Bump version to 0.2.28

sync dev -> main

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,42 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-# READ BEFORE POSTING
-
-### Are you up-to-date?
-
-Upgrade to the latest version and confirm the issue/bug is still there.
-
-`$ pip install yfinance --upgrade --no-cache-dir`
-
-Confirm by running:
-
-`import yfinance as yf ; print(yf.__version__)`
-
-and comparing against [PIP](https://pypi.org/project/yfinance/#history).
-
-### Does Yahoo actually have the data?
-
-Visit `finance.yahoo.com` and confim they have your data. Maybe your ticker was delisted.
-
-Then check that you are spelling ticker *exactly* same as Yahoo.
-
-### Are you spamming Yahoo?
-
-Yahoo Finance free service has limit on query rate (roughly 100/s). Them delaying or blocking your spam is not a bug.
-
-### Still think it's a bug?
-
-Delete this default message and submit your bug report here, providing the following as best you can:
-
-- Info about your system:
-  - yfinance version
-  - operating system
-- Simple code that reproduces your problem
-- The error message

.github/ISSUE_TEMPLATE/bug_report.yaml

@@ -0,0 +1,88 @@
+name: Bug report
+description: Report a bug in our project
+labels: ["bug"]
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        # IMPORTANT - Read and follow these instructions carefully. Help us help you.
+
+        ### Does issue already exist?
+
+        Use the search tool. Don't annoy everyone by duplicating existing Issues.
+
+        ### Are you up-to-date?
+
+        Upgrade to the latest version and confirm the issue/bug is still there.
+
+        `$ pip install yfinance --upgrade --no-cache-dir`
+
+        Confirm by running:
+
+        `import yfinance as yf ; print(yf.__version__)`
+
+        and comparing against [PIP](https://pypi.org/project/yfinance/#history).
+
+        ### Does Yahoo actually have the data?
+
+        Are you spelling symbol *exactly* same as Yahoo?
+
+        Then visit `finance.yahoo.com` and confirm they have the data you want. Maybe your symbol was delisted, or your expectations of `yfinance` are wrong.
+
+        ### Are you spamming Yahoo?
+
+        Yahoo Finance free service has rate-limiting https://github.com/ranaroussi/yfinance/discussions/1513. Once limit hit, Yahoo can delay, block, or return bad data -> not a `yfinance` bug.
+        
+  - type: markdown
+    attributes:
+      value: |
+        ---
+        ## Still think it's a bug?
+
+        Provide the following as best you can:
+
+  - type: textarea
+    id: code
+    attributes:
+      label: "Simple code that reproduces your problem"
+      description: "Provide a snippet of code that we can copy-paste-run. Wrap code in Python Markdown code blocks for proper formatting (```` ```python ... ``` ````)."
+    validations:
+      required: true
+
+  - type: textarea
+    id: debug-log
+    attributes:
+      label: "Debug log"
+      description: "Run code with debug logging enabled and post the full output. Instructions: https://github.com/ranaroussi/yfinance/tree/main#logging"
+    validations:
+      required: true
+
+  - type: textarea
+    id: bad-data-proof
+    attributes:
+      label: "Bad data proof"
+      description: "If you think `yfinance` returning bad data, provide your proof here."
+    validations:
+      required: false
+
+  - type: input
+    id: version-yfinance
+    attributes:
+      label: "`yfinance` version"
+    validations:
+      required: true
+
+  - type: input
+    id: version-python
+    attributes:
+      label: "Python version"
+    validations:
+      required: false
+
+  - type: input
+    id: os
+    attributes:
+      label: "Operating system"
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/feature_request.md

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

.github/workflows/python-publish.yml

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

.gitignore

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

CHANGELOG.rst

@@ -1,6 +1,156 @@
 Change Log
 ===========
 
+0.2.28
+------
+- Fix TypeError: 'FastInfo' object is not callable #1636
+- Improve & fix price repair #1633 #1660
+- option_chain() also return underlying data #1606
+
+0.2.27
+------
+Bug fixes:
+- fix merging 1d-prices with out-of-range divs/splits #1635
+- fix multithread error 'tz already in cache' #1648
+
+0.2.26
+------
+Proxy improvements
+- bug fixes #1371
+- security fix #1625
+
+0.2.25
+------
+Fix single ISIN as ticker #1611
+Fix 'Only 100 years allowed' error #1576
+
+0.2.24
+------
+Fix info[] missing values #1603
+
+0.2.23
+------
+Fix 'Unauthorized' error #1595
+
+0.2.22
+------
+Fix unhandled 'sqlite3.DatabaseError' #1574
+
+0.2.21
+------
+Fix financials tables #1568
+Price repair update: fix Yahoo messing up dividend and split adjustments #1543
+Fix logging behaviour #1562
+Fix merge future div/split into prices #1567
+
+0.2.20
+------
+Switch to `logging` module #1493 #1522 #1541
+Price history:
+- optimise #1514
+- fixes #1523
+- fix TZ-cache corruption #1528
+
+0.2.18
+------
+Fix 'fast_info' error '_np not found' #1496
+Fix bug in timezone cache #1498
+
+0.2.17
+------
+Fix prices error with Pandas 2.0 #1488
+
+0.2.16
+------
+Fix 'fast_info deprecated' msg appearing at Ticker() init
+
+0.2.15
+------
+Restore missing Ticker.info keys #1480
+
+0.2.14
+------
+Fix Ticker.info dict by fetching from API #1461
+
+0.2.13
+------
+Price bug fixes:
+- fetch big-interval with Capital Gains #1455
+- merging dividends & splits with prices #1452
+
+0.2.12
+------
+Disable annoying 'backup decrypt' msg
+
+0.2.11
+------
+Fix history_metadata accesses for unusual symbols #1411
+
+0.2.10
+------
+General
+- allow using sqlite3 < 3.8.2 #1380
+- add another backup decrypt option #1379
+Prices
+- restore original download() timezone handling #1385
+- fix & improve price repair #1289 2a2928b 86d6acc
+- drop intraday intervals if in post-market but prepost=False #1311
+Info
+- fast_info improvements:
+  - add camelCase keys, add dict functions values() & items() #1368
+  - fix fast_info["previousClose"] #1383
+- catch TypeError Exception #1397
+
+0.2.9
+-----
+- Fix fast_info bugs #1362
+
+0.2.7
+-----
+- Fix Yahoo decryption, smarter this time #1353
+- Rename basic_info -> fast_info #1354
+
+0.2.6
+-----
+- Fix Ticker.basic_info lazy-loading #1342
+
+0.2.5
+-----
+- Fix Yahoo data decryption again #1336
+- New: Ticker.basic_info - faster Ticker.info #1317
+
+0.2.4
+-----
+- Fix Yahoo data decryption #1297
+- New feature: 'Ticker.get_shares_full()' #1301
+- Improve caching of financials data #1284
+- Restore download() original alignment behaviour #1283
+- Fix the database lock error in multithread download #1276
+
+0.2.3
+-----
+- Make financials API '_' use consistent
+
+0.2.2
+-----
+- Restore 'financials' attribute (map to 'income_stmt')
+
+0.2.1
+-----
+Release!
+
+0.2.0rc5
+--------
+- Improve financials error handling #1243
+- Fix '100x price' repair #1244
+
+0.2.0rc4
+--------
+- Access to old financials tables via `get_income_stmt(legacy=True)`
+- Optimise scraping financials & fundamentals, 2x faster
+- Add 'capital gains' alongside dividends & splits for ETFs, and metadata available via `history_metadata`, plus a bunch of price fixes
+For full list of changes see #1238
+
 0.2.0rc2
 --------
 Financials

README.md

@@ -53,30 +53,23 @@ 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 meta information about the history (requires history() to be called first)
 msft.history_metadata
 
 # show actions (dividends, splits, capital gains)
 msft.actions
-
-# show dividends
 msft.dividends
-
-# show splits
 msft.splits
-
-
-# show capital gains (for mutual funds & etfs)
-msft.capital_gains
+msft.capital_gains  # only for mutual funds & etfs
 
 # show share count
-msft.shares
+msft.get_shares_full(start="2022-01-01", end=None)
 
 # show financials:
 # - income statement
@@ -90,34 +83,11 @@ msft.cashflow
 msft.quarterly_cashflow
 # see `Ticker.get_income_stmt()` for more options
 
-# show major holders
+# show holders
 msft.major_holders
-
-# show institutional holders
 msft.institutional_holders
-
-# show mutualfund holders
 msft.mutualfund_holders
 
-# show earnings
-msft.earnings
-msft.quarterly_earnings
-
-# show sustainability
-msft.sustainability
-
-# show analysts recommendations
-msft.recommendations
-msft.recommendations_summary
-# show analysts other work
-msft.analyst_price_target
-msft.revenue_forecasts
-msft.earnings_forecasts
-msft.earnings_trend
-
-# show next event (earnings, etc)
-msft.calendar
-
 # Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default. 
 # Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.
 msft.earnings_dates
@@ -155,18 +125,7 @@ msft.option_chain(..., proxy="PROXY_SERVER")
 ...
 ```
 
-To use a custom `requests` session (for example to cache calls to the
-API or customize the `User-agent` header), pass a `session=` argument to
-the Ticker constructor.
-
-```python
-import requests_cache
-session = requests_cache.CachedSession('yfinance.cache')
-session.headers['User-agent'] = 'my-program/1.0'
-ticker = yf.Ticker('msft', session=session)
-# The scraped response will be stored in the cache
-ticker.actions
-```
+### Multiple tickers
 
 To initialize multiple `Ticker` objects, use
 
@@ -181,70 +140,48 @@ tickers.tickers['AAPL'].history(period="1mo")
 tickers.tickers['GOOG'].actions
 ```
 
-### Fetching data for multiple tickers
+To download price history into one table:
 
 ```python
 import yfinance as yf
-data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
+data = yf.download("SPY AAPL", period="1mo")
 ```
 
-I've also added some options to make life easier :)
+#### `yf.download()` and `Ticker.history()` have many options for configuring fetching and processing. [Review the Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
+
+### Logging
+
+`yfinance` now uses the `logging` module to handle messages, default behaviour is only print errors. If debugging, use `yf.enable_debug_mode()` to switch logging to debug with custom formatting.
+
+### Smarter scraping
+
+To use a custom `requests` session (for example to cache calls to the
+API or customize the `User-agent` header), pass a `session=` argument to
+the Ticker constructor.
 
 ```python
-data = yf.download(  # or pdr.get_data_yahoo(...
-        # tickers list or string as well
-        tickers = "SPY AAPL MSFT",
-
-        # use "period" instead of start/end
-        # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
-        # (optional, default is '1mo')
-        period = "ytd",
-
-        # fetch data by interval (including intraday if period < 60 days)
-        # valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
-        # (optional, default is '1d')
-        interval = "5d",
-
-        # Whether to ignore timezone when aligning ticker data from 
-        # different timezones. Default is 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,
-
-        # identify and attempt repair of currency unit mixups e.g. $/cents
-        repair = False,
-
-        # download pre/post regular market hours data
-        # (optional, default is False)
-        prepost = True,
-
-        # use threads for mass downloading? (True/False/Integer)
-        # (optional, default is True)
-        threads = True,
-
-        # proxy URL scheme use use when downloading?
-        # (optional, default is None)
-        proxy = None
-    )
+import requests_cache
+session = requests_cache.CachedSession('yfinance.cache')
+session.headers['User-agent'] = 'my-program/1.0'
+ticker = yf.Ticker('msft', session=session)
+# The scraped response will be stored in the cache
+ticker.actions
 ```
 
-### Timezone cache store
-
-When fetching price data, all dates are localized to stock exchange timezone. 
-But timezone retrieval is relatively slow, so yfinance attemps to cache them 
-in your users cache folder. 
-You can direct cache to use a different location with `set_tz_cache_location()`:
+Combine a `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-limiter/blocker that can corrupt data.
 ```python
-import yfinance as yf
-yf.set_tz_cache_location("custom/cache/location")
-...
+from requests import Session
+from requests_cache import CacheMixin, SQLiteCache
+from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
+from pyrate_limiter import Duration, RequestRate, Limiter
+class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
+    pass
+
+session = CachedLimiterSession(
+    limiter=Limiter(RequestRate(2, Duration.SECOND*5)),  # max 2 requests per 5 seconds
+    bucket_class=MemoryQueueBucket,
+    backend=SQLiteCache("yfinance.cache"),
+)
 ```
 
 ### Managing Multi-Level Columns
@@ -262,9 +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()`
@@ -281,6 +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
@@ -291,6 +238,11 @@ Install `yfinance` using `pip`:
 $ pip install yfinance --upgrade --no-cache-dir
 ```
 
+Test new features by installing betas, provide feedback in [corresponding Discussion](https://github.com/ranaroussi/yfinance/discussions):
+``` {.sourceCode .bash}
+$ pip install yfinance --upgrade --no-cache-dir --pre
+```
+
 To install `yfinance` using `conda`, see
 [this](https://anaconda.org/ranaroussi/yfinance).
 
@@ -299,7 +251,7 @@ To install `yfinance` using `conda`, see
 -   [Python](https://www.python.org) \>= 2.7, 3.4+
 -   [Pandas](https://github.com/pydata/pandas) \>= 1.3.0
 -   [Numpy](http://www.numpy.org) \>= 1.16.5
--   [requests](http://docs.python-requests.org/en/master) \>= 2.26
+-   [requests](http://docs.python-requests.org/en/master) \>= 2.31
 -   [lxml](https://pypi.org/project/lxml) \>= 4.9.1
 -   [appdirs](https://pypi.org/project/appdirs) \>= 1.4.4
 -   [pytz](https://pypi.org/project/pytz) \>=2022.5
@@ -307,11 +259,15 @@ To install `yfinance` using `conda`, see
 -   [beautifulsoup4](https://pypi.org/project/beautifulsoup4) \>= 4.11.1
 -   [html5lib](https://pypi.org/project/html5lib) \>= 1.1
 
-### Optional (if you want to use `pandas_datareader`)
+#### Optional (if you want to use `pandas_datareader`)
 
 -   [pandas\_datareader](https://github.com/pydata/pandas-datareader)
     \>= 0.4.0
 
+## Developers: want to contribute?
+
+`yfinance` relies on community to investigate bugs and contribute code. Developer guide: https://github.com/ranaroussi/yfinance/discussions/1084
+
 ---
 
 ### Legal Stuff

meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.2.0" %}
+{% set version = "0.2.28" %}
 
 package:
   name: "{{ name|lower }}"
@@ -18,7 +18,7 @@ requirements:
   host:
     - pandas >=1.3.0
     - numpy >=1.16.5
-    - requests >=2.26
+    - requests >=2.31
     - multitasking >=0.0.7
     - lxml >=4.9.1
     - appdirs >=1.4.4
@@ -26,13 +26,14 @@ requirements:
     - frozendict >=2.3.4
     - beautifulsoup4 >=4.11.1
     - html5lib >=1.1
+    # - pycryptodome >=3.6.6
     - pip
     - python
 
   run:
     - pandas >=1.3.0
     - numpy >=1.16.5
-    - requests >=2.26
+    - requests >=2.31
     - multitasking >=0.0.7
     - lxml >=4.9.1
     - appdirs >=1.4.4
@@ -40,6 +41,7 @@ requirements:
     - frozendict >=2.3.4
     - beautifulsoup4 >=4.11.1
     - html5lib >=1.1
+    # - pycryptodome >=3.6.6
     - python
 
 test:

requirements.txt

@@ -1,6 +1,6 @@
 pandas>=1.3.0
 numpy>=1.16.5
-requests>=2.26
+requests>=2.31
 multitasking>=0.0.7
 lxml>=4.9.1
 appdirs>=1.4.4

setup.py

@@ -39,7 +39,7 @@ setup(
         'License :: OSI Approved :: Apache Software License',
         # 'Development Status :: 3 - Alpha',
         'Development Status :: 4 - Beta',
-        #'Development Status :: 5 - Production/Stable',
+        # 'Development Status :: 5 - Production/Stable',
 
 
         'Operating System :: OS Independent',
@@ -60,10 +60,11 @@ setup(
     keywords='pandas, yahoo finance, pandas datareader',
     packages=find_packages(exclude=['contrib', 'docs', 'tests', 'examples']),
     install_requires=['pandas>=1.3.0', 'numpy>=1.16.5',
-                      'requests>=2.26', 'multitasking>=0.0.7',
+                      'requests>=2.31', 'multitasking>=0.0.7',
                       'lxml>=4.9.1', 'appdirs>=1.4.4', 'pytz>=2022.5',
-                      'frozendict>=2.3.4',
+                      '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]

tests/context.py

@@ -7,3 +7,37 @@ _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-1d-bad-stock-split-fixed.csv

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

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

@@ -0,0 +1,23 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-04-14 00:00:00+09:00,4126,4130,4055,4129,4129,7459400,0,0
+2023-04-13 00:00:00+09:00,4064,4099,4026,4081,4081,5160200,0,0
+2023-04-12 00:00:00+09:00,3968,4084,3966,4064,4064,6372000,0,0
+2023-04-11 00:00:00+09:00,3990,4019,3954,3960,3960,6476500,0,0
+2023-04-10 00:00:00+09:00,3996,4009,3949,3964,3964,3485200,0,0
+2023-04-07 00:00:00+09:00,3897,3975,3892,3953,3953,4554700,0,0
+2023-04-06 00:00:00+09:00,4002,4004,3920,3942,3942,8615200,0,0
+2023-04-05 00:00:00+09:00,4150,4150,4080,4088,4088,6063700,0,0
+2023-04-04 00:00:00+09:00,4245,4245,4144,4155,4155,6780600,0,0
+2023-04-03 00:00:00+09:00,4250,4259,4162,4182,4182,7076800,0,0
+2023-03-31 00:00:00+09:00,4229,4299,4209,4275,4275,9608400,0,0
+2023-03-30 00:00:00+09:00,4257,4268,4119,4161,4161,5535200,55,5
+2023-03-29 00:00:00+09:00,4146,4211,4146,4206,4151,6514500,0,0
+2023-03-28 00:00:00+09:00,21000,21035,20620,20710,20439.185546875,901100,0,0
+2023-03-27 00:00:00+09:00,20980,21020,20755,20960,20685.916015625,1191900,0,0
+2023-03-24 00:00:00+09:00,20650,20935,20615,20885,20611.896484375,1792300,0,0
+2023-03-23 00:00:00+09:00,20280,20530,20195,20430,20162.845703125,1096000,0,0
+2023-03-22 00:00:00+09:00,20330,20640,20285,20610,20340.4921875,1748300,0,0
+2023-03-20 00:00:00+09:00,20000,20135,19900,19900,19639.77734375,1401300,0,0
+2023-03-17 00:00:00+09:00,20090,20275,20080,20155,19891.44140625,1392300,0,0
+2023-03-16 00:00:00+09:00,19880,20225,19860,20175,19911.181640625,1003800,0,0
+2023-03-15 00:00:00+09:00,20170,20250,20015,20205,19940.7890625,1224400,0,0

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

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

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

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

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

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

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

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

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

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

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

@@ -0,0 +1,25 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2022-08-15 00:00:00+01:00,27.6000,28.2000,26.2000,27.6000,27.6000,3535668,0,0
+2022-08-12 00:00:00+01:00,27.3000,29.8000,26.4030,27.0000,27.0000,7223353,0,0
+2022-08-11 00:00:00+01:00,26.0000,29.8000,24.2000,27.1000,27.1000,12887933,0,0
+2022-08-10 00:00:00+01:00,25.0000,29.2000,22.5000,25.0000,25.0000,26572680,0,0
+2022-08-09 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-08 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-05 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-04 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-03 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-02 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-08-01 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-29 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-28 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-27 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-26 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-25 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-22 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-21 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-20 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-19 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-18 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-15 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-14 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
+2022-07-13 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -0,0 +1,23 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2022-06-01 00:00:00+02:00,5.72999992370606,5.78199996948242,5.3939998626709,5.3939998626709,5.3939998626709,3095860,0,0
+2022-06-02 00:00:00+02:00,5.38600006103516,5.38600006103516,5.26800003051758,5.2939998626709,5.2939998626709,1662880,0,0
+2022-06-03 00:00:00+02:00,5.34599990844727,5.34599990844727,5.15800018310547,5.16800003051758,5.16800003051758,1698900,0,0
+2022-06-06 00:00:00+02:00,5.16800003051758,5.25200004577637,5.13800010681152,5.18800010681152,5.18800010681152,1074910,0,0
+2022-06-07 00:00:00+02:00,5.21800003051758,5.22200012207031,5.07400016784668,5.1560001373291,5.1560001373291,1850680,0,0
+2022-06-08 00:00:00+02:00,5.1560001373291,5.17599983215332,5.07200012207031,5.10200004577637,5.10200004577637,1140360,0,0
+2022-06-09 00:00:00+02:00,5.09799995422363,5.09799995422363,4.87599983215332,4.8939998626709,4.8939998626709,2025480,0,0
+2022-06-10 00:00:00+02:00,4.87999992370606,4.87999992370606,4.50400009155274,4.50400009155274,4.50400009155274,2982730,0,0
+2022-06-13 00:00:00+02:00,4.3,4.37599983215332,3.83600006103516,3.83600006103516,3.83600006103516,4568210,0,0.1
+2022-06-14 00:00:00+02:00,3.87750015258789,4.15999984741211,3.85200004577637,3.9439998626709,3.9439998626709,5354500,0,0
+2022-06-15 00:00:00+02:00,4.03400001525879,4.16450004577637,3.73050003051758,3.73050003051758,3.73050003051758,6662610,0,0
+2022-06-16 00:00:00+02:00,3.73050003051758,3.98499984741211,3.72400016784668,3.82550010681152,3.82550010681152,13379960,0,0
+2022-06-17 00:00:00+02:00,3.8,4.29949989318848,3.75,4.29949989318848,4.29949989318848,12844160,0,0
+2022-06-20 00:00:00+02:00,2.19422197341919,2.2295401096344,2.13992595672607,2.2295401096344,2.2295401096344,12364104,0,0
+2022-06-21 00:00:00+02:00,2.24719905853272,2.28515291213989,2.19712090492249,2.21557092666626,2.21557092666626,8434013,0,0
+2022-06-22 00:00:00+02:00,1.98679196834564,2.00365996360779,1.73798203468323,1.73798203468323,1.73798203468323,26496542,0,0
+2022-06-23 00:00:00+02:00,1.62411904335022,1.68526804447174,1.37320005893707,1.59776198863983,1.59776198863983,48720201,0,0
+2022-06-24 00:00:00+02:00,1.47599303722382,1.54610300064087,1.1739410161972,1.24932205677032,1.24932205677032,56877192,0,0
+2022-06-27 00:00:00+02:00,1.49899995326996,1.79849994182587,1.49899995326996,1.79849994182587,1.79849994182587,460673,0,0
+2022-06-28 00:00:00+02:00,2.15799999237061,3.05100011825562,2.12599992752075,3.05100011825562,3.05100011825562,3058635,0,0
+2022-06-29 00:00:00+02:00,2.90000009536743,3.73799991607666,2.85899996757507,3.26399993896484,3.26399993896484,6516761,0,0
+2022-06-30 00:00:00+02:00,3.24900007247925,3.28099989891052,2.5,2.5550000667572,2.5550000667572,4805984,0,0

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

@@ -0,0 +1,23 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2022-06-01 00:00:00+02:00,57.29999923706055,57.81999969482422,53.939998626708984,53.939998626708984,53.939998626708984,309586,0.0,0.0
+2022-06-02 00:00:00+02:00,53.86000061035156,53.86000061035156,52.68000030517578,52.939998626708984,52.939998626708984,166288,0.0,0.0
+2022-06-03 00:00:00+02:00,53.459999084472656,53.459999084472656,51.58000183105469,51.68000030517578,51.68000030517578,169890,0.0,0.0
+2022-06-06 00:00:00+02:00,51.68000030517578,52.52000045776367,51.380001068115234,51.880001068115234,51.880001068115234,107491,0.0,0.0
+2022-06-07 00:00:00+02:00,52.18000030517578,52.220001220703125,50.7400016784668,51.560001373291016,51.560001373291016,185068,0.0,0.0
+2022-06-08 00:00:00+02:00,51.560001373291016,51.7599983215332,50.720001220703125,51.02000045776367,51.02000045776367,114036,0.0,0.0
+2022-06-09 00:00:00+02:00,50.97999954223633,50.97999954223633,48.7599983215332,48.939998626708984,48.939998626708984,202548,0.0,0.0
+2022-06-10 00:00:00+02:00,48.79999923706055,48.79999923706055,45.040000915527344,45.040000915527344,45.040000915527344,298273,0.0,0.0
+2022-06-13 00:00:00+02:00,43.0,43.7599983215332,38.36000061035156,38.36000061035156,38.36000061035156,456821,0.0,0.1
+2022-06-14 00:00:00+02:00,38.775001525878906,41.599998474121094,38.52000045776367,39.439998626708984,39.439998626708984,535450,0.0,0.0
+2022-06-15 00:00:00+02:00,40.34000015258789,41.64500045776367,37.30500030517578,37.30500030517578,37.30500030517578,666261,0.0,0.0
+2022-06-16 00:00:00+02:00,37.30500030517578,39.849998474121094,37.2400016784668,38.255001068115234,38.255001068115234,1337996,0.0,0.0
+2022-06-17 00:00:00+02:00,38.0,42.994998931884766,37.5,42.994998931884766,42.994998931884766,1284416,0.0,0.0
+2022-06-20 00:00:00+02:00,2.1942219734191895,2.2295401096343994,2.139925956726074,2.2295401096343994,2.2295401096343994,12364104,0.0,0.0
+2022-06-21 00:00:00+02:00,2.247199058532715,2.2851529121398926,2.1971209049224854,2.2155709266662598,2.2155709266662598,8434013,0.0,0.0
+2022-06-22 00:00:00+02:00,1.986791968345642,2.003659963607788,1.7379820346832275,1.7379820346832275,1.7379820346832275,26496542,0.0,0.0
+2022-06-23 00:00:00+02:00,1.6241190433502197,1.6852680444717407,1.3732000589370728,1.5977619886398315,1.5977619886398315,48720201,0.0,0.0
+2022-06-24 00:00:00+02:00,1.475993037223816,1.5461030006408691,1.1739410161972046,1.2493220567703247,1.2493220567703247,56877192,0.0,0.0
+2022-06-27 00:00:00+02:00,1.4989999532699585,1.7984999418258667,1.4989999532699585,1.7984999418258667,1.7984999418258667,460673,0.0,0.0
+2022-06-28 00:00:00+02:00,2.1579999923706055,3.0510001182556152,2.125999927520752,3.0510001182556152,3.0510001182556152,3058635,0.0,0.0
+2022-06-29 00:00:00+02:00,2.9000000953674316,3.73799991607666,2.8589999675750732,3.2639999389648438,3.2639999389648438,6516761,0.0,0.0
+2022-06-30 00:00:00+02:00,3.249000072479248,3.2809998989105225,2.5,2.555000066757202,2.555000066757202,4805984,0.0,0.0

tests/data/SSW-JO-1d-100x-error-fixed.csv

@@ -0,0 +1,30 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-06-09 00:00:00+02:00,34.7000,34.7100,33.2400,33.6200,33.6200,7148409,0,0
+2023-06-08 00:00:00+02:00,34.9000,34.9900,34.0400,34.3600,34.3600,10406999,0,0
+2023-06-07 00:00:00+02:00,34.5500,35.6400,34.3200,35.0900,35.0900,10118918,0,0
+2023-06-06 00:00:00+02:00,34.5000,34.8200,34.0500,34.4600,34.4600,9109709,0,0
+2023-06-05 00:00:00+02:00,35.0000,35.3000,34.2000,34.7000,34.7000,8791993,0,0
+2023-06-02 00:00:00+02:00,35.6900,36.1800,34.6000,34.9700,34.9700,8844549,0,0
+2023-06-01 00:00:00+02:00,35.2300,35.3800,34.2400,35.3500,35.3500,6721030,0,0
+2023-05-31 00:00:00+02:00,34.8,35.48,34.26,35.01,35.01,32605833,0,0
+2023-05-30 00:00:00+02:00,34.39,35.37,33.85,34.23,34.23,8970804,0,0
+2023-05-29 00:00:00+02:00,34.66,35.06,34.02,34.32,34.32,3912803,0,0
+2023-05-26 00:00:00+02:00,34.75,35.99,34.33,34.53,34.53,6744718,0,0
+2023-05-25 00:00:00+02:00,35.4,36.09,34.63,35.07,35.07,16900221,0,0
+2023-05-24 00:00:00+02:00,36.2,36.5,35.26,35.4,35.4,9049505,0,0
+2023-05-23 00:00:00+02:00,36.9,36.67,35.56,36.1,36.1,10797373,0,0
+2023-05-22 00:00:00+02:00,37.05,37.36,36.09,36.61,36.61,7132641,0,0
+2023-05-19 00:00:00+02:00,36.2,37.15,36.25,36.9,36.9,12648518,0,0
+2023-05-18 00:00:00+02:00,36.57,36.99,35.84,36.46,36.46,10674542,0,0
+2023-05-17 00:00:00+02:00,36.87,37.31,36.56,36.71,36.71,9892791,0,0
+2023-05-16 00:00:00+02:00,37.15,37.73,36.96,37.03,37.03,4706789,0,0
+2023-05-15 00:00:00+02:00,37.74,38.05,36.96,37.27,37.27,7890969,0,0
+2023-05-12 00:00:00+02:00,37.5,38.44,36.71,37.74,37.74,8724303,0,0
+2023-05-11 00:00:00+02:00,38.8,38.88,37.01,37.32,37.32,14371855,0,0
+2023-05-10 00:00:00+02:00,38.93,38.8,36.42,38.1,38.1,30393389,0,0
+2023-05-09 00:00:00+02:00,44.41,44.41,39.39,39.66,39.66,19833428,0,0
+2023-05-08 00:00:00+02:00,44.63,45.78,44.56,44.71,44.71,11092519,0,0
+2023-05-05 00:00:00+02:00,42.99,44.9,42.87,44.58,44.58,28539048,0,0
+2023-05-04 00:00:00+02:00,41.49,43.3,41.23,42.83,42.83,15506868,0,0
+2023-05-03 00:00:00+02:00,39.75,40.98,39.68,40.95,40.95,14657028,0,0
+2023-05-02 00:00:00+02:00,40.37,40.32,39.17,39.65,39.65,11818133,0,0

tests/data/SSW-JO-1d-100x-error.csv

@@ -0,0 +1,30 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
+2023-06-09 00:00:00+02:00,34.700001,34.709999,33.240002,33.619999,33.619999,7148409,0,0
+2023-06-08 00:00:00+02:00,34.900002,34.990002,34.040001,34.360001,34.360001,10406999,0,0
+2023-06-07 00:00:00+02:00,34.549999,35.639999,34.320000,35.090000,35.090000,10118918,0,0
+2023-06-06 00:00:00+02:00,34.500000,34.820000,34.049999,34.459999,34.459999,9109709,0,0
+2023-06-05 00:00:00+02:00,35.000000,35.299999,34.200001,34.700001,34.700001,8791993,0,0
+2023-06-02 00:00:00+02:00,35.689999,36.180000,34.599998,34.970001,34.970001,8844549,0,0
+2023-06-01 00:00:00+02:00,35.230000,35.380001,34.240002,35.349998,35.349998,6721030,0,0
+2023-05-31 00:00:00+02:00,3480,3548,3426,3501,3501,32605833,0,0
+2023-05-30 00:00:00+02:00,3439,3537,3385,3423,3423,8970804,0,0
+2023-05-29 00:00:00+02:00,3466,3506,3402,3432,3432,3912803,0,0
+2023-05-26 00:00:00+02:00,3475,3599,3433,3453,3453,6744718,0,0
+2023-05-25 00:00:00+02:00,3540,3609,3463,3507,3507,16900221,0,0
+2023-05-24 00:00:00+02:00,3620,3650,3526,3540,3540,9049505,0,0
+2023-05-23 00:00:00+02:00,3690,3667,3556,3610,3610,10797373,0,0
+2023-05-22 00:00:00+02:00,3705,3736,3609,3661,3661,7132641,0,0
+2023-05-19 00:00:00+02:00,3620,3715,3625,3690,3690,12648518,0,0
+2023-05-18 00:00:00+02:00,3657,3699,3584,3646,3646,10674542,0,0
+2023-05-17 00:00:00+02:00,3687,3731,3656,3671,3671,9892791,0,0
+2023-05-16 00:00:00+02:00,3715,3773,3696,3703,3703,4706789,0,0
+2023-05-15 00:00:00+02:00,3774,3805,3696,3727,3727,7890969,0,0
+2023-05-12 00:00:00+02:00,3750,3844,3671,3774,3774,8724303,0,0
+2023-05-11 00:00:00+02:00,3880,3888,3701,3732,3732,14371855,0,0
+2023-05-10 00:00:00+02:00,3893,3880,3642,3810,3810,30393389,0,0
+2023-05-09 00:00:00+02:00,4441,4441,3939,3966,3966,19833428,0,0
+2023-05-08 00:00:00+02:00,4463,4578,4456,4471,4471,11092519,0,0
+2023-05-05 00:00:00+02:00,4299,4490,4287,4458,4458,28539048,0,0
+2023-05-04 00:00:00+02:00,4149,4330,4123,4283,4283,15506868,0,0
+2023-05-03 00:00:00+02:00,3975,4098,3968,4095,4095,14657028,0,0
+2023-05-02 00:00:00+02:00,4037,4032,3917,3965,3965,11818133,0,0

tests/prices.py

@@ -1,21 +1,19 @@
 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
 
-import requests_cache
-
 
 class TestPriceHistory(unittest.TestCase):
-    session = None
-
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -24,9 +22,7 @@ class TestPriceHistory(unittest.TestCase):
 
     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)
 
@@ -36,32 +32,43 @@ class TestPriceHistory(unittest.TestCase):
                 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(debug_mode=False, proxy=None, timeout=None)
+            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))
-
-            df = dat.history(start=dt.date() - _dt.timedelta(days=1), interval="1h")
+            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:
+            except AssertionError:
                 print("Ticker = ", tkr)
                 raise
 
-
     def test_duplicatingDaily(self):
         tkrs = ["IMP.JO", "BHG.JO", "SSW.JO", "BP.L", "INTC"]
         test_run = False
         for tkr in tkrs:
             dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(debug_mode=False, proxy=None, timeout=None)
+            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))
@@ -75,7 +82,7 @@ class TestPriceHistory(unittest.TestCase):
             dt1 = df.index[-1]
             try:
                 self.assertNotEqual(dt0, dt1)
-            except:
+            except AssertionError:
                 print("Ticker = ", tkr)
                 raise
 
@@ -87,7 +94,7 @@ class TestPriceHistory(unittest.TestCase):
         test_run = False
         for tkr in tkrs:
             dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(debug_mode=False, proxy=None, timeout=None)
+            tz = dat._get_ticker_tz(proxy=None, timeout=None)
 
             dt = _tz.timezone(tz).localize(_dt.datetime.now())
             if dt.date().weekday() not in [1, 2, 3, 4]:
@@ -99,7 +106,7 @@ class TestPriceHistory(unittest.TestCase):
             dt1 = df.index[-1]
             try:
                 self.assertNotEqual(dt0.week, dt1.week)
-            except:
+            except AssertionError:
                 print("Ticker={}: Last two rows within same week:".format(tkr))
                 print(df.iloc[df.shape[0] - 2:])
                 raise
@@ -108,26 +115,82 @@ class TestPriceHistory(unittest.TestCase):
             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
 
-        tkr = "ICL.TA"
-        # tkr = "ESLT.TA"
-        # tkr = "ONE.TA"
-        # tkr = "MGDL.TA"
-        start_d = _dt.date.today() - _dt.timedelta(days=60)
-        end_d = None
-        df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
-        df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
-        if df_daily_divs.shape[0] == 0:
-            self.skipTest("Skipping test_intraDayWithEvents() because 'ICL.TA' has no dividend in last 60 days")
+        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 = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
-        self.assertTrue((df["Dividends"] != 0.0).any())
+            last_div_date = df_daily_divs.index[-1]
+            start_d = last_div_date.date()
+            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df_intraday["Dividends"] != 0.0).any())
+
+            df_intraday_divs = df_intraday["Dividends"][df_intraday["Dividends"] != 0]
+            df_intraday_divs.index = df_intraday_divs.index.floor('D')
+            self.assertTrue(df_daily_divs.equals(df_intraday_divs))
+
+            test_run = True
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents_tase() because no tickers had a dividend in last 60 days")
 
     def test_dailyWithEvents(self):
+        start_d = _dt.date(2022, 1, 1)
+        end_d = _dt.date(2023, 1, 1)
+
+        tkr_div_dates = {'BHP.AX': [_dt.date(2022, 9, 1), _dt.date(2022, 2, 24)],  # Yahoo claims 23-Feb but wrong because DST
+                         'IMP.JO': [_dt.date(2022, 9, 21), _dt.date(2022, 3, 16)],
+                         'BP.L': [_dt.date(2022, 11, 10), _dt.date(2022, 8, 11), _dt.date(2022, 5, 12),
+                                  _dt.date(2022, 2, 17)],
+                         'INTC': [_dt.date(2022, 11, 4), _dt.date(2022, 8, 4), _dt.date(2022, 5, 5),
+                                  _dt.date(2022, 2, 4)]}
+
+        for tkr, dates in tkr_div_dates.items():
+            df = yf.Ticker(tkr, session=self.session).history(interval='1d', start=start_d, end=end_d)
+            df_divs = df[df['Dividends'] != 0].sort_index(ascending=False)
+            try:
+                self.assertTrue((df_divs.index.date == dates).all())
+            except AssertionError:
+                print(f'- ticker = {tkr}')
+                print('- response:') ; print(df_divs.index.date)
+                print('- answer:') ; print(dates)
+                raise
+
+    def test_dailyWithEvents_bugs(self):
         # Reproduce issue #521
         tkr1 = "QQQ"
         tkr2 = "GDX"
@@ -139,7 +202,7 @@ class TestPriceHistory(unittest.TestCase):
         self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
         try:
             self.assertTrue(df1.index.equals(df2.index))
-        except:
+        except AssertionError:
             missing_from_df1 = df2.index.difference(df1.index)
             missing_from_df2 = df1.index.difference(df2.index)
             print("{} missing these dates: {}".format(tkr1, missing_from_df1))
@@ -154,13 +217,76 @@ class TestPriceHistory(unittest.TestCase):
             self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
             try:
                 self.assertTrue(df1.index.equals(df2.index))
-            except:
+            except AssertionError:
                 missing_from_df1 = df2.index.difference(df1.index)
                 missing_from_df2 = df1.index.difference(df2.index)
                 print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
                 print("{}-without-events missing these dates: {}".format(tkr, missing_from_df2))
                 raise
 
+        # Reproduce issue #1634 - 1d dividend out-of-range, should be prepended to prices
+        div_dt = _pd.Timestamp(2022, 7, 21).tz_localize("America/New_York")
+        df_dividends = _pd.DataFrame(data={"Dividends":[1.0]}, index=[div_dt])
+        df_prices = _pd.DataFrame(data={c:[1.0] for c in yf.const.price_colnames}|{'Volume':0}, index=[div_dt+_dt.timedelta(days=1)])
+        df_merged = yf.utils.safe_merge_dfs(df_prices, df_dividends, '1d')
+        self.assertEqual(df_merged.shape[0], 2)
+        self.assertTrue(df_merged[df_prices.columns].iloc[1:].equals(df_prices))
+        self.assertEqual(df_merged.index[0], div_dt)
+
+    def test_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"
@@ -173,7 +299,7 @@ class TestPriceHistory(unittest.TestCase):
         self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
         try:
             self.assertTrue(df1.index.equals(df2.index))
-        except:
+        except AssertionError:
             missing_from_df1 = df2.index.difference(df1.index)
             missing_from_df2 = df1.index.difference(df2.index)
             print("{} missing these dates: {}".format(tkr1, missing_from_df1))
@@ -188,7 +314,7 @@ class TestPriceHistory(unittest.TestCase):
             self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
             try:
                 self.assertTrue(df1.index.equals(df2.index))
-            except:
+            except AssertionError:
                 missing_from_df1 = df2.index.difference(df1.index)
                 missing_from_df2 = df1.index.difference(df2.index)
                 print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
@@ -206,7 +332,7 @@ class TestPriceHistory(unittest.TestCase):
         self.assertTrue(((df2["Dividends"] > 0) | (df2["Stock Splits"] > 0)).any())
         try:
             self.assertTrue(df1.index.equals(df2.index))
-        except:
+        except AssertionError:
             missing_from_df1 = df2.index.difference(df1.index)
             missing_from_df2 = df1.index.difference(df2.index)
             print("{} missing these dates: {}".format(tkr1, missing_from_df1))
@@ -221,16 +347,31 @@ class TestPriceHistory(unittest.TestCase):
             self.assertTrue(((df1["Dividends"] > 0) | (df1["Stock Splits"] > 0)).any())
             try:
                 self.assertTrue(df1.index.equals(df2.index))
-            except:
+            except AssertionError:
                 missing_from_df1 = df2.index.difference(df1.index)
                 missing_from_df2 = df1.index.difference(df2.index)
                 print("{}-with-events missing these dates: {}".format(tkr, missing_from_df1))
                 print("{}-without-events missing these dates: {}".format(tkr, missing_from_df2))
                 raise
 
+    def test_monthlyWithEvents2(self):
+        # Simply check no exception from internal merge
+        dfm = yf.Ticker("ABBV").history(period="max", interval="1mo")
+        dfd = yf.Ticker("ABBV").history(period="max", interval="1d")
+        dfd = dfd[dfd.index > dfm.index[0]]
+        dfm_divs = dfm[dfm['Dividends'] != 0]
+        dfd_divs = dfd[dfd['Dividends'] != 0]
+        self.assertEqual(dfm_divs.shape[0], dfd_divs.shape[0])
+
+        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:
@@ -257,10 +398,120 @@ class TestPriceHistory(unittest.TestCase):
         df = dat.history(start=start, end=end, interval=interval)
         try:
             self.assertTrue((df.index.weekday == 0).all())
-        except:
+        except AssertionError:
             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 d not 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)
@@ -270,46 +521,88 @@ class TestPriceHistory(unittest.TestCase):
         df = dat.history(start=start, interval="1wk")
         self.assertTrue((df.index.weekday == 0).all())
 
-    def test_repair_100x_weekly(self):
+    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.info["exchangeTimezoneName"]
+        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],
+                                 "Adj Close": [470.1, 468.6, 467.1, 468.6],
                                  "Volume": [2295613, 2245604, 3000287, 2635611]},
-                                index=_pd.to_datetime([_dt.date(2022, 10, 23),
-                                                       _dt.date(2022, 10, 16),
-                                                       _dt.date(2022, 10, 9),
-                                                       _dt.date(2022, 10, 2)]))
+                           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["2022-10-23", "Close"] *= 100
-        df_bad.loc["2022-10-16", "Low"] *= 100
-        df_bad.loc["2022-10-2", "Open"] *= 100
+        df_bad.loc["2022-10-24", "Close"] *= 100
+        df_bad.loc["2022-10-17", "Low"] *= 100
+        df_bad.loc["2022-10-03", "Open"] *= 100
         df.index = df.index.tz_localize(tz_exchange)
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
 
         # Run test
 
-        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange)
+        df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
             try:
                 self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-            except:
+            except AssertionError:
                 print(df[c])
                 print(df_repaired[c])
                 raise
 
-
         # Second test - all differences should be either ~1x or ~100x
         ratio = df_bad[data_cols].values / df[data_cols].values
         ratio = ratio.round(2)
@@ -321,24 +614,27 @@ class TestPriceHistory(unittest.TestCase):
         f_1 = ratio == 1
         self.assertTrue((f_100 | f_1).all())
 
-    def test_repair_100x_weekly_preSplit(self):
+        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.info["exchangeTimezoneName"]
+        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],
+        df = _pd.DataFrame(data={"Open":      [400,    398,    392.5,  417],
+                                 "High":      [421,    425,    419,    420.5],
+                                 "Low":       [400,    380.5,  376.5,  396],
+                                 "Close":     [410,    409.5,  402,    399],
+                                 "Adj Close": [393.91, 393.43, 386.22, 383.34],
                                  "Volume": [3232600, 3773900, 10835000, 4257900]},
-                                index=_pd.to_datetime([_dt.date(2020, 3, 30),
-                                                       _dt.date(2020, 3, 23),
-                                                       _dt.date(2020, 3, 16),
-                                                       _dt.date(2020, 3, 9)]))
+                           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
@@ -353,13 +649,13 @@ class TestPriceHistory(unittest.TestCase):
         df.index = df.index.tz_localize(tz_exchange)
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
 
-        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange)
+        df_repaired = dat._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
             try:
                 self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-            except:
+            except AssertionError:
                 print("Mismatch in column", c)
                 print("- df_repaired:")
                 print(df_repaired[c])
@@ -378,10 +674,13 @@ class TestPriceHistory(unittest.TestCase):
         f_1 = ratio == 1
         self.assertTrue((f_100 | f_1).all())
 
-    def test_repair_100x_daily(self):
+        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.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
         data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
         df = _pd.DataFrame(data={"Open":      [478,    476,   476,   472],
@@ -390,10 +689,10 @@ class TestPriceHistory(unittest.TestCase):
                                  "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)]))
+                           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()
@@ -403,7 +702,7 @@ class TestPriceHistory(unittest.TestCase):
         df.index = df.index.tz_localize(tz_exchange)
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
 
-        df_repaired = dat._fix_unit_mixups(df_bad, "1d", tz_exchange)
+        df_repaired = dat._fix_unit_random_mixups(df_bad, "1d", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
@@ -420,10 +719,67 @@ class TestPriceHistory(unittest.TestCase):
         f_1 = ratio == 1
         self.assertTrue((f_100 | f_1).all())
 
+        self.assertTrue("Repaired?" in df_repaired.columns)
+        self.assertFalse(df_repaired["Repaired?"].isna().any())
+
+    def test_repair_100x_block_daily(self):
+        # Some 100x errors are not sporadic.
+        # Sometimes Yahoo suddenly shifts from cents->$ from some recent date.
+
+        tkrs = ['AET.L', 'SSW.JO']
+        for tkr in tkrs:
+            for interval in ['1d', '1wk']:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+
+                data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
+                _dp = os.path.dirname(__file__)
+                fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error.csv")
+                if not os.path.isfile(fp):
+                    continue
+                df_bad = _pd.read_csv(fp, index_col="Date")
+                df_bad.index = _pd.to_datetime(df_bad.index, utc=True).tz_convert(tz_exchange)
+                df_bad = df_bad.sort_index()
+
+                df = df_bad.copy()
+                fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error-fixed.csv")
+                df = _pd.read_csv(fp, index_col="Date")
+                df.index = _pd.to_datetime(df.index, utc=True).tz_convert(tz_exchange)
+                df = df.sort_index()
+
+                df_repaired = dat._fix_unit_switch(df_bad, interval, tz_exchange)
+                df_repaired = df_repaired.sort_index()
+
+                # First test - no errors left
+                for c in data_cols:
+                    try:
+                        self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
+                    except:
+                        print("- repaired:")
+                        print(df_repaired[c])
+                        print("- correct:")
+                        print(df[c])
+                        print(f"TEST FAIL on column '{c}' (tkr={tkr} interval={interval})")
+                        raise
+
+                # Second test - all differences should be either ~1x or ~100x
+                ratio = df_bad[data_cols].values / df[data_cols].values
+                ratio = ratio.round(2)
+                # - round near-100 ratio to 100:
+                f = ratio > 90
+                ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
+                # - now test
+                f_100 = (ratio == 100) | (ratio == 0.01)
+                f_1 = ratio == 1
+                self.assertTrue((f_100 | f_1).all())
+
+                self.assertTrue("Repaired?" in df_repaired.columns)
+                self.assertFalse(df_repaired["Repaired?"].isna().any())
+
     def test_repair_zeroes_daily(self):
         tkr = "BBIL.L"
         dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
         df_bad = _pd.DataFrame(data={"Open":      [0,      102.04, 102.04],
                                      "High":      [0,      102.1,  102.11],
@@ -431,14 +787,14 @@ class TestPriceHistory(unittest.TestCase):
                                      "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)]))
+                               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)
+        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
@@ -447,53 +803,203 @@ class TestPriceHistory(unittest.TestCase):
         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.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
-        df_bad = _pd.DataFrame(data={"Open":      [29.68, 29.49, 29.545, _np.nan, 29.485],
-                                     "High":      [29.68, 29.625, 29.58, _np.nan, 29.49],
-                                     "Low":       [29.46, 29.4, 29.45, _np.nan, 29.31],
-                                     "Close":     [29.485, 29.545, 29.485, _np.nan, 29.325],
-                                     "Adj Close": [29.485, 29.545, 29.485, _np.nan, 29.325],
-                                     "Volume": [3258528, 2140195, 1621010, 0, 0]},
-                                index=_pd.to_datetime([_dt.datetime(2022,11,25, 9,30),
-                                                       _dt.datetime(2022,11,25, 10,30),
-                                                       _dt.datetime(2022,11,25, 11,30),
-                                                       _dt.datetime(2022,11,25, 12,30),
-                                                       _dt.datetime(2022,11,25, 13,00)]))
-        df_bad = df_bad.sort_index()
-        df_bad.index.name = "Date"
-        df_bad.index = df_bad.index.tz_localize(tz_exchange)
+        correct_df = dat.history(period="1wk", interval="1h", auto_adjust=False, repair=True)
 
-        repaired_df = dat._fix_zeroes(df_bad, "1h", tz_exchange)
+        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)
 
-        correct_df = df_bad.copy()
-        idx = _pd.Timestamp(2022,11,25, 12,30).tz_localize(tz_exchange)
-        correct_df.loc[idx, "Open"] = 29.485001
-        correct_df.loc[idx, "High"] = 29.49
-        correct_df.loc[idx, "Low"] = 29.43
-        correct_df.loc[idx, "Close"] = 29.455
-        correct_df.loc[idx, "Adj Close"] = 29.455
-        correct_df.loc[idx, "Volume"] = 609164
         for c in ["Open", "Low", "High", "Close"]:
             try:
                 self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-7).all())
-            except:
+            except AssertionError:
                 print("COLUMN", c)
+                print("- repaired_df")
                 print(repaired_df)
+                print("- correct_df[c]:")
                 print(correct_df[c])
+                print("- diff:")
                 print(repaired_df[c] - correct_df[c])
                 raise
 
+        self.assertTrue("Repaired?" in repaired_df.columns)
+        self.assertFalse(repaired_df["Repaired?"].isna().any())
+
+    def test_repair_bad_stock_split(self):
+        # Stocks that split in 2022 but no problems in Yahoo data, 
+        # so repair should change nothing
+        good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
+        good_tkrs += ['AEI', '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(start='2020-01-01', end=_dt.date.today(), 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
+
+        bad_tkrs = ['4063.T', 'ALPHA.PA', 'AV.L', 'CNE.L', 'MOB.ST', 'SPM.MI']
+        bad_tkrs.append('LA.V')  # special case - stock split error is 3 years ago! why not fixed?
+        for tkr in bad_tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+            tz_exchange = dat.fast_info["timezone"]
+
+            _dp = os.path.dirname(__file__)
+            interval = '1d'
+            fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
+            if not os.path.isfile(fp):
+                interval = '1wk'
+                fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
+            df_bad = _pd.read_csv(fp, index_col="Date")
+            df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
+
+            repaired_df = dat._fix_bad_stock_split(df_bad, "1d", tz_exchange)
+
+            fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split-fixed.csv")
+            correct_df = _pd.read_csv(fp, index_col="Date")
+            correct_df.index = _pd.to_datetime(correct_df.index)
+
+            repaired_df = repaired_df.sort_index()
+            correct_df = correct_df.sort_index()
+            for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                try:
+                    self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
+                except AssertionError:
+                    print(f"tkr={tkr} COLUMN={c}")
+                    # print("- repaired_df")
+                    # print(repaired_df)
+                    # print("- correct_df[c]:")
+                    # print(correct_df[c])
+                    # print("- diff:")
+                    # print(repaired_df[c] - correct_df[c])
+                    raise
+
+        # Had very high price volatility in Jan-2021 around split date that could 
+        # be mistaken for missing stock split adjustment. And old logic did think 
+        # column 'High' required fixing - wrong!
+        sketchy_tkrs = ['FIZZ']
+        intervals = ['1wk']
+        for tkr in sketchy_tkrs:
+            for interval in intervals:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+
+                _dp = os.path.dirname(__file__)
+                df_good = dat.history(start='2020-11-30', end='2021-04-01', 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 AssertionError:
+                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
+                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
+                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
+                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
+                        raise
+
+    def test_repair_missing_div_adjust(self):
+        tkr = '8TRA.DE'
+
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+
+        _dp = os.path.dirname(__file__)
+        df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust.csv"), index_col="Date")
+        df_bad.index = _pd.to_datetime(df_bad.index)
+
+        repaired_df = dat._fix_missing_div_adjust(df_bad, "1d", tz_exchange)
+
+        correct_df = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust-fixed.csv"), index_col="Date")
+        correct_df.index = _pd.to_datetime(correct_df.index)
+
+        repaired_df = repaired_df.sort_index()
+        correct_df = correct_df.sort_index()
+        for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+            try:
+                self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
+            except:
+                print(f"tkr={tkr} COLUMN={c}")
+                print("- repaired_df")
+                print(repaired_df)
+                print("- correct_df[c]:")
+                print(correct_df[c])
+                print("- diff:")
+                print(repaired_df[c] - correct_df[c])
+                raise
+
+
 if __name__ == '__main__':
     unittest.main()
-
-# # Run tests sequentially:
-# import inspect
-# test_src = inspect.getsource(TestPriceHistory)
-# unittest.TestLoader.sortTestMethodsUsing = lambda _, x, y: (
-#     test_src.index(f"def {x}") - test_src.index(f"def {y}")
-# )
-# unittest.main(verbosity=2)

tests/ticker.py

@@ -9,27 +9,23 @@ Specific test class:
 
 """
 import pandas as pd
+import numpy as np
 
 from .context import yfinance as yf
+from .context import session_gbl
 
 import unittest
 import requests_cache
 
-# Set this to see the exact requests that are made during tests
-DEBUG_LOG_REQUESTS = False
-
-if DEBUG_LOG_REQUESTS:
-    import logging
-
-    logging.basicConfig(level=logging.DEBUG)
-
 
 class TestTicker(unittest.TestCase):
     session = None
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
+
+        cls.proxy = None
 
     @classmethod
     def tearDownClass(cls):
@@ -44,19 +40,27 @@ class TestTicker(unittest.TestCase):
 
             # Test:
             dat = yf.Ticker(tkr, session=self.session)
-            tz = dat._get_ticker_tz(debug_mode=False, proxy=None, timeout=None)
+            tz = dat._get_ticker_tz(proxy=None, timeout=None)
 
             self.assertIsNotNone(tz)
 
     def test_badTicker(self):
         # Check yfinance doesn't die when ticker delisted
 
-        tkr = "AM2Z.TA"
+        tkr = "DJI"  # typo of "^DJI"
         dat = yf.Ticker(tkr, session=self.session)
+
         dat.history(period="1wk")
         dat.history(start="2022-01-01")
         dat.history(start="2022-01-01", end="2022-03-01")
-        yf.download([tkr], period="1wk")
+        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
@@ -64,85 +68,244 @@ class TestTicker(unittest.TestCase):
         dat.dividends
         dat.splits
         dat.actions
-        dat.shares
-        dat.info
-        dat.calendar
-        dat.recommendations
-        dat.earnings
-        dat.quarterly_earnings
+        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
-        dat.recommendations_summary
-        dat.analyst_price_target
-        dat.revenue_forecasts
-        dat.sustainability
-        dat.options
-        dat.news
-        dat.earnings_trend
-        dat.earnings_dates
-        dat.earnings_forecasts
+
+        # 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
+
+    def test_goodTicker_withProxy(self):
+        # that yfinance works when full api is called on same instance of ticker
+
         tkr = "IBM"
         dat = yf.Ticker(tkr, session=self.session)
 
-        dat.isin
-        dat.major_holders
-        dat.institutional_holders
-        dat.mutualfund_holders
-        dat.dividends
-        dat.splits
-        dat.actions
-        dat.shares
-        dat.info
-        dat.calendar
-        dat.recommendations
-        dat.earnings
-        dat.quarterly_earnings
-        dat.income_stmt
-        dat.quarterly_income_stmt
-        dat.balance_sheet
-        dat.quarterly_balance_sheet
-        dat.cashflow
-        dat.quarterly_cashflow
-        dat.recommendations_summary
-        dat.analyst_price_target
-        dat.revenue_forecasts
-        dat.sustainability
-        dat.options
-        dat.news
-        dat.earnings_trend
-        dat.earnings_dates
-        dat.earnings_forecasts
+        dat._fetch_ticker_tz(proxy=self.proxy, timeout=5, debug_mode=False, raise_errors=False)
+        dat._get_ticker_tz(proxy=self.proxy, timeout=5, debug_mode=False, raise_errors=False)
+        dat.history(period="1wk", proxy=self.proxy)
 
-        dat.history(period="1wk")
-        dat.history(start="2022-01-01")
-        dat.history(start="2022-01-01", end="2022-03-01")
-        yf.download([tkr], period="1wk")
+        v = dat.stats(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(len(v) > 0)
+
+        v = dat.get_recommendations(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_calendar(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_major_holders(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_institutional_holders(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_mutualfund_holders(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_info(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(len(v) > 0)
+
+        v = dat.get_sustainability(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_recommendations_summary(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_analyst_price_target(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_rev_forecast(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_earnings_forecast(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_trend_details(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_earnings_trend(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_earnings(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_income_stmt(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_incomestmt(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_financials(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_balance_sheet(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_balancesheet(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_cash_flow(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_cashflow(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_shares(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_shares_full(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        v = dat.get_isin(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(v != "")
+
+        v = dat.get_news(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertTrue(len(v) > 0)
+
+        v = dat.get_earnings_dates(proxy=self.proxy)
+        self.assertIsNotNone(v)
+        self.assertFalse(v.empty)
+
+        # TODO: enable after merge
+        # dat.get_history_metadata(proxy=self.proxy)
+        # self.assertIsNotNone(v)
+        # self.assertTrue(len(v) > 0)
 
 
 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.ticker = yf.Ticker("IBM")
+        self.symbol = "IBM"
+        self.ticker = yf.Ticker(self.symbol, session=self.session)
+
+        self.symbols = ["AMZN", "MSFT", "NVDA"]
 
     def tearDown(self):
         self.ticker = None
 
     def test_history(self):
-        with self.assertRaises(RuntimeError):
-            self.ticker.history_metadata
+        md = self.ticker.history_metadata
+        self.assertIn("IBM", md.values(), "metadata missing")
         data = self.ticker.history("1y")
-        self.assertIn("IBM", self.ticker.history_metadata.values(), "metadata missing")
         self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
         self.assertFalse(data.empty, "data is empty")
 
+    def test_download(self):
+        for t in [False, True]:
+            for i in [False, True]:
+                data = yf.download(self.symbols, threads=t, ignore_tz=i)
+                self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+                self.assertFalse(data.empty, "data is empty")
+
     def test_no_expensive_calls_introduced(self):
         """
         Make sure calling history to get price data has not introduced more calls to yahoo than absolutely necessary.
@@ -155,7 +318,7 @@ class TestTickerHistory(unittest.TestCase):
         actual_urls_called = tuple([r.url for r in session.cache.filter()])
         session.close()
         expected_urls = (
-            'https://query2.finance.yahoo.com/v8/finance/chart/GOOGL?range=1y&interval=1d&includePrePost=False&events=div%2Csplits%2CcapitalGains',
+            '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.")
 
@@ -175,71 +338,92 @@ class TestTickerHistory(unittest.TestCase):
         self.assertFalse(data.empty, "data is empty")
 
 
-class TestTickerEarnings(unittest.TestCase):
+# Below will fail because not ported to Yahoo API
+# class TestTickerEarnings(unittest.TestCase):
+#     session = None
 
-    def setUp(self):
-        self.ticker = yf.Ticker("GOOGL")
+#     @classmethod
+#     def setUpClass(cls):
+#         cls.session = session_gbl
 
-    def tearDown(self):
-        self.ticker = None
+#     @classmethod
+#     def tearDownClass(cls):
+#         if cls.session is not None:
+#             cls.session.close()
 
-    def test_earnings(self):
-        data = self.ticker.earnings
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
+#     def setUp(self):
+#         self.ticker = yf.Ticker("GOOGL", session=self.session)
 
-        data_cached = self.ticker.earnings
-        self.assertIs(data, data_cached, "data not cached")
+#     def tearDown(self):
+#         self.ticker = None
 
-    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")
+#     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.quarterly_earnings
-        self.assertIs(data, data_cached, "data not cached")
+#         data_cached = self.ticker.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")
+#     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.earnings_forecasts
-        self.assertIs(data, data_cached, "data not cached")
+#         data_cached = self.ticker.quarterly_earnings
+#         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")
+#     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_dates
-        self.assertIs(data, data_cached, "data not cached")
+#         data_cached = self.ticker.earnings_forecasts
+#         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")
+#     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_trend
-        self.assertIs(data, data_cached, "data not cached")
+#         data_cached = self.ticker.earnings_dates
+#         self.assertIs(data, data_cached, "data not cached")
 
-    def test_earnings_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")
+#     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 = ticker.get_earnings_dates(limit=limit)
-        self.assertIs(data, data_cached, "data not cached")
+#         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")
+        self.ticker = yf.Ticker("GOOGL", session=self.session)
 
     def tearDown(self):
         self.ticker = None
@@ -274,7 +458,7 @@ class TestTickerMiscFinancials(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        cls.session = requests_cache.CachedSession(backend='memory')
+        cls.session = session_gbl
 
     @classmethod
     def tearDownClass(cls):
@@ -283,7 +467,7 @@ class TestTickerMiscFinancials(unittest.TestCase):
 
     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.
@@ -292,6 +476,24 @@ class TestTickerMiscFinancials(unittest.TestCase):
     def tearDown(self):
         self.ticker = None
 
+    def test_isin(self):
+        data = self.ticker.isin
+        self.assertIsInstance(data, str, "data has wrong type")
+        self.assertEqual("ARDEUT116159", data, "data is empty")
+
+        data_cached = self.ticker.isin
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_options(self):
+        data = self.ticker.options
+        self.assertIsInstance(data, tuple, "data has wrong type")
+        self.assertTrue(len(data) > 1, "data is empty")
+
+    def test_shares_full(self):
+        data = self.ticker.get_shares_full()
+        self.assertIsInstance(data, pd.Series, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
     def test_income_statement(self):
         expected_keys = ["Total Revenue", "Basic EPS"]
         expected_periods_days = 365
@@ -321,7 +523,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_income_stmt(as_dict=True)
         self.assertIsInstance(data, dict, "data has wrong type")
 
-
     def test_quarterly_income_statement(self):
         expected_keys = ["Total Revenue", "Basic EPS"]
         expected_periods_days = 365//4
@@ -351,16 +552,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_income_stmt(as_dict=True)
         self.assertIsInstance(data, dict, "data has wrong type")
 
-    def test_quarterly_income_statement_old_fmt(self):
-        expected_row = "TotalRevenue"
-        data = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertIn(expected_row, data.index, "Did not find expected row in index")
-
-        data_cached = self.ticker_old_fmt.get_income_stmt(freq="quarterly", legacy=True)
-        self.assertIs(data, data_cached, "data not cached")
-
     def test_balance_sheet(self):
         expected_keys = ["Total Assets", "Net PPE"]
         expected_periods_days = 365
@@ -419,16 +610,6 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_balance_sheet(as_dict=True, freq="quarterly")
         self.assertIsInstance(data, dict, "data has wrong type")
 
-    def test_quarterly_balance_sheet_old_fmt(self):
-        expected_row = "TotalAssets"
-        data = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertIn(expected_row, data.index, "Did not find expected row in index")
-
-        data_cached = self.ticker_old_fmt.get_balance_sheet(freq="quarterly", legacy=True)
-        self.assertIs(data, data_cached, "data not cached")
-
     def test_cash_flow(self):
         expected_keys = ["Operating Cash Flow", "Net PPE Purchase And Sale"]
         expected_periods_days = 365
@@ -487,91 +668,259 @@ class TestTickerMiscFinancials(unittest.TestCase):
         data = self.ticker.get_cashflow(as_dict=True)
         self.assertIsInstance(data, dict, "data has wrong type")
 
-    def test_quarterly_cashflow_old_fmt(self):
-        expected_row = "NetIncome"
-        data = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-        self.assertIn(expected_row, data.index, "Did not find expected row in index")
+    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))
 
-        data_cached = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
-        self.assertIs(data, data_cached, "data not cached")
+        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))
 
-    def test_sustainability(self):
-        data = self.ticker.sustainability
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
+        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))
 
-        data_cached = self.ticker.sustainability
-        self.assertIs(data, data_cached, "data not cached")
+        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_recommendations(self):
-        data = self.ticker.recommendations
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
+    def test_balance_sheet_alt_names(self):
+        i1 = self.ticker.balance_sheet
+        i2 = self.ticker.balancesheet
+        self.assertTrue(i1.equals(i2))
 
-        data_cached = self.ticker.recommendations
-        self.assertIs(data, data_cached, "data not cached")
+        i1 = self.ticker.get_balance_sheet()
+        i2 = self.ticker.get_balancesheet()
+        self.assertTrue(i1.equals(i2))
 
-    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")
+        i1 = self.ticker.quarterly_balance_sheet
+        i2 = self.ticker.quarterly_balancesheet
+        self.assertTrue(i1.equals(i2))
 
-        data_cached = self.ticker.recommendations_summary
-        self.assertIs(data, data_cached, "data not cached")
+        i1 = self.ticker.get_balance_sheet(freq="quarterly")
+        i2 = self.ticker.get_balancesheet(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
 
-    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")
+    def test_cash_flow_alt_names(self):
+        i1 = self.ticker.cash_flow
+        i2 = self.ticker.cashflow
+        self.assertTrue(i1.equals(i2))
 
-        data_cached = self.ticker.analyst_price_target
-        self.assertIs(data, data_cached, "data not cached")
+        i1 = self.ticker.get_cash_flow()
+        i2 = self.ticker.get_cashflow()
+        self.assertTrue(i1.equals(i2))
 
-    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")
+        i1 = self.ticker.quarterly_cash_flow
+        i2 = self.ticker.quarterly_cashflow
+        self.assertTrue(i1.equals(i2))
 
-        data_cached = self.ticker.revenue_forecasts
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_calendar(self):
-        data = self.ticker.calendar
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        data_cached = self.ticker.calendar
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_isin(self):
-        data = self.ticker.isin
-        self.assertIsInstance(data, str, "data has wrong type")
-        self.assertEqual("ARDEUT116159", data, "data is empty")
-
-        data_cached = self.ticker.isin
-        self.assertIs(data, data_cached, "data not cached")
-
-    def test_options(self):
-        data = self.ticker.options
-        self.assertIsInstance(data, tuple, "data has wrong type")
-        self.assertTrue(len(data) > 1, "data is empty")
-
-    def test_shares(self):
-        data = self.ticker.shares
-        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-    def test_info(self):
-        data = self.ticker.info
-        self.assertIsInstance(data, dict, "data has wrong type")
-        self.assertIn("symbol", data.keys(), "Did not find expected key in info dict")
-        self.assertEqual("GOOGL", data["symbol"], "Wrong symbol value in info dict")
+        i1 = self.ticker.get_cash_flow(freq="quarterly")
+        i2 = self.ticker.get_cashflow(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
 
     def test_bad_freq_value_raises_exception(self):
         self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))
 
+    # Below will fail because not ported to Yahoo API
+
+    # def test_sustainability(self):
+    #     data = self.ticker.sustainability
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.sustainability
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_recommendations(self):
+    #     data = self.ticker.recommendations
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.recommendations
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_recommendations_summary(self):
+    #     data = self.ticker.recommendations_summary
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.recommendations_summary
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_analyst_price_target(self):
+    #     data = self.ticker.analyst_price_target
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.analyst_price_target
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_revenue_forecasts(self):
+    #     data = self.ticker.revenue_forecasts
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.revenue_forecasts
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_calendar(self):
+    #     data = self.ticker.calendar
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+    #     data_cached = self.ticker.calendar
+    #     self.assertIs(data, data_cached, "data not cached")
+
+    # def test_shares(self):
+    #     data = self.ticker.shares
+    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+    #     self.assertFalse(data.empty, "data is empty")
+
+
+class TestTickerInfo(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        self.symbols = []
+        self.symbols += ["ESLT.TA", "BP.L", "GOOGL"]
+        self.symbols.append("QCSTIX")  # good for testing, doesn't trade
+        self.symbols += ["BTC-USD", "IWO", "VFINX", "^GSPC"]
+        self.symbols += ["SOKE.IS", "ADS.DE"]  # detected bugs
+        self.tickers = [yf.Ticker(s, session=self.session) for s in self.symbols]
+
+    def tearDown(self):
+        self.ticker = None
+
+    def test_fast_info(self):
+        f = yf.Ticker("AAPL", session=self.session).fast_info
+        for k in f:
+            self.assertIsNotNone(f[k])
+
+    def test_info(self):
+        data = self.tickers[0].info
+        self.assertIsInstance(data, dict, "data has wrong type")
+        expected_keys = ['industry', 'currentPrice', 'exchange', 'floatShares', 'companyOfficers', 'bid']
+        for k in expected_keys:
+            print(k)
+            self.assertIn("symbol", data.keys(), f"Did not find expected key '{k}' in info dict")
+        self.assertEqual(self.symbols[0], data["symbol"], "Wrong symbol value in info dict")
+
+    # def test_fast_info_matches_info(self):
+    #     fast_info_keys = set()
+    #     for ticker in self.tickers:
+    #         fast_info_keys.update(set(ticker.fast_info.keys()))
+    #     fast_info_keys = sorted(list(fast_info_keys))
+
+    #     key_rename_map = {}
+    #     key_rename_map["currency"] = "currency"
+    #     key_rename_map["quote_type"] = "quoteType"
+    #     key_rename_map["timezone"] = "exchangeTimezoneName"
+
+    #     key_rename_map["last_price"] = ["currentPrice", "regularMarketPrice"]
+    #     key_rename_map["open"] = ["open", "regularMarketOpen"]
+    #     key_rename_map["day_high"] = ["dayHigh", "regularMarketDayHigh"]
+    #     key_rename_map["day_low"] = ["dayLow", "regularMarketDayLow"]
+    #     key_rename_map["previous_close"] = ["previousClose"]
+    #     key_rename_map["regular_market_previous_close"] = ["regularMarketPreviousClose"]
+
+    #     key_rename_map["fifty_day_average"] = "fiftyDayAverage"
+    #     key_rename_map["two_hundred_day_average"] = "twoHundredDayAverage"
+    #     key_rename_map["year_change"] = ["52WeekChange", "fiftyTwoWeekChange"]
+    #     key_rename_map["year_high"] = "fiftyTwoWeekHigh"
+    #     key_rename_map["year_low"] = "fiftyTwoWeekLow"
+
+    #     key_rename_map["last_volume"] = ["volume", "regularMarketVolume"]
+    #     key_rename_map["ten_day_average_volume"] = ["averageVolume10days", "averageDailyVolume10Day"]
+    #     key_rename_map["three_month_average_volume"] = "averageVolume"
+
+    #     key_rename_map["market_cap"] = "marketCap"
+    #     key_rename_map["shares"] = "sharesOutstanding"
+
+    #     for k in list(key_rename_map.keys()):
+    #         if '_' in k:
+    #             key_rename_map[yf.utils.snake_case_2_camelCase(k)] = key_rename_map[k]
+
+    #     # Note: share count items in info[] are bad. Sometimes the float > outstanding!
+    #     # So often fast_info["shares"] does not match. 
+    #     # Why isn't fast_info["shares"] wrong? Because using it to calculate market cap always correct.
+    #     bad_keys = {"shares"}
+
+    #     # Loose tolerance for averages, no idea why don't match info[]. Is info wrong?
+    #     custom_tolerances = {}
+    #     custom_tolerances["year_change"] = 1.0
+    #     # custom_tolerances["ten_day_average_volume"] = 1e-3
+    #     custom_tolerances["ten_day_average_volume"] = 1e-1
+    #     # custom_tolerances["three_month_average_volume"] = 1e-2
+    #     custom_tolerances["three_month_average_volume"] = 5e-1
+    #     custom_tolerances["fifty_day_average"] = 1e-2
+    #     custom_tolerances["two_hundred_day_average"] = 1e-2
+    #     for k in list(custom_tolerances.keys()):
+    #         if '_' in k:
+    #             custom_tolerances[yf.utils.snake_case_2_camelCase(k)] = custom_tolerances[k]
+
+    #     for k in fast_info_keys:
+    #         if k in key_rename_map:
+    #             k2 = key_rename_map[k]
+    #         else:
+    #             k2 = k
+
+    #         if not isinstance(k2, list):
+    #             k2 = [k2]
+
+    #         for m in k2:
+    #             for ticker in self.tickers:
+    #                 if not m in ticker.info:
+    #                     # print(f"symbol={ticker.ticker}: fast_info key '{k}' mapped to info key '{m}' but not present in info")
+    #                     continue
+
+    #                 if k in bad_keys:
+    #                     continue
+
+    #                 if k in custom_tolerances:
+    #                     rtol = custom_tolerances[k]
+    #                 else:
+    #                     rtol = 5e-3
+    #                     # rtol = 1e-4
+
+    #                 correct = ticker.info[m]
+    #                 test = ticker.fast_info[k]
+    #                 # print(f"Testing: symbol={ticker.ticker} m={m} k={k}: test={test} vs correct={correct}")
+    #                 if k in ["market_cap","marketCap"] and ticker.fast_info["currency"] in ["GBp", "ILA"]:
+    #                     # Adjust for currency to match Yahoo:
+    #                     test *= 0.01
+    #                 try:
+    #                     if correct is None:
+    #                         self.assertTrue(test is None or (not np.isnan(test)), f"{k}: {test} must be None or real value because correct={correct}")
+    #                     elif isinstance(test, float) or isinstance(correct, int):
+    #                         self.assertTrue(np.isclose(test, correct, rtol=rtol), f"{ticker.ticker} {k}: {test} != {correct}")
+    #                     else:
+    #                         self.assertEqual(test, correct, f"{k}: {test} != {correct}")
+    #                 except:
+    #                     if k in ["regularMarketPreviousClose"] and ticker.ticker in ["ADS.DE"]:
+    #                         # Yahoo is wrong, is returning post-market close not regular
+    #                         continue
+    #                     else:
+    #                         raise
+
+
 
 def suite():
     suite = unittest.TestSuite()
@@ -580,6 +929,7 @@ def suite():
     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
 
 

tests/utils.py

@@ -0,0 +1,51 @@
+"""
+Tests for utils
+
+To run all tests in suite from commandline:
+   python -m unittest tests.utils
+
+Specific test class:
+   python -m unittest tests.utils.TestTicker
+
+"""
+# import pandas as pd
+# import numpy as np
+
+from .context import yfinance as yf
+from .context import session_gbl
+
+import unittest
+# import requests_cache
+import tempfile
+
+
+class TestUtils(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.tempCacheDir = tempfile.TemporaryDirectory()
+        yf.set_tz_cache_location(cls.tempCacheDir.name)
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.tempCacheDir.cleanup()
+
+    def test_storeTzNoRaise(self):
+        # storing TZ to cache should never raise exception
+        tkr = 'AMZN'
+        tz1 = "America/New_York"
+        tz2 = "London/Europe"
+        cache = yf.utils.get_tz_cache()
+        cache.store(tkr, tz1)
+        cache.store(tkr, tz2)
+
+
+def suite():
+    suite = unittest.TestSuite()
+    suite.addTest(TestUtils('Test utils'))
+    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 +1,118 @@
+_BASE_URL_ = 'https://query2.finance.yahoo.com'
+_ROOT_URL_ = 'https://finance.yahoo.com'
+
+fundamentals_keys = {
+    'financials': ["TaxEffectOfUnusualItems", "TaxRateForCalcs", "NormalizedEBITDA", "NormalizedDilutedEPS",
+                   "NormalizedBasicEPS", "TotalUnusualItems", "TotalUnusualItemsExcludingGoodwill",
+                   "NetIncomeFromContinuingOperationNetMinorityInterest", "ReconciledDepreciation",
+                   "ReconciledCostOfRevenue", "EBITDA", "EBIT", "NetInterestIncome", "InterestExpense",
+                   "InterestIncome", "ContinuingAndDiscontinuedDilutedEPS", "ContinuingAndDiscontinuedBasicEPS",
+                   "NormalizedIncome", "NetIncomeFromContinuingAndDiscontinuedOperation", "TotalExpenses",
+                   "RentExpenseSupplemental", "ReportedNormalizedDilutedEPS", "ReportedNormalizedBasicEPS",
+                   "TotalOperatingIncomeAsReported", "DividendPerShare", "DilutedAverageShares", "BasicAverageShares",
+                   "DilutedEPS", "DilutedEPSOtherGainsLosses", "TaxLossCarryforwardDilutedEPS",
+                   "DilutedAccountingChange", "DilutedExtraordinary", "DilutedDiscontinuousOperations",
+                   "DilutedContinuousOperations", "BasicEPS", "BasicEPSOtherGainsLosses", "TaxLossCarryforwardBasicEPS",
+                   "BasicAccountingChange", "BasicExtraordinary", "BasicDiscontinuousOperations",
+                   "BasicContinuousOperations", "DilutedNIAvailtoComStockholders", "AverageDilutionEarnings",
+                   "NetIncomeCommonStockholders", "OtherunderPreferredStockDividend", "PreferredStockDividends",
+                   "NetIncome", "MinorityInterests", "NetIncomeIncludingNoncontrollingInterests",
+                   "NetIncomeFromTaxLossCarryforward", "NetIncomeExtraordinary", "NetIncomeDiscontinuousOperations",
+                   "NetIncomeContinuousOperations", "EarningsFromEquityInterestNetOfTax", "TaxProvision",
+                   "PretaxIncome", "OtherIncomeExpense", "OtherNonOperatingIncomeExpenses", "SpecialIncomeCharges",
+                   "GainOnSaleOfPPE", "GainOnSaleOfBusiness", "OtherSpecialCharges", "WriteOff",
+                   "ImpairmentOfCapitalAssets", "RestructuringAndMergernAcquisition", "SecuritiesAmortization",
+                   "EarningsFromEquityInterest", "GainOnSaleOfSecurity", "NetNonOperatingInterestIncomeExpense",
+                   "TotalOtherFinanceCost", "InterestExpenseNonOperating", "InterestIncomeNonOperating",
+                   "OperatingIncome", "OperatingExpense", "OtherOperatingExpenses", "OtherTaxes",
+                   "ProvisionForDoubtfulAccounts", "DepreciationAmortizationDepletionIncomeStatement",
+                   "DepletionIncomeStatement", "DepreciationAndAmortizationInIncomeStatement", "Amortization",
+                   "AmortizationOfIntangiblesIncomeStatement", "DepreciationIncomeStatement", "ResearchAndDevelopment",
+                   "SellingGeneralAndAdministration", "SellingAndMarketingExpense", "GeneralAndAdministrativeExpense",
+                   "OtherGandA", "InsuranceAndClaims", "RentAndLandingFees", "SalariesAndWages", "GrossProfit",
+                   "CostOfRevenue", "TotalRevenue", "ExciseTaxes", "OperatingRevenue"],
+    'balance-sheet': ["TreasurySharesNumber", "PreferredSharesNumber", "OrdinarySharesNumber", "ShareIssued", "NetDebt",
+                      "TotalDebt", "TangibleBookValue", "InvestedCapital", "WorkingCapital", "NetTangibleAssets",
+                      "CapitalLeaseObligations", "CommonStockEquity", "PreferredStockEquity", "TotalCapitalization",
+                      "TotalEquityGrossMinorityInterest", "MinorityInterest", "StockholdersEquity",
+                      "OtherEquityInterest", "GainsLossesNotAffectingRetainedEarnings", "OtherEquityAdjustments",
+                      "FixedAssetsRevaluationReserve", "ForeignCurrencyTranslationAdjustments",
+                      "MinimumPensionLiabilities", "UnrealizedGainLoss", "TreasuryStock", "RetainedEarnings",
+                      "AdditionalPaidInCapital", "CapitalStock", "OtherCapitalStock", "CommonStock", "PreferredStock",
+                      "TotalPartnershipCapital", "GeneralPartnershipCapital", "LimitedPartnershipCapital",
+                      "TotalLiabilitiesNetMinorityInterest", "TotalNonCurrentLiabilitiesNetMinorityInterest",
+                      "OtherNonCurrentLiabilities", "LiabilitiesHeldforSaleNonCurrent", "RestrictedCommonStock",
+                      "PreferredSecuritiesOutsideStockEquity", "DerivativeProductLiabilities", "EmployeeBenefits",
+                      "NonCurrentPensionAndOtherPostretirementBenefitPlans", "NonCurrentAccruedExpenses",
+                      "DuetoRelatedPartiesNonCurrent", "TradeandOtherPayablesNonCurrent",
+                      "NonCurrentDeferredLiabilities", "NonCurrentDeferredRevenue",
+                      "NonCurrentDeferredTaxesLiabilities", "LongTermDebtAndCapitalLeaseObligation",
+                      "LongTermCapitalLeaseObligation", "LongTermDebt", "LongTermProvisions", "CurrentLiabilities",
+                      "OtherCurrentLiabilities", "CurrentDeferredLiabilities", "CurrentDeferredRevenue",
+                      "CurrentDeferredTaxesLiabilities", "CurrentDebtAndCapitalLeaseObligation",
+                      "CurrentCapitalLeaseObligation", "CurrentDebt", "OtherCurrentBorrowings", "LineOfCredit",
+                      "CommercialPaper", "CurrentNotesPayable", "PensionandOtherPostRetirementBenefitPlansCurrent",
+                      "CurrentProvisions", "PayablesAndAccruedExpenses", "CurrentAccruedExpenses", "InterestPayable",
+                      "Payables", "OtherPayable", "DuetoRelatedPartiesCurrent", "DividendsPayable", "TotalTaxPayable",
+                      "IncomeTaxPayable", "AccountsPayable", "TotalAssets", "TotalNonCurrentAssets",
+                      "OtherNonCurrentAssets", "DefinedPensionBenefit", "NonCurrentPrepaidAssets",
+                      "NonCurrentDeferredAssets", "NonCurrentDeferredTaxesAssets", "DuefromRelatedPartiesNonCurrent",
+                      "NonCurrentNoteReceivables", "NonCurrentAccountsReceivable", "FinancialAssets",
+                      "InvestmentsAndAdvances", "OtherInvestments", "InvestmentinFinancialAssets",
+                      "HeldToMaturitySecurities", "AvailableForSaleSecurities",
+                      "FinancialAssetsDesignatedasFairValueThroughProfitorLossTotal", "TradingSecurities",
+                      "LongTermEquityInvestment", "InvestmentsinJointVenturesatCost",
+                      "InvestmentsInOtherVenturesUnderEquityMethod", "InvestmentsinAssociatesatCost",
+                      "InvestmentsinSubsidiariesatCost", "InvestmentProperties", "GoodwillAndOtherIntangibleAssets",
+                      "OtherIntangibleAssets", "Goodwill", "NetPPE", "AccumulatedDepreciation", "GrossPPE", "Leases",
+                      "ConstructionInProgress", "OtherProperties", "MachineryFurnitureEquipment",
+                      "BuildingsAndImprovements", "LandAndImprovements", "Properties", "CurrentAssets",
+                      "OtherCurrentAssets", "HedgingAssetsCurrent", "AssetsHeldForSaleCurrent", "CurrentDeferredAssets",
+                      "CurrentDeferredTaxesAssets", "RestrictedCash", "PrepaidAssets", "Inventory",
+                      "InventoriesAdjustmentsAllowances", "OtherInventories", "FinishedGoods", "WorkInProcess",
+                      "RawMaterials", "Receivables", "ReceivablesAdjustmentsAllowances", "OtherReceivables",
+                      "DuefromRelatedPartiesCurrent", "TaxesReceivable", "AccruedInterestReceivable", "NotesReceivable",
+                      "LoansReceivable", "AccountsReceivable", "AllowanceForDoubtfulAccountsReceivable",
+                      "GrossAccountsReceivable", "CashCashEquivalentsAndShortTermInvestments",
+                      "OtherShortTermInvestments", "CashAndCashEquivalents", "CashEquivalents", "CashFinancial"],
+    'cash-flow': ["ForeignSales", "DomesticSales", "AdjustedGeographySegmentData", "FreeCashFlow",
+                  "RepurchaseOfCapitalStock", "RepaymentOfDebt", "IssuanceOfDebt", "IssuanceOfCapitalStock",
+                  "CapitalExpenditure", "InterestPaidSupplementalData", "IncomeTaxPaidSupplementalData",
+                  "EndCashPosition", "OtherCashAdjustmentOutsideChangeinCash", "BeginningCashPosition",
+                  "EffectOfExchangeRateChanges", "ChangesInCash", "OtherCashAdjustmentInsideChangeinCash",
+                  "CashFlowFromDiscontinuedOperation", "FinancingCashFlow", "CashFromDiscontinuedFinancingActivities",
+                  "CashFlowFromContinuingFinancingActivities", "NetOtherFinancingCharges", "InterestPaidCFF",
+                  "ProceedsFromStockOptionExercised", "CashDividendsPaid", "PreferredStockDividendPaid",
+                  "CommonStockDividendPaid", "NetPreferredStockIssuance", "PreferredStockPayments",
+                  "PreferredStockIssuance", "NetCommonStockIssuance", "CommonStockPayments", "CommonStockIssuance",
+                  "NetIssuancePaymentsOfDebt", "NetShortTermDebtIssuance", "ShortTermDebtPayments",
+                  "ShortTermDebtIssuance", "NetLongTermDebtIssuance", "LongTermDebtPayments", "LongTermDebtIssuance",
+                  "InvestingCashFlow", "CashFromDiscontinuedInvestingActivities",
+                  "CashFlowFromContinuingInvestingActivities", "NetOtherInvestingChanges", "InterestReceivedCFI",
+                  "DividendsReceivedCFI", "NetInvestmentPurchaseAndSale", "SaleOfInvestment", "PurchaseOfInvestment",
+                  "NetInvestmentPropertiesPurchaseAndSale", "SaleOfInvestmentProperties",
+                  "PurchaseOfInvestmentProperties", "NetBusinessPurchaseAndSale", "SaleOfBusiness",
+                  "PurchaseOfBusiness", "NetIntangiblesPurchaseAndSale", "SaleOfIntangibles", "PurchaseOfIntangibles",
+                  "NetPPEPurchaseAndSale", "SaleOfPPE", "PurchaseOfPPE", "CapitalExpenditureReported",
+                  "OperatingCashFlow", "CashFromDiscontinuedOperatingActivities",
+                  "CashFlowFromContinuingOperatingActivities", "TaxesRefundPaid", "InterestReceivedCFO",
+                  "InterestPaidCFO", "DividendReceivedCFO", "DividendPaidCFO", "ChangeInWorkingCapital",
+                  "ChangeInOtherWorkingCapital", "ChangeInOtherCurrentLiabilities", "ChangeInOtherCurrentAssets",
+                  "ChangeInPayablesAndAccruedExpense", "ChangeInAccruedExpense", "ChangeInInterestPayable",
+                  "ChangeInPayable", "ChangeInDividendPayable", "ChangeInAccountPayable", "ChangeInTaxPayable",
+                  "ChangeInIncomeTaxPayable", "ChangeInPrepaidAssets", "ChangeInInventory", "ChangeInReceivables",
+                  "ChangesInAccountReceivables", "OtherNonCashItems", "ExcessTaxBenefitFromStockBasedCompensation",
+                  "StockBasedCompensation", "UnrealizedGainLossOnInvestmentSecurities", "ProvisionandWriteOffofAssets",
+                  "AssetImpairmentCharge", "AmortizationOfSecurities", "DeferredTax", "DeferredIncomeTax",
+                  "DepreciationAmortizationDepletion", "Depletion", "DepreciationAndAmortization",
+                  "AmortizationCashFlow", "AmortizationOfIntangibles", "Depreciation", "OperatingGainsLosses",
+                  "PensionAndEmployeeBenefitExpense", "EarningsLossesFromEquityInvestments",
+                  "GainLossOnInvestmentSecurities", "NetForeignCurrencyExchangeGainLoss", "GainLossOnSaleOfPPE",
+                  "GainLossOnSaleOfBusiness", "NetIncomeFromContinuingOperations",
+                  "CashFlowsfromusedinOperatingActivitiesDirect", "TaxesRefundPaidDirect", "InterestReceivedDirect",
+                  "InterestPaidDirect", "DividendsReceivedDirect", "DividendsPaidDirect", "ClassesofCashPayments",
+                  "OtherCashPaymentsfromOperatingActivities", "PaymentsonBehalfofEmployees",
+                  "PaymentstoSuppliersforGoodsandServices", "ClassesofCashReceiptsfromOperatingActivities",
+                  "OtherCashReceiptsfromOperatingActivities", "ReceiptsfromGovernmentGrants", "ReceiptsfromCustomers"]}
+
+price_colnames = ['Open', 'High', 'Low', 'Close', 'Adj Close']

yfinance/data.py

@@ -1,15 +1,16 @@
 import functools
 from functools import lru_cache
 
+import logging
+
 import requests as requests
 import re
+import random
+import time
 
 from frozendict import frozendict
 
-try:
-    import ujson as json
-except ImportError:
-    import json as json
+from . import utils
 
 cache_maxsize = 64
 
@@ -35,9 +36,6 @@ def lru_cache_freezeargs(func):
     return wrapped
 
 
-_SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
-
-
 class TickerData:
     """
     Have one place to retrieve data from Yahoo API in order to ease caching and speed up operations
@@ -67,32 +65,12 @@ class TickerData:
     def _get_proxy(self, proxy):
         # setup proxy in requests format
         if proxy is not None:
-            if isinstance(proxy, dict) and "https" in proxy:
+            if isinstance(proxy, (dict, frozendict)) and "https" in proxy:
                 proxy = proxy["https"]
             proxy = {"https": proxy}
         return proxy
 
-    @lru_cache_freezeargs
-    @lru_cache(maxsize=cache_maxsize)
-    def get_json_data_stores(self, sub_page: str = None, proxy=None) -> dict:
-        '''
-        get_json_data_stores returns a python dictionary of the data stores in yahoo finance web page.
-        '''
-        if sub_page:
-            ticker_url = "{}/{}/{}".format(_SCRAPE_URL_, self.ticker, sub_page)
-        else:
-            ticker_url = "{}/{}".format(_SCRAPE_URL_, self.ticker)
-
-        html = self.get(url=ticker_url, proxy=proxy).text
-
-        # The actual json-data for stores is in a javascript assignment in the webpage
-        json_str = html.split('root.App.main =')[1].split(
-            '(this)')[0].split(';\n}')[0].strip()
-        data = json.loads(json_str)['context']['dispatcher']['stores']
-
-        # return data
-        new_data = json.dumps(data).replace('{}', 'null')
-        new_data = re.sub(
-            r'{[\'|\"]raw[\'|\"]:(.*?),(.*?)}', r'\1', new_data)
-
-        return json.loads(new_data)
+    def 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

@@ -1,6 +1,12 @@
-class YFianceException(Exception):
+class YFinanceException(Exception):
     pass
 
 
-class YFianceDataException(YFianceException):
+class YFinanceDataException(YFinanceException):
     pass
+
+
+class YFNotImplementedError(NotImplementedError):
+    def __init__(self, method_name):
+        super().__init__(f"Have not implemented fetching '{method_name}' from Yahoo API")
+

yfinance/multi.py

@@ -21,7 +21,10 @@
 
 from __future__ import print_function
 
+import logging
 import time as _time
+import traceback
+
 import multitasking as _multitasking
 import pandas as _pd
 
@@ -29,10 +32,11 @@ from . import Ticker, utils
 from . import shared
 
 
-def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=True,
+@utils.log_indent_decorator
+def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=None,
              group_by='column', auto_adjust=False, back_adjust=False, repair=False, keepna=False,
-             progress=True, period="max", show_errors=True, interval="1d", prepost=False,
-             proxy=None, rounding=False, timeout=10):
+             progress=True, period="max", show_errors=None, interval="1d", prepost=False,
+             proxy=None, rounding=False, timeout=10, session=None):
     """Download yahoo tickers
     :Parameters:
         tickers : str, list
@@ -44,11 +48,13 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
             Intraday data cannot extend last 60 days
         start: str
-            Download start date string (YYYY-MM-DD) or _datetime.
-            Default is 1900-01-01
+            Download start date string (YYYY-MM-DD) or _datetime, inclusive.
+            Default is 99 years ago
+            E.g. for start="2020-01-01", the first data point will be on "2020-01-01"
         end: str
-            Download end date string (YYYY-MM-DD) or _datetime.
+            Download end date string (YYYY-MM-DD) or _datetime, exclusive.
             Default is now
+            E.g. for end="2023-01-01", the last data point will be on "2022-12-31"
         group_by : str
             Group by 'ticker' or 'column' (default)
         prepost : bool
@@ -68,17 +74,48 @@ 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(
@@ -90,7 +127,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
     for ticker in tickers:
         if utils.is_isin(ticker):
             isin = ticker
-            ticker = utils.get_ticker_by_isin(ticker, proxy)
+            ticker = utils.get_ticker_by_isin(ticker, proxy, session=session)
             shared._ISINS[ticker] = isin
         _tickers_.append(ticker)
 
@@ -104,6 +141,7 @@ 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:
@@ -116,10 +154,9 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
                                    actions=actions, auto_adjust=auto_adjust,
                                    back_adjust=back_adjust, repair=repair, keepna=keepna,
                                    progress=(progress and i > 0), proxy=proxy,
-                                   rounding=rounding, timeout=timeout)
+                                   rounding=rounding, timeout=timeout, session=session)
         while len(shared._DFS) < len(tickers):
             _time.sleep(0.01)
-
     # download synchronously
     else:
         for i, ticker in enumerate(tickers):
@@ -128,20 +165,42 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
                                  actions=actions, auto_adjust=auto_adjust,
                                  back_adjust=back_adjust, repair=repair, keepna=keepna,
                                  proxy=proxy,
-                                 rounding=rounding, timeout=timeout)
-            shared._DFS[ticker.upper()] = data
+                                 rounding=rounding, timeout=timeout, session=session)
             if progress:
                 shared._PROGRESS_BAR.animate()
-
+    
     if progress:
         shared._PROGRESS_BAR.completed()
 
-    if shared._ERRORS and show_errors:
-        print('\n%.f Failed download%s:' % (
+    if shared._ERRORS:
+        # Send errors to logging module
+        logger = utils.get_yf_logger()
+        logger.error('\n%.f Failed download%s:' % (
             len(shared._ERRORS), 's' if len(shared._ERRORS) > 1 else ''))
-        # print(shared._ERRORS)
-        print("\n".join(['- %s: %s' %
-                         v for v in list(shared._ERRORS.items())]))
+
+        # Log each distinct error once, with list of symbols affected
+        errors = {}
+        for ticker in shared._ERRORS:
+            err = shared._ERRORS[ticker]
+            err = err.replace(f'{ticker}', '%ticker%')
+            if err not in errors:
+                errors[err] = [ticker]
+            else:
+                errors[err].append(ticker)
+        for err in errors.keys():
+            logger.error(f'{errors[err]}: ' + err)
+
+        # Log each distinct traceback once, with list of symbols affected
+        tbs = {}
+        for ticker in shared._TRACEBACKS:
+            tb = shared._TRACEBACKS[ticker]
+            tb = tb.replace(f'{ticker}', '%ticker%')
+            if tb not in tbs:
+                tbs[tb] = [ticker]
+            else:
+                tbs[tb].append(ticker)
+        for tb in tbs.keys():
+            logger.debug(f'{tbs[tb]}: ' + tb)
 
     if ignore_tz:
         for tkr in shared._DFS.keys():
@@ -150,7 +209,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
 
     if len(tickers) == 1:
         ticker = tickers[0]
-        return shared._DFS[shared._ISINS.get(ticker, ticker)]
+        return shared._DFS[ticker]
 
     try:
         data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
@@ -198,17 +257,10 @@ def _download_one_threaded(ticker, start=None, end=None,
                            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=10):
-    try:
-        data = _download_one(ticker, start, end, auto_adjust, back_adjust, repair,
-                             actions, period, interval, prepost, proxy, rounding,
-                             keepna, timeout)
-    except Exception as e:
-        # glob try/except needed as current thead implementation breaks if exception is raised.
-        shared._DFS[ticker] = utils.empty_df()
-        shared._ERRORS[ticker] = repr(e)
-    else:
-        shared._DFS[ticker.upper()] = data
+                           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, session)
     if progress:
         shared._PROGRESS_BAR.animate()
 
@@ -217,12 +269,23 @@ def _download_one(ticker, start=None, end=None,
                   auto_adjust=False, back_adjust=False, repair=False,
                   actions=False, period="max", interval="1d",
                   prepost=False, proxy=None, rounding=False,
-                  keepna=False, timeout=10):
-    return Ticker(ticker).history(
-        period=period, interval=interval,
-        start=start, end=end, prepost=prepost,
-        actions=actions, auto_adjust=auto_adjust,
-        back_adjust=back_adjust, repair=repair, proxy=proxy,
-        rounding=rounding, keepna=keepna, timeout=timeout,
-        debug=False, raise_errors=False  # debug and raise_errors false to not log and raise errors in threads
-    )
+                  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 data

yfinance/scrapers/analysis.py

@@ -2,6 +2,7 @@ import pandas as pd
 
 from yfinance import utils
 from yfinance.data import TickerData
+from yfinance.exceptions import YFNotImplementedError
 
 
 class Analysis:
@@ -20,99 +21,29 @@ class Analysis:
     @property
     def earnings_trend(self) -> pd.DataFrame:
         if self._earnings_trend is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('earnings_trend')
         return self._earnings_trend
 
     @property
     def analyst_trend_details(self) -> pd.DataFrame:
         if self._analyst_trend_details is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('analyst_trend_details')
         return self._analyst_trend_details
 
     @property
     def analyst_price_target(self) -> pd.DataFrame:
         if self._analyst_price_target is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('analyst_price_target')
         return self._analyst_price_target
 
     @property
     def rev_est(self) -> pd.DataFrame:
         if self._rev_est is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('rev_est')
         return self._rev_est
 
     @property
     def eps_est(self) -> pd.DataFrame:
         if self._eps_est is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('eps_est')
         return self._eps_est
-
-    def _scrape(self, proxy):
-        if self._already_scraped:
-            return
-        self._already_scraped = True
-
-        # Analysis Data/Analyst Forecasts
-        analysis_data = self._data.get_json_data_stores("analysis", proxy=proxy)
-        try:
-            analysis_data = analysis_data['QuoteSummaryStore']
-        except KeyError as e:
-            err_msg = "No analysis data found, symbol may be delisted"
-            print('- %s: %s' % (self._data.ticker, err_msg))
-            return
-
-        if isinstance(analysis_data.get('earningsTrend'), dict):
-            try:
-                analysis = pd.DataFrame(analysis_data['earningsTrend']['trend'])
-                analysis['endDate'] = pd.to_datetime(analysis['endDate'])
-                analysis.set_index('period', inplace=True)
-                analysis.index = analysis.index.str.upper()
-                analysis.index.name = 'Period'
-                analysis.columns = utils.camel2title(analysis.columns)
-
-                dict_cols = []
-
-                for idx, row in analysis.iterrows():
-                    for colname, colval in row.items():
-                        if isinstance(colval, dict):
-                            dict_cols.append(colname)
-                            for k, v in colval.items():
-                                new_colname = colname + ' ' + \
-                                              utils.camel2title([k])[0]
-                                analysis.loc[idx, new_colname] = v
-
-                self._earnings_trend = analysis[[
-                    c for c in analysis.columns if c not in dict_cols]]
-            except Exception:
-                pass
-
-        try:
-            self._analyst_trend_details = pd.DataFrame(analysis_data['recommendationTrend']['trend'])
-        except Exception as e:
-            self._analyst_trend_details = None
-        try:
-            self._analyst_price_target = pd.DataFrame(analysis_data['financialData'], index=[0])[
-                ['targetLowPrice', 'currentPrice', 'targetMeanPrice', 'targetHighPrice', 'numberOfAnalystOpinions']].T
-        except Exception as e:
-            self._analyst_price_target = None
-        earnings_estimate = []
-        revenue_estimate = []
-        if self._analyst_trend_details is not None :
-            for key in analysis_data['earningsTrend']['trend']:
-                try:
-                    earnings_dict = key['earningsEstimate']
-                    earnings_dict['period'] = key['period']
-                    earnings_dict['endDate'] = key['endDate']
-                    earnings_estimate.append(earnings_dict)
-
-                    revenue_dict = key['revenueEstimate']
-                    revenue_dict['period'] = key['period']
-                    revenue_dict['endDate'] = key['endDate']
-                    revenue_estimate.append(revenue_dict)
-                except Exception as e:
-                    pass
-            self._rev_est = pd.DataFrame(revenue_estimate)
-            self._eps_est = pd.DataFrame(earnings_estimate)
-        else:
-            self._rev_est = pd.DataFrame()
-            self._eps_est = pd.DataFrame()

yfinance/scrapers/fundamentals.py

@@ -2,11 +2,10 @@ import datetime
 import json
 
 import pandas as pd
-import numpy as np
 
-from yfinance import utils
+from yfinance import utils, const
 from yfinance.data import TickerData
-from yfinance.exceptions import YFianceDataException, YFianceException
+from yfinance.exceptions import YFinanceException, YFNotImplementedError
 
 
 class Fundamentals:
@@ -22,109 +21,51 @@ class Fundamentals:
         self._financials_data = None
         self._fin_data_quote = None
         self._basics_already_scraped = False
-        self._financials = Fiancials(data)
+        self._financials = Financials(data)
 
     @property
-    def financials(self) -> "Fiancials":
+    def financials(self) -> "Financials":
         return self._financials
 
     @property
     def earnings(self) -> dict:
         if self._earnings is None:
-            self._scrape_earnings(self.proxy)
+            raise YFNotImplementedError('earnings')
         return self._earnings
 
     @property
     def shares(self) -> pd.DataFrame:
         if self._shares is None:
-            self._scrape_shares(self.proxy)
+            raise YFNotImplementedError('shares')
         return self._shares
 
-    def _scrape_basics(self, proxy):
-        if self._basics_already_scraped:
-            return
-        self._basics_already_scraped = True
 
-        self._financials_data = self._data.get_json_data_stores('financials', proxy)
-        try:
-            self._fin_data_quote = self._financials_data['QuoteSummaryStore']
-        except KeyError:
-            err_msg = "No financials data found, symbol may be delisted"
-            print('- %s: %s' % (self._data.ticker, err_msg))
-            return None
-
-    def _scrape_earnings(self, proxy):
-        self._scrape_basics(proxy)
-        # earnings
-        self._earnings = {"yearly": pd.DataFrame(), "quarterly": pd.DataFrame()}
-        if self._fin_data_quote is None:
-            return
-        if isinstance(self._fin_data_quote.get('earnings'), dict):
-            try:
-                earnings = self._fin_data_quote['earnings']['financialsChart']
-                earnings['financialCurrency'] = self._fin_data_quote['earnings'].get('financialCurrency', 'USD')
-                self._earnings['financialCurrency'] = earnings['financialCurrency']
-                df = pd.DataFrame(earnings['yearly']).set_index('date')
-                df.columns = utils.camel2title(df.columns)
-                df.index.name = 'Year'
-                self._earnings['yearly'] = df
-
-                df = pd.DataFrame(earnings['quarterly']).set_index('date')
-                df.columns = utils.camel2title(df.columns)
-                df.index.name = 'Quarter'
-                self._earnings['quarterly'] = df
-            except Exception:
-                pass
-
-    def _scrape_shares(self, proxy):
-        self._scrape_basics(proxy)
-        # shares outstanding
-        try:
-            # keep only years with non None data
-            available_shares = [shares_data for shares_data in
-                                self._financials_data['QuoteTimeSeriesStore']['timeSeries']['annualBasicAverageShares']
-                                if
-                                shares_data]
-            shares = pd.DataFrame(available_shares)
-            shares['Year'] = shares['asOfDate'].agg(lambda x: int(x[:4]))
-            shares.set_index('Year', inplace=True)
-            shares.drop(columns=['dataId', 'asOfDate',
-                                 'periodType', 'currencyCode'], inplace=True)
-            shares.rename(
-                columns={'reportedValue': "BasicShares"}, inplace=True)
-            self._shares = shares
-        except Exception:
-            pass
-
-
-class Fiancials:
+class Financials:
     def __init__(self, data: TickerData):
         self._data = data
         self._income_time_series = {}
         self._balance_sheet_time_series = {}
         self._cash_flow_time_series = {}
-        self._income_scraped = {}
-        self._balance_sheet_scraped = {}
-        self._cash_flow_scraped = {}
 
     def get_income_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
         res = self._income_time_series
         if freq not in res:
-            res[freq] = self._fetch_time_series("income", freq, proxy=None)
+            res[freq] = self._fetch_time_series("income", freq, proxy)
         return res[freq]
 
     def get_balance_sheet_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
         res = self._balance_sheet_time_series
         if freq not in res:
-            res[freq] = self._fetch_time_series("balance-sheet", freq, proxy=None)
+            res[freq] = self._fetch_time_series("balance-sheet", freq, proxy)
         return res[freq]
 
     def get_cash_flow_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
         res = self._cash_flow_time_series
         if freq not in res:
-            res[freq] = self._fetch_time_series("cash-flow", freq, proxy=None)
+            res[freq] = self._fetch_time_series("cash-flow", freq, proxy)
         return res[freq]
 
+    @utils.log_indent_decorator
     def _fetch_time_series(self, name, timescale, proxy=None):
         # Fetching time series preferred over scraping 'QuoteSummaryStore',
         # because it matches what Yahoo shows. But for some tickers returns nothing, 
@@ -134,17 +75,17 @@ class Fiancials:
         allowed_timescales = ["yearly", "quarterly"]
 
         if name not in allowed_names:
-            raise ValueError("Illegal argument: name must be one of: {}".format(allowed_names))
+            raise ValueError(f"Illegal argument: name must be one of: {allowed_names}")
         if timescale not in allowed_timescales:
-            raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
+            raise ValueError(f"Illegal argument: timescale must be one of: {allowed_names}")
 
         try:
             statement = self._create_financials_table(name, timescale, proxy)
 
             if statement is not None:
                 return statement
-        except YFianceException as e:
-            print("Failed to create financials table for {} reason: {}".format(name, repr(e)))
+        except YFinanceException as e:
+            utils.get_yf_logger().error(f"{self._data.ticker}: Failed to create {name} financials table for reason: {e}")
         return pd.DataFrame()
 
     def _create_financials_table(self, name, timescale, proxy):
@@ -152,57 +93,24 @@ class Fiancials:
             # Yahoo stores the 'income' table internally under 'financials' key
             name = "financials"
 
-        keys = self._get_datastore_keys(name, proxy)
+        keys = const.fundamentals_keys[name]
 
         try:
-            # Developers note: TTM and template stuff allows for reproducing the nested structure
-            # visible on Yahoo website. But more work needed to make it user-friendly! Ideally
-            # return a tree data structure instead of Pandas MultiIndex
-            # So until this is implemented, just return simple tables
             return self.get_financials_time_series(timescale, keys, proxy)
-
         except Exception as e:
             pass
 
-    def _get_datastore_keys(self, sub_page, proxy) -> list:
-        data_stores = self._data.get_json_data_stores(sub_page, proxy)
-
-        # Step 1: get the keys:
-        def _finditem1(key, obj):
-            values = []
-            if isinstance(obj, dict):
-                if key in obj.keys():
-                    values.append(obj[key])
-                for k, v in obj.items():
-                    values += _finditem1(key, v)
-            elif isinstance(obj, list):
-                for v in obj:
-                    values += _finditem1(key, v)
-            return values
-
-        try:
-            keys = _finditem1("key", data_stores['FinancialTemplateStore'])
-        except KeyError as e:
-            raise YFianceDataException("Parsing FinancialTemplateStore failed, reason: {}".format(repr(e)))
-
-        if not keys:
-            raise YFianceDataException("No keys in FinancialTemplateStore")
-        return keys
-
     def get_financials_time_series(self, timescale, keys: list, proxy=None) -> pd.DataFrame:
         timescale_translation = {"yearly": "annual", "quarterly": "quarterly"}
         timescale = timescale_translation[timescale]
 
         # Step 2: construct url:
-        ts_url_base = \
-            "https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{0}?symbol={0}" \
-                .format(self._data.ticker)
-
+        ts_url_base = f"https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._data.ticker}?symbol={self._data.ticker}"
         url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
         # Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
         start_dt = datetime.datetime(2016, 12, 31)
-        end = (datetime.datetime.now() + datetime.timedelta(days=366))
-        url += "&period1={}&period2={}".format(int(start_dt.timestamp()), int(end.timestamp()))
+        end = pd.Timestamp.utcnow().ceil("D")
+        url += f"&period1={int(start_dt.timestamp())}&period2={int(end.timestamp())}"
 
         # Step 3: fetch and reshape data
         json_str = self._data.cache_get(url=url, proxy=proxy).text
@@ -237,87 +145,3 @@ class Fiancials:
         df = df[sorted(df.columns, reverse=True)]
 
         return df
-
-    def get_income_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._income_scraped
-        if freq not in res:
-            res[freq] = self._scrape("income", freq, proxy=None)
-        return res[freq]
-
-    def get_balance_sheet_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._balance_sheet_scraped
-        if freq not in res:
-            res[freq] = self._scrape("balance-sheet", freq, proxy=None)
-        return res[freq]
-
-    def get_cash_flow_scrape(self, freq="yearly", proxy=None) -> pd.DataFrame:
-        res = self._cash_flow_scraped
-        if freq not in res:
-            res[freq] = self._scrape("cash-flow", freq, proxy=None)
-        return res[freq]
-
-    def _scrape(self, name, timescale, proxy=None):
-        # Backup in case _fetch_time_series() fails to return data
-
-        allowed_names = ["income", "balance-sheet", "cash-flow"]
-        allowed_timescales = ["yearly", "quarterly"]
-
-        if name not in allowed_names:
-            raise ValueError("Illegal argument: name must be one of: {}".format(allowed_names))
-        if timescale not in allowed_timescales:
-            raise ValueError("Illegal argument: timescale must be one of: {}".format(allowed_names))
-
-        try:
-            statement = self._create_financials_table_old(name, timescale, proxy)
-
-            if statement is not None:
-                return statement
-        except YFianceException as e:
-            print("Failed to create financials table for {} reason: {}".format(name, repr(e)))
-        return pd.DataFrame()
-
-    def _create_financials_table_old(self, name, timescale, proxy):
-        data_stores = self._data.get_json_data_stores("financials", proxy)
-
-        # Fetch raw data
-        if not "QuoteSummaryStore" in data_stores:
-            return pd.DataFrame()
-        data = data_stores["QuoteSummaryStore"]
-
-        if name == "cash-flow":
-            key1 = "cashflowStatement"
-            key2 = "cashflowStatements"
-        elif name == "balance-sheet":
-            key1 = "balanceSheet"
-            key2 = "balanceSheetStatements"
-        else:
-            key1 = "incomeStatement"
-            key2 = "incomeStatementHistory"
-        key1 += "History"
-        if timescale == "quarterly":
-            key1 += "Quarterly"
-        data = data.get(key1)[key2]
-
-        # Tabulate
-        df = pd.DataFrame(data)
-        if len(df) == 0:
-            return pd.DataFrame()
-        df = df.drop(columns=['maxAge'])
-        for col in df.columns:
-            df[col] = df[col].replace('-', np.nan)
-        df.set_index('endDate', inplace=True)
-        try:
-            df.index = pd.to_datetime(df.index, unit='s')
-        except ValueError:
-            df.index = pd.to_datetime(df.index)
-        df = df.T
-        df.columns.name = ''
-        df.index.name = 'Breakdown'
-        # rename incorrect yahoo key
-        df.rename(index={'treasuryStock': 'gainsLossesNotAffectingRetainedEarnings'}, inplace=True)
-
-        # Upper-case first letter, leave rest unchanged:
-        s0 = df.index[0]
-        df.index = [s[0].upper()+s[1:] for s in df.index]
-
-        return df

yfinance/scrapers/holders.py

@@ -2,6 +2,7 @@ import pandas as pd
 
 from yfinance.data import TickerData
 
+
 class Holders:
     _SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
 
@@ -32,9 +33,9 @@ class Holders:
         return self._mutualfund
 
     def _scrape(self, proxy):
-        ticker_url = "{}/{}".format(self._SCRAPE_URL_, self._data.ticker)
+        ticker_url = f"{self._SCRAPE_URL_}/{self._data.ticker}"
         try:
-            resp = self._data.cache_get(ticker_url + '/holders', proxy)
+            resp = self._data.cache_get(ticker_url + '/holders', proxy=proxy)
             holders = pd.read_html(resp.text)
         except Exception:
             holders = []

yfinance/scrapers/quote.py

@@ -1,10 +1,552 @@
 import datetime
 import json
+import logging
+import warnings
+from collections.abc import MutableMapping
 
+import numpy as _np
 import pandas as pd
 
 from yfinance import utils
 from yfinance.data import TickerData
+from yfinance.exceptions import YFNotImplementedError
+
+info_retired_keys_price = {"currentPrice", "dayHigh", "dayLow", "open", "previousClose", "volume", "volume24Hr"}
+info_retired_keys_price.update({"regularMarket"+s for s in ["DayHigh", "DayLow", "Open", "PreviousClose", "Price", "Volume"]})
+info_retired_keys_price.update({"fiftyTwoWeekLow", "fiftyTwoWeekHigh", "fiftyTwoWeekChange", "52WeekChange", "fiftyDayAverage", "twoHundredDayAverage"})
+info_retired_keys_price.update({"averageDailyVolume10Day", "averageVolume10days", "averageVolume"})
+info_retired_keys_exchange = {"currency", "exchange", "exchangeTimezoneName", "exchangeTimezoneShortName", "quoteType"}
+info_retired_keys_marketCap = {"marketCap"}
+info_retired_keys_symbol = {"symbol"}
+info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_retired_keys_marketCap | info_retired_keys_symbol
+
+
+_BASIC_URL_ = "https://query2.finance.yahoo.com/v6/finance/quoteSummary"
+
+
+class InfoDictWrapper(MutableMapping):
+    """ Simple wrapper around info dict, intercepting 'gets' to 
+    print how-to-migrate messages for specific keys. Requires
+    override dict API"""
+
+    def __init__(self, info):
+        self.info = info
+
+    def keys(self):
+        return self.info.keys()
+
+    def __str__(self):
+        return self.info.__str__()
+
+    def __repr__(self):
+        return self.info.__repr__()
+
+    def __contains__(self, k):
+        return k in self.info.keys()
+
+    def __getitem__(self, k):
+        if k in info_retired_keys_price:
+            warnings.warn(f"Price data removed from info (key='{k}'). Use Ticker.fast_info or history() instead", DeprecationWarning)
+            return None
+        elif k in info_retired_keys_exchange:
+            warnings.warn(f"Exchange data removed from info (key='{k}'). Use Ticker.fast_info or Ticker.get_history_metadata() instead", DeprecationWarning)
+            return None
+        elif k in info_retired_keys_marketCap:
+            warnings.warn(f"Market cap removed from info (key='{k}'). Use Ticker.fast_info instead", DeprecationWarning)
+            return None
+        elif k in info_retired_keys_symbol:
+            warnings.warn(f"Symbol removed from info (key='{k}'). You know this already", DeprecationWarning)
+            return None
+        return self.info[self._keytransform(k)]
+
+    def __setitem__(self, k, value):
+        self.info[self._keytransform(k)] = value
+
+    def __delitem__(self, k):
+        del self.info[self._keytransform(k)]
+
+    def __iter__(self):
+        return iter(self.info)
+    
+    def __len__(self):
+        return len(self.info)
+
+    def _keytransform(self, k):
+        return k
+
+
+class FastInfo:
+    # Contain small subset of info[] items that can be fetched faster elsewhere.
+    # Imitates a dict.
+    def __init__(self, tickerBaseObject, proxy=None):
+        self._tkr = tickerBaseObject
+        self.proxy = proxy
+
+        self._prices_1y = None
+        self._prices_1wk_1h_prepost = None
+        self._prices_1wk_1h_reg = None
+        self._md = None
+
+        self._currency = None
+        self._quote_type = None
+        self._exchange = None
+        self._timezone = None
+
+        self._shares = None
+        self._mcap = None
+
+        self._open = None
+        self._day_high = None
+        self._day_low = None
+        self._last_price = None
+        self._last_volume = None
+
+        self._prev_close = None
+
+        self._reg_prev_close = None
+
+        self._50d_day_average = None
+        self._200d_day_average = None
+        self._year_high = None
+        self._year_low = None
+        self._year_change = None
+
+        self._10d_avg_vol = None
+        self._3mo_avg_vol = None
+
+        # attrs = utils.attributes(self)
+        # self.keys = attrs.keys()
+        # utils.attributes is calling each method, bad! Have to hardcode
+        _properties = ["currency", "quote_type", "exchange", "timezone"]
+        _properties += ["shares", "market_cap"]
+        _properties += ["last_price", "previous_close", "open", "day_high", "day_low"]
+        _properties += ["regular_market_previous_close"]
+        _properties += ["last_volume"]
+        _properties += ["fifty_day_average", "two_hundred_day_average", "ten_day_average_volume", "three_month_average_volume"]
+        _properties += ["year_high", "year_low", "year_change"]
+
+        # Because released before fixing key case, need to officially support 
+        # camel-case but also secretly support snake-case
+        base_keys = [k for k in _properties if '_' not in k]
+
+        sc_keys = [k for k in _properties if '_' in k]
+
+        self._sc_to_cc_key = {k: utils.snake_case_2_camelCase(k) for k in sc_keys}
+        self._cc_to_sc_key = {v: k for k, v in self._sc_to_cc_key.items()}
+ 
+        self._public_keys = sorted(base_keys + list(self._sc_to_cc_key.values()))
+        self._keys = sorted(self._public_keys + sc_keys)
+
+    # dict imitation:
+    def keys(self):
+        return self._public_keys
+
+    def items(self):
+        return [(k, self[k]) for k in self._public_keys]
+
+    def values(self):
+        return [self[k] for k in self._public_keys]
+
+    def get(self, key, default=None):
+        if key in self.keys():
+            if key in self._cc_to_sc_key:
+                key = self._cc_to_sc_key[key]
+            return self[key]
+        return default
+
+    def __getitem__(self, k):
+        if not isinstance(k, str):
+            raise KeyError(f"key must be a string")
+        if k not in self._keys:
+            raise KeyError(f"'{k}' not valid key. Examine 'FastInfo.keys()'")
+        if k in self._cc_to_sc_key:
+            k = self._cc_to_sc_key[k]
+        return getattr(self, k)
+
+    def __contains__(self, k):
+        return k in self.keys()
+
+    def __iter__(self):
+        return iter(self.keys())
+
+    def __str__(self):
+        return "lazy-loading dict with keys = " + str(self.keys())
+
+    def __repr__(self):
+        return self.__str__()
+
+    def toJSON(self, indent=4):
+        d = {k: self[k] for k in self.keys()}
+        return json.dumps({k: self[k] for k in self.keys()}, indent=indent)
+
+    def _get_1y_prices(self, fullDaysOnly=False):
+        if self._prices_1y is None:
+            # Temporarily disable error printing
+            logging.disable(logging.CRITICAL)
+            self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, keepna=True, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
+            self._md = self._tkr.get_history_metadata(proxy=self.proxy)
+            try:
+                ctp = self._md["currentTradingPeriod"]
+                self._today_open = pd.to_datetime(ctp["regular"]["start"], unit='s', utc=True).tz_convert(self.timezone)
+                self._today_close = pd.to_datetime(ctp["regular"]["end"], unit='s', utc=True).tz_convert(self.timezone)
+                self._today_midnight = self._today_close.ceil("D")
+            except Exception:
+                self._today_open = None
+                self._today_close = None
+                self._today_midnight = None
+                raise
+
+        if self._prices_1y.empty:
+            return self._prices_1y
+
+        dnow = pd.Timestamp.utcnow().tz_convert(self.timezone).date()
+        d1 = dnow
+        d0 = (d1 + datetime.timedelta(days=1)) - utils._interval_to_timedelta("1y")
+        if fullDaysOnly and self._exchange_open_now():
+            # Exclude today
+            d1 -= utils._interval_to_timedelta("1d")
+        return self._prices_1y.loc[str(d0):str(d1)]
+
+    def _get_1wk_1h_prepost_prices(self):
+        if self._prices_1wk_1h_prepost is None:
+            # Temporarily disable error printing
+            logging.disable(logging.CRITICAL)
+            self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
+        return self._prices_1wk_1h_prepost
+
+    def _get_1wk_1h_reg_prices(self):
+        if self._prices_1wk_1h_reg is None:
+            # Temporarily disable error printing
+            logging.disable(logging.CRITICAL)
+            self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False, proxy=self.proxy)
+            logging.disable(logging.NOTSET)
+        return self._prices_1wk_1h_reg
+
+    def _get_exchange_metadata(self):
+        if self._md is not None:
+            return self._md
+
+        self._get_1y_prices()
+        self._md = self._tkr.get_history_metadata(proxy=self.proxy)
+        return self._md
+
+    def _exchange_open_now(self):
+        t = pd.Timestamp.utcnow()
+        self._get_exchange_metadata()
+
+        # if self._today_open is None and self._today_close is None:
+        #     r = False
+        # else:
+        #     r = self._today_open <= t and t < self._today_close
+
+        # if self._today_midnight is None:
+        #     r = False
+        # elif self._today_midnight.date() > t.tz_convert(self.timezone).date():
+        #     r = False
+        # else:
+        #     r = t < self._today_midnight
+
+        last_day_cutoff = self._get_1y_prices().index[-1] + datetime.timedelta(days=1)
+        last_day_cutoff += datetime.timedelta(minutes=20)
+        r = t < last_day_cutoff
+
+        # print("_exchange_open_now() returning", r)
+        return r
+
+    @property
+    def currency(self):
+        if self._currency is not None:
+            return self._currency
+
+        if self._tkr._history_metadata is None:
+            self._get_1y_prices()
+        md = self._tkr.get_history_metadata(proxy=self.proxy)
+        self._currency = md["currency"]
+        return self._currency
+
+    @property
+    def quote_type(self):
+        if self._quote_type is not None:
+            return self._quote_type
+
+        if self._tkr._history_metadata is None:
+            self._get_1y_prices()
+        md = self._tkr.get_history_metadata(proxy=self.proxy)
+        self._quote_type = md["instrumentType"]
+        return self._quote_type
+
+    @property
+    def exchange(self):
+        if self._exchange is not None:
+            return self._exchange
+
+        self._exchange = self._get_exchange_metadata()["exchangeName"]
+        return self._exchange
+
+    @property
+    def timezone(self):
+        if self._timezone is not None:
+            return self._timezone
+
+        self._timezone = self._get_exchange_metadata()["exchangeTimezoneName"]
+        return self._timezone
+
+    @property
+    def shares(self):
+        if self._shares is not None:
+            return self._shares
+
+        shares = self._tkr.get_shares_full(start=pd.Timestamp.utcnow().date()-pd.Timedelta(days=548), proxy=self.proxy)
+        # if shares is None:
+        #     # Requesting 18 months failed, so fallback to shares which should include last year
+        #     shares = self._tkr.get_shares()
+        if shares is not None:
+            if isinstance(shares, pd.DataFrame):
+                shares = shares[shares.columns[0]]
+            self._shares = int(shares.iloc[-1])
+        return self._shares
+
+    @property
+    def last_price(self):
+        if self._last_price is not None:
+            return self._last_price
+        prices = self._get_1y_prices()
+        if prices.empty:
+            md = self._get_exchange_metadata()
+            if "regularMarketPrice" in md:
+                self._last_price = md["regularMarketPrice"]
+        else:
+            self._last_price = float(prices["Close"].iloc[-1])
+            if _np.isnan(self._last_price):
+                md = self._get_exchange_metadata()
+                if "regularMarketPrice" in md:
+                    self._last_price = md["regularMarketPrice"]
+        return self._last_price
+
+    @property
+    def previous_close(self):
+        if self._prev_close is not None:
+            return self._prev_close
+        prices = self._get_1wk_1h_prepost_prices()
+        fail = False
+        if prices.empty:
+            fail = True
+        else:
+            prices = prices[["Close"]].groupby(prices.index.date).last()
+            if prices.shape[0] < 2:
+                # Very few symbols have previousClose despite no 
+                # no trading data e.g. 'QCSTIX'.
+                fail = True
+            else:
+                self._prev_close = float(prices["Close"].iloc[-2])
+        if fail:
+            # Fallback to original info[] if available.
+            self._tkr.info  # trigger fetch
+            k = "previousClose"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._prev_close = self._tkr._quote._retired_info[k]
+        return self._prev_close
+
+    @property
+    def regular_market_previous_close(self):
+        if self._reg_prev_close is not None:
+            return self._reg_prev_close
+        prices = self._get_1y_prices()
+        if prices.shape[0] == 1:
+            # Tiny % of tickers don't return daily history before last trading day, 
+            # so backup option is hourly history:
+            prices = self._get_1wk_1h_reg_prices()
+            prices = prices[["Close"]].groupby(prices.index.date).last()
+        if prices.shape[0] < 2:
+            # Very few symbols have regularMarketPreviousClose despite no 
+            # no trading data. E.g. 'QCSTIX'.
+            # So fallback to original info[] if available.
+            self._tkr.info  # trigger fetch
+            k = "regularMarketPreviousClose"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._reg_prev_close = self._tkr._quote._retired_info[k]
+        else:
+            self._reg_prev_close = float(prices["Close"].iloc[-2])
+        return self._reg_prev_close
+
+    @property
+    def open(self):
+        if self._open is not None:
+            return self._open
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._open = None
+        else:
+            self._open = float(prices["Open"].iloc[-1])
+            if _np.isnan(self._open):
+                self._open = None
+        return self._open
+
+    @property
+    def day_high(self):
+        if self._day_high is not None:
+            return self._day_high
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._day_high = None
+        else:
+            self._day_high = float(prices["High"].iloc[-1])
+            if _np.isnan(self._day_high):
+                self._day_high = None
+        return self._day_high
+
+    @property
+    def day_low(self):
+        if self._day_low is not None:
+            return self._day_low
+        prices = self._get_1y_prices()
+        if prices.empty:
+            self._day_low = None
+        else:
+            self._day_low = float(prices["Low"].iloc[-1])
+            if _np.isnan(self._day_low):
+                self._day_low = None
+        return self._day_low
+
+    @property
+    def last_volume(self):
+        if self._last_volume is not None:
+            return self._last_volume
+        prices = self._get_1y_prices()
+        self._last_volume = None if prices.empty else int(prices["Volume"].iloc[-1])
+        return self._last_volume
+
+    @property
+    def fifty_day_average(self):
+        if self._50d_day_average is not None:
+            return self._50d_day_average
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._50d_day_average = None
+        else:
+            n = prices.shape[0]
+            a = n-50
+            b = n
+            if a < 0:
+                a = 0
+            self._50d_day_average = float(prices["Close"].iloc[a:b].mean())
+
+        return self._50d_day_average
+
+    @property
+    def two_hundred_day_average(self):
+        if self._200d_day_average is not None:
+            return self._200d_day_average
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._200d_day_average = None
+        else:
+            n = prices.shape[0]
+            a = n-200
+            b = n
+            if a < 0:
+                a = 0
+
+            self._200d_day_average = float(prices["Close"].iloc[a:b].mean())
+
+        return self._200d_day_average
+
+    @property
+    def ten_day_average_volume(self):
+        if self._10d_avg_vol is not None:
+            return self._10d_avg_vol
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._10d_avg_vol = None
+        else:
+            n = prices.shape[0]
+            a = n-10
+            b = n
+            if a < 0:
+                a = 0
+            self._10d_avg_vol = int(prices["Volume"].iloc[a:b].mean())
+
+        return self._10d_avg_vol
+
+    @property
+    def three_month_average_volume(self):
+        if self._3mo_avg_vol is not None:
+            return self._3mo_avg_vol
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._3mo_avg_vol = None
+        else:
+            dt1 = prices.index[-1]
+            dt0 = dt1 - utils._interval_to_timedelta("3mo") + utils._interval_to_timedelta("1d")
+            self._3mo_avg_vol = int(prices.loc[dt0:dt1, "Volume"].mean())
+
+        return self._3mo_avg_vol
+
+    @property
+    def year_high(self):
+        if self._year_high is not None:
+            return self._year_high
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            prices = self._get_1y_prices(fullDaysOnly=False)
+        self._year_high = float(prices["High"].max())
+        return self._year_high
+
+    @property
+    def year_low(self):
+        if self._year_low is not None:
+            return self._year_low
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            prices = self._get_1y_prices(fullDaysOnly=False)
+        self._year_low = float(prices["Low"].min())
+        return self._year_low
+
+    @property
+    def year_change(self):
+        if self._year_change is not None:
+            return self._year_change
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.shape[0] >= 2:
+            self._year_change = (prices["Close"].iloc[-1] - prices["Close"].iloc[0]) / prices["Close"].iloc[0]
+            self._year_change = float(self._year_change)
+        return self._year_change
+
+    @property
+    def market_cap(self):
+        if self._mcap is not None:
+            return self._mcap
+
+        try:
+            shares = self.shares
+        except Exception as e:
+            if "Cannot retrieve share count" in str(e):
+                shares = None
+            elif "failed to decrypt Yahoo" in str(e):
+                shares = None
+            else:
+                raise
+
+        if shares is None:
+            # Very few symbols have marketCap despite no share count.
+            # E.g. 'BTC-USD'
+            # So fallback to original info[] if available.
+            self._tkr.info
+            k = "marketCap"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._mcap = self._tkr._quote._retired_info[k]
+        else:
+            self._mcap = float(shares * self.last_price)
+        return self._mcap
 
 
 class Quote:
@@ -14,153 +556,92 @@ class Quote:
         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_scraped_complementary = False
+        self._already_fetched = False
+        self._already_fetched_complementary = False
 
     @property
     def info(self) -> dict:
         if self._info is None:
-            self._scrape(self.proxy)
-            self._scrape_complementary(self.proxy)
+            self._fetch(self.proxy)
+            self._fetch_complementary(self.proxy)
 
         return self._info
 
     @property
     def sustainability(self) -> pd.DataFrame:
         if self._sustainability is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('sustainability')
         return self._sustainability
 
     @property
     def recommendations(self) -> pd.DataFrame:
         if self._recommendations is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('recommendations')
         return self._recommendations
 
     @property
     def calendar(self) -> pd.DataFrame:
         if self._calendar is None:
-            self._scrape(self.proxy)
+            raise YFNotImplementedError('calendar')
         return self._calendar
 
-    def _scrape(self, proxy):
-        if self._already_scraped:
+    def _fetch(self, proxy):
+        if self._already_fetched:
             return
-        self._already_scraped = True
+        self._already_fetched = True
+        modules = ['financialData', 'quoteType', 'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
+        params_dict = {"modules": modules, "ssl": "true"}
+        result = self._data.get_raw_json(
+            _BASIC_URL_ + f"/{self._data.ticker}", params=params_dict, proxy=proxy
+        )
+        result["quoteSummary"]["result"][0]["symbol"] = self._data.ticker
+        query1_info = next(
+            (info for info in result.get("quoteSummary", {}).get("result", []) if info["symbol"] == self._data.ticker),
+            None,
+        )
+        # Most keys that appear in multiple dicts have same value. Except 'maxAge' because
+        # Yahoo not consistent with days vs seconds. Fix it here:
+        for k in query1_info:
+            if "maxAge" in query1_info[k] and query1_info[k]["maxAge"] == 1:
+                query1_info[k]["maxAge"] = 86400
+        query1_info = {
+            k1: v1 
+            for k, v in query1_info.items() 
+            if isinstance(v, dict) 
+            for k1, v1 in v.items() 
+            if v1
+        }
+        # recursively format but only because of 'companyOfficers'
 
-        # get info and sustainability
-        json_data = self._data.get_json_data_stores(proxy=proxy)
-        try:
-            quote_summary_store = json_data['QuoteSummaryStore']
-        except KeyError:
-            err_msg = "No summary info found, symbol may be delisted"
-            print('- %s: %s' % (self._data.ticker, err_msg))
-            return None
-
-        # sustainability
-        d = {}
-        try:
-            if isinstance(quote_summary_store.get('esgScores'), dict):
-                for item in quote_summary_store['esgScores']:
-                    if not isinstance(quote_summary_store['esgScores'][item], (dict, list)):
-                        d[item] = quote_summary_store['esgScores'][item]
-
-                s = pd.DataFrame(index=[0], data=d)[-1:].T
-                s.columns = ['Value']
-                s.index.name = '%.f-%.f' % (
-                    s[s.index == 'ratingYear']['Value'].values[0],
-                    s[s.index == 'ratingMonth']['Value'].values[0])
-
-                self._sustainability = s[~s.index.isin(
-                    ['maxAge', 'ratingYear', 'ratingMonth'])]
-        except Exception:
-            pass
-
-        self._info = {}
-        try:
-            items = ['summaryProfile', 'financialData', 'quoteType',
-                     'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
-            for item in items:
-                if isinstance(quote_summary_store.get(item), dict):
-                    self._info.update(quote_summary_store[item])
-        except Exception:
-            pass
-
-        # For ETFs, provide this valuable data: the top holdings of the ETF
-        try:
-            if 'topHoldings' in quote_summary_store:
-                self._info.update(quote_summary_store['topHoldings'])
-        except Exception:
-            pass
-
-        try:
-            if not isinstance(quote_summary_store.get('summaryDetail'), dict):
-                # For some reason summaryDetail did not give any results. The price dict
-                # usually has most of the same info
-                self._info.update(quote_summary_store.get('price', {}))
-        except Exception:
-            pass
-
-        try:
-            # self._info['regularMarketPrice'] = self._info['regularMarketOpen']
-            self._info['regularMarketPrice'] = quote_summary_store.get('price', {}).get(
-                'regularMarketPrice', self._info.get('regularMarketOpen', None))
-        except Exception:
-            pass
-
-        try:
-            self._info['preMarketPrice'] = quote_summary_store.get('price', {}).get(
-                'preMarketPrice', self._info.get('preMarketPrice', None))
-        except Exception:
-            pass
-
-        self._info['logo_url'] = ""
-        try:
-            if not 'website' in self._info:
-                self._info['logo_url'] = 'https://logo.clearbit.com/%s.com' % \
-                                         self._info['shortName'].split(' ')[0].split(',')[0]
+        def _format(k, v):
+            if isinstance(v, dict) and "raw" in v and "fmt" in v:
+                v2 = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
+            elif isinstance(v, list):
+                v2 = [_format(None, x) for x in v]
+            elif isinstance(v, dict):
+                v2 = {k: _format(k, x) for k, x in v.items()}
+            elif isinstance(v, str):
+                v2 = v.replace("\xa0", " ")
             else:
-                domain = self._info['website'].split(
-                    '://')[1].split('/')[0].replace('www.', '')
-                self._info['logo_url'] = 'https://logo.clearbit.com/%s' % domain
-        except Exception:
-            pass
+                v2 = v
+            return v2
+        for k, v in query1_info.items():
+            query1_info[k] = _format(k, v)
+        self._info = query1_info
 
-        # events
-        try:
-            cal = pd.DataFrame(quote_summary_store['calendarEvents']['earnings'])
-            cal['earningsDate'] = pd.to_datetime(
-                cal['earningsDate'], unit='s')
-            self._calendar = cal.T
-            self._calendar.index = utils.camel2title(self._calendar.index)
-            self._calendar.columns = ['Value']
-        except Exception as e:
-            pass
-
-        # analyst recommendations
-        try:
-            rec = pd.DataFrame(
-                quote_summary_store['upgradeDowngradeHistory']['history'])
-            rec['earningsDate'] = pd.to_datetime(
-                rec['epochGradeDate'], unit='s')
-            rec.set_index('earningsDate', inplace=True)
-            rec.index.name = 'Date'
-            rec.columns = utils.camel2title(rec.columns)
-            self._recommendations = rec[[
-                'Firm', 'To Grade', 'From Grade', 'Action']].sort_index()
-        except Exception:
-            pass
-
-    def _scrape_complementary(self, proxy):
-        if self._already_scraped_complementary:
+    def _fetch_complementary(self, proxy):
+        if self._already_fetched_complementary:
             return
-        self._already_scraped_complementary = True
+        self._already_fetched_complementary = True
 
-        self._scrape(proxy)
+        # self._scrape(proxy)  # decrypt broken
+        self._fetch(proxy)
         if self._info is None:
             return
 
@@ -189,22 +670,26 @@ class Quote:
             #     pass
             #
             # For just one/few variable is faster to query directly:
-            url = "https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{}?symbol={}".format(
-                self._data.ticker, self._data.ticker)
+            url = f"https://query1.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._data.ticker}?symbol={self._data.ticker}"
             for k in keys:
                 url += "&type=" + k
             # Request 6 months of data
-            url += "&period1={}".format(
-                int((datetime.datetime.now() - datetime.timedelta(days=365 // 2)).timestamp()))
-            url += "&period2={}".format(int((datetime.datetime.now() + datetime.timedelta(days=1)).timestamp()))
+            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)
-            key_stats = json_data["timeseries"]["result"][0]
-            if k not in key_stats:
-                # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+            try:
+                key_stats = json_data["timeseries"]["result"][0]
+                if k not in key_stats:
+                    # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+                    v = None
+                else:
+                    # Select most recent (last) raw value in list:
+                    v = key_stats[k][-1]["reportedValue"]["raw"]
+            except Exception:
                 v = None
-            else:
-                # Select most recent (last) raw value in list:
-                v = key_stats[k][-1]["reportedValue"]["raw"]
             self._info[k] = v

yfinance/scrapers/yahoo-keys.txt

@@ -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

@@ -22,10 +22,10 @@
 from __future__ import print_function
 
 import datetime as _datetime
-import pandas as _pd
-
 from collections import namedtuple as _namedtuple
 
+import pandas as _pd
+
 from .base import TickerBase
 
 
@@ -33,25 +33,29 @@ class Ticker(TickerBase):
     def __init__(self, ticker, session=None):
         super(Ticker, self).__init__(ticker, session=session)
         self._expirations = {}
+        self._underlying  = {}
 
     def __repr__(self):
-        return 'yfinance.Ticker object <%s>' % self.ticker
+        return f'yfinance.Ticker object <{self.ticker}>'
 
     def _download_options(self, date=None, proxy=None):
         if date is None:
-            url = "{}/v7/finance/options/{}".format(
-                self._base_url, self.ticker)
+            url = f"{self._base_url}/v7/finance/options/{self.ticker}"
         else:
-            url = "{}/v7/finance/options/{}?date={}".format(
-                self._base_url, self.ticker, date)
+            url = f"{self._base_url}/v7/finance/options/{self.ticker}?date={date}"
 
         r = self._data.get(url=url, proxy=proxy).json()
         if len(r.get('optionChain', {}).get('result', [])) > 0:
             for exp in r['optionChain']['result'][0]['expirationDates']:
                 self._expirations[_datetime.datetime.utcfromtimestamp(
                     exp).strftime('%Y-%m-%d')] = exp
+
+            self._underlying = r['optionChain']['result'][0].get('quote', {})
+
             opt = r['optionChain']['result'][0].get('options', [])
-            return opt[0] if len(opt) > 0 else []
+
+            return dict(**opt[0],underlying=self._underlying) if len(opt) > 0 else {}
+        return {}
 
     def _options2df(self, opt, tz=None):
         data = _pd.DataFrame(opt).reindex(columns=[
@@ -84,15 +88,15 @@ class Ticker(TickerBase):
                 self._download_options()
             if date not in self._expirations:
                 raise ValueError(
-                    "Expiration `%s` cannot be found. "
-                    "Available expiration are: [%s]" % (
-                        date, ', '.join(self._expirations)))
+                    f"Expiration `{date}` cannot be found. "
+                    f"Available expirations are: [{', '.join(self._expirations)}]")
             date = self._expirations[date]
             options = self._download_options(date, proxy=proxy)
 
-        return _namedtuple('Options', ['calls', 'puts'])(**{
+        return _namedtuple('Options', ['calls', 'puts', 'underlying'])(**{
             "calls": self._options2df(options['calls'], tz=tz),
-            "puts": self._options2df(options['puts'], tz=tz)
+            "puts": self._options2df(options['puts'], tz=tz),
+            "underlying": options['underlying']
         })
 
     # ------------------------
@@ -137,6 +141,10 @@ class Ticker(TickerBase):
     def info(self) -> dict:
         return self.get_info()
 
+    @property
+    def fast_info(self):
+        return self.get_fast_info()
+
     @property
     def calendar(self) -> _pd.DataFrame:
         return self.get_calendar()
@@ -161,6 +169,22 @@ class Ticker(TickerBase):
     def quarterly_income_stmt(self) -> _pd.DataFrame:
         return self.get_income_stmt(pretty=True, freq='quarterly')
 
+    @property
+    def incomestmt(self) -> _pd.DataFrame:
+        return self.income_stmt
+
+    @property
+    def quarterly_incomestmt(self) -> _pd.DataFrame:
+        return self.quarterly_income_stmt
+
+    @property
+    def financials(self) -> _pd.DataFrame:
+        return self.income_stmt
+
+    @property
+    def quarterly_financials(self) -> _pd.DataFrame:
+        return self.quarterly_income_stmt
+
     @property
     def balance_sheet(self) -> _pd.DataFrame:
         return self.get_balance_sheet(pretty=True)
@@ -177,13 +201,21 @@ class Ticker(TickerBase):
     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.get_cashflow(pretty=True, freq="yearly")
+        return self.cash_flow
 
     @property
     def quarterly_cashflow(self) -> _pd.DataFrame:
-        return self.get_cashflow(pretty=True, freq='quarterly')
+        return self.quarterly_cash_flow
 
     @property
     def recommendations_summary(self):
@@ -211,6 +243,10 @@ class Ticker(TickerBase):
     def news(self):
         return self.get_news()
 
+    @property
+    def trend_details(self) -> _pd.DataFrame:
+        return self.get_trend_details()
+
     @property
     def earnings_trend(self) -> _pd.DataFrame:
         return self.get_earnings_trend()

yfinance/tickers.py

@@ -22,24 +22,22 @@
 from __future__ import print_function
 
 from . import Ticker, multi
+
+
 # from collections import namedtuple as _namedtuple
 
 
 class Tickers:
 
     def __repr__(self):
-        return 'yfinance.Tickers object <%s>' % ",".join(self.symbols)
+        return f"yfinance.Tickers object <{','.join(self.symbols)}>"
 
     def __init__(self, tickers, session=None):
         tickers = tickers if isinstance(
             tickers, list) else tickers.replace(',', ' ').split()
         self.symbols = [ticker.upper() for ticker in tickers]
-        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())
@@ -91,10 +89,4 @@ class Tickers:
         return data
 
     def news(self):
-        collection = {}
-        for ticker in self.symbols:
-            collection[ticker] = []
-            items = Ticker(ticker).news
-            for item in items:
-                collection[ticker].append(item)
-        return collection
+        return {ticker: [item for item in Ticker(ticker).news] for ticker in self.symbols}

yfinance/utils.py

@@ -21,25 +21,32 @@
 
 from __future__ import print_function
 
-import datetime as _datetime
-import dateutil as _dateutil
-from typing import Dict, Union, List, Optional
-
-import pytz as _tz
-import requests as _requests
-import re as _re
-import pandas as _pd
-import numpy as _np
-import sys as _sys
-import os as _os
-import appdirs as _ad
-import sqlite3 as _sqlite3
 import atexit as _atexit
 
+import datetime as _datetime
+import logging
+import os as _os
+import re as _re
+import sqlite3 as _sqlite3
+import sys as _sys
+import threading
+from functools import lru_cache
+from inspect import getmembers
 from threading import Lock
+from types import FunctionType
+from typing import Dict, Union, List, Optional
 
+import appdirs as _ad
+import numpy as _np
+import pandas as _pd
+import pytz as _tz
+import requests as _requests
+from dateutil.relativedelta import relativedelta
 from pytz import UnknownTimeZoneError
 
+from yfinance import const
+from .const import _BASE_URL_
+
 try:
     import ujson as _json
 except ImportError:
@@ -49,17 +56,144 @@ user_agent_headers = {
     'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
 
 
+# From https://stackoverflow.com/a/59128615
+def attributes(obj):
+    disallowed_names = {
+        name for name, value in getmembers(type(obj))
+        if isinstance(value, FunctionType)}
+    return {
+        name: getattr(obj, name) for name in dir(obj)
+        if name[0] != '_' and name not in disallowed_names and hasattr(obj, name)}
+
+
+@lru_cache(maxsize=20)
+def print_once(msg):
+    # 'warnings' module suppression of repeat messages does not work. 
+    # This function replicates correct behaviour
+    print(msg)
+
+
+# Logging
+# Note: most of this logic is adding indentation with function depth,
+#       so that DEBUG log is readable.
+class IndentLoggerAdapter(logging.LoggerAdapter):
+    def process(self, msg, kwargs):
+        if get_yf_logger().isEnabledFor(logging.DEBUG):
+            i = ' ' * self.extra['indent']
+            if not isinstance(msg, str):
+                msg = str(msg)
+            msg = '\n'.join([i + m for m in msg.split('\n')])
+        return msg, kwargs
+
+
+_indentation_level = threading.local()
+
+
+class IndentationContext:
+    def __init__(self, increment=1):
+        self.increment = increment
+
+    def __enter__(self):
+        _indentation_level.indent = getattr(_indentation_level, 'indent', 0) + self.increment
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        _indentation_level.indent -= self.increment
+
+
+def get_indented_logger(name=None):
+    # Never cache the returned value! Will break indentation.
+    return IndentLoggerAdapter(logging.getLogger(name), {'indent': getattr(_indentation_level, 'indent', 0)})
+
+
+def log_indent_decorator(func):
+    def wrapper(*args, **kwargs):
+        logger = get_indented_logger('yfinance')
+        logger.debug(f'Entering {func.__name__}()')
+
+        with IndentationContext():
+            result = func(*args, **kwargs)
+
+        logger.debug(f'Exiting {func.__name__}()')
+        return result
+
+    return wrapper
+
+
+class MultiLineFormatter(logging.Formatter):
+    # The 'fmt' formatting further down is only applied to first line
+    # of log message, specifically the padding after %level%.
+    # For multi-line messages, need to manually copy over padding.
+    def __init__(self, fmt):
+        super().__init__(fmt)
+        # Extract amount of padding
+        match = _re.search(r'%\(levelname\)-(\d+)s', fmt)
+        self.level_length = int(match.group(1)) if match else 0
+
+    def format(self, record):
+        original = super().format(record)
+        lines = original.split('\n')
+        levelname = lines[0].split(' ')[0]
+        if len(lines) <= 1:
+            return original
+        else:
+            # Apply padding to all lines below first
+            formatted = [lines[0]]
+            if self.level_length == 0:
+                padding = ' ' * len(levelname)
+            else:
+                padding = ' ' * self.level_length
+            padding += ' '  # +1 for space between level and message
+            formatted.extend(padding + line for line in lines[1:])
+            return '\n'.join(formatted)
+
+
+yf_logger = None
+yf_log_indented = False
+
+
+def get_yf_logger():
+    global yf_logger
+    if yf_logger is None:
+        yf_logger = logging.getLogger('yfinance')
+    global yf_log_indented
+    if yf_log_indented:
+        yf_logger = get_indented_logger('yfinance')
+    return yf_logger
+
+
+def setup_debug_formatting():
+    global yf_logger
+    yf_logger = get_yf_logger()
+
+    if not yf_logger.isEnabledFor(logging.DEBUG):
+        yf_logger.warning("logging mode not set to 'DEBUG', so not setting up debug formatting")
+        return
+
+    if yf_logger.handlers is None or len(yf_logger.handlers) == 0:
+        h = logging.StreamHandler()
+        # Ensure different level strings don't interfere with indentation
+        formatter = MultiLineFormatter(fmt='%(levelname)-8s %(message)s')
+        h.setFormatter(formatter)
+        yf_logger.addHandler(h)
+
+    global yf_log_indented
+    yf_log_indented = True
+
+
+def enable_debug_mode():
+    get_yf_logger().setLevel(logging.DEBUG)
+    setup_debug_formatting()
+
+
 def is_isin(string):
-    return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9]{1})$", string))
+    return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9])$", string))
 
 
 def get_all_by_isin(isin, proxy=None, session=None):
     if not (is_isin(isin)):
         raise ValueError("Invalid ISIN number")
-
-    from .base import _BASE_URL_
     session = session or _requests
-    url = "{}/v1/finance/search?q={}".format(_BASE_URL_, isin)
+    url = f"{_BASE_URL_}/v1/finance/search?q={isin}"
     data = session.get(url=url, proxies=proxy, headers=user_agent_headers)
     try:
         data = data.json()
@@ -111,7 +245,7 @@ def empty_earnings_dates_df():
 
 
 def build_template(data):
-    '''
+    """
     build_template returns the details required to rebuild any of the yahoo finance financial statements in the same order as the yahoo finance webpage. The function is built to be used on the "FinancialTemplateStore" json which appears in any one of the three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
 
     Returns:
@@ -120,95 +254,80 @@ def build_template(data):
         - template_order: The order that quarterlies should be in (note that quarterlies have no pre-fix - hence why this is required).
         - level_detail: The level of each individual line item. E.g. for the "/financials" webpage, "Total Revenue" is a level 0 item and is the summation of "Operating Revenue" and "Excise Taxes" which are level 1 items.
 
-    '''
+    """
     template_ttm_order = []  # Save the TTM (Trailing Twelve Months) ordering to an object.
     template_annual_order = []  # Save the annual ordering to an object.
     template_order = []  # Save the ordering to an object (this can be utilized for quarterlies)
     level_detail = []  # Record the level of each line item of the income statement ("Operating Revenue" and "Excise Taxes" sum to return "Total Revenue" we need to keep track of this)
-    for key in data['template']:
-        # Loop through the json to retreive the exact financial order whilst appending to the objects
-        template_ttm_order.append('trailing{}'.format(key['key']))
-        template_annual_order.append('annual{}'.format(key['key']))
-        template_order.append('{}'.format(key['key']))
-        level_detail.append(0)
-        if 'children' in key:
-            for child1 in key['children']:  # Level 1
-                template_ttm_order.append('trailing{}'.format(child1['key']))
-                template_annual_order.append('annual{}'.format(child1['key']))
-                template_order.append('{}'.format(child1['key']))
-                level_detail.append(1)
-                if 'children' in child1:
-                    for child2 in child1['children']:  # Level 2
-                        template_ttm_order.append('trailing{}'.format(child2['key']))
-                        template_annual_order.append('annual{}'.format(child2['key']))
-                        template_order.append('{}'.format(child2['key']))
-                        level_detail.append(2)
-                        if 'children' in child2:
-                            for child3 in child2['children']:  # Level 3
-                                template_ttm_order.append('trailing{}'.format(child3['key']))
-                                template_annual_order.append('annual{}'.format(child3['key']))
-                                template_order.append('{}'.format(child3['key']))
-                                level_detail.append(3)
-                                if 'children' in child3:
-                                    for child4 in child3['children']:  # Level 4
-                                        template_ttm_order.append('trailing{}'.format(child4['key']))
-                                        template_annual_order.append('annual{}'.format(child4['key']))
-                                        template_order.append('{}'.format(child4['key']))
-                                        level_detail.append(4)
-                                        if 'children' in child4:
-                                            for child5 in child4['children']:  # Level 5
-                                                template_ttm_order.append('trailing{}'.format(child5['key']))
-                                                template_annual_order.append('annual{}'.format(child5['key']))
-                                                template_order.append('{}'.format(child5['key']))
-                                                level_detail.append(5)
+
+    def traverse(node, level):
+        """
+        A recursive function that visits a node and its children.
+
+        Args:
+            node: The current node in the data structure.
+            level: The depth of the current node in the data structure.
+        """
+        if level > 5:  # Stop when level is above 5
+            return
+        template_ttm_order.append(f"trailing{node['key']}")
+        template_annual_order.append(f"annual{node['key']}")
+        template_order.append(f"{node['key']}")
+        level_detail.append(level)
+        if 'children' in node:  # Check if the node has children
+            for child in node['children']:  # If yes, traverse each child
+                traverse(child, level + 1)  # Increment the level by 1 for each child
+
+    for key in data['template']:  # Loop through the data
+        traverse(key, 0)  # Call the traverse function with initial level being 0
+
     return template_ttm_order, template_annual_order, template_order, level_detail
 
 
-def retreive_financial_details(data):
-    '''
-    retreive_financial_details returns all of the available financial details under the "QuoteTimeSeriesStore" for any of the following three yahoo finance webpages: "/financials", "/cash-flow" and "/balance-sheet".
+def retrieve_financial_details(data):
+    """
+    retrieve_financial_details returns all of the available financial details under the
+    "QuoteTimeSeriesStore" for any of the following three yahoo finance webpages:
+    "/financials", "/cash-flow" and "/balance-sheet".
 
     Returns:
         - TTM_dicts: A dictionary full of all of the available Trailing Twelve Month figures, this can easily be converted to a pandas dataframe.
         - Annual_dicts: A dictionary full of all of the available Annual figures, this can easily be converted to a pandas dataframe.
-    '''
+    """
     TTM_dicts = []  # Save a dictionary object to store the TTM financials.
     Annual_dicts = []  # Save a dictionary object to store the Annual financials.
-    for key in data['timeSeries']:  # Loop through the time series data to grab the key financial figures.
+
+    for key, timeseries in data.get('timeSeries', {}).items():  # Loop through the time series data to grab the key financial figures.
         try:
-            if len(data['timeSeries'][key]) > 0:
-                time_series_dict = {}
-                time_series_dict['index'] = key
-                for each in data['timeSeries'][key]:  # Loop through the years
-                    if each == None:
+            if timeseries:
+                time_series_dict = {'index': key}
+                for each in timeseries:  # Loop through the years
+                    if not each:
                         continue
-                    else:
-                        time_series_dict[each['asOfDate']] = each['reportedValue']
-                    # time_series_dict["{}".format(each['asOfDate'])] = data['timeSeries'][key][each]['reportedValue']
+                    time_series_dict[each.get('asOfDate')] = each.get('reportedValue')
                 if 'trailing' in key:
                     TTM_dicts.append(time_series_dict)
                 elif 'annual' in key:
                     Annual_dicts.append(time_series_dict)
-        except Exception as e:
-            pass
+        except KeyError as e:
+            print(f"An error occurred while processing the key: {e}")
     return TTM_dicts, Annual_dicts
 
 
 def format_annual_financial_statement(level_detail, annual_dicts, annual_order, ttm_dicts=None, ttm_order=None):
-    '''
+    """
     format_annual_financial_statement formats any annual financial statement
 
     Returns:
         - _statement: A fully formatted annual financial statement in pandas dataframe.
-    '''
+    """
     Annual = _pd.DataFrame.from_dict(annual_dicts).set_index("index")
     Annual = Annual.reindex(annual_order)
     Annual.index = Annual.index.str.replace(r'annual', '')
 
     # Note: balance sheet is the only financial statement with no ttm detail
-    if (ttm_dicts not in [[], None]) and (ttm_order not in [[], None]):
-        TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index")
-        TTM = TTM.reindex(ttm_order)
+    if ttm_dicts and ttm_order:
+        TTM = _pd.DataFrame.from_dict(ttm_dicts).set_index("index").reindex(ttm_order)
         # Add 'TTM' prefix to all column names, so if combined we can tell
         # the difference between actuals and TTM (similar to yahoo finance).
         TTM.columns = ['TTM ' + str(col) for col in TTM.columns]
@@ -226,12 +345,12 @@ def format_annual_financial_statement(level_detail, annual_dicts, annual_order,
 
 
 def format_quarterly_financial_statement(_statement, level_detail, order):
-    '''
+    """
     format_quarterly_financial_statements formats any quarterly financial statement
 
     Returns:
         - _statement: A fully formatted quarterly financial statement in pandas dataframe.
-    '''
+    """
     _statement = _statement.reindex(order)
     _statement.index = camel2title(_statement.T)
     _statement['level_detail'] = level_detail
@@ -282,12 +401,17 @@ def camel2title(strings: List[str], sep: str = ' ', acronyms: Optional[List[str]
 
     # Apply str.title() to non-acronym words
     strings = [s.split(sep) for s in strings]
-    strings = [[j.title() if not j in acronyms else j for j in s] for s in strings]
+    strings = [[j.title() if j not in acronyms else j for j in s] for s in strings]
     strings = [sep.join(s) for s in strings]
 
     return strings
 
 
+def snake_case_2_camelCase(s):
+    sc = s.split('_')[0] + ''.join(x.title() for x in s.split('_')[1:])
+    return sc
+
+
 def _parse_user_dt(dt, exchange_tz):
     if isinstance(dt, int):
         # Should already be epoch, test with conversion:
@@ -307,20 +431,24 @@ def _parse_user_dt(dt, exchange_tz):
 
 def _interval_to_timedelta(interval):
     if interval == "1mo":
-        return _dateutil.relativedelta(months=1)
+        return relativedelta(months=1)
+    elif interval == "3mo":
+        return relativedelta(months=3)
+    elif interval == "1y":
+        return relativedelta(years=1)
     elif interval == "1wk":
-        return _pd.Timedelta(days=7, unit='d')
-    else: 
+        return _pd.Timedelta(days=7)
+    else:
         return _pd.Timedelta(interval)
 
 
 def auto_adjust(data):
     col_order = data.columns
     df = data.copy()
-    ratio = df["Close"] / df["Adj Close"]
-    df["Adj Open"] = df["Open"] / ratio
-    df["Adj High"] = df["High"] / ratio
-    df["Adj Low"] = df["Low"] / ratio
+    ratio = (df["Adj Close"] / df["Close"]).to_numpy()
+    df["Adj Open"] = df["Open"] * ratio
+    df["Adj High"] = df["High"] * ratio
+    df["Adj Low"] = df["Low"] * ratio
 
     df.drop(
         ["Open", "High", "Low", "Close"],
@@ -383,12 +511,9 @@ def parse_quotes(data):
 
 
 def parse_actions(data):
-    dividends = _pd.DataFrame(
-        columns=["Dividends"], index=_pd.DatetimeIndex([]))
-    capital_gains = _pd.DataFrame(
-        columns=["Capital Gains"], index=_pd.DatetimeIndex([]))
-    splits = _pd.DataFrame(
-        columns=["Stock Splits"], index=_pd.DatetimeIndex([]))
+    dividends = None
+    capital_gains = None
+    splits = None
 
     if "events" in data:
         if "dividends" in data["events"]:
@@ -413,10 +538,19 @@ def parse_actions(data):
             splits.set_index("date", inplace=True)
             splits.index = _pd.to_datetime(splits.index, unit="s")
             splits.sort_index(inplace=True)
-            splits["Stock Splits"] = splits["numerator"] / \
-                                     splits["denominator"]
+            splits["Stock Splits"] = splits["numerator"] / splits["denominator"]
             splits = splits[["Stock Splits"]]
 
+    if dividends is None:
+        dividends = _pd.DataFrame(
+            columns=["Dividends"], index=_pd.DatetimeIndex([]))
+    if capital_gains is None:
+        capital_gains = _pd.DataFrame(
+            columns=["Capital Gains"], index=_pd.DatetimeIndex([]))
+    if splits is None:
+        splits = _pd.DataFrame(
+            columns=["Stock Splits"], index=_pd.DatetimeIndex([]))
+
     return dividends, splits, capital_gains
 
 
@@ -427,6 +561,34 @@ def set_df_tz(df, interval, tz):
     return df
 
 
+def fix_Yahoo_returning_prepost_unrequested(quotes, interval, tradingPeriods):
+    # Sometimes Yahoo returns post-market data despite not requesting it.
+    # Normally happens on half-day early closes.
+    #
+    # And sometimes returns pre-market data despite not requesting it.
+    # E.g. some London tickers.
+    tps_df = tradingPeriods.copy()
+    tps_df["_date"] = tps_df.index.date
+    quotes["_date"] = quotes.index.date
+    idx = quotes.index.copy()
+    quotes = quotes.merge(tps_df, how="left")
+    quotes.index = idx
+    # "end" = end of regular trading hours (including any auction)
+    f_drop = quotes.index >= quotes["end"]
+    f_drop = f_drop | (quotes.index < quotes["start"])
+    if f_drop.any():
+        # When printing report, ignore rows that were already NaNs:
+        # f_na = quotes[["Open","Close"]].isna().all(axis=1)
+        # n_nna = quotes.shape[0] - _np.sum(f_na)
+        # n_drop_nna = _np.sum(f_drop & ~f_na)
+        # quotes_dropped = quotes[f_drop]
+        # if debug and n_drop_nna > 0:
+        #     print(f"Dropping {n_drop_nna}/{n_nna} intervals for falling outside regular trading hours")
+        quotes = quotes[~f_drop]
+    quotes = quotes.drop(["_date", "start", "end"], axis=1)
+    return quotes
+
+
 def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
     # Yahoo bug fix. If market is open today then Yahoo normally returns 
     # todays data as a separate row from rest-of week/month interval in above row. 
@@ -455,22 +617,30 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
             elif interval == "3mo":
                 last_rows_same_interval = dt1.year == dt2.year and dt1.quarter == dt2.quarter
             else:
-                last_rows_same_interval = (dt1-dt2) < _pd.Timedelta(interval)
+                last_rows_same_interval = (dt1 - dt2) < _pd.Timedelta(interval)
 
             if last_rows_same_interval:
                 # Last two rows are within same interval
                 idx1 = quotes.index[n - 1]
                 idx2 = quotes.index[n - 2]
+                if idx1 == idx2:
+                    # Yahoo returning last interval duplicated, which means
+                    # Yahoo is not returning live data (phew!)
+                    return quotes
                 if _np.isnan(quotes.loc[idx2, "Open"]):
                     quotes.loc[idx2, "Open"] = quotes["Open"][n - 1]
-                # Note: nanmax() & nanmin() ignores NaNs
-                quotes.loc[idx2, "High"] = _np.nanmax([quotes["High"][n - 1], quotes["High"][n - 2]])
-                quotes.loc[idx2, "Low"] = _np.nanmin([quotes["Low"][n - 1], quotes["Low"][n - 2]])
+                # Note: nanmax() & nanmin() ignores NaNs, but still need to check not all are NaN to avoid warnings
+                if not _np.isnan(quotes["High"][n - 1]):
+                    quotes.loc[idx2, "High"] = _np.nanmax([quotes["High"][n - 1], quotes["High"][n - 2]])
+                    if "Adj High" in quotes.columns:
+                        quotes.loc[idx2, "Adj High"] = _np.nanmax([quotes["Adj High"][n - 1], quotes["Adj High"][n - 2]])
+
+                if not _np.isnan(quotes["Low"][n - 1]):
+                    quotes.loc[idx2, "Low"] = _np.nanmin([quotes["Low"][n - 1], quotes["Low"][n - 2]])
+                    if "Adj Low" in quotes.columns:
+                        quotes.loc[idx2, "Adj Low"] = _np.nanmin([quotes["Adj Low"][n - 1], quotes["Adj Low"][n - 2]])
+
                 quotes.loc[idx2, "Close"] = quotes["Close"][n - 1]
-                if "Adj High" in quotes.columns:
-                    quotes.loc[idx2, "Adj High"] = _np.nanmax([quotes["Adj High"][n - 1], quotes["Adj High"][n - 2]])
-                if "Adj Low" in quotes.columns:
-                    quotes.loc[idx2, "Adj Low"] = _np.nanmin([quotes["Adj Low"][n - 1], quotes["Adj Low"][n - 2]])
                 if "Adj Close" in quotes.columns:
                     quotes.loc[idx2, "Adj Close"] = quotes["Adj Close"][n - 1]
                 quotes.loc[idx2, "Volume"] += quotes["Volume"][n - 1]
@@ -480,11 +650,6 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
 
 
 def safe_merge_dfs(df_main, df_sub, interval):
-    # Carefully merge 'df_sub' onto 'df_main'
-    # If naive merge fails, try again with reindexing df_sub:
-    # 1) if interval is weekly or monthly, then try with index set to start of week/month
-    # 2) if still failing then manually search through df_main.index to reindex df_sub
-
     if df_sub.shape[0] == 0:
         raise Exception("No data to merge")
 
@@ -494,6 +659,72 @@ def safe_merge_dfs(df_main, df_sub, interval):
         raise Exception("Expected 1 data col")
     data_col = data_cols[0]
 
+    df_main = df_main.sort_index()
+    intraday = interval.endswith('m') or interval.endswith('s')
+
+    td = _interval_to_timedelta(interval)
+    if intraday:
+        # On some exchanges the event can occur before market open.
+        # Problem when combining with intraday data.
+        # Solution = use dates, not datetimes, to map/merge.
+        df_main['_date'] = df_main.index.date
+        df_sub['_date'] = df_sub.index.date
+        indices = _np.searchsorted(_np.append(df_main['_date'], [df_main['_date'].iloc[-1]+td]), df_sub['_date'], side='left')
+        df_main = df_main.drop('_date', axis=1)
+        df_sub = df_sub.drop('_date', axis=1)
+    else:
+        indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1] + td), df_sub.index, side='right')
+        indices -= 1  # Convert from [[i-1], [i]) to [[i], [i+1])
+        # Numpy.searchsorted does not handle out-of-range well, so handle manually:
+        for i in range(len(df_sub.index)):
+            dt = df_sub.index[i]
+            if dt < df_main.index[0] or dt >= df_main.index[-1] + td:
+                # Out-of-range
+                indices[i] = -1
+
+    f_outOfRange = indices == -1
+    if f_outOfRange.any() and not intraday:
+        empty_row_data = {c:[_np.nan] for c in const.price_colnames}|{'Volume':[0]}
+        if interval == '1d':
+            # For 1d, add all out-of-range event dates
+            for i in _np.where(f_outOfRange)[0]:
+                dt = df_sub.index[i]
+                get_yf_logger().debug(f"Adding out-of-range {data_col} @ {dt.date()} in new prices row of NaNs")
+                empty_row = _pd.DataFrame(data=empty_row_data, index=[dt])
+                df_main = _pd.concat([df_main, empty_row], sort=True)
+        else:
+            # Else, only add out-of-range event dates if occurring in interval 
+            # immediately after last pricfe row
+            last_dt = df_main.index[-1]
+            next_interval_start_dt = last_dt + td
+            next_interval_end_dt = next_interval_start_dt + td
+            for i in _np.where(f_outOfRange)[0]:
+                dt = df_sub.index[i]
+                if next_interval_start_dt <= dt < next_interval_end_dt:
+                    new_dt = next_interval_start_dt
+                    get_yf_logger().debug(f"Adding out-of-range {data_col} @ {dt.date()} in new prices row of NaNs")
+                    empty_row = _pd.DataFrame(data=empty_row_data, index=[dt])
+                    df_main = _pd.concat([df_main, empty_row], sort=True)
+        df_main = df_main.sort_index()
+
+        # Re-calculate indices
+        indices = _np.searchsorted(_np.append(df_main.index, df_main.index[-1] + td), df_sub.index, side='right')
+        indices -= 1  # Convert from [[i-1], [i]) to [[i], [i+1])
+        # Numpy.searchsorted does not handle out-of-range well, so handle manually:
+        for i in range(len(df_sub.index)):
+            dt = df_sub.index[i]
+            if dt < df_main.index[0] or dt >= df_main.index[-1] + td:
+                # Out-of-range
+                indices[i] = -1
+
+    f_outOfRange = indices == -1
+    if f_outOfRange.any():
+        if intraday or interval in ['1d', '1wk']:
+            raise Exception(f"The following '{data_col}' events are out-of-range, did not expect with interval {interval}: {df_sub.index[f_outOfRange]}")
+        get_yf_logger().debug(f'Discarding these {data_col} events:' + '\n' + str(df_sub[f_outOfRange]))
+        df_sub = df_sub[~f_outOfRange].copy()
+        indices = indices[~f_outOfRange]
+
     def _reindex_events(df, new_index, data_col_name):
         if len(new_index) == len(set(new_index)):
             # No duplicates, easy
@@ -502,7 +733,7 @@ def safe_merge_dfs(df_main, df_sub, interval):
 
         df["_NewIndex"] = new_index
         # Duplicates present within periods but can aggregate
-        if data_col_name == "Dividends":
+        if data_col_name in ["Dividends", "Capital Gains"]:
             # Add
             df = df.groupby("_NewIndex").sum()
             df.index.name = None
@@ -511,110 +742,19 @@ def safe_merge_dfs(df_main, df_sub, interval):
             df = df.groupby("_NewIndex").prod()
             df.index.name = None
         else:
-            raise Exception("New index contains duplicates but unsure how to aggregate for '{}'".format(data_col_name))
+            raise Exception(f"New index contains duplicates but unsure how to aggregate for '{data_col_name}'")
         if "_NewIndex" in df.columns:
             df = df.drop("_NewIndex", axis=1)
         return df
 
-    df = df_main.join(df_sub)
-
-    f_na = df[data_col].isna()
-    data_lost = sum(~f_na) < df_sub.shape[0]
-    if not data_lost:
-        return df
-    # Lost data during join()
-    # Backdate all df_sub.index dates to start of week/month
-    if interval == "1wk":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='W').to_timestamp()
-    elif interval == "1mo":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='M').to_timestamp()
-    elif interval == "3mo":
-        new_index = _pd.PeriodIndex(df_sub.index, freq='Q').to_timestamp()
-    else:
-        new_index = None
-
-    if new_index is not None:
-        new_index = new_index.tz_localize(df.index.tz, ambiguous=True, nonexistent='shift_forward')
-        df_sub = _reindex_events(df_sub, new_index, data_col)
-        df = df_main.join(df_sub)
-
-    f_na = df[data_col].isna()
-    data_lost = sum(~f_na) < df_sub.shape[0]
-    if not data_lost:
-        return df
-    # Lost data during join(). Manually check each df_sub.index date against df_main.index to
-    # find matching interval
-    df_sub = df_sub_backup.copy()
-    new_index = [-1] * df_sub.shape[0]
-    for i in range(df_sub.shape[0]):
-        dt_sub_i = df_sub.index[i]
-        if dt_sub_i in df_main.index:
-            new_index[i] = dt_sub_i
-            continue
-        # Found a bad index date, need to search for near-match in df_main (same week/month)
-        fixed = False
-        for j in range(df_main.shape[0] - 1):
-            dt_main_j0 = df_main.index[j]
-            dt_main_j1 = df_main.index[j + 1]
-            if (dt_main_j0 <= dt_sub_i) and (dt_sub_i < dt_main_j1):
-                fixed = True
-                if interval.endswith('h') or interval.endswith('m'):
-                    # Must also be same day
-                    fixed = (dt_main_j0.date() == dt_sub_i.date()) and (dt_sub_i.date() == dt_main_j1.date())
-                if fixed:
-                    dt_sub_i = dt_main_j0
-                    break
-        if not fixed:
-            last_main_dt = df_main.index[df_main.shape[0] - 1]
-            diff = dt_sub_i - last_main_dt
-            if interval == "1mo" and last_main_dt.month == dt_sub_i.month:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "3mo" and last_main_dt.year == dt_sub_i.year and last_main_dt.quarter == dt_sub_i.quarter:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "1wk":
-                if last_main_dt.week == dt_sub_i.week:
-                    dt_sub_i = last_main_dt
-                    fixed = True
-                elif (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < _datetime.timedelta(weeks=1)):
-                    # With some specific start dates (e.g. around early Jan), Yahoo
-                    # messes up start-of-week, is Saturday not Monday. So check
-                    # if same week another way
-                    dt_sub_i = last_main_dt
-                    fixed = True
-            elif interval == "1d" and last_main_dt.day == dt_sub_i.day:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval == "1h" and last_main_dt.hour == dt_sub_i.hour:
-                dt_sub_i = last_main_dt
-                fixed = True
-            elif interval.endswith('m') or interval.endswith('h'):
-                td = _pd.to_timedelta(interval)
-                if (dt_sub_i >= last_main_dt) and (dt_sub_i - last_main_dt < td):
-                    dt_sub_i = last_main_dt
-                    fixed = True
-        new_index[i] = dt_sub_i
+    new_index = df_main.index[indices]
     df_sub = _reindex_events(df_sub, new_index, data_col)
-    df = df_main.join(df_sub)
 
+    df = df_main.join(df_sub)
     f_na = df[data_col].isna()
     data_lost = sum(~f_na) < df_sub.shape[0]
     if data_lost:
-        ## Not always possible to match events with trading, e.g. when released pre-market.
-        ## So have to append to bottom with nan prices.
-        ## But should only be impossible with intra-day price data.
-        if interval.endswith('m') or interval.endswith('h') or interval == "1d":
-            # Update: is possible with daily data when dividend very recent
-            f_missing = ~df_sub.index.isin(df.index)
-            df_sub_missing = df_sub[f_missing]
-            keys = {"Adj Open", "Open", "Adj High", "High", "Adj Low", "Low", "Adj Close",
-                    "Close"}.intersection(df.columns)
-            df_sub_missing[list(keys)] = _np.nan
-            col_ordering = df.columns
-            df = _pd.concat([df, df_sub_missing], sort=True)[col_ordering]
-        else:
-            raise Exception("Lost data during merge despite all attempts to align data (see above)")
+        raise Exception('Data was lost in merge, investigate')
 
     return df
 
@@ -640,6 +780,66 @@ def is_valid_timezone(tz: str) -> bool:
     return True
 
 
+def format_history_metadata(md, tradingPeriodsOnly=True):
+    if not isinstance(md, dict):
+        return md
+    if len(md) == 0:
+        return md
+
+    tz = md["exchangeTimezoneName"]
+
+    if not tradingPeriodsOnly:
+        for k in ["firstTradeDate", "regularMarketTime"]:
+            if k in md and md[k] is not None:
+                if isinstance(md[k], int):
+                    md[k] = _pd.to_datetime(md[k], unit='s', utc=True).tz_convert(tz)
+
+        if "currentTradingPeriod" in md:
+            for m in ["regular", "pre", "post"]:
+                if m in md["currentTradingPeriod"] and isinstance(md["currentTradingPeriod"][m]["start"], int):
+                    for t in ["start", "end"]:
+                        md["currentTradingPeriod"][m][t] = \
+                            _pd.to_datetime(md["currentTradingPeriod"][m][t], unit='s', utc=True).tz_convert(tz)
+                    del md["currentTradingPeriod"][m]["gmtoffset"]
+                    del md["currentTradingPeriod"][m]["timezone"]
+
+    if "tradingPeriods" in md:
+        tps = md["tradingPeriods"]
+        if tps == {"pre": [], "post": []}:
+            # Ignore
+            pass
+        elif isinstance(tps, (list, dict)):
+            if isinstance(tps, list):
+                # Only regular times
+                df = _pd.DataFrame.from_records(_np.hstack(tps))
+                df = df.drop(["timezone", "gmtoffset"], axis=1)
+                df["start"] = _pd.to_datetime(df["start"], unit='s', utc=True).dt.tz_convert(tz)
+                df["end"] = _pd.to_datetime(df["end"], unit='s', utc=True).dt.tz_convert(tz)
+            elif isinstance(tps, dict):
+                # Includes pre- and post-market
+                pre_df = _pd.DataFrame.from_records(_np.hstack(tps["pre"]))
+                post_df = _pd.DataFrame.from_records(_np.hstack(tps["post"]))
+                regular_df = _pd.DataFrame.from_records(_np.hstack(tps["regular"]))
+
+                pre_df = pre_df.rename(columns={"start": "pre_start", "end": "pre_end"}).drop(["timezone", "gmtoffset"], axis=1)
+                post_df = post_df.rename(columns={"start": "post_start", "end": "post_end"}).drop(["timezone", "gmtoffset"], axis=1)
+                regular_df = regular_df.drop(["timezone", "gmtoffset"], axis=1)
+
+                cols = ["pre_start", "pre_end", "start", "end", "post_start", "post_end"]
+                df = regular_df.join(pre_df).join(post_df)
+                for c in cols:
+                    df[c] = _pd.to_datetime(df[c], unit='s', utc=True).dt.tz_convert(tz)
+                df = df[cols]
+
+            df.index = _pd.to_datetime(df["start"].dt.date)
+            df.index = df.index.tz_localize(tz)
+            df.index.name = "Date"
+
+            md["tradingPeriods"] = df
+
+    return md
+
+
 class ProgressBar:
     def __init__(self, iterations, text='completed'):
         self.text = text
@@ -672,19 +872,16 @@ class ProgressBar:
     def update_iteration(self, val=None):
         val = val if val is not None else self.elapsed / float(self.iterations)
         self.__update_amount(val * 100.0)
-        self.prog_bar += '  %s of %s %s' % (
-            self.elapsed, self.iterations, self.text)
+        self.prog_bar += f"  {self.elapsed} of {self.iterations} {self.text}"
 
     def __update_amount(self, new_amount):
         percent_done = int(round((new_amount / 100.0) * 100.0))
         all_full = self.width - 2
         num_hashes = int(round((percent_done / 100.0) * all_full))
-        self.prog_bar = '[' + self.fill_char * \
-                        num_hashes + ' ' * (all_full - num_hashes) + ']'
+        self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'
         pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
-        pct_string = '%d%%' % percent_done
-        self.prog_bar = self.prog_bar[0:pct_place] + \
-                        (pct_string + self.prog_bar[pct_place + len(pct_string):])
+        pct_string = f'{percent_done}%%'
+        self.prog_bar = self.prog_bar[0:pct_place] + (pct_string + self.prog_bar[pct_place + len(pct_string):])
 
     def __str__(self):
         return str(self.prog_bar)
@@ -695,14 +892,21 @@ class ProgressBar:
 # ---------------------------------
 
 class _KVStore:
-    """Simpel Sqlite backed key/value store, key and value are strings. Should be thread safe."""
+    """Simple Sqlite backed key/value store, key and value are strings. Should be thread safe."""
 
     def __init__(self, filename):
         self._cache_mutex = Lock()
         with self._cache_mutex:
             self.conn = _sqlite3.connect(filename, timeout=10, check_same_thread=False)
             self.conn.execute('pragma journal_mode=wal')
-            self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT) without rowid')
+            try:
+                self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT) without rowid')
+            except Exception as e:
+                if 'near "without": syntax error' in str(e):
+                    # "without rowid" requires sqlite 3.8.2. Older versions will raise exception
+                    self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT)')
+                else:
+                    raise
             self.conn.commit()
         _atexit.register(self.close)
 
@@ -714,14 +918,21 @@ class _KVStore:
 
     def get(self, key: str) -> Union[str, None]:
         """Get value for key if it exists else returns None"""
-        item = self.conn.execute('select value from "kv" where key=?', (key,))
+        try:
+            item = self.conn.execute('select value from "kv" where key=?', (key,))
+        except _sqlite3.IntegrityError as e:
+            self.delete(key)
+            return None
         if item:
             return next(item, (None,))[0]
 
     def set(self, key: str, value: str) -> None:
-        with self._cache_mutex:
-            self.conn.execute('replace into "kv" (key, value) values (?,?)', (key, value))
-            self.conn.commit()
+        if value is None:
+            self.delete(key)
+        else:
+            with self._cache_mutex:
+                self.conn.execute('replace into "kv" (key, value) values (?,?)', (key, value))
+                self.conn.commit()
 
     def bulk_set(self, kvdata: Dict[str, str]):
         records = tuple(i for i in kvdata.items())
@@ -743,20 +954,23 @@ class _TzCache:
     """Simple sqlite file cache of ticker->timezone"""
 
     def __init__(self):
-        self._tz_db = None
         self._setup_cache_folder()
+        # Must init db here, where is thread-safe
+        try:
+            self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
+        except _sqlite3.DatabaseError as err:
+            raise _TzCacheException(f"Error creating TzCache folder: '{self._db_dir}' reason: {err}")
+        self._migrate_cache_tkr_tz()
 
     def _setup_cache_folder(self):
         if not _os.path.isdir(self._db_dir):
             try:
                 _os.makedirs(self._db_dir)
             except OSError as err:
-                raise _TzCacheException("Error creating TzCache folder: '{}' reason: {}"
-                                        .format(self._db_dir, err))
+                raise _TzCacheException(f"Error creating TzCache folder: '{self._db_dir}' reason: {err}")
 
         elif not (_os.access(self._db_dir, _os.R_OK) and _os.access(self._db_dir, _os.W_OK)):
-            raise _TzCacheException("Cannot read and write in TzCache folder: '{}'"
-                                    .format(self._db_dir, ))
+            raise _TzCacheException(f"Cannot read and write in TzCache folder: '{self._db_dir}'")
 
     def lookup(self, tkr):
         return self.tz_db.get(tkr)
@@ -764,10 +978,14 @@ class _TzCache:
     def store(self, tkr, tz):
         if tz is None:
             self.tz_db.delete(tkr)
-        elif self.tz_db.get(tkr) is not None:
-            raise Exception("Tkr {} tz already in cache".format(tkr))
         else:
-            self.tz_db.set(tkr, tz)
+            tz_db = self.tz_db.get(tkr)
+            if tz_db is not None:
+                if tz != tz_db:
+                    get_yf_logger().debug(f'{tkr}: Overwriting cached TZ "{tz_db}" with different TZ "{tz}"')
+                    self.tz_db.set(tkr, tz)
+            else:
+                self.tz_db.set(tkr, tz)
 
     @property
     def _db_dir(self):
@@ -776,11 +994,6 @@ class _TzCache:
 
     @property
     def tz_db(self):
-        # lazy init
-        if self._tz_db is None:
-            self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
-            self._migrate_cache_tkr_tz()
-
         return self._tz_db
 
     def _migrate_cache_tkr_tz(self):
@@ -790,11 +1003,23 @@ class _TzCache:
         if not _os.path.isfile(old_cache_file_path):
             return None
         try:
-            df = _pd.read_csv(old_cache_file_path, index_col="Ticker")
+            df = _pd.read_csv(old_cache_file_path, index_col="Ticker", on_bad_lines="skip")
         except _pd.errors.EmptyDataError:
             _os.remove(old_cache_file_path)
+        except TypeError:
+            _os.remove(old_cache_file_path)
         else:
-            self.tz_db.bulk_set(df.to_dict()['Tz'])
+            # Discard corrupt data:
+            df = df[~df["Tz"].isna().to_numpy()]
+            df = df[~(df["Tz"] == '').to_numpy()]
+            df = df[~df.index.isna()]
+            if not df.empty:
+                try:
+                    self.tz_db.bulk_set(df.to_dict()['Tz'])
+                except Exception as e:
+                    # Ignore
+                    pass
+
             _os.remove(old_cache_file_path)
 
 
@@ -825,9 +1050,9 @@ def get_tz_cache():
             try:
                 _tz_cache = _TzCache()
             except _TzCacheException as err:
-                print("Failed to create TzCache, reason: {}".format(err))
-                print("TzCache will not be used.")
-                print("Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'")
+                get_yf_logger().info(f"Failed to create TzCache, reason: {err}. "
+                                     "TzCache will not be used. "
+                                     "Tip: You can direct cache to use a different location with 'set_tz_cache_location(mylocation)'")
                 _tz_cache = _TzCacheDummy()
 
         return _tz_cache

yfinance/version.py

@@ -1 +1 @@
-version = "0.2.0rc2"
+version = "0.2.28"