception #1107
+
+0.1.80
+------
+- Fix `download(ignore_tz=True)` for single ticker #1097
+- Fix rare case of error "Cannot infer DST time" #1100
+
+0.1.79
+------
+- Fix when Yahoo returns price=NaNs on dividend day
+
+0.1.78
+------
+- Fix download() when different timezones #1085
+
+0.1.77
+------
+- Fix user experience bug #1078
+
+0.1.75
+------
+- Fixed datetime-related issues: #1048
+- Add 'keepna' argument #1032
+- Speedup Ticker() creation #1042
+- Improve a bugfix #1033
+
+0.1.74
+------
+- Fixed bug introduced in 0.1.73 (sorry :/)
+
+0.1.73
+------
+- Merged several PR that fixed misc issues
+
+0.1.72
+------
+- Misc bugfixs
+
+0.1.71
+------
+- Added Tickers(…).news()
+- Return empty DF if YF missing earnings dates
+- Fix EPS % to 0->1
+- Fix timezone handling
+- Fix handling of missing data
+- Clean&format earnings_dates table
+- Add ``.get_earnings_dates()`` to retreive earnings calendar
+- Added ``.get_earnings_history()`` to fetch earnings data
+
+0.1.70
+------
+- Bug fixed - Closes #937
+
+0.1.69
+------
+- Bug fixed - #920
+
+0.1.68
+------
+- Upgraded requests dependency
+- Removed Python 3.5 support
+
+0.1.67
+------
+- Added legal disclaimers to make sure people are aware that this library is not affiliated, endorsed, or vetted by Yahoo, Inc.
+
 0.1.66
 ------
 - Merged PR to allow yfinance to be pickled

README.md

@@ -0,0 +1,333 @@
+# Download market data from Yahoo! Finance's API
+
+<table border=1 cellpadding=10><tr><td>
+
+#### \*\*\* IMPORTANT LEGAL DISCLAIMER \*\*\*
+
+---
+
+**Yahoo!, Y!Finance, and Yahoo! finance are registered trademarks of
+Yahoo, Inc.**
+
+yfinance is **not** affiliated, endorsed, or vetted by Yahoo, Inc. It's
+an open-source tool that uses Yahoo's publicly available APIs, and is
+intended for research and educational purposes.
+
+**You should refer to Yahoo!'s terms of use**
+([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. Remember - the
+Yahoo! finance API is intended for personal use only.**
+
+</td></tr></table>
+
+---
+
+<a target="new" href="https://pypi.python.org/pypi/yfinance"><img border=0 src="https://img.shields.io/badge/python-2.7,%203.6+-blue.svg?style=flat" alt="Python version"></a>
+<a target="new" href="https://pypi.python.org/pypi/yfinance"><img border=0 src="https://img.shields.io/pypi/v/yfinance.svg?maxAge=60%" alt="PyPi version"></a>
+<a target="new" href="https://pypi.python.org/pypi/yfinance"><img border=0 src="https://img.shields.io/pypi/status/yfinance.svg?maxAge=60" alt="PyPi status"></a>
+<a target="new" href="https://pypi.python.org/pypi/yfinance"><img border=0 src="https://img.shields.io/pypi/dm/yfinance.svg?maxAge=2592000&label=installs&color=%2327B1FF" alt="PyPi downloads"></a>
+<a target="new" href="https://travis-ci.com/github/ranaroussi/yfinance"><img border=0 src="https://img.shields.io/travis/ranaroussi/yfinance/main.svg?maxAge=1" alt="Travis-CI build status"></a>
+<a target="new" href="https://www.codefactor.io/repository/github/ranaroussi/yfinance"><img border=0 src="https://www.codefactor.io/repository/github/ranaroussi/yfinance/badge" alt="CodeFactor"></a>
+<a target="new" href="https://github.com/ranaroussi/yfinance"><img border=0 src="https://img.shields.io/github/stars/ranaroussi/yfinance.svg?style=social&label=Star&maxAge=60" alt="Star this repo"></a>
+<a target="new" href="https://twitter.com/aroussi"><img border=0 src="https://img.shields.io/twitter/follow/aroussi.svg?style=social&label=Follow&maxAge=60" alt="Follow me on twitter"></a>
+
+
+**yfinance** offers a threaded and Pythonic way to download market data from [Yahoo!Ⓡ finance](https://finance.yahoo.com).
+
+→ Check out this [Blog post](https://aroussi.com/#post/python-yahoo-finance) for a detailed tutorial with code examples.
+
+[Changelog »](https://github.com/ranaroussi/yfinance/blob/main/CHANGELOG.rst)
+
+---
+
+## News [2023-01-27]
+Since December 2022 Yahoo has been encrypting the web data that `yfinance` scrapes for non-market data. Fortunately the decryption keys are available, although Yahoo moved/changed them several times hence `yfinance` breaking several times. `yfinance` is now better prepared for any future changes by Yahoo.
+
+Why is Yahoo doing this? We don't know. Is it to stop scrapers? Maybe, so we've implemented changes to reduce load on Yahoo. In December we rolled out version 0.2 with optimised scraping. ~Then in 0.2.6 introduced `Ticker.fast_info`, providing much faster access to some `info` elements wherever possible e.g. price stats and forcing users to switch (sorry but we think necessary). `info` will continue to exist for as long as there are elements without a fast alternative.~ `info` now fixed and much faster than before.
+
+## Quick Start
+
+### The Ticker module
+
+The `Ticker` module, which allows you to access ticker data in a more Pythonic way:
+
+```python
+import yfinance as yf
+
+msft = yf.Ticker("MSFT")
+
+# get all stock info
+msft.info
+
+# get historical market data
+hist = msft.history(period="1mo")
+
+# show meta information about the history (requires history() to be called first)
+msft.history_metadata
+
+# show actions (dividends, splits, capital gains)
+msft.actions
+msft.dividends
+msft.splits
+msft.capital_gains  # only for mutual funds & etfs
+
+# show share count
+# - yearly summary:
+msft.shares
+# - accurate time-series count:
+msft.get_shares_full(start="2022-01-01", end=None)
+
+# show financials:
+# - income statement
+msft.income_stmt
+msft.quarterly_income_stmt
+# - balance sheet
+msft.balance_sheet
+msft.quarterly_balance_sheet
+# - cash flow statement
+msft.cashflow
+msft.quarterly_cashflow
+# see `Ticker.get_income_stmt()` for more options
+
+# show holders
+msft.major_holders
+msft.institutional_holders
+msft.mutualfund_holders
+
+# show earnings
+msft.earnings
+msft.quarterly_earnings
+
+# show sustainability
+msft.sustainability
+
+# show analysts recommendations
+msft.recommendations
+msft.recommendations_summary
+# show analysts other work
+msft.analyst_price_target
+msft.revenue_forecasts
+msft.earnings_forecasts
+msft.earnings_trend
+
+# show next event (earnings, etc)
+msft.calendar
+
+# Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default. 
+# Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.
+msft.earnings_dates
+
+# show ISIN code - *experimental*
+# ISIN = International Securities Identification Number
+msft.isin
+
+# show options expirations
+msft.options
+
+# show news
+msft.news
+
+# get option chain for specific expiration
+opt = msft.option_chain('YYYY-MM-DD')
+# data available via: opt.calls, opt.puts
+```
+
+If you want to use a proxy server for downloading data, use:
+
+```python
+import yfinance as yf
+
+msft = yf.Ticker("MSFT")
+
+msft.history(..., proxy="PROXY_SERVER")
+msft.get_actions(proxy="PROXY_SERVER")
+msft.get_dividends(proxy="PROXY_SERVER")
+msft.get_splits(proxy="PROXY_SERVER")
+msft.get_capital_gains(proxy="PROXY_SERVER")
+msft.get_balance_sheet(proxy="PROXY_SERVER")
+msft.get_cashflow(proxy="PROXY_SERVER")
+msft.option_chain(..., proxy="PROXY_SERVER")
+...
+```
+
+### Multiple tickers
+
+To initialize multiple `Ticker` objects, use
+
+```python
+import yfinance as yf
+
+tickers = yf.Tickers('msft aapl goog')
+
+# access each ticker using (example)
+tickers.tickers['MSFT'].info
+tickers.tickers['AAPL'].history(period="1mo")
+tickers.tickers['GOOG'].actions
+```
+
+To download price history into one table:
+
+```python
+import yfinance as yf
+data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
+```
+
+`yf.download()` and `Ticker.history()` have many options for configuring fetching and processing, e.g.:
+
+```python
+yf.download(tickers = "SPY AAPL",  # list of tickers
+            period = "1y",         # time period
+            interval = "1d",       # trading interval
+            prepost = False,       # download pre/post market hours data?
+            repair = True)         # repair obvious price errors e.g. 100x?
+```
+
+Review the [Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
+
+### Logging
+
+`yfinance` now uses the `logging` module. To control the detail of printed messages you simply change the level:
+```
+import logging
+logger = logging.getLogger('yfinance')
+logger.setLevel(logging.ERROR)  # default: only print errors
+logger.setLevel(logging.CRITICAL)  # disable printing
+logger.setLevel(logging.DEBUG)  # verbose: print errors & debug info
+```
+
+### Smarter scraping
+
+To use a custom `requests` session (for example to cache calls to the
+API or customize the `User-agent` header), pass a `session=` argument to
+the Ticker constructor.
+
+```python
+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
+```
+
+Combine a `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
+
+session = CachedLimiterSession(
+    limiter=Limiter(RequestRate(2, Duration.SECOND*5),  # max 2 requests per 5 seconds
+    bucket_class=MemoryQueueBucket,
+    backend=SQLiteCache("yfinance.cache"),
+)
+```
+
+### Managing Multi-Level Columns
+
+The following answer on Stack Overflow is for [How to deal with
+multi-level column names downloaded with
+yfinance?](https://stackoverflow.com/questions/63107801)
+
+-   `yfinance` returns a `pandas.DataFrame` with multi-level column
+    names, with a level for the ticker and a level for the stock price
+    data
+    -   The answer discusses:
+        -   How to correctly read the the multi-level columns after
+            saving the dataframe to a csv with `pandas.DataFrame.to_csv`
+        -   How to download single or multiple tickers into a single
+            dataframe with single level column names and a ticker column
+
+### `pandas_datareader` override
+
+If your code uses `pandas_datareader` and you want to download data
+faster, you can "hijack" `pandas_datareader.data.get_data_yahoo()`
+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.pdr_override() # <== that's all it takes :-)
+
+# download dataframe
+data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")
+```
+
+### Timezone cache store
+
+When fetching price data, all dates are localized to stock exchange timezone. 
+But timezone retrieval is relatively slow, so yfinance attemps to cache them 
+in your users cache folder. 
+You can direct cache to use a different location with `set_tz_cache_location()`:
+```python
+import yfinance as yf
+yf.set_tz_cache_location("custom/cache/location")
+...
+```
+
+---
+
+## Installation
+
+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) \>= 1.3.0
+-   [Numpy](http://www.numpy.org) \>= 1.16.5
+-   [requests](http://docs.python-requests.org/en/master) \>= 2.26
+-   [lxml](https://pypi.org/project/lxml) \>= 4.9.1
+-   [appdirs](https://pypi.org/project/appdirs) \>= 1.4.4
+-   [pytz](https://pypi.org/project/pytz) \>=2022.5
+-   [frozendict](https://pypi.org/project/frozendict) \>= 2.3.4
+-   [beautifulsoup4](https://pypi.org/project/beautifulsoup4) \>= 4.11.1
+-   [html5lib](https://pypi.org/project/html5lib) \>= 1.1
+-   [cryptography](https://pypi.org/project/cryptography) \>= 3.3.2
+
+#### Optional (if you want to use `pandas_datareader`)
+
+-   [pandas\_datareader](https://github.com/pydata/pandas-datareader)
+    \>= 0.4.0
+
+## Developers: want to contribute?
+
+`yfinance` relies on community to investigate bugs and contribute code. Developer guide: https://github.com/ranaroussi/yfinance/discussions/1084
+
+---
+
+### Legal Stuff
+
+**yfinance** is distributed under the **Apache Software License**. See
+the [LICENSE.txt](./LICENSE.txt) file in the release for details.
+
+
+AGAIN - yfinance is **not** affiliated, endorsed, or vetted by Yahoo, Inc. It's
+an open-source tool that uses Yahoo's publicly available APIs, and is
+intended for research and educational purposes. You should refer to Yahoo!'s terms of use
+([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
+detailes on your rights to use the actual data downloaded.
+
+---
+
+### P.S.
+
+Please drop me an note with any feedback you have.
+
+**Ran Aroussi**

README.rst

@@ -1,301 +0,0 @@
-Yahoo! Finance market data downloader
-=====================================
-
-.. image:: https://img.shields.io/badge/python-2.7,%203.4+-blue.svg?style=flat
-    :target: https://pypi.python.org/pypi/yfinance
-    :alt: Python version
-
-.. image:: https://img.shields.io/pypi/v/yfinance.svg?maxAge=60
-    :target: https://pypi.python.org/pypi/yfinance
-    :alt: PyPi version
-
-.. image:: https://img.shields.io/pypi/status/yfinance.svg?maxAge=60
-    :target: https://pypi.python.org/pypi/yfinance
-    :alt: PyPi status
-
-.. image:: https://img.shields.io/pypi/dm/yfinance.svg?maxAge=2592000&label=installs&color=%2327B1FF
-    :target: https://pypi.python.org/pypi/yfinance
-    :alt: PyPi downloads
-
-.. image:: https://img.shields.io/travis/ranaroussi/yfinance/main.svg?maxAge=1
-    :target: https://travis-ci.com/github/ranaroussi/yfinance
-    :alt: Travis-CI build status
-
-.. image:: https://www.codefactor.io/repository/github/ranaroussi/yfinance/badge
-    :target: https://www.codefactor.io/repository/github/ranaroussi/yfinance
-    :alt: CodeFactor
-
-.. image:: https://img.shields.io/github/stars/ranaroussi/yfinance.svg?style=social&label=Star&maxAge=60
-    :target: https://github.com/ranaroussi/yfinance
-    :alt: Star this repo
-
-.. image:: https://img.shields.io/twitter/follow/aroussi.svg?style=social&label=Follow&maxAge=60
-    :target: https://twitter.com/aroussi
-    :alt: Follow me on twitter
-
-\
-
-Ever since `Yahoo! finance <https://finance.yahoo.com>`_ decommissioned
-their historical data API, many programs that relied on it to stop working.
-
-**yfinance** aims to solve this problem by offering a reliable, threaded,
-and Pythonic way to download historical market data from Yahoo! finance.
-
-
-NOTE
-~~~~
-
-The library was originally named ``fix-yahoo-finance``, but
-I've since renamed it to ``yfinance`` as I no longer consider it a mere "fix".
-For reasons of backward-compatibility, ``fix-yahoo-finance`` now import and
-uses ``yfinance``, but you should install and use ``yfinance`` directly.
-
-`Changelog » <https://github.com/ranaroussi/yfinance/blob/main/CHANGELOG.rst>`__
-
------
-
-==> Check out this `Blog post <https://aroussi.com/#post/python-yahoo-finance>`_ for a detailed tutorial with code examples.
-
------
-
-Quick Start
-===========
-
-The Ticker module
-~~~~~~~~~~~~~~~~~
-
-The ``Ticker`` module, which allows you to access
-ticker data in a more Pythonic way:
-
-Note: yahoo finance datetimes are received as UTC.
-
-.. code:: python
-
-    import yfinance as yf
-
-    msft = yf.Ticker("MSFT")
-
-    # get stock info
-    msft.info
-
-    # get historical market data
-    hist = msft.history(period="max")
-
-    # show actions (dividends, splits)
-    msft.actions
-
-    # show dividends
-    msft.dividends
-
-    # show splits
-    msft.splits
-
-    # show financials
-    msft.financials
-    msft.quarterly_financials
-
-    # show major holders
-    msft.major_holders
-
-    # show institutional holders
-    msft.institutional_holders
-
-    # show balance sheet
-    msft.balance_sheet
-    msft.quarterly_balance_sheet
-
-    # show cashflow
-    msft.cashflow
-    msft.quarterly_cashflow
-
-    # show earnings
-    msft.earnings
-    msft.quarterly_earnings
-
-    # show sustainability
-    msft.sustainability
-
-    # show analysts recommendations
-    msft.recommendations
-
-    # show next event (earnings, etc)
-    msft.calendar
-
-    # show ISIN code - *experimental*
-    # ISIN = International Securities Identification Number
-    msft.isin
-
-    # show options expirations
-    msft.options
-
-    # show news
-    msft.news
-
-    # get option chain for specific expiration
-    opt = msft.option_chain('YYYY-MM-DD')
-    # data available via: opt.calls, opt.puts
-
-If you want to use a proxy server for downloading data, use:
-
-.. code:: python
-
-    import yfinance as yf
-
-    msft = yf.Ticker("MSFT")
-
-    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_balance_sheet(proxy="PROXY_SERVER")
-    msft.get_cashflow(proxy="PROXY_SERVER")
-    msft.option_chain(..., proxy="PROXY_SERVER")
-    ...
-
-To use a custom ``requests`` session (for example to cache calls to the API
-or customize the ``User-agent`` header), pass a ``session=`` argument to the
-Ticker constructor.
-
-.. code:: 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
-
-.. code:: python
-
-    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
-
-
-Fetching data for multiple tickers
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. code:: python
-
-    import yfinance as yf
-    data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
-
-
-I've also added some options to make life easier :)
-
-.. code:: python
-
-    data = yf.download(  # or pdr.get_data_yahoo(...
-            # tickers list or string as well
-            tickers = "SPY AAPL MSFT",
-
-            # use "period" instead of start/end
-            # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
-            # (optional, default is '1mo')
-            period = "ytd",
-
-            # fetch data by interval (including intraday if period < 60 days)
-            # valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
-            # (optional, default is '1d')
-            interval = "1m",
-
-            # group by ticker (to access via data['SPY'])
-            # (optional, default is 'column')
-            group_by = 'ticker',
-
-            # adjust all OHLC automatically
-            # (optional, default is False)
-            auto_adjust = True,
-
-            # download pre/post regular market hours data
-            # (optional, default is False)
-            prepost = True,
-
-            # use threads for mass downloading? (True/False/Integer)
-            # (optional, default is True)
-            threads = True,
-
-            # proxy URL scheme use use when downloading?
-            # (optional, default is None)
-            proxy = None
-        )
-
-
-Managing Multi-Level Columns
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The following answer on Stack Overflow is for `How to deal with multi-level column names downloaded with yfinance? <https://stackoverflow.com/questions/63107801>`_
-
-* ``yfinance`` returns a ``pandas.DataFrame`` with multi-level column names, with a level for the ticker and a level for the stock price data
-
-  * The answer discusses:
-
-    * How to correctly read the the multi-level columns after saving the dataframe to a csv with ``pandas.DataFrame.to_csv``
-    * How to download single or multiple tickers into a single dataframe with single level column names and a ticker column
-
-
-``pandas_datareader`` override
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-If your code uses ``pandas_datareader`` and you want to download data faster,
-you can "hijack" ``pandas_datareader.data.get_data_yahoo()`` method to use
-**yfinance** while making sure the returned data is in the same format as
-**pandas_datareader**'s ``get_data_yahoo()``.
-
-.. code:: python
-
-    from pandas_datareader import data as pdr
-
-    import yfinance as yf
-    yf.pdr_override() # <== that's all it takes :-)
-
-    # download dataframe
-    data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")
-
-
-Installation
-------------
-
-Install ``yfinance`` using ``pip``:
-
-.. code:: 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
-
-Optional (if you want to use ``pandas_datareader``)
----------------------------------------------------
-
-* `pandas_datareader <https://github.com/pydata/pandas-datareader>`_ >= 0.4.0
-
-Legal Stuff
-------------
-
-**yfinance** is distributed under the **Apache Software License**. See the `LICENSE.txt <./LICENSE.txt>`_ file in the release for details.
-
-
-P.S.
-------------
-
-Please drop me an note with any feedback you have.
-
-**Ran Aroussi**

docs/Ticker.md

@@ -1,3 +0,0 @@
-# Ticker
-
-::: yfinance.ticker.Ticker

docs/TickerBase.md

@@ -1,3 +0,0 @@
-# TickerBase Reference
-
-::: yfinance.base.TickerBase

docs/Tickers.md

@@ -1,3 +0,0 @@
-# Tickers Reference
-
-::: yfinance.tickers.Tickers

docs/index.md

@@ -1,18 +0,0 @@
-Yahoo! Finance market data downloader
-=====================================
-
-Ever since [Yahoo! finance](https://finance.yahoo.com) decommissioned
-their historical data API, many programs that relied on it to stop
-working.
-
-**yfinance** aims to solve this problem by offering a reliable,
-threaded, and Pythonic way to download historical market data from
-Yahoo! finance.
-
-NOTE
-----
-
-The library was originally named `fix-yahoo-finance`, but I've since
-renamed it to `yfinance` as I no longer consider it a mere "fix". For
-reasons of backward-compatibility, `fix-yahoo-finance` now import and
-uses `yfinance`, but you should install and use `yfinance` directly.

docs/installation.md

@@ -1,28 +0,0 @@
-Installation
-============
-
-Install `yfinance` using `pip`:
-
-``` {.sourceCode .bash}
-$ pip install yfinance --upgrade --no-cache-dir
-```
-
-Install `yfinance` using `conda`:
-
-``` {.sourceCode .bash}
-$ conda install -c 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
-
-### Optional (if you want to use `pandas_datareader`)
-
--   [pandas\_datareader](https://github.com/pydata/pandas-datareader)
-    \>= 0.4.0

docs/multi.md

@@ -1,3 +0,0 @@
-# multi.py Reference
-
-::: yfinance.multi

docs/quickstart.md

@@ -1,201 +0,0 @@
-Quick Start
-===========
-
-The Ticker module
------------------
-
-The `Ticker` module, which allows you to access ticker data in a more
-Pythonic way:
-
-Note: yahoo finance datetimes are received as UTC.
-
-``` {.sourceCode .python}
-import yfinance as yf
-
-msft = yf.Ticker("MSFT")
-
-# get stock info
-msft.info
-
-# get historical market data
-hist = msft.history(period="max")
-
-# show actions (dividends, splits)
-msft.actions
-
-# show dividends
-msft.dividends
-
-# show splits
-msft.splits
-
-# show financials
-msft.financials
-msft.quarterly_financials
-
-# show major holders
-msft.major_holders
-
-# show institutional holders
-msft.institutional_holders
-
-# show balance sheet
-msft.balance_sheet
-msft.quarterly_balance_sheet
-
-# show cashflow
-msft.cashflow
-msft.quarterly_cashflow
-
-# show earnings
-msft.earnings
-msft.quarterly_earnings
-
-# show sustainability
-msft.sustainability
-
-# show analysts recommendations
-msft.recommendations
-
-# show next event (earnings, etc)
-msft.calendar
-
-# show ISIN code - *experimental*
-# ISIN = International Securities Identification Number
-msft.isin
-
-# show options expirations
-msft.options
-
-# show news
-msft.news
-
-# get option chain for specific expiration
-opt = msft.option_chain('YYYY-MM-DD')
-# data available via: opt.calls, opt.puts
-```
-
-If you want to use a proxy server for downloading data, use:
-
-``` {.sourceCode .python}
-import yfinance as yf
-
-msft = yf.Ticker("MSFT")
-
-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_balance_sheet(proxy="PROXY_SERVER")
-msft.get_cashflow(proxy="PROXY_SERVER")
-msft.option_chain(..., proxy="PROXY_SERVER")
-...
-```
-
-To use a custom `requests` session (for example to cache calls to the
-API or customize the `User-agent` header), pass a `session=` argument to
-the Ticker constructor.
-
-``` {.sourceCode .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
-
-``` {.sourceCode .python}
-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
-```
-
-Fetching data for multiple tickers
-----------------------------------
-
-``` {.sourceCode .python}
-import yfinance as yf
-data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
-```
-
-I've also added some options to make life easier :)
-
-``` {.sourceCode .python}
-data = yf.download(  # or pdr.get_data_yahoo(...
-        # tickers list or string as well
-        tickers = "SPY AAPL MSFT",
-
-        # use "period" instead of start/end
-        # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
-        # (optional, default is '1mo')
-        period = "ytd",
-
-        # fetch data by interval (including intraday if period < 60 days)
-        # valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
-        # (optional, default is '1d')
-        interval = "1m",
-
-        # group by ticker (to access via data['SPY'])
-        # (optional, default is 'column')
-        group_by = 'ticker',
-
-        # adjust all OHLC automatically
-        # (optional, default is False)
-        auto_adjust = True,
-
-        # download pre/post regular market hours data
-        # (optional, default is False)
-        prepost = True,
-
-        # use threads for mass downloading? (True/False/Integer)
-        # (optional, default is True)
-        threads = True,
-
-        # proxy URL scheme use use when downloading?
-        # (optional, default is None)
-        proxy = None
-    )
-```
-
-Managing Multi-Level Columns
-----------------------------
-
-The following answer on Stack Overflow is for [How to deal with
-multi-level column names downloaded with
-yfinance?](https://stackoverflow.com/questions/63107801)
-
--   `yfinance` returns a `pandas.DataFrame` with multi-level column
-    names, with a level for the ticker and a level for the stock price
-    data
-    -   The answer discusses:
-        -   How to correctly read the the multi-level columns after
-            saving the dataframe to a csv with `pandas.DataFrame.to_csv`
-        -   How to download single or multiple tickers into a single
-            dataframe with single level column names and a ticker column
-
-`pandas_datareader` override
-----------------------------
-
-If your code uses `pandas_datareader` and you want to download data
-faster, you can "hijack" `pandas_datareader.data.get_data_yahoo()`
-method to use **yfinance** while making sure the returned data is in the
-same format as **pandas\_datareader**'s `get_data_yahoo()`.
-
-``` {.sourceCode .python}
-from pandas_datareader import data as pdr
-
-import yfinance as yf
-yf.pdr_override() # <== that's all it takes :-)
-
-# download dataframe
-data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")
-```

docs/utils.md

@@ -1,3 +0,0 @@
-# utils.py Reference
-
-::: yfinance.utils

meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.1.58" %}
+{% set version = "0.2.19b4" %}
 
 package:
   name: "{{ name|lower }}"
@@ -16,20 +16,34 @@ build:
 
 requirements:
   host:
-    - pandas >=0.24.0
+    - pandas >=1.3.0
     - numpy >=1.16.5
-    - requests >=2.21
+    - requests >=2.26
     - multitasking >=0.0.7
-    - lxml >=4.5.1
+    - lxml >=4.9.1
+    - appdirs >=1.4.4
+    - pytz >=2022.5
+    - frozendict >=2.3.4
+    - beautifulsoup4 >=4.11.1
+    - html5lib >=1.1
+    # - pycryptodome >=3.6.6
+    - cryptography >=3.3.2
     - pip
     - python
 
   run:
-    - pandas >=0.24.0
+    - pandas >=1.3.0
     - numpy >=1.16.5
-    - requests >=2.21
+    - requests >=2.26
     - multitasking >=0.0.7
-    - lxml >=4.5.1
+    - lxml >=4.9.1
+    - appdirs >=1.4.4
+    - pytz >=2022.5
+    - frozendict >=2.3.4
+    - beautifulsoup4 >=4.11.1
+    - html5lib >=1.1
+    # - pycryptodome >=3.6.6
+    - cryptography >=3.3.2
     - python
 
 test:

requirements.txt

@@ -1,5 +1,11 @@
-pandas>=0.24.0
+pandas>=1.3.0
 numpy>=1.16.5
-requests>=2.21
+requests>=2.26
 multitasking>=0.0.7
-lxml>=4.5.1
+lxml>=4.9.1
+appdirs>=1.4.4
+pytz>=2022.5
+frozendict>=2.3.4
+beautifulsoup4>=4.11.1
+html5lib>=1.1
+cryptography>=3.3.2

setup.py

@@ -1,10 +1,10 @@
 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 #
-# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
+# yfinance - market data downloader
 # https://github.com/ranaroussi/yfinance
 
-"""Yahoo! Finance market data downloader (+fix for Pandas Datareader)"""
+"""yfinance - market data downloader"""
 
 from setuptools import setup, find_packages
 # from codecs import open
@@ -22,14 +22,15 @@ with open("yfinance/version.py") as f:
 here = path.abspath(path.dirname(__file__))
 
 # Get the long description from the README file
-with io.open(path.join(here, 'README.rst'), encoding='utf-8') as f:
+with io.open(path.join(here, 'README.md'), encoding='utf-8') as f:
     long_description = f.read()
 
 setup(
     name='yfinance',
     version=version,
-    description='Yahoo! Finance market data downloader',
+    description='Download market data from Yahoo! Finance API',
     long_description=long_description,
+    long_description_content_type='text/markdown',
     url='https://github.com/ranaroussi/yfinance',
     author='Ran Aroussi',
     author_email='ran@aroussi.com',
@@ -37,8 +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',
@@ -49,23 +50,31 @@ setup(
         'Topic :: Software Development :: Libraries',
         'Topic :: Software Development :: Libraries :: Python Modules',
 
-        'Programming Language :: Python :: 2.7',
-        'Programming Language :: Python :: 3.4',
-        'Programming Language :: Python :: 3.5',
         'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
         'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
     ],
     platforms=['any'],
     keywords='pandas, yahoo finance, pandas datareader',
     packages=find_packages(exclude=['contrib', 'docs', 'tests', 'examples']),
-    install_requires=['pandas>=0.24', 'numpy>=1.15',
-                      'requests>=2.20', 'multitasking>=0.0.7',
-                      'lxml>=4.5.1'],
+    install_requires=['pandas>=1.3.0', 'numpy>=1.16.5',
+                      'requests>=2.26', 'multitasking>=0.0.7',
+                      'lxml>=4.9.1', 'appdirs>=1.4.4', 'pytz>=2022.5',
+                      'frozendict>=2.3.4', 
+                      # 'pycryptodome>=3.6.6',
+                      'cryptography>=3.3.2',
+                      'beautifulsoup4>=4.11.1', 'html5lib>=1.1'],
     entry_points={
         'console_scripts': [
             'sample=sample:main',
         ],
     },
 )
+
+print("""
+NOTE: yfinance is not affiliated, endorsed, or vetted by Yahoo, Inc.
+
+You should refer to Yahoo!'s terms of use for details on your rights
+to use the actual data downloaded.""")

test_yfinance.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 #
-# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
+# yfinance - market data downloader
 # https://github.com/ranaroussi/yfinance
 
 """
@@ -15,6 +15,9 @@ Sanity check for most common library uses all working
 
 import yfinance as yf
 import unittest
+import logging
+
+logging.basicConfig(level=logging.DEBUG)
 
 symbols = ['MSFT', 'IWO', 'VFINX', '^GSPC', 'BTC-USD']
 tickers = [yf.Ticker(symbol) for symbol in symbols]
@@ -25,7 +28,8 @@ class TestTicker(unittest.TestCase):
         for ticker in tickers:
             # always should have info and history for valid symbols
             assert(ticker.info is not None and ticker.info != {})
-            assert(ticker.history(period="max").empty is False)
+            history = ticker.history(period="max")
+            assert(history.empty is False and history is not None)
 
     def test_attributes(self):
         for ticker in tickers:
@@ -36,20 +40,27 @@ class TestTicker(unittest.TestCase):
             ticker.dividends
             ticker.splits
             ticker.actions
+            ticker.shares
             ticker.info
             ticker.calendar
             ticker.recommendations
             ticker.earnings
             ticker.quarterly_earnings
-            ticker.financials
-            ticker.quarterly_financials
+            ticker.income_stmt
+            ticker.quarterly_income_stmt
             ticker.balance_sheet
             ticker.quarterly_balance_sheet
             ticker.cashflow
             ticker.quarterly_cashflow
+            ticker.recommendations_summary
+            ticker.analyst_price_target
+            ticker.revenue_forecasts
             ticker.sustainability
             ticker.options
             ticker.news
+            ticker.earnings_trend
+            ticker.earnings_dates
+            ticker.earnings_forecasts
 
     def test_holders(self):
         for ticker in tickers:

tests/__init__.py

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

tests/context.py

@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+
+import sys
+import os
+_parent_dp = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
+_src_dp = _parent_dp
+sys.path.insert(0, _src_dp)
+
+import yfinance
+
+
+# Optional: see the exact requests that are made during tests:
+# import logging
+# logging.basicConfig(level=logging.DEBUG)
+
+
+# Setup a session to rate-limit and cache persistently:
+import datetime as _dt
+import os
+import appdirs as _ad
+from requests import Session
+from requests_cache import CacheMixin, SQLiteCache
+from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
+class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
+    pass
+from pyrate_limiter import Duration, RequestRate, Limiter
+history_rate = RequestRate(1, Duration.SECOND*2)
+limiter = Limiter(history_rate)
+session_gbl = CachedLimiterSession(
+    limiter=limiter,
+    bucket_class=MemoryQueueBucket,
+    backend=SQLiteCache(os.path.join(_ad.user_cache_dir(), "py-yfinance", "unittests-cache"), 
+    					expire_after=_dt.timedelta(hours=1)),
+)
+# Use this instead if only want rate-limiting:
+# from requests_ratelimiter import LimiterSession
+# session_gbl = LimiterSession(limiter=limiter)
+

tests/prices.py

@@ -0,0 +1,664 @@
+from .context import yfinance as yf
+from .context import session_gbl
+
+import unittest
+
+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_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 = _tz.timezone("UTC").localize(_dt.datetime.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:
+                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 = _tz.timezone("UTC").localize(_dt.datetime.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:
+                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:
+                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_intraDayWithEvents(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:
+                # self.skipTest("Skipping test_intraDayWithEvents() because 'ICL.TA' has no dividend in last 60 days")
+                continue
+
+            last_div_date = df_daily_divs.index[-1]
+            start_d = last_div_date.date()
+            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            df = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df["Dividends"] != 0.0).any())
+            test_run = True
+            break
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
+
+    def test_dailyWithEvents(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:
+            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:
+                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_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:
+            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:
+                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:
+            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:
+                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
+        tkr = "ABBV"
+        yf.Ticker("ABBV").history(period="max", interval="1mo")
+
+    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:
+            print("Weekly data not aligned to Monday")
+            raise
+
+    def test_prune_post_intraday_us(self):
+        # Half-day before USA Thanksgiving. Yahoo normally 
+        # returns an interval starting when regular trading closes, 
+        # even if prepost=False.
+
+        # Setup
+        tkr = "AMZN"
+        interval = "1h"
+        interval_td = _dt.timedelta(hours=1)
+        time_open = _dt.time(9, 30)
+        time_close = _dt.time(16)
+        special_day = _dt.date(2022, 11, 25)
+        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=interval, 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()
+        f_early_close = (last_dts+interval_td).dt.time < time_close
+        early_close_dates = last_dts.index[f_early_close].values
+        self.assertEqual(len(early_close_dates), 1)
+        self.assertEqual(early_close_dates[0], special_day)
+
+        first_dts = _pd.Series(df.index).groupby(df.index.date).first()
+        f_late_open = first_dts.dt.time > time_open
+        late_open_dates = first_dts.index[f_late_open]
+        self.assertEqual(len(late_open_dates), 0)
+
+    def test_prune_post_intraday_omx(self):
+        # Half-day before Sweden Christmas. Yahoo normally 
+        # returns an interval starting when regular trading closes, 
+        # even if prepost=False.
+        # If prepost=False, test that yfinance is removing prepost intervals.
+
+        # Setup
+        tkr = "AEC.ST"
+        interval = "1h"
+        interval_td = _dt.timedelta(hours=1)
+        time_open = _dt.time(9)
+        time_close = _dt.time(17,30)
+        special_day = _dt.date(2022, 12, 23)
+        time_early_close = _dt.time(13, 2)
+        dat = yf.Ticker(tkr, session=self.session)
+
+        # Half trading day Jan 5, Apr 14, May 25, Jun 23, Nov 4, Dec 23, Dec 30
+        half_days = [_dt.date(special_day.year, x[0], x[1]) for x in [(1,5), (4,14), (5,25), (6,23), (11,4), (12,23), (12,30)]]
+
+        # Yahoo has incorrectly classified afternoon of 2022-04-13 as post-market.
+        # Nothing yfinance can do because Yahoo doesn't return data with prepost=False.
+        # But need to handle in this test.
+        expected_incorrect_half_days = [_dt.date(2022,4,13)]
+        half_days = sorted(half_days+expected_incorrect_half_days)
+
+        # 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=interval, 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()
+        f_early_close = (last_dts+interval_td).dt.time < time_close
+        early_close_dates = last_dts.index[f_early_close].values
+        unexpected_early_close_dates = [d for d in early_close_dates if not d in half_days]
+        self.assertEqual(len(unexpected_early_close_dates), 0)
+        self.assertEqual(len(early_close_dates), len(half_days))
+        self.assertTrue(_np.equal(early_close_dates, half_days).all())
+
+        first_dts = _pd.Series(df.index).groupby(df.index.date).first()
+        f_late_open = first_dts.dt.time > time_open
+        late_open_dates = first_dts.index[f_late_open]
+        self.assertEqual(len(late_open_dates), 0)
+
+    def test_prune_post_intraday_asx(self):
+        # Setup
+        tkr = "BHP.AX"
+        interval = "1h"
+        interval_td = _dt.timedelta(hours=1)
+        time_open = _dt.time(10)
+        time_close = _dt.time(16,12)
+        # No early closes in 2022
+        dat = yf.Ticker(tkr, session=self.session)
+
+        # Test no afternoons (or mornings) were pruned
+        start_d = _dt.date(2022, 1, 1)
+        end_d = _dt.date(2022+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()
+        f_early_close = (last_dts+interval_td).dt.time < time_close
+        early_close_dates = last_dts.index[f_early_close].values
+        self.assertEqual(len(early_close_dates), 0)
+
+        first_dts = _pd.Series(df.index).groupby(df.index.date).first()
+        f_late_open = first_dts.dt.time > time_open
+        late_open_dates = first_dts.index[f_late_open]
+        self.assertEqual(len(late_open_dates), 0)
+
+    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"
+
+        df = 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_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_7d = _dt.timedelta(days=7)
+        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
+            df = dat.history(start=start_dt, end=end_dt, interval="2m", repair=True)
+
+    def test_repair_100x_weekly(self):
+        # Setup:
+        tkr = "PNL.L"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+
+        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": [475,   473.5, 472,   473.5],
+                                 "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 = dat._fix_unit_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:
+                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_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"]
+
+        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
+        df = _pd.DataFrame(data={"Open":      [400,   398,    392.5,   417],
+                                 "High":      [421,   425,    419,     420.5],
+                                 "Low":       [400,   380.5,  376.5,   396],
+                                 "Close":     [410,   409.5,  402,     399],
+                                 "Adj Close": [398.02, 397.53, 390.25, 387.34],
+                                 "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 = dat._fix_unit_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:
+                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_daily(self):
+        tkr = "PNL.L"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+
+        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 = dat._fix_unit_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_zeroes_daily(self):
+        tkr = "BBIL.L"
+        dat = yf.Ticker(tkr, session=self.session)
+        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 = dat._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_hourly(self):
+        tkr = "INTC"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+
+        correct_df = dat.history(period="1wk", 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 = dat._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:
+                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())
+
+if __name__ == '__main__':
+    unittest.main()

tests/ticker.py

@@ -0,0 +1,841 @@
+"""
+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
+import numpy as np
+
+from .context import yfinance as yf
+from .context import session_gbl
+
+import unittest
+import requests_cache
+
+
+class TestTicker(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_getTz(self):
+        tkrs = ["IMP.JO", "BHG.JO", "SSW.JO", "BP.L", "INTC"]
+        for tkr in tkrs:
+            # First step: remove ticker from tz-cache
+            yf.utils.get_tz_cache().store(tkr, None)
+
+            # Test:
+            dat = yf.Ticker(tkr, session=self.session)
+            tz = dat._get_ticker_tz(proxy=None, timeout=None)
+
+            self.assertIsNotNone(tz)
+
+    def test_badTicker(self):
+        # Check yfinance doesn't die when ticker delisted
+
+        tkr = "DJI"  # typo of "^DJI"
+        dat = yf.Ticker(tkr, session=self.session)
+
+        dat.history(period="1wk")
+        dat.history(start="2022-01-01")
+        dat.history(start="2022-01-01", end="2022-03-01")
+        yf.download([tkr], period="1wk", threads=False, ignore_tz=False)
+        yf.download([tkr], period="1wk", threads=True, ignore_tz=False)
+        yf.download([tkr], period="1wk", threads=False, ignore_tz=True)
+        yf.download([tkr], period="1wk", threads=True, ignore_tz=True)
+
+        for k in dat.fast_info:
+            dat.fast_info[k]
+
+        dat.isin
+        dat.major_holders
+        dat.institutional_holders
+        dat.mutualfund_holders
+        dat.dividends
+        dat.splits
+        dat.actions
+        dat.get_shares_full()
+        dat.options
+        dat.news
+        dat.earnings_dates
+
+        # These require decryption which is broken:
+        # dat.shares
+        # dat.info
+        # dat.calendar
+        # dat.recommendations
+        # dat.earnings
+        # dat.quarterly_earnings
+        # dat.income_stmt
+        # dat.quarterly_income_stmt
+        # dat.balance_sheet
+        # dat.quarterly_balance_sheet
+        # dat.cashflow
+        # dat.quarterly_cashflow
+        # dat.recommendations_summary
+        # dat.analyst_price_target
+        # dat.revenue_forecasts
+        # dat.sustainability
+        # dat.earnings_trend
+        # dat.earnings_forecasts
+
+    def test_goodTicker(self):
+        # that yfinance works when full api is called on same instance of ticker
+
+        tkrs = ["IBM"]
+        tkrs.append("QCSTIX")  # weird ticker, no price history but has previous close
+        for tkr in tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+
+            dat.history(period="1wk")
+            dat.history(start="2022-01-01")
+            dat.history(start="2022-01-01", end="2022-03-01")
+            yf.download([tkr], period="1wk", threads=False, ignore_tz=False)
+            yf.download([tkr], period="1wk", threads=True, ignore_tz=False)
+            yf.download([tkr], period="1wk", threads=False, ignore_tz=True)
+            yf.download([tkr], period="1wk", threads=True, ignore_tz=True)
+
+            for k in dat.fast_info:
+                dat.fast_info[k]
+
+            dat.isin
+            dat.major_holders
+            dat.institutional_holders
+            dat.mutualfund_holders
+            dat.dividends
+            dat.splits
+            dat.actions
+            dat.get_shares_full()
+            dat.options
+            dat.news
+            dat.earnings_dates
+
+            # These require decryption which is broken:
+            # dat.shares
+            # dat.info
+            # dat.calendar
+            # dat.recommendations
+            # dat.earnings
+            # dat.quarterly_earnings
+            # dat.income_stmt
+            # dat.quarterly_income_stmt
+            # dat.balance_sheet
+            # dat.quarterly_balance_sheet
+            # dat.cashflow
+            # dat.quarterly_cashflow
+            # dat.recommendations_summary
+            # dat.analyst_price_target
+            # dat.revenue_forecasts
+            # dat.sustainability
+            # dat.earnings_trend
+            # dat.earnings_forecasts
+
+
+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.
+        """
+        session = requests_cache.CachedSession(backend='memory')
+        ticker = yf.Ticker("GOOGL", session=session)
+        ticker.history("1y")
+        actual_urls_called = tuple([r.url for r in session.cache.filter()])
+        session.close()
+        expected_urls = (
+            'https://query2.finance.yahoo.com/v8/finance/chart/GOOGL?events=div,splits,capitalGains&includePrePost=False&interval=1d&range=1y',
+        )
+        self.assertEqual(expected_urls, actual_urls_called, "Different than expected url used to fetch history.")
+
+    def test_dividends(self):
+        data = self.ticker.dividends
+        self.assertIsInstance(data, pd.Series, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+    def test_splits(self):
+        data = self.ticker.splits
+        self.assertIsInstance(data, pd.Series, "data has wrong type")
+        # self.assertFalse(data.empty, "data is empty")
+
+    def test_actions(self):
+        data = self.ticker.actions
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+
+# Below will fail because decryption broken
+# class TestTickerEarnings(unittest.TestCase):
+#     session = None
+
+#     @classmethod
+#     def setUpClass(cls):
+#         cls.session = session_gbl
+
+#     @classmethod
+#     def tearDownClass(cls):
+#         if cls.session is not None:
+#             cls.session.close()
+
+#     def setUp(self):
+#         self.ticker = yf.Ticker("GOOGL", session=self.session)
+
+#     def tearDown(self):
+#         self.ticker = None
+
+#     def test_earnings(self):
+#         data = self.ticker.earnings
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+
+#         data_cached = self.ticker.earnings
+#         self.assertIs(data, data_cached, "data not cached")
+
+#     def test_quarterly_earnings(self):
+#         data = self.ticker.quarterly_earnings
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+
+#         data_cached = self.ticker.quarterly_earnings
+#         self.assertIs(data, data_cached, "data not cached")
+
+#     def test_earnings_forecasts(self):
+#         data = self.ticker.earnings_forecasts
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+
+#         data_cached = self.ticker.earnings_forecasts
+#         self.assertIs(data, data_cached, "data not cached")
+
+#     def test_earnings_dates(self):
+#         data = self.ticker.earnings_dates
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+
+#         data_cached = self.ticker.earnings_dates
+#         self.assertIs(data, data_cached, "data not cached")
+
+#     def test_earnings_trend(self):
+#         data = self.ticker.earnings_trend
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+
+#         data_cached = self.ticker.earnings_trend
+#         self.assertIs(data, data_cached, "data not cached")
+
+#     def test_earnings_dates_with_limit(self):
+#         # use ticker with lots of historic earnings
+#         ticker = yf.Ticker("IBM")
+#         limit = 110
+#         data = ticker.get_earnings_dates(limit=limit)
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+#         self.assertEqual(len(data), limit, "Wrong number or rows")
+
+#         data_cached = ticker.get_earnings_dates(limit=limit)
+#         self.assertIs(data, data_cached, "data not cached")
+
+
+class TestTickerHolders(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        self.ticker = yf.Ticker("GOOGL", session=self.session)
+
+    def tearDown(self):
+        self.ticker = None
+
+    def test_major_holders(self):
+        data = self.ticker.major_holders
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        data_cached = self.ticker.major_holders
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_institutional_holders(self):
+        data = self.ticker.institutional_holders
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        data_cached = self.ticker.institutional_holders
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_mutualfund_holders(self):
+        data = self.ticker.mutualfund_holders
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        data_cached = self.ticker.mutualfund_holders
+        self.assertIs(data, data_cached, "data not cached")
+
+
+class TestTickerMiscFinancials(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        self.ticker = yf.Ticker("GOOGL", session=self.session)
+        
+        # For ticker 'BSE.AX' (and others), Yahoo not returning 
+        # full quarterly financials (usually cash-flow) with all entries, 
+        # instead returns a smaller version in different data store.
+        self.ticker_old_fmt = yf.Ticker("BSE.AX", session=self.session)
+
+    def tearDown(self):
+        self.ticker = None
+
+    def test_isin(self):
+        data = self.ticker.isin
+        self.assertIsInstance(data, str, "data has wrong type")
+        self.assertEqual("ARDEUT116159", data, "data is empty")
+
+        data_cached = self.ticker.isin
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_options(self):
+        data = self.ticker.options
+        self.assertIsInstance(data, tuple, "data has wrong type")
+        self.assertTrue(len(data) > 1, "data is empty")
+
+    def test_shares_full(self):
+        data = self.ticker.get_shares_full()
+        self.assertIsInstance(data, pd.Series, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+    # Below will fail because decryption broken
+
+    # 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_quarterly_income_statement_old_fmt(self):
+    #     expected_row = "TotalRevenue"
+    #     data = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+    #     self.assertIn(expected_row, data.index, "Did not find expected row in index")
+
+    #     data_cached = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_balance_sheet(self):
+    #     expected_keys = ["Total Assets", "Net PPE"]
+    #     expected_periods_days = 365
+
+    #     # 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_quarterly_balance_sheet_old_fmt(self):
+    #     expected_row = "TotalAssets"
+    #     data = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+    #     self.assertIn(expected_row, data.index, "Did not find expected row in index")
+
+    #     data_cached = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_cash_flow(self):
+    #     expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
+    #     expected_periods_days = 365
+
+    #     # 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_quarterly_cashflow_old_fmt(self):
+    #     expected_row = "NetIncome"
+    #     data = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+    #     self.assertIn(expected_row, data.index, "Did not find expected row in index")
+
+    #     data_cached = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_income_alt_names(self):
+    #     i1 = self.ticker.income_stmt
+    #     i2 = self.ticker.incomestmt
+    #     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_sustainability(self):
+    #     data = self.ticker.sustainability
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.sustainability
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_recommendations(self):
+    #     data = self.ticker.recommendations
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.recommendations
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_recommendations_summary(self):
+    #     data = self.ticker.recommendations_summary
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.recommendations_summary
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_analyst_price_target(self):
+    #     data = self.ticker.analyst_price_target
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.analyst_price_target
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_revenue_forecasts(self):
+    #     data = self.ticker.revenue_forecasts
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.revenue_forecasts
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_calendar(self):
+    #     data = self.ticker.calendar
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.calendar
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_shares(self):
+    #     data = self.ticker.shares
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    # def test_bad_freq_value_raises_exception(self):
+    #     self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))
+
+
+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])
+
+    # Below will fail because decryption broken
+
+    # def test_info(self):
+    #     data = self.tickers[0].info
+    #     self.assertIsInstance(data, dict, "data has wrong type")
+    #     self.assertIn("symbol", data.keys(), "Did not find expected key in info dict")
+    #     self.assertEqual(self.symbols[0], data["symbol"], "Wrong symbol value in info dict")
+
+    # def test_fast_info_matches_info(self):
+    #     yf.scrapers.quote.PRUNE_INFO = False
+
+    #     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()

yfinance/__init__.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 #
-# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
+# yfinance - market data downloader
 # https://github.com/ranaroussi/yfinance
 #
 # Copyright 2017-2019 Ran Aroussi
@@ -23,6 +23,7 @@ from . import version
 from .ticker import Ticker
 from .tickers import Tickers
 from .multi import download
+from .utils import set_tz_cache_location
 
 __version__ = version.version
 __author__ = "Ran Aroussi"
@@ -42,4 +43,4 @@ def pdr_override():
         pass
 
 
-__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override']
+__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override', 'set_tz_cache_location']

yfinance/data.py

@@ -0,0 +1,336 @@
+import functools
+from functools import lru_cache
+
+import logging
+import hashlib
+from base64 import b64decode
+usePycryptodome = False  # slightly faster
+# usePycryptodome = True
+if usePycryptodome:
+    from Crypto.Cipher import AES
+    from Crypto.Util.Padding import unpad
+else:
+    from cryptography.hazmat.primitives import padding
+    from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+
+import requests as requests
+import re
+from bs4 import BeautifulSoup
+import random
+import time
+
+from frozendict import frozendict
+
+try:
+    import ujson as json
+except ImportError:
+    import json as json
+
+from . import utils
+
+cache_maxsize = 64
+
+logger = utils.get_yf_logger()
+
+
+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
+
+
+def _extract_extra_keys_from_stores(data):
+    new_keys = [k for k in data.keys() if k not in ["context", "plugins"]]
+    new_keys_values = set([data[k] for k in new_keys])
+
+    # Maybe multiple keys have same value - keep one of each
+    new_keys_uniq = []
+    new_keys_uniq_values = set()
+    for k in new_keys:
+        v = data[k]
+        if not v in new_keys_uniq_values:
+            new_keys_uniq.append(k)
+            new_keys_uniq_values.add(v)
+
+    return [data[k] for k in new_keys_uniq]
+
+
+def decrypt_cryptojs_aes_stores(data, keys=None):
+    encrypted_stores = data['context']['dispatcher']['stores']
+
+    password = None
+    if keys is not None:
+        if not isinstance(keys, list):
+            raise TypeError("'keys' must be list")
+        candidate_passwords = keys
+    else:
+        candidate_passwords = []
+
+    if "_cs" in data and "_cr" in data:
+        _cs = data["_cs"]
+        _cr = data["_cr"]
+        _cr = b"".join(int.to_bytes(i, length=4, byteorder="big", signed=True) for i in json.loads(_cr)["words"])
+        password = hashlib.pbkdf2_hmac("sha1", _cs.encode("utf8"), _cr, 1, dklen=32).hex()
+
+    encrypted_stores = b64decode(encrypted_stores)
+    assert encrypted_stores[0:8] == b"Salted__"
+    salt = encrypted_stores[8:16]
+    encrypted_stores = encrypted_stores[16:]
+
+    def _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5") -> tuple:
+        """OpenSSL EVP Key Derivation Function
+        Args:
+            password (Union[str, bytes, bytearray]): Password to generate key from.
+            salt (Union[bytes, bytearray]): Salt to use.
+            keySize (int, optional): Output key length in bytes. Defaults to 32.
+            ivSize (int, optional): Output Initialization Vector (IV) length in bytes. Defaults to 16.
+            iterations (int, optional): Number of iterations to perform. Defaults to 1.
+            hashAlgorithm (str, optional): Hash algorithm to use for the KDF. Defaults to 'md5'.
+        Returns:
+            key, iv: Derived key and Initialization Vector (IV) bytes.
+
+        Taken from: https://gist.github.com/rafiibrahim8/0cd0f8c46896cafef6486cb1a50a16d3
+        OpenSSL original code: https://github.com/openssl/openssl/blob/master/crypto/evp/evp_key.c#L78
+        """
+
+        assert iterations > 0, "Iterations can not be less than 1."
+
+        if isinstance(password, str):
+            password = password.encode("utf-8")
+
+        final_length = keySize + ivSize
+        key_iv = b""
+        block = None
+
+        while len(key_iv) < final_length:
+            hasher = hashlib.new(hashAlgorithm)
+            if block:
+                hasher.update(block)
+            hasher.update(password)
+            hasher.update(salt)
+            block = hasher.digest()
+            for _ in range(1, iterations):
+                block = hashlib.new(hashAlgorithm, block).digest()
+            key_iv += block
+
+        key, iv = key_iv[:keySize], key_iv[keySize:final_length]
+        return key, iv
+
+    def _decrypt(encrypted_stores, password, key, iv):
+        if usePycryptodome:
+            cipher = AES.new(key, AES.MODE_CBC, iv=iv)
+            plaintext = cipher.decrypt(encrypted_stores)
+            plaintext = unpad(plaintext, 16, style="pkcs7")
+        else:
+            cipher = Cipher(algorithms.AES(key), modes.CBC(iv))
+            decryptor = cipher.decryptor()
+            plaintext = decryptor.update(encrypted_stores) + decryptor.finalize()
+            unpadder = padding.PKCS7(128).unpadder()
+            plaintext = unpadder.update(plaintext) + unpadder.finalize()
+            plaintext = plaintext.decode("utf-8")
+        return plaintext
+
+    if not password is None:
+        try:
+            key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
+        except:
+            raise Exception("yfinance failed to decrypt Yahoo data response")
+        plaintext = _decrypt(encrypted_stores, password, key, iv)
+    else:
+        success = False
+        for i in range(len(candidate_passwords)):
+            # print(f"Trying candiate pw {i+1}/{len(candidate_passwords)}")
+            password = candidate_passwords[i]
+            try:
+                key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
+
+                plaintext = _decrypt(encrypted_stores, password, key, iv)
+
+                success = True
+                break
+            except:
+                pass
+        if not success:
+            raise Exception("yfinance failed to decrypt Yahoo data response")
+
+    decoded_stores = json.loads(plaintext)
+    return decoded_stores
+
+
+_SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
+
+
+class TickerData:
+    """
+    Have one place to retrieve data from Yahoo API in order to ease caching and speed up operations
+    """
+    user_agent_headers = {
+        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
+
+    def __init__(self, ticker: str, session=None):
+        self.ticker = ticker
+        self._session = session or requests
+
+    def get(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        proxy = self._get_proxy(proxy)
+        response = self._session.get(
+            url=url,
+            params=params,
+            proxies=proxy,
+            timeout=timeout,
+            headers=user_agent_headers or self.user_agent_headers)
+        return response
+
+    @lru_cache_freezeargs
+    @lru_cache(maxsize=cache_maxsize)
+    def cache_get(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        return self.get(url, user_agent_headers, params, proxy, timeout)
+
+    def _get_proxy(self, proxy):
+        # setup proxy in requests format
+        if proxy is not None:
+            if isinstance(proxy, dict) and "https" in proxy:
+                proxy = proxy["https"]
+            proxy = {"https": proxy}
+        return proxy
+
+    def get_raw_json(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        response = self.get(url, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
+        response.raise_for_status()
+        return response.json()
+
+    def _get_decryption_keys_from_yahoo_js(self, soup):
+        result = None
+
+        key_count = 4
+        re_script = soup.find("script", string=re.compile("root.App.main")).text
+        re_data = json.loads(re.search("root.App.main\s+=\s+(\{.*\})", re_script).group(1))
+        re_data.pop("context", None)
+        key_list = list(re_data.keys())
+        if re_data.get("plugins"):  # 1) attempt to get last 4 keys after plugins
+            ind = key_list.index("plugins")
+            if len(key_list) > ind+1:
+                sub_keys = key_list[ind+1:]
+                if len(sub_keys) == key_count:
+                    re_obj = {}
+                    missing_val = False
+                    for k in sub_keys:
+                        if not re_data.get(k):
+                            missing_val = True
+                            break
+                        re_obj.update({k: re_data.get(k)})
+                    if not missing_val:
+                        result = re_obj
+
+        if not result is None:
+            return [''.join(result.values())]
+
+        re_keys = []    # 2) attempt scan main.js file approach to get keys
+        prefix = "https://s.yimg.com/uc/finance/dd-site/js/main."
+        tags = [tag['src'] for tag in soup.find_all('script') if prefix in tag.get('src', '')]
+        for t in tags:
+            response_js = self.cache_get(t)
+            #
+            if response_js.status_code != 200:
+                time.sleep(random.randrange(10, 20))
+                response_js.close()
+            else:
+                r_data = response_js.content.decode("utf8")
+                re_list = [
+                    x.group() for x in re.finditer(r"context.dispatcher.stores=JSON.parse((?:.*?\r?\n?)*)toString", r_data)
+                ]
+                for rl in re_list:
+                    re_sublist = [x.group() for x in re.finditer(r"t\[\"((?:.*?\r?\n?)*)\"\]", rl)]
+                    if len(re_sublist) == key_count:
+                        re_keys = [sl.replace('t["', '').replace('"]', '') for sl in re_sublist]
+                        break
+                response_js.close()
+            if len(re_keys) == key_count:
+                break
+        if len(re_keys) > 0:
+            re_obj = {}
+            missing_val = False
+            for k in re_keys:
+                if not re_data.get(k):
+                    missing_val = True
+                    break
+                re_obj.update({k: re_data.get(k)})
+            if not missing_val:
+                return [''.join(re_obj.values())]
+
+        return []
+
+    @lru_cache_freezeargs
+    @lru_cache(maxsize=cache_maxsize)
+    def get_json_data_stores(self, sub_page: str = None, proxy=None) -> dict:
+        '''
+        get_json_data_stores returns a python dictionary of the data stores in yahoo finance web page.
+        '''
+        if sub_page:
+            ticker_url = "{}/{}/{}".format(_SCRAPE_URL_, self.ticker, sub_page)
+        else:
+            ticker_url = "{}/{}".format(_SCRAPE_URL_, self.ticker)
+
+        response = self.get(url=ticker_url, proxy=proxy)
+        html = response.text
+
+        # The actual json-data for stores is in a javascript assignment in the webpage
+        try:
+            json_str = html.split('root.App.main =')[1].split(
+                '(this)')[0].split(';\n}')[0].strip()
+        except IndexError:
+            # Fetch failed, probably because Yahoo spam triggered
+            return {}
+
+        data = json.loads(json_str)
+
+        # Gather decryption keys:
+        soup = BeautifulSoup(response.content, "html.parser")
+        keys = self._get_decryption_keys_from_yahoo_js(soup)
+        if len(keys) == 0:
+            msg = "No decryption keys could be extracted from JS file."
+            if "requests_cache" in str(type(response)):
+                msg += " Try flushing your 'requests_cache', probably parsing old JS."
+            logger.warning("%s Falling back to backup decrypt methods.", msg)
+        if len(keys) == 0:
+            keys = []
+            try:
+                extra_keys = _extract_extra_keys_from_stores(data)
+                keys = [''.join(extra_keys[-4:])]
+            except:
+                pass
+            #
+            keys_url = "https://github.com/ranaroussi/yfinance/raw/main/yfinance/scrapers/yahoo-keys.txt"
+            response_gh = self.cache_get(keys_url)
+            keys += response_gh.text.splitlines()
+
+        # Decrypt!
+        stores = decrypt_cryptojs_aes_stores(data, keys)
+        if stores is None:
+            # Maybe Yahoo returned old format, not encrypted
+            if "context" in data and "dispatcher" in data["context"]:
+                stores = data['context']['dispatcher']['stores']
+        if stores is None:
+            raise Exception(f"{self.ticker}: Failed to extract data stores from web request")
+
+        # return data
+        new_data = json.dumps(stores).replace('{}', 'null')
+        new_data = re.sub(
+            r'{[\'|\"]raw[\'|\"]:(.*?),(.*?)}', r'\1', new_data)
+
+        return json.loads(new_data)

yfinance/exceptions.py

@@ -0,0 +1,6 @@
+class YFinanceException(Exception):
+    pass
+
+
+class YFinanceDataException(YFinanceException):
+    pass

yfinance/multi.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 #
-# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
+# yfinance - market data downloader
 # https://github.com/ranaroussi/yfinance
 #
 # Copyright 2017-2019 Ran Aroussi
@@ -21,6 +21,8 @@
 
 from __future__ import print_function
 
+import logging
+import traceback
 import time as _time
 import multitasking as _multitasking
 import pandas as _pd
@@ -28,11 +30,10 @@ import pandas as _pd
 from . import Ticker, utils
 from . import shared
 
-
-def download(tickers, start=None, end=None, actions=False, threads=True,
-             group_by='column', auto_adjust=False, back_adjust=False,
-             progress=True, period="max", show_errors=True, interval="1d", prepost=False,
-             proxy=None, rounding=False, timeout=None, **kwargs):
+def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=None,
+             group_by='column', auto_adjust=False, back_adjust=False, repair=False, keepna=False,
+             progress=True, period="max", show_errors=None, interval="1d", prepost=False,
+             proxy=None, rounding=False, timeout=10):
     """Download yahoo tickers
     :Parameters:
         tickers : str, list
@@ -44,11 +45,13 @@ def download(tickers, start=None, end=None, actions=False, threads=True,
             Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
             Intraday data cannot extend last 60 days
         start: str
-            Download start date string (YYYY-MM-DD) or _datetime.
+            Download start date string (YYYY-MM-DD) or _datetime, inclusive.
             Default is 1900-01-01
+            E.g. for start="2020-01-01", the first data point will be on "2020-01-01"
         end: str
-            Download end date string (YYYY-MM-DD) or _datetime.
+            Download end date string (YYYY-MM-DD) or _datetime, exclusive.
             Default is now
+            E.g. for end="2023-01-01", the last data point will be on "2022-12-31"
         group_by : str
             Group by 'ticker' or 'column' (default)
         prepost : bool
@@ -56,21 +59,47 @@ def download(tickers, start=None, end=None, actions=False, threads=True,
             Default is False
         auto_adjust: bool
             Adjust all OHLC automatically? Default is False
+        repair: bool
+            Detect currency unit 100x mixups and attempt repair
+            Default is False
+        keepna: bool
+            Keep NaN rows returned by Yahoo?
+            Default is False
         actions: bool
             Download dividend + stock splits data. Default is False
         threads: bool / int
             How many threads to use for mass downloading. Default is True
+        ignore_tz: bool
+            When combining from different timezones, ignore that part of datetime.
+            Default depends on interval. Intraday = False. Day+ = True.
         proxy: str
             Optional. Proxy server URL scheme. Default is None
         rounding: bool
             Optional. Round values to 2 decimal places?
         show_errors: bool
-            Optional. Doesn't print errors if True
+            Optional. Doesn't print errors if False
+            DEPRECATED, will be removed in future version
         timeout: None or float
             If not None stops waiting for a response after given number of
             seconds. (Can also be a fraction of a second e.g. 0.01)
     """
 
+    if show_errors is not None:
+        if show_errors:
+            utils.print_once(f"yfinance: download(show_errors={show_errors}) argument is deprecated and will be removed in future version. Do this instead: logging.getLogger('yfinance').setLevel(logging.ERROR)")
+            logging.getLogger('yfinance').setLevel(logging.ERROR)
+        else:
+            utils.print_once(f"yfinance: download(show_errors={show_errors}) argument is deprecated and will be removed in future version. Do this instead to suppress error messages: logging.getLogger('yfinance').setLevel(logging.CRITICAL)")
+            logging.getLogger('yfinance').setLevel(logging.CRITICAL)
+
+    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(
         tickers, (list, set, tuple)) else tickers.replace(',', ' ').split()
@@ -95,6 +124,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True,
     # reset shared._DFS
     shared._DFS = {}
     shared._ERRORS = {}
+    shared._TRACEBACKS = {}
 
     # download using threads
     if threads:
@@ -105,7 +135,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True,
             _download_one_threaded(ticker, period=period, interval=interval,
                                    start=start, end=end, prepost=prepost,
                                    actions=actions, auto_adjust=auto_adjust,
-                                   back_adjust=back_adjust,
+                                   back_adjust=back_adjust, repair=repair, keepna=keepna,
                                    progress=(progress and i > 0), proxy=proxy,
                                    rounding=rounding, timeout=timeout)
         while len(shared._DFS) < len(tickers):
@@ -117,32 +147,58 @@ def download(tickers, start=None, end=None, actions=False, threads=True,
             data = _download_one(ticker, period=period, interval=interval,
                                  start=start, end=end, prepost=prepost,
                                  actions=actions, auto_adjust=auto_adjust,
-                                 back_adjust=back_adjust, proxy=proxy,
+                                 back_adjust=back_adjust, repair=repair, keepna=keepna,
+                                 proxy=proxy,
                                  rounding=rounding, timeout=timeout)
-            shared._DFS[ticker.upper()] = data
             if progress:
                 shared._PROGRESS_BAR.animate()
 
     if progress:
         shared._PROGRESS_BAR.completed()
 
-    if shared._ERRORS and show_errors:
-        print('\n%.f Failed download%s:' % (
+    if shared._ERRORS:
+        # Send errors to logging module
+        logger = utils.get_yf_logger()
+        logger.error('\n%.f Failed download%s:' % (
             len(shared._ERRORS), 's' if len(shared._ERRORS) > 1 else ''))
-        # print(shared._ERRORS)
-        print("\n".join(['- %s: %s' %
-                         v for v in list(shared._ERRORS.items())]))
+
+        # Log each distinct error once, with list of symbols affected
+        errors = {}
+        for ticker in shared._ERRORS:
+            err = shared._ERRORS[ticker]
+            if not err 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]
+            if not tb in tbs:
+                tbs[tb] = [ticker]
+            else:
+                tbs[tb].append(ticker)
+        for tb in tbs.keys():
+            logger.debug(f'{tbs[tb]}: ' + tb)
+
+    if ignore_tz:
+        for tkr in shared._DFS.keys():
+            if (shared._DFS[tkr] is not None) and (shared._DFS[tkr].shape[0] > 0):
+                shared._DFS[tkr].index = shared._DFS[tkr].index.tz_localize(None)
 
     if len(tickers) == 1:
         ticker = tickers[0]
         return shared._DFS[shared._ISINS.get(ticker, ticker)]
 
     try:
-        data = _pd.concat(shared._DFS.values(), axis=1,
+        data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
                           keys=shared._DFS.keys())
     except Exception:
         _realign_dfs()
-        data = _pd.concat(shared._DFS.values(), axis=1,
+        data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
                           keys=shared._DFS.keys())
 
     # switch names back to isins if applicable
@@ -180,28 +236,38 @@ def _realign_dfs():
 
 @_multitasking.task
 def _download_one_threaded(ticker, start=None, end=None,
-                           auto_adjust=False, back_adjust=False,
+                           auto_adjust=False, back_adjust=False, repair=False,
                            actions=False, progress=True, period="max",
                            interval="1d", prepost=False, proxy=None,
-                           rounding=False, timeout=None):
-
-    data = _download_one(ticker, start, end, auto_adjust, back_adjust,
+                           keepna=False, rounding=False, timeout=10):
+    data = _download_one(ticker, start, end, auto_adjust, back_adjust, repair,
                          actions, period, interval, prepost, proxy, rounding,
-                         timeout)
-    shared._DFS[ticker.upper()] = data
+                         keepna, timeout)
     if progress:
         shared._PROGRESS_BAR.animate()
 
 
 def _download_one(ticker, start=None, end=None,
-                  auto_adjust=False, back_adjust=False,
+                  auto_adjust=False, back_adjust=False, repair=False,
                   actions=False, period="max", interval="1d",
                   prepost=False, proxy=None, rounding=False,
-                  timeout=None):
+                  keepna=False, timeout=10):
+    data = None
+    try:
+        data = Ticker(ticker).history(
+                period=period, interval=interval,
+                start=start, end=end, prepost=prepost,
+                actions=actions, auto_adjust=auto_adjust,
+                back_adjust=back_adjust, repair=repair, proxy=proxy,
+                rounding=rounding, keepna=keepna, timeout=timeout,
+                raise_errors=True
+        )
+    except Exception as e:
+        # glob try/except needed as current thead implementation breaks if exception is raised.
+        shared._DFS[ticker.upper()] = utils.empty_df()
+        shared._ERRORS[ticker.upper()] = repr(e)
+        shared._TRACEBACKS[ticker.upper()] = traceback.format_exc()
+    else:
+        shared._DFS[ticker.upper()] = data
 
-    return Ticker(ticker).history(period=period, interval=interval,
-                                  start=start, end=end, prepost=prepost,
-                                  actions=actions, auto_adjust=auto_adjust,
-                                  back_adjust=back_adjust, proxy=proxy,
-                                  rounding=rounding, many=True,
-                                  timeout=timeout)
+    return data

yfinance/scrapers/analysis.py

@@ -0,0 +1,118 @@
+import pandas as pd
+
+from yfinance import utils
+from yfinance.data import TickerData
+
+
+class Analysis:
+
+    def __init__(self, data: TickerData, proxy=None):
+        self._data = data
+        self.proxy = proxy
+
+        self._earnings_trend = None
+        self._analyst_trend_details = None
+        self._analyst_price_target = None
+        self._rev_est = None
+        self._eps_est = None
+        self._already_scraped = False
+
+    @property
+    def earnings_trend(self) -> pd.DataFrame:
+        if self._earnings_trend is None:
+            self._scrape(self.proxy)
+        return self._earnings_trend
+
+    @property
+    def analyst_trend_details(self) -> pd.DataFrame:
+        if self._analyst_trend_details is None:
+            self._scrape(self.proxy)
+        return self._analyst_trend_details
+
+    @property
+    def analyst_price_target(self) -> pd.DataFrame:
+        if self._analyst_price_target is None:
+            self._scrape(self.proxy)
+        return self._analyst_price_target
+
+    @property
+    def rev_est(self) -> pd.DataFrame:
+        if self._rev_est is None:
+            self._scrape(self.proxy)
+        return self._rev_est
+
+    @property
+    def eps_est(self) -> pd.DataFrame:
+        if self._eps_est is None:
+            self._scrape(self.proxy)
+        return self._eps_est
+
+    def _scrape(self, proxy):
+        if self._already_scraped:
+            return
+        self._already_scraped = True
+
+        # Analysis Data/Analyst Forecasts
+        analysis_data = self._data.get_json_data_stores("analysis", proxy=proxy)
+        try:
+            analysis_data = analysis_data['QuoteSummaryStore']
+        except KeyError as e:
+            err_msg = "No analysis data found, symbol may be delisted"
+            logger.error('%s: %s', self._data.ticker, err_msg)
+            return
+
+        if isinstance(analysis_data.get('earningsTrend'), dict):
+            try:
+                analysis = pd.DataFrame(analysis_data['earningsTrend']['trend'])
+                analysis['endDate'] = pd.to_datetime(analysis['endDate'])
+                analysis.set_index('period', inplace=True)
+                analysis.index = analysis.index.str.upper()
+                analysis.index.name = 'Period'
+                analysis.columns = utils.camel2title(analysis.columns)
+
+                dict_cols = []
+
+                for idx, row in analysis.iterrows():
+                    for colname, colval in row.items():
+                        if isinstance(colval, dict):
+                            dict_cols.append(colname)
+                            for k, v in colval.items():
+                                new_colname = colname + ' ' + \
+                                              utils.camel2title([k])[0]
+                                analysis.loc[idx, new_colname] = v
+
+                self._earnings_trend = analysis[[
+                    c for c in analysis.columns if c not in dict_cols]]
+            except Exception:
+                pass
+
+        try:
+            self._analyst_trend_details = pd.DataFrame(analysis_data['recommendationTrend']['trend'])
+        except Exception as e:
+            self._analyst_trend_details = None
+        try:
+            self._analyst_price_target = pd.DataFrame(analysis_data['financialData'], index=[0])[
+                ['targetLowPrice', 'currentPrice', 'targetMeanPrice', 'targetHighPrice', 'numberOfAnalystOpinions']].T
+        except Exception as e:
+            self._analyst_price_target = None
+        earnings_estimate = []
+        revenue_estimate = []
+        if self._analyst_trend_details is not None :
+            for key in analysis_data['earningsTrend']['trend']:
+                try:
+                    earnings_dict = key['earningsEstimate']
+                    earnings_dict['period'] = key['period']
+                    earnings_dict['endDate'] = key['endDate']
+                    earnings_estimate.append(earnings_dict)
+
+                    revenue_dict = key['revenueEstimate']
+                    revenue_dict['period'] = key['period']
+                    revenue_dict['endDate'] = key['endDate']
+                    revenue_estimate.append(revenue_dict)
+                except Exception as e:
+                    pass
+            self._rev_est = pd.DataFrame(revenue_estimate)
+            self._eps_est = pd.DataFrame(earnings_estimate)
+        else:
+            self._rev_est = pd.DataFrame()
+            self._eps_est = pd.DataFrame()

yfinance/scrapers/fundamentals.py

@@ -0,0 +1,321 @@
+import datetime
+import logging
+import json
+
+import pandas as pd
+import numpy as np
+
+from yfinance import utils
+from yfinance.data import TickerData
+from yfinance.exceptions import YFinanceDataException, YFinanceException
+
+logger = utils.get_yf_logger()
+
+class Fundamentals:
+
+    def __init__(self, data: TickerData, proxy=None):
+        self._data = data
+        self.proxy = proxy
+
+        self._earnings = None
+        self._financials = None
+        self._shares = None
+
+        self._financials_data = None
+        self._fin_data_quote = None
+        self._basics_already_scraped = False
+        self._financials = Financials(data)
+
+    @property
+    def financials(self) -> "Financials":
+        return self._financials
+
+    @property
+    def earnings(self) -> dict:
+        if self._earnings is None:
+            self._scrape_earnings(self.proxy)
+        return self._earnings
+
+    @property
+    def shares(self) -> pd.DataFrame:
+        if self._shares is None:
+            self._scrape_shares(self.proxy)
+        return self._shares
+
+    def _scrape_basics(self, proxy):
+        if self._basics_already_scraped:
+            return
+        self._basics_already_scraped = True
+
+        self._financials_data = self._data.get_json_data_stores('financials', proxy)
+        try:
+            self._fin_data_quote = self._financials_data['QuoteSummaryStore']
+        except KeyError:
+            err_msg = "No financials data found, symbol may be delisted"
+            logger.error('%s: %s', self._data.ticker, err_msg)
+            return None
+
+    def _scrape_earnings(self, proxy):
+        self._scrape_basics(proxy)
+        # earnings
+        self._earnings = {"yearly": pd.DataFrame(), "quarterly": pd.DataFrame()}
+        if self._fin_data_quote is None:
+            return
+        if isinstance(self._fin_data_quote.get('earnings'), dict):
+            try:
+                earnings = self._fin_data_quote['earnings']['financialsChart']
+                earnings['financialCurrency'] = self._fin_data_quote['earnings'].get('financialCurrency', 'USD')
+                self._earnings['financialCurrency'] = earnings['financialCurrency']
+                df = pd.DataFrame(earnings['yearly']).set_index('date')
+                df.columns = utils.camel2title(df.columns)
+                df.index.name = 'Year'
+                self._earnings['yearly'] = df
+
+                df = pd.DataFrame(earnings['quarterly']).set_index('date')
+                df.columns = utils.camel2title(df.columns)
+                df.index.name = 'Quarter'
+                self._earnings['quarterly'] = df
+            except Exception:
+                pass
+
+    def _scrape_shares(self, proxy):
+        self._scrape_basics(proxy)
+        # shares outstanding
+        try:
+            # keep only years with non None data
+            available_shares = [shares_data for shares_data in
+                                self._financials_data['QuoteTimeSeriesStore']['timeSeries']['annualBasicAverageShares']
+                                if
+                                shares_data]
+            shares = pd.DataFrame(available_shares)
+            shares['Year'] = shares['asOfDate'].agg(lambda x: int(x[:4]))
+            shares.set_index('Year', inplace=True)
+            shares.drop(columns=['dataId', 'asOfDate',
+                                 'periodType', 'currencyCode'], inplace=True)
+            shares.rename(
+                columns={'reportedValue': "BasicShares"}, inplace=True)
+            self._shares = shares
+        except Exception:
+            pass
+
+
+class Financials:
+    def __init__(self, data: TickerData):
+        self._data = data
+        self._income_time_series = {}
+        self._balance_sheet_time_series = {}
+        self._cash_flow_time_series = {}
+        self._income_scraped = {}
+        self._balance_sheet_scraped = {}
+        self._cash_flow_scraped = {}
+
+    def get_income_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
+        res = self._income_time_series
+        if freq not in res:
+            res[freq] = self._fetch_time_series("income", freq, proxy=None)
+        return res[freq]
+
+    def get_balance_sheet_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
+        res = self._balance_sheet_time_series
+        if freq not in res:
+            res[freq] = self._fetch_time_series("balance-sheet", freq, proxy=None)
+        return res[freq]
+
+    def get_cash_flow_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
+        res = self._cash_flow_time_series
+        if freq not in res:
+            res[freq] = self._fetch_time_series("cash-flow", freq, proxy=None)
+        return res[freq]
+
+    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("Illegal argument: name must be one of: {}".format(allowed_names))
+        if timescale not in allowed_timescales:
+            raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
+
+        try:
+            statement = self._create_financials_table(name, timescale, proxy)
+
+            if statement is not None:
+                return statement
+        except YFinanceException as e:
+            logger.error("%s: Failed to create %s financials table for reason: %r", self._data.ticker, name, 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 = self._get_datastore_keys(name, proxy)
+        try:
+            return self.get_financials_time_series(timescale, keys, proxy)
+        except Exception as e:
+            pass
+
+    def _get_datastore_keys(self, sub_page, proxy) -> list:
+        data_stores = self._data.get_json_data_stores(sub_page, proxy)
+
+        # Step 1: get the keys:
+        def _finditem1(key, obj):
+            values = []
+            if isinstance(obj, dict):
+                if key in obj.keys():
+                    values.append(obj[key])
+                for k, v in obj.items():
+                    values += _finditem1(key, v)
+            elif isinstance(obj, list):
+                for v in obj:
+                    values += _finditem1(key, v)
+            return values
+
+        try:
+            keys = _finditem1("key", data_stores['FinancialTemplateStore'])
+        except KeyError as e:
+            raise YFinanceDataException("Parsing FinancialTemplateStore failed, reason: {}".format(repr(e)))
+
+        if not keys:
+            raise YFinanceDataException("No keys in FinancialTemplateStore")
+        return keys
+
+    def get_financials_time_series(self, timescale, keys: list, proxy=None) -> pd.DataFrame:
+        timescale_translation = {"yearly": "annual", "quarterly": "quarterly"}
+        timescale = timescale_translation[timescale]
+
+        # Step 2: construct url:
+        ts_url_base = \
+            "https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{0}?symbol={0}" \
+                .format(self._data.ticker)
+
+        url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
+        # Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
+        start_dt = datetime.datetime(2016, 12, 31)
+        end = pd.Timestamp.utcnow().ceil("D")
+        url += "&period1={}&period2={}".format(int(start_dt.timestamp()), int(end.timestamp()))
+
+        # 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
+
+    def get_income_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
+        res = self._income_scraped
+        if freq not in res:
+            res[freq] = self._scrape("income", freq, proxy=None)
+        return res[freq]
+
+    def get_balance_sheet_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
+        res = self._balance_sheet_scraped
+        if freq not in res:
+            res[freq] = self._scrape("balance-sheet", freq, proxy=None)
+        return res[freq]
+
+    def get_cash_flow_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
+        res = self._cash_flow_scraped
+        if freq not in res:
+            res[freq] = self._scrape("cash-flow", freq, proxy=None)
+        return res[freq]
+
+    def _scrape(self, name, timescale, proxy=None):
+        # Backup in case _fetch_time_series() fails to return data
+
+        allowed_names = ["income", "balance-sheet", "cash-flow"]
+        allowed_timescales = ["yearly", "quarterly"]
+
+        if name not in allowed_names:
+            raise ValueError("Illegal argument: name must be one of: {}".format(allowed_names))
+        if timescale not in allowed_timescales:
+            raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
+
+        try:
+            statement = self._create_financials_table_old(name, timescale, proxy)
+
+            if statement is not None:
+                return statement
+        except YFinanceException as e:
+            logger.error("%s: Failed to create financials table for %s reason: %r", self._data.ticker, name, e)
+        return pd.DataFrame()
+
+    def _create_financials_table_old(self, name, timescale, proxy):
+        data_stores = self._data.get_json_data_stores("financials", proxy)
+
+        # Fetch raw data
+        if not "QuoteSummaryStore" in data_stores:
+            raise YFinanceDataException(f"Yahoo not returning legacy financials data")
+        data = data_stores["QuoteSummaryStore"]
+
+        if name == "cash-flow":
+            key1 = "cashflowStatement"
+            key2 = "cashflowStatements"
+        elif name == "balance-sheet":
+            key1 = "balanceSheet"
+            key2 = "balanceSheetStatements"
+        else:
+            key1 = "incomeStatement"
+            key2 = "incomeStatementHistory"
+        key1 += "History"
+        if timescale == "quarterly":
+            key1 += "Quarterly"
+        if key1 not in data or data[key1] is None or key2 not in data[key1]:
+            raise YFinanceDataException(f"Yahoo not returning legacy {name} financials data")
+        data = data[key1][key2]
+
+        # Tabulate
+        df = pd.DataFrame(data)
+        if len(df) == 0:
+            raise YFinanceDataException(f"Yahoo not returning legacy {name} financials data")
+        df = df.drop(columns=['maxAge'])
+        for col in df.columns:
+            df[col] = df[col].replace('-', np.nan)
+        df.set_index('endDate', inplace=True)
+        try:
+            df.index = pd.to_datetime(df.index, unit='s')
+        except ValueError:
+            df.index = pd.to_datetime(df.index)
+        df = df.T
+        df.columns.name = ''
+        df.index.name = 'Breakdown'
+        # rename incorrect yahoo key
+        df.rename(index={'treasuryStock': 'gainsLossesNotAffectingRetainedEarnings'}, inplace=True)
+
+        # Upper-case first letter, leave rest unchanged:
+        s0 = df.index[0]
+        df.index = [s[0].upper()+s[1:] for s in df.index]
+
+        return df

yfinance/scrapers/holders.py

@@ -0,0 +1,66 @@
+import pandas as pd
+
+from yfinance.data import TickerData
+
+class Holders:
+    _SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
+
+    def __init__(self, data: TickerData, proxy=None):
+        self._data = data
+        self.proxy = proxy
+
+        self._major = None
+        self._institutional = None
+        self._mutualfund = None
+
+    @property
+    def major(self) -> pd.DataFrame:
+        if self._major is None:
+            self._scrape(self.proxy)
+        return self._major
+
+    @property
+    def institutional(self) -> pd.DataFrame:
+        if self._institutional is None:
+            self._scrape(self.proxy)
+        return self._institutional
+
+    @property
+    def mutualfund(self) -> pd.DataFrame:
+        if self._mutualfund is None:
+            self._scrape(self.proxy)
+        return self._mutualfund
+
+    def _scrape(self, proxy):
+        ticker_url = "{}/{}".format(self._SCRAPE_URL_, self._data.ticker)
+        try:
+            resp = self._data.cache_get(ticker_url + '/holders', proxy)
+            holders = pd.read_html(resp.text)
+        except Exception:
+            holders = []
+
+        if len(holders) >= 3:
+            self._major = holders[0]
+            self._institutional = holders[1]
+            self._mutualfund = holders[2]
+        elif len(holders) >= 2:
+            self._major = holders[0]
+            self._institutional = holders[1]
+        elif len(holders) >= 1:
+            self._major = holders[0]
+
+        if self._institutional is not None:
+            if 'Date Reported' in self._institutional:
+                self._institutional['Date Reported'] = pd.to_datetime(
+                    self._institutional['Date Reported'])
+            if '% Out' in self._institutional:
+                self._institutional['% Out'] = self._institutional[
+                                                   '% Out'].str.replace('%', '').astype(float) / 100
+
+        if self._mutualfund is not None:
+            if 'Date Reported' in self._mutualfund:
+                self._mutualfund['Date Reported'] = pd.to_datetime(
+                    self._mutualfund['Date Reported'])
+            if '% Out' in self._mutualfund:
+                self._mutualfund['% Out'] = self._mutualfund[
+                                                '% Out'].str.replace('%', '').astype(float) / 100

yfinance/scrapers/quote.py

@@ -0,0 +1,818 @@
+import datetime
+import logging
+import json
+import warnings
+
+import pandas as pd
+import numpy as _np
+
+from yfinance import utils
+from yfinance.data import TickerData
+
+logger = utils.get_yf_logger()
+
+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
+
+
+PRUNE_INFO = True
+# PRUNE_INFO = False
+_BASIC_URL_ = "https://query2.finance.yahoo.com/v10/finance/quoteSummary"
+
+
+from collections.abc import MutableMapping
+class InfoDictWrapper(MutableMapping):
+    """ Simple wrapper around info dict, intercepting 'gets' to 
+    print how-to-migrate messages for specific keys. Requires
+    override dict API"""
+
+    def __init__(self, info):
+        self.info = info
+
+    def keys(self):
+        return self.info.keys()
+
+    def __str__(self):
+        return self.info.__str__()
+
+    def __repr__(self):
+        return self.info.__repr__()
+
+    def __contains__(self, k):
+        return k in self.info.keys()
+
+    def __getitem__(self, k):
+        if k in info_retired_keys_price:
+            warnings.warn(f"Price data removed from info (key='{k}'). Use Ticker.fast_info or history() instead", DeprecationWarning)
+            return None
+        elif k in info_retired_keys_exchange:
+            warnings.warn(f"Exchange data removed from info (key='{k}'). Use Ticker.fast_info or Ticker.get_history_metadata() instead", DeprecationWarning)
+            return None
+        elif k in info_retired_keys_marketCap:
+            warnings.warn(f"Market cap removed from info (key='{k}'). Use Ticker.fast_info instead", DeprecationWarning)
+            return None
+        elif k in info_retired_keys_symbol:
+            warnings.warn(f"Symbol removed from info (key='{k}'). You know this already", DeprecationWarning)
+            return None
+        return self.info[self._keytransform(k)]
+
+    def __setitem__(self, k, value):
+        self.info[self._keytransform(k)] = value
+
+    def __delitem__(self, k):
+        del self.info[self._keytransform(k)]
+
+    def __iter__(self):
+        return iter(self.info)
+    
+    def __len__(self):
+        return len(self.info)
+
+    def _keytransform(self, k):
+        return k
+
+
+class FastInfo:
+    # Contain small subset of info[] items that can be fetched faster elsewhere.
+    # Imitates a dict.
+    def __init__(self, tickerBaseObject):
+        utils.print_once("yfinance: Note: 'Ticker.info' dict is now fixed & improved, 'fast_info' is no longer faster")
+
+        self._tkr = tickerBaseObject
+
+        self._prices_1y = None
+        self._prices_1wk_1h_prepost = None
+        self._prices_1wk_1h_reg = None
+        self._md = None
+
+        self._currency = None
+        self._quote_type = None
+        self._exchange = None
+        self._timezone = None
+
+        self._shares = None
+        self._mcap = None
+
+        self._open = None
+        self._day_high = None
+        self._day_low = None
+        self._last_price = None
+        self._last_volume = None
+
+        self._prev_close = None
+
+        self._reg_prev_close = None
+
+        self._50d_day_average = None
+        self._200d_day_average = None
+        self._year_high = None
+        self._year_low = None
+        self._year_change = None
+
+        self._10d_avg_vol = None
+        self._3mo_avg_vol = None
+
+        # attrs = utils.attributes(self)
+        # self.keys = attrs.keys()
+        # utils.attributes is calling each method, bad! Have to hardcode
+        _properties = ["currency", "quote_type", "exchange", "timezone"]
+        _properties += ["shares", "market_cap"]
+        _properties += ["last_price", "previous_close", "open", "day_high", "day_low"]
+        _properties += ["regular_market_previous_close"]
+        _properties += ["last_volume"]
+        _properties += ["fifty_day_average", "two_hundred_day_average", "ten_day_average_volume", "three_month_average_volume"]
+        _properties += ["year_high", "year_low", "year_change"]
+
+        # Because released before fixing key case, need to officially support 
+        # camel-case but also secretly support snake-case
+        base_keys = [k for k in _properties if not '_' in k]
+
+        sc_keys = [k for k in _properties if '_' in k]
+
+        self._sc_to_cc_key = {k:utils.snake_case_2_camelCase(k) for k in sc_keys}
+        self._cc_to_sc_key = {v:k for k,v in self._sc_to_cc_key.items()}
+ 
+        self._public_keys = sorted(base_keys + list(self._sc_to_cc_key.values()))
+        self._keys = sorted(self._public_keys + sc_keys)
+
+    # dict imitation:
+    def keys(self):
+        return self._public_keys
+    def items(self):
+        return [(k,self[k]) for k in self._public_keys]
+    def values(self):
+        return [self[k] for k in self._public_keys]
+    def get(self, key, default=None):
+        if key in self.keys():
+            if key in self._cc_to_sc_key:
+                key = self._cc_to_sc_key[key]
+            return self[key]
+        return default
+    def __getitem__(self, k):
+        if not isinstance(k, str):
+            raise KeyError(f"key must be a string")
+        if not k in self._keys:
+            raise KeyError(f"'{k}' not valid key. Examine 'FastInfo.keys()'")
+        if k in self._cc_to_sc_key:
+            k = self._cc_to_sc_key[k]
+        return getattr(self, k)
+    def __contains__(self, k):
+        return k in self.keys()
+    def __iter__(self):
+        return iter(self.keys())
+
+    def __str__(self):
+        return "lazy-loading dict with keys = " + str(self.keys())
+    def __repr__(self):
+        return self.__str__()
+
+    def toJSON(self, indent=4):
+        d = {k:self[k] for k in self.keys()}
+        return _json.dumps({k:self[k] for k in self.keys()}, indent=indent)
+
+    def _get_1y_prices(self, fullDaysOnly=False):
+        if self._prices_1y is None:
+            # Temporarily disable error printing
+            l = logger.level
+            logger.setLevel(logging.CRITICAL)
+            self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, keepna=True)
+            logger.setLevel(l)
+            self._md = self._tkr.get_history_metadata()
+            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:
+                self._today_open = None
+                self._today_close = None
+                self._today_midnight = None
+                raise
+
+        if self._prices_1y.empty:
+            return self._prices_1y
+
+        dnow = pd.Timestamp.utcnow().tz_convert(self.timezone).date()
+        d1 = dnow
+        d0 = (d1 + datetime.timedelta(days=1)) - utils._interval_to_timedelta("1y")
+        if fullDaysOnly and self._exchange_open_now():
+            # Exclude today
+            d1 -= utils._interval_to_timedelta("1d")
+        return self._prices_1y.loc[str(d0):str(d1)]
+
+    def _get_1wk_1h_prepost_prices(self):
+        if self._prices_1wk_1h_prepost is None:
+            # Temporarily disable error printing
+            l = logger.level
+            logger.setLevel(logging.CRITICAL)
+            self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True)
+            logger.setLevel(l)
+        return self._prices_1wk_1h_prepost
+
+    def _get_1wk_1h_reg_prices(self):
+        if self._prices_1wk_1h_reg is None:
+            # Temporarily disable error printing
+            l = logger.level
+            logger.setLevel(logging.CRITICAL)
+            self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False)
+            logger.setLevel(l)
+        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()
+        return self._md
+
+    def _exchange_open_now(self):
+        t = pd.Timestamp.utcnow()
+        self._get_exchange_metadata()
+
+        # if self._today_open is None and self._today_close is None:
+        #     r = False
+        # else:
+        #     r = self._today_open <= t and t < self._today_close
+
+        # if self._today_midnight is None:
+        #     r = False
+        # elif self._today_midnight.date() > t.tz_convert(self.timezone).date():
+        #     r = False
+        # else:
+        #     r = t < self._today_midnight
+
+        last_day_cutoff = self._get_1y_prices().index[-1] + datetime.timedelta(days=1)
+        last_day_cutoff += datetime.timedelta(minutes=20)
+        r = t < last_day_cutoff
+
+        # print("_exchange_open_now() returning", r)
+        return r
+
+    @property
+    def currency(self):
+        if self._currency is not None:
+            return self._currency
+
+        if self._tkr._history_metadata is None:
+            self._get_1y_prices()
+        md = self._tkr.get_history_metadata()
+        self._currency = md["currency"]
+        return self._currency
+
+    @property
+    def quote_type(self):
+        if self._quote_type is not None:
+            return self._quote_type
+
+        if self._tkr._history_metadata is None:
+            self._get_1y_prices()
+        md = self._tkr.get_history_metadata()
+        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))
+        if shares is None:
+            # Requesting 18 months failed, so fallback to shares which should include last year
+            shares = self._tkr.get_shares()
+        if shares is not None:
+            if isinstance(shares, pd.DataFrame):
+                shares = shares[shares.columns[0]]
+            self._shares = int(shares.iloc[-1])
+        return self._shares
+
+    @property
+    def last_price(self):
+        if self._last_price is not None:
+            return self._last_price
+        prices = self._get_1y_prices()
+        if prices.empty:
+            md = self._get_exchange_metadata()
+            if "regularMarketPrice" in md:
+                self._last_price = md["regularMarketPrice"]
+        else:
+            self._last_price = float(prices["Close"].iloc[-1])
+            if _np.isnan(self._last_price):
+                md = self._get_exchange_metadata()
+                if "regularMarketPrice" in md:
+                    self._last_price = md["regularMarketPrice"]
+        return self._last_price
+
+    @property
+    def previous_close(self):
+        if self._prev_close is not None:
+            return self._prev_close
+        prices = self._get_1wk_1h_prepost_prices()
+        fail = False
+        if prices.empty:
+            fail = True
+        else:
+            prices = prices[["Close"]].groupby(prices.index.date).last()
+            if prices.shape[0] < 2:
+                # Very few symbols have previousClose despite no 
+                # no trading data e.g. 'QCSTIX'.
+                fail = True
+            else:
+                self._prev_close = float(prices["Close"].iloc[-2])
+        if fail:
+            # Fallback to original info[] if available.
+            self._tkr.info  # trigger fetch
+            k = "previousClose"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._prev_close = self._tkr._quote._retired_info[k]
+        return self._prev_close
+
+    @property
+    def regular_market_previous_close(self):
+        if self._reg_prev_close is not None:
+            return self._reg_prev_close
+        prices = self._get_1y_prices()
+        if prices.shape[0] == 1:
+            # Tiny % of tickers don't return daily history before last trading day, 
+            # so backup option is hourly history:
+            prices = self._get_1wk_1h_reg_prices()
+            prices = prices[["Close"]].groupby(prices.index.date).last()
+        if prices.shape[0] < 2:
+            # Very few symbols have regularMarketPreviousClose despite no 
+            # no trading data. E.g. 'QCSTIX'.
+            # So fallback to original info[] if available.
+            self._tkr.info  # trigger fetch
+            k = "regularMarketPreviousClose"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._reg_prev_close = self._tkr._quote._retired_info[k]
+        else:
+            self._reg_prev_close = float(prices["Close"].iloc[-2])
+        return self._reg_prev_close
+
+    @property
+    def open(self):
+        if self._open is not None:
+            return self._open
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._open = None
+        else:
+            self._open = float(prices["Open"].iloc[-1])
+            if _np.isnan(self._open):
+                self._open = None
+        return self._open
+
+    @property
+    def day_high(self):
+        if self._day_high is not None:
+            return self._day_high
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._day_high = None
+        else:
+            self._day_high = float(prices["High"].iloc[-1])
+            if _np.isnan(self._day_high):
+                self._day_high = None
+        return self._day_high
+
+    @property
+    def day_low(self):
+        if self._day_low is not None:
+            return self._day_low
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._day_low = None
+        else:
+            self._day_low = float(prices["Low"].iloc[-1])
+            if _np.isnan(self._day_low):
+                self._day_low = None
+        return self._day_low
+
+    @property
+    def last_volume(self):
+        if self._last_volume is not None:
+            return self._last_volume
+        prices = self._get_1y_prices()
+        self._last_volume = None if prices.empty else int(prices["Volume"].iloc[-1])
+        return self._last_volume
+
+    @property
+    def fifty_day_average(self):
+        if self._50d_day_average is not None:
+            return self._50d_day_average
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._50d_day_average = None
+        else:
+            n = prices.shape[0]
+            a = n-50
+            b = n
+            if a < 0:
+                a = 0
+            self._50d_day_average = float(prices["Close"].iloc[a:b].mean())
+
+        return self._50d_day_average
+
+    @property
+    def two_hundred_day_average(self):
+        if self._200d_day_average is not None:
+            return self._200d_day_average
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._200d_day_average = None
+        else:
+            n = prices.shape[0]
+            a = n-200
+            b = n
+            if a < 0:
+                a = 0
+
+            self._200d_day_average = float(prices["Close"].iloc[a:b].mean())
+
+        return self._200d_day_average
+
+    @property
+    def ten_day_average_volume(self):
+        if self._10d_avg_vol is not None:
+            return self._10d_avg_vol
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._10d_avg_vol = None
+        else:
+            n = prices.shape[0]
+            a = n-10
+            b = n
+            if a < 0:
+                a = 0
+            self._10d_avg_vol = int(prices["Volume"].iloc[a:b].mean())
+
+        return self._10d_avg_vol
+
+    @property
+    def three_month_average_volume(self):
+        if self._3mo_avg_vol is not None:
+            return self._3mo_avg_vol
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._3mo_avg_vol = None
+        else:
+            dt1 = prices.index[-1]
+            dt0 = dt1 - utils._interval_to_timedelta("3mo") + utils._interval_to_timedelta("1d")
+            self._3mo_avg_vol = int(prices.loc[dt0:dt1, "Volume"].mean())
+
+        return self._3mo_avg_vol
+
+    @property
+    def year_high(self):
+        if self._year_high is not None:
+            return self._year_high
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            prices = self._get_1y_prices(fullDaysOnly=False)
+        self._year_high = float(prices["High"].max())
+        return self._year_high
+
+    @property
+    def year_low(self):
+        if self._year_low is not None:
+            return self._year_low
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            prices = self._get_1y_prices(fullDaysOnly=False)
+        self._year_low = float(prices["Low"].min())
+        return self._year_low
+
+    @property
+    def year_change(self):
+        if self._year_change is not None:
+            return self._year_change
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.shape[0] >= 2:
+            self._year_change = (prices["Close"].iloc[-1] - prices["Close"].iloc[0]) / prices["Close"].iloc[0]
+            self._year_change = float(self._year_change)
+        return self._year_change
+
+    @property
+    def market_cap(self):
+        if self._mcap is not None:
+            return self._mcap
+
+        try:
+            shares = self.shares
+        except Exception as e:
+            if "Cannot retrieve share count" in str(e):
+                shares = None
+            elif "failed to decrypt Yahoo" in str(e):
+                shares = None
+            else:
+                raise
+
+        if shares is None:
+            # Very few symbols have marketCap despite no share count.
+            # E.g. 'BTC-USD'
+            # So fallback to original info[] if available.
+            self._tkr.info
+            k = "marketCap"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._mcap = self._tkr._quote._retired_info[k]
+        else:
+            self._mcap = float(shares * self.last_price)
+        return self._mcap
+
+
+class Quote:
+
+    def __init__(self, data: TickerData, proxy=None):
+        self._data = data
+        self.proxy = proxy
+
+        self._info = None
+        self._retired_info = None
+        self._sustainability = None
+        self._recommendations = None
+        self._calendar = None
+
+        self._already_scraped = False
+        self._already_fetched = False
+        self._already_fetched_complementary = False
+
+    @property
+    def info(self) -> dict:
+        if self._info is None:
+            # self._scrape(self.proxy)  # decrypt broken
+            self._fetch(self.proxy)
+
+            self._fetch_complementary(self.proxy)
+
+        return self._info
+
+    @property
+    def sustainability(self) -> pd.DataFrame:
+        if self._sustainability is None:
+            self._scrape(self.proxy)
+        return self._sustainability
+
+    @property
+    def recommendations(self) -> pd.DataFrame:
+        if self._recommendations is None:
+            self._scrape(self.proxy)
+        return self._recommendations
+
+    @property
+    def calendar(self) -> pd.DataFrame:
+        if self._calendar is None:
+            self._scrape(self.proxy)
+        return self._calendar
+
+    def _scrape(self, proxy):
+        if self._already_scraped:
+            return
+        self._already_scraped = True
+
+        # get info and sustainability
+        json_data = self._data.get_json_data_stores(proxy=proxy)
+        try:
+            quote_summary_store = json_data['QuoteSummaryStore']
+        except KeyError:
+            err_msg = "No summary info found, symbol may be delisted"
+            logger.error('%s: %s', self._data.ticker, err_msg)
+            return None
+
+        # sustainability
+        d = {}
+        try:
+            if isinstance(quote_summary_store.get('esgScores'), dict):
+                for item in quote_summary_store['esgScores']:
+                    if not isinstance(quote_summary_store['esgScores'][item], (dict, list)):
+                        d[item] = quote_summary_store['esgScores'][item]
+
+                s = pd.DataFrame(index=[0], data=d)[-1:].T
+                s.columns = ['Value']
+                s.index.name = '%.f-%.f' % (
+                    s[s.index == 'ratingYear']['Value'].values[0],
+                    s[s.index == 'ratingMonth']['Value'].values[0])
+
+                self._sustainability = s[~s.index.isin(
+                    ['maxAge', 'ratingYear', 'ratingMonth'])]
+        except Exception:
+            pass
+
+        self._info = {}
+        try:
+            items = ['summaryProfile', 'financialData', 'quoteType',
+                     'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
+            for item in items:
+                if isinstance(quote_summary_store.get(item), dict):
+                    self._info.update(quote_summary_store[item])
+        except Exception:
+            pass
+
+        # For ETFs, provide this valuable data: the top holdings of the ETF
+        try:
+            if 'topHoldings' in quote_summary_store:
+                self._info.update(quote_summary_store['topHoldings'])
+        except Exception:
+            pass
+
+        try:
+            if not isinstance(quote_summary_store.get('summaryDetail'), dict):
+                # For some reason summaryDetail did not give any results. The price dict
+                # usually has most of the same info
+                self._info.update(quote_summary_store.get('price', {}))
+        except Exception:
+            pass
+
+        try:
+            # self._info['regularMarketPrice'] = self._info['regularMarketOpen']
+            self._info['regularMarketPrice'] = quote_summary_store.get('price', {}).get(
+                'regularMarketPrice', self._info.get('regularMarketOpen', None))
+        except Exception:
+            pass
+
+        try:
+            self._info['preMarketPrice'] = quote_summary_store.get('price', {}).get(
+                'preMarketPrice', self._info.get('preMarketPrice', None))
+        except Exception:
+            pass
+
+        self._info['logo_url'] = ""
+        try:
+            if not 'website' in self._info:
+                self._info['logo_url'] = 'https://logo.clearbit.com/%s.com' % \
+                                         self._info['shortName'].split(' ')[0].split(',')[0]
+            else:
+                domain = self._info['website'].split(
+                    '://')[1].split('/')[0].replace('www.', '')
+                self._info['logo_url'] = 'https://logo.clearbit.com/%s' % domain
+        except Exception:
+            pass
+
+        # Delete redundant info[] keys, because values can be accessed faster
+        # elsewhere - e.g. price keys. Hope is reduces Yahoo spam effect.
+        # But record the dropped keys, because in rare cases they are needed.
+        self._retired_info = {}
+        for k in info_retired_keys:
+            if k in self._info:
+                self._retired_info[k] = self._info[k]
+                if PRUNE_INFO:
+                    del self._info[k]
+        if PRUNE_INFO:
+            # InfoDictWrapper will explain how to access above data elsewhere
+            self._info = InfoDictWrapper(self._info)
+
+        # events
+        try:
+            cal = pd.DataFrame(quote_summary_store['calendarEvents']['earnings'])
+            cal['earningsDate'] = pd.to_datetime(
+                cal['earningsDate'], unit='s')
+            self._calendar = cal.T
+            self._calendar.index = utils.camel2title(self._calendar.index)
+            self._calendar.columns = ['Value']
+        except Exception as e:
+            pass
+
+        # analyst recommendations
+        try:
+            rec = pd.DataFrame(
+                quote_summary_store['upgradeDowngradeHistory']['history'])
+            rec['earningsDate'] = pd.to_datetime(
+                rec['epochGradeDate'], unit='s')
+            rec.set_index('earningsDate', inplace=True)
+            rec.index.name = 'Date'
+            rec.columns = utils.camel2title(rec.columns)
+            self._recommendations = rec[[
+                'Firm', 'To Grade', 'From Grade', 'Action']].sort_index()
+        except Exception:
+            pass
+
+    def _fetch(self, proxy):
+        if self._already_fetched:
+            return
+        self._already_fetched = True
+        modules = ['summaryProfile', 'financialData', 'quoteType',
+                     'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
+        result = self._data.get_raw_json(
+            _BASIC_URL_ + f"/{self._data.ticker}", params={"modules": ",".join(modules), "ssl": "true"}, proxy=proxy
+        )
+        result["quoteSummary"]["result"][0]["symbol"] = self._data.ticker
+        query1_info = next(
+            (info for info in result.get("quoteSummary", {}).get("result", []) if info["symbol"] == self._data.ticker),
+            None,
+        )
+        # Most keys that appear in multiple dicts have same value. Except 'maxAge' because
+        # Yahoo not consistent with days vs seconds. Fix it here:
+        for k in query1_info:
+            if "maxAge" in query1_info[k] and query1_info[k]["maxAge"] == 1:
+                query1_info[k]["maxAge"] = 86400
+        query1_info = {
+            k1: v1 
+            for k, v in query1_info.items() 
+            if isinstance(v, dict) 
+            for k1, v1 in v.items() 
+            if v1
+        }
+        # recursively format but only because of 'companyOfficers'
+        def _format(k, v):
+            if isinstance(v, dict) and "raw" in v and "fmt" in v:
+                v2 = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
+            elif isinstance(v, list):
+                v2 = [_format(None, x) for x in v]
+            elif isinstance(v, dict):
+                v2 = {k:_format(k, x) for k, x in v.items()}
+            elif isinstance(v, str):
+                v2 = v.replace("\xa0", " ")
+            else:
+                v2 = v
+            return v2
+        for k, v in query1_info.items():
+            query1_info[k] = _format(k, v)
+        self._info = query1_info
+
+    def _fetch_complementary(self, proxy):
+        if self._already_fetched_complementary:
+            return
+        self._already_fetched_complementary = True
+
+        # self._scrape(proxy)  # decrypt broken
+        self._fetch(proxy)
+        if self._info is None:
+            return
+
+        # Complementary key-statistics. For now just want 'trailing PEG ratio'
+        keys = {"trailingPegRatio"}
+        if keys:
+            # Simplified the original scrape code for key-statistics. Very expensive for fetching
+            # just one value, best if scraping most/all:
+            #
+            # p = _re.compile(r'root\.App\.main = (.*);')
+            # url = 'https://finance.yahoo.com/quote/{}/key-statistics?p={}'.format(self._ticker.ticker, self._ticker.ticker)
+            # try:
+            #     r = session.get(url, headers=utils.user_agent_headers)
+            #     data = _json.loads(p.findall(r.text)[0])
+            #     key_stats = data['context']['dispatcher']['stores']['QuoteTimeSeriesStore']["timeSeries"]
+            #     for k in keys:
+            #         if k not in key_stats or len(key_stats[k])==0:
+            #             # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+            #             v = None
+            #         else:
+            #             # Select most recent (last) raw value in list:
+            #             v = key_stats[k][-1]["reportedValue"]["raw"]
+            #         self._info[k] = v
+            # except Exception:
+            #     raise
+            #     pass
+            #
+            # For just one/few variable is faster to query directly:
+            url = "https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{}?symbol={}".format(
+                self._data.ticker, self._data.ticker)
+            for k in keys:
+                url += "&type=" + k
+            # Request 6 months of data
+            start = pd.Timestamp.utcnow().floor("D") - datetime.timedelta(days=365 // 2)
+            start = int(start.timestamp())
+            end = pd.Timestamp.utcnow().ceil("D")
+            end = int(end.timestamp())
+            url += f"&period1={start}&period2={end}"
+
+            json_str = self._data.cache_get(url=url, proxy=proxy).text
+            json_data = json.loads(json_str)
+            try:
+                key_stats = json_data["timeseries"]["result"][0]
+                if k not in key_stats:
+                    # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+                    v = None
+                else:
+                    # Select most recent (last) raw value in list:
+                    v = key_stats[k][-1]["reportedValue"]["raw"]
+            except Exception:
+                v = None
+            self._info[k] = v

yfinance/scrapers/yahoo-keys.txt

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

yfinance/shared.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 #
-# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
+# yfinance - market data downloader
 # https://github.com/ranaroussi/yfinance
 #
 # Copyright 2017-2019 Ran Aroussi
@@ -22,4 +22,5 @@
 _DFS = {}
 _PROGRESS_BAR = None
 _ERRORS = {}
+_TRACEBACKS = {}
 _ISINS = {}

yfinance/ticker.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 #
-# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
+# yfinance - market data downloader
 # https://github.com/ranaroussi/yfinance
 #
 # Copyright 2017-2019 Ran Aroussi
@@ -21,21 +21,18 @@
 
 from __future__ import print_function
 
-# import time as _time
 import datetime as _datetime
-import requests as _requests
 import pandas as _pd
-# import numpy as _np
 
-# import json as _json
-# import re as _re
 from collections import namedtuple as _namedtuple
 
-from . import utils
 from .base import TickerBase
 
 
 class Ticker(TickerBase):
+    def __init__(self, ticker, session=None):
+        super(Ticker, self).__init__(ticker, session=session)
+        self._expirations = {}
 
     def __repr__(self):
         return 'yfinance.Ticker object <%s>' % self.ticker
@@ -48,17 +45,7 @@ class Ticker(TickerBase):
             url = "{}/v7/finance/options/{}?date={}".format(
                 self._base_url, self.ticker, date)
 
-        # setup proxy in requests format
-        if proxy is not None:
-            if isinstance(proxy, dict) and "https" in proxy:
-                proxy = proxy["https"]
-            proxy = {"https": proxy}
-
-        r = _requests.get(
-            url=url,
-            proxies=proxy,
-            headers=utils.user_agent_headers
-        ).json()
+        r = self._data.get(url=url, proxy=proxy).json()
         if len(r.get('optionChain', {}).get('result', [])) > 0:
             for exp in r['optionChain']['result'][0]['expirationDates']:
                 self._expirations[_datetime.datetime.utcfromtimestamp(
@@ -84,9 +71,9 @@ class Ticker(TickerBase):
             'currency'])
 
         data['lastTradeDate'] = _pd.to_datetime(
-            data['lastTradeDate'], unit='s')
+            data['lastTradeDate'], unit='s', utc=True)
         if tz is not None:
-            data['lastTradeDate'] = data['lastTradeDate'].tz_localize(tz)
+            data['lastTradeDate'] = data['lastTradeDate'].dt.tz_convert(tz)
         return data
 
     def option_chain(self, date=None, proxy=None, tz=None):
@@ -115,35 +102,43 @@ class Ticker(TickerBase):
         return self.get_isin()
 
     @property
-    def major_holders(self):
+    def major_holders(self) -> _pd.DataFrame:
         return self.get_major_holders()
 
     @property
-    def institutional_holders(self):
+    def institutional_holders(self) -> _pd.DataFrame:
         return self.get_institutional_holders()
 
     @property
-    def mutualfund_holders(self):
+    def mutualfund_holders(self) -> _pd.DataFrame:
         return self.get_mutualfund_holders()
 
     @property
-    def dividends(self):
+    def dividends(self) -> _pd.Series:
         return self.get_dividends()
 
     @property
-    def splits(self):
+    def capital_gains(self):
+        return self.get_capital_gains()
+
+    @property
+    def splits(self) -> _pd.Series:
         return self.get_splits()
 
     @property
-    def actions(self):
+    def actions(self) -> _pd.DataFrame:
         return self.get_actions()
 
     @property
-    def info(self):
+    def shares(self) -> _pd.DataFrame :
+        return self.get_shares()
+
+    @property
+    def info(self) -> dict:
         return self.get_info()
 
     @property
-    def calendar(self):
+    def calendar(self) -> _pd.DataFrame:
         return self.get_calendar()
 
     @property
@@ -151,51 +146,87 @@ class Ticker(TickerBase):
         return self.get_recommendations()
 
     @property
-    def earnings(self):
+    def earnings(self) -> _pd.DataFrame:
         return self.get_earnings()
 
     @property
-    def quarterly_earnings(self):
+    def quarterly_earnings(self) -> _pd.DataFrame:
         return self.get_earnings(freq='quarterly')
 
     @property
-    def financials(self):
-        return self.get_financials()
+    def income_stmt(self) -> _pd.DataFrame:
+        return self.get_income_stmt(pretty=True)
 
     @property
-    def quarterly_financials(self):
-        return self.get_financials(freq='quarterly')
+    def quarterly_income_stmt(self) -> _pd.DataFrame:
+        return self.get_income_stmt(pretty=True, freq='quarterly')
 
     @property
-    def balance_sheet(self):
-        return self.get_balancesheet()
+    def incomestmt(self) -> _pd.DataFrame:
+        return self.income_stmt
 
     @property
-    def quarterly_balance_sheet(self):
-        return self.get_balancesheet(freq='quarterly')
+    def quarterly_incomestmt(self) -> _pd.DataFrame:
+        return self.quarterly_income_stmt
 
     @property
-    def balancesheet(self):
-        return self.get_balancesheet()
+    def financials(self) -> _pd.DataFrame:
+        return self.income_stmt
 
     @property
-    def quarterly_balancesheet(self):
-        return self.get_balancesheet(freq='quarterly')
+    def quarterly_financials(self) -> _pd.DataFrame:
+        return self.quarterly_income_stmt
 
     @property
-    def cashflow(self):
-        return self.get_cashflow()
+    def balance_sheet(self) -> _pd.DataFrame:
+        return self.get_balance_sheet(pretty=True)
 
     @property
-    def quarterly_cashflow(self):
-        return self.get_cashflow(freq='quarterly')
+    def quarterly_balance_sheet(self) -> _pd.DataFrame:
+        return self.get_balance_sheet(pretty=True, freq='quarterly')
 
     @property
-    def sustainability(self):
+    def balancesheet(self) -> _pd.DataFrame:
+        return self.balance_sheet
+
+    @property
+    def quarterly_balancesheet(self) -> _pd.DataFrame:
+        return self.quarterly_balance_sheet
+
+    @property
+    def cash_flow(self) -> _pd.DataFrame:
+        return self.get_cash_flow(pretty=True, freq="yearly")
+
+    @property
+    def quarterly_cash_flow(self) -> _pd.DataFrame:
+        return self.get_cash_flow(pretty=True, freq='quarterly')
+
+    @property
+    def cashflow(self) -> _pd.DataFrame:
+        return self.cash_flow
+
+    @property
+    def quarterly_cashflow(self) -> _pd.DataFrame:
+        return self.quarterly_cash_flow
+
+    @property
+    def recommendations_summary(self):
+        return self.get_recommendations_summary()
+
+    @property
+    def analyst_price_target(self) -> _pd.DataFrame:
+        return self.get_analyst_price_target()
+
+    @property
+    def revenue_forecasts(self) -> _pd.DataFrame:
+        return self.get_rev_forecast()
+
+    @property
+    def sustainability(self) -> _pd.DataFrame:
         return self.get_sustainability()
 
     @property
-    def options(self):
+    def options(self) -> tuple:
         if not self._expirations:
             self._download_options()
         return tuple(self._expirations.keys())
@@ -205,5 +236,17 @@ class Ticker(TickerBase):
         return self.get_news()
 
     @property
-    def analysis(self):
-        return self.get_analysis()
+    def earnings_trend(self) -> _pd.DataFrame:
+        return self.get_earnings_trend()
+
+    @property
+    def earnings_dates(self) -> _pd.DataFrame:
+        return self.get_earnings_dates()
+
+    @property
+    def earnings_forecasts(self) -> _pd.DataFrame:
+        return self.get_earnings_forecast()
+
+    @property
+    def history_metadata(self) -> dict:
+        return self.get_history_metadata()

yfinance/tickers.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 #
-# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
+# yfinance - market data downloader
 # https://github.com/ranaroussi/yfinance
 #
 # Copyright 2017-2019 Ran Aroussi
@@ -25,48 +25,48 @@ from . import Ticker, multi
 # from collections import namedtuple as _namedtuple
 
 
-class Tickers():
+class Tickers:
 
     def __repr__(self):
         return 'yfinance.Tickers object <%s>' % ",".join(self.symbols)
 
-    def __init__(self, tickers):
+    def __init__(self, tickers, session=None):
         tickers = tickers if isinstance(
             tickers, list) else tickers.replace(',', ' ').split()
         self.symbols = [ticker.upper() for ticker in tickers]
-        ticker_objects = {}
+        self.tickers = {ticker:Ticker(ticker, session=session) for ticker in self.symbols}
 
-        for ticker in self.symbols:
-            ticker_objects[ticker] = Ticker(ticker)
-
-        self.tickers = ticker_objects
         # self.tickers = _namedtuple(
         #     "Tickers", ticker_objects.keys(), rename=True
         # )(*ticker_objects.values())
 
     def history(self, period="1mo", interval="1d",
                 start=None, end=None, prepost=False,
-                actions=True, auto_adjust=True, proxy=None,
+                actions=True, auto_adjust=True, repair=False,
+                proxy=None,
                 threads=True, group_by='column', progress=True,
-                timeout=None, **kwargs):
+                timeout=10, **kwargs):
 
         return self.download(
-                period, interval,
-                start, end, prepost,
-                actions, auto_adjust, proxy,
-                threads, group_by, progress,
-                timeout, **kwargs)
+            period, interval,
+            start, end, prepost,
+            actions, auto_adjust, repair, 
+            proxy,
+            threads, group_by, progress,
+            timeout, **kwargs)
 
     def download(self, period="1mo", interval="1d",
                  start=None, end=None, prepost=False,
-                 actions=True, auto_adjust=True, proxy=None,
+                 actions=True, auto_adjust=True, repair=False, 
+                 proxy=None,
                  threads=True, group_by='column', progress=True,
-                 timeout=None, **kwargs):
+                 timeout=10, **kwargs):
 
         data = multi.download(self.symbols,
                               start=start, end=end,
                               actions=actions,
                               auto_adjust=auto_adjust,
+                              repair=repair,
                               period=period,
                               interval=interval,
                               prepost=prepost,
@@ -85,3 +85,6 @@ class Tickers():
             data.sort_index(level=0, axis=1, inplace=True)
 
         return data
+
+    def news(self):
+        return {ticker: [item for item in Ticker(ticker).news] for ticker in self.symbols}

yfinance/utils.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 #
-# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
+# yfinance - market data downloader
 # https://github.com/ranaroussi/yfinance
 #
 # Copyright 2017-2019 Ran Aroussi
@@ -21,19 +21,67 @@
 
 from __future__ import print_function
 
+import datetime as _datetime
+import dateutil as _dateutil
+from typing import Dict, Union, List, Optional
+
+import pytz as _tz
 import requests as _requests
 import re as _re
 import pandas as _pd
 import numpy as _np
 import sys as _sys
+import os as _os
+import appdirs as _ad
+import sqlite3 as _sqlite3
+import atexit as _atexit
+from functools import lru_cache
+import logging
+
+from threading import Lock
+
+from pytz import UnknownTimeZoneError
 
 try:
     import ujson as _json
 except ImportError:
     import json as _json
 
+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'}
 
-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
+from types import FunctionType
+from inspect import getmembers
+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)
+
+
+yf_logger = None
+def get_yf_logger():
+    global yf_logger
+    if yf_logger is None:
+        yf_logger = logging.getLogger("yfinance")
+        if yf_logger.handlers is None or len(yf_logger.handlers) == 0:
+            # Add stream handler if user not already added one
+            h = logging.StreamHandler()
+            formatter = logging.Formatter(fmt='%(levelname)s %(message)s')
+            h.setFormatter(formatter)
+            yf_logger.addHandler(h)
+    return yf_logger
 
 
 def is_isin(string):
@@ -41,7 +89,7 @@ def is_isin(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_
@@ -80,7 +128,9 @@ def get_news_by_isin(isin, proxy=None, session=None):
     return data.get('news', {})
 
 
-def empty_df(index=[]):
+def empty_df(index=None):
+    if index is None:
+        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})
@@ -88,44 +138,233 @@ def empty_df(index=[]):
     return empty
 
 
-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 empty_earnings_dates_df():
+    empty = _pd.DataFrame(
+        columns=["Symbol", "Company", "Earnings Date",
+                 "EPS Estimate", "Reported EPS", "Surprise(%)"])
+    return empty
 
 
-def get_json(url, proxy=None, session=None):
-    session = session or _requests
-    html = session.get(url=url, proxies=proxy, headers=user_agent_headers).text
+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".
 
-    if "QuoteSummaryStore" not in html:
-        html = session.get(url=url, proxies=proxy).text
-        if "QuoteSummaryStore" not in html:
-            return {}
+    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.
 
-    json_str = html.split('root.App.main =')[1].split(
-        '(this)')[0].split(';\n}')[0].strip()
-    data = _json.loads(json_str)[
-        'context']['dispatcher']['stores']['QuoteSummaryStore']
-
-    # return data
-    new_data = _json.dumps(data).replace('{}', 'null')
-    new_data = _re.sub(
-        r'\{[\'|\"]raw[\'|\"]:(.*?),(.*?)\}', r'\1', new_data)
-
-    return _json.loads(new_data)
+    '''
+    template_ttm_order = []  # Save the TTM (Trailing Twelve Months) ordering to an object.
+    template_annual_order = []  # Save the annual ordering to an object.
+    template_order = []  # Save the ordering to an object (this can be utilized for quarterlies)
+    level_detail = []  # Record the level of each line item of the income statement ("Operating Revenue" and "Excise Taxes" sum to return "Total Revenue" we need to keep track of this)
+    for key in data['template']:
+        # Loop through the json to retreive the exact financial order whilst appending to the objects
+        template_ttm_order.append('trailing{}'.format(key['key']))
+        template_annual_order.append('annual{}'.format(key['key']))
+        template_order.append('{}'.format(key['key']))
+        level_detail.append(0)
+        if 'children' in key:
+            for child1 in key['children']:  # Level 1
+                template_ttm_order.append('trailing{}'.format(child1['key']))
+                template_annual_order.append('annual{}'.format(child1['key']))
+                template_order.append('{}'.format(child1['key']))
+                level_detail.append(1)
+                if 'children' in child1:
+                    for child2 in child1['children']:  # Level 2
+                        template_ttm_order.append('trailing{}'.format(child2['key']))
+                        template_annual_order.append('annual{}'.format(child2['key']))
+                        template_order.append('{}'.format(child2['key']))
+                        level_detail.append(2)
+                        if 'children' in child2:
+                            for child3 in child2['children']:  # Level 3
+                                template_ttm_order.append('trailing{}'.format(child3['key']))
+                                template_annual_order.append('annual{}'.format(child3['key']))
+                                template_order.append('{}'.format(child3['key']))
+                                level_detail.append(3)
+                                if 'children' in child3:
+                                    for child4 in child3['children']:  # Level 4
+                                        template_ttm_order.append('trailing{}'.format(child4['key']))
+                                        template_annual_order.append('annual{}'.format(child4['key']))
+                                        template_order.append('{}'.format(child4['key']))
+                                        level_detail.append(4)
+                                        if 'children' in child4:
+                                            for child5 in child4['children']:  # Level 5
+                                                template_ttm_order.append('trailing{}'.format(child5['key']))
+                                                template_annual_order.append('annual{}'.format(child5['key']))
+                                                template_order.append('{}'.format(child5['key']))
+                                                level_detail.append(5)
+    return template_ttm_order, template_annual_order, template_order, level_detail
 
 
-def camel2title(o):
-    return [_re.sub("([a-z])([A-Z])", r"\g<1> \g<2>", i).title() for i in o]
+def retreive_financial_details(data):
+    '''
+    retreive_financial_details returns all of the available financial details under the "QuoteTimeSeriesStore" for any of the following three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
+
+    Returns:
+        - TTM_dicts: A dictionary full of all of the available Trailing Twelve Month figures, this can easily be converted to a pandas dataframe.
+        - Annual_dicts: A dictionary full of all of the available Annual figures, this can easily be converted to a pandas dataframe.
+    '''
+    TTM_dicts = []  # Save a dictionary object to store the TTM financials.
+    Annual_dicts = []  # Save a dictionary object to store the Annual financials.
+    for key in data['timeSeries']:  # Loop through the time series data to grab the key financial figures.
+        try:
+            if len(data['timeSeries'][key]) > 0:
+                time_series_dict = {}
+                time_series_dict['index'] = key
+                for each in data['timeSeries'][key]:  # Loop through the years
+                    if each == None:
+                        continue
+                    else:
+                        time_series_dict[each['asOfDate']] = each['reportedValue']
+                    # time_series_dict["{}".format(each['asOfDate'])] = data['timeSeries'][key][each]['reportedValue']
+                if 'trailing' in key:
+                    TTM_dicts.append(time_series_dict)
+                elif 'annual' in key:
+                    Annual_dicts.append(time_series_dict)
+        except Exception as e:
+            pass
+    return TTM_dicts, Annual_dicts
+
+
+def format_annual_financial_statement(level_detail, annual_dicts, annual_order, ttm_dicts=None, ttm_order=None):
+    '''
+    format_annual_financial_statement formats any annual financial statement
+
+    Returns:
+        - _statement: A fully formatted annual financial statement in pandas dataframe.
+    '''
+    Annual = _pd.DataFrame.from_dict(annual_dicts).set_index("index")
+    Annual = Annual.reindex(annual_order)
+    Annual.index = Annual.index.str.replace(r'annual', '')
+
+    # Note: balance sheet is the only financial statement with no ttm detail
+    if (ttm_dicts not in [[], None]) and (ttm_order not in [[], None]):
+        TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index")
+        TTM = TTM.reindex(ttm_order)
+        # 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 not j in acronyms else j for j in s] for s in strings]
+    strings = [sep.join(s) for s in strings]
+
+    return strings
+
+
+def snake_case_2_camelCase(s):
+    sc = s.split('_')[0] + ''.join(x.title() for x in s.split('_')[1:])
+    return sc
+
+
+def _parse_user_dt(dt, exchange_tz):
+    if isinstance(dt, int):
+        # Should already be epoch, test with conversion:
+        _datetime.datetime.fromtimestamp(dt)
+    else:
+        # Convert str/date -> datetime, set tzinfo=exchange, get timestamp:
+        if isinstance(dt, str):
+            dt = _datetime.datetime.strptime(str(dt), '%Y-%m-%d')
+        if isinstance(dt, _datetime.date) and not isinstance(dt, _datetime.datetime):
+            dt = _datetime.datetime.combine(dt, _datetime.time(0))
+        if isinstance(dt, _datetime.datetime) and dt.tzinfo is None:
+            # Assume user is referring to exchange's timezone
+            dt = _tz.timezone(exchange_tz).localize(dt)
+        dt = int(dt.timestamp())
+    return dt
+
+
+def _interval_to_timedelta(interval):
+    if interval == "1mo":
+        return _dateutil.relativedelta.relativedelta(months=1)
+    elif interval == "3mo":
+        return _dateutil.relativedelta.relativedelta(months=3)
+    elif interval == "1y":
+        return _dateutil.relativedelta.relativedelta(years=1)
+    elif interval == "1wk":
+        return _pd.Timedelta(days=7, unit='d')
+    else: 
+        return _pd.Timedelta(interval)
 
 
 def auto_adjust(data):
+    col_order = data.columns
     df = data.copy()
-    ratio = df["Close"] / df["Adj Close"]
-    df["Adj Open"] = df["Open"] / ratio
-    df["Adj High"] = df["High"] / ratio
-    df["Adj Low"] = df["Low"] / ratio
+    ratio = (df["Adj Close"] / df["Close"]).to_numpy()
+    df["Adj Open"] = df["Open"] * ratio
+    df["Adj High"] = df["High"] * ratio
+    df["Adj Low"] = df["Low"] * ratio
 
     df.drop(
         ["Open", "High", "Low", "Close"],
@@ -136,13 +375,13 @@ def auto_adjust(data):
         "Adj Low": "Low", "Adj Close": "Close"
     }, inplace=True)
 
-    df = df[["Open", "High", "Low", "Close", "Volume"]]
-    return df[["Open", "High", "Low", "Close", "Volume"]]
+    return df[[c for c in col_order if c in df.columns]]
 
 
 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
@@ -158,10 +397,10 @@ def back_adjust(data):
         "Adj Low": "Low"
     }, inplace=True)
 
-    return df[["Open", "High", "Low", "Close", "Volume"]]
+    return df[[c for c in col_order if c in df.columns]]
 
 
-def parse_quotes(data, tz=None):
+def parse_quotes(data):
     timestamps = data["timestamp"]
     ohlc = data["indicators"]["quote"][0]
     volumes = ohlc["volume"]
@@ -184,15 +423,13 @@ def parse_quotes(data, tz=None):
     quotes.index = _pd.to_datetime(timestamps, unit="s")
     quotes.sort_index(inplace=True)
 
-    if tz is not None:
-        quotes.index = quotes.index.tz_localize(tz)
-
     return quotes
 
 
-def parse_actions(data, tz=None):
-    dividends = _pd.DataFrame(columns=["Dividends"])
-    splits = _pd.DataFrame(columns=["Stock Splits"])
+def parse_actions(data):
+    dividends = None
+    capital_gains = None
+    splits = None
 
     if "events" in data:
         if "dividends" in data["events"]:
@@ -201,25 +438,353 @@ def parse_actions(data, tz=None):
             dividends.set_index("date", inplace=True)
             dividends.index = _pd.to_datetime(dividends.index, unit="s")
             dividends.sort_index(inplace=True)
-            if tz is not None:
-                dividends.index = dividends.index.tz_localize(tz)
-
             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"]
+
         if "splits" in data["events"]:
             splits = _pd.DataFrame(
                 data=list(data["events"]["splits"].values()))
             splits.set_index("date", inplace=True)
             splits.index = _pd.to_datetime(splits.index, unit="s")
             splits.sort_index(inplace=True)
-            if tz is not None:
-                splits.index = splits.index.tz_localize(tz)
             splits["Stock Splits"] = splits["numerator"] / \
-                splits["denominator"]
-            splits = splits["Stock Splits"]
+                                     splits["denominator"]
+            splits = splits[["Stock Splits"]]
 
-    return dividends, 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"][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"][n - 1]):
+                    quotes.loc[idx2, "High"] = _np.nanmax([quotes["High"][n - 1], quotes["High"][n - 2]])
+                    if "Adj High" in quotes.columns:
+                        quotes.loc[idx2, "Adj High"] = _np.nanmax([quotes["Adj High"][n - 1], quotes["Adj High"][n - 2]])
+
+                if not _np.isnan(quotes["Low"][n - 1]):
+                    quotes.loc[idx2, "Low"] = _np.nanmin([quotes["Low"][n - 1], quotes["Low"][n - 2]])
+                    if "Adj Low" in quotes.columns:
+                        quotes.loc[idx2, "Adj Low"] = _np.nanmin([quotes["Adj Low"][n - 1], quotes["Adj Low"][n - 2]])
+
+                quotes.loc[idx2, "Close"] = quotes["Close"][n - 1]
+                if "Adj Close" in quotes.columns:
+                    quotes.loc[idx2, "Adj Close"] = quotes["Adj Close"][n - 1]
+                quotes.loc[idx2, "Volume"] += quotes["Volume"][n - 1]
+                quotes = quotes.drop(quotes.index[n - 1])
+
+    return quotes
+
+
+def safe_merge_dfs(df_main, df_sub, interval):
+    # Carefully merge 'df_sub' onto 'df_main'
+    # If naive merge fails, try again with reindexing df_sub:
+    # 1) if interval is weekly or monthly, then try with index set to start of week/month
+    # 2) if still failing then manually search through df_main.index to reindex df_sub
+
+    if df_sub.shape[0] == 0:
+        raise Exception("No data to merge")
+
+    df_sub_backup = df_sub.copy()
+    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]
+
+    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("New index contains duplicates but unsure how to aggregate for '{}'".format(data_col_name))
+        if "_NewIndex" in df.columns:
+            df = df.drop("_NewIndex", axis=1)
+        return df
+
+    df = df_main.join(df_sub)
+
+    f_na = df[data_col].isna()
+    data_lost = sum(~f_na) < df_sub.shape[0]
+    if not data_lost:
+        return df
+    # Lost data during join()
+    # Backdate all df_sub.index dates to start of week/month
+    if interval == "1wk":
+        new_index = _pd.PeriodIndex(df_sub.index, freq='W').to_timestamp()
+    elif interval == "1mo":
+        new_index = _pd.PeriodIndex(df_sub.index, freq='M').to_timestamp()
+    elif interval == "3mo":
+        new_index = _pd.PeriodIndex(df_sub.index, freq='Q').to_timestamp()
+    else:
+        new_index = None
+
+    if new_index is not None:
+        new_index = new_index.tz_localize(df.index.tz, ambiguous=True, nonexistent='shift_forward')
+        df_sub = _reindex_events(df_sub, new_index, data_col)
+        df = df_main.join(df_sub)
+
+    f_na = df[data_col].isna()
+    data_lost = sum(~f_na) < df_sub.shape[0]
+    if not data_lost:
+        return df
+    # Lost data during join(). Manually check each df_sub.index date against df_main.index to
+    # find matching interval
+    df_sub = df_sub_backup.copy()
+    new_index = [-1] * df_sub.shape[0]
+    for i in range(df_sub.shape[0]):
+        dt_sub_i = df_sub.index[i]
+        if dt_sub_i in df_main.index:
+            new_index[i] = dt_sub_i
+            continue
+        # Found a bad index date, need to search for near-match in df_main (same week/month)
+        fixed = False
+        for j in range(df_main.shape[0] - 1):
+            dt_main_j0 = df_main.index[j]
+            dt_main_j1 = df_main.index[j + 1]
+            if (dt_main_j0 <= dt_sub_i) and (dt_sub_i < dt_main_j1):
+                fixed = True
+                if interval.endswith('h') or interval.endswith('m'):
+                    # Must also be same day
+                    fixed = (dt_main_j0.date() == dt_sub_i.date()) and (dt_sub_i.date() == dt_main_j1.date())
+                if fixed:
+                    dt_sub_i = dt_main_j0
+                    break
+        if not fixed:
+            last_main_dt = df_main.index[df_main.shape[0] - 1]
+            diff = dt_sub_i - last_main_dt
+            if interval == "1mo" and last_main_dt.month == dt_sub_i.month:
+                dt_sub_i = last_main_dt
+                fixed = True
+            elif interval == "3mo" and last_main_dt.year == dt_sub_i.year and last_main_dt.quarter == dt_sub_i.quarter:
+                dt_sub_i = last_main_dt
+                fixed = True
+            elif interval == "1wk":
+                if last_main_dt.week == dt_sub_i.week:
+                    dt_sub_i = last_main_dt
+                    fixed = True
+                elif (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < _datetime.timedelta(weeks=1)):
+                    # With some specific start dates (e.g. around early Jan), Yahoo
+                    # messes up start-of-week, is Saturday not Monday. So check
+                    # if same week another way
+                    dt_sub_i = last_main_dt
+                    fixed = True
+            elif interval == "1d" and last_main_dt.day == dt_sub_i.day:
+                dt_sub_i = last_main_dt
+                fixed = True
+            elif interval == "1h" and last_main_dt.hour == dt_sub_i.hour:
+                dt_sub_i = last_main_dt
+                fixed = True
+            elif interval.endswith('m') or interval.endswith('h'):
+                td = _pd.to_timedelta(interval)
+                if (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < td):
+                    dt_sub_i = last_main_dt
+                    fixed = True
+        new_index[i] = dt_sub_i
+    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:
+        ## Not always possible to match events with trading, e.g. when released pre-market.
+        ## So have to append to bottom with nan prices.
+        ## But should only be impossible with intra-day price data.
+        if interval.endswith('m') or interval.endswith('h') or interval == "1d":
+            # Update: is possible with daily data when dividend very recent
+            f_missing = ~df_sub.index.isin(df.index)
+            df_sub_missing = df_sub[f_missing].copy()
+            keys = {"Adj Open", "Open", "Adj High", "High", "Adj Low", "Low", "Adj Close",
+                    "Close"}.intersection(df.columns)
+            df_sub_missing[list(keys)] = _np.nan
+            col_ordering = df.columns
+            df = _pd.concat([df, df_sub_missing], sort=True)[col_ordering]
+        else:
+            raise Exception("Lost data during merge despite all attempts to align data (see above)")
+
+    return df
+
+
+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, 
+        # 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:
+        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'):
@@ -261,11 +826,179 @@ class ProgressBar:
         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) + ']'
+                        num_hashes + ' ' * (all_full - num_hashes) + ']'
         pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
         pct_string = '%d%%' % percent_done
         self.prog_bar = self.prog_bar[0:pct_place] + \
-            (pct_string + self.prog_bar[pct_place + len(pct_string):])
+                        (pct_string + self.prog_bar[pct_place + len(pct_string):])
 
     def __str__(self):
         return str(self.prog_bar)
+
+
+# ---------------------------------
+# TimeZone cache related code
+# ---------------------------------
+
+class _KVStore:
+    """Simpel Sqlite backed key/value store, key and value are strings. Should be thread safe."""
+
+    def __init__(self, filename):
+        self._cache_mutex = Lock()
+        with self._cache_mutex:
+            self.conn = _sqlite3.connect(filename, timeout=10, check_same_thread=False)
+            self.conn.execute('pragma journal_mode=wal')
+            try:
+                self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT) without rowid')
+            except Exception as e:
+                if 'near "without": syntax error' in str(e):
+                    # "without rowid" requires sqlite 3.8.2. Older versions will raise exception
+                    self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT)')
+                else:
+                    raise
+            self.conn.commit()
+        _atexit.register(self.close)
+
+    def close(self):
+        if self.conn is not None:
+            with self._cache_mutex:
+                self.conn.close()
+                self.conn = None
+
+    def get(self, key: str) -> Union[str, None]:
+        """Get value for key if it exists else returns None"""
+        item = self.conn.execute('select value from "kv" where key=?', (key,))
+        if item:
+            return next(item, (None,))[0]
+
+    def set(self, key: str, value: str) -> None:
+        with self._cache_mutex:
+            self.conn.execute('replace into "kv" (key, value) values (?,?)', (key, value))
+            self.conn.commit()
+
+    def bulk_set(self, kvdata: Dict[str, str]):
+        records = tuple(i for i in kvdata.items())
+        with self._cache_mutex:
+            self.conn.executemany('replace into "kv" (key, value) values (?,?)', records)
+            self.conn.commit()
+
+    def delete(self, key: str):
+        with self._cache_mutex:
+            self.conn.execute('delete from "kv" where key=?', (key,))
+            self.conn.commit()
+
+
+class _TzCacheException(Exception):
+    pass
+
+
+class _TzCache:
+    """Simple sqlite file cache of ticker->timezone"""
+
+    def __init__(self):
+        self._setup_cache_folder()
+        # Must init db here, where is thread-safe
+        self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
+        self._migrate_cache_tkr_tz()
+
+    def _setup_cache_folder(self):
+        if not _os.path.isdir(self._db_dir):
+            try:
+                _os.makedirs(self._db_dir)
+            except OSError as err:
+                raise _TzCacheException("Error creating TzCache folder: '{}' reason: {}"
+                                        .format(self._db_dir, err))
+
+        elif not (_os.access(self._db_dir, _os.R_OK) and _os.access(self._db_dir, _os.W_OK)):
+            raise _TzCacheException("Cannot read and write in TzCache folder: '{}'"
+                                    .format(self._db_dir, ))
+
+    def lookup(self, tkr):
+        return self.tz_db.get(tkr)
+
+    def store(self, tkr, tz):
+        if tz is None:
+            self.tz_db.delete(tkr)
+        elif self.tz_db.get(tkr) is not None:
+            raise Exception("Tkr {} tz already in cache".format(tkr))
+        else:
+            self.tz_db.set(tkr, tz)
+
+    @property
+    def _db_dir(self):
+        global _cache_dir
+        return _os.path.join(_cache_dir, "py-yfinance")
+
+    @property
+    def tz_db(self):
+        return self._tz_db
+
+    def _migrate_cache_tkr_tz(self):
+        """Migrate contents from old ticker CSV-cache to SQLite db"""
+        old_cache_file_path = _os.path.join(self._db_dir, "tkr-tz.csv")
+
+        if not _os.path.isfile(old_cache_file_path):
+            return None
+        try:
+            df = _pd.read_csv(old_cache_file_path, index_col="Ticker", on_bad_lines="skip")
+        except _pd.errors.EmptyDataError:
+            _os.remove(old_cache_file_path)
+        except TypeError:
+            _os.remove(old_cache_file_path)
+        else:
+            self.tz_db.bulk_set(df.to_dict()['Tz'])
+            _os.remove(old_cache_file_path)
+
+
+class _TzCacheDummy:
+    """Dummy cache to use if tz cache is disabled"""
+
+    def lookup(self, tkr):
+        return None
+
+    def store(self, tkr, tz):
+        pass
+
+    @property
+    def tz_db(self):
+        return None
+
+
+def get_tz_cache():
+    """
+    Get the timezone cache, initializes it and creates cache folder if needed on first call.
+    If folder cannot be created for some reason it will fall back to initialize a
+    dummy cache with same interface as real cash.
+    """
+    # as this can be called from multiple threads, protect it.
+    with _cache_init_lock:
+        global _tz_cache
+        if _tz_cache is None:
+            try:
+                _tz_cache = _TzCache()
+            except _TzCacheException as err:
+                logger.error("Failed to create TzCache, reason: %s. "
+                             "TzCache will not be used. "
+                             "Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'",
+                             err)
+                _tz_cache = _TzCacheDummy()
+
+        return _tz_cache
+
+
+_cache_dir = _ad.user_cache_dir()
+_cache_init_lock = Lock()
+_tz_cache = None
+
+
+def set_tz_cache_location(cache_dir: str):
+    """
+    Sets the path to create the "py-yfinance" cache folder in.
+    Useful if the default folder returned by "appdir.user_cache_dir()" is not writable.
+    Must be called before cache is used (that is, before fetching tickers).
+    :param cache_dir: Path to use for caches
+    :return: None
+    """
+    global _cache_dir, _tz_cache
+    assert _tz_cache is None, "Time Zone cache already initialized, setting path must be done before cache is created"
+    _cache_dir = cache_dir

yfinance/version.py

@@ -1 +1 @@
-version = "0.1.66"
+version = "0.2.19b4"