Fix Market.summary

Improve Market.status formatting
Remove unused raise_errors
2024-12-21 12:46:43 +00:00 · 2024-12-21 12:36:18 +00:00 · 2024-12-20 18:51:40 +00:00 · 2024-12-20 18:47:10 +00:00 · 2024-12-14 22:05:59 +00:00 · 2024-12-14 18:21:15 +01:00
88 changed files with 12327 additions and 6466 deletions
--- a/.DS_Store
+++ b/.DS_Store
--- a/.github/ISSUE_TEMPLATE/bug_report.yaml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yaml
@@ -63,7 +63,7 @@ body:
    id: bad-data-proof
    attributes:
      label: "Bad data proof"
-      description: "If you think `yfinance` returning bad data, provide your proof here."
+      description: "If `yfinance` returning bad data, show proof here. Best is screenshot of finance.yahoo.com"
    validations:
      required: false

--- a/.github/workflows/deploy_doc.yml
+++ b/.github/workflows/deploy_doc.yml
@@ -0,0 +1,43 @@
+name: Build and Deploy Sphinx Docs
+
+on:
+  push:
+    branches:
+      - dev-documented
+  workflow_dispatch:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Check out the repository
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.x'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+        pip install Sphinx==8.0.2 pydata-sphinx-theme==0.15.4 Jinja2==3.1.4 sphinx-copybutton==0.5.2
+
+    - name: Build Sphinx documentation
+      run: |
+        sphinx-build -b html doc/source doc/_build/html -v
+
+    - name: List generated HTML files
+      run: |
+        ls -l -R doc/_build/html
+
+    - name: Deploy to GitHub Pages
+      uses: peaceiris/actions-gh-pages@v4
+      with:
+        github_token: ${{ secrets.GITHUB_TOKEN }}
+        publish_branch: documentation
+        publish_dir: doc/_build/html
+        destination_dir: docs
+        enable_jekyll: false
--- a/.github/workflows/pyright.yml
+++ b/.github/workflows/pyright.yml
@@ -0,0 +1,27 @@
+name: Pyright
+
+on:
+  pull_request:
+    branches:
+      - master
+      - main
+      - dev
+
+jobs:
+  pyright:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pyright
+
+    - name: Run Pyright
+      run: pyright . --level error
--- a/.gitignore
+++ b/.gitignore
@@ -18,3 +18,10 @@ test.ipynb
 env/
 venv/
 ENV/
+
+# Documentation
+/doc/build/
+/doc/_build/
+/doc/source/reference/api
+!yfinance.css
+!/doc/source/development/assets/branches.png
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,39 @@
 Change Log
 ===========

+0.2.49
+------
+Fix prices-clean rarely discarding good data #2122
+
+0.2.47 and 0.2.48
+-----------------
+Add yf.download(multi_level_index)
+
+0.2.46
+------
+Fix regression in 0.2.45 #2094
+
+0.2.45
+------
+Features:
+- Screener #2066 @ericpien
+Fixes
+- Tickers keyerror #2068 @antoniouaa
+- IndexError in some history() debug messages #2087
+- improve dividend repair #2090
+Maintenance
+- fix unit tests contextual imports #2067
+- fix typos #2072 @algonell
+- add Pyright type checking #2059 @marco-carvalho
+
+0.2.44
+------
+Features:
+- fetch funds #2041
+- fetch sector & industry #2058
+Fixes:
+- improve dividend repair #2062
+
 0.2.43
 ------
 Fix price-repair bug introduced in 0.2.42 #2036
@@ -351,7 +384,7 @@ Jumping to 0.2 for this big update. 0.1.* will continue to receive bug-fixes
 - Fix timezone handling
 - Fix handling of missing data
 - Clean&format earnings_dates table
- Add ``.get_earnings_dates()`` to retreive earnings calendar
+- Add ``.get_earnings_dates()`` to retrieve earnings calendar
 - Added ``.get_earnings_history()`` to fetch earnings data

 0.1.70
@@ -663,7 +696,7 @@ Jumping to 0.2 for this big update. 0.1.* will continue to receive bug-fixes
 - Removed 5 second wait for every failed fetch
 - Reduced TTL for Yahoo!'s cookie
 - Keeps track of failed downloads and tries to re-download all failed downloads one more time before giving up
- Added progress bar (can be turned off useing ``progress=False``)
+- Added progress bar (can be turned off using ``progress=False``)

 0.0.7
 -------
--- a/README.md
+++ b/README.md
@@ -28,248 +28,36 @@ Yahoo! finance API is intended for personal use only.**
 <a target="new" href="https://pypi.python.org/pypi/yfinance"><img border=0 src="https://img.shields.io/pypi/v/yfinance.svg?maxAge=60%" alt="PyPi version"></a>
 <a target="new" href="https://pypi.python.org/pypi/yfinance"><img border=0 src="https://img.shields.io/pypi/status/yfinance.svg?maxAge=60" alt="PyPi status"></a>
 <a target="new" href="https://pypi.python.org/pypi/yfinance"><img border=0 src="https://img.shields.io/pypi/dm/yfinance.svg?maxAge=2592000&label=installs&color=%2327B1FF" alt="PyPi downloads"></a>
-<a target="new" href="https://travis-ci.com/github/ranaroussi/yfinance"><img border=0 src="https://img.shields.io/travis/ranaroussi/yfinance/main.svg?maxAge=1" alt="Travis-CI build status"></a>
 <a target="new" href="https://www.codefactor.io/repository/github/ranaroussi/yfinance"><img border=0 src="https://www.codefactor.io/repository/github/ranaroussi/yfinance/badge" alt="CodeFactor"></a>
 <a target="new" href="https://github.com/ranaroussi/yfinance"><img border=0 src="https://img.shields.io/github/stars/ranaroussi/yfinance.svg?style=social&label=Star&maxAge=60" alt="Star this repo"></a>
 <a target="new" href="https://twitter.com/aroussi"><img border=0 src="https://img.shields.io/twitter/follow/aroussi.svg?style=social&label=Follow&maxAge=60" alt="Follow me on twitter"></a>

+**yfinance** offers a Pythonic way to fetch financial & market data from [Yahoo!Ⓡ finance](https://finance.yahoo.com).

-**yfinance** offers a threaded and Pythonic way to download market data from [Yahoo!Ⓡ finance](https://finance.yahoo.com).
+## Main components

-→ Check out this [Blog post](https://aroussi.com/#post/python-yahoo-finance) for a detailed tutorial with code examples.
+- `Ticker`: single ticker data
+- `Tickers`: multiple tickers' data
+- `download`: download market data for multiple tickers
+- `Search`: quotes and news from search
+- `Sector` and `Industry`: sector and industry information
+- `EquityQuery` and `Screener`: build query to screen market

-[Changelog »](https://github.com/ranaroussi/yfinance/blob/main/CHANGELOG.rst)
-
---
-
- [Installation](#installation)
- [Quick start](#quick-start)
- [Advanced](#logging)
- [Wiki](https://github.com/ranaroussi/yfinance/wiki)
- [Contribute](#developers-want-to-contribute)
-
---
+## **NEW DOCUMENTATION WEBSITE**: [ranaroussi.github.io/yfinance](https://ranaroussi.github.io/yfinance/index.html)

 ## Installation

-Install `yfinance` using `pip`:
+Install `yfinance` from PYPI using `pip`:

 ``` {.sourceCode .bash}
-$ pip install yfinance --upgrade --no-cache-dir
+$ pip install yfinance
 ```

-[With Conda](https://anaconda.org/ranaroussi/yfinance).
-
-To install with optional dependencies, replace `optional` with: `nospam` for [caching-requests](#smarter-scraping), `repair` for [price repair](https://github.com/ranaroussi/yfinance/wiki/Price-repair), or `nospam,repair` for both:
-
-``` {.sourceCode .bash}
-$ pip install "yfinance[optional]"
-```
-
-[Required dependencies](./requirements.txt) , [all dependencies](./setup.py#L62).
-
---
-
-## Quick Start
-
-### The Ticker module
-
-The `Ticker` module, which allows you to access ticker data in a more Pythonic way:
-
-```python
-import yfinance as yf
-
-msft = yf.Ticker("MSFT")
-
-# get all stock info
-msft.info
-
-# get historical market data
-hist = msft.history(period="1mo")
-
-# show meta information about the history (requires history() to be called first)
-msft.history_metadata
-
-# show actions (dividends, splits, capital gains)
-msft.actions
-msft.dividends
-msft.splits
-msft.capital_gains  # only for mutual funds & etfs
-
-# show share count
-msft.get_shares_full(start="2022-01-01", end=None)
-
-# show financials:
-msft.calendar
-msft.sec_filings
-# - income statement
-msft.income_stmt
-msft.quarterly_income_stmt
-# - balance sheet
-msft.balance_sheet
-msft.quarterly_balance_sheet
-# - cash flow statement
-msft.cashflow
-msft.quarterly_cashflow
-# see `Ticker.get_income_stmt()` for more options
-
-# show holders
-msft.major_holders
-msft.institutional_holders
-msft.mutualfund_holders
-msft.insider_transactions
-msft.insider_purchases
-msft.insider_roster_holders
-
-msfs.sustainability
-
-# show recommendations
-msft.recommendations
-msft.recommendations_summary
-msft.upgrades_downgrades
-
-# show analysts data
-msft.analyst_price_targets
-msft.earnings_estimate
-msft.revenue_estimate
-msft.earnings_history
-msft.eps_trend
-msft.eps_revisions
-msft.growth_estimates
-
-# Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default.
-# Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.
-msft.earnings_dates
-
-# show ISIN code - *experimental*
-# ISIN = International Securities Identification Number
-msft.isin
-
-# show options expirations
-msft.options
-
-# show news
-msft.news
-
-# get option chain for specific expiration
-opt = msft.option_chain('YYYY-MM-DD')
-# data available via: opt.calls, opt.puts
-```
-
-If you want to use a proxy server for downloading data, use:
-
-```python
-import yfinance as yf
-
-msft = yf.Ticker("MSFT")
-
-msft.history(..., proxy="PROXY_SERVER")
-msft.get_actions(proxy="PROXY_SERVER")
-msft.get_dividends(proxy="PROXY_SERVER")
-msft.get_splits(proxy="PROXY_SERVER")
-msft.get_capital_gains(proxy="PROXY_SERVER")
-msft.get_balance_sheet(proxy="PROXY_SERVER")
-msft.get_cashflow(proxy="PROXY_SERVER")
-msft.option_chain(..., proxy="PROXY_SERVER")
-...
-```
-
-### Multiple tickers
-
-To initialize multiple `Ticker` objects, use
-
-```python
-import yfinance as yf
-
-tickers = yf.Tickers('msft aapl goog')
-
-# access each ticker using (example)
-tickers.tickers['MSFT'].info
-tickers.tickers['AAPL'].history(period="1mo")
-tickers.tickers['GOOG'].actions
-```
-
-To download price history into one table:
-
-```python
-import yfinance as yf
-data = yf.download("SPY AAPL", period="1mo")
-```
-
-#### `yf.download()` and `Ticker.history()` have many options for configuring fetching and processing. [Review the Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
-
-### Logging
-
-`yfinance` now uses the `logging` module to handle messages, default behaviour is only print errors. If debugging, use `yf.enable_debug_mode()` to switch logging to debug with custom formatting.
-
-### Smarter scraping
-
-Install the `nospam` packages for smarter scraping using `pip` (see [Installation](#installation)). These packages help cache calls such that Yahoo is not spammed with requests.
-
-To use a custom `requests` session, pass a `session=` argument to
-the Ticker constructor. This allows for caching calls to the API as well as a custom way to modify requests via  the `User-agent` header.
-
-```python
-import requests_cache
-session = requests_cache.CachedSession('yfinance.cache')
-session.headers['User-agent'] = 'my-program/1.0'
-ticker = yf.Ticker('msft', session=session)
-# The scraped response will be stored in the cache
-ticker.actions
-```
-
-Combine `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-limiter/blocker that can corrupt data.
-```python
-from requests import Session
-from requests_cache import CacheMixin, SQLiteCache
-from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
-from pyrate_limiter import Duration, RequestRate, Limiter
-class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
-    pass
-
-session = CachedLimiterSession(
-    limiter=Limiter(RequestRate(2, Duration.SECOND*5)),  # max 2 requests per 5 seconds
-    bucket_class=MemoryQueueBucket,
-    backend=SQLiteCache("yfinance.cache"),
-)
-```
-
-### Managing Multi-Level Columns
-
-The following answer on Stack Overflow is for [How to deal with
-multi-level column names downloaded with
-yfinance?](https://stackoverflow.com/questions/63107801)
-
-   `yfinance` returns a `pandas.DataFrame` with multi-level column
-    names, with a level for the ticker and a level for the stock price
-    data
-    -   The answer discusses:
-        -   How to correctly read the the multi-level columns after
-            saving the dataframe to a csv with `pandas.DataFrame.to_csv`
-        -   How to download single or multiple tickers into a single
-            dataframe with single level column names and a ticker column
-
-### Persistent cache store
-
-To reduce Yahoo, yfinance store some data locally: timezones to localize dates, and cookie. Cache location is:
-
- Windows = C:/Users/\<USER\>/AppData/Local/py-yfinance
- Linux = /home/\<USER\>/.cache/py-yfinance
- MacOS = /Users/\<USER\>/Library/Caches/py-yfinance
-
-You can direct cache to use a different location with `set_tz_cache_location()`:
-
-```python
-import yfinance as yf
-yf.set_tz_cache_location("custom/cache/location")
-...
-```
-
---
+The list of changes can be found in the [Changelog](https://github.com/ranaroussi/yfinance/blob/main/CHANGELOG.rst)

 ## Developers: want to contribute?

-`yfinance` relies on community to investigate bugs and contribute code. Developer guide: https://github.com/ranaroussi/yfinance/discussions/1084
+`yfinance` relies on community to investigate bugs, review code, and contribute code. Developer guide: https://github.com/ranaroussi/yfinance/discussions/1084

 ---

@@ -278,7 +66,6 @@ yf.set_tz_cache_location("custom/cache/location")
 **yfinance** is distributed under the **Apache Software License**. See
 the [LICENSE.txt](./LICENSE.txt) file in the release for details.

-
 AGAIN - yfinance is **not** affiliated, endorsed, or vetted by Yahoo, Inc. It's
 an open-source tool that uses Yahoo's publicly available APIs, and is
 intended for research and educational purposes. You should refer to Yahoo!'s terms of use
@@ -291,6 +78,7 @@ details on your rights to use the actual data downloaded.

 ### P.S.

-Please drop me an note with any feedback you have.
+Please drop me a note with any feedback you have.

 **Ran Aroussi**
+
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
--- a/doc/make.bat
+++ b/doc/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
--- a/doc/source/_static/yfinance.css
+++ b/doc/source/_static/yfinance.css
@@ -0,0 +1,4 @@
+/* Hide the "Section Navigation" title */
+p.bd-links__title {
+    display: none;
+}
--- a/doc/source/_templates/autosummary/class.rst
+++ b/doc/source/_templates/autosummary/class.rst
@@ -0,0 +1,29 @@
+:orphan:
+
+{{ objname | escape | underline }}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: Attributes
+   
+   {% for item in attributes %}
+   .. autoattribute:: {{ item }}
+      :noindex:
+   {%- endfor %}
+   {% endif %}
+   {% endblock attributes %}
+
+   {% block methods %}
+   {% if methods %}
+   .. rubric:: Methods
+
+   {% for item in methods %}
+   .. automethod:: {{ item }}
+      :noindex:
+   {%- endfor %}
+   {% endif %}
+   {% endblock methods %}
--- a/doc/source/advanced/caching.rst
+++ b/doc/source/advanced/caching.rst
@@ -0,0 +1,59 @@
+Caching
+=======
+
+Smarter Scraping
+----------------
+
+Install the `nospam` package to cache API calls and reduce spam to Yahoo:
+
+.. code-block:: bash
+
+   pip install yfinance[nospam]
+
+To use a custom `requests` session, pass a `session=` argument to
+the Ticker constructor. This allows for caching calls to the API as well as a custom way to modify requests via  the `User-agent` header.
+
+.. code-block:: python
+
+   import requests_cache
+   session = requests_cache.CachedSession('yfinance.cache')
+   session.headers['User-agent'] = 'my-program/1.0'
+   ticker = yf.Ticker('MSFT', session=session)
+   
+   # The scraped response will be stored in the cache
+   ticker.actions
+
+
+Combine `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-limiter/blocker that can corrupt data.
+
+.. code-block:: python
+
+   from requests import Session
+   from requests_cache import CacheMixin, SQLiteCache
+   from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
+   from pyrate_limiter import Duration, RequestRate, Limiter
+   class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
+      pass
+
+   session = CachedLimiterSession(
+      limiter=Limiter(RequestRate(2, Duration.SECOND*5)),  # max 2 requests per 5 seconds
+      bucket_class=MemoryQueueBucket,
+      backend=SQLiteCache("yfinance.cache"),
+   )
+
+
+Persistent Cache
+----------------
+
+To reduce Yahoo, yfinance store some data locally: timezones to localize dates, and cookie. Cache location is:
+
+- Windows = C:/Users/\<USER\>/AppData/Local/py-yfinance
+- Linux = /home/\<USER\>/.cache/py-yfinance
+- MacOS = /Users/\<USER\>/Library/Caches/py-yfinance
+
+You can direct cache to use a different location with :attr:`set_tz_cache_location <yfinance.set_tz_cache_location>`:
+
+.. code-block:: python
+
+    import yfinance as yf
+    yf.set_tz_cache_location("custom/cache/location")
--- a/doc/source/advanced/index.rst
+++ b/doc/source/advanced/index.rst
@@ -0,0 +1,11 @@
+========
+Advanced
+========
+
+.. toctree::
+   :maxdepth: 2
+
+   logging
+   proxy
+   caching
+   multi_level_columns
--- a/doc/source/advanced/logging.rst
+++ b/doc/source/advanced/logging.rst
@@ -0,0 +1,11 @@
+Logging
+=======
+
+`yfinance` uses the `logging` module to handle messages. By default, only errors are logged.
+
+If debugging, you can switch to debug mode with custom formatting using:
+
+.. code-block:: python
+
+   import yfinance as yf
+   yf.enable_debug_mode()
--- a/doc/source/advanced/multi_level_columns.rst
+++ b/doc/source/advanced/multi_level_columns.rst
@@ -0,0 +1,13 @@
+************************
+Multi-Level Column Index
+************************
+
+The following answer on Stack Overflow is for `How to deal with
+multi-level column names downloaded with yfinance? <https://stackoverflow.com/questions/63107801>`_
+
+- `yfinance` returns a `pandas.DataFrame` with multi-level column names, with a level for the ticker and a level for the stock price data
+
+The answer discusses:
+
+- How to correctly read the the multi-level columns after saving the dataframe to a csv with `pandas.DataFrame.to_csv`
+- How to download single or multiple tickers into a singledataframe with single level column names and a ticker column
--- a/doc/source/advanced/proxy.rst
+++ b/doc/source/advanced/proxy.rst
@@ -0,0 +1,11 @@
+************
+Proxy Server
+************
+
+You can download data via a proxy:
+
+.. code-block:: python
+
+   msft = yf.Ticker("MSFT")
+   msft.history(..., proxy="PROXY_SERVER")
+
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -0,0 +1,45 @@
+import os
+import sys
+sys.path.insert(0, os.path.abspath('../..')) 
+
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = 'yfinance - market data downloader'
+copyright = '2017-2019 Ran Aroussi'
+author = 'Ran Aroussi'
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = ['sphinx.ext.autodoc',
+              'sphinx.ext.napoleon',
+              "sphinx.ext.githubpages",
+              "sphinx.ext.autosectionlabel",
+              "sphinx.ext.autosummary",
+              "sphinx_copybutton"]
+
+templates_path = ['_templates']
+exclude_patterns = []
+autoclass_content = 'both'
+autosummary_generate = True
+autodoc_default_options = {
+    'exclude-members': '__init__'
+}
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_title = 'yfinance'
+html_theme = 'pydata_sphinx_theme'
+html_theme_options = {
+    "github_url": "https://github.com/ranaroussi/yfinance",
+    "navbar_align": "left"
+}
+html_static_path = ['_static']
+html_css_files = ['yfinance.css']
--- a/doc/source/development/assets/branches.png
+++ b/doc/source/development/assets/branches.png
--- a/doc/source/development/contributing.rst
+++ b/doc/source/development/contributing.rst
@@ -0,0 +1,109 @@
+********************************
+Contributiong to yfinance
+********************************
+
+`yfinance` relies on the community to investigate bugs and contribute code. Here’s how you can help:
+
+Contributing
+------------
+
+1. Fork the repository on GitHub.
+2. Clone your forked repository:
+
+   .. code-block:: bash
+
+      git clone https://github.com/your-username/yfinance.git
+
+3. Create a new branch for your feature or bug fix:
+
+   .. code-block:: bash
+
+      git checkout -b feature-branch-name
+
+4. Make your changes, commit them, and push your branch to GitHub. To keep the commit history and `network graph <https://github.com/ranaroussi/yfinance/network>`_ compact:
+
+   Use short summaries for commits
+
+   .. code-block:: shell
+
+      git commit -m "short summary" -m "full commit message"
+
+   **Squash** tiny or negligible commits with meaningful ones.
+
+   .. code-block:: shell
+
+      git rebase -i HEAD~2
+      git push --force-with-lease origin <branch-name>
+
+5. Open a pull request on the `yfinance` GitHub page.
+
+For more information, see the `Developer Guide <https://github.com/ranaroussi/yfinance/discussions/1084>`_.
+
+Branches
+---------
+
+To support rapid development without breaking stable versions, this project uses a two-layer branch model:
+
+.. image:: assets/branches.png
+   :alt: Branching Model
+
+`Inspiration <https://miro.medium.com/max/700/1*2YagIpX6LuauC3ASpwHekg.png>`_
+
+- **dev**: New features and some bug fixes are merged here. This branch allows collective testing, conflict resolution, and further stabilization before merging into the stable branch.
+- **main**: Stable branch where PIP releases are created.
+
+By default, branches target **main**, but most contributions should target **dev**. 
+
+**Exceptions**:
+Direct merges to **main** are allowed if:
+
+- `yfinance` is massively broken
+- Part of `yfinance` is broken, and the fix is simple and isolated
+
+Unit Tests
+----------
+
+Tests are written using Python’s `unittest` module. Here are some ways to run tests:
+
+- **Run all price tests**:
+
+  .. code-block:: shell
+
+     python -m unittest tests.test_prices
+
+- **Run a subset of price tests**:
+
+  .. code-block:: shell
+
+     python -m unittest tests.test_prices.TestPriceRepair
+
+- **Run a specific test**:
+
+  .. code-block:: shell
+
+     python -m unittest tests.test_prices.TestPriceRepair.test_ticker_missing
+
+- **Run all tests**:
+
+  .. code-block:: shell
+
+     python -m unittest discover -s tests
+
+Rebasing
+--------------
+
+If asked to move your branch from **main** to **dev**:
+
+1. Ensure all relevant branches are pulled.
+2. Run:
+
+   .. code-block:: shell
+
+      git checkout <your-branch>
+      git rebase --onto dev main <branch-name>
+      git push --force-with-lease origin <branch-name>
+
+Running the GitHub Version of yfinance
+--------------------------------------
+
+To download and run a GitHub version of `yfinance`, refer to `GitHub discussion <https://github.com/ranaroussi/yfinance/discussions/1080>`_
--- a/doc/source/development/documentation.rst
+++ b/doc/source/development/documentation.rst
@@ -0,0 +1,46 @@
+*************************************
+Contribution to the documentation
+*************************************
+
+.. contents:: Documentation:
+   :local:
+
+About documentation
+------------------------
+* yfinance documentation is written in reStructuredText (rst) and built using Sphinx.
+* The documentation file is in `doc/source/..`.
+* Most of the notes under API References read from class and methods docstrings. These documentations, found in `doc/source/reference/api` is autogenerated by Sphinx and not included in git.
+
+Building documentation locally
+-------------------------------
+To build the documentation locally, follow these steps:
+
+1. **Install Required Dependencies**:
+
+   * Make sure `Sphinx` and any other dependencies are installed. If a `requirements.txt` file is available, you can install dependencies by running:
+   
+   .. code-block:: console
+
+      pip install -r requirements.txt
+   
+
+2. **Build with Sphinx**:
+    
+   * After dependencies are installed, use the sphinx-build command to generate HTML documentation. 
+   * Go to `doc/` directory Run:
+
+   .. code-block:: console
+
+      make clean && make html 
+
+3. **View Documentation Locally**:
+
+   * Open `doc/build/html/index.html` in the browser to view the generated documentation.
+
+Building documentation on main 
+-------------------------------
+The documentation updates are built on merge to `main` branch. This is done via GitHub Actions workflow based on `/yfinance/.github/workflows/deploy_doc.yml`.
+
+1. Reivew the changes locally and push to `dev`.
+
+2. When `dev` gets merged to `main`, GitHub Actions workflow is automated to build documentation.
--- a/doc/source/development/index.rst
+++ b/doc/source/development/index.rst
@@ -0,0 +1,10 @@
+===========
+Development
+===========
+
+.. toctree::
+   :maxdepth: 1
+
+   contributing
+   documentation
+   reporting_bug
--- a/doc/source/development/reporting_bug.rst
+++ b/doc/source/development/reporting_bug.rst
@@ -0,0 +1,5 @@
+********************************
+Reporting a Bug
+********************************
+
+Open a new issue on our `GitHub <https://github.com/ranaroussi/yfinance/issues>`_.
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -0,0 +1,74 @@
+yfinance documentation
+======================
+
+Download Market Data from Yahoo! Finance's API
+----------------------------------------------
+
+.. admonition:: IMPORTANT LEGAL DISCLAIMER
+
+   **Yahoo!, Y!Finance, and Yahoo! finance are registered trademarks of Yahoo, Inc.**
+
+   yfinance is **not** affiliated, endorsed, or vetted by Yahoo, Inc. It's
+   an open-source tool that uses Yahoo's publicly available APIs, and is
+   intended for research and educational purposes.
+
+   **You should refer to Yahoo!'s terms of use**
+   (`here <https://policies.yahoo.com/us/en/yahoo/terms/product-atos/apiforydn/index.htm>`__),
+   (`here <https://legal.yahoo.com/us/en/yahoo/terms/otos/index.html>`__),
+   and (`here <https://policies.yahoo.com/us/en/yahoo/terms/index.htm>`__)
+   for details on your rights to use the actual data downloaded.
+   Remember - the Yahoo! finance API is intended for personal use only.
+
+Install
+-------
+
+.. code-block:: bash
+
+    $ pip install yfinance
+
+Quick start
+-----------
+
+Showing a small sample of yfinance API, the full API is much bigger and covered in :doc:`reference/index`.
+
+.. code-block:: python
+
+   import yfinance as yf
+   dat = yf.Ticker("MSFT")
+
+
+One ticker symbol
+
+.. code-block:: python
+
+   dat = yf.Ticker("MSFT")
+   dat.info
+   dat.calendar
+   dat.analyst_price_targets
+   dat.quarterly_income_stmt
+   dat.history(period='1mo')
+   dat.option_chain(dat.options[0]).calls
+
+Multiple ticker symbols
+
+.. code-block:: python
+
+   tickers = yf.Tickers('MSFT AAPL GOOG')
+   tickers.tickers['MSFT'].info
+   yf.download(['MSFT', 'AAPL', 'GOOG'], period='1mo')
+
+Funds
+
+.. code-block:: python
+
+   spy = yf.Ticker('SPY').funds_data
+   spy.description
+   spy.top_holdings
+
+.. toctree::
+   :maxdepth: 1
+   :titlesonly:
+
+   advanced/index
+   reference/index
+   development/index
--- a/doc/source/reference/examples/download.py
+++ b/doc/source/reference/examples/download.py
@@ -0,0 +1,2 @@
+import yfinance as yf
+data = yf.download("SPY AAPL", period="1mo")
--- a/doc/source/reference/examples/funds_data.py
+++ b/doc/source/reference/examples/funds_data.py
@@ -0,0 +1,18 @@
+import yfinance as yf
+spy = yf.Ticker('SPY')
+data = spy.funds_data
+
+# show fund description
+data.description
+
+# show operational information
+data.fund_overview
+data.fund_operations
+
+# show holdings related information
+data.asset_classes
+data.top_holdings
+data.equity_holdings
+data.bond_holdings
+data.bond_ratings
+data.sector_weightings
--- a/doc/source/reference/examples/proxy.py
+++ b/doc/source/reference/examples/proxy.py
@@ -0,0 +1,13 @@
+import yfinance as yf
+
+msft = yf.Ticker("MSFT")
+
+msft.history(..., proxy="PROXY_SERVER")
+msft.get_actions(proxy="PROXY_SERVER")
+msft.get_dividends(proxy="PROXY_SERVER")
+msft.get_splits(proxy="PROXY_SERVER")
+msft.get_capital_gains(proxy="PROXY_SERVER")
+msft.get_balance_sheet(proxy="PROXY_SERVER")
+msft.get_cashflow(proxy="PROXY_SERVER")
+msft.option_chain(..., proxy="PROXY_SERVER")
+...
--- a/doc/source/reference/examples/search.py
+++ b/doc/source/reference/examples/search.py
@@ -0,0 +1,7 @@
+import yfinance as yf
+
+# get list of quotes
+quotes = yf.Search("AAPL", max_results=10).quotes
+
+# get list of news
+news = yf.Search("Google", news_count=10).news
--- a/doc/source/reference/examples/sector_industry.py
+++ b/doc/source/reference/examples/sector_industry.py
@@ -0,0 +1,25 @@
+import yfinance as yf
+
+tech = yf.Sector('technology')
+software = yf.Industry('software-infrastructure')
+
+# Common information
+tech.key
+tech.name
+tech.symbol
+tech.ticker
+tech.overview
+tech.top_companies
+tech.research_reports
+
+# Sector information
+tech.top_etfs
+tech.top_mutual_funds
+tech.industries
+
+# Industry information
+software.sector_key
+software.sector_name
+software.top_performing_companies
+software.top_growth_companies
+
--- a/doc/source/reference/examples/sector_industry_ticker.py
+++ b/doc/source/reference/examples/sector_industry_ticker.py
@@ -0,0 +1,11 @@
+import yfinance as yf
+# Ticker to Sector and Industry
+msft = yf.Ticker('MSFT')
+tech = yf.Sector(msft.info.get('sectorKey'))
+software = yf.Industry(msft.info.get('industryKey'))
+
+# Sector and Industry to Ticker
+tech_ticker = tech.ticker
+tech_ticker.info
+software_ticker = software.ticker
+software_ticker.history()
--- a/doc/source/reference/examples/ticker.py
+++ b/doc/source/reference/examples/ticker.py
@@ -0,0 +1,22 @@
+import yfinance as yf
+
+dat = yf.Ticker("MSFT")
+
+# get historical market data
+dat.history(period='1mo')
+
+# options
+dat.option_chain(dat.options[0]).calls
+
+# get financials
+dat.balance_sheet
+dat.quarterly_income_stmt
+
+# dates
+dat.calendar
+
+# general info
+dat.info
+
+# analysis
+dat.analyst_price_targets
--- a/doc/source/reference/examples/tickers.py
+++ b/doc/source/reference/examples/tickers.py
@@ -0,0 +1,8 @@
+import yfinance as yf
+
+tickers = yf.Tickers('msft aapl goog')
+
+# access each ticker using (example)
+tickers.tickers['MSFT'].info
+tickers.tickers['AAPL'].history(period="1mo")
+tickers.tickers['GOOG'].actions
--- a/doc/source/reference/index.rst
+++ b/doc/source/reference/index.rst
@@ -0,0 +1,41 @@
+=============
+API Reference
+=============
+
+Overview
+--------
+
+The `yfinance` package provides easy access to Yahoo! Finance's API to retrieve market data. It includes classes and functions for downloading historical market data, accessing ticker information, managing cache, and more.
+
+
+Public API
+==========
+
+The following are the publicly available classes, and functions exposed by the `yfinance` package:
+
+- :attr:`Ticker <yfinance.Ticker>`: Class for accessing single ticker data.
+- :attr:`Tickers <yfinance.Tickers>`: Class for handling multiple tickers.
+- :attr:`Search <yfinance.Search>`: Class for accessing search results.
+- :attr:`Sector <yfinance.Sector>`: Domain class for accessing sector information.
+- :attr:`Industry <yfinance.Industry>`: Domain class for accessing industry information.
+- :attr:`download <yfinance.download>`: Function to download market data for multiple tickers.
+- :attr:`EquityQuery <yfinance.EquityQuery>`: Class to build equity market query.
+- :attr:`Screener <yfinance.Screener>`: Class to screen the market using defined query.
+- :attr:`enable_debug_mode <yfinance.enable_debug_mode>`: Function to enable debug mode for logging.
+- :attr:`set_tz_cache_location <yfinance.set_tz_cache_location>`: Function to set the timezone cache location.
+
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+
+   yfinance.ticker_tickers
+   yfinance.stock
+   yfinance.financials
+   yfinance.analysis
+   yfinance.search
+   yfinance.sector_industry
+   yfinance.functions
+
+   yfinance.funds_data
+   yfinance.price_history
--- a/doc/source/reference/yfinance.analysis.rst
+++ b/doc/source/reference/yfinance.analysis.rst
@@ -0,0 +1,81 @@
+===================
+Analysis & Holdings
+===================
+
+.. currentmodule:: yfinance.Ticker
+
+Analysis
+--------
+
+.. autosummary::
+   :toctree: api/
+   :recursive:
+
+   get_recommendations
+   recommendations
+
+   get_recommendations_summary
+   recommendations_summary
+
+   get_upgrades_downgrades
+   upgrades_downgrades
+
+   get_sustainability
+   sustainability
+
+   get_analyst_price_targets
+   analyst_price_targets
+
+   get_earnings_estimate
+   earnings_estimate
+
+   get_revenue_estimate
+   revenue_estimate
+
+   get_earnings_history
+   earnings_history
+
+   get_eps_trend
+   eps_trend
+
+   get_eps_revisions
+   eps_revisions
+
+   get_growth_estimates
+   growth_estimates
+
+
+Holdings
+--------
+
+.. autosummary::
+   :toctree: api/
+   :recursive:
+
+   get_funds_data
+   funds_data
+
+.. seealso::
+   :meth:`yfinance.scrapers.funds.FundsData`
+
+.. autosummary::
+   :toctree: api/
+   :recursive:
+
+   get_insider_purchases
+   insider_purchases
+
+   get_insider_transactions
+   insider_transactions
+
+   get_insider_roster_holders
+   insider_roster_holders
+
+   get_major_holders
+   major_holders
+
+   get_institutional_holders
+   institutional_holders
+
+   get_mutualfund_holders
+   mutualfund_holders
--- a/doc/source/reference/yfinance.financials.rst
+++ b/doc/source/reference/yfinance.financials.rst
@@ -0,0 +1,29 @@
+==========
+Financials
+==========
+
+.. currentmodule:: yfinance.Ticker
+
+.. autosummary::
+   :toctree: api/
+   :recursive:
+
+   get_income_stmt
+   income_stmt
+
+   get_balance_sheet
+   balance_sheet
+
+   get_cashflow
+   cashflow
+
+   get_earnings
+   earnings
+
+   calendar
+
+   get_earnings_dates
+   earnings_dates
+
+   get_sec_filings
+   sec_filings
--- a/doc/source/reference/yfinance.functions.rst
+++ b/doc/source/reference/yfinance.functions.rst
@@ -0,0 +1,51 @@
+=========================
+Functions and Utilities
+=========================
+
+.. currentmodule:: yfinance
+   
+Download Market Data
+~~~~~~~~~~~~~~~~~~~~~
+The `download` function allows you to retrieve market data for multiple tickers at once.
+
+.. autosummary:: 
+   :toctree: api/
+
+   download
+
+Query Market Data
+~~~~~~~~~~~~~~~~~~~~~
+The `Sector` and `Industry` modules allow you to access the sector and industry information.
+
+.. autosummary:: 
+   :toctree: api/
+
+   EquityQuery
+   Screener
+
+.. seealso::
+   :attr:`EquityQuery.valid_operand_fields <yfinance.EquityQuery.valid_operand_fields>`
+      supported operand values for query
+   :attr:`EquityQuery.valid_eq_operand_map <yfinance.EquityQuery.valid_eq_operand_map>`
+      supported `EQ query operand parameters`
+   :attr:`Screener.predefined_bodies <yfinance.Screener.predefined_bodies>`
+      supported predefined screens
+   
+
+Enable Debug Mode
+~~~~~~~~~~~~~~~~~
+Enables logging of debug information for the `yfinance` package.
+
+.. autosummary:: 
+   :toctree: api/
+
+   enable_debug_mode
+
+Set Timezone Cache Location
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Sets the cache location for timezone data.
+
+.. autosummary:: 
+   :toctree: api/
+
+   set_tz_cache_location
--- a/doc/source/reference/yfinance.funds_data.rst
+++ b/doc/source/reference/yfinance.funds_data.rst
@@ -0,0 +1,11 @@
+====================
+`FundsData` class
+====================
+
+.. currentmodule:: yfinance.scrapers.funds
+
+.. autosummary::
+   :toctree: api/
+   :recursive:
+
+   FundsData
--- a/doc/source/reference/yfinance.price_history.rst
+++ b/doc/source/reference/yfinance.price_history.rst
@@ -0,0 +1,9 @@
+====================
+`PriceHistory` class
+====================
+
+.. currentmodule:: yfinance.scrapers.history
+
+.. autoclass:: PriceHistory
+   :members:
+   :undoc-members:
--- a/doc/source/reference/yfinance.search.rst
+++ b/doc/source/reference/yfinance.search.rst
@@ -0,0 +1,22 @@
+=====================
+Search & News
+=====================
+
+.. currentmodule:: yfinance
+
+
+Class
+------------
+The `Search` module, allows you to access search data in a Pythonic way.
+
+.. autosummary::
+   :toctree: api/
+
+   Search
+
+Search Sample Code
+------------------
+The `Search` module, allows you to access search data in a Pythonic way.
+
+.. literalinclude:: examples/search.py
+   :language: python
--- a/doc/source/reference/yfinance.sector_industry.rst
+++ b/doc/source/reference/yfinance.sector_industry.rst
@@ -0,0 +1,32 @@
+=======================
+Sector and Industry
+=======================
+
+.. currentmodule:: yfinance
+
+Sector class
+--------------
+The `Sector` and `Industry` modules provide access to the Sector and Industry information.
+
+.. autosummary::
+   :toctree: api/
+   :recursive:
+
+   Sector
+   Industry
+
+.. seealso::
+   :attr:`Sector.industries <yfinance.Sector.industries>`
+      Map of sector and industry
+
+Sample Code
+---------------------
+To initialize, use the relevant sector or industry key as below.
+
+.. literalinclude:: examples/sector_industry.py
+   :language: python
+
+The modules can be chained with Ticker as below.
+
+.. literalinclude:: examples/sector_industry_ticker.py
+   :language: python
--- a/doc/source/reference/yfinance.stock.rst
+++ b/doc/source/reference/yfinance.stock.rst
@@ -0,0 +1,50 @@
+=====
+Stock
+=====
+
+.. currentmodule:: yfinance.Ticker
+
+Ticker stock methods
+--------------------
+
+.. autosummary::
+   :toctree: api/
+   :recursive:
+
+   get_isin
+   isin
+
+   history
+
+.. seealso::
+   :meth:`yfinance.scrapers.history.PriceHistory.history`
+      Documentation for history
+
+.. autosummary::
+   :toctree: api/
+   :recursive:
+
+   get_history_metadata
+
+   get_dividends
+   dividends
+
+   get_splits
+   splits
+
+   get_actions
+   actions
+
+   get_capital_gains
+   capital_gains
+
+   get_shares_full
+
+   get_info
+   info
+
+   get_fast_info
+   fast_info
+
+   get_news
+   news
--- a/doc/source/reference/yfinance.ticker_tickers.rst
+++ b/doc/source/reference/yfinance.ticker_tickers.rst
@@ -0,0 +1,46 @@
+=====================
+Ticker and Tickers
+=====================
+
+.. currentmodule:: yfinance
+
+
+Class
+------------
+The `Ticker` module, allows you to access ticker data in a Pythonic way.
+
+.. autosummary::
+   :toctree: api/
+
+   Ticker
+   Tickers
+
+
+Ticker Sample Code
+------------------
+The `Ticker` module, allows you to access ticker data in a Pythonic way.
+
+.. literalinclude:: examples/ticker.py
+   :language: python
+
+To initialize multiple `Ticker` objects, use
+
+.. literalinclude:: examples/tickers.py
+   :language: python
+
+For tickers that are ETFs/Mutual Funds, `Ticker.funds_data` provides access to fund related data. 
+
+Funds' Top Holdings and other data with category average is returned as `pd.DataFrame`.
+
+.. literalinclude:: examples/funds_data.py
+   :language: python
+
+If you want to use a proxy server for downloading data, use:
+
+.. literalinclude:: examples/proxy.py
+   :language: python
+
+To initialize multiple `Ticker` objects, use `Tickers` module
+
+.. literalinclude:: examples/tickers.py
+   :language: python
--- a/meta.yaml
+++ b/meta.yaml
@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.2.43" %}
+{% set version = "0.2.49" %}

 package:
  name: "{{ name|lower }}"
--- a/pyrightconfig.json
+++ b/pyrightconfig.json
@@ -0,0 +1,15 @@
+{
+    "typeCheckingMode": "basic",
+    "reportGeneralTypeIssues": "warning",
+    "reportArgumentType": "warning",
+    "reportOptionalMemberAccess": "warning",
+    "reportOperatorIssue": "warning",
+    "reportAttributeAccessIssue": "warning",
+    "reportMissingImports": "warning",
+    "reportReturnType": "warning",
+    "reportAssignmentType": "warning",
+    "reportOptionalSubscript": "warning",
+    "reportOptionalIterable": "warning",
+    "reportCallIssue": "warning",
+    "reportUnhashable": "warning"
+  }
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,4 +8,4 @@ pytz>=2022.5
 frozendict>=2.3.4
 beautifulsoup4>=4.11.1
 html5lib>=1.1
-peewee>=3.16.2
+peewee>=3.16.2
--- a/tests/data/4063-T-1d-bad-div-fixed.csv
+++ b/tests/data/4063-T-1d-bad-div-fixed.csv
@@ -0,0 +1,668 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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--- a/tests/data/4063-T-1d-bad-div.csv
+++ b/tests/data/4063-T-1d-bad-div.csv
@@ -0,0 +1,668 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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--- a/tests/data/AET-L-1d-100x-error-fixed.csv
+++ b/tests/data/AET-L-1d-100x-error-fixed.csv
@@ -1,24 +1,24 @@
 Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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+2022-05-20 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-19 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-18 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,532454,0,0
+2022-05-17 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-16 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-13 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-12 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-11 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-10 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-09 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-06 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-05 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-04 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-03 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
--- a/tests/data/AET-L-1wk-100x-error-fixed.csv.old
+++ b/tests/data/AET-L-1wk-100x-error-fixed.csv.old
@@ -1,25 +0,0 @@
-Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
-2022-08-15 00:00:00+01:00,27.6000,28.2000,26.2000,27.6000,27.6000,3535668,0,0
-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
--- a/tests/data/AET-L-1wk-100x-error.csv.old
+++ b/tests/data/AET-L-1wk-100x-error.csv.old
@@ -1,25 +0,0 @@
-Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
-2022-08-15 00:00:00+01:00,27.600000381469727,28.200000762939453,26.200000762939453,27.600000381469727,27.600000381469727,3535668,0.0,0.0
-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
--- a/tests/data/KWS-L-1d-bad-div-fixed.csv
+++ b/tests/data/KWS-L-1d-bad-div-fixed.csv
--- a/tests/data/KWS-L-1d-bad-div.csv
+++ b/tests/data/KWS-L-1d-bad-div.csv
--- a/tests/data/LSC-L-1d-bad-div-fixed.csv
+++ b/tests/data/LSC-L-1d-bad-div-fixed.csv
@@ -12,70 +12,70 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
 2022-01-18 00:00:00+00:00,0.275,0.275,0.275,0.275,0.2529500382534256,0,0.0,0.0,True
 2022-01-19 00:00:00+00:00,0.275,0.275,0.275,0.275,0.2529500382534256,0,0.0,0.0,True
 2022-01-20 00:00:00+00:00,0.275,0.27299999237060546,0.27299999237060546,0.275,0.2529500382534256,36,0.0,0.0,True
-2022-01-21 00:00:00+00:00,0.275,0.29,0.29,0.28500000000000003,0.2621482159594605,206,0.0,0.0,True
-2022-01-24 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-01-25 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-01-26 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-01-27 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-01-28 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-01-31 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-01 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-02 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-03 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-04 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-07 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-08 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-09 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-10 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-11 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True
-2022-02-14 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True
-2022-02-15 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True
-2022-02-16 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True
-2022-02-17 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True
-2022-02-18 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-21 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-22 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-23 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-24 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-25 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-28 00:00:00+00:00,0.295,0.3,0.29,0.305,0.28054459028871354,119,0.0,0.0,True
-2022-03-01 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28054459028871354,0,0.0,0.0,True
-2022-03-02 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28054459028871354,0,0.0,0.0,True
-2022-03-03 00:00:00+00:00,0.305,0.32,0.32,0.315,0.28974278691193167,100,0.0,0.0,True
-2022-03-04 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-07 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-08 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-09 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-10 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-11 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-14 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-15 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-16 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-17 00:00:00+00:00,0.315,0.3036000061035156,0.3036000061035156,0.315,0.28974278691193167,9,0.0,0.0,True
-2022-03-18 00:00:00+00:00,0.315,0.3036000061035156,0.3036000061035156,0.315,0.28974278691193167,6,0.0,0.0,True
-2022-03-21 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
+2022-01-21 00:00:00+00:00,0.275,0.29,0.29,0.28500000000000003,0.2621482348766438,206,0.0,0.0,True
+2022-01-24 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-01-25 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-01-26 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-01-27 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-01-28 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-01-31 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-02-01 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-02-02 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-02-03 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-02-04 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-02-07 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-02-08 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-02-09 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
+2022-02-10 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482348766438,0,0.0,0.0,True
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@@ -122,89 +122,89 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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@@ -226,7 +226,7 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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 2022-11-29 00:00:00+00:00,0.24700000762939453,0.2670000076293945,0.24,0.26,0.24591193824852078,201,0.0,0.0,True
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 2022-12-01 00:00:00+00:00,0.26,0.26,0.26,0.26,0.24591193824852078,0,0.0,0.0,True
@@ -242,8 +242,8 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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 2022-12-16 00:00:00+00:00,0.27,0.27,0.27,0.27,0.25537009622586265,0,0.0,0.0,True
 2022-12-19 00:00:00+00:00,0.27,0.27,0.27,0.27,0.25537009622586265,0,0.0,0.0,True
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@@ -251,77 +251,77 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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+2023-04-14 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-04-17 00:00:00+01:00,0.315,0.31,0.2929999923706055,0.305,0.28847362498050666,35,0.0,0.0,True
 2023-04-18 00:00:00+01:00,0.305,0.30950000762939456,0.30950000762939456,0.31,0.2932027039691775,24,0.0,0.0,True
 2023-04-19 00:00:00+01:00,0.31,0.31,0.31,0.31,0.2932027039691775,0,0.0,0.0,True
 2023-04-20 00:00:00+01:00,0.31,0.31,0.31,0.31,0.2932027039691775,0,0.0,0.0,True
@@ -343,12 +343,12 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
 2023-05-16 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2600991752145336,0,0.0,0.0,True
 2023-05-17 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2600991752145336,0,0.0,0.0,True
 2023-05-18 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2600991752145336,0,0.0,0.0,True
-2023-05-19 00:00:00+01:00,0.275,0.29649999618530276,0.29649999618530276,0.28,0.264828230037152,33,0.0,0.0,True
-2023-05-22 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True
-2023-05-23 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True
-2023-05-24 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True
-2023-05-25 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True
-2023-05-26 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True
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+2023-05-22 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True
+2023-05-23 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True
+2023-05-24 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True
+2023-05-25 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True
+2023-05-26 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True
 2023-05-30 00:00:00+01:00,0.29,0.29,0.29,0.29,0.27428638801449384,0,0.0,0.0,True
 2023-05-31 00:00:00+01:00,0.29,0.29,0.29,0.29,0.27428638801449384,0,0.0,0.0,True
 2023-06-01 00:00:00+01:00,0.29,0.29,0.29,0.29,0.27428638801449384,0,0.0,0.0,True
@@ -662,4 +662,10 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
 2024-08-16 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0,False
 2024-08-19 00:00:00+01:00,37.5,38.1,38.1,39.0,39.0,14,0.0,0.0,False
 2024-08-20 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0,False
-2024-08-21 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0,False
+2024-08-21 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-22 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-23 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-27 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-28 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-29 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-30 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
--- a/tests/data/LSC-L-1d-bad-div.csv
+++ b/tests/data/LSC-L-1d-bad-div.csv
@@ -12,70 +12,70 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2022-01-18 00:00:00+00:00,0.275,0.275,0.275,0.275,0.01594000220298767,0,0.0,0.0
 2022-01-19 00:00:00+00:00,0.275,0.275,0.275,0.275,0.01594000220298767,0,0.0,0.0
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 2022-03-23 00:00:00+00:00,0.34,0.34,0.34,0.34,0.019707638025283813,0,0.0,0.0
 2022-03-24 00:00:00+00:00,0.34,0.34,0.34,0.34,0.019707638025283813,0,0.0,0.0
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@@ -122,89 +122,89 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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 2022-10-28 00:00:00+01:00,0.245,0.26,0.26,0.26,-0.12558789253234864,200,0.0,0.0
 2022-10-31 00:00:00+00:00,0.26,0.26,0.26,0.26,-0.12558789253234864,0,0.0,0.0
 2022-11-01 00:00:00+00:00,0.26,0.26,0.26,0.26,-0.12558789253234864,0,0.0,0.0
@@ -226,7 +226,7 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2022-11-23 00:00:00+00:00,0.255,0.22670000076293945,0.215,0.23,0.17169532775878907,1258,0.0,0.0
 2022-11-24 00:00:00+00:00,0.225,0.225,0.225,0.225,0.16796281814575195,0,0.0,0.0
 2022-11-25 00:00:00+00:00,0.225,0.225,0.225,0.225,0.16796281814575195,0,0.0,0.0
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+2022-11-28 00:00:00+00:00,0.225,0.24760000228881837,0.23450000762939455,0.24700000762939453,0.18438585281372072,912,0.0,0.0
 2022-11-29 00:00:00+00:00,0.24700000762939453,0.2670000076293945,0.24,0.26,0.1940903663635254,201,0.0,0.0
 2022-11-30 00:00:00+00:00,0.26,0.26,0.26,0.26,0.1940903663635254,0,0.0,0.0
 2022-12-01 00:00:00+00:00,0.26,0.26,0.26,0.26,0.1940903663635254,0,0.0,0.0
@@ -242,8 +242,8 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2022-12-15 00:00:00+00:00,0.27,0.27,0.27,0.27,0.20155538558959962,0,0.0,0.0
 2022-12-16 00:00:00+00:00,0.27,0.27,0.27,0.27,0.20155538558959962,0,0.0,0.0
 2022-12-19 00:00:00+00:00,0.27,0.27,0.27,0.27,0.20155538558959962,0,0.0,0.0
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+2022-12-21 00:00:00+00:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0
 2022-12-22 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0
 2022-12-23 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0
 2022-12-28 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0
@@ -251,77 +251,77 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2022-12-30 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0
 2023-01-03 00:00:00+00:00,0.28500000000000003,0.2720000076293945,0.2720000076293945,0.28500000000000003,0.21275291442871094,15,0.0,0.0
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+2023-04-04 00:00:00+01:00,0.305,0.31,0.31,0.305,0.22768293380737306,4,0.0,0.0
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+2023-04-06 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0
+2023-04-11 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0
+2023-04-12 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0
+2023-04-13 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0
+2023-04-14 00:00:00+01:00,0.305,0.305,0.305,0.305,0.22768293380737306,0,0.0,0.0
+2023-04-17 00:00:00+01:00,0.315,0.31,0.2929999923706055,0.305,0.22768293380737306,35,0.0,0.0
 2023-04-18 00:00:00+01:00,0.305,0.30950000762939456,0.30950000762939456,0.31,0.23141544342041015,24,0.0,0.0
 2023-04-19 00:00:00+01:00,0.31,0.31,0.31,0.31,0.23141544342041015,0,0.0,0.0
 2023-04-20 00:00:00+01:00,0.31,0.31,0.31,0.31,0.23141544342041015,0,0.0,0.0
@@ -343,12 +343,12 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2023-05-16 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2052878952026367,0,0.0,0.0
 2023-05-17 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2052878952026367,0,0.0,0.0
 2023-05-18 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2052878952026367,0,0.0,0.0
-2023-05-19 00:00:00+01:00,0.275,0.29649999618530276,0.29649999618530276,0.28,0.2090203857421875,33,0.0,0.0
-2023-05-22 00:00:00+01:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0
-2023-05-23 00:00:00+01:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0
-2023-05-24 00:00:00+01:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0
-2023-05-25 00:00:00+01:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0
-2023-05-26 00:00:00+01:00,0.28,0.28,0.28,0.28,0.2090203857421875,0,0.0,0.0
+2023-05-19 00:00:00+01:00,0.275,0.29649999618530276,0.29649999618530276,0.28,0.20902040481567383,33,0.0,0.0
+2023-05-22 00:00:00+01:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0
+2023-05-23 00:00:00+01:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0
+2023-05-24 00:00:00+01:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0
+2023-05-25 00:00:00+01:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0
+2023-05-26 00:00:00+01:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0
 2023-05-30 00:00:00+01:00,0.29,0.29,0.29,0.29,0.2164854049682617,0,0.0,0.0
 2023-05-31 00:00:00+01:00,0.29,0.29,0.29,0.29,0.2164854049682617,0,0.0,0.0
 2023-06-01 00:00:00+01:00,0.29,0.29,0.29,0.29,0.2164854049682617,0,0.0,0.0
@@ -662,4 +662,10 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2024-08-16 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0
 2024-08-19 00:00:00+01:00,37.5,38.1,38.1,39.0,39.0,14,0.0,0.0
 2024-08-20 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0
-2024-08-21 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0
+2024-08-21 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0
+2024-08-22 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0
+2024-08-23 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0
+2024-08-27 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0
+2024-08-28 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0
+2024-08-29 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0
+2024-08-30 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0
--- a/tests/data/NPK-1d-no-bad-divs.csv
+++ b/tests/data/NPK-1d-no-bad-divs.csv
@@ -1,663 +0,0 @@
-Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
-2022-01-03 00:00:00-05:00,82.0,83.0999984741211,81.93000030517578,82.7699966430664,69.97517395019531,31600,0.0,0.0
-2022-01-04 00:00:00-05:00,83.11000061035156,83.93000030517578,83.11000061035156,83.58000183105469,70.65996551513672,53300,0.0,0.0
-2022-01-05 00:00:00-05:00,83.93000030517578,84.66999816894531,83.16999816894531,83.80000305175781,70.84595489501953,39600,0.0,0.0
-2022-01-06 00:00:00-05:00,83.75,83.75,82.77999877929688,83.38999938964844,70.49932861328125,25400,0.0,0.0
-2022-01-07 00:00:00-05:00,83.36000061035156,83.87999725341797,83.16000366210938,83.5199966430664,70.60923767089844,19700,0.0,0.0
-2022-01-10 00:00:00-05:00,82.98999786376953,84.05999755859375,82.87000274658203,83.25,70.38097381591797,30300,0.0,0.0
-2022-01-11 00:00:00-05:00,83.41999816894531,83.58000183105469,81.9000015258789,82.55000305175781,69.78919219970703,25300,0.0,0.0
-2022-01-12 00:00:00-05:00,82.41999816894531,84.2699966430664,81.95999908447266,83.6500015258789,70.71914672851562,41300,0.0,0.0
-2022-01-13 00:00:00-05:00,83.4800033569336,86.26000213623047,83.44000244140625,85.31999969482422,72.13098907470703,82200,0.0,0.0
-2022-01-14 00:00:00-05:00,85.31999969482422,87.70999908447266,84.48999786376953,87.55000305175781,74.01627349853516,59100,0.0,0.0
-2022-01-18 00:00:00-05:00,87.73999786376953,89.05000305175781,87.02999877929688,87.05999755859375,73.60200500488281,37000,0.0,0.0
-2022-01-19 00:00:00-05:00,87.25,87.25,84.55000305175781,84.7300033569336,71.63219451904297,24500,0.0,0.0
-2022-01-20 00:00:00-05:00,85.2699966430664,85.76000213623047,82.5,82.72000122070312,69.93290710449219,18300,0.0,0.0
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--- a/tests/data/NVT-L-1d-bad-div-fixed.csv
+++ b/tests/data/NVT-L-1d-bad-div-fixed.csv
--- a/tests/data/NVT-L-1d-bad-div.csv
+++ b/tests/data/NVT-L-1d-bad-div.csv
--- a/tests/data/SCR-TO-1d-bad-div-fixed.csv
+++ b/tests/data/SCR-TO-1d-bad-div-fixed.csv
--- a/tests/data/SCR-TO-1d-bad-div.csv
+++ b/tests/data/SCR-TO-1d-bad-div.csv
--- a/tests/data/SOLB-BR-1d-bad-div-fixed.csv
+++ b/tests/data/SOLB-BR-1d-bad-div-fixed.csv
--- a/tests/data/SOLB-BR-1d-bad-div.csv
+++ b/tests/data/SOLB-BR-1d-bad-div.csv
@@ -1,399 +1,399 @@
 Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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+2022-01-03 00:00:00+01:00,20.400468826293945,20.80828094482422,20.400468826293945,20.629241943359375,11.042256355285645,559352,0.0,0.0
 2022-01-04 00:00:00+01:00,20.649133682250977,20.897798538208008,20.5098819732666,20.848066329956055,11.15938663482666,930594,0.0,0.0
 2022-01-05 00:00:00+01:00,20.897798538208008,21.126571655273438,20.838119506835938,21.126571655273438,11.308463096618652,572100,0.0,0.0
-2022-01-06 00:00:00+01:00,20.887853622436523,21.126571655273438,20.599401473999023,20.967424392700195,11.223275184631348,532086,0.0,0.0
+2022-01-06 00:00:00+01:00,20.887853622436523,21.126571655273438,20.599401473999023,20.967424392700195,11.22327709197998,532086,0.0,0.0
 2022-01-07 00:00:00+01:00,21.007211685180664,21.186250686645508,20.867958068847656,21.126571655273438,11.308463096618652,698666,0.0,0.0
-2022-01-10 00:00:00+01:00,21.186250686645508,21.245929718017578,20.997264862060547,21.05694580078125,11.271193504333496,972438,0.0,0.0
-2022-01-11 00:00:00+01:00,21.226036071777344,21.514488220214844,21.226036071777344,21.37523651123047,11.441567420959473,894602,0.0,0.0
-2022-01-12 00:00:00+01:00,21.564220428466797,21.604007720947266,21.0469970703125,21.216089248657227,11.356378555297852,838352,0.0,0.0
-2022-01-13 00:00:00+01:00,20.897798538208008,21.14646339416504,20.897798538208008,21.14646339416504,12.180269241333008,514241,1.5,0.0
-2022-01-14 00:00:00+01:00,20.897798538208008,21.066890716552734,20.80828094482422,21.0271053314209,12.111518859863281,708327,0.0,0.0
-2022-01-17 00:00:00+01:00,21.106678009033203,21.633846282958984,21.0469970703125,21.554275512695312,12.415165901184082,947841,0.0,0.0
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@@ -402,123 +402,123 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2023-07-25 00:00:00+02:00,20.80828094482422,21.156410217285156,20.7386531829834,21.14646339416504,19.358171463012695,815268,0.0,0.0
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 2023-11-29 00:00:00+01:00,20.60934829711914,21.037052154541016,20.569562911987305,20.977371215820312,19.203378677368164,957061,0.0,0.0
 2023-11-30 00:00:00+01:00,20.947532653808594,21.27577018737793,20.818225860595703,21.11662483215332,19.330856323242188,2370750,0.0,0.0
 2023-12-01 00:00:00+01:00,21.166357040405273,21.6437931060791,21.166357040405273,21.604007720947266,19.777023315429688,1195222,0.0,0.0
 2023-12-04 00:00:00+01:00,21.484647750854492,21.94219207763672,21.464754104614258,21.74325942993164,19.904497146606445,1089262,0.0,0.0
 2023-12-05 00:00:00+01:00,21.6239013671875,21.9322452545166,21.59406089782715,21.882511138916016,20.03197479248047,1150906,0.0,0.0
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 2023-12-08 00:00:00+01:00,22.330108642578125,22.479307174682617,21.991924285888672,22.35000228881836,20.459930419921875,1410130,0.0,0.0
 2023-12-11 00:00:00+01:00,19.450000762939453,23.649999618530273,17.895000457763672,20.6200008392334,18.876230239868164,8998324,0.0,0.0
 2023-12-12 00:00:00+01:00,20.790000915527344,22.3700008392334,20.75,22.139999389648438,20.26768684387207,3236690,0.0,0.0
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 2023-12-14 00:00:00+01:00,24.5,25.010000228881836,23.950000762939453,24.540000915527344,22.4647274017334,1535694,0.0,0.0
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 2024-01-04 00:00:00+01:00,27.5,28.110000610351562,27.5,28.09000015258789,25.714515686035156,353356,0.0,0.0
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 2024-01-08 00:00:00+01:00,28.290000915527344,28.329999923706055,27.540000915527344,28.0,25.632125854492188,503233,0.0,0.0
 2024-01-09 00:00:00+01:00,27.190000534057617,27.549999237060547,27.079999923706055,27.299999237060547,24.991321563720703,592677,0.0,0.0
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 2024-01-12 00:00:00+01:00,26.639999389648438,26.860000610351562,26.06999969482422,26.139999389648438,23.929418563842773,803481,0.0,0.0
 2024-01-15 00:00:00+01:00,25.059999465942383,25.329999923706055,25.0,25.299999237060547,24.690631866455078,726646,1.62,0.0
 2024-01-16 00:00:00+01:00,25.149999618530273,25.200000762939453,24.579999923706055,24.989999771118164,24.388099670410156,493029,0.0,0.0
@@ -675,4 +675,67 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2024-08-19 00:00:00+02:00,31.030000686645508,31.299999237060547,30.670000076293945,31.030000686645508,31.030000686645508,180272,0.0,0.0
 2024-08-20 00:00:00+02:00,31.170000076293945,31.170000076293945,30.469999313354492,30.530000686645508,30.530000686645508,152575,0.0,0.0
 2024-08-21 00:00:00+02:00,30.530000686645508,31.020000457763672,30.530000686645508,30.969999313354492,30.969999313354492,128994,0.0,0.0
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--- a/tests/data/TENT-L-1d-bad-div-fixed.csv
+++ b/tests/data/TENT-L-1d-bad-div-fixed.csv
@@ -0,0 +1,686 @@
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--- a/tests/data/TENT-L-1d-bad-div.csv
+++ b/tests/data/TENT-L-1d-bad-div.csv
@@ -0,0 +1,686 @@
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+2024-09-19 00:00:00+01:00,0.4640000152587891,0.4909999847412109,0.4520000076293945,0.4640000152587891,0.4640000152587891,794932,0.25,0.0
+2024-09-20 00:00:00+01:00,0.4458200073242188,0.480999984741211,0.4458200073242188,0.46950000762939453,0.46950000762939453,328898,0.0,0.0
--- a/tests/test_price_repair.py
+++ b/tests/test_price_repair.py
@@ -1,5 +1,5 @@
-from .context import yfinance as yf
-from .context import session_gbl
+from tests.context import yfinance as yf
+from tests.context import session_gbl

 import unittest

@@ -35,7 +35,6 @@ class TestPriceRepairAssumptions(unittest.TestCase):
                    continue
                for j in range(i, len(periods)):
                    period = periods[j]
-                    print(f"- interval={interval} period={period}")

                    df_truth = dat.history(interval=interval, period=period)
                    # df_1d = dat.history(interval='1d', period=period)
@@ -115,7 +114,6 @@ class TestPriceRepair(unittest.TestCase):
        self.assertIsInstance(reconstructed, _pd.DataFrame, "data has wrong type")
        self.assertFalse(data.empty, "data is empty")

-    # pass
    def test_reconstruct_2m(self):
        # 2m repair requires 1m data.
        # Yahoo restricts 1m fetches to 7 days max within last 30 days.
@@ -135,7 +133,6 @@ class TestPriceRepair(unittest.TestCase):
            start_dt = end_dt - td_60d
            dat.history(start=start_dt, end=end_dt, interval="2m", repair=True)

-    # pass
    def test_repair_100x_random_weekly(self):
        # Setup:
        tkr = "PNL.L"
@@ -190,7 +187,6 @@ class TestPriceRepair(unittest.TestCase):
        self.assertTrue("Repaired?" in df_repaired.columns)
        self.assertFalse(df_repaired["Repaired?"].isna().any())

-    # pass
    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.

@@ -252,7 +248,6 @@ class TestPriceRepair(unittest.TestCase):
        self.assertTrue("Repaired?" in df_repaired.columns)
        self.assertFalse(df_repaired["Repaired?"].isna().any())

-    # pass
    def test_repair_100x_random_daily(self):
        tkr = "PNL.L"
        dat = yf.Ticker(tkr, session=self.session)
@@ -301,7 +296,6 @@ class TestPriceRepair(unittest.TestCase):
        self.assertTrue("Repaired?" in df_repaired.columns)
        self.assertFalse(df_repaired["Repaired?"].isna().any())

-    # pass
    def test_repair_100x_block_daily(self):
        # Some 100x errors are not sporadic.
        # Sometimes Yahoo suddenly shifts from cents->$ from some recent date.
@@ -361,7 +355,6 @@ class TestPriceRepair(unittest.TestCase):
                self.assertTrue("Repaired?" in df_repaired.columns)
                self.assertFalse(df_repaired["Repaired?"].isna().any())

-    # pass
    def test_repair_zeroes_daily(self):
        tkr = "BBIL.L"
        dat = yf.Ticker(tkr, session=self.session)
@@ -393,7 +386,6 @@ class TestPriceRepair(unittest.TestCase):
        self.assertTrue("Repaired?" in repaired_df.columns)
        self.assertFalse(repaired_df["Repaired?"].isna().any())

-    # pass
    def test_repair_zeroes_daily_adjClose(self):
        # Test that 'Adj Close' is reconstructed correctly,
        # particularly when a dividend occurred within 1 day.
@@ -431,7 +423,6 @@ class TestPriceRepair(unittest.TestCase):
                self.assertTrue("Repaired?" in df_slice_bad_repaired.columns)
                self.assertFalse(df_slice_bad_repaired["Repaired?"].isna().any())

-    # pass
    def test_repair_zeroes_hourly(self):
        tkr = "INTC"
        dat = yf.Ticker(tkr, session=self.session)
@@ -467,7 +458,6 @@ class TestPriceRepair(unittest.TestCase):
        self.assertTrue("Repaired?" in repaired_df.columns)
        self.assertFalse(repaired_df["Repaired?"].isna().any())

-    # pass
    def test_repair_bad_stock_splits(self):
        # Stocks that split in 2022 but no problems in Yahoo data,
        # so repair should change nothing
@@ -562,37 +552,6 @@ class TestPriceRepair(unittest.TestCase):
                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
                        raise

-    # pass
-    def test_repair_missing_div_adjust(self):
-        tkr = '8TRA.DE'
-
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        df_bad = _pd.read_csv(os.path.join(self.dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust.csv"), index_col="Date")
-        df_bad.index = _pd.to_datetime(df_bad.index)
-
-        repaired_df = hist._fix_missing_div_adjust(df_bad, "1d", tz_exchange)
-
-        correct_df = _pd.read_csv(os.path.join(self.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 Exception:
-                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
-
    def test_repair_bad_div_adjusts(self):
        interval = '1d'
        bad_tkrs = []
@@ -606,11 +565,11 @@ class TestPriceRepair(unittest.TestCase):
        # These tickers were exceptionally bad
        bad_tkrs += ['LSC.L']
        bad_tkrs += ['TEM.L']
-        bad_tkrs += ['CLC.L']

        # Other special sits
        bad_tkrs += ['KME.MI']  # 2023 dividend paid to savings share, not common/preferred
        bad_tkrs += ['REL.L']  # 100x div also missing adjust
+        bad_tkrs.append('4063.T')  # Div with same-day split not split adjusted

        # Adj too small
        bad_tkrs += ['ADIG.L']
@@ -623,8 +582,14 @@ class TestPriceRepair(unittest.TestCase):
        bad_tkrs += ['ELCO.L']
        bad_tkrs += ['KWS.L']
        bad_tkrs += ['PSH.L']
+
+        # Div 100x and adjust too big
        bad_tkrs += ['SCR.TO']

+        # Div 0.01x
+        bad_tkrs += ['NVT.L']
+        bad_tkrs += ['TENT.L']
+
        # Missing div adjusts:
        bad_tkrs += ['1398.HK']
        bad_tkrs += ['3988.HK']
@@ -652,12 +617,13 @@ class TestPriceRepair(unittest.TestCase):
            hist = dat._lazy_load_price_history()
            hist.history(period='1mo')  # init metadata for currency
            currency = hist._history_metadata['currency']
+            tz = hist._history_metadata['exchangeTimezoneName']

            fp = os.path.join(self.dp, "data", tkr.replace('.','-') + '-' + interval + "-no-bad-divs.csv")
            if not os.path.isfile(fp):
                continue
            df = _pd.read_csv(fp, index_col='Datetime')
-            df.index = _pd.to_datetime(df.index, utc=True)
+            df.index = _pd.to_datetime(df.index, utc=True).tz_convert(tz)

            repaired_df = hist._fix_bad_div_adjust(df, interval, currency)

@@ -683,15 +649,16 @@ class TestPriceRepair(unittest.TestCase):
            hist = dat._lazy_load_price_history()
            hist.history(period='1mo')  # init metadata for currency
            currency = hist._history_metadata['currency']
+            tz = hist._history_metadata['exchangeTimezoneName']

            fp = os.path.join(self.dp, "data", tkr.replace('.','-') + '-' + interval + "-bad-div.csv")
            if not os.path.isfile(fp):
                continue
            df_bad = _pd.read_csv(fp, index_col='Datetime')
-            df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
+            df_bad.index = _pd.to_datetime(df_bad.index, utc=True).tz_convert(tz)
            fp = os.path.join(self.dp, "data", tkr.replace('.','-') + '-' + interval + "-bad-div-fixed.csv")
            correct_df = _pd.read_csv(fp, index_col='Datetime')
-            correct_df.index = _pd.to_datetime(correct_df.index, utc=True)
+            correct_df.index = _pd.to_datetime(correct_df.index, utc=True).tz_convert(tz)

            repaired_df = hist._fix_bad_div_adjust(df_bad, interval, currency)

--- a/tests/test_prices.py
+++ b/tests/test_prices.py
@@ -1,5 +1,5 @@
-from .context import yfinance as yf
-from .context import session_gbl
+from tests.context import yfinance as yf
+from tests.context import session_gbl

 import unittest

--- a/tests/test_screener.py
+++ b/tests/test_screener.py
@@ -0,0 +1,153 @@
+import unittest
+from unittest.mock import patch, MagicMock
+from yfinance.const import PREDEFINED_SCREENER_BODY_MAP
+from yfinance.screener.screener import Screener
+from yfinance.screener.screener_query import EquityQuery
+
+
+class TestScreener(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(self):
+        self.screener = Screener()
+        self.query = EquityQuery('gt',['eodprice',3])
+
+    def test_set_default_body(self):
+        result = self.screener.set_default_body(self.query)
+
+        self.assertEqual(self.screener.body['offset'], 0)
+        self.assertEqual(self.screener.body['size'], 100)
+        self.assertEqual(self.screener.body['sortField'], 'ticker')
+        self.assertEqual(self.screener.body['sortType'], 'desc')
+        self.assertEqual(self.screener.body['quoteType'], 'equity')
+        self.assertEqual(self.screener.body['query'], self.query.to_dict())
+        self.assertEqual(self.screener.body['userId'], '')
+        self.assertEqual(self.screener.body['userIdType'], 'guid')
+        self.assertEqual(self.screener, result)
+
+    def test_set_predefined_body(self):
+        k = 'most_actives'
+        result = self.screener.set_predefined_body(k)
+        self.assertEqual(self.screener.body, PREDEFINED_SCREENER_BODY_MAP[k])
+        self.assertEqual(self.screener, result)
+
+    def test_set_predefined_body_invalid_key(self):
+        with self.assertRaises(ValueError):
+            self.screener.set_predefined_body('invalid_key')
+
+    def test_set_body(self):
+        body = {
+            "offset": 0,
+            "size": 100,
+            "sortField": "ticker",
+            "sortType": "desc",
+            "quoteType": "equity",
+            "query": self.query.to_dict(),
+            "userId": "",
+            "userIdType": "guid"
+        }
+        result = self.screener.set_body(body)
+
+        self.assertEqual(self.screener.body, body)
+        self.assertEqual(self.screener, result)
+
+    def test_set_body_missing_keys(self):
+        body = {
+            "offset": 0,
+            "size": 100,
+            "sortField": "ticker",
+            "sortType": "desc",
+            "quoteType": "equity"
+        }
+        with self.assertRaises(ValueError):
+            self.screener.set_body(body)
+
+    def test_set_body_extra_keys(self):
+        body = {
+            "offset": 0,
+            "size": 100,
+            "sortField": "ticker",
+            "sortType": "desc",
+            "quoteType": "equity",
+            "query": self.query.to_dict(),
+            "userId": "",
+            "userIdType": "guid",
+            "extraKey": "extraValue"
+        }
+        with self.assertRaises(ValueError):
+            self.screener.set_body(body)
+
+    def test_patch_body(self):
+        initial_body = {
+            "offset": 0,
+            "size": 100,
+            "sortField": "ticker",
+            "sortType": "desc",
+            "quoteType": "equity",
+            "query": self.query.to_dict(),
+            "userId": "",
+            "userIdType": "guid"
+        }
+        self.screener.set_body(initial_body)
+        patch_values = {"size": 50}
+        result = self.screener.patch_body(patch_values)
+
+        self.assertEqual(self.screener.body['size'], 50)
+        self.assertEqual(self.screener.body['query'], self.query.to_dict())
+        self.assertEqual(self.screener, result)
+
+    def test_patch_body_extra_keys(self):
+        initial_body = {
+            "offset": 0,
+            "size": 100,
+            "sortField": "ticker",
+            "sortType": "desc",
+            "quoteType": "equity",
+            "query": self.query.to_dict(),
+            "userId": "",
+            "userIdType": "guid"
+        }
+        self.screener.set_body(initial_body)
+        patch_values = {"extraKey": "extraValue"}
+        with self.assertRaises(ValueError):
+            self.screener.patch_body(patch_values)
+
+    @patch('yfinance.screener.screener.YfData.post')
+    def test_set_large_size_in_body(self, mock_post):
+        body = {
+            "offset": 0,
+            "size": 251, # yahoo limits at 250
+            "sortField": "ticker",
+            "sortType": "desc",
+            "quoteType": "equity",
+            "query": self.query.to_dict(),
+            "userId": "",
+            "userIdType": "guid"
+        }
+
+        with self.assertRaises(ValueError):
+            self.screener.set_body(body).response            
+
+    @patch('yfinance.screener.screener.YfData.post')
+    def test_fetch(self, mock_post):
+        mock_response = MagicMock()
+        mock_response.json.return_value = {'finance': {'result': [{}]}}
+        mock_post.return_value = mock_response
+
+        self.screener.set_default_body(self.query)
+        response = self.screener._fetch()
+
+        self.assertEqual(response, {'finance': {'result': [{}]}})
+
+    @patch('yfinance.screener.screener.YfData.post')
+    def test_fetch_and_parse(self, mock_post):
+        mock_response = MagicMock()
+        mock_response.json.return_value = {'finance': {'result': [{'key': 'value'}]}}
+        mock_post.return_value = mock_response
+
+        self.screener.set_default_body(self.query)
+        self.screener._fetch_and_parse()
+        self.assertEqual(self.screener.response, {'key': 'value'})
+
+if __name__ == '__main__':
+    unittest.main()
--- a/tests/test_search.py
+++ b/tests/test_search.py
@@ -0,0 +1,31 @@
+import unittest
+
+from tests.context import yfinance as yf
+
+
+class TestSearch(unittest.TestCase):
+    def test_valid_query(self):
+        search = yf.Search(query="AAPL", max_results=5, news_count=3)
+
+        self.assertEqual(len(search.quotes), 5)
+        self.assertEqual(len(search.news), 3)
+        self.assertIn("AAPL", search.quotes[0]['symbol'])
+
+    def test_invalid_query(self):
+        search = yf.Search(query="XYZXYZ")
+
+        self.assertEqual(len(search.quotes), 0)
+        self.assertEqual(len(search.news), 0)
+
+    def test_empty_query(self):
+        search = yf.Search(query="")
+
+        self.assertEqual(len(search.quotes), 0)
+        self.assertEqual(len(search.news), 0)
+
+    def test_fuzzy_query(self):
+        search = yf.Search(query="Appel", enable_fuzzy_query=True)
+
+        # Check if the fuzzy search retrieves relevant results despite the typo
+        self.assertGreater(len(search.quotes), 0)
+        self.assertIn("AAPL", search.quotes[0]['symbol'])
--- a/tests/test_ticker.py
+++ b/tests/test_ticker.py
@@ -10,9 +10,9 @@ Specific test class:
 """
 import pandas as pd

-from .context import yfinance as yf
-from .context import session_gbl
-from yfinance.exceptions import YFChartError, YFInvalidPeriodError, YFNotImplementedError, YFTickerMissingError, YFTzMissingError
+from tests.context import yfinance as yf
+from tests.context import session_gbl
+from yfinance.exceptions import YFPricesMissingError, YFInvalidPeriodError, YFNotImplementedError, YFTickerMissingError, YFTzMissingError, YFDataException


 import unittest
@@ -142,14 +142,14 @@ class TestTicker(unittest.TestCase):
        # META call option, 2024 April 26th @ strike of 180000
        tkr = 'META240426C00180000'
        dat = yf.Ticker(tkr, session=self.session)
-        with self.assertRaises(YFChartError):
+        with self.assertRaises(YFPricesMissingError):
            dat.history(period="5d", interval="1m", raise_errors=True)

    def test_ticker_missing(self):
        tkr = 'ATVI'
        dat = yf.Ticker(tkr, session=self.session)
        # A missing ticker can trigger either a niche error or the generalized error
-        with self.assertRaises((YFTickerMissingError, YFTzMissingError, YFChartError)):
+        with self.assertRaises((YFTickerMissingError, YFTzMissingError, YFPricesMissingError)):
            dat.history(period="3mo", interval="1d", raise_errors=True)

    def test_goodTicker(self):
@@ -216,11 +216,24 @@ class TestTickerHistory(unittest.TestCase):
        self.assertFalse(data.empty, "data is empty")

    def test_download(self):
+        tomorrow = pd.Timestamp.now().date() + pd.Timedelta(days=1)  # helps with caching
        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")
+                for m in [False, True]:
+                    for n in [1, 'all']:
+                        symbols = self.symbols[0] if n == 1 else self.symbols
+                        data = yf.download(symbols, end=tomorrow, session=self.session, 
+                                           threads=t, ignore_tz=i, multi_level_index=m)
+                        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+                        self.assertFalse(data.empty, "data is empty")
+                        if i:
+                            self.assertIsNone(data.index.tz)
+                        else:
+                            self.assertIsNotNone(data.index.tz)
+                        if (not m) and n == 1:
+                            self.assertFalse(isinstance(data.columns, pd.MultiIndex))
+                        else:
+                            self.assertIsInstance(data.columns, pd.MultiIndex)

    def test_no_expensive_calls_introduced(self):
        """
@@ -825,7 +838,7 @@ class TestTickerAnalysts(unittest.TestCase):
        columns = ['stock', 'industry', 'sector', 'index']
        self.assertEqual(data.columns.values.tolist(), columns, "data has wrong column names")

-        index = ['0q', '+1q', '0y', '+1y', '+5y', '-5y']
+        index = ['0q', '+1q', '0y', '+1y']
        self.assertEqual(data.index.values.tolist(), index, "data has wrong row names")

        data_cached = self.ticker.growth_estimates
@@ -848,7 +861,7 @@ class TestTickerAnalysts(unittest.TestCase):
                self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
                self.assertTrue(data.empty, "data is not empty")
            except Exception as e:
-                self.fail(f"Excpetion raised for attribute '{attribute}': {e}")
+                self.fail(f"Exception raised for attribute '{attribute}': {e}")



@@ -997,7 +1010,84 @@ class TestTickerInfo(unittest.TestCase):
    #                     else:
    #                         raise

+class TestTickerFundsData(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.test_tickers = [yf.Ticker("SPY", session=self.session),    # equity etf
+                            yf.Ticker("JNK", session=self.session),     # bonds etf
+                            yf.Ticker("VTSAX", session=self.session)]   # mutual fund
+
+    def tearDown(self):
+        self.ticker = None
+
+    def test_fetch_and_parse(self):
+        try:
+            for ticker in self.test_tickers:
+                ticker.funds_data._fetch_and_parse()
+
+        except Exception as e:
+            self.fail(f"_fetch_and_parse raised an exception unexpectedly: {e}")
+
+        with self.assertRaises(YFDataException):
+            ticker = yf.Ticker("AAPL", session=self.session) # stock, not funds
+            ticker.funds_data._fetch_and_parse()
+            self.fail("_fetch_and_parse should have failed when calling for non-funds data")
+
+    def test_description(self):
+        for ticker in self.test_tickers:
+            description = ticker.funds_data.description
+            self.assertIsInstance(description, str)
+            self.assertTrue(len(description) > 0)
+
+    def test_fund_overview(self):
+        for ticker in self.test_tickers:
+            fund_overview = ticker.funds_data.fund_overview
+            self.assertIsInstance(fund_overview, dict)
+
+    def test_fund_operations(self):
+        for ticker in self.test_tickers:
+            fund_operations = ticker.funds_data.fund_operations
+            self.assertIsInstance(fund_operations, pd.DataFrame)
+
+    def test_asset_classes(self):
+        for ticker in self.test_tickers:
+            asset_classes = ticker.funds_data.asset_classes
+            self.assertIsInstance(asset_classes, dict)
+
+    def test_top_holdings(self):
+        for ticker in self.test_tickers:
+            top_holdings = ticker.funds_data.top_holdings
+            self.assertIsInstance(top_holdings, pd.DataFrame)
+
+    def test_equity_holdings(self):
+        for ticker in self.test_tickers:
+            equity_holdings = ticker.funds_data.equity_holdings
+            self.assertIsInstance(equity_holdings, pd.DataFrame)
+
+    def test_bond_holdings(self):
+        for ticker in self.test_tickers:
+            bond_holdings = ticker.funds_data.bond_holdings
+            self.assertIsInstance(bond_holdings, pd.DataFrame)
+
+    def test_bond_ratings(self):
+        for ticker in self.test_tickers:
+            bond_ratings = ticker.funds_data.bond_ratings
+            self.assertIsInstance(bond_ratings, dict)
+
+    def test_sector_weightings(self):
+        for ticker in self.test_tickers:
+            sector_weightings = ticker.funds_data.sector_weightings
+            self.assertIsInstance(sector_weightings, dict)

 def suite():
    suite = unittest.TestSuite()
@@ -1007,6 +1097,7 @@ def suite():
    suite.addTest(TestTickerHistory('Test Ticker history'))
    suite.addTest(TestTickerMiscFinancials('Test misc financials'))
    suite.addTest(TestTickerInfo('Test info & fast_info'))
+    suite.addTest(TestTickerFundsData('Test Funds Data'))
    return suite


--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -14,7 +14,7 @@ from unittest import TestSuite
 import pandas as pd
 # import numpy as np

-from .context import yfinance as yf
+from tests.context import yfinance as yf

 import unittest
 # import requests_cache
--- a/yfinance/init.py
+++ b/yfinance/init.py
@@ -20,11 +20,17 @@
 #

 from . import version
+from .search import Search
 from .ticker import Ticker
 from .tickers import Tickers
 from .multi import download
 from .utils import enable_debug_mode
 from .cache import set_tz_cache_location
+from .domain.sector import Sector
+from .domain.industry import Industry
+from .domain.market import Market
+from .screener.screener import Screener
+from .screener.screener_query import EquityQuery

 __version__ = version.version
 __author__ = "Ran Aroussi"
@@ -32,4 +38,5 @@ __author__ = "Ran Aroussi"
 import warnings
 warnings.filterwarnings('default', category=DeprecationWarning, module='^yfinance')

-__all__ = ['download', 'Ticker', 'Tickers', 'enable_debug_mode', 'set_tz_cache_location']
+__all__ = ['download', 'Search', 'Ticker', 'Tickers', 'enable_debug_mode', 'set_tz_cache_location', 'Sector',
+           'Industry', 'EquityQuery', 'Screener', 'Market']
--- a/yfinance/base.py
+++ b/yfinance/base.py
@@ -38,6 +38,7 @@ from .scrapers.fundamentals import Fundamentals
 from .scrapers.holders import Holders
 from .scrapers.quote import Quote, FastInfo
 from .scrapers.history import PriceHistory
+from .scrapers.funds import FundsData

 from .const import _BASE_URL_, _ROOT_URL_

@@ -70,6 +71,7 @@ class TickerBase:
        self._holders = Holders(self._data, self.ticker)
        self._quote = Quote(self._data, self.ticker)
        self._fundamentals = Fundamentals(self._data, self.ticker)
+        self._funds_data = None

        self._fast_info = None

@@ -532,38 +534,60 @@ class TickerBase:
        self._isin = data.split(search_str)[1].split('"')[0].split('|')[0]
        return self._isin

-    def get_news(self, proxy=None) -> list:
+    def get_news(self, count=10, tab="news", proxy=None) -> list:
+        """Allowed options for tab: "news", "all", "press releases"""
        if self._news:
            return self._news

-        # Getting data from json
-        url = f"{_BASE_URL_}/v1/finance/search?q={self.ticker}"
-        data = self._data.cache_get(url=url, proxy=proxy)
+        logger = utils.get_yf_logger()
+
+        tab_queryrefs = {
+            "all": "newsAll",
+            "news": "latestNews",
+            "press releases": "pressRelease",
+        }
+
+        query_ref = tab_queryrefs.get(tab.lower())
+        if not query_ref:
+            raise ValueError(f"Invalid tab name '{tab}'. Choose from: {', '.join(tab_queryrefs.keys())}")
+
+        url = f"{_ROOT_URL_}/xhr/ncp?queryRef={query_ref}&serviceKey=ncp_fin"
+        payload = {
+            "serviceConfig": {
+                "snippetCount": count,
+                "s": [self.ticker]
+            }
+        }
+
+        data = self._data.post(url, body=payload, proxy=proxy)
        if data is None or "Will be right back" in data.text:
            raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
                               "Our engineers are working quickly to resolve "
                               "the issue. Thank you for your patience.")
        try:
            data = data.json()
-        except (_json.JSONDecodeError): 
-            logger = utils.get_yf_logger()
+        except _json.JSONDecodeError:
            logger.error(f"{self.ticker}: Failed to retrieve the news and received faulty response instead.")
            data = {}

-        # parse news
-        self._news = data.get("news", [])
+        news = data.get("data", {}).get("tickerStream", {}).get("stream", [])
+
+        self._news = [article for article in news if not article.get('ad', [])]
        return self._news

    @utils.log_indent_decorator
    def get_earnings_dates(self, limit=12, proxy=None) -> Optional[pd.DataFrame]:
        """
        Get earning dates (future and historic)
-        :param limit: max amount of upcoming and recent earnings dates to return.
-                      Default value 12 should return next 4 quarters and last 8 quarters.
-                      Increase if more history is needed.
-
-        :param proxy: requests proxy to use.
-        :return: pandas dataframe
+        
+        Args:
+            limit (int): max amount of upcoming and recent earnings dates to return.
+                Default value 12 should return next 4 quarters and last 8 quarters.
+                Increase if more history is needed.
+            proxy: requests proxy to use.
+        
+        Returns:
+            pd.DataFrame
        """
        if self._earnings_dates and limit in self._earnings_dates:
            return self._earnings_dates[limit]
@@ -647,3 +671,9 @@ class TickerBase:

    def get_history_metadata(self, proxy=None) -> dict:
        return self._lazy_load_price_history().get_history_metadata(proxy)
+
+    def get_funds_data(self, proxy=None) -> Optional[FundsData]:
+        if not self._funds_data:
+            self._funds_data = FundsData(self._data, self.ticker)
+        
+        return self._funds_data
--- a/yfinance/const.py
+++ b/yfinance/const.py
@@ -1,3 +1,4 @@
+_QUERY1_URL_ = 'https://query1.finance.yahoo.com'
 _BASE_URL_ = 'https://query2.finance.yahoo.com'
 _ROOT_URL_ = 'https://finance.yahoo.com'

@@ -155,3 +156,395 @@ quote_summary_valid_modules = (
    "recommendationTrend",
    "futuresChain",
 )
+
+# map last updated as of 2024.09.18
+SECTOR_INDUSTY_MAPPING = {
+    'basic-materials': {'specialty-chemicals',
+                        'gold',
+                        'building-materials',
+                        'copper',
+                        'steel',
+                        'agricultural-inputs',
+                        'chemicals',
+                        'other-industrial-metals-mining',
+                        'lumber-wood-production',
+                        'aluminum',
+                        'other-precious-metals-mining',
+                        'coking-coal',
+                        'paper-paper-products',
+                        'silver'},
+    'communication-services': {'internet-content-information',
+                                'telecom-services',
+                                'entertainment',
+                                'electronic-gaming-multimedia',
+                                'advertising-agencies',
+                                'broadcasting',
+                                'publishing'},
+    'consumer-cyclical': {'internet-retail',
+                            'auto-manufacturers',
+                            'restaurants',
+                            'home-improvement-retail',
+                            'travel-services',
+                            'specialty-retail',
+                            'apparel-retail',
+                            'residential-construction',
+                            'footwear-accessories',
+                            'packaging-containers',
+                            'lodging',
+                            'auto-parts',
+                            'auto-truck-dealerships',
+                            'gambling',
+                            'resorts-casinos',
+                            'leisure',
+                            'apparel-manufacturing',
+                            'personal-services',
+                            'furnishings-fixtures-appliances',
+                            'recreational-vehicles',
+                            'luxury-goods',
+                            'department-stores',
+                            'textile-manufacturing'},
+    'consumer-defensive': {'discount-stores',
+                            'beverages-non-alcoholic',
+                            'household-personal-products',
+                            'packaged-foods',
+                            'tobacco',
+                            'confectioners',
+                            'farm-products',
+                            'food-distribution',
+                            'grocery-stores',
+                            'beverages-brewers',
+                            'education-training-services',
+                            'beverages-wineries-distilleries'},
+    'energy': {'oil-gas-integrated',
+                'oil-gas-midstream',
+                'oil-gas-e-p',
+                'oil-gas-equipment-services',
+                'oil-gas-refining-marketing',
+                'uranium',
+                'oil-gas-drilling',
+                'thermal-coal'},
+    'financial-services': {'banks-diversified',
+                            'credit-services',
+                            'asset-management',
+                            'insurance-diversified',
+                            'banks-regional',
+                            'capital-markets',
+                            'financial-data-stock-exchanges',
+                            'insurance-property-casualty',
+                            'insurance-brokers',
+                            'insurance-life',
+                            'insurance-specialty',
+                            'mortgage-finance',
+                            'insurance-reinsurance',
+                            'shell-companies',
+                            'financial-conglomerates'},
+    'healthcare': {'drug-manufacturers-general',
+                    'healthcare-plans',
+                    'biotechnology',
+                    'medical-devices',
+                    'diagnostics-research',
+                    'medical-instruments-supplies',
+                    'medical-care-facilities',
+                    'drug-manufacturers-specialty-generic',
+                    'health-information-services',
+                    'medical-distribution',
+                    'pharmaceutical-retailers'},
+    'industrials': {'aerospace-defense',
+                    'specialty-industrial-machinery',
+                    'railroads',
+                    'building-products-equipment',
+                    'farm-heavy-construction-machinery',
+                    'specialty-business-services',
+                    'integrated-freight-logistics',
+                    'waste-management',
+                    'conglomerates',
+                    'industrial-distribution',
+                    'engineering-construction',
+                    'rental-leasing-services',
+                    'consulting-services',
+                    'trucking',
+                    'electrical-equipment-parts',
+                    'airlines',
+                    'tools-accessories',
+                    'pollution-treatment-controls',
+                    'security-protection-services',
+                    'marine-shipping',
+                    'metal-fabrication',
+                    'infrastructure-operations',
+                    'staffing-employment-services',
+                    'airports-air-services',
+                    'business-equipment-supplies'},
+    'real-estate': {'reit-specialty',
+                    'reit-industrial',
+                    'reit-retail',
+                    'reit-residential',
+                    'reit-healthcare-facilities',
+                    'real-estate-services',
+                    'reit-office',
+                    'reit-diversified',
+                    'reit-mortgage',
+                    'reit-hotel-motel',
+                    'real-estate-development',
+                    'real-estate-diversified'},
+    'technology': {'software-infrastructure',
+                    'semiconductors',
+                    'consumer-electronics',
+                    'software-application',
+                    'information-technology-services',
+                    'semiconductor-equipment-materials',
+                    'communication-equipment',
+                    'computer-hardware',
+                    'electronic-components',
+                    'scientific-technical-instruments',
+                    'solar',
+                    'electronics-computer-distribution'},
+    'utilities': {'utilities-regulated-electric',
+                    'utilities-renewable',
+                    'utilities-diversified',
+                    'utilities-regulated-gas',
+                    'utilities-independent-power-producers',
+                    'utilities-regulated-water'}
+}
+EQUITY_SCREENER_EQ_MAP = {
+    "region": {
+        "za", "ve", "vn", "us", "tw", "th", "tr", "sr", "sg", "sa", "se", "ru", "ro", "qa", "pt", "pk", "pl",
+        "ph", "nz", "nl", "mx", "pe", "no", "my", "lv", "lt", "kw", "jp", "is", "il", "lk", "kr", "it", "in",
+        "ie", "hu", "id", "hk", "gb", "fi", "eg", "dk", "gr", "fr", "es", "ee", "de", "cz", "cl", "ca", "be",
+        "at", "cn", "br", "au", "ar", "ch"
+    },
+    "sector": {
+        "Basic Materials", "Industrials", "Communication Services", "Healthcare",
+        "Real Estate", "Technology", "Energy", "Utilities", "Financial Services",
+        "Consumer Defensive", "Consumer Cyclical"
+    },
+    "exchanges": {
+        "NMS", "NAS", "YHD", "NYQ", "NGM", "NCM", "BSE"
+    },
+    "peer_group": {
+        "US Fund Equity Energy",
+        "US CE Convertibles",
+        "EAA CE UK Large-Cap Equity",
+        "EAA CE Other",
+        "US Fund Financial",
+        "India CE Multi-Cap",
+        "US Fund Foreign Large Blend",
+        "US Fund Consumer Cyclical",
+        "EAA Fund Global Equity Income",
+        "China Fund Sector Equity Financial and Real Estate",
+        "US Fund Equity Precious Metals",
+        "EAA Fund RMB Bond - Onshore",
+        "China Fund QDII Greater China Equity",
+        "US Fund Large Growth",
+        "EAA Fund Germany Equity",
+        "EAA Fund Hong Kong Equity",
+        "EAA CE UK Small-Cap Equity",
+        "US Fund Natural Resources",
+        "US CE Preferred Stock",
+        "India Fund Sector - Financial Services",
+        "US Fund Diversified Emerging Mkts",
+        "EAA Fund South Africa & Namibia Equity",
+        "China Fund QDII Sector Equity",
+        "EAA CE Sector Equity Biotechnology",
+        "EAA Fund Switzerland Equity",
+        "US Fund Large Value",
+        "EAA Fund Asia ex-Japan Equity",
+        "US Fund Health",
+        "US Fund China Region",
+        "EAA Fund Emerging Europe ex-Russia Equity",
+        "EAA Fund Sector Equity Industrial Materials",
+        "EAA Fund Japan Large-Cap Equity",
+        "EAA Fund EUR Corporate Bond",
+        "US Fund Technology",
+        "EAA CE Global Large-Cap Blend Equity",
+        "Mexico Fund Mexico Equity",
+        "US Fund Trading--Leveraged Equity",
+        "EAA Fund Sector Equity Consumer Goods & Services",
+        "US Fund Large Blend",
+        "EAA Fund Global Flex-Cap Equity",
+        "EAA Fund EUR Aggressive Allocation - Global",
+        "EAA Fund China Equity",
+        "EAA Fund Global Large-Cap Growth Equity",
+        "US CE Options-based",
+        "EAA Fund Sector Equity Financial Services",
+        "EAA Fund Europe Large-Cap Blend Equity",
+        "EAA Fund China Equity - A Shares",
+        "EAA Fund USD Corporate Bond",
+        "EAA Fund Eurozone Large-Cap Equity",
+        "China Fund Aggressive Allocation Fund",
+        "EAA Fund Sector Equity Technology",
+        "EAA Fund Global Emerging Markets Equity",
+        "EAA Fund EUR Moderate Allocation - Global",
+        "EAA Fund Other Bond",
+        "EAA Fund Denmark Equity",
+        "EAA Fund US Large-Cap Blend Equity",
+        "India Fund Large-Cap",
+        "Paper & Forestry",
+        "Containers & Packaging",
+        "US Fund Miscellaneous Region",
+        "Energy Services",
+        "EAA Fund Other Equity",
+        "Homebuilders",
+        "Construction Materials",
+        "China Fund Equity Funds",
+        "Steel",
+        "Consumer Durables",
+        "EAA Fund Global Large-Cap Blend Equity",
+        "Transportation Infrastructure",
+        "Precious Metals",
+        "Building Products",
+        "Traders & Distributors",
+        "Electrical Equipment",
+        "Auto Components",
+        "Construction & Engineering",
+        "Aerospace & Defense",
+        "Refiners & Pipelines",
+        "Diversified Metals",
+        "Textiles & Apparel",
+        "Industrial Conglomerates",
+        "Household Products",
+        "Commercial Services",
+        "Food Retailers",
+        "Semiconductors",
+        "Media",
+        "Automobiles",
+        "Consumer Services",
+        "Technology Hardware",
+        "Transportation",
+        "Telecommunication Services",
+        "Oil & Gas Producers",
+        "Machinery",
+        "Retailing",
+        "Healthcare",
+        "Chemicals",
+        "Food Products",
+        "Diversified Financials",
+        "Real Estate",
+        "Insurance",
+        "Utilities",
+        "Pharmaceuticals",
+        "Software & Services",
+        "Banks"
+    }
+}
+EQUITY_SCREENER_FIELDS = {
+    "eq_fields": {
+        "region",
+        "sector",
+        "peer_group",
+        "exchanges"}, 
+    "price":{
+        "eodprice",
+        "intradaypricechange",
+        "lastclosemarketcap.lasttwelvemonths",
+        "percentchange",
+        "lastclose52weekhigh.lasttwelvemonths",
+        "fiftytwowkpercentchange",
+        "intradayprice",
+        "lastclose52weeklow.lasttwelvemonths",
+        "intradaymarketcap"},
+    "trading":{
+        "beta",
+        "avgdailyvol3m",
+        "pctheldinsider",
+        "pctheldinst",
+        "dayvolume",
+        "eodvolume"},
+    "short_interest":{
+        "short_percentage_of_shares_outstanding.value",
+        "short_interest.value",
+        "short_percentage_of_float.value",
+        "days_to_cover_short.value",
+        "short_interest_percentage_change.value"},
+    "valuation":{
+        "bookvalueshare.lasttwelvemonths",
+        "lastclosemarketcaptotalrevenue.lasttwelvemonths",
+        "lastclosetevtotalrevenue.lasttwelvemonths",
+        "pricebookratio.quarterly",
+        "peratio.lasttwelvemonths",
+        "lastclosepricetangiblebookvalue.lasttwelvemonths",
+        "lastclosepriceearnings.lasttwelvemonths",
+        "pegratio_5y"},
+    "profitability":{
+        "consecutive_years_of_dividend_growth_count",
+        "returnonassets.lasttwelvemonths",
+        "returnonequity.lasttwelvemonths",
+        "forward_dividend_per_share",
+        "forward_dividend_yield",
+        "returnontotalcapital.lasttwelvemonths"},
+    "leverage":{
+        "lastclosetevebit.lasttwelvemonths",
+        "netdebtebitda.lasttwelvemonths",
+        "totaldebtequity.lasttwelvemonths",
+        "ltdebtequity.lasttwelvemonths",
+        "ebitinterestexpense.lasttwelvemonths",
+        "ebitdainterestexpense.lasttwelvemonths",
+        "lastclosetevebitda.lasttwelvemonths",
+        "totaldebtebitda.lasttwelvemonths"},
+    "liquidity":{
+        "quickratio.lasttwelvemonths",
+        "altmanzscoreusingtheaveragestockinformationforaperiod.lasttwelvemonths",
+        "currentratio.lasttwelvemonths",
+        "operatingcashflowtocurrentliabilities.lasttwelvemonths"},
+    "income_statement":{
+        "totalrevenues.lasttwelvemonths",
+        "netincomemargin.lasttwelvemonths",
+        "grossprofit.lasttwelvemonths",
+        "ebitda1yrgrowth.lasttwelvemonths",
+        "dilutedepscontinuingoperations.lasttwelvemonths",
+        "quarterlyrevenuegrowth.quarterly",
+        "epsgrowth.lasttwelvemonths",
+        "netincomeis.lasttwelvemonths",
+        "ebitda.lasttwelvemonths",
+        "dilutedeps1yrgrowth.lasttwelvemonths",
+        "totalrevenues1yrgrowth.lasttwelvemonths",
+        "operatingincome.lasttwelvemonths",
+        "netincome1yrgrowth.lasttwelvemonths",
+        "grossprofitmargin.lasttwelvemonths",
+        "ebitdamargin.lasttwelvemonths",
+        "ebit.lasttwelvemonths",
+        "basicepscontinuingoperations.lasttwelvemonths",
+        "netepsbasic.lasttwelvemonths"
+        "netepsdiluted.lasttwelvemonths"},
+    "balance_sheet":{
+        "totalassets.lasttwelvemonths",
+        "totalcommonsharesoutstanding.lasttwelvemonths",
+        "totaldebt.lasttwelvemonths",
+        "totalequity.lasttwelvemonths",
+        "totalcurrentassets.lasttwelvemonths",
+        "totalcashandshortterminvestments.lasttwelvemonths",
+        "totalcommonequity.lasttwelvemonths",
+        "totalcurrentliabilities.lasttwelvemonths",
+        "totalsharesoutstanding"},
+    "cash_flow":{
+        "forward_dividend_yield",
+        "leveredfreecashflow.lasttwelvemonths",
+        "capitalexpenditure.lasttwelvemonths",
+        "cashfromoperations.lasttwelvemonths",
+        "leveredfreecashflow1yrgrowth.lasttwelvemonths",
+        "unleveredfreecashflow.lasttwelvemonths",
+        "cashfromoperations1yrgrowth.lasttwelvemonths"},
+    "esg":{
+        "esg_score",
+        "environmental_score",
+        "governance_score",
+        "social_score",
+        "highest_controversy"}
+}
+
+PREDEFINED_SCREENER_BODY_MAP = {
+    'aggressive_small_caps': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]},{"operator":"or","operands":[{"operator":"LT","operands":["epsgrowth.lasttwelvemonths",15]}]}]},"userId":"","userIdType":"guid"},
+    'day_gainers': {"offset":0,"size":25,"sortField":"percentchange","sortType":"DESC","quoteType":"EQUITY","query":{"operator":"AND","operands":[{"operator":"gt","operands":["percentchange",3]},{"operator":"eq","operands":["region","us"]},{"operator":"or","operands":[{"operator":"BTWN","operands":["intradaymarketcap",2000000000,10000000000]},{"operator":"BTWN","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"GT","operands":["intradaymarketcap",100000000000]}]},{"operator":"gte","operands":["intradayprice",5]},{"operator":"gt","operands":["dayvolume",15000]}]},"userId":"","userIdType":"guid"},
+    'day_losers': {"offset":0,"size":25,"sortField":"percentchange","sortType":"ASC","quoteType":"EQUITY","query":{"operator":"AND","operands":[{"operator":"lt","operands":["percentchange",-2.5]},{"operator":"eq","operands":["region","us"]},{"operator":"or","operands":[{"operator":"BTWN","operands":["intradaymarketcap",2000000000,10000000000]},{"operator":"BTWN","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"GT","operands":["intradaymarketcap",100000000000]}]},{"operator":"gte","operands":["intradayprice",5]},{"operator":"gt","operands":["dayvolume",20000]}]},"userId":"","userIdType":"guid"},
+    'growth_technology_stocks': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"BTWN","operands":["quarterlyrevenuegrowth.quarterly",50,100]},{"operator":"GT","operands":["quarterlyrevenuegrowth.quarterly",100]},{"operator":"BTWN","operands":["quarterlyrevenuegrowth.quarterly",25,50]}]},{"operator":"or","operands":[{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",25,50]},{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",50,100]},{"operator":"GT","operands":["epsgrowth.lasttwelvemonths",100]}]},{"operator":"eq","operands":["sector","Technology"]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
+    'most_actives': {"offset":0,"size":25,"sortField":"dayvolume","sortType":"DESC","quoteType":"EQUITY","query":{"operator":"AND","operands":[{"operator":"eq","operands":["region","us"]},{"operator":"or","operands":[{"operator":"BTWN","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"GT","operands":["intradaymarketcap",100000000000]},{"operator":"BTWN","operands":["intradaymarketcap",2000000000,10000000000]}]},{"operator":"gt","operands":["dayvolume",5000000]}]},"userId":"","userIdType":"guid"},
+    'most_shorted_stocks': {"size":25,"offset":0,"sortField":"short_percentage_of_shares_outstanding.value","sortType":"DESC","quoteType":"EQUITY","topOperator":"AND","query":{"operator":"AND","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["region","us"]}]},{"operator":"gt","operands":["intradayprice",1]},{"operator":"gt","operands":["avgdailyvol3m",200000]}]},"userId":"","userIdType":"guid"},
+    'small_cap_gainers': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"lt","operands":["intradaymarketcap",2000000000]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
+    'undervalued_growth_stocks': {"offset":0,"size":25,"sortType":"DESC","sortField":"eodvolume","quoteType":"EQUITY","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"BTWN","operands":["peratio.lasttwelvemonths",0,20]}]},{"operator":"or","operands":[{"operator":"LT","operands":["pegratio_5y",1]}]},{"operator":"or","operands":[{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",25,50]},{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",50,100]},{"operator":"GT","operands":["epsgrowth.lasttwelvemonths",100]}]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
+    'undervalued_large_caps': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"BTWN","operands":["peratio.lasttwelvemonths",0,20]}]},{"operator":"lt","operands":["pegratio_5y",1]},{"operator":"btwn","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
+    'conservative_foreign_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Foreign Large Value"]},{"operator":"EQ","operands":["categoryname","Foreign Large Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Large Growth"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Growth"]},{"operator":"EQ","operands":["categoryname","Foreign Large Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",4]},{"operator":"EQ","operands":["performanceratingoverall",5]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"EQ","operands":["riskratingoverall",1]},{"operator":"EQ","operands":["riskratingoverall",3]},{"operator":"EQ","operands":["riskratingoverall",2]}]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
+    'high_yield_bond': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",4]},{"operator":"EQ","operands":["performanceratingoverall",5]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"EQ","operands":["riskratingoverall",1]},{"operator":"EQ","operands":["riskratingoverall",3]},{"operator":"EQ","operands":["riskratingoverall",2]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","High Yield Bond"]}]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
+    'portfolio_anchors': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Large Blend"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",4]},{"operator":"EQ","operands":["performanceratingoverall",5]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
+    'solid_large_growth_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Large Growth"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",5]},{"operator":"EQ","operands":["performanceratingoverall",4]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
+    'solid_midcap_growth_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Mid-Cap Growth"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",5]},{"operator":"EQ","operands":["performanceratingoverall",4]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
+    'top_mutual_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"percentchange","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"gt","operands":["intradayprice",15]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",5]},{"operator":"EQ","operands":["performanceratingoverall",4]}]},{"operator":"gt","operands":["initialinvestment",1000]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"}
+}
--- a/yfinance/data.py
+++ b/yfinance/data.py
@@ -330,6 +330,14 @@ class YfData(metaclass=SingletonMeta):

    @utils.log_indent_decorator
    def get(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        return self._make_request(url, request_method = self._session.get, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
+    
+    @utils.log_indent_decorator
+    def post(self, url, body, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        return self._make_request(url, request_method = self._session.post, user_agent_headers=user_agent_headers, body=body, params=params, proxy=proxy, timeout=timeout)
+    
+    @utils.log_indent_decorator
+    def _make_request(self, url, request_method, user_agent_headers=None, body=None, params=None, proxy=None, timeout=30):
        # Important: treat input arguments as immutable.

        if len(url) > 200:
@@ -363,7 +371,11 @@ class YfData(metaclass=SingletonMeta):
            'timeout': timeout,
            'headers': user_agent_headers or self.user_agent_headers
        }
-        response = self._session.get(**request_args)
+
+        if body:
+            request_args['json'] = body
+            
+        response = request_method(**request_args)
        utils.get_yf_logger().debug(f'response code={response.status_code}')
        if response.status_code >= 400:
            # Retry with other cookie strategy
@@ -375,7 +387,7 @@ class YfData(metaclass=SingletonMeta):
            request_args['params']['crumb'] = crumb
            if strategy == 'basic':
                request_args['cookies'] = {cookie.name: cookie.value}
-            response = self._session.get(**request_args)
+            response = request_method(**request_args)
            utils.get_yf_logger().debug(f'response code={response.status_code}')

        return response
@@ -397,4 +409,4 @@ class YfData(metaclass=SingletonMeta):
        utils.get_yf_logger().debug(f'get_raw_json(): {url}')
        response = self.get(url, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
        response.raise_for_status()
-        return response.json()
+        return response.json()
--- a/yfinance/domain/init.py
+++ b/yfinance/domain/init.py
@@ -0,0 +1,6 @@
+# domain/__init__.py
+from .sector import Sector
+from .industry import Industry
+from .market import Market
+
+__all__ = ['Sector', 'Industry', 'Market']
--- a/yfinance/domain/domain.py
+++ b/yfinance/domain/domain.py
@@ -0,0 +1,197 @@
+from abc import ABC, abstractmethod
+from ..ticker import Ticker
+from ..const import _QUERY1_URL_
+from ..data import YfData
+from typing import Dict, List, Optional
+import pandas as _pd
+
+_QUERY_URL_ = f'{_QUERY1_URL_}/v1/finance'
+
+class Domain(ABC):
+    """
+    Abstract base class representing a domain entity in financial data, with key attributes 
+    and methods for fetching and parsing data. Derived classes must implement the `_fetch_and_parse()` method.
+    """
+
+    def __init__(self, key: str, session=None, proxy=None):
+        """
+        Initializes the Domain object with a key, session, and proxy.
+
+        Args:
+            key (str): Unique key identifying the domain entity.
+            session (Optional[requests.Session]): Session object for HTTP requests. Defaults to None.
+            proxy (Optional[Dict]): Proxy settings. Defaults to None.
+        """
+        self._key: str = key
+        self.proxy = proxy
+        self.session = session
+        self._data: YfData = YfData(session=session)
+        
+        self._name: Optional[str] = None
+        self._symbol: Optional[str] = None
+        self._overview: Optional[Dict] = None
+        self._top_companies: Optional[_pd.DataFrame] = None
+        self._research_reports: Optional[List[Dict[str, str]]] = None
+
+    @property
+    def key(self) -> str:
+        """
+        Retrieves the key of the domain entity.
+
+        Returns:
+            str: The unique key of the domain entity.
+        """
+        return self._key
+
+    @property
+    def name(self) -> str:
+        """
+        Retrieves the name of the domain entity.
+
+        Returns:
+            str: The name of the domain entity.
+        """
+        self._ensure_fetched(self._name)
+        return self._name
+
+    @property
+    def symbol(self) -> str:
+        """
+        Retrieves the symbol of the domain entity.
+
+        Returns:
+            str: The symbol representing the domain entity.
+        """
+        self._ensure_fetched(self._symbol)
+        return self._symbol
+
+    @property
+    def ticker(self) -> Ticker:
+        """
+        Retrieves a Ticker object based on the domain entity's symbol.
+
+        Returns:
+            Ticker: A Ticker object associated with the domain entity.
+        """
+        self._ensure_fetched(self._symbol)
+        return Ticker(self._symbol)
+
+    @property
+    def overview(self) -> Dict:
+        """
+        Retrieves the overview information of the domain entity.
+
+        Returns:
+            Dict: A dictionary containing an overview of the domain entity.
+        """
+        self._ensure_fetched(self._overview)
+        return self._overview
+
+    @property
+    def top_companies(self) -> Optional[_pd.DataFrame]:
+        """
+        Retrieves the top companies within the domain entity.
+
+        Returns:
+            pandas.DataFrame: A DataFrame containing the top companies in the domain.
+        """
+        self._ensure_fetched(self._top_companies)
+        return self._top_companies 
+
+    @property
+    def research_reports(self) -> List[Dict[str, str]]:
+        """
+        Retrieves research reports related to the domain entity.
+
+        Returns:
+            List[Dict[str, str]]: A list of research reports, where each report is a dictionary with metadata.
+        """
+        self._ensure_fetched(self._research_reports)
+        return self._research_reports
+
+    def _fetch(self, query_url, proxy) -> Dict:
+        """
+        Fetches data from the given query URL.
+
+        Args:
+            query_url (str): The URL used for the data query.
+            proxy (Dict): Proxy settings for the request.
+
+        Returns:
+            Dict: The JSON response data from the request.
+        """
+        params_dict = {"formatted": "true", "withReturns": "true", "lang": "en-US", "region": "US"}
+        result = self._data.get_raw_json(query_url, user_agent_headers=self._data.user_agent_headers, params=params_dict, proxy=proxy)
+        return result
+
+    def _parse_and_assign_common(self, data) -> None:
+        """
+        Parses and assigns common data fields such as name, symbol, overview, and top companies.
+
+        Args:
+            data (Dict): The raw data received from the API.
+        """
+        self._name = data.get('name')
+        self._symbol = data.get('symbol')
+        self._overview = self._parse_overview(data.get('overview', {}))
+        self._top_companies = self._parse_top_companies(data.get('topCompanies', {}))
+        self._research_reports = data.get('researchReports')
+
+    def _parse_overview(self, overview) -> Dict:
+        """
+        Parses the overview data for the domain entity.
+
+        Args:
+            overview (Dict): The raw overview data.
+
+        Returns:
+            Dict: A dictionary containing parsed overview information.
+        """
+        return {
+            "companies_count": overview.get('companiesCount', None),
+            "market_cap": overview.get('marketCap', {}).get('raw', None),
+            "message_board_id": overview.get('messageBoardId', None),
+            "description": overview.get('description', None),
+            "industries_count": overview.get('industriesCount', None),
+            "market_weight": overview.get('marketWeight', {}).get('raw', None),
+            "employee_count": overview.get('employeeCount', {}).get('raw', None)
+        }
+
+    def _parse_top_companies(self, top_companies) -> Optional[_pd.DataFrame]:
+        """
+        Parses the top companies data and converts it into a pandas DataFrame.
+
+        Args:
+            top_companies (Dict): The raw top companies data.
+
+        Returns:
+            Optional[pandas.DataFrame]: A DataFrame containing top company data, or None if no data is available.
+        """
+        top_companies_column = ['symbol', 'name', 'rating', 'market weight']
+        top_companies_values = [(c.get('symbol'), 
+                                c.get('name'), 
+                                c.get('rating'), 
+                                c.get('marketWeight',{}).get('raw',None)) for c in top_companies]
+
+        if not top_companies_values: 
+            return None
+        
+        return _pd.DataFrame(top_companies_values, columns=top_companies_column).set_index('symbol')
+
+    @abstractmethod
+    def _fetch_and_parse(self) -> None:
+        """
+        Abstract method for fetching and parsing domain-specific data. 
+        Must be implemented by derived classes.
+        """
+        raise NotImplementedError("_fetch_and_parse() needs to be implemented by children classes")
+
+    def _ensure_fetched(self, attribute) -> None:
+        """
+        Ensures that the given attribute is fetched by calling `_fetch_and_parse()` if the attribute is None.
+
+        Args:
+            attribute: The attribute to check and potentially fetch.
+        """
+        if attribute is None:
+            self._fetch_and_parse()
--- a/yfinance/domain/industry.py
+++ b/yfinance/domain/industry.py
@@ -0,0 +1,148 @@
+from __future__ import print_function
+from typing import Dict, Optional
+
+import pandas as _pd
+
+from .domain import Domain, _QUERY_URL_
+from .. import utils
+
+class Industry(Domain):
+    """
+    Represents an industry within a sector.
+    """
+
+    def __init__(self, key, session=None, proxy=None):
+        """
+        Args:
+            key (str): The key identifier for the industry.
+            session (optional): The session to use for requests.
+            proxy (optional): The proxy to use for requests.
+        """
+        super(Industry, self).__init__(key, session, proxy)
+        self._query_url = f'{_QUERY_URL_}/industries/{self._key}'
+
+        self._sector_key = None
+        self._sector_name = None
+        self._top_performing_companies = None
+        self._top_growth_companies = None
+
+    def __repr__(self):
+        """
+        Returns a string representation of the Industry instance.
+        
+        Returns:
+            str: String representation of the Industry instance.
+        """
+        return f'yfinance.Industry object <{self._key}>'
+    
+    @property
+    def sector_key(self) -> str:
+        """
+        Returns the sector key of the industry.
+        
+        Returns:
+            str: The sector key.
+        """
+        self._ensure_fetched(self._sector_key)
+        return self._sector_key
+    
+    @property
+    def sector_name(self) -> str:
+        """
+        Returns the sector name of the industry.
+        
+        Returns:
+            str: The sector name.
+        """
+        self._ensure_fetched(self._sector_name)
+        return self._sector_name
+    
+    @property
+    def top_performing_companies(self) -> Optional[_pd.DataFrame]:
+        """
+        Returns the top performing companies in the industry.
+        
+        Returns:
+            Optional[pd.DataFrame]: DataFrame containing top performing companies.
+        """
+        self._ensure_fetched(self._top_performing_companies)
+        return self._top_performing_companies
+    
+    @property
+    def top_growth_companies(self) -> Optional[_pd.DataFrame]:
+        """
+        Returns the top growth companies in the industry.
+        
+        Returns:
+            Optional[pd.DataFrame]: DataFrame containing top growth companies.
+        """
+        self._ensure_fetched(self._top_growth_companies)
+        return self._top_growth_companies
+    
+    def _parse_top_performing_companies(self, top_performing_companies: Dict) -> Optional[_pd.DataFrame]:
+        """
+        Parses the top performing companies data.
+        
+        Args:
+            top_performing_companies (Dict): Dictionary containing top performing companies data.
+        
+        Returns:
+            Optional[pd.DataFrame]: DataFrame containing parsed top performing companies data.
+        """
+        compnaies_column = ['symbol','name','ytd return',' last price','target price']
+        compnaies_values = [(c.get('symbol', None),
+                             c.get('name', None),
+                             c.get('ytdReturn',{}).get('raw', None),
+                             c.get('lastPrice',{}).get('raw', None),
+                             c.get('targetPrice',{}).get('raw', None),) for c in top_performing_companies]
+        
+        if not compnaies_values: 
+            return None
+
+        return _pd.DataFrame(compnaies_values, columns = compnaies_column).set_index('symbol')
+    
+    def _parse_top_growth_companies(self, top_growth_companies: Dict) -> Optional[_pd.DataFrame]:
+        """
+        Parses the top growth companies data.
+        
+        Args:
+            top_growth_companies (Dict): Dictionary containing top growth companies data.
+        
+        Returns:
+            Optional[pd.DataFrame]: DataFrame containing parsed top growth companies data.
+        """
+        compnaies_column = ['symbol','name','ytd return',' growth estimate']
+        compnaies_values = [(c.get('symbol', None),
+                             c.get('name', None),
+                             c.get('ytdReturn',{}).get('raw', None),
+                             c.get('growthEstimate',{}).get('raw', None),) for c in top_growth_companies]
+        
+        if not compnaies_values: 
+            return None
+
+        return _pd.DataFrame(compnaies_values, columns = compnaies_column).set_index('symbol')
+
+    def _fetch_and_parse(self) -> None:
+        """
+        Fetches and parses the industry data.
+        """
+        result = None
+        
+        try:
+            result = self._fetch(self._query_url, self.proxy)
+            data = result['data']
+            self._parse_and_assign_common(data)
+
+            self._sector_key = data.get('sectorKey')
+            self._sector_name = data.get('sectorName')
+            self._top_performing_companies = self._parse_top_performing_companies(data.get('topPerformingCompanies'))
+            self._top_growth_companies = self._parse_top_growth_companies(data.get('topGrowthCompanies'))
+
+            return result
+        except Exception as e:
+            logger = utils.get_yf_logger()
+            logger.error(f"Failed to get industry data for '{self._key}' reason: {e}")
+            logger.debug("Got response: ")
+            logger.debug("-------------")
+            logger.debug(f" {result}")
+            logger.debug("-------------")
--- a/yfinance/domain/market.py
+++ b/yfinance/domain/market.py
@@ -0,0 +1,85 @@
+from ..data import YfData
+from ..data import utils
+from ..const import _QUERY1_URL_
+# from .domain import Domain
+
+import pandas as pd
+
+class Market():
+    def __init__(self, market:'str', session=None, proxy=None, timeout=30):
+        self.market = market
+        self.session = session
+        self.proxy = proxy
+        self.timeout = timeout
+
+        self._data = YfData(session=self.session)
+        self._logger = utils.get_yf_logger()
+        
+        self._status = None
+        self._summary = None
+
+    def _fetch_json(self, url, params):
+        data = self._data.cache_get(url=url, params=params, proxy=self.proxy, timeout=self.timeout)
+        if data is None or "Will be right back" in data.text:
+            raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
+                               "Our engineers are working quickly to resolve "
+                               "the issue. Thank you for your patience.")
+        try:
+            return data.json()
+        except _json.JSONDecodeError:
+            self._logger.error(f"{self.market}: Failed to retrieve market data and recieved faulty data.")
+            return {}
+
+    @property
+    def status(self):
+        if self._status is not None:
+            return self._status
+
+        url = f"{_QUERY1_URL_}/v6/finance/markettime"
+        params = {
+            "formatted": True,
+            "key": "finance",
+            "lang": "en-GB",
+            "market": self.market
+        }
+        self._status = self._fetch_json(url, params)
+        try:
+            # Unpack
+            self._status = self._status['finance']['marketTimes'][0]['marketTime'][0]
+            self._status['timezone'] = self._status['timezone'][0]
+            del self._status['time']  # redundant
+        except:
+            pass
+        try:
+            # Localise
+            tz = self._status['timezone']['$text']
+            open2 = pd.Timestamp(self._status['open']).tz_convert(tz)
+            close2 = pd.Timestamp(self._status['close']).tz_convert(tz)
+            self._status['open'] = open2
+            self._status['close'] = close2
+        except:
+            pass
+
+        return self._status
+
+    @property
+    def summary(self):
+        if self._summary is not None:
+            return self._summary
+
+        url = f"{_QUERY1_URL_}/v6/finance/quote/marketSummary"
+        fields = ["shortName", "regularMarketPrice", "regularMarketChange", "regularMarketChangePercent"]
+        params = {
+            "fields": ",".join(fields),
+            "formatted": False,
+            "lang": "en-US",
+            "market": self.market
+        }
+        self._summary = self._fetch_json(url, params)
+        try:
+            self._summary = self._summary['marketSummaryResponse']['result']
+            self._summary = {x['exchange']:x for x in self._summary}
+        except:
+            pass
+        return self._summary
+
--- a/yfinance/domain/sector.py
+++ b/yfinance/domain/sector.py
@@ -0,0 +1,150 @@
+from __future__ import print_function
+from typing import Dict, Optional
+from ..utils import dynamic_docstring, generate_list_table_from_dict
+from ..const import SECTOR_INDUSTY_MAPPING
+
+import pandas as _pd
+
+from .domain import Domain, _QUERY_URL_
+from .. import utils
+
+class Sector(Domain):
+    """
+    Represents a financial market sector and allows retrieval of sector-related data 
+    such as top ETFs, top mutual funds, and industry data.
+    """
+
+    def __init__(self, key, session=None, proxy=None):
+        """
+        Args:
+            key (str): The key representing the sector.
+            session (requests.Session, optional): A session for making requests. Defaults to None.
+            proxy (dict, optional): A dictionary containing proxy settings for the request. Defaults to None.
+        
+        .. seealso::
+   
+            :attr:`Sector.industries <yfinance.Sector.industries>`
+                Map of sector and industry
+        """
+        super(Sector, self).__init__(key, session, proxy)
+        self._query_url: str = f'{_QUERY_URL_}/sectors/{self._key}'
+        self._top_etfs: Optional[Dict] = None
+        self._top_mutual_funds: Optional[Dict] = None
+        self._industries: Optional[_pd.DataFrame] = None
+
+    def __repr__(self):
+        """
+        Returns the string representation of the Sector object.
+
+        Returns:
+            str: A string representation of the object.
+        """
+        return f'yfinance.Sector object <{self._key}>'
+    
+    @property
+    def top_etfs(self) -> Dict[str, str]:
+        """
+        Gets the top ETFs for the sector.
+
+        Returns:
+            Dict[str, str]: A dictionary of ETF symbols and names.
+        """
+        self._ensure_fetched(self._top_etfs)
+        return self._top_etfs
+
+    @property
+    def top_mutual_funds(self) -> Dict[str, str]:
+        """
+        Gets the top mutual funds for the sector.
+
+        Returns:
+            Dict[str, str]: A dictionary of mutual fund symbols and names.
+        """
+        self._ensure_fetched(self._top_mutual_funds)
+        return self._top_mutual_funds
+
+    @dynamic_docstring({"sector_industry": generate_list_table_from_dict(SECTOR_INDUSTY_MAPPING,bullets=True)})
+    @property
+    def industries(self) -> _pd.DataFrame:
+        """
+        Gets the industries within the sector.
+
+        Returns:
+            pandas.DataFrame: A DataFrame with industries' key, name, symbol, and market weight.
+
+        {sector_industry}
+        """
+        self._ensure_fetched(self._industries)
+        return self._industries
+    
+    def _parse_top_etfs(self, top_etfs: Dict) -> Dict[str, str]:
+        """
+        Parses top ETF data from the API response.
+
+        Args:
+            top_etfs (Dict): The raw ETF data from the API response.
+
+        Returns:
+            Dict[str, str]: A dictionary of ETF symbols and names.
+        """
+        return {e.get('symbol'): e.get('name') for e in top_etfs}
+
+    def _parse_top_mutual_funds(self, top_mutual_funds: Dict) -> Dict[str, str]:
+        """
+        Parses top mutual funds data from the API response.
+
+        Args:
+            top_mutual_funds (Dict): The raw mutual fund data from the API response.
+
+        Returns:
+            Dict[str, str]: A dictionary of mutual fund symbols and names.
+        """
+        return {e.get('symbol'): e.get('name') for e in top_mutual_funds}
+    
+    def _parse_industries(self, industries: Dict) -> _pd.DataFrame:
+        """
+        Parses industry data from the API response into a DataFrame.
+
+        Args:
+            industries (Dict): The raw industry data from the API response.
+
+        Returns:
+            pandas.DataFrame: A DataFrame containing industry key, name, symbol, and market weight.
+        """
+        industries_column = ['key','name','symbol','market weight']
+        industries_values = [(i.get('key'),
+                              i.get('name'),
+                              i.get('symbol'),
+                              i.get('marketWeight',{}).get('raw', None)
+                              ) for i in industries if i.get('name') != 'All Industries']
+        return _pd.DataFrame(industries_values, columns=industries_column).set_index('key')
+
+    def _fetch_and_parse(self) -> None:
+        """
+        Fetches and parses sector data from the API.
+
+        Fetches data for the sector and parses the top ETFs, top mutual funds, 
+        and industries within the sector. Stores the parsed data in the corresponding
+        attributes `_top_etfs`, `_top_mutual_funds`, and `_industries`.
+
+        Raises:
+            Exception: If fetching or parsing the sector data fails.
+        """
+        result = None
+        
+        try:
+            result = self._fetch(self._query_url, self.proxy)
+            data = result['data']
+            self._parse_and_assign_common(data)
+
+            self._top_etfs = self._parse_top_etfs(data.get('topETFs', {}))
+            self._top_mutual_funds = self._parse_top_mutual_funds(data.get('topMutualFunds', {}))
+            self._industries = self._parse_industries(data.get('industries', {}))
+
+        except Exception as e:
+            logger = utils.get_yf_logger()
+            logger.error(f"Failed to get sector data for '{self._key}' reason: {e}")
+            logger.debug("Got response: ")
+            logger.debug("-------------")
+            logger.debug(f" {result}")
+            logger.debug("-------------")
--- a/yfinance/multi.py
+++ b/yfinance/multi.py
@@ -24,6 +24,7 @@ from __future__ import print_function
 import logging
 import time as _time
 import traceback
+from typing import Union

 import multitasking as _multitasking
 import pandas as _pd
@@ -34,11 +35,13 @@ from . import shared


@utils.log_indent_decorator
-def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=None,
-             group_by='column', auto_adjust=False, back_adjust=False, repair=False, keepna=False,
-             progress=True, period="max", interval="1d", prepost=False,
-             proxy=None, rounding=False, timeout=10, session=None):
-    """Download yahoo tickers
+def download(tickers, start=None, end=None, actions=False, threads=True,
+             ignore_tz=None, group_by='column', auto_adjust=True, back_adjust=False,
+             repair=False, keepna=False, progress=True, period="max", interval="1d",
+             prepost=False, proxy=None, rounding=False, timeout=10, session=None,
+             multi_level_index=True) -> Union[_pd.DataFrame, None]:
+    """
+    Download yahoo tickers
    :Parameters:
        tickers : str, list
            List of tickers to download
@@ -62,7 +65,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
            Include Pre and Post market data in results?
            Default is False
        auto_adjust: bool
-            Adjust all OHLC automatically? Default is False
+            Adjust all OHLC automatically? Default is True
        repair: bool
            Detect currency unit 100x mixups and attempt repair
            Default is False
@@ -85,6 +88,8 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
            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
+        multi_level_index: bool
+            Optional. Always return a MultiIndex DataFrame? Default is True
    """
    logger = utils.get_yf_logger()

@@ -200,10 +205,6 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
            if (shared._DFS[tkr] is not None) and (shared._DFS[tkr].shape[0] > 0):
                shared._DFS[tkr].index = shared._DFS[tkr].index.tz_localize(None)

-    if len(tickers) == 1:
-        ticker = tickers[0]
-        return shared._DFS[ticker]
-
    try:
        data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
                          keys=shared._DFS.keys(), names=['Ticker', 'Price'])
@@ -211,7 +212,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
        _realign_dfs()
        data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
                          keys=shared._DFS.keys(), names=['Ticker', 'Price'])
-    data.index = _pd.to_datetime(data.index, utc=True)
+    data.index = _pd.to_datetime(data.index, utc=not ignore_tz)
    # switch names back to isins if applicable
    data.rename(columns=shared._ISINS, inplace=True)

@@ -219,6 +220,9 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
        data.columns = data.columns.swaplevel(0, 1)
        data.sort_index(level=0, axis=1, inplace=True)

+    if not multi_level_index and len(tickers) == 1:
+        data = data.droplevel(0 if group_by == 'ticker' else 1, axis=1).rename_axis(None, axis=1)
+
    return data


--- a/yfinance/scrapers/analysis.py
+++ b/yfinance/scrapers/analysis.py
@@ -205,13 +205,18 @@ class Analysis:
            self._growth_estimates = pd.DataFrame()
            return self._growth_estimates

+        # LTG is not defined in yahoo finance front-end as at 2024-11-14.
+        # But its addition is breaking the retrieval of growth estimates.
+        # Also, support for 5 year seem to have dropped.
+        # TODO: Revisit this change and consider permanently removing these keys.
        data_dict = {
            '0q': [],
            '+1q': [],
            '0y': [],
            '+1y': [],
-            '+5y': [],
-            '-5y': []
+            # 'LTG': [],
+            # '+5y': [],
+            # '-5y': []
        }

        # make sure no column is empty
@@ -222,19 +227,23 @@ class Analysis:

        for item in self._earnings_trend:
            period = item['period']
-            data_dict[period].append(item.get('growth', {}).get('raw', None))
+            if period in data_dict:
+                data_dict[period].append(item.get('growth', {}).get('raw', None))

        for item in industry_trend:
            period = item['period']
-            data_dict[period].append(item.get('growth', None))
+            if period in data_dict:
+                data_dict[period].append(item.get('growth', None))

        for item in sector_trend:
            period = item['period']
-            data_dict[period].append(item.get('growth', None))
+            if period in data_dict:
+                data_dict[period].append(item.get('growth', None))

        for item in index_trend:
            period = item['period']
-            data_dict[period].append(item.get('growth', None))
+            if period in data_dict:
+                data_dict[period].append(item.get('growth', None))

        cols = ['stock', 'industry', 'sector', 'index']
        self._growth_estimates = pd.DataFrame(data_dict, index=cols).T
--- a/yfinance/scrapers/funds.py
+++ b/yfinance/scrapers/funds.py
@@ -0,0 +1,337 @@
+import pandas as pd
+
+from yfinance.data import YfData
+from yfinance.const import _BASE_URL_
+from yfinance.exceptions import YFDataException
+from yfinance import utils
+
+from typing import Dict, Optional
+
+_QUOTE_SUMMARY_URL_ = f"{_BASE_URL_}/v10/finance/quoteSummary/"
+
+class FundsData:
+    """
+    ETF and Mutual Funds Data
+    Queried Modules: quoteType, summaryProfile, fundProfile, topHoldings
+
+    Notes: 
+    - fundPerformance module is not implemented as better data is queryable using history
+    """
+    def __init__(self, data: YfData, symbol: str, proxy=None):
+        """
+        Args:
+            data (YfData): The YfData object for fetching data.
+            symbol (str): The symbol of the fund.
+            proxy (optional): Proxy settings for fetching data.
+        """
+        self._data = data
+        self._symbol = symbol
+        self.proxy = proxy
+        
+        # quoteType
+        self._quote_type = None
+
+        # summaryProfile
+        self._description = None
+
+        # fundProfile
+        self._fund_overview = None
+        self._fund_operations = None
+
+        # topHoldings
+        self._asset_classes = None
+        self._top_holdings = None
+        self._equity_holdings = None
+        self._bond_holdings = None
+        self._bond_ratings = None
+        self._sector_weightings = None
+
+    def quote_type(self) -> str:
+        """
+        Returns the quote type of the fund.
+
+        Returns:
+            str: The quote type.
+        """
+        if self._quote_type is None:
+            self._fetch_and_parse()
+        return self._quote_type
+    
+    @property
+    def description(self) -> str:
+        """
+        Returns the description of the fund.
+
+        Returns:
+            str: The description.
+        """
+        if self._description is None:
+            self._fetch_and_parse()
+        return self._description
+    
+    @property
+    def fund_overview(self) -> Dict[str, Optional[str]]:
+        """
+        Returns the fund overview.
+
+        Returns:
+            Dict[str, Optional[str]]: The fund overview.
+        """
+        if self._fund_overview is None:
+            self._fetch_and_parse()
+        return self._fund_overview
+
+    @property
+    def fund_operations(self) -> pd.DataFrame:
+        """
+        Returns the fund operations.
+
+        Returns:
+            pd.DataFrame: The fund operations.
+        """
+        if self._fund_operations is None:
+            self._fetch_and_parse()
+        return self._fund_operations
+
+    @property
+    def asset_classes(self) -> Dict[str, float]:
+        """
+        Returns the asset classes of the fund.
+
+        Returns:
+            Dict[str, float]: The asset classes.
+        """
+        if self._asset_classes is None:
+            self._fetch_and_parse()
+        return self._asset_classes
+
+    @property
+    def top_holdings(self) -> pd.DataFrame:
+        """
+        Returns the top holdings of the fund.
+
+        Returns:
+            pd.DataFrame: The top holdings.
+        """
+        if self._top_holdings is None:
+            self._fetch_and_parse()
+        return self._top_holdings
+
+    @property
+    def equity_holdings(self) -> pd.DataFrame:
+        """
+        Returns the equity holdings of the fund.
+
+        Returns:
+            pd.DataFrame: The equity holdings.
+        """
+        if self._equity_holdings is None:
+            self._fetch_and_parse()
+        return self._equity_holdings
+
+    @property
+    def bond_holdings(self) -> pd.DataFrame:
+        """
+        Returns the bond holdings of the fund.
+
+        Returns:
+            pd.DataFrame: The bond holdings.
+        """
+        if self._bond_holdings is None:
+            self._fetch_and_parse()
+        return self._bond_holdings
+
+    @property
+    def bond_ratings(self) -> Dict[str, float]:
+        """
+        Returns the bond ratings of the fund.
+
+        Returns:
+            Dict[str, float]: The bond ratings.
+        """
+        if self._bond_ratings is None:
+            self._fetch_and_parse()
+        return self._bond_ratings
+
+    @property
+    def sector_weightings(self) -> Dict[str,float]:
+        """
+        Returns the sector weightings of the fund.
+
+        Returns:
+            Dict[str, float]: The sector weightings.
+        """
+        if self._sector_weightings is None:
+            self._fetch_and_parse()
+        return self._sector_weightings
+
+    def _fetch(self, proxy):
+        """
+        Fetches the raw JSON data from the API.
+
+        Args:
+            proxy: Proxy settings for fetching data.
+
+        Returns:
+            dict: The raw JSON data.
+        """
+        modules = ','.join(["quoteType", "summaryProfile", "topHoldings", "fundProfile"])
+        params_dict = {"modules": modules, "corsDomain": "finance.yahoo.com", "symbol": self._symbol, "formatted": "false"}
+        result = self._data.get_raw_json(_QUOTE_SUMMARY_URL_+self._symbol, user_agent_headers=self._data.user_agent_headers, params=params_dict, proxy=proxy)
+        return result
+
+    def _fetch_and_parse(self) -> None:
+        """
+        Fetches and parses the data from the API.
+        """
+        result = self._fetch(self.proxy)
+        try:
+            data = result["quoteSummary"]["result"][0]
+            # check quote type
+            self._quote_type = data["quoteType"]["quoteType"]
+            
+            # parse "summaryProfile", "topHoldings", "fundProfile"
+            self._parse_description(data["summaryProfile"])
+            self._parse_top_holdings(data["topHoldings"])
+            self._parse_fund_profile(data["fundProfile"])
+        except KeyError:
+            raise YFDataException("No Fund data found.")
+        except Exception as e:
+            logger = utils.get_yf_logger()
+            logger.error(f"Failed to get fund data for '{self._symbol}' reason: {e}")
+            logger.debug("Got response: ")
+            logger.debug("-------------")
+            logger.debug(f" {data}")
+            logger.debug("-------------")
+
+    @staticmethod
+    def _parse_raw_values(data, default=None):
+        """
+        Parses raw values from the data.
+
+        Args:
+            data: The data to parse.
+            default: The default value if data is not a dictionary.
+
+        Returns:
+            The parsed value or the default value.
+        """
+        if not isinstance(data, dict):
+            return data
+        
+        return data.get("raw", default)
+
+    def _parse_description(self, data) -> None:
+        """
+        Parses the description from the data.
+
+        Args:
+            data: The data to parse.
+        """
+        self._description = data.get("longBusinessSummary", "")
+
+    def _parse_top_holdings(self, data) -> None:
+        """
+        Parses the top holdings from the data.
+
+        Args:
+            data: The data to parse.
+        """
+        # asset classes
+        self._asset_classes = {
+            "cashPosition": self._parse_raw_values(data.get("cashPosition", None)),
+            "stockPosition": self._parse_raw_values(data.get("stockPosition", None)),
+            "bondPosition": self._parse_raw_values(data.get("bondPosition", None)),
+            "preferredPosition": self._parse_raw_values(data.get("preferredPosition", None)),
+            "convertiblePosition": self._parse_raw_values(data.get("convertiblePosition", None)),
+            "otherPosition": self._parse_raw_values(data.get("otherPosition", None))
+        }
+
+        # top holdings
+        _holdings = data.get("holdings", [])
+        _symbol, _name, _holding_percent = [], [], []
+
+        for item in _holdings:
+            _symbol.append(item["symbol"])
+            _name.append(item["holdingName"])
+            _holding_percent.append(item["holdingPercent"])
+        
+        self._top_holdings = pd.DataFrame({
+            "Symbol": _symbol,
+            "Name": _name,
+            "Holding Percent": _holding_percent
+        }).set_index("Symbol")
+
+        # equity holdings
+        _equity_holdings = data.get("equityHoldings", {})
+        self._equity_holdings = pd.DataFrame({
+            "Average": ["Price/Earnings", "Price/Book", "Price/Sales", "Price/Cashflow", "Median Market Cap", "3 Year Earnings Growth"],
+            self._symbol: [
+                self._parse_raw_values(_equity_holdings.get("priceToEarnings", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToBook", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToSales", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToCashflow", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("medianMarketCap", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("threeYearEarningsGrowth", pd.NA)),
+            ],
+            "Category Average": [
+                self._parse_raw_values(_equity_holdings.get("priceToEarningsCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToBookCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToSalesCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToCashflowCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("medianMarketCapCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("threeYearEarningsGrowthCat", pd.NA)),
+            ]
+        }).set_index("Average")
+        
+        # bond holdings
+        _bond_holdings = data.get("bondHoldings", {})
+        self._bond_holdings = pd.DataFrame({
+            "Average": ["Duration", "Maturity", "Credit Quality"],
+            self._symbol: [
+                self._parse_raw_values(_bond_holdings.get("duration", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("maturity", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("creditQuality", pd.NA)),
+            ],
+            "Category Average": [
+                self._parse_raw_values(_bond_holdings.get("durationCat", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("maturityCat", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("creditQualityCat", pd.NA)),
+            ]
+        }).set_index("Average")
+
+        # bond ratings
+        self._bond_ratings = dict((key, d[key]) for d in data.get("bondRatings", []) for key in d)
+
+        # sector weightings
+        self._sector_weightings = dict((key, d[key]) for d in data.get("sectorWeightings", []) for key in d)
+        
+    def _parse_fund_profile(self, data):
+        """
+        Parses the fund profile from the data.
+
+        Args:
+            data: The data to parse.
+        """
+        self._fund_overview = {
+            "categoryName": data.get("categoryName", None), 
+            "family":       data.get("family", None), 
+            "legalType":    data.get("legalType", None)
+        }
+        
+        _fund_operations = data.get("feesExpensesInvestment", {})
+        _fund_operations_cat = data.get("feesExpensesInvestmentCat", {})
+
+        self._fund_operations = pd.DataFrame({
+            "Attributes": ["Annual Report Expense Ratio", "Annual Holdings Turnover", "Total Net Assets"],
+            self._symbol: [
+                self._parse_raw_values(_fund_operations.get("annualReportExpenseRatio", pd.NA)),
+                self._parse_raw_values(_fund_operations.get("annualHoldingsTurnover", pd.NA)),
+                self._parse_raw_values(_fund_operations.get("totalNetAssets", pd.NA))
+            ],
+            "Category Average": [
+                self._parse_raw_values(_fund_operations_cat.get("annualReportExpenseRatio", pd.NA)),
+                self._parse_raw_values(_fund_operations_cat.get("annualHoldingsTurnover", pd.NA)),
+                self._parse_raw_values(_fund_operations_cat.get("totalNetAssets", pd.NA))
+            ]
+        }).set_index("Attributes")
--- a/yfinance/scrapers/history.py
+++ b/yfinance/scrapers/history.py
@@ -3,6 +3,7 @@ import dateutil as _dateutil
 import logging
 import numpy as np
 import pandas as pd
+from math import isclose
 import time as _time
 import bisect

@@ -145,7 +146,7 @@ class PriceHistory:
        params["interval"] = interval.lower()
        params["includePrePost"] = prepost

-        # 1) fix weired bug with Yahoo! - returning 60m for 30m bars
+        # 1) fix weird bug with Yahoo! - returning 60m for 30m bars
        if params["interval"] == "30m":
            params["interval"] = "15m"

@@ -253,9 +254,15 @@ class PriceHistory:
            endDt = pd.to_datetime(end, unit='s')
            if quotes.index[quotes.shape[0] - 1] >= endDt:
                quotes = quotes.iloc[0:quotes.shape[0] - 1]
-        logger.debug(f'{self.ticker}: yfinance received OHLC data: {quotes.index[0]} -> {quotes.index[-1]}')
+        if quotes.empty:
+            msg = f'{self.ticker}: yfinance received OHLC data: EMPTY'
+        elif len(quotes) == 1:
+            msg = f'{self.ticker}: yfinance received OHLC data: {quotes.index[0]} only'
+        else:
+            msg = f'{self.ticker}: yfinance received OHLC data: {quotes.index[0]} -> {quotes.index[-1]}'
+        logger.debug(msg)

-        # 2) fix weired bug with Yahoo! - returning 60m for 30m bars
+        # 2) fix weird bug with Yahoo! - returning 60m for 30m bars
        if interval.lower() == "30m":
            logger.debug(f'{self.ticker}: resampling 30m OHLC from 15m')
            quotes2 = quotes.resample('30min')
@@ -290,7 +297,13 @@ class PriceHistory:
                self._history_metadata_formatted = True
                tps = self._history_metadata["tradingPeriods"]
            quotes = utils.fix_Yahoo_returning_prepost_unrequested(quotes, params["interval"], tps)
-        logger.debug(f'{self.ticker}: OHLC after cleaning: {quotes.index[0]} -> {quotes.index[-1]}')
+        if quotes.empty:
+            msg = f'{self.ticker}: OHLC after cleaning: EMPTY'
+        elif len(quotes) == 1:
+            msg = f'{self.ticker}: OHLC after cleaning: {quotes.index[0]} only'
+        else:
+            msg = f'{self.ticker}: OHLC after cleaning: {quotes.index[0]} -> {quotes.index[-1]}'
+        logger.debug(msg)

        # actions
        dividends, splits, capital_gains = utils.parse_actions(data["chart"]["result"][0])
@@ -353,7 +366,13 @@ class PriceHistory:
                df.loc[df["Capital Gains"].isna(), "Capital Gains"] = 0
            else:
                df["Capital Gains"] = 0.0
-        logger.debug(f'{self.ticker}: OHLC after combining events: {quotes.index[0]} -> {quotes.index[-1]}')
+        if df.empty:
+            msg = f'{self.ticker}: OHLC after combining events: EMPTY'
+        elif len(df) == 1:
+            msg = f'{self.ticker}: OHLC after combining events: {df.index[0]} only'
+        else:
+            msg = f'{self.ticker}: OHLC after combining events: {df.index[0]} -> {df.index[-1]}'
+        logger.debug(msg)

        df = utils.fix_Yahoo_returning_live_separate(df, params["interval"], tz_exchange, repair=repair, currency=currency)

@@ -425,7 +444,13 @@ class PriceHistory:
        if interval != interval_user:
            df = self._resample(df, interval, interval_user, period_user)

-        logger.debug(f'{self.ticker}: yfinance returning OHLC: {df.index[0]} -> {df.index[-1]}')
+        if df.empty:
+            msg = f'{self.ticker}: yfinance returning OHLC: EMPTY'
+        elif len(df) == 1:
+            msg = f'{self.ticker}: yfinance returning OHLC: {df.index[0]} only'
+        else:
+            msg = f'{self.ticker}: yfinance returning OHLC: {df.index[0]} -> {df.index[-1]}'
+        logger.debug(msg)

        if self._reconstruct_start_interval is not None and self._reconstruct_start_interval == interval:
            self._reconstruct_start_interval = None
@@ -682,7 +707,7 @@ class PriceHistory:
            if min_dt is not None:
                fetch_start = max(min_dt.date(), fetch_start)
            logger.debug(f"Fetching {sub_interval} prepost={prepost} {fetch_start}->{fetch_end}", extra=log_extras)
-            # Temp disable erors printing
+            # Temp disable errors printing
            logger = utils.get_yf_logger()
            if hasattr(logger, 'level'):
                # YF's custom indented logger doesn't expose level
@@ -928,7 +953,7 @@ class PriceHistory:
        if prices_in_subunits:
            for c in _PRICE_COLNAMES_:
                df[c] *= m
-        self._history_metadata["currency"] = currency
+        self._history_metadata["currency"] = currency2

        f_div = df['Dividends']!=0.0
        if f_div.any():
@@ -1297,14 +1322,6 @@ class PriceHistory:

        # Split df2 into: nan data, and non-nan data
        f_nan = df2['Close'].isna().to_numpy()
-        f_no_change_inter = df2['Close'].iloc[1:].to_numpy() == df2['Close'].iloc[:-1].to_numpy()
-        f_no_change_inter = np.append([False], f_no_change_inter)
-        f_no_change_intra = (df2['High'] == df2['Low']).to_numpy()
-        f_no_change = f_no_change_intra & f_no_change_inter
-        f_nan = f_nan | f_no_change
-        # Sometimes a dividend occurs on day with zero volume, and sometimes they are bad dividends that need correction!
-        # So don't discard volume=0
-        f_nan = f_nan & (df2['Dividends']==0.0).to_numpy()
        df2_nan = df2[f_nan].copy()
        df2 = df2[~f_nan].copy()

@@ -1325,8 +1342,11 @@ class PriceHistory:
            if div_idx == 0:
                continue
            prices_before = df2.iloc[div_idx-1]
-            if prices_before['Close'] < prices_before['Low']:
-                div = df2['Dividends'].iloc[div_idx]
+            diff = prices_before['Low'] - prices_before['Close']
+            div = df2['Dividends'].iloc[div_idx]
+            if diff > 0 and (diff/div-1)<0.01:
+                # Close<Low and the difference not bigger than the dividend, 
+                # because if diff > dividend then something else caused problem.
                dt_before = df2.index[div_idx-1]
                new_close = prices_before['Close'] + div
                if new_close >= prices_before['Low'] and new_close <= prices_before['High']:
@@ -1353,15 +1373,30 @@ class PriceHistory:
            div_pct = div / df2['Close'].iloc[div_idx-1]

            # Check if dividend is 100x market movement.
-            div_too_big = False
-            div_too_small = False
            div_too_small_improvement_threshold = 1
            # div_too_big_improvement_threshold = 1
            div_too_big_improvement_threshold = 2

-            drop_c2l = df2['Close'].iloc[div_idx-1] - df2['Low'].iloc[div_idx]
-            # drop_c2c = df2['Close'].iloc[div_idx-1] - df2['Close'].iloc[div_idx]
-            # drop = drop_c2c
+            if isclose(df2['Low'].iloc[div_idx], df2['Close'].iloc[div_idx-1]*100, rel_tol = 0.025):
+                # Price has jumped ~100x on ex-div day, need to fix immediately.
+                drop_c2l = df2['Close'].iloc[div_idx-1]*100 - df2['Low'].iloc[div_idx]
+                div_pct = div / (df2['Close'].iloc[div_idx-1]*100)
+                true_adjust = 1.0 - div / (df2['Close'].iloc[div_idx-1]*100)
+                present_adj = df2['Adj Close'].iloc[div_idx-1] / df2['Close'].iloc[div_idx-1]
+                if not isclose(present_adj, true_adjust, rel_tol = 0.025):
+                    df2.loc[:dt-_datetime.timedelta(seconds=1), 'Adj Close'] = true_adjust * df2['Close'].loc[:dt-_datetime.timedelta(seconds=1)]
+                    df2.loc[:dt-_datetime.timedelta(seconds=1), 'Repaired?'] = True
+            elif isclose(df2['Low'].iloc[div_idx], df2['Close'].iloc[div_idx-1]*0.01, rel_tol = 0.025):
+                # Price has dropped ~100x on ex-div day, need to fix immediately.
+                drop_c2l = df2['Close'].iloc[div_idx-1]*0.01 - df2['Low'].iloc[div_idx]
+                div_pct = div / (df2['Close'].iloc[div_idx-1]*0.01)
+                true_adjust = 1.0 - div / (df2['Close'].iloc[div_idx-1]*100)
+                present_adj = df2['Adj Close'].iloc[div_idx-1] / df2['Close'].iloc[div_idx-1]
+                if not isclose(present_adj, true_adjust, rel_tol = 0.025):
+                    df2.loc[:dt-_datetime.timedelta(seconds=1), 'Adj Close'] = true_adjust * df2['Close'].loc[:dt-_datetime.timedelta(seconds=1)]
+                    df2.loc[:dt-_datetime.timedelta(seconds=1), 'Repaired?'] = True
+            else:
+                drop_c2l = df2['Close'].iloc[div_idx-1] - df2['Low'].iloc[div_idx]
            drop = drop_c2l
            if div_idx < len(df2)-1:
                # # In low-volume scenarios, the price drop is day after not today.
@@ -1371,10 +1406,13 @@ class PriceHistory:
                # elif df2['Volume'].iloc[div_idx]==0:
                #     if drop == 0.0:
                #         drop = np.max(df2['Close'].iloc[div_idx-1:div_idx+1].to_numpy() - df2['Low'].iloc[div_idx:div_idx+2].to_numpy())
+                #
                # Hmm, can I always look ahead 1 day? Catch: increases FP rate of div-too-small for tiny divs.
-                drops = df2['Close'].iloc[div_idx-1:div_idx+1].to_numpy() - df2['Low'].iloc[div_idx:div_idx+2].to_numpy()
+                # drops = df2['Close'].iloc[div_idx-1:div_idx+1].to_numpy() - df2['Low'].iloc[div_idx:div_idx+2].to_numpy()
+                drops = np.array([drop, df2['Close'].iloc[div_idx] - df2['Low'].iloc[div_idx+1]])
                drop_2Dmax = np.max(drops)
            else:
+                drops = np.array([drop])
                drop_2Dmax = drop

            if (len(df2)-div_idx) < 4:
@@ -1388,44 +1426,95 @@ class PriceHistory:
                typical_volatility = np.nan
            else:
                diffs = df2['Close'].iloc[start:end-1].to_numpy() - df2['Low'].iloc[start+1:end].to_numpy()
-                typical_volatility = np.median(np.abs(diffs))
+                typical_volatility = np.mean(np.abs(diffs))

-            if drop == 0.0 and df2['Volume'].iloc[div_idx]==0:
+            possibilities = []
+            if (drops==0.0).all() and df2['Volume'].iloc[div_idx]==0:
                # Can't analyse price action so use crude heuristics
-                # if div_pct*100 < 0.1:
-                if div_pct*100 < 0.08:  # lowered for $AG and their tiny divs
-                    div_too_small = True  # Could be a 0.01x error
-                elif div_pct > 1.0:
-                    div_too_big = True  # Could be a 100x error
-            if not (div_too_small or div_too_big):
+                pct_zero_vol = np.sum(df2['Volume']==0.0)/len(df2)
+                if div_pct*100 < 0.1:
+                    # Could be a 0.01x error
+                    possibilities.append({'state':'div-too-small', 'diff':0.0})
+                # elif div_pct > 1.0:
+                # Update: lower threshold for illiquid stocks, because why paying mega dividends?
+                elif (pct_zero_vol > 0.75 and div_pct > 0.25) or (div_pct > 1.0):
+                    # Could be a 100x error
+                    possibilities.append({'state':'div-too-big', 'diff':0.0})
+            else:
+                split = df2['Stock Splits'].loc[dt]
+                if split == 0.0:
+                    div_postSplit = None
+                else:
+                    # Maybe Yahoo has not applied coincident split to dividend
+                    div_postSplit = div / split
+
+                    if div_postSplit > div:
+                        # Use volatility-adjusted drop
+                        _drop = drop - typical_volatility
+                    else:
+                        _drop = drop_2Dmax
+                    if _drop > 0:
+                        diff = abs(div-_drop)
+                        diff_postSplit = abs(div_postSplit-_drop)
+                        if (diff_postSplit * div_too_big_improvement_threshold) <= diff:
+                            possibilities.append({'state':'div-pre-split', 'diff':diff_postSplit})
+
                # Check for div-too-big
                if div_pct > too_big_check_threshold:
                    if drop_2Dmax <= 0.0:
-                        div_too_big = True
+                        possibilities.append({'state':'div-too-big', 'diff':0.0})
                    else:
                        diff = abs(div-drop_2Dmax)
-                        diff2 = abs((div/currency_divide)-drop_2Dmax)
-                        if (diff2 * div_too_big_improvement_threshold) <= diff:
-                            # div exceeds market move AND 0.01x div fits drop better
-                            div_too_big = True
+                        diff_fx = abs((div/currency_divide)-drop_2Dmax)
+                        if div_postSplit is None:
+                            if (diff_fx * div_too_big_improvement_threshold) <= diff:
+                                possibilities.append({'state':'div-too-big', 'diff':diff_fx})
+                        else:
+                            diff_fxPostSplit = abs((div_postSplit/currency_divide)-drop_2Dmax)
+                            if diff_fx < diff_fxPostSplit:
+                                if (diff_fx * div_too_big_improvement_threshold) <= diff:
+                                    possibilities.append({'state':'div-too-big', 'diff':diff_fx})
+                            else:
+                                if (diff_fxPostSplit * div_too_big_improvement_threshold) <= diff:
+                                    possibilities.append({'state':'div-too-big-and-pre-split', 'diff':diff_fxPostSplit})

                # Check for div-too-small - can be tricked by normal price volatility
-                if not div_too_big and not np.isnan(typical_volatility):
-                    drop_wo_vol = drop_2Dmax - typical_volatility
+                if not np.isnan(typical_volatility):
+                    # drop_wo_vol = drop_2Dmax - typical_volatility
+                    # Update: only use same-day change for too-small, to reduce false-positives
+                    drop_wo_vol = drop - typical_volatility
                    if drop_wo_vol > 0:
                        diff = abs(div-drop_wo_vol)
-                        diff2 = abs((div*currency_divide)-drop_wo_vol)
-                        if (diff2 * div_too_small_improvement_threshold) <= diff:
-                            # market move exceeds div AND 100x div fits drop better
-                            div_too_small = True
+                        diff_fx = abs((div*currency_divide)-drop_wo_vol)
+                        if div_postSplit is None:
+                            if (diff_fx * div_too_small_improvement_threshold) <= diff:
+                                possibilities.append({'state':'div-too-small', 'diff':diff_fx})
+                        else:
+                            diff_fxPostSplit = abs((div_postSplit*currency_divide)-drop_wo_vol)
+                            if diff_fx < diff_fxPostSplit:
+                                if (diff_fx * div_too_big_improvement_threshold) <= diff:
+                                    possibilities.append({'state':'div-too-small', 'diff':diff_fx})
+                            else:
+                                if (diff_fxPostSplit * div_too_big_improvement_threshold) <= diff:
+                                    possibilities.append({'state':'div-too-small-and-pre-split', 'diff':diff_fxPostSplit})

            div_status = {'date': dt, 'idx':div_idx, 'div': div, '%': div_pct}
            div_status['drop'] = drop
            div_status['drop_2Dmax'] = drop_2Dmax
            div_status['volume'] = df2['Volume'].iloc[div_idx]
            div_status['vol'] = typical_volatility
-            div_status['div_too_big'] = div_too_big
-            div_status['div_too_small'] = div_too_small
+
+            div_status['div_too_big'] = False
+            div_status['div_too_small'] = False
+            div_status['div_pre_split'] = False
+            div_status['div_too_big_and_pre_split'] = False
+            div_status['div_too_small_and_pre_split'] = False
+            if len(possibilities) > 0:
+                # Something is wrong with dividend - pick the best correction
+                possibilities = sorted(possibilities, key=lambda k: k['diff'])
+                p = possibilities[0]
+                div_status[p['state'].replace('-', '_')] = True
+
            row = pd.DataFrame([div_status]).set_index('date')
            if div_status_df is None:
                div_status_df = row
@@ -1434,9 +1523,10 @@ class PriceHistory:

        if div_status_df is None and not df_modified:
            return df
+        checks = [c for c in div_status_df.columns if c.startswith('div_')]
        div_status_df = div_status_df.sort_index()

-        def cluster_dividends(df, column='div', threshold=10):
+        def cluster_dividends(df, column='div', threshold=7):
            n = len(df)
            sorted_df = df.sort_values(column)
            clusters = []
@@ -1465,6 +1555,8 @@ class PriceHistory:
            return cluster_labels

        # Check if the present div-adjustment is too big/small, or missing
+        # - too-big determined from Adj Close movement vs Close
+        # - too-small compares Adj Close vs dividends
        for i in range(len(div_status_df)):
            div_idx = div_status_df['idx'].iloc[i]
            dt = div_status_df.index[i]
@@ -1480,28 +1572,6 @@ class PriceHistory:
            post_adj = df2['Adj Close'].iloc[div_idx] / df2['Close'].iloc[div_idx]
            div_missing_from_adjclose = post_adj == pre_adj

-            # Adj Close should drop by LESS than Close on ex-div, at least for big dividends.
-            # Update: Yahoo might be reporting dividend slightly early, meaning
-            # Mr Market's price drop happens tomorrow e.g. UNTC in december 2023
-            lookahead_date = dt+_datetime.timedelta(days=20)
-            lookahead_idx = bisect.bisect_left(df2.index, lookahead_date)
-            lookahead_idx = min(lookahead_idx, len(df2)-1)
-            # In rare cases, the price dropped 1 day before dividend (DVD.OL @ 2024-05-15)
-            lookback_idx = div_idx-2 if div_idx > 1 else div_idx-1
-            div_adj_exceeds_prices = False
-            if lookahead_idx > lookback_idx:
-                close_deltas = np.diff(df2['Close'].iloc[lookback_idx:lookahead_idx+1].to_numpy())
-                adjClose_deltas = np.diff(df2['Adj Close'].iloc[lookback_idx:lookahead_idx+1].to_numpy())
-                close_chgs_pct = close_deltas / df2['Close'].iloc[lookback_idx:lookahead_idx].to_numpy()
-                adjClose_chgs_pct = adjClose_deltas / df2['Adj Close'].iloc[lookback_idx:lookahead_idx].to_numpy()
-                # Check if adjustment is too much. Need a big dividend % to be sure.
-                if div_pct > 0.15:
-                    adjClose_chg_pct = np.max(adjClose_chgs_pct)
-                    close_chg_pct = np.max(close_chgs_pct)
-                    if adjClose_chg_pct > 0.1 and adjClose_chg_pct > 1.0001*close_chg_pct:
-                        # Bigger drop
-                        div_adj_exceeds_prices = True
-
            # Check if adjustment too small
            present_adj = pre_adj / post_adj
            implied_div_yield = 1.0 - present_adj
@@ -1510,39 +1580,33 @@ class PriceHistory:
            # ... and use same method for adjustment too big:
            div_adj_exceeds_div = implied_div_yield > (10*div_pct)

-            # Have done 4 different checks that can interact, so handle very carefully.
            # Can prune the space:
            if div_missing_from_adjclose:
                div_adj_is_too_small = False  # redundant information

-            if div_adj_exceeds_prices and div_adj_is_too_small:
-                # Contradiction. Assume former tricked by low-liquidity price action
-                div_adj_exceeds_prices = False
-
            div_status = {'present adj': present_adj}
            div_status['adj_missing'] = div_missing_from_adjclose
-            div_status['adj_exceeds_prices'] = div_adj_exceeds_prices
            div_status['adj_exceeds_div'] = div_adj_exceeds_div
            div_status['div_exceeds_adj'] = div_adj_is_too_small

            for k,v in div_status.items():
                if k not in div_status_df:
-                    if isinstance(v, (bool, np.bool)):
+                    if isinstance(v, (bool, np.bool_)):
                        div_status_df[k] = False
                    elif isinstance(v, int):
                        div_status_df[k] = 0
                    elif isinstance(v, float):
                        div_status_df[k] = 0.0
+                    # elif k == 'div_true_date':
+                    #     div_status_df[k] = pd.Series(dtype='datetime64[ns, UTC]')
                    else:
                        raise Exception(k,v,type(v))
                div_status_df.loc[dt, k] = v
-
-        checks = ['adj_missing', 'adj_exceeds_div', 'adj_exceeds_prices', 'div_exceeds_adj', 'div_too_big', 'div_too_small', 'fx_mixup']
-        checks = [c for c in checks if c in div_status_df.columns]
+        checks += ['adj_missing', 'adj_exceeds_div', 'div_exceeds_adj']

        div_status_df['phantom'] = False
        phantom_proximity_threshold = _datetime.timedelta(days=7)
-        f = div_status_df['div_too_big'] | div_status_df['div_exceeds_adj']
+        f = div_status_df[['div_too_big', 'div_exceeds_adj']].any(axis=1)
        if f.any():
            # One/some of these may be phantom dividends. Clue is if another correct dividend is very close
            indices = np.where(f)[0]
@@ -1558,7 +1622,7 @@ class PriceHistory:
                    divergence = min(abs(ratio1-1.0), abs(ratio2-1.0))
                    if abs(div_dt-prev_div.name) <= phantom_proximity_threshold and not prev_div['phantom'] and divergence < 0.01:
                        if prev_div.name in dts_to_check:
-                            # Both this and previous are anomolous, so mark smallest drop as phantom
+                            # Both this and previous are anomalous, so mark smallest drop as phantom
                            drop = div['drop']
                            drop_prev = prev_div['drop']
                            if drop > 1.5*drop_prev:
@@ -1574,7 +1638,7 @@ class PriceHistory:
                    divergence = min(abs(ratio1-1.0), abs(ratio2-1.0))
                    if abs(div_dt-next_div.name) <= phantom_proximity_threshold and divergence < 0.01:
                        if next_div.name in dts_to_check:
-                            # Both this and previous are anomolous, so mark smallest drop as phantom
+                            # Both this and previous are anomalous, so mark smallest drop as phantom
                            drop = div['drop']
                            drop_next = next_div['drop']
                            if drop > 1.5*drop_next:
@@ -1617,13 +1681,35 @@ class PriceHistory:
                        div_status_df.loc[phantom_div_dt, c] = False
        checks.append('phantom')

+        # Remove phantoms early
+        if 'phantom' in div_status_df.columns:
+            f_phantom = div_status_df['phantom']
+            # ... but only if no other problems
+            f_phantom = f_phantom & (~div_status_df[[c for c in checks if c != 'phantom']].any(axis=1))
+            if f_phantom.any():
+                div_dts = div_status_df.index[f_phantom]
+                msg = f'Removing phantom div(s): {[str(dt.date()) for dt in div_dts]}'
+                logger.info(msg, extra=log_extras)
+                phantom_div_dts = div_status_df.index[f_phantom]
+                for dt in phantom_div_dts:
+                    enddt = dt-_datetime.timedelta(seconds=1)
+                    df2.loc[    :enddt, 'Adj Close'] /= div_status_df['present adj'].loc[dt]
+                    df2.loc[    :enddt, 'Repaired?'] = True
+                    df2_nan.loc[:enddt, 'Adj Close'] /= div_status_df['present adj'].loc[dt]
+                    df2_nan.loc[:enddt, 'Repaired?'] = True
+                    df2.loc[dt, 'Dividends'] = 0
+                    df_modified = True
+                    div_status_df = div_status_df.drop(dt)
+                div_status_df.loc[f_phantom, 'phantom'] = False
+            div_status_df = div_status_df.drop('phantom', axis=1)
+            if 'phantom' in checks:
+                checks.remove('phantom')
+
        if not div_status_df[checks].any().any():
            # Maybe failed to detect a too-small div. If div is ~0.01x of previous and next, then
            # treat as a 0.01x error
            if len(div_status_df) > 1:
                for i in range(0, len(div_status_df)):
-                    if div_status_df['phantom'].iloc[i]:
-                        continue
                    r_pre, r_post = None, None
                    if i > 0:
                        r_pre = div_status_df['%'].iloc[i-1] / div_status_df['%'].iloc[i]
@@ -1637,37 +1723,166 @@ class PriceHistory:

        if not div_status_df[checks].any().any():
            # Perfect
-            return df
+            if df_modified:
+                return df2
+            else:
+                return df
+
+        # Check if the present div-adjustment contradicts price action
+        for i in range(len(div_status_df)):
+            div_idx = div_status_df['idx'].iloc[i]
+            dt = div_status_df.index[i]
+            div = div_status_df['div'].iloc[i]
+            if div_idx == 0:
+                continue
+            div_pct = div / df2['Close'].iloc[div_idx-1]
+
+            # Adj Close should drop by LESS than Close on ex-div, at least for big dividends.
+            # Update: Yahoo might be reporting dividend slightly early, meaning
+            # Mr Market's price drop happens tomorrow e.g. UNTC in december 2023.
+            # Or worse, Yahoo is 1 month early e.g. GWI.L ex-div was mid-April not mid-March
+            lookahead_date = dt+_datetime.timedelta(days=35)
+            lookahead_idx = bisect.bisect_left(df2.index, lookahead_date)
+            lookahead_idx = min(lookahead_idx, len(df2)-1)
+            # In rare cases, the price dropped 1 day before dividend (DVD.OL @ 2024-05-15)
+            lookback_idx = div_idx-2 if div_idx > 1 else div_idx-1
+            # Check for bad stock splits in the lookahead period - 
+            # if present, reduce lookahead to before.
+            future_changes = df2['Close'].iloc[div_idx:lookahead_idx+1].pct_change()
+            f_big_change = (future_changes > 2).to_numpy() | (future_changes < -0.9).to_numpy()
+            if f_big_change.any():
+                lookahead_idx = div_idx + np.where(f_big_change)[0][0]-1
+                lookahead_date = df2.index[lookahead_idx]
+            
+            div_adj_exceeds_prices = False
+            div_date_wrong = False
+            div_true_date = pd.NaT
+            if lookahead_idx > lookback_idx:
+                x = df2.iloc[lookback_idx:lookahead_idx+1].copy()
+                x['Adj'] = x['Adj Close'] / x['Close']
+                x['Adj Low'] = x['Adj'] * x['Low']
+                deltas = x['Low'].iloc[1:].to_numpy() - x['Close'].iloc[:-1].to_numpy()
+                deltas = np.append([0.0], deltas)
+                x['delta'] = deltas
+                adjDeltas = x['Adj Low'].iloc[1:].to_numpy() - x['Adj Close'].iloc[:-1].to_numpy()
+                adjDeltas = np.append([0.0], adjDeltas)
+                x['adjDelta'] = adjDeltas
+                for i in np.where(x['Dividends']>0)[0]:
+                    x.loc[x.index[i], 'adjDelta'] += x['Dividends'].iloc[i]*x['Adj'].iloc[i]
+                deltas = x[['delta', 'adjDelta']]
+                if div_pct > 0.05 and div_pct < 1.0:
+                    adjDiv = div * x['Adj'].iloc[0]
+                    f = deltas['adjDelta'] > (adjDiv*0.6)
+                    if f.any():
+                        indices = np.where(f)[0]
+                        for idx in indices:
+                            adjDelta_drop = deltas['adjDelta'].iloc[idx]
+                            if adjDelta_drop > 1.001*deltas['delta'].iloc[idx]:
+                                # Adjusted price has risen by more than unadjusted, should not happen.
+                                # See if Adjusted price later falls by a similar amount. This would mean
+                                # dividend has been applied too early.
+                                ratios = (-1*deltas['adjDelta'])/adjDelta_drop
+                                f_near1_or_above = ratios>=0.8
+                                # Update: only check for wrong date if no coincident split.
+                                # Because if a split, more likely the div is missing split
+                                split = df2['Stock Splits'].loc[dt]
+                                pre_split = div_status_df['div_pre_split'].loc[dt]
+                                if (split==0.0 or (not pre_split)) and f_near1_or_above.any():
+                                    near_indices = np.where(f_near1_or_above)[0]
+                                    if len(near_indices) > 1:
+                                        penalties = np.zeros(len(near_indices))
+                                        for i in range(len(near_indices)):
+                                            idx = near_indices[i]
+                                            dti = ratios.index[idx]
+                                            if dti < dt:
+                                                penalties[i] += (dt-dti).days
+                                            else:
+                                                penalties[i] += 0.1*(dti-dt).days
+                                        i = np.argmin(penalties)
+                                        reversal_idx = near_indices[i]
+                                    else:
+                                        reversal_idx = near_indices[0]
+                                    div_date_wrong = True
+                                    div_true_date = ratios.index[reversal_idx]
+                                    break
+                                elif adjDelta_drop > 0.39*adjDiv:
+                                    # Still true that applied adjustment exceeds price action, 
+                                    # just not clear what solution is (if any).
+                                    if (x['Adj']<1.0).any():
+                                        div_adj_exceeds_prices = True
+                                    break
+
+            # Can prune the space:
+            div_adj_is_too_small = div_status_df.loc[dt, 'div_exceeds_adj']
+            if div_adj_exceeds_prices and div_adj_is_too_small:
+                # Contradiction. Assume former tricked by low-liquidity price action
+                div_adj_exceeds_prices = False
+
+            div_status = {}
+            div_status['adj_exceeds_prices'] = div_adj_exceeds_prices
+            div_status['div_date_wrong'] = div_date_wrong
+            div_status['div_true_date'] = div_true_date
+
+            if div_adj_exceeds_prices:
+                split = df2['Stock Splits'].loc[dt]
+                if split != 0.0:
+                    # Check again if div missing split. Use looser tolerance
+                    # as we know the adjustment seems wrong.
+                    div_postSplit = div / split
+                    if div_postSplit > div:
+                        # Use volatility-adjusted drop
+                        typical_volatility = div_status_df['vol'].loc[dt]
+                        drop = div_status_df['drop'].loc[dt]
+                        _drop = drop - typical_volatility
+                    else:
+                        drop_2Dmax = div_status_df['drop_2Dmax'].loc[dt]
+                        _drop = drop_2Dmax
+                    if _drop > 0:
+                        diff = abs(div-_drop)
+                        diff_postSplit = abs(div_postSplit-_drop)
+                        if diff_postSplit <= (diff*1.1):
+                            # possibilities.append({'state':'div-pre-split', 'diff':diff_postSplit})
+                            div_status_df.loc[dt, 'div_pre_split'] = True
+
+            for k,v in div_status.items():
+                if k not in div_status_df:
+                    if isinstance(v, (bool, np.bool_)):
+                        div_status_df[k] = False
+                    elif isinstance(v, int):
+                        div_status_df[k] = 0
+                    elif isinstance(v, float):
+                        div_status_df[k] = 0.0
+                    elif k == 'div_true_date':
+                        div_status_df[k] = pd.Series(dtype='datetime64[ns, UTC]')
+                    else:
+                        raise Exception(k,v,type(v))
+                div_status_df.loc[dt, k] = v
+            if 'div_too_big' in div_status_df.columns and 'div_date_wrong' in div_status_df.columns:
+                # Where div_date_wrong = True, discard div_too_big. Helps with false-positive handling later.
+                div_status_df.loc[div_status_df['div_date_wrong'].to_numpy(), 'div_too_big'] = False
+
+        checks += ['adj_exceeds_prices', 'div_date_wrong']
+
        for c in checks:
            if not div_status_df[c].any():
                div_status_df = div_status_df.drop(c, axis=1)
+        c = 'div_true_date'
+        if c in div_status_df.columns and div_status_df[c].isna().all():
+            div_status_df = div_status_df.drop(c, axis=1)
        checks = [c for c in checks if c in div_status_df.columns]

        # With small dividends e.g. < 10%, my error detecting logic can be tricked by price volatility.
        # But by looking at all the dividends, can find errors that previous logic missed.

        div_status_df = div_status_df.sort_values('%')
-        div_status_df['cluster'] = cluster_dividends(div_status_df, column='%', )
-
-        # Discard columns with no problems
-        for c in checks:
-            if (~div_status_df[c]).all():
-                div_status_df = div_status_df.drop(c, axis=1)
-        checks = [c for c in checks if c in div_status_df.columns]
-        if len(checks) == 0:
-            return df
+        div_status_df['cluster'] = cluster_dividends(div_status_df, column='%')

+        # Check for inconsistencies
        cluster_ids = div_status_df['cluster'].unique()
        for cid in cluster_ids:
            fc = div_status_df['cluster'] == cid
            cluster = div_status_df[fc].sort_index()
-
-            if 'phantom' in cluster.columns:
-                cluster = cluster[~cluster['phantom']]
            n = len(cluster)
-            if n == 0:
-                # this cluster is just phantom
-                continue
            div_pcts = cluster[['%']].copy()
            if len(div_pcts) > 1:
                time_diffs = div_pcts['%'].index.to_series().diff().dt.total_seconds() / (365.25 * 24 * 60 * 60)
@@ -1676,15 +1891,36 @@ class PriceHistory:
                div_pcts['avg yr yield'] = div_pcts['%'] / div_pcts['period']

            for c in checks:
+                if not cluster[c].to_numpy().any():
+                    cluster = cluster.drop(c, axis=1)
+            cluster_checks = [c for c in checks if c in cluster.columns]
+
+            for c in cluster_checks:
                f_fail = cluster[c].to_numpy()
                n_fail = np.sum(f_fail)
                if n_fail in [0, n]:
                    continue
-                pct_fail = np.sum(f_fail) / n
+                pct_fail = n_fail / n
                if c == 'div_too_big':
                    true_threshold = 1.0
                    fals_threshold = 0.2

+                    if 'div_date_wrong' in cluster.columns and (cluster[c] == cluster['div_date_wrong']).all():
+                        continue
+
+                    if 'adj_exceeds_prices' in cluster.columns and (cluster[c] == (cluster[c] & cluster['adj_exceeds_prices'])).all():
+                        # Treat div_too_big=False as false positives IFF adj_exceeds_prices=true AND 
+                        # true ratio above (lowered) threshold.
+                        true_threshold = 0.5
+                        f_adj_exceeds_prices = cluster['adj_exceeds_prices'].to_numpy()
+                        n = np.sum(f_adj_exceeds_prices)
+                        n_fail = np.sum(f_fail[f_adj_exceeds_prices])
+                        pct_fail = n_fail / n
+                        if pct_fail > true_threshold:
+                            f = fc & div_status_df['adj_exceeds_prices'].to_numpy()
+                            div_status_df.loc[f, c] = True
+                        continue
+
                    if 'div_exceeds_adj' in cluster.columns and cluster['div_exceeds_adj'].all():
                        # Dividend too big for prices AND the present adjustment,
                        # more likely the dividends are too big.
@@ -1693,19 +1929,23 @@ class PriceHistory:
                            fals_threshold = 2/3
                        else:
                            # Relax thresholds
-                            true_threshold = 2/5
+                            true_threshold = 0.25

-                    elif 'adj_exceeds_prices' in cluster.columns and cluster['adj_exceeds_prices'].all():
+                    elif 'adj_exceeds_prices' in cluster.columns and (cluster[c]==cluster['adj_exceeds_prices']).all():
                        # Both dividend and present adjust too big for prices,
                        # more likely the dividends are too big.
                        true_threshold = 1/2

-                    elif not ('div_exceeds_adj' in cluster.columns or 'adj_exceeds_prices' in cluster.columns):
-                        # Present adjustment seems correct, so more likely that 'div_too_big' are false positives: NOT too big
+                    else:
                        fals_threshold = 1/2

                    if pct_fail >= true_threshold:
                        div_status_df.loc[fc, c] = True
+                        if 'div_date_wrong' in div_status_df.columns:
+                            # reset this as well
+                            div_status_df.loc[fc, 'div_date_wrong'] = False
+                            div_status_df.loc[fc, 'div_true_date'] = pd.NaT
+                            cluster = div_status_df[fc].sort_index()
                        continue
                    elif pct_fail <= fals_threshold:
                        div_status_df.loc[fc, c] = False
@@ -1716,7 +1956,8 @@ class PriceHistory:
                    fals_threshold = 0.1
                    if 'adj_exceeds_div' not in cluster.columns:
                        # Adjustment confirms dividends => more likely that 'div_too_small' are false positives: NOT too small
-                        fals_threshold = 2/3
+                        true_threshold = 6/11
+                        fals_threshold = 1/2
                    if pct_fail >= true_threshold:
                        div_status_df.loc[fc, c] = True
                        continue
@@ -1731,53 +1972,40 @@ class PriceHistory:
                if c == 'div_exceeds_adj':
                    continue

+                if c == 'adj_exceeds_prices':
+                    continue
+
                if c == 'phantom' and self.ticker in ['KAP.IL', 'SAND']:
                    # Manually approve, but these are probably safe to assume ok
                    continue

+                if c == 'div_date_wrong':
+                    # Fine, these should be rare
+                    continue
+                if c == 'div_pre_split':
+                    # Fine, these should be rare
+                    continue
+
        div_status_df = div_status_df.sort_index()

        # Discard dividends with no problems
        div_status_df = div_status_df[div_status_df[checks].any(axis=1)]
        if div_status_df.empty:
-            return df
-
-        # Process phantoms first 
-        if 'phantom' in div_status_df.columns:
-            f_phantom = div_status_df['phantom']
-            # ... but only if no other problems
-            f_phantom = f_phantom & (~div_status_df[[c for c in checks if c != 'phantom']].any(axis=1))
-            if f_phantom.any():
-                div_dts = div_status_df.index[f_phantom]
-                msg = f'Removing phantom div(s): {[str(dt.date()) for dt in div_dts]}'
-                logger.info(msg, extra=log_extras)
-                for i in np.where(f_phantom)[0]:
-                    dt = div_status_df.index[i]
-                    enddt = dt-_datetime.timedelta(seconds=1)
-                    df2.loc[    :enddt, 'Adj Close'] /= div_status_df['present adj'].iloc[i]
-                    df2.loc[    :enddt, 'Repaired?'] = True
-                    df2_nan.loc[:enddt, 'Adj Close'] /= div_status_df['present adj'].iloc[i]
-                    df2_nan.loc[:enddt, 'Repaired?'] = True
-                    df2.loc[dt, 'Dividends'] = 0
-                    df_modified = True
-                div_status_df.loc[f_phantom, 'phantom'] = False
-            # Discard dividends with no problems
-            div_status_df = div_status_df[div_status_df[checks].any(axis=1)]
-            
-            if div_status_df.empty:
-                if not df2_nan.empty:
-                    df2 = pd.concat([df2, df2_nan]).sort_index()
-                return df2
+            if not df2_nan.empty:
+                df2 = pd.concat([df2, df2_nan]).sort_index()
+            return df2

        # These arrays track changes for constructing compact log messages
        div_repairs = {}
-        for cid in div_status_df['cluster'].unique():
+        for cid in list(div_status_df['cluster'].unique()):
            cluster = div_status_df[div_status_df['cluster']==cid]
            cluster = cluster.sort_index(ascending=False)
            cluster['Fixed?'] = False
-            for i in range(len(cluster)):
+            # Reverse order because may delete false-positives
+            for i in range(len(cluster)-1, -1, -1):
                row = cluster.iloc[i]
                dt = row.name
+                enddt = dt-_datetime.timedelta(seconds=1)

                adj_missing = 'adj_missing' in row and row['adj_missing']
                div_exceeds_adj = 'div_exceeds_adj' in row and row['div_exceeds_adj']
@@ -1785,15 +2013,43 @@ class PriceHistory:
                adj_exceeds_prices = 'adj_exceeds_prices' in row and row['adj_exceeds_prices']
                div_too_small = 'div_too_small' in row and row['div_too_small']
                div_too_big = 'div_too_big' in row and row['div_too_big']
-
+                div_pre_split = 'div_pre_split' in row and row['div_pre_split']
+                # div_too_small_and_pre_split = 'div_too_small_and_pre_split' in row and row['div_too_small_and_pre_split']  # not happened yet
+                # div_too_big_and_pre_split = 'div_too_big_and_pre_split' in row and row['div_too_big_and_pre_split']  # not happened yet
+                div_date_wrong = 'div_date_wrong' in row and row['div_date_wrong']
                n_failed_checks = np.sum([row[c] for c in checks if c in row])
+
+                if div_too_big and adj_exceeds_prices and n_failed_checks == 2:
+                    # adj_exceeds_prices is redundant information, fixing div-too-big
+                    # will fix adjustment
+                    adj_exceeds_prices = False
+                    n_failed_checks -= 1
+
+                if div_date_wrong:
+                    if div_too_big:
+                        # redundant information
+                        div_too_big = False
+                        cluster.loc[dt, 'div_too_big'] = False
+                        n_failed_checks -= 1
+                    if div_exceeds_adj:
+                        # false-positive
+                        div_exceeds_adj = False
+                        cluster.loc[dt, 'div_exceeds_adj'] = False
+                        n_failed_checks -= 1
+
+                if div_pre_split:
+                    if adj_exceeds_prices:
+                        # redundant information
+                        adj_exceeds_prices = False
+                        cluster.loc[dt, 'adj_exceeds_prices'] = False
+                        n_failed_checks -= 1
+
                if n_failed_checks == 1:
                    if div_exceeds_adj or adj_exceeds_div:
                        # Simply recalculate Adj Close
                        k = 'too-small div-adjust' if div_exceeds_adj else 'too-big div-adjust'
                        div_repairs.setdefault(k, []).append(dt)
                        adj_correction = (1.0 - row['%']) / row['present adj']
-                        enddt = dt-_datetime.timedelta(seconds=1)
                        df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                        df2.loc[    :enddt, 'Repaired?'] = True
                        df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
@@ -1812,7 +2068,6 @@ class PriceHistory:
                        k += ' & div-adjust'
                        target_adj = 1.0 - ((1.0 - row['present adj']) * correction)
                        adj_correction = target_adj / row['present adj']
-                        enddt = dt-_datetime.timedelta(seconds=1)
                        df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                        df2.loc[    :enddt, 'Repaired?'] = True
                        df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
@@ -1827,13 +2082,11 @@ class PriceHistory:
                        correct_div = row['div'] * correction
                        df2.loc[dt, 'Dividends'] = correct_div

-                        # Also correct adjustment to match corrected dividend
-                        target_div_pct = row['%']/currency_divide
+                        target_div_pct = row['%'] * correction
                        target_adj = 1.0 - target_div_pct
                        present_adj = row['present adj']
                        k += ' & div-adjust'
-                        adj_correction = target_adj / row['present adj']
-                        enddt = dt-_datetime.timedelta(seconds=1)
+                        adj_correction = target_adj / present_adj
                        df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                        df2.loc[    :enddt, 'Repaired?'] = True
                        df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
@@ -1845,7 +2098,6 @@ class PriceHistory:
                    elif adj_missing:
                        k = 'missing div-adjust'
                        div_repairs.setdefault(k, []).append(dt)
-                        enddt = dt-_datetime.timedelta(seconds=1)
                        adj_correction = 1.0-row['%']
                        df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                        df2.loc[    :enddt, 'Repaired?'] = True
@@ -1853,6 +2105,69 @@ class PriceHistory:
                        df2_nan.loc[:enddt, 'Repaired?'] = True
                        cluster.loc[dt, 'Fixed?'] = True

+                    elif div_date_wrong:
+                        k = 'wrong ex-div date'
+                        div_repairs.setdefault(k, []).append(dt)
+
+                        # First rollback the present adj
+                        adj_correction = 1.0/row['present adj']
+                        df2.loc[    :enddt, 'Adj Close'] *= adj_correction
+                        df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
+
+                        # Apply correct adj from correct date
+                        div_true_date = row['div_true_date']
+                        close_before = df2['Close'].iloc[row['idx']]
+                        div = row['div']
+                        true_adj = 1.0 - div/close_before
+                        enddt2 = div_true_date-_datetime.timedelta(seconds=1)
+                        df2.loc[    :enddt2, 'Adj Close'] *= true_adj
+                        df2_nan.loc[:enddt2, 'Adj Close'] *= true_adj
+
+                        # Move div to correct date
+                        df2.loc[div_true_date, 'Dividends'] += div
+                        df2.loc[dt, 'Dividends'] = 0
+
+                        df2.loc[    :enddt, 'Repaired?'] = True
+                        df2_nan.loc[:enddt, 'Repaired?'] = True
+                        cluster.loc[dt, 'Fixed?'] = True
+
+                    elif adj_exceeds_prices:
+                        # Nothing else wrong => probably false positive, 
+                        # but no harm checking the adjustment
+                        target_adj = 1.0 - row['%']
+                        present_adj = row['present adj']
+                        if abs((target_adj/present_adj)-1) > 0.05:
+                            # Also correct adjustment to match corrected dividend
+                            k += ' & div-adjust'
+                            adj_correction = target_adj / present_adj
+                            df2.loc[    :enddt, 'Adj Close'] *= adj_correction
+                            df2.loc[    :enddt, 'Repaired?'] = True
+                            df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
+                            df2_nan.loc[:enddt, 'Repaired?'] = True
+                            cluster.loc[dt, 'Fixed?'] = True
+                        else:
+                            div_status_df = div_status_df.drop(dt)
+                            cluster = cluster.drop(dt)
+
+                    elif div_pre_split:
+                        k = 'pre-split div'
+                        correction = 1.0/df2['Stock Splits'].loc[dt]
+                        correct_div = row['div'] * correction
+                        df2.loc[dt, 'Dividends'] = correct_div
+
+                        target_div_pct = row['%'] * correction
+                        target_adj = 1.0 - target_div_pct
+                        present_adj = row['present adj']
+                        # Also correct adjustment to match corrected dividend
+                        k += ' & div-adjust'
+                        adj_correction = target_adj / present_adj
+                        df2.loc[    :enddt, 'Adj Close'] *= adj_correction
+                        df2.loc[    :enddt, 'Repaired?'] = True
+                        df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
+                        df2_nan.loc[:enddt, 'Repaired?'] = True
+                        cluster.loc[dt, 'Fixed?'] = True
+                        div_repairs.setdefault(k, []).append(dt)
+
                elif n_failed_checks == 2:
                    if div_too_big and adj_missing:
                        # A currency unit mixup AND adjustment missing
@@ -1860,7 +2175,6 @@ class PriceHistory:
                        div_repairs.setdefault(k, []).append(dt)
                        adj_correction = 1.0 - row['%']/currency_divide
                        df2.loc[dt, 'Dividends'] /= currency_divide
-                        enddt = dt-_datetime.timedelta(seconds=1)
                        df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                        df2.loc[    :enddt, 'Repaired?'] = True
                        df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
@@ -1879,7 +2193,6 @@ class PriceHistory:
                            msg = None
                            div_adj = 1.0 - (row['%']/currency_divide)
                            adj_correction = div_adj / row['present adj']
-                            enddt = dt-_datetime.timedelta(seconds=1)
                            df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                            df2.loc[    :enddt, 'Repaired?'] = True
                            df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
@@ -1899,13 +2212,15 @@ class PriceHistory:
                        target_adj = 1.0 - target_div_pct
                        adj_correction = target_adj / row['present adj']
                        df2.loc[dt, 'Dividends'] /= currency_divide
-                        enddt = dt-_datetime.timedelta(seconds=1)
                        df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                        df2.loc[    :enddt, 'Repaired?'] = True
                        df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
                        df2_nan.loc[:enddt, 'Repaired?'] = True
                        cluster.loc[dt, 'Fixed?'] = True

+            if cluster.empty:
+                continue
+
        for k in div_repairs:
            msg = f"Repaired {k}: {[str(dt.date()) for dt in sorted(div_repairs[k])]}"
            logger.info(msg, extra=log_extras)
@@ -2158,6 +2473,14 @@ class PriceHistory:

        r = _1d_change_x / split_rcp
        f_down = _1d_change_x < 1.0 / threshold
+        if f_down.any():
+            # Discard where triggered by negative Adj Close after dividend
+            f_neg = _1d_change_x < 0.0
+            f_div = (df2['Dividends']>0).to_numpy()
+            f_div_before = np.roll(f_div, 1)
+            if f_down.ndim == 2:
+                f_div_before = f_div_before[:, np.newaxis].repeat(f_down.shape[1], axis=1)
+            f_down = f_down & ~(f_neg + f_div_before)
        f_up = _1d_change_x > threshold
        f_up_ndims = len(f_up.shape)
        f_up_shifts = f_up if f_up_ndims==1 else f_up.any(axis=1)
@@ -2166,12 +2489,24 @@ class PriceHistory:
            for i in np.where(f_up_shifts)[0]:
                v = df2['Volume'].iloc[i]
                vol_change_pct = 0 if v == 0 else df2['Volume'].iloc[i-1] / v
-                if multiday:
-                    v = df2['Volume'].iloc[i+1]
-                    if v > 0:
-                        vol_change_pct = max(vol_change_pct, df2['Volume'].iloc[i] / v)
+                if multiday and (i+1 < len(df2)):
+                    next_v = df2['Volume'].iloc[i+1]
+                    if next_v > 0:
+                        vol_change_pct = max(vol_change_pct, df2['Volume'].iloc[i] / next_v)
                if vol_change_pct > 5:
-                    # big volume change +500% = false positive
+                    # big volume change +500%
+                    # Could be false-positive, but need some more checks
+                    lookback = max(0, i-10)
+                    lookahead = min(len(df2), i+10)
+                    if (df2['Stock Splits'].iloc[lookback:lookahead]!=0.0).any():
+                        # There's a stock split near the volume spike, so 
+                        # assume false positive
+                        continue
+                    avg_vol_after = df2['Volume'].iloc[lookback:i-1].mean()
+                    if not np.isnan(avg_vol_after) and v/avg_vol_after < 2.0:
+                        # volume spike is actually a step-change, so 
+                        # probably missing stock split
+                        continue
                    if f_up_ndims == 1:
                        f_up[i] = False
                    else:
@@ -2219,13 +2554,13 @@ class PriceHistory:
                    logger.info('100x changes are too soon after stock split events, aborting', extra=log_extras)
                    return df

-        # if logger.isEnabledFor(logging.DEBUG):
+        if logger.isEnabledFor(logging.DEBUG):
            df_debug['i'] = list(range(0, df_debug.shape[0]))
            df_debug['i_rev'] = df_debug.shape[0]-1 - df_debug['i']
            if correct_columns_individually:
                f_change = df_debug[[c+'_down' for c in debug_cols]].any(axis=1) | df_debug[[c+'_up' for c in debug_cols]].any(axis=1)
            else:
-                f_change = df_debug['f_down'] | df_debug['f_up']
+                f_change = df_debug['down'] | df_debug['up']
            f_change = f_change | np.roll(f_change, -1) | np.roll(f_change, 1) | np.roll(f_change, -2) | np.roll(f_change, 2)
            with pd.option_context('display.max_rows', None, 'display.max_columns', 10, 'display.width', 1000):  # more options can be specified also
                logger.debug("price-repair-split: my workings:" + '\n' + str(df_debug[f_change]))
@@ -2457,7 +2792,12 @@ class PriceHistory:
                logger.debug(msg, extra=log_extras)
                n_corrected += r[1] - r[0]

-            msg = f"Corrected: {n_corrected}x"
+            if len(ranges) <= 2:
+                msg = "Corrected:"
+                for r in ranges:
+                    msg += f" {df2.index[r[1]-1].date()} -> {df2.index[r[0]].date()}"
+            else:
+                msg = f"Corrected: {n_corrected}x"
            logger.info(msg, extra=log_extras)

        if correct_volume:
--- a/yfinance/screener/init.py
+++ b/yfinance/screener/init.py
@@ -0,0 +1,4 @@
+from .screener import Screener
+from .screener_query import EquityQuery
+
+__all__ = ['EquityQuery', 'Screener']
--- a/yfinance/screener/screener.py
+++ b/yfinance/screener/screener.py
@@ -0,0 +1,225 @@
+from typing import Dict
+
+from yfinance import utils
+from yfinance.data import YfData
+from yfinance.const import _BASE_URL_, PREDEFINED_SCREENER_BODY_MAP
+from .screener_query import Query
+from ..utils import dynamic_docstring, generate_list_table_from_dict_of_dict
+
+_SCREENER_URL_ = f"{_BASE_URL_}/v1/finance/screener"
+
+class Screener:
+    """
+    The `Screener` class is used to execute the queries and return the filtered results.
+
+    The Screener class provides methods to set and manipulate the body of a screener request,
+    fetch and parse the screener results, and access predefined screener bodies.
+    """
+    def __init__(self, session=None, proxy=None):
+        """
+        Args:
+            session (requests.Session, optional): A requests session object to be used for making HTTP requests. Defaults to None.
+            proxy (str, optional): A proxy URL to be used for making HTTP requests. Defaults to None.
+        
+        .. seealso::
+
+            :attr:`Screener.predefined_bodies <yfinance.Screener.predefined_bodies>`
+                supported predefined screens
+        """
+        self.proxy = proxy
+        self.session = session
+
+        self._data: YfData = YfData(session=session)
+        self._body: Dict = {}
+        self._response: Dict = {}
+        self._body_updated = False
+        self._accepted_body_keys = {"offset","size","sortField","sortType","quoteType","query","userId","userIdType"}
+        self._predefined_bodies = PREDEFINED_SCREENER_BODY_MAP.keys()
+
+    @property
+    def body(self) -> Dict:
+        return self._body
+    
+    @property
+    def response(self) -> Dict:
+        """
+        Fetch screen result
+
+        Example:
+
+            .. code-block:: python
+
+                result = screener.response
+                symbols = [quote['symbol'] for quote in result['quotes']]
+        """
+        if self._body_updated or self._response is None:
+            self._fetch_and_parse()
+        
+        self._body_updated = False
+        return self._response
+    
+    @dynamic_docstring({"predefined_screeners": generate_list_table_from_dict_of_dict(PREDEFINED_SCREENER_BODY_MAP,bullets=False)})
+    @property
+    def predefined_bodies(self) -> Dict:
+        """
+        Predefined Screeners
+        {predefined_screeners}
+        """
+        return self._predefined_bodies
+
+    def set_default_body(self, query: Query, offset: int = 0, size: int = 100, sortField: str = "ticker", sortType: str = "desc", quoteType: str = "equity", userId: str = "", userIdType: str = "guid") -> 'Screener':
+        """
+        Set the default body using a custom query.
+
+        Args:
+            query (Query): The Query object to set as the body.
+            offset (Optional[int]): The offset for the results. Defaults to 0.
+            size (Optional[int]): The number of results to return. Defaults to 100. Maximum is 250 as set by Yahoo.
+            sortField (Optional[str]): The field to sort the results by. Defaults to "ticker".
+            sortType (Optional[str]): The type of sorting (e.g., "asc" or "desc"). Defaults to "desc".
+            quoteType (Optional[str]): The type of quote (e.g., "equity"). Defaults to "equity".
+            userId (Optional[str]): The user ID. Defaults to an empty string.
+            userIdType (Optional[str]): The type of user ID (e.g., "guid"). Defaults to "guid".
+        
+        Returns:
+            Screener: self
+
+        Example:
+
+            .. code-block:: python
+
+                screener.set_default_body(qf)
+        """
+        self._body_updated = True
+
+        self._body = {
+            "offset": offset,
+            "size": size,
+            "sortField": sortField,
+            "sortType": sortType,
+            "quoteType": quoteType,
+            "query": query.to_dict(),
+            "userId": userId,
+            "userIdType": userIdType
+        }
+        return self
+
+    def set_predefined_body(self, predefined_key: str) -> 'Screener':
+        """
+        Set a predefined body
+
+        Args: 
+            predefined_key (str): key to one of predefined screens 
+
+        Returns:
+            Screener: self
+
+        Example:
+
+            .. code-block:: python
+
+                screener.set_predefined_body('day_gainers')
+        
+                
+        .. seealso::
+
+            :attr:`Screener.predefined_bodies <yfinance.Screener.predefined_bodies>`
+                supported predefined screens
+        """
+        body = PREDEFINED_SCREENER_BODY_MAP.get(predefined_key, None)
+        if not body:
+            raise ValueError(f'Invalid key {predefined_key} provided for predefined screener')
+        
+        self._body_updated = True
+        self._body = body
+        return self
+
+    def set_body(self, body: Dict) -> 'Screener':
+        """
+        Set the fully custom body using dictionary input
+
+        Args: 
+            body (Dict): full query body
+
+        Returns:
+            Screener: self
+        
+        Example:
+
+            .. code-block:: python
+
+                screener.set_body({
+                    "offset": 0,
+                    "size": 100,
+                    "sortField": "ticker",
+                    "sortType": "desc",
+                    "quoteType": "equity",
+                    "query": qf.to_dict(),
+                    "userId": "",
+                    "userIdType": "guid"
+                })
+        """
+        missing_keys = [key for key in self._accepted_body_keys if key not in body]
+        if missing_keys:
+            raise ValueError(f"Missing required keys in body: {missing_keys}")
+
+        extra_keys = [key for key in body if key not in self._accepted_body_keys]
+        if extra_keys:
+            raise ValueError(f"Body contains extra keys: {extra_keys}")
+
+        self._body_updated = True
+        self._body = body
+        return self
+
+    def patch_body(self, values: Dict) -> 'Screener':
+        """
+        Patch parts of the body using dictionary input
+
+        Args: 
+            body (Dict): partial query body
+
+        Returns:
+            Screener: self
+
+        Example:
+
+            .. code-block:: python
+
+                screener.patch_body({"offset": 100})
+        """
+        extra_keys = [key for key in values if key not in self._accepted_body_keys]
+        if extra_keys:
+            raise ValueError(f"Body contains extra keys: {extra_keys}")
+        
+        self._body_updated = True
+        for k in values:
+            self._body[k] = values[k]
+        return self
+
+    def _validate_body(self) -> None:
+        if not all(k in self._body for k in self._accepted_body_keys):
+            raise ValueError("Missing required keys in body")
+        
+        if self._body["size"] > 250:
+            raise ValueError("Yahoo limits query size to 250. Please decrease the size of the query.")
+
+    def _fetch(self) -> Dict:
+        params_dict = {"corsDomain": "finance.yahoo.com", "formatted": "false", "lang": "en-US", "region": "US"}
+        response = self._data.post(_SCREENER_URL_, body=self.body, user_agent_headers=self._data.user_agent_headers, params=params_dict, proxy=self.proxy)
+        response.raise_for_status()
+        return response.json()
+    
+    def _fetch_and_parse(self) -> None:
+        response = None
+        self._validate_body()
+        
+        try:
+            response = self._fetch()
+            self._response = response['finance']['result'][0]
+        except Exception as e:
+            logger = utils.get_yf_logger()
+            logger.error(f"Failed to get screener data for '{self._body.get('query', 'query not set')}' reason: {e}")
+            logger.debug("Got response: ")
+            logger.debug("-------------")
+            logger.debug(f" {response}")
+            logger.debug("-------------")
--- a/yfinance/screener/screener_query.py
+++ b/yfinance/screener/screener_query.py
@@ -0,0 +1,145 @@
+from abc import ABC, abstractmethod
+import numbers
+from typing import List, Union, Dict
+
+from yfinance.const import EQUITY_SCREENER_EQ_MAP, EQUITY_SCREENER_FIELDS
+from yfinance.exceptions import YFNotImplementedError
+from ..utils import dynamic_docstring, generate_list_table_from_dict
+
+class Query(ABC):
+    def __init__(self, operator: str, operand: Union[numbers.Real, str, List['Query']]):
+        self.operator = operator
+        self.operands = operand
+
+    @abstractmethod
+    def to_dict(self) -> Dict:
+        raise YFNotImplementedError('to_dict() needs to be implemented by children classes')
+
+class EquityQuery(Query):
+    """
+    The `EquityQuery` class constructs filters for stocks based on specific criteria such as region, sector, exchange, and peer group.
+
+    The queries support operators: `GT` (greater than), `LT` (less than), `BTWN` (between), `EQ` (equals), and logical operators `AND` and `OR` for combining multiple conditions.
+
+    Example:
+        Screen for stocks where the end-of-day price is greater than 3.
+        
+        .. code-block:: python
+
+            gt = yf.EquityQuery('gt', ['eodprice', 3])
+
+        Screen for stocks where the average daily volume over the last 3 months is less than a very large number.
+
+        .. code-block:: python
+
+            lt = yf.EquityQuery('lt', ['avgdailyvol3m', 99999999999])
+
+        Screen for stocks where the intraday market cap is between 0 and 100 million.
+
+        .. code-block:: python
+
+            btwn = yf.EquityQuery('btwn', ['intradaymarketcap', 0, 100000000])
+
+        Screen for stocks in the Technology sector.
+
+        .. code-block:: python
+
+            eq = yf.EquityQuery('eq', ['sector', 'Technology'])
+
+        Combine queries using AND/OR.
+
+        .. code-block:: python
+
+            qt = yf.EquityQuery('and', [gt, lt])
+            qf = yf.EquityQuery('or', [qt, btwn, eq])
+    """
+    def __init__(self, operator: str, operand: Union[numbers.Real, str, List['EquityQuery']]):
+        """
+        .. seealso::
+            
+            :attr:`EquityQuery.valid_operand_fields <yfinance.EquityQuery.valid_operand_fields>`
+                supported operand values for query
+            :attr:`EquityQuery.valid_eq_operand_map <yfinance.EquityQuery.valid_eq_operand_map>`
+                supported `EQ query operand parameters`
+        """
+        operator = operator.upper()
+
+        if not isinstance(operand, list):
+            raise TypeError('Invalid operand type')
+        if len(operand) <= 0:
+            raise ValueError('Invalid field for Screener')
+            
+        if operator in {'OR','AND'}: 
+            self._validate_or_and_operand(operand)
+        elif operator == 'EQ': 
+            self._validate_eq_operand(operand)
+        elif operator == 'BTWN': 
+            self._validate_btwn_operand(operand)
+        elif operator in {'GT','LT'}: 
+            self._validate_gt_lt(operand)
+        else: 
+            raise ValueError('Invalid Operator Value')
+
+        self.operator = operator
+        self.operands = operand
+        self._valid_eq_operand_map = EQUITY_SCREENER_EQ_MAP
+        self._valid_operand_fields = EQUITY_SCREENER_FIELDS
+    
+    @dynamic_docstring({"valid_eq_operand_map_table": generate_list_table_from_dict(EQUITY_SCREENER_EQ_MAP)})
+    @property
+    def valid_eq_operand_map(self) -> Dict:
+        """
+        Valid Operand Map for Operator "EQ"
+        {valid_eq_operand_map_table}
+        """
+        return self._valid_eq_operand_map
+
+    @dynamic_docstring({"valid_operand_fields_table": generate_list_table_from_dict(EQUITY_SCREENER_FIELDS)})
+    @property
+    def valid_operand_fields(self) -> Dict:
+        """
+        Valid Operand Fields
+        {valid_operand_fields_table}
+        """
+        return self._valid_operand_fields
+    
+    def _validate_or_and_operand(self, operand: List['EquityQuery']) -> None:
+        if len(operand) <= 1: 
+            raise ValueError('Operand must be length longer than 1')
+        if all(isinstance(e, EquityQuery) for e in operand) is False: 
+            raise TypeError('Operand must be type EquityQuery for OR/AND')
+
+    def _validate_eq_operand(self, operand: List[Union[str, numbers.Real]]) -> None:
+        if len(operand) != 2:
+            raise ValueError('Operand must be length 2 for EQ')
+        
+        if  not any(operand[0] in fields_by_type for fields_by_type in EQUITY_SCREENER_FIELDS.values()):
+            raise ValueError('Invalid field for Screener')
+        if operand[0] not in EQUITY_SCREENER_EQ_MAP:
+            raise ValueError('Invalid EQ key')
+        if operand[1] not in EQUITY_SCREENER_EQ_MAP[operand[0]]:
+            raise ValueError('Invalid EQ value')
+    
+    def _validate_btwn_operand(self, operand: List[Union[str, numbers.Real]]) -> None:
+        if len(operand) != 3: 
+            raise ValueError('Operand must be length 3 for BTWN')
+        if  not any(operand[0] in fields_by_type for fields_by_type in EQUITY_SCREENER_FIELDS.values()):
+            raise ValueError('Invalid field for Screener')
+        if isinstance(operand[1], numbers.Real) is False:
+            raise TypeError('Invalid comparison type for BTWN')
+        if isinstance(operand[2], numbers.Real) is False:
+            raise TypeError('Invalid comparison type for BTWN')
+
+    def _validate_gt_lt(self, operand: List[Union[str, numbers.Real]]) -> None:
+        if len(operand) != 2:
+            raise ValueError('Operand must be length 2 for GT/LT')
+        if  not any(operand[0] in fields_by_type for fields_by_type in EQUITY_SCREENER_FIELDS.values()):
+            raise ValueError('Invalid field for Screener')
+        if isinstance(operand[1], numbers.Real) is False:
+            raise TypeError('Invalid comparison type for GT/LT')
+
+    def to_dict(self) -> Dict:
+        return {
+            "operator": self.operator,
+            "operands": [operand.to_dict() if isinstance(operand, EquityQuery) else operand for operand in self.operands]
+        }
--- a/yfinance/search.py
+++ b/yfinance/search.py
@@ -0,0 +1,95 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# yfinance - market data downloader
+# https://github.com/ranaroussi/yfinance
+#
+# Copyright 2017-2019 Ran Aroussi
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import json as _json
+
+from . import utils
+from .const import _BASE_URL_
+from .data import YfData
+
+
+class Search:
+    def __init__(self, query, max_results=8, news_count=8, enable_fuzzy_query=False,
+                 session=None, proxy=None, timeout=30, raise_errors=True):
+        """
+        Fetches and organizes search results from Yahoo Finance, including stock quotes and news articles.
+
+        Args:
+            query: The search query (ticker symbol or company name).
+            max_results: Maximum number of stock quotes to return (default 8).
+            news_count: Number of news articles to include (default 8).
+            enable_fuzzy_query: Enable fuzzy search for typos (default False).
+            session: Custom HTTP session for requests (default None).
+            proxy: Proxy settings for requests (default None).
+            timeout: Request timeout in seconds (default 30).
+            raise_errors: Raise exceptions on error (default True).
+        """
+        self.query = query
+        self.max_results = max_results
+        self.enable_fuzzy_query = enable_fuzzy_query
+        self.news_count = news_count
+        self.session = session
+        self.proxy = proxy
+        self.timeout = timeout
+        self.raise_errors = raise_errors
+
+        self._data = YfData(session=self.session)
+        self._logger = utils.get_yf_logger()
+
+        self._response = self._fetch_results()
+        self._quotes = self._response.get("quotes", [])
+        self._news = self._response.get("news", [])
+
+    def _fetch_results(self):
+        url = f"{_BASE_URL_}/v1/finance/search"
+        params = {
+            "q": self.query,
+            "quotesCount": self.max_results,
+            "enableFuzzyQuery": self.enable_fuzzy_query,
+            "newsCount": self.news_count,
+            "quotesQueryId": "tss_match_phrase_query",
+            "newsQueryId": "news_cie_vespa"
+        }
+
+        self._logger.debug(f'{self.query}: Yahoo GET parameters: {str(dict(params))}')
+
+        data = self._data.cache_get(url=url, params=params, proxy=self.proxy, timeout=self.timeout)
+        if data is None or "Will be right back" in data.text:
+            raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
+                               "Our engineers are working quickly to resolve "
+                               "the issue. Thank you for your patience.")
+        try:
+            data = data.json()
+        except _json.JSONDecodeError:
+            self._logger.error(f"{self.query}: Failed to retrieve the news and received faulty response instead.")
+            data = {}
+
+        return data
+
+    @property
+    def quotes(self):
+        """Get the quotes from the search results."""
+        return self._quotes
+
+    @property
+    def news(self):
+        """Get the news from the search results."""
+        return self._news
--- a/yfinance/ticker.py
+++ b/yfinance/ticker.py
@@ -22,6 +22,7 @@
 from __future__ import print_function

 from collections import namedtuple as _namedtuple
+from .scrapers.funds import FundsData

 import pandas as _pd

@@ -297,3 +298,7 @@ class Ticker(TickerBase):
    @property
    def history_metadata(self) -> dict:
        return self.get_history_metadata()
+
+    @property
+    def funds_data(self) -> FundsData:
+        return self.get_funds_data()
--- a/yfinance/utils.py
+++ b/yfinance/utils.py
@@ -39,7 +39,6 @@ from dateutil.relativedelta import relativedelta
 from pytz import UnknownTimeZoneError

 from yfinance import const
-from .const import _BASE_URL_

 user_agent_headers = {
    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
@@ -189,24 +188,27 @@ def is_isin(string):
 def get_all_by_isin(isin, proxy=None, session=None):
    if not (is_isin(isin)):
        raise ValueError("Invalid ISIN number")
+
+    # Deferred this to prevent circular imports
+    from .search import Search
+
    session = session or _requests
-    url = f"{_BASE_URL_}/v1/finance/search?q={isin}"
-    data = session.get(url=url, proxies=proxy, headers=user_agent_headers)
-    try:
-        data = data.json()
-        ticker = data.get('quotes', [{}])[0]
-        return {
-            'ticker': {
-                'symbol': ticker['symbol'],
-                'shortname': ticker['shortname'],
-                'longname': ticker['longname'],
-                'type': ticker['quoteType'],
-                'exchange': ticker['exchDisp'],
-            },
-            'news': data.get('news', [])
-        }
-    except Exception:
-        return {}
+    search = Search(query=isin, max_results=1, session=session, proxy=proxy)
+
+    # Extract the first quote and news
+    ticker = search.quotes[0] if search.quotes else {}
+    news = search.news
+
+    return {
+        'ticker': {
+            'symbol': ticker.get('symbol', ''),
+            'shortname': ticker.get('shortname', ''),
+            'longname': ticker.get('longname', ''),
+            'type': ticker.get('quoteType', ''),
+            'exchange': ticker.get('exchDisp', ''),
+        },
+        'news': news
+    }


 def get_ticker_by_isin(isin, proxy=None, session=None):
@@ -613,7 +615,7 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange, repair=Fals
            # - exception is volume, *slightly* greater on final row (and matches website)
            if dt1.date() == dt2.date():
                # Last two rows are on same day. Drop second-to-last row
-                quotes = quotes.drop(quotes.index[n - 2])
+                quotes = _pd.concat([quotes.iloc[:-2], quotes.iloc[-1:]])
        else:
            if interval == "1wk":
                last_rows_same_interval = dt1.year == dt2.year and dt1.week == dt2.week
@@ -932,3 +934,64 @@ class ProgressBar:
    def __str__(self):
        return str(self.prog_bar)

+def dynamic_docstring(placeholders: dict):
+    """
+    A decorator to dynamically update the docstring of a function or method.
+    
+    Args:
+        placeholders (dict): A dictionary where keys are placeholder names and values are the strings to insert.
+    """
+    def decorator(func):
+        if func.__doc__:
+            docstring = func.__doc__
+            # Replace each placeholder with its corresponding value
+            for key, value in placeholders.items():
+                docstring = docstring.replace(f"{{{key}}}", value)
+            func.__doc__ = docstring
+        return func
+    return decorator
+
+def _generate_table_configurations() -> str:
+    import textwrap
+    table = textwrap.dedent("""
+    .. list-table:: Permitted Keys/Values
+       :widths: 25 75
+       :header-rows: 1
+
+       * - Key
+         - Values
+    """)
+
+    return table
+
+def generate_list_table_from_dict(data: dict, bullets: bool=True) -> str:
+    """
+    Generate a list-table for the docstring showing permitted keys/values.
+    """
+    table = _generate_table_configurations()
+    for key, values in data.items():
+        value_str = ', '.join(sorted(values))
+        table += f"   * - {key}\n"
+        if bullets:
+            table += "     -\n"
+            for value in sorted(values):
+                table += f"       - {value}\n"
+        else:
+            table += f"     - {value_str}\n"
+    return table
+
+def generate_list_table_from_dict_of_dict(data: dict, bullets: bool=True) -> str:
+    """
+    Generate a list-table for the docstring showing permitted keys/values.
+    """
+    table = _generate_table_configurations()
+    for key, values in data.items():
+        value_str = values
+        table += f"   * - {key}\n"
+        if bullets:
+            table += "     -\n"
+            for value in sorted(values):
+                table += f"       - {value}\n"
+        else:
+            table += f"     - {value_str}\n"
+    return table
--- a/yfinance/version.py
+++ b/yfinance/version.py
@@ -1 +1 @@
-version = "0.2.43"
+version = "0.2.49"
Author	SHA1	Message	Date
ValueRaider	91666197c6	Fix Market.summary	2024-12-21 12:46:43 +00:00
ValueRaider	63698c56c3	Improve Market.status formatting	2024-12-21 12:36:18 +00:00
ValueRaider	05d744773f	Remove unused raise_errors	2024-12-20 18:51:40 +00:00
ValueRaider	48bdfa06c8	Proof-of-concept Market class	2024-12-20 18:47:10 +00:00
ValueRaider	59d0974773	Merge pull request #2173 from dhruvan2006/feature/full-news Feature: Fetch upto 200 news articles	2024-12-14 22:05:59 +00:00
Dhruvan Gnanadhandayuthapani	5bc3ec01d0	Refactor get_news() to fetch more articles	2024-12-14 18:21:15 +01:00
ValueRaider	3ac85397cb	Merge pull request #2160 from dhruvan2006/feature/search Feature: Add search feature	2024-12-06 19:44:02 +00:00
Dhruvan Gnanadhandayuthapani	873ab0f574	Added new search feature Refactor Search Added unittest for Search Documentation for Search Fix unused import	2024-12-05 23:53:33 +01:00
ValueRaider	f5264649b9	Merge pull request #2168 from ericpien/screener_return_self Update Screener	2024-12-05 22:36:20 +00:00
Eric Pien	90731511ed	Update Screener - Setting Body to Screener returns the Screener Object to enable method chaining. (i.e. `r = yf.Screener().set_predefined_body("day_gainers").response`) - Limit query size to 250 and raise error if larger. This also avoids unnecessary network calls and makes the issue clearer.	2024-12-04 12:45:14 -08:00
ValueRaider	a8f0998ab8	Merge pull request #2147 from dhruvan2006/fix/price-auto-adjust Default yf.download() to auto adjust prices	2024-11-23 19:57:22 +00:00
Dhruvan Gnanadhandayuthapani	02785d61a1	Default yf.download() to auto adjust prices	2024-11-23 19:50:51 +01:00
ValueRaider	2167fb380f	Merge pull request #2139 from ranaroussi/fix/price-repair-missing-some Prices dividend repair: fixes	2024-11-19 21:38:13 +00:00
ValueRaider	006649d800	Prices dividend repair: fixes Fix blind spot (regresssion) Improve fixing div-too-big	2024-11-19 21:36:24 +00:00
ValueRaider	d3ec9c63ff	Merge branch 'main' into dev	2024-11-17 15:13:14 +00:00
ValueRaider	1d27b56b80	Merge pull request #2132 from ranaroussi/dev-documented New Sphinx docs	2024-11-17 11:50:20 +00:00
ValueRaider	fffe89e691	README: simplify a little	2024-11-17 11:49:05 +00:00
ValueRaider	a9e70b42c0	Merge pull request #2120 from ValueRaider/dev-documented Improve Sphinx docs	2024-11-16 19:01:51 +00:00
ValueRaider	ce8f8a0b30	Reorganise documentation Add minimal quick-start to root Simplify examples/ticker.py Separate Ticker API into more groups in reference/index.rst Fix some duplicate labels	2024-11-16 18:57:07 +00:00
ValueRaider	9f517a77ea	Merge pull request #2127 from ericpien/fix_growth_estimate Fix growth_estimates for new key `LTG`	2024-11-16 16:55:04 +00:00
Eric Pien	0a3f9c74bb	Fix growth_esitmates for new key LTG - LTG (long-term growth ?) is new to the API but its meaning is not shown on yahoo yet. - Support for 5 year numbers seem to have been removed. so commenting it out. - Amend unit test	2024-11-15 10:19:05 -08:00
ValueRaider	c535c44fd3	Merge pull request #2099 from DamienDuv/bugfix-get-by-isin fixed a bug throwing an error when trying to get a ticker from certain isin	2024-11-12 18:54:33 +00:00
DamienDuv	272b6f6453	fixed a bug throwing an error when trying to get a ticker from certain isin	2024-11-12 12:22:12 +01:00
ValueRaider	dfe3ec8df7	Version 0.2.49	2024-11-10 21:37:06 +00:00
ValueRaider	bc52389135	Merge pull request #2122 from ranaroussi/hotfix/prices-missing-latest-day Fix prices-clean discarding both duplicate last rows instead of 1/2	2024-11-10 21:32:22 +00:00
ValueRaider	e077a2b50c	Fix prices-clean discarding both duplicate last rows instead of 1/2	2024-11-10 11:14:49 +00:00
ValueRaider	5d8b444281	Action deploy_doc: enable manual trigger	2024-11-05 22:05:45 +00:00
ValueRaider	e27b90d875	Merge pull request #2113 from ericpien/docs Add Sphinx Documentation	2024-11-05 18:56:01 +00:00
Eric Pien	deafc88520	Add Sphinx Documentation - Initial draft of Sphinx documentation using rst - update docstrings - add deploy_doc.yml for automated deployment through GitHub Actions	2024-11-04 13:59:35 -08:00
ValueRaider	c012ceac01	Merge pull request #2111 from ranaroussi/fix/price-repair-fx Price repair: when fix currency, update metadata	2024-11-03 18:25:25 +00:00
ValueRaider	13d006827f	Merge pull request #2109 from ranaroussi/fix/download-tz Fix times appearing in output from yf.download(ignore_tz=True)	2024-11-03 18:24:46 +00:00
ValueRaider	2d5d752b87	Price repair: when fix currency, update metadata	2024-11-03 18:23:36 +00:00
ValueRaider	f05f99c2b8	Merge pull request #2110 from dhruvan2006/fix/multi-level-index-typo Fix typo in multi.py download() docs	2024-11-03 12:04:30 +00:00
Dhruvan Gnanadhandayuthapani	ca7f11bb41	Fix typo in multi.py download()	2024-11-03 12:45:42 +01:00
ValueRaider	4157b9e170	Fix times appearing in output from yf.download(ignore_tz=True)	2024-11-02 12:53:14 +00:00
ValueRaider	0bc22f82bb	Merge pull request #2108 from ranaroussi/main sync main -> dev	2024-11-02 12:38:00 +00:00
Ran Aroussi	5e942fdec4	Refactor multi.py to handle single tickers and rename columns	2024-10-25 19:02:07 +01:00
Ran Aroussi	16072e87df	Bump version to 0.2.48	2024-10-25 18:59:19 +01:00
Ran Aroussi	6946f99e30	Refactor multi.py to handle single tickers and rename columns	2024-10-25 18:59:03 +01:00
Ran Aroussi	1397dcc4bc	Update version.py	2024-10-25 11:34:13 +01:00
Ran Aroussi	7fee3039f4	Delete .DS_Store	2024-10-25 11:33:08 +01:00
Ran Aroussi	4beb489f82	Refactor multi.py to handle single tickers and rename columns	2024-10-25 11:31:20 +01:00
ValueRaider	0f570c4684	Merge pull request #2097 from eltociear/patch-1 docs: update README.md	2024-10-22 18:55:03 +01:00
Ikko Eltociear Ashimine	b32e57fcfe	docs: update README.md minor fix	2024-10-22 17:19:56 +09:00
ValueRaider	0ec5151ff3	Version 0.2.46	2024-10-21 18:37:53 +01:00
ValueRaider	b88a0a6bf5	Merge pull request #2094 from FX196/patch-1 Fix malformed f-string in release `0.2.45`	2024-10-21 18:36:49 +01:00
Yuhong Chen	c218848a69	Fix malformed f-string	2024-10-20 17:36:22 -07:00
ValueRaider	f19dff247e	Version 0.2.45	2024-10-20 22:59:16 +01:00
ValueRaider	1de113eccd	Merge pull request #2091 from ranaroussi/dev sync dev -> main	2024-10-20 22:56:26 +01:00
ValueRaider	8daa477167	Merge pull request #2090 from ranaroussi/feature/divident-repair-improve Feature/dividend repair improve	2024-10-20 22:55:52 +01:00
ValueRaider	3152715d45	Improve divident repair Dividend repair tweaks: - handle coincident split missing from dividend - avoid 100x price changes confusing detection of div too-big/small - improve detecting div-date-wrong	2024-10-20 21:50:08 +01:00
ValueRaider	ee657b24d5	Merge pull request #2087 from ranaroussi/fix/debug-msg Fix IndexError in some history() debug messages	2024-10-20 16:31:33 +01:00
ValueRaider	4672715a86	Merge pull request #2066 from ericpien/screener Implement Screener Feature	2024-10-19 22:13:46 +01:00
Eric Pien	4a70b59db9	Simplify README for Screener - Reduce the information provided in README with the expectation of migrating to separate documentation.	2024-10-19 09:27:32 -07:00
Ran Aroussi	12e815d977	Update README.md	2024-10-17 21:51:59 +01:00
ValueRaider	e282d1b59c	Fix IndexError in some history() debug messages	2024-10-12 11:48:11 +01:00
ValueRaider	7e0946ed37	Merge pull request #2080 from ranaroussi/main sync main -> dev	2024-10-07 21:30:03 +01:00
ValueRaider	a21fc073b8	Merge pull request #2059 from marco-carvalho/patch-1 Add Pyright type checking	2024-10-07 19:50:24 +01:00
ValueRaider	a9e3cf0780	Merge pull request #2072 from algonell/main Fix typos	2024-10-05 20:08:10 +01:00
ValueRaider	0f63ecc2bd	Merge pull request #2068 from antoniouaa/fix_tickers_single fix keyerror with single element list passed to Tickers	2024-10-03 20:09:52 +01:00
Andrew Kreimer	10800a1070	Fix typos	2024-10-03 22:01:53 +03:00
Andrew Kreimer	db39b3fca4	Fix a typo	2024-10-03 22:00:57 +03:00
Andrew Kreimer	10d6221718	Fix typos	2024-10-03 22:00:29 +03:00
ValueRaider	17f07e08ef	Merge pull request #2069 from ranaroussi/main sync main -> dev	2024-10-02 20:12:48 +01:00
ValueRaider	f82823c624	Merge pull request #2067 from ranaroussi/fix/tests-context Fix unit tests contextual imports	2024-10-02 20:11:30 +01:00
antoniouaa	ebc4e200c1	fix keyerror with single element list passed to Tickers	2024-10-01 23:15:54 +01:00
ValueRaider	a8df88b2d2	Fix unit tests contextual imports	2024-10-01 21:24:51 +01:00
Eric Pien	048378ea20	Implement Screener - Add a new class Screener, Query, and EquityQuery - Refactor YfData.get() to enable YfData.post() to all of get's implementations - Add test for the Screener - Add new set and map to const Screener can be used to filter yahoo finance. This can be used to get the top gainers of the day, and more customized queries that a user may want to automate.	2024-09-30 18:01:11 -07:00
ValueRaider	9e2252e451	Version 0.2.44	2024-09-30 19:57:57 +01:00
ValueRaider	4c3c228724	Merge pull request #2063 from ranaroussi/dev sync dev -> main	2024-09-30 19:55:01 +01:00
ValueRaider	3535fb9ed7	Fix test fails in _fix_prices_sudden_change	2024-09-29 23:03:44 +01:00
ValueRaider	dd663a404a	Merge pull request #2062 from ranaroussi/feature/improve-div-repair Improve dividend repair	2024-09-29 20:32:54 +01:00
ValueRaider	ce530b0855	Improve dividend repair Improve dividend repair: - detect wrong date - detect split missing from coincident dividend - improve detecting adjust contradicting price action - improve detecting when Close has adjustment (remove phantoms before, then use AdjClose -> AdjLow deltas) Stop negative 'Adj Close' triggering a 'sudden change' repair.	2024-09-29 16:44:23 +01:00
ValueRaider	1890ff2b6d	Merge pull request #2058 from ericpien/sectors Feature Proposal: Support Sector and Industry data from Markets Tab	2024-09-22 17:53:39 +01:00
Eric Pien	3a0b6c2315	Implement Sector and Industry Sector and Industry class to query the data available on: https://finance.yahoo.com/sectors/: - sector / industry overview - sector / industry top companies - sector / industry research reports - sector top ETFs - sector top Mutual Funds - industries within sector - industry's sector - industry's top performing companies - industry's top growth companies	2024-09-22 08:35:12 -07:00
ValueRaider	676500854c	Bug template: clarify 'bad data proof'	2024-09-21 11:56:54 +01:00
Marco Carvalho	f1d5c1d06f	Update pyright.yml	2024-09-19 16:08:40 -03:00
Marco Carvalho	d65bf2d761	Update requirements.txt	2024-09-19 15:57:55 -03:00
Marco Carvalho	81deec0d6c	Update pyrightconfig.json	2024-09-19 11:32:57 -03:00
Marco Carvalho	74f44a4e1e	Update pyright.yml	2024-09-19 11:14:09 -03:00
Marco Carvalho	691904202b	Create pyright.yml	2024-09-19 11:09:13 -03:00
Marco Carvalho	3fc54ab249	Create pyrightconfig.json	2024-09-19 11:08:56 -03:00
Marco Carvalho	d7c6f5f320	Update requirements.txt	2024-09-19 11:08:08 -03:00
ValueRaider	6b7a511765	Merge pull request #2041 from ericpien/funds implement support for funds data	2024-09-08 12:53:20 +01:00
Eric Pien	f584b0c7d0	implementing support for funds data - adding to scraper a new class FundsData. - Ticker can now reference the data via ticker.funds_data when appropriate. - Add unit tests in test_ticker.py for the funds data	2024-09-07 13:49:54 -07:00
ValueRaider	8cadad2cbb	Merge pull request #2048 from ranaroussi/main sync main -> dev	2024-09-04 21:44:28 +01:00
ValueRaider	3fe87cb132	Merge pull request #2039 from joncokisler/main Fix typo in README.md #2038	2024-08-26 19:27:55 +01:00
Jon Cokisler	72684b1784	fix typo in README	2024-08-26 13:51:45 -04:00