Bump version to 0.2.23

Update quote.py

.github/ISSUE_TEMPLATE/bug_report.md

@@ -7,14 +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 and provide the following, as best you can:
+Confirm by running:
 
-- Simple code that reproduces your problem
-- The error message
+`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#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/workflows/python-publish.yml

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

.gitignore

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

CHANGELOG.rst

@@ -1,6 +1,153 @@
 Change Log
 ===========
 
+0.2.23
+------
+Fix 'Unauthorized' error #1595
+
+0.2.22
+------
+Fix unhandled 'sqlite3.DatabaseError' #1574
+
+0.2.21
+------
+Fix financials tables #1568
+Price repair update: fix Yahoo messing up dividend and split adjustments #1543
+Fix logging behaviour #1562
+Fix merge future div/split into prices #1567
+
+0.2.20
+------
+Switch to `logging` module #1493 #1522 #1541
+Price history:
+- optimise #1514
+- fixes #1523
+- fix TZ-cache corruption #1528
+
+0.2.18
+------
+Fix 'fast_info' error '_np not found' #1496
+Fix bug in timezone cache #1498
+
+0.2.17
+------
+Fix prices error with Pandas 2.0 #1488
+
+0.2.16
+------
+Fix 'fast_info deprecated' msg appearing at Ticker() init
+
+0.2.15
+------
+Restore missing Ticker.info keys #1480
+
+0.2.14
+------
+Fix Ticker.info dict by fetching from API #1461
+
+0.2.13
+------
+Price bug fixes:
+- fetch big-interval with Capital Gains #1455
+- merging dividends & splits with prices #1452
+
+0.2.12
+------
+Disable annoying 'backup decrypt' msg
+
+0.2.11
+------
+Fix history_metadata accesses for unusual symbols #1411
+
+0.2.10
+------
+General
+- allow using sqlite3 < 3.8.2 #1380
+- add another backup decrypt option #1379
+Prices
+- restore original download() timezone handling #1385
+- fix & improve price repair #1289 2a2928b 86d6acc
+- drop intraday intervals if in post-market but prepost=False #1311
+Info
+- fast_info improvements:
+  - add camelCase keys, add dict functions values() & items() #1368
+  - fix fast_info["previousClose"] #1383
+- catch TypeError Exception #1397
+
+0.2.9
+-----
+- Fix fast_info bugs #1362
+
+0.2.7
+-----
+- Fix Yahoo decryption, smarter this time #1353
+- Rename basic_info -> fast_info #1354
+
+0.2.6
+-----
+- Fix Ticker.basic_info lazy-loading #1342
+
+0.2.5
+-----
+- Fix Yahoo data decryption again #1336
+- New: Ticker.basic_info - faster Ticker.info #1317
+
+0.2.4
+-----
+- Fix Yahoo data decryption #1297
+- New feature: 'Ticker.get_shares_full()' #1301
+- Improve caching of financials data #1284
+- Restore download() original alignment behaviour #1283
+- Fix the database lock error in multithread download #1276
+
+0.2.3
+-----
+- Make financials API '_' use consistent
+
+0.2.2
+-----
+- Restore 'financials' attribute (map to 'income_stmt')
+
+0.2.1
+-----
+Release!
+
+0.2.0rc5
+--------
+- Improve financials error handling #1243
+- Fix '100x price' repair #1244
+
+0.2.0rc4
+--------
+- Access to old financials tables via `get_income_stmt(legacy=True)`
+- Optimise scraping financials & fundamentals, 2x faster
+- Add 'capital gains' alongside dividends & splits for ETFs, and metadata available via `history_metadata`, plus a bunch of price fixes
+For full list of changes see #1238
+
+0.2.0rc2
+--------
+Financials
+- fix financials tables to match website  #1128 #1157
+- lru_cache to optimise web requests  #1147
+Prices
+- improve price repair  #1148
+- fix merging dividends/splits with day/week/monthly prices  #1161
+- fix the Yahoo DST fixes  #1143
+- improve bad/delisted ticker handling  #1140
+Misc
+- fix 'trailingPegRatio'  #1138
+- improve error handling  #1118
+
+0.2.0rc1
+--------
+Jumping to 0.2 for this big update. 0.1.* will continue to receive bug-fixes
+- timezone cache performance massively improved. Thanks @fredrik-corneliusson #1113 #1112 #1109 #1105 #1099
+- price repair feature #1110
+- fix merging of dividends/splits with prices #1069 #1086 #1102
+- fix Yahoo returning latest price interval across 2 rows #1070
+- optional: raise errors as exceptions: raise_errors=True #1104
+- add proper unit tests #1069
+
 0.1.81
 ------
 - Fix unhandled tz-cache exception #1107

README.md

@@ -53,53 +53,43 @@ import yfinance as yf
 
 msft = yf.Ticker("MSFT")
 
-# get stock info
+# get all stock info
 msft.info
 
 # get historical market data
-hist = msft.history(period="max")
+hist = msft.history(period="1mo")
 
-# show actions (dividends, splits)
+# show meta information about the history (requires history() to be called first)
+msft.history_metadata
+
+# show actions (dividends, splits, capital gains)
 msft.actions
-
-# show dividends
 msft.dividends
-
-# show splits
 msft.splits
+msft.capital_gains  # only for mutual funds & etfs
 
-# show financials
-msft.financials
-msft.quarterly_financials
+# show share count
+msft.get_shares_full(start="2022-01-01", end=None)
 
-# show major holders
-msft.major_holders
-
-# show institutional holders
-msft.institutional_holders
-
-# show balance sheet
+# show financials:
+# - income statement
+msft.income_stmt
+msft.quarterly_income_stmt
+# - balance sheet
 msft.balance_sheet
 msft.quarterly_balance_sheet
-
-# show cashflow
+# - cash flow statement
 msft.cashflow
 msft.quarterly_cashflow
+# see `Ticker.get_income_stmt()` for more options
 
-# show earnings
-msft.earnings
-msft.quarterly_earnings
+# show holders
+msft.major_holders
+msft.institutional_holders
+msft.mutualfund_holders
 
-# show sustainability
-msft.sustainability
-
-# show analysts recommendations
-msft.recommendations
-
-# show next event (earnings, etc)
-msft.calendar
-
-# show all earnings dates
+# 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*
@@ -128,12 +118,43 @@ msft.history(..., proxy="PROXY_SERVER")
 msft.get_actions(proxy="PROXY_SERVER")
 msft.get_dividends(proxy="PROXY_SERVER")
 msft.get_splits(proxy="PROXY_SERVER")
+msft.get_capital_gains(proxy="PROXY_SERVER")
 msft.get_balance_sheet(proxy="PROXY_SERVER")
 msft.get_cashflow(proxy="PROXY_SERVER")
 msft.option_chain(..., proxy="PROXY_SERVER")
 ...
 ```
 
+### Multiple tickers
+
+To initialize multiple `Ticker` objects, use
+
+```python
+import yfinance as yf
+
+tickers = yf.Tickers('msft aapl goog')
+
+# access each ticker using (example)
+tickers.tickers['MSFT'].info
+tickers.tickers['AAPL'].history(period="1mo")
+tickers.tickers['GOOG'].actions
+```
+
+To download price history into one table:
+
+```python
+import yfinance as yf
+data = yf.download("SPY AAPL", period="1mo")
+```
+
+#### `yf.download()` and `Ticker.history()` have many options for configuring fetching and processing. [Review the Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
+
+### Logging
+
+`yfinance` now uses the `logging` module to handle messages, default behaviour is only print errors. If debugging, use `yf.enable_debug_mode()` to switch logging to debug with custom formatting.
+
+### Smarter scraping
+
 To use a custom `requests` session (for example to cache calls to the
 API or customize the `User-agent` header), pass a `session=` argument to
 the Ticker constructor.
@@ -142,86 +163,25 @@ the Ticker constructor.
 import requests_cache
 session = requests_cache.CachedSession('yfinance.cache')
 session.headers['User-agent'] = 'my-program/1.0'
-ticker = yf.Ticker('msft aapl goog', session=session)
+ticker = yf.Ticker('msft', session=session)
 # The scraped response will be stored in the cache
 ticker.actions
 ```
 
-To initialize multiple `Ticker` objects, use
-
+Combine a `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-limiter/blocker that can corrupt data.
 ```python
-import yfinance as yf
+from requests import Session
+from requests_cache import CacheMixin, SQLiteCache
+from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
+from pyrate_limiter import Duration, RequestRate, Limiter
+class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
+    pass
 
-tickers = yf.Tickers('msft aapl goog')
-# ^ returns a named tuple of Ticker objects
-
-# access each ticker using (example)
-tickers.tickers.MSFT.info
-tickers.tickers.AAPL.history(period="1mo")
-tickers.tickers.GOOG.actions
-```
-
-### Fetching data for multiple tickers
-
-```python
-import yfinance as yf
-data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
-```
-
-I've also added some options to make life easier :)
-
-```python
-data = yf.download(  # or pdr.get_data_yahoo(...
-        # tickers list or string as well
-        tickers = "SPY AAPL MSFT",
-
-        # use "period" instead of start/end
-        # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
-        # (optional, default is '1mo')
-        period = "ytd",
-
-        # fetch data by interval (including intraday if period < 60 days)
-        # valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
-        # (optional, default is '1d')
-        interval = "1m",
-
-        # Whether to ignore timezone when aligning ticker data from 
-        # different timezones. Default is True. False may be useful for 
-        # minute/hourly data.
-        ignore_tz = False,
-
-        # group by ticker (to access via data['SPY'])
-        # (optional, default is 'column')
-        group_by = 'ticker',
-
-        # adjust all OHLC automatically
-        # (optional, default is False)
-        auto_adjust = True,
-
-        # download pre/post regular market hours data
-        # (optional, default is False)
-        prepost = True,
-
-        # use threads for mass downloading? (True/False/Integer)
-        # (optional, default is True)
-        threads = True,
-
-        # proxy URL scheme use use when downloading?
-        # (optional, default is None)
-        proxy = None
-    )
-```
-
-### 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")
-...
+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
@@ -239,9 +199,7 @@ yfinance?](https://stackoverflow.com/questions/63107801)
         -   How to download single or multiple tickers into a single
             dataframe with single level column names and a ticker column
 
----
-
-## `pandas_datareader` override
+### `pandas_datareader` override
 
 If your code uses `pandas_datareader` and you want to download data
 faster, you can "hijack" `pandas_datareader.data.get_data_yahoo()`
@@ -258,6 +216,18 @@ yf.pdr_override() # <== that's all it takes :-)
 data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")
 ```
 
+### Timezone cache store
+
+When fetching price data, all dates are localized to stock exchange timezone. 
+But timezone retrieval is relatively slow, so yfinance attemps to cache them 
+in your users cache folder. 
+You can direct cache to use a different location with `set_tz_cache_location()`:
+```python
+import yfinance as yf
+yf.set_tz_cache_location("custom/cache/location")
+...
+```
+
 ---
 
 ## Installation
@@ -268,24 +238,37 @@ Install `yfinance` using `pip`:
 $ pip install yfinance --upgrade --no-cache-dir
 ```
 
+Test new features by installing betas, provide feedback in [corresponding Discussion](https://github.com/ranaroussi/yfinance/discussions):
+``` {.sourceCode .bash}
+$ pip install yfinance --upgrade --no-cache-dir --pre
+```
+
 To install `yfinance` using `conda`, see
 [this](https://anaconda.org/ranaroussi/yfinance).
 
 ### Requirements
 
 -   [Python](https://www.python.org) \>= 2.7, 3.4+
--   [Pandas](https://github.com/pydata/pandas) (tested to work with
-    \>=0.23.1)
--   [Numpy](http://www.numpy.org) \>= 1.11.1
--   [requests](http://docs.python-requests.org/en/master/) \>= 2.14.2
--   [lxml](https://pypi.org/project/lxml/) \>= 4.5.1
--   [appdirs](https://pypi.org/project/appdirs) \>=1.4.4
+-   [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`)
+#### Optional (if you want to use `pandas_datareader`)
 
 -   [pandas\_datareader](https://github.com/pydata/pandas-datareader)
     \>= 0.4.0
 
+## Developers: want to contribute?
+
+`yfinance` relies on community to investigate bugs and contribute code. Developer guide: https://github.com/ranaroussi/yfinance/discussions/1084
+
 ---
 
 ### Legal Stuff

meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.1.58" %}
+{% set version = "0.2.23" %}
 
 package:
   name: "{{ name|lower }}"
@@ -16,22 +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
-    - appdirs >= 1.4.4
+    - 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
-    - appdirs >= 1.4.4
+    - 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

@@ -1,6 +1,11 @@
-pandas>=0.24.0
+pandas>=1.3.0
 numpy>=1.16.5
 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

@@ -38,8 +38,8 @@ setup(
     classifiers=[
         'License :: OSI Approved :: Apache Software License',
         # 'Development Status :: 3 - Alpha',
-        # 'Development Status :: 4 - Beta',
-        'Development Status :: 5 - Production/Stable',
+        'Development Status :: 4 - Beta',
+        #'Development Status :: 5 - Production/Stable',
 
 
         'Operating System :: OS Independent',
@@ -50,20 +50,21 @@ setup(
         'Topic :: Software Development :: Libraries',
         'Topic :: Software Development :: Libraries :: Python Modules',
 
-        'Programming Language :: Python :: 2.7',
-        'Programming Language :: Python :: 3.4',
-        'Programming Language :: Python :: 3.5',
-        # 'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
         'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
     ],
     platforms=['any'],
     keywords='pandas, yahoo finance, pandas datareader',
     packages=find_packages(exclude=['contrib', 'docs', 'tests', 'examples']),
-    install_requires=['pandas>=0.24.0', 'numpy>=1.15',
+    install_requires=['pandas>=1.3.0', 'numpy>=1.16.5',
                       'requests>=2.26', 'multitasking>=0.0.7',
-                      'lxml>=4.5.1', 'appdirs>=1.4.4'],
+                      'lxml>=4.9.1', 'appdirs>=1.4.4', 'pytz>=2022.5',
+                      'frozendict>=2.3.4', 
+                      'beautifulsoup4>=4.11.1', 'html5lib>=1.1'],
+    # Note: Pandas.read_html() needs html5lib & beautifulsoup4
     entry_points={
         'console_scripts': [
             'sample=sample:main',

test_yfinance.py

@@ -15,6 +15,9 @@ Sanity check for most common library uses all working
 
 import yfinance as yf
 import unittest
+import logging
+
+logging.basicConfig(level=logging.DEBUG)
 
 symbols = ['MSFT', 'IWO', 'VFINX', '^GSPC', 'BTC-USD']
 tickers = [yf.Ticker(symbol) for symbol in symbols]
@@ -37,23 +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.shares
-            ticker.earnings_history
+            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 +1,43 @@
+# -*- 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)
+cache_fp = os.path.join(_ad.user_cache_dir(), "py-yfinance", "unittests-cache")
+if os.path.isfile(cache_fp + '.sqlite'):
+    # Delete local cache if older than 1 day:
+    mod_dt = _dt.datetime.fromtimestamp(os.path.getmtime(cache_fp + '.sqlite'))
+    if mod_dt.date() < _dt.date.today():
+        os.remove(cache_fp + '.sqlite')
+session_gbl = CachedLimiterSession(
+    limiter=limiter,
+    bucket_class=MemoryQueueBucket,
+    backend=SQLiteCache(cache_fp, expire_after=_dt.timedelta(hours=1)),
+)
+# Use this instead if only want rate-limiting:
+# from requests_ratelimiter import LimiterSession
+# session_gbl = LimiterSession(limiter=limiter)
+

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

@@ -0,0 +1,23 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-04-14 00:00:00+09:00,4126,4130,4055,4129,4129,7459400,0,0
+2023-04-13 00:00:00+09:00,4064,4099,4026,4081,4081,5160200,0,0
+2023-04-12 00:00:00+09:00,3968,4084,3966,4064,4064,6372000,0,0
+2023-04-11 00:00:00+09:00,3990,4019,3954,3960,3960,6476500,0,0
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tests/data/4063-T-bad-stock-split.csv

@@ -0,0 +1,23 @@
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tests/data/ALPHA-PA-bad-stock-split-fixed.csv

@@ -0,0 +1,30 @@
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tests/data/ALPHA-PA-bad-stock-split.csv

@@ -0,0 +1,30 @@
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tests/data/CNE-L-bad-stock-split-fixed.csv

@@ -0,0 +1,11 @@
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tests/data/CNE-L-bad-stock-split.csv

@@ -0,0 +1,11 @@
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tests/data/DEX-AX-bad-stock-split-fixed.csv

@@ -0,0 +1,24 @@
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tests/data/DEX-AX-bad-stock-split.csv

@@ -0,0 +1,24 @@
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tests/data/MOB-ST-bad-stock-split-fixed.csv

@@ -0,0 +1,17 @@
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tests/data/MOB-ST-bad-stock-split.csv

@@ -0,0 +1,17 @@
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tests/data/SPM-MI-bad-stock-split-fixed.csv

@@ -0,0 +1,23 @@
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tests/data/SPM-MI-bad-stock-split.csv

@@ -0,0 +1,23 @@
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tests/data/SSW-JO-100x-error.csv

@@ -0,0 +1,30 @@
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+2023-05-17 00:00:00+02:00,3687,3731,3656,3671,3671,9892791,0,0
+2023-05-16 00:00:00+02:00,3715,3773,3696,3703,3703,4706789,0,0
+2023-05-15 00:00:00+02:00,3774,3805,3696,3727,3727,7890969,0,0
+2023-05-12 00:00:00+02:00,3750,3844,3671,3774,3774,8724303,0,0
+2023-05-11 00:00:00+02:00,3880,3888,3701,3732,3732,14371855,0,0
+2023-05-10 00:00:00+02:00,3893,3880,3642,3810,3810,30393389,0,0
+2023-05-09 00:00:00+02:00,4441,4441,3939,3966,3966,19833428,0,0
+2023-05-08 00:00:00+02:00,4463,4578,4456,4471,4471,11092519,0,0
+2023-05-05 00:00:00+02:00,4299,4490,4287,4458,4458,28539048,0,0
+2023-05-04 00:00:00+02:00,4149,4330,4123,4283,4283,15506868,0,0
+2023-05-03 00:00:00+02:00,3975,4098,3968,4095,4095,14657028,0,0
+2023-05-02 00:00:00+02:00,4037,4032,3917,3965,3965,11818133,0,0

tests/prices.py

@@ -0,0 +1,921 @@
+from .context import yfinance as yf
+from .context import session_gbl
+
+import unittest
+
+import os
+import datetime as _dt
+import pytz as _tz
+import numpy as _np
+import pandas as _pd
+
+
+class TestPriceHistory(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def test_daily_index(self):
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+        intervals = ["1d", "1wk", "1mo"]
+        for tkr in tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+
+            for interval in intervals:
+                df = dat.history(period="5y", interval=interval)
+
+                f = df.index.time == _dt.time(0)
+                self.assertTrue(f.all())
+
+    def test_download(self):
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+        intervals = ["1d", "1wk", "1mo"]
+        for interval in intervals:
+            df = yf.download(tkrs, period="5y", interval=interval)
+
+            f = df.index.time == _dt.time(0)
+            self.assertTrue(f.all())
+
+            df_tkrs = df.columns.levels[1]
+            self.assertEqual(sorted(tkrs), sorted(df_tkrs))
+
+    def test_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):
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+        test_run = False
+        for tkr in tkrs:
+            start_d = _dt.date.today() - _dt.timedelta(days=59)
+            end_d = None
+            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
+            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
+            if df_daily_divs.shape[0] == 0:
+                continue
+
+            last_div_date = df_daily_divs.index[-1]
+            start_d = last_div_date.date()
+            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
+            test_run = True
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
+
+    def test_intraDayWithEvents_tase(self):
+        # TASE dividend release pre-market, doesn't merge nicely with intra-day data so check still present
+
+        tase_tkrs = ["ICL.TA", "ESLT.TA", "ONE.TA", "MGDL.TA"]
+        test_run = False
+        for tkr in tase_tkrs:
+            start_d = _dt.date.today() - _dt.timedelta(days=59)
+            end_d = None
+            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
+            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
+            if df_daily_divs.shape[0] == 0:
+                continue
+
+            last_div_date = df_daily_divs.index[-1]
+            start_d = last_div_date.date()
+            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
+            test_run = True
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents_tase() because no tickers had a dividend in last 60 days")
+
+    def test_dailyWithEvents(self):
+        start_d = _dt.date(2022, 1, 1)
+        end_d = _dt.date(2023, 1, 1)
+
+        tkr_div_dates = {}
+        tkr_div_dates['BHP.AX'] = [_dt.date(2022, 9, 1), _dt.date(2022, 2, 24)]  # Yahoo claims 23-Feb but wrong because DST
+        tkr_div_dates['IMP.JO'] = [_dt.date(2022, 9, 21), _dt.date(2022, 3, 16)]
+        tkr_div_dates['BP.L'] = [_dt.date(2022, 11, 10), _dt.date(2022, 8, 11), _dt.date(2022, 5, 12), _dt.date(2022, 2, 17)]
+        tkr_div_dates['INTC'] = [_dt.date(2022, 11, 4), _dt.date(2022, 8, 4), _dt.date(2022, 5, 5), _dt.date(2022, 2, 4)]
+
+        for tkr,dates in tkr_div_dates.items():
+            df = yf.Ticker(tkr, session=self.session).history(interval='1d', start=start_d, end=end_d)
+            df_divs = df[df['Dividends']!=0].sort_index(ascending=False)
+            try:
+                self.assertTrue((df_divs.index.date == dates).all())
+            except:
+                print(f'- ticker = {tkr}')
+                print('- response:') ; print(df_divs.index.date)
+                print('- answer:') ; print(dates)
+                raise
+
+    def test_dailyWithEvents_bugs(self):
+        # Reproduce issue #521
+        tkr1 = "QQQ"
+        tkr2 = "GDX"
+        start_d = "2014-12-29"
+        end_d = "2020-11-29"
+        df1 = yf.Ticker(tkr1).history(start=start_d, end=end_d, interval="1d", actions=True)
+        df2 = yf.Ticker(tkr2).history(start=start_d, end=end_d, interval="1d", actions=True)
+        self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
+        self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
+        try:
+            self.assertTrue(df1.index.equals(df2.index))
+        except:
+            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_intraDayWithEvents(self):
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+        test_run = False
+        for tkr in tkrs:
+            start_d = _dt.date.today() - _dt.timedelta(days=59)
+            end_d = None
+            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
+            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
+            if df_daily_divs.shape[0] == 0:
+                continue
+
+            last_div_date = df_daily_divs.index[-1]
+            start_d = last_div_date.date()
+            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
+            test_run = True
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
+
+    def test_intraDayWithEvents_tase(self):
+        # TASE dividend release pre-market, doesn't merge nicely with intra-day data so check still present
+
+        tase_tkrs = ["ICL.TA", "ESLT.TA", "ONE.TA", "MGDL.TA"]
+        test_run = False
+        for tkr in tase_tkrs:
+            start_d = _dt.date.today() - _dt.timedelta(days=59)
+            end_d = None
+            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
+            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
+            if df_daily_divs.shape[0] == 0:
+                continue
+
+            last_div_date = df_daily_divs.index[-1]
+            start_d = last_div_date.date()
+            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
+            test_run = True
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents_tase() because no tickers had a dividend in last 60 days")
+
+    def test_weeklyWithEvents(self):
+        # Reproduce issue #521
+        tkr1 = "QQQ"
+        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
+        dfm = yf.Ticker("ABBV").history(period="max", interval="1mo")
+        dfd = yf.Ticker("ABBV").history(period="max", interval="1d")
+        dfd = dfd[dfd.index > dfm.index[0]]
+        dfm_divs = dfm[dfm['Dividends']!=0]
+        dfd_divs = dfd[dfd['Dividends']!=0]
+        self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
+
+        dfm = yf.Ticker("F").history(period="50mo",interval="1mo")
+        dfd = yf.Ticker("F").history(period="50mo", interval="1d")
+        dfd = dfd[dfd.index > dfm.index[0]]
+        dfm_divs = dfm[dfm['Dividends']!=0]
+        dfd_divs = dfd[dfd['Dividends']!=0]
+        self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
+
+    def test_tz_dst_ambiguous(self):
+        # Reproduce issue #1100
+        try:
+            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_random_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_random_mixups(df_bad, "1wk", tz_exchange, prepost=False, silent=True)
+
+        # 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_random_weekly_preSplit(self):
+        # PNL.L has a stock-split in 2022. Sometimes requesting data before 2022 is not split-adjusted.
+
+        tkr = "PNL.L"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+
+        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_random_mixups(df_bad, "1wk", tz_exchange, prepost=False, silent=True)
+
+        # 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_random_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_random_mixups(df_bad, "1d", tz_exchange, prepost=False, silent=True)
+
+        # First test - no errors left
+        for c in data_cols:
+            self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
+
+        # Second test - all differences should be either ~1x or ~100x
+        ratio = df_bad[data_cols].values / df[data_cols].values
+        ratio = ratio.round(2)
+        # - round near-100 ratio to 100:
+        f = ratio > 90
+        ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
+        # - now test
+        f_100 = ratio == 100
+        f_1 = ratio == 1
+        self.assertTrue((f_100 | f_1).all())
+
+        self.assertTrue("Repaired?" in df_repaired.columns)
+        self.assertFalse(df_repaired["Repaired?"].isna().any())
+
+    def test_repair_100x_block_daily(self):
+        # Some 100x errors are not sporadic.
+        # Sometimes Yahoo suddenly shifts from cents->$ from some recent date.
+
+        tkr = "SSW.JO"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+
+        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
+        _dp = os.path.dirname(__file__)
+        df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-100x-error.csv"), index_col="Date")
+        df_bad.index = _pd.to_datetime(df_bad.index)
+        df_bad = df_bad.sort_index()
+
+        df = df_bad.copy()
+        for d in data_cols:
+            df.loc[:'2023-05-31', d] *= 0.01  # fix error
+
+        df_repaired = dat._fix_unit_switch(df_bad, "1d", tz_exchange)
+        df_repaired = df_repaired.sort_index()
+
+        # First test - no errors left
+        for c in data_cols:
+            try:
+                self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
+            except:
+                print(df_repaired[c])
+                print(df[c])
+                print(f"TEST FAIL on column '{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_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_daily_adjClose(self):
+        # Test that 'Adj Close' is reconstructed correctly, 
+        # particularly when a dividend occurred within 1 day.
+
+        tkr = "INTC"
+        df = _pd.DataFrame(data={"Open":      [28.95, 28.65, 29.55, 29.62, 29.25],
+                                 "High":      [29.12, 29.27, 29.65, 31.17, 30.30],
+                                 "Low":       [28.21, 28.43, 28.61, 29.53, 28.80],
+                                 "Close":     [28.24, 29.05, 28.69, 30.32, 30.19],
+                                 "Adj Close": [28.12, 28.93, 28.57, 29.83, 29.70],
+                                 "Volume":    [36e6, 51e6, 49e6, 58e6, 62e6],
+                                 "Dividends": [0, 0, 0.365, 0, 0]},
+                                index=_pd.to_datetime([_dt.datetime(2023, 2, 8),
+                                                       _dt.datetime(2023, 2, 7),
+                                                       _dt.datetime(2023, 2, 6),
+                                                       _dt.datetime(2023, 2, 3),
+                                                       _dt.datetime(2023, 2, 2)]))
+        df = df.sort_index()
+        df.index.name = "Date"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+        df.index = df.index.tz_localize(tz_exchange)
+
+        rtol = 5e-3
+        for i in [0, 1, 2]:
+            df_slice = df.iloc[i:i+3]
+            for j in range(3):
+                df_slice_bad = df_slice.copy()
+                df_slice_bad.loc[df_slice_bad.index[j], "Adj Close"] = 0.0
+
+                df_slice_bad_repaired = dat._fix_zeroes(df_slice_bad, "1d", tz_exchange, prepost=False)
+                for c in ["Close", "Adj Close"]:
+                    self.assertTrue(_np.isclose(df_slice_bad_repaired[c], df_slice[c], rtol=rtol).all())
+                self.assertTrue("Repaired?" in df_slice_bad_repaired.columns)
+                self.assertFalse(df_slice_bad_repaired["Repaired?"].isna().any())
+
+    def test_repair_zeroes_hourly(self):
+        tkr = "INTC"
+        dat = yf.Ticker(tkr, session=self.session)
+        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())
+
+    def test_repair_bad_stock_split(self):
+        bad_tkrs = ['4063.T', 'ALPHA.PA', 'CNE.L', 'MOB.ST', 'SPM.MI']
+        for tkr in bad_tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+            tz_exchange = dat.fast_info["timezone"]
+
+            _dp = os.path.dirname(__file__)
+            df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-bad-stock-split.csv"), index_col="Date")
+            df_bad.index = _pd.to_datetime(df_bad.index)
+
+            repaired_df = dat._fix_bad_stock_split(df_bad, "1d", tz_exchange)
+
+            correct_df = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-bad-stock-split-fixed.csv"), index_col="Date")
+            correct_df.index = _pd.to_datetime(correct_df.index)
+
+            repaired_df = repaired_df.sort_index()
+            correct_df = correct_df.sort_index()
+            for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                try:
+                    self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
+                except:
+                    print(f"tkr={tkr} COLUMN={c}")
+                    print("- repaired_df")
+                    print(repaired_df)
+                    print("- correct_df[c]:")
+                    print(correct_df[c])
+                    print("- diff:")
+                    print(repaired_df[c] - correct_df[c])
+                    raise
+
+        # Stocks that split in 2022 but no problems in Yahoo data, 
+        # so repair should change nothing
+        good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
+        good_tkrs += ['AEI', 'CHRA', 'GHI', 'IRON', 'LXU', 'NUZE', 'RSLS', 'TISI']
+        good_tkrs += ['BOL.ST', 'TUI1.DE']
+        intervals = ['1d', '1wk', '1mo', '3mo']
+        for tkr in good_tkrs:
+            for interval in intervals:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+
+                _dp = os.path.dirname(__file__)
+                df_good = dat.history(period='2y', interval=interval, auto_adjust=False)
+
+                repaired_df = dat._fix_bad_stock_split(df_good, interval, tz_exchange)
+
+                # Expect no change from repair
+                df_good = df_good.sort_index()
+                repaired_df = repaired_df.sort_index()
+                for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                    try:
+                        self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
+                    except:
+                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
+                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
+                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
+                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
+                        raise
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/ticker.py

@@ -0,0 +1,809 @@
+"""
+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
+
+        dat.income_stmt
+        dat.quarterly_income_stmt
+        dat.balance_sheet
+        dat.quarterly_balance_sheet
+        dat.cashflow
+        dat.quarterly_cashflow
+
+        # These haven't been ported Yahoo API
+        # dat.shares
+        # dat.info
+        # dat.calendar
+        # dat.recommendations
+        # dat.earnings
+        # dat.quarterly_earnings
+        # dat.recommendations_summary
+        # dat.analyst_price_target
+        # dat.revenue_forecasts
+        # dat.sustainability
+        # dat.earnings_trend
+        # dat.earnings_forecasts
+
+    def test_goodTicker(self):
+        # that yfinance works when full api is called on same instance of ticker
+
+        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
+
+            dat.income_stmt
+            dat.quarterly_income_stmt
+            dat.balance_sheet
+            dat.quarterly_balance_sheet
+            dat.cashflow
+            dat.quarterly_cashflow
+
+            # These require decryption which is broken:
+            # dat.shares
+            # dat.info
+            # dat.calendar
+            # dat.recommendations
+            # dat.earnings
+            # dat.quarterly_earnings
+            # dat.recommendations_summary
+            # dat.analyst_price_target
+            # dat.revenue_forecasts
+            # dat.sustainability
+            # dat.earnings_trend
+            # dat.earnings_forecasts
+
+
+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 not ported to Yahoo API
+# class TestTickerEarnings(unittest.TestCase):
+#     session = None
+
+#     @classmethod
+#     def setUpClass(cls):
+#         cls.session = session_gbl
+
+#     @classmethod
+#     def tearDownClass(cls):
+#         if cls.session is not None:
+#             cls.session.close()
+
+#     def setUp(self):
+#         self.ticker = yf.Ticker("GOOGL", session=self.session)
+
+#     def tearDown(self):
+#         self.ticker = None
+
+#     def test_earnings(self):
+#         data = self.ticker.earnings
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+
+#         data_cached = self.ticker.earnings
+#         self.assertIs(data, data_cached, "data not cached")
+
+#     def test_quarterly_earnings(self):
+#         data = self.ticker.quarterly_earnings
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+
+#         data_cached = self.ticker.quarterly_earnings
+#         self.assertIs(data, data_cached, "data not cached")
+
+#     def test_earnings_forecasts(self):
+#         data = self.ticker.earnings_forecasts
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+
+#         data_cached = self.ticker.earnings_forecasts
+#         self.assertIs(data, data_cached, "data not cached")
+
+#     def test_earnings_dates(self):
+#         data = self.ticker.earnings_dates
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+
+#         data_cached = self.ticker.earnings_dates
+#         self.assertIs(data, data_cached, "data not cached")
+
+#     def test_earnings_trend(self):
+#         data = self.ticker.earnings_trend
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+
+#         data_cached = self.ticker.earnings_trend
+#         self.assertIs(data, data_cached, "data not cached")
+
+#     def test_earnings_dates_with_limit(self):
+#         # use ticker with lots of historic earnings
+#         ticker = yf.Ticker("IBM")
+#         limit = 110
+#         data = ticker.get_earnings_dates(limit=limit)
+#         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+#         self.assertFalse(data.empty, "data is empty")
+#         self.assertEqual(len(data), limit, "Wrong number or rows")
+
+#         data_cached = ticker.get_earnings_dates(limit=limit)
+#         self.assertIs(data, data_cached, "data not cached")
+
+
+class TestTickerHolders(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        self.ticker = yf.Ticker("GOOGL", session=self.session)
+
+    def tearDown(self):
+        self.ticker = None
+
+    def test_major_holders(self):
+        data = self.ticker.major_holders
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        data_cached = self.ticker.major_holders
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_institutional_holders(self):
+        data = self.ticker.institutional_holders
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        data_cached = self.ticker.institutional_holders
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_mutualfund_holders(self):
+        data = self.ticker.mutualfund_holders
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        data_cached = self.ticker.mutualfund_holders
+        self.assertIs(data, data_cached, "data not cached")
+
+
+class TestTickerMiscFinancials(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        self.ticker = yf.Ticker("GOOGL", session=self.session)
+        
+        # For ticker 'BSE.AX' (and others), Yahoo not returning 
+        # full quarterly financials (usually cash-flow) with all entries, 
+        # instead returns a smaller version in different data store.
+        self.ticker_old_fmt = yf.Ticker("BSE.AX", session=self.session)
+
+    def tearDown(self):
+        self.ticker = None
+
+    def test_isin(self):
+        data = self.ticker.isin
+        self.assertIsInstance(data, str, "data has wrong type")
+        self.assertEqual("ARDEUT116159", data, "data is empty")
+
+        data_cached = self.ticker.isin
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_options(self):
+        data = self.ticker.options
+        self.assertIsInstance(data, tuple, "data has wrong type")
+        self.assertTrue(len(data) > 1, "data is empty")
+
+    def test_shares_full(self):
+        data = self.ticker.get_shares_full()
+        self.assertIsInstance(data, pd.Series, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+    def test_income_statement(self):
+        expected_keys = ["Total Revenue", "Basic EPS"]
+        expected_periods_days = 365
+
+        # Test contents of table
+        data = self.ticker.get_income_stmt(pretty=True)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
+
+        # Test property defaults
+        data2 = self.ticker.income_stmt
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_income_stmt(pretty=False)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        data = self.ticker.get_income_stmt(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")
+
+    def test_quarterly_income_statement(self):
+        expected_keys = ["Total Revenue", "Basic EPS"]
+        expected_periods_days = 365//4
+
+        # Test contents of table
+        data = self.ticker.get_income_stmt(pretty=True, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
+
+        # Test property defaults
+        data2 = self.ticker.quarterly_income_stmt
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_income_stmt(pretty=False, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        data = self.ticker.get_income_stmt(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")
+
+    def test_balance_sheet(self):
+        expected_keys = ["Total Assets", "Net PPE"]
+        expected_periods_days = 365
+
+        # Test contents of table
+        data = self.ticker.get_balance_sheet(pretty=True)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
+
+        # Test property defaults
+        data2 = self.ticker.balance_sheet
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_balance_sheet(pretty=False)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        data = self.ticker.get_balance_sheet(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")
+
+    def test_quarterly_balance_sheet(self):
+        expected_keys = ["Total Assets", "Net PPE"]
+        expected_periods_days = 365//4
+
+        # Test contents of table
+        data = self.ticker.get_balance_sheet(pretty=True, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
+
+        # Test property defaults
+        data2 = self.ticker.quarterly_balance_sheet
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_balance_sheet(pretty=False, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        data = self.ticker.get_balance_sheet(as_dict=True, freq="quarterly")
+        self.assertIsInstance(data, dict, "data has wrong type")
+
+    def test_cash_flow(self):
+        expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
+        expected_periods_days = 365
+
+        # Test contents of table
+        data = self.ticker.get_cashflow(pretty=True)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning annual financials")
+
+        # Test property defaults
+        data2 = self.ticker.cashflow
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_cashflow(pretty=False)
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        data = self.ticker.get_cashflow(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")
+
+    def test_quarterly_cash_flow(self):
+        expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
+        expected_periods_days = 365//4
+
+        # Test contents of table
+        data = self.ticker.get_cashflow(pretty=True, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+        period = abs((data.columns[0]-data.columns[1]).days)
+        self.assertLess(abs(period-expected_periods_days), 20, "Not returning quarterly financials")
+
+        # Test property defaults
+        data2 = self.ticker.quarterly_cashflow
+        self.assertTrue(data.equals(data2), "property not defaulting to 'pretty=True'")
+
+        # Test pretty=False
+        expected_keys = [k.replace(' ', '') for k in expected_keys]
+        data = self.ticker.get_cashflow(pretty=False, freq="quarterly")
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+        for k in expected_keys:
+            self.assertIn(k, data.index, "Did not find expected row in index")
+
+        # Test to_dict
+        data = self.ticker.get_cashflow(as_dict=True)
+        self.assertIsInstance(data, dict, "data has wrong type")
+
+    def test_income_alt_names(self):
+        i1 = self.ticker.income_stmt
+        i2 = self.ticker.incomestmt
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.financials
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.get_income_stmt()
+        i2 = self.ticker.get_incomestmt()
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.get_financials()
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.quarterly_income_stmt
+        i2 = self.ticker.quarterly_incomestmt
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.quarterly_financials
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.get_income_stmt(freq="quarterly")
+        i2 = self.ticker.get_incomestmt(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.get_financials(freq="quarterly")
+        self.assertTrue(i1.equals(i3))
+
+    def test_balance_sheet_alt_names(self):
+        i1 = self.ticker.balance_sheet
+        i2 = self.ticker.balancesheet
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_balance_sheet()
+        i2 = self.ticker.get_balancesheet()
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.quarterly_balance_sheet
+        i2 = self.ticker.quarterly_balancesheet
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_balance_sheet(freq="quarterly")
+        i2 = self.ticker.get_balancesheet(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+
+    def test_cash_flow_alt_names(self):
+        i1 = self.ticker.cash_flow
+        i2 = self.ticker.cashflow
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_cash_flow()
+        i2 = self.ticker.get_cashflow()
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.quarterly_cash_flow
+        i2 = self.ticker.quarterly_cashflow
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_cash_flow(freq="quarterly")
+        i2 = self.ticker.get_cashflow(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+
+    def test_bad_freq_value_raises_exception(self):
+        self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))
+
+    # Below will fail because not ported to Yahoo API
+
+    # def test_sustainability(self):
+    #     data = self.ticker.sustainability
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.sustainability
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_recommendations(self):
+    #     data = self.ticker.recommendations
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.recommendations
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_recommendations_summary(self):
+    #     data = self.ticker.recommendations_summary
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.recommendations_summary
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_analyst_price_target(self):
+    #     data = self.ticker.analyst_price_target
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.analyst_price_target
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_revenue_forecasts(self):
+    #     data = self.ticker.revenue_forecasts
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.revenue_forecasts
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_calendar(self):
+    #     data = self.ticker.calendar
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.calendar
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_shares(self):
+    #     data = self.ticker.shares
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+
+class TestTickerInfo(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        self.symbols = []
+        self.symbols += ["ESLT.TA", "BP.L", "GOOGL"]
+        self.symbols.append("QCSTIX")  # good for testing, doesn't trade
+        self.symbols += ["BTC-USD", "IWO", "VFINX", "^GSPC"]
+        self.symbols += ["SOKE.IS", "ADS.DE"]  # detected bugs
+        self.tickers = [yf.Ticker(s, session=self.session) for s in self.symbols]
+
+    def tearDown(self):
+        self.ticker = None
+
+    def test_fast_info(self):
+        f = yf.Ticker("AAPL", session=self.session).fast_info
+        for k in f:
+            self.assertIsNotNone(f[k])
+
+    def test_info(self):
+        data = self.tickers[0].info
+        self.assertIsInstance(data, dict, "data has wrong type")
+        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):
+    #     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

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

yfinance/const.py

@@ -0,0 +1,8 @@
+
+fundamentals_keys = {}
+
+fundamentals_keys['financials'] = ["TaxEffectOfUnusualItems","TaxRateForCalcs","NormalizedEBITDA","NormalizedDilutedEPS","NormalizedBasicEPS","TotalUnusualItems","TotalUnusualItemsExcludingGoodwill","NetIncomeFromContinuingOperationNetMinorityInterest","ReconciledDepreciation","ReconciledCostOfRevenue","EBITDA","EBIT","NetInterestIncome","InterestExpense","InterestIncome","ContinuingAndDiscontinuedDilutedEPS","ContinuingAndDiscontinuedBasicEPS","NormalizedIncome","NetIncomeFromContinuingAndDiscontinuedOperation","TotalExpenses","RentExpenseSupplemental","ReportedNormalizedDilutedEPS","ReportedNormalizedBasicEPS","TotalOperatingIncomeAsReported","DividendPerShare","DilutedAverageShares","BasicAverageShares","DilutedEPS","DilutedEPSOtherGainsLosses","TaxLossCarryforwardDilutedEPS","DilutedAccountingChange","DilutedExtraordinary","DilutedDiscontinuousOperations","DilutedContinuousOperations","BasicEPS","BasicEPSOtherGainsLosses","TaxLossCarryforwardBasicEPS","BasicAccountingChange","BasicExtraordinary","BasicDiscontinuousOperations","BasicContinuousOperations","DilutedNIAvailtoComStockholders","AverageDilutionEarnings","NetIncomeCommonStockholders","OtherunderPreferredStockDividend","PreferredStockDividends","NetIncome","MinorityInterests","NetIncomeIncludingNoncontrollingInterests","NetIncomeFromTaxLossCarryforward","NetIncomeExtraordinary","NetIncomeDiscontinuousOperations","NetIncomeContinuousOperations","EarningsFromEquityInterestNetOfTax","TaxProvision","PretaxIncome","OtherIncomeExpense","OtherNonOperatingIncomeExpenses","SpecialIncomeCharges","GainOnSaleOfPPE","GainOnSaleOfBusiness","OtherSpecialCharges","WriteOff","ImpairmentOfCapitalAssets","RestructuringAndMergernAcquisition","SecuritiesAmortization","EarningsFromEquityInterest","GainOnSaleOfSecurity","NetNonOperatingInterestIncomeExpense","TotalOtherFinanceCost","InterestExpenseNonOperating","InterestIncomeNonOperating","OperatingIncome","OperatingExpense","OtherOperatingExpenses","OtherTaxes","ProvisionForDoubtfulAccounts","DepreciationAmortizationDepletionIncomeStatement","DepletionIncomeStatement","DepreciationAndAmortizationInIncomeStatement","Amortization","AmortizationOfIntangiblesIncomeStatement","DepreciationIncomeStatement","ResearchAndDevelopment","SellingGeneralAndAdministration","SellingAndMarketingExpense","GeneralAndAdministrativeExpense","OtherGandA","InsuranceAndClaims","RentAndLandingFees","SalariesAndWages","GrossProfit","CostOfRevenue","TotalRevenue","ExciseTaxes","OperatingRevenue"]
+
+fundamentals_keys['balance-sheet'] = ["TreasurySharesNumber","PreferredSharesNumber","OrdinarySharesNumber","ShareIssued","NetDebt","TotalDebt","TangibleBookValue","InvestedCapital","WorkingCapital","NetTangibleAssets","CapitalLeaseObligations","CommonStockEquity","PreferredStockEquity","TotalCapitalization","TotalEquityGrossMinorityInterest","MinorityInterest","StockholdersEquity","OtherEquityInterest","GainsLossesNotAffectingRetainedEarnings","OtherEquityAdjustments","FixedAssetsRevaluationReserve","ForeignCurrencyTranslationAdjustments","MinimumPensionLiabilities","UnrealizedGainLoss","TreasuryStock","RetainedEarnings","AdditionalPaidInCapital","CapitalStock","OtherCapitalStock","CommonStock","PreferredStock","TotalPartnershipCapital","GeneralPartnershipCapital","LimitedPartnershipCapital","TotalLiabilitiesNetMinorityInterest","TotalNonCurrentLiabilitiesNetMinorityInterest","OtherNonCurrentLiabilities","LiabilitiesHeldforSaleNonCurrent","RestrictedCommonStock","PreferredSecuritiesOutsideStockEquity","DerivativeProductLiabilities","EmployeeBenefits","NonCurrentPensionAndOtherPostretirementBenefitPlans","NonCurrentAccruedExpenses","DuetoRelatedPartiesNonCurrent","TradeandOtherPayablesNonCurrent","NonCurrentDeferredLiabilities","NonCurrentDeferredRevenue","NonCurrentDeferredTaxesLiabilities","LongTermDebtAndCapitalLeaseObligation","LongTermCapitalLeaseObligation","LongTermDebt","LongTermProvisions","CurrentLiabilities","OtherCurrentLiabilities","CurrentDeferredLiabilities","CurrentDeferredRevenue","CurrentDeferredTaxesLiabilities","CurrentDebtAndCapitalLeaseObligation","CurrentCapitalLeaseObligation","CurrentDebt","OtherCurrentBorrowings","LineOfCredit","CommercialPaper","CurrentNotesPayable","PensionandOtherPostRetirementBenefitPlansCurrent","CurrentProvisions","PayablesAndAccruedExpenses","CurrentAccruedExpenses","InterestPayable","Payables","OtherPayable","DuetoRelatedPartiesCurrent","DividendsPayable","TotalTaxPayable","IncomeTaxPayable","AccountsPayable","TotalAssets","TotalNonCurrentAssets","OtherNonCurrentAssets","DefinedPensionBenefit","NonCurrentPrepaidAssets","NonCurrentDeferredAssets","NonCurrentDeferredTaxesAssets","DuefromRelatedPartiesNonCurrent","NonCurrentNoteReceivables","NonCurrentAccountsReceivable","FinancialAssets","InvestmentsAndAdvances","OtherInvestments","InvestmentinFinancialAssets","HeldToMaturitySecurities","AvailableForSaleSecurities","FinancialAssetsDesignatedasFairValueThroughProfitorLossTotal","TradingSecurities","LongTermEquityInvestment","InvestmentsinJointVenturesatCost","InvestmentsInOtherVenturesUnderEquityMethod","InvestmentsinAssociatesatCost","InvestmentsinSubsidiariesatCost","InvestmentProperties","GoodwillAndOtherIntangibleAssets","OtherIntangibleAssets","Goodwill","NetPPE","AccumulatedDepreciation","GrossPPE","Leases","ConstructionInProgress","OtherProperties","MachineryFurnitureEquipment","BuildingsAndImprovements","LandAndImprovements","Properties","CurrentAssets","OtherCurrentAssets","HedgingAssetsCurrent","AssetsHeldForSaleCurrent","CurrentDeferredAssets","CurrentDeferredTaxesAssets","RestrictedCash","PrepaidAssets","Inventory","InventoriesAdjustmentsAllowances","OtherInventories","FinishedGoods","WorkInProcess","RawMaterials","Receivables","ReceivablesAdjustmentsAllowances","OtherReceivables","DuefromRelatedPartiesCurrent","TaxesReceivable","AccruedInterestReceivable","NotesReceivable","LoansReceivable","AccountsReceivable","AllowanceForDoubtfulAccountsReceivable","GrossAccountsReceivable","CashCashEquivalentsAndShortTermInvestments","OtherShortTermInvestments","CashAndCashEquivalents","CashEquivalents","CashFinancial"]
+
+fundamentals_keys['cash-flow'] = ["ForeignSales","DomesticSales","AdjustedGeographySegmentData","FreeCashFlow","RepurchaseOfCapitalStock","RepaymentOfDebt","IssuanceOfDebt","IssuanceOfCapitalStock","CapitalExpenditure","InterestPaidSupplementalData","IncomeTaxPaidSupplementalData","EndCashPosition","OtherCashAdjustmentOutsideChangeinCash","BeginningCashPosition","EffectOfExchangeRateChanges","ChangesInCash","OtherCashAdjustmentInsideChangeinCash","CashFlowFromDiscontinuedOperation","FinancingCashFlow","CashFromDiscontinuedFinancingActivities","CashFlowFromContinuingFinancingActivities","NetOtherFinancingCharges","InterestPaidCFF","ProceedsFromStockOptionExercised","CashDividendsPaid","PreferredStockDividendPaid","CommonStockDividendPaid","NetPreferredStockIssuance","PreferredStockPayments","PreferredStockIssuance","NetCommonStockIssuance","CommonStockPayments","CommonStockIssuance","NetIssuancePaymentsOfDebt","NetShortTermDebtIssuance","ShortTermDebtPayments","ShortTermDebtIssuance","NetLongTermDebtIssuance","LongTermDebtPayments","LongTermDebtIssuance","InvestingCashFlow","CashFromDiscontinuedInvestingActivities","CashFlowFromContinuingInvestingActivities","NetOtherInvestingChanges","InterestReceivedCFI","DividendsReceivedCFI","NetInvestmentPurchaseAndSale","SaleOfInvestment","PurchaseOfInvestment","NetInvestmentPropertiesPurchaseAndSale","SaleOfInvestmentProperties","PurchaseOfInvestmentProperties","NetBusinessPurchaseAndSale","SaleOfBusiness","PurchaseOfBusiness","NetIntangiblesPurchaseAndSale","SaleOfIntangibles","PurchaseOfIntangibles","NetPPEPurchaseAndSale","SaleOfPPE","PurchaseOfPPE","CapitalExpenditureReported","OperatingCashFlow","CashFromDiscontinuedOperatingActivities","CashFlowFromContinuingOperatingActivities","TaxesRefundPaid","InterestReceivedCFO","InterestPaidCFO","DividendReceivedCFO","DividendPaidCFO","ChangeInWorkingCapital","ChangeInOtherWorkingCapital","ChangeInOtherCurrentLiabilities","ChangeInOtherCurrentAssets","ChangeInPayablesAndAccruedExpense","ChangeInAccruedExpense","ChangeInInterestPayable","ChangeInPayable","ChangeInDividendPayable","ChangeInAccountPayable","ChangeInTaxPayable","ChangeInIncomeTaxPayable","ChangeInPrepaidAssets","ChangeInInventory","ChangeInReceivables","ChangesInAccountReceivables","OtherNonCashItems","ExcessTaxBenefitFromStockBasedCompensation","StockBasedCompensation","UnrealizedGainLossOnInvestmentSecurities","ProvisionandWriteOffofAssets","AssetImpairmentCharge","AmortizationOfSecurities","DeferredTax","DeferredIncomeTax","DepreciationAmortizationDepletion","Depletion","DepreciationAndAmortization","AmortizationCashFlow","AmortizationOfIntangibles","Depreciation","OperatingGainsLosses","PensionAndEmployeeBenefitExpense","EarningsLossesFromEquityInvestments","GainLossOnInvestmentSecurities","NetForeignCurrencyExchangeGainLoss","GainLossOnSaleOfPPE","GainLossOnSaleOfBusiness","NetIncomeFromContinuingOperations","CashFlowsfromusedinOperatingActivitiesDirect","TaxesRefundPaidDirect","InterestReceivedDirect","InterestPaidDirect","DividendsReceivedDirect","DividendsPaidDirect","ClassesofCashPayments","OtherCashPaymentsfromOperatingActivities","PaymentsonBehalfofEmployees","PaymentstoSuppliersforGoodsandServices","ClassesofCashReceiptsfromOperatingActivities","OtherCashReceiptsfromOperatingActivities","ReceiptsfromGovernmentGrants","ReceiptsfromCustomers"]

yfinance/data.py

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

yfinance/exceptions.py

@@ -0,0 +1,12 @@
+class YFinanceException(Exception):
+    pass
+
+
+class YFinanceDataException(YFinanceException):
+    pass
+
+
+class YFNotImplementedError(NotImplementedError):
+    def __init__(self, method_name):
+        super().__init__(f"Have not implemented fetching '{method_name}' from Yahoo API")
+

yfinance/multi.py

@@ -21,6 +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 +30,11 @@ import pandas as _pd
 from . import Ticker, utils
 from . import shared
 
-
-def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=True, 
-             group_by='column', auto_adjust=False, back_adjust=False, keepna=False,
-             progress=True, period="max", show_errors=True, interval="1d", prepost=False,
-             proxy=None, rounding=False, timeout=None, **kwargs):
+@utils.log_indent_decorator
+def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=None,
+             group_by='column', auto_adjust=False, back_adjust=False, repair=False, keepna=False,
+             progress=True, period="max", show_errors=None, interval="1d", prepost=False,
+             proxy=None, rounding=False, timeout=10, session=None):
     """Download yahoo tickers
     :Parameters:
         tickers : str, list
@@ -44,11 +46,13 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
             Intraday data cannot extend last 60 days
         start: str
-            Download start date string (YYYY-MM-DD) or _datetime.
+            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
@@ -56,6 +60,9 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             Default is False
         auto_adjust: bool
             Adjust all OHLC automatically? Default is False
+        repair: bool
+            Detect currency unit 100x mixups and attempt repair
+            Default is False
         keepna: bool
             Keep NaN rows returned by Yahoo?
             Default is False
@@ -65,17 +72,48 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             How many threads to use for mass downloading. Default is True
         ignore_tz: bool
             When combining from different timezones, ignore that part of datetime.
-            Default is True
+            Default depends on interval. Intraday = False. Day+ = True.
         proxy: str
             Optional. Proxy server URL scheme. Default is None
         rounding: bool
             Optional. Round values to 2 decimal places?
         show_errors: bool
             Optional. Doesn't print errors if False
+            DEPRECATED, will be removed in future version
         timeout: None or float
             If not None stops waiting for a response after given number of
             seconds. (Can also be a fraction of a second e.g. 0.01)
+        session: None or Session
+            Optional. Pass your own session object to be used for all requests
     """
+    logger = utils.get_yf_logger()
+
+    if show_errors is not None:
+        if show_errors:
+            utils.print_once(f"yfinance: download(show_errors={show_errors}) argument is deprecated and will be removed in future version. Do this instead: logging.getLogger('yfinance').setLevel(logging.ERROR)")
+            logger.setLevel(logging.ERROR)
+        else:
+            utils.print_once(f"yfinance: download(show_errors={show_errors}) argument is deprecated and will be removed in future version. Do this instead to suppress error messages: logging.getLogger('yfinance').setLevel(logging.CRITICAL)")
+            logger.setLevel(logging.CRITICAL)
+
+    if logger.isEnabledFor(logging.DEBUG):
+        if threads:
+            # With DEBUG, each thread generates a lot of log messages.
+            # And with multi-threading, these messages will be interleaved, bad!
+            # So disable multi-threading to make log readable.
+            logger.debug('Disabling multithreading because DEBUG logging enabled')
+            threads = False
+        if progress:
+            # Disable progress bar, interferes with display of log messages
+            progress = False
+
+    if ignore_tz is None:
+        # Set default value depending on interval
+        if interval[1:] in ['m', 'h']:
+            # Intraday
+            ignore_tz = False
+        else:
+            ignore_tz = True
 
     # create ticker list
     tickers = tickers if isinstance(
@@ -87,7 +125,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
     for ticker in tickers:
         if utils.is_isin(ticker):
             isin = ticker
-            ticker = utils.get_ticker_by_isin(ticker, proxy)
+            ticker = utils.get_ticker_by_isin(ticker, proxy, session=session)
             shared._ISINS[ticker] = isin
         _tickers_.append(ticker)
 
@@ -101,6 +139,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
     # reset shared._DFS
     shared._DFS = {}
     shared._ERRORS = {}
+    shared._TRACEBACKS = {}
 
     # download using threads
     if threads:
@@ -111,37 +150,59 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             _download_one_threaded(ticker, period=period, interval=interval,
                                    start=start, end=end, prepost=prepost,
                                    actions=actions, auto_adjust=auto_adjust,
-                                   back_adjust=back_adjust, keepna=keepna,
+                                   back_adjust=back_adjust, repair=repair, keepna=keepna,
                                    progress=(progress and i > 0), proxy=proxy,
-                                   rounding=rounding, timeout=timeout)
+                                   rounding=rounding, timeout=timeout, session=session)
         while len(shared._DFS) < len(tickers):
             _time.sleep(0.01)
-
     # download synchronously
     else:
         for i, ticker in enumerate(tickers):
             data = _download_one(ticker, period=period, interval=interval,
                                  start=start, end=end, prepost=prepost,
                                  actions=actions, auto_adjust=auto_adjust,
-                                 back_adjust=back_adjust, keepna=keepna, proxy=proxy,
-                                 rounding=rounding, timeout=timeout)
-            shared._DFS[ticker.upper()] = data
+                                 back_adjust=back_adjust, repair=repair, keepna=keepna,
+                                 proxy=proxy,
+                                 rounding=rounding, timeout=timeout, session=session)
             if progress:
                 shared._PROGRESS_BAR.animate()
-
+    
     if progress:
         shared._PROGRESS_BAR.completed()
 
-    if shared._ERRORS and show_errors:
-        print('\n%.f Failed download%s:' % (
+    if shared._ERRORS:
+        # Send errors to logging module
+        logger = utils.get_yf_logger()
+        logger.error('\n%.f Failed download%s:' % (
             len(shared._ERRORS), 's' if len(shared._ERRORS) > 1 else ''))
-        # print(shared._ERRORS)
-        print("\n".join(['- %s: %s' %
-                         v for v in list(shared._ERRORS.items())]))
+
+        # Log each distinct error once, with list of symbols affected
+        errors = {}
+        for ticker in shared._ERRORS:
+            err = shared._ERRORS[ticker]
+            err = err.replace(f'{ticker}', '%ticker%')
+            if 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]
+            tb = tb.replace(f'{ticker}', '%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):
+            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:
@@ -191,28 +252,38 @@ 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,
-                           keepna=False, rounding=False, timeout=None):
-
-    data = _download_one(ticker, start, end, auto_adjust, back_adjust,
+                           keepna=False, rounding=False, timeout=10, session=None):
+    data = _download_one(ticker, start, end, auto_adjust, back_adjust, repair,
                          actions, period, interval, prepost, proxy, rounding,
-                         keepna, timeout)
-    shared._DFS[ticker.upper()] = data
+                         keepna, timeout, session)
     if progress:
         shared._PROGRESS_BAR.animate()
 
 
 def _download_one(ticker, start=None, end=None,
-                  auto_adjust=False, back_adjust=False,
+                  auto_adjust=False, back_adjust=False, repair=False,
                   actions=False, period="max", interval="1d",
                   prepost=False, proxy=None, rounding=False,
-                  keepna=False, timeout=None):
+                  keepna=False, timeout=10, session=None):
+    data = None
+    try:
+        data = Ticker(ticker, session=session).history(
+                period=period, interval=interval,
+                start=start, end=end, prepost=prepost,
+                actions=actions, auto_adjust=auto_adjust,
+                back_adjust=back_adjust, repair=repair, proxy=proxy,
+                rounding=rounding, keepna=keepna, timeout=timeout,
+                raise_errors=True
+        )
+    except Exception as e:
+        # glob try/except needed as current thead implementation breaks if exception is raised.
+        shared._DFS[ticker.upper()] = utils.empty_df()
+        shared._ERRORS[ticker.upper()] = repr(e)
+        shared._TRACEBACKS[ticker.upper()] = traceback.format_exc()
+    else:
+        shared._DFS[ticker.upper()] = data
 
-    return Ticker(ticker).history(period=period, interval=interval,
-                                  start=start, end=end, prepost=prepost,
-                                  actions=actions, auto_adjust=auto_adjust,
-                                  back_adjust=back_adjust, proxy=proxy,
-                                  rounding=rounding, keepna=keepna, many=True,
-                                  timeout=timeout)
+    return data

yfinance/scrapers/analysis.py

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

yfinance/scrapers/fundamentals.py

@@ -0,0 +1,151 @@
+import datetime
+import logging
+import json
+
+import pandas as pd
+import numpy as np
+
+from yfinance import utils, const
+from yfinance.data import TickerData
+from yfinance.exceptions import YFinanceException, YFNotImplementedError
+
+class Fundamentals:
+
+    def __init__(self, data: TickerData, proxy=None):
+        self._data = data
+        self.proxy = proxy
+
+        self._earnings = None
+        self._financials = None
+        self._shares = None
+
+        self._financials_data = None
+        self._fin_data_quote = None
+        self._basics_already_scraped = False
+        self._financials = Financials(data)
+
+    @property
+    def financials(self) -> "Financials":
+        return self._financials
+
+    @property
+    def earnings(self) -> dict:
+        if self._earnings is None:
+            raise YFNotImplementedError('earnings')
+        return self._earnings
+
+    @property
+    def shares(self) -> pd.DataFrame:
+        if self._shares is None:
+            raise YFNotImplementedError('shares')
+        return self._shares
+
+
+class Financials:
+    def __init__(self, data: TickerData):
+        self._data = data
+        self._income_time_series = {}
+        self._balance_sheet_time_series = {}
+        self._cash_flow_time_series = {}
+
+    def get_income_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
+        res = self._income_time_series
+        if freq not in res:
+            res[freq] = self._fetch_time_series("income", freq, proxy=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]
+
+    @utils.log_indent_decorator
+    def _fetch_time_series(self, name, timescale, proxy=None):
+        # Fetching time series preferred over scraping 'QuoteSummaryStore',
+        # because it matches what Yahoo shows. But for some tickers returns nothing, 
+        # despite 'QuoteSummaryStore' containing valid data.
+
+        allowed_names = ["income", "balance-sheet", "cash-flow"]
+        allowed_timescales = ["yearly", "quarterly"]
+
+        if name not in allowed_names:
+            raise ValueError("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:
+            utils.get_yf_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 = const.fundamentals_keys[name]
+
+        try:
+            return self.get_financials_time_series(timescale, keys, proxy)
+        except Exception as e:
+            pass
+
+    def get_financials_time_series(self, timescale, keys: list, proxy=None) -> pd.DataFrame:
+        timescale_translation = {"yearly": "annual", "quarterly": "quarterly"}
+        timescale = timescale_translation[timescale]
+
+        # Step 2: construct url:
+        ts_url_base = \
+            "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

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 +1,690 @@
+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
+from yfinance.exceptions import YFNotImplementedError
+
+info_retired_keys_price = {"currentPrice", "dayHigh", "dayLow", "open", "previousClose", "volume", "volume24Hr"}
+info_retired_keys_price.update({"regularMarket"+s for s in ["DayHigh", "DayLow", "Open", "PreviousClose", "Price", "Volume"]})
+info_retired_keys_price.update({"fiftyTwoWeekLow", "fiftyTwoWeekHigh", "fiftyTwoWeekChange", "52WeekChange", "fiftyDayAverage", "twoHundredDayAverage"})
+info_retired_keys_price.update({"averageDailyVolume10Day", "averageVolume10days", "averageVolume"})
+info_retired_keys_exchange = {"currency", "exchange", "exchangeTimezoneName", "exchangeTimezoneShortName", "quoteType"}
+info_retired_keys_marketCap = {"marketCap"}
+info_retired_keys_symbol = {"symbol"}
+info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_retired_keys_marketCap | info_retired_keys_symbol
+
+
+_BASIC_URL_ = "https://query2.finance.yahoo.com/v6/finance/quoteSummary"
+
+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):
+        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
+            logging.disable(logging.CRITICAL)
+            self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, keepna=True)
+            logging.disable(logging.NOTSET)
+            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
+            logging.disable(logging.CRITICAL)
+            self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True)
+            logging.disable(logging.NOTSET)
+        return self._prices_1wk_1h_prepost
+
+    def _get_1wk_1h_reg_prices(self):
+        if self._prices_1wk_1h_reg is None:
+            # Temporarily disable error printing
+            logging.disable(logging.CRITICAL)
+            self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False)
+            logging.disable(logging.NOTSET)
+        return self._prices_1wk_1h_reg
+
+    def _get_exchange_metadata(self):
+        if self._md is not None:
+            return self._md
+
+        self._get_1y_prices()
+        self._md = self._tkr.get_history_metadata()
+        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._fetch(self.proxy)
+            self._fetch_complementary(self.proxy)
+
+        return self._info
+
+    @property
+    def sustainability(self) -> pd.DataFrame:
+        if self._sustainability is None:
+            raise YFNotImplementedError('sustainability')
+        return self._sustainability
+
+    @property
+    def recommendations(self) -> pd.DataFrame:
+        if self._recommendations is None:
+            raise YFNotImplementedError('recommendations')
+        return self._recommendations
+
+    @property
+    def calendar(self) -> pd.DataFrame:
+        if self._calendar is None:
+            raise YFNotImplementedError('calendar')
+        return self._calendar
+
+    def _fetch(self, proxy):
+        if self._already_fetched:
+            return
+        self._already_fetched = True
+        modules = ['summaryProfile', 'financialData', 'quoteType',
+                     'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
+        params_dict = {}
+        for m in modules:
+            params_dict["modules"] = m
+        params_dict["ssl"] = "true"
+        result = self._data.get_raw_json(
+            _BASIC_URL_ + f"/{self._data.ticker}", params=params_dict, proxy=proxy
+        )
+        result["quoteSummary"]["result"][0]["symbol"] = self._data.ticker
+        query1_info = next(
+            (info for info in result.get("quoteSummary", {}).get("result", []) if info["symbol"] == self._data.ticker),
+            None,
+        )
+        # Most keys that appear in multiple dicts have same value. Except 'maxAge' because
+        # Yahoo not consistent with days vs seconds. Fix it here:
+        for k in query1_info:
+            if "maxAge" in query1_info[k] and query1_info[k]["maxAge"] == 1:
+                query1_info[k]["maxAge"] = 86400
+        query1_info = {
+            k1: v1 
+            for k, v in query1_info.items() 
+            if isinstance(v, dict) 
+            for k1, v1 in v.items() 
+            if v1
+        }
+        # recursively format but only because of 'companyOfficers'
+        def _format(k, v):
+            if isinstance(v, dict) and "raw" in v and "fmt" in v:
+                v2 = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
+            elif isinstance(v, list):
+                v2 = [_format(None, x) for x in v]
+            elif isinstance(v, dict):
+                v2 = {k:_format(k, x) for k, x in v.items()}
+            elif isinstance(v, str):
+                v2 = v.replace("\xa0", " ")
+            else:
+                v2 = v
+            return v2
+        for k, v in query1_info.items():
+            query1_info[k] = _format(k, v)
+        self._info = query1_info
+
+    def _fetch_complementary(self, proxy):
+        if self._already_fetched_complementary:
+            return
+        self._already_fetched_complementary = True
+
+        # self._scrape(proxy)  # decrypt broken
+        self._fetch(proxy)
+        if self._info is None:
+            return
+
+        # Complementary key-statistics. For now just want 'trailing PEG ratio'
+        keys = {"trailingPegRatio"}
+        if keys:
+            # Simplified the original scrape code for key-statistics. Very expensive for fetching
+            # just one value, best if scraping most/all:
+            #
+            # p = _re.compile(r'root\.App\.main = (.*);')
+            # url = 'https://finance.yahoo.com/quote/{}/key-statistics?p={}'.format(self._ticker.ticker, self._ticker.ticker)
+            # try:
+            #     r = session.get(url, headers=utils.user_agent_headers)
+            #     data = _json.loads(p.findall(r.text)[0])
+            #     key_stats = data['context']['dispatcher']['stores']['QuoteTimeSeriesStore']["timeSeries"]
+            #     for k in keys:
+            #         if k not in key_stats or len(key_stats[k])==0:
+            #             # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+            #             v = None
+            #         else:
+            #             # Select most recent (last) raw value in list:
+            #             v = key_stats[k][-1]["reportedValue"]["raw"]
+            #         self._info[k] = v
+            # except Exception:
+            #     raise
+            #     pass
+            #
+            # For just one/few variable is faster to query directly:
+            url = "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

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

yfinance/ticker.py

@@ -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(
@@ -115,39 +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 shares(self):
+    def shares(self) -> _pd.DataFrame :
         return self.get_shares()
 
     @property
-    def info(self):
+    def info(self) -> dict:
         return self.get_info()
 
     @property
-    def calendar(self):
+    def calendar(self) -> _pd.DataFrame:
         return self.get_calendar()
 
     @property
@@ -155,51 +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())
@@ -209,13 +236,21 @@ class Ticker(TickerBase):
         return self.get_news()
 
     @property
-    def analysis(self):
-        return self.get_analysis()
+    def trend_details(self) -> _pd.DataFrame:
+        return self.get_trend_details()
 
     @property
-    def earnings_history(self):
-        return self.get_earnings_history()
+    def earnings_trend(self) -> _pd.DataFrame:
+        return self.get_earnings_trend()
 
     @property
-    def earnings_dates(self):
+    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

@@ -25,7 +25,7 @@ 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)
@@ -34,39 +34,39 @@ class Tickers():
         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, session=session)
-
-        self.tickers = ticker_objects
         # self.tickers = _namedtuple(
         #     "Tickers", ticker_objects.keys(), rename=True
         # )(*ticker_objects.values())
 
     def history(self, period="1mo", interval="1d",
                 start=None, end=None, prepost=False,
-                actions=True, auto_adjust=True, 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,
+            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,
@@ -87,10 +87,4 @@ class Tickers():
         return data
 
     def news(self):
-        collection = {}
-        for ticker in self.symbols:
-            collection[ticker] = []
-            items = Ticker(ticker).news
-            for item in items:
-                collection[ticker].append(item)
-        return collection
+        return {ticker: [item for item in Ticker(ticker).news] for ticker in self.symbols}

yfinance/version.py

@@ -1 +1 @@
-version = "0.1.81"
+version = "0.2.23"