Version 0.2.51

Merge pull request #2182 from ranaroussi/dev
dev -> main
2024-12-19 22:46:16 +00:00 · 2024-12-19 22:42:48 +00:00 · 2024-12-19 22:42:36 +00:00 · 2024-12-18 21:07:30 +00:00 · 2024-12-18 21:04:21 +00:00 · 2024-12-14 22:05:59 +00:00
69 changed files with 5907 additions and 4020 deletions
--- a/.DS_Store
+++ b/.DS_Store
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -1,18 +0,0 @@
-name: ci
-on:
-  push:
-    branches:
-      - master
-      - main
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
-        with:
-          python-version: 3.x
-      - run: pip install -r requirements.txt
-      - run: pip install mkdocstrings==0.14.0
-      - run: pip install mkdocs-material
-      - run: mkdocs gh-deploy --force
--- 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/.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 +1,24 @@
 Change Log
 ===========

+0.2.51
+------
+Fixes:
+- earnings_dates #2169
+Features:
+- Screener tweaks #2168
+- Search #2160
+- get_news() expose count #2173
+
+0.2.50
+------
+Fixes:
+- price repair #2111 #2139
+- download() appearance 2109
+- isin() error #2099
+- growth_estimates #2127
+Also new docs #2132
+
 0.2.49
 ------
 Fix prices-clean rarely discarding good data #2122
--- a/README.md
+++ b/README.md
@@ -32,328 +32,32 @@ Yahoo! finance API is intended for personal use only.**
 <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
-
-msft.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
-```
-
-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`.
-
-```python
-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
-```
-
-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.
-
-### Sector and Industry
-
-The `Sector` and `Industry` modules allow you to access the US market information.
-
-To initialize, use the relevant sector or industry key as below. (Complete mapping of the keys is available in `const.py`.)
-
-```python
-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
-```
-
-The modules can be chained with Ticker as below.
-```python
-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()
-```
-
-### Market Screener
-The `Screener` module allows you to screen the market based on specified queries.
-
-#### Query Construction
-To create a query, you can use the `EquityQuery` class to construct your filters step by step. The queries support operators: `GT` (greater than), `LT` (less than), `BTWN` (between), `EQ` (equals), and logical operators `AND` and `OR` for combining multiple conditions.
-
-#### Screener
-The `Screener` class is used to execute the queries and return the filtered results. You can set a custom body for the screener or use predefined configurations.
-
-<!-- TODO: link to Github Pages for more including list of predefined bodies, supported fields, operands, and sample code -->
-
-### 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

 ---

@@ -362,7 +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
@@ -378,3 +81,4 @@ details on your rights to use the actual data downloaded.
 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 = "0.2.49" %}
+{% set version = "0.2.51" %}

 package:
  name: "{{ name|lower }}"
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,19 +0,0 @@
-# site_name: My Docs
-
-# # mkdocs.yml
-# theme:
-#   name: "material"
-
-# plugins:
-# - search
-# - mkdocstrings
-
-# nav:
-#   - Introduction: 'index.md'
-#   - Installation: 'installation.md'
-#   - Quick Start: 'quickstart.md'
-#   # - Ticker: 'Ticker.md'
-#   - TickerBase: 'TickerBase.md'
-#   # - Tickers: 'Tickers.md'
-#   - utils: 'utils.md'
-#   - multi: 'multi.md'
--- 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/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/NVT-L-1d-bad-div-fixed.csv
+++ b/tests/data/NVT-L-1d-bad-div-fixed.csv
@@ -1,391 +1,391 @@
 Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gains,Repaired?
-2022-01-04 00:00:00+00:00,0.6875,0.6940000152587891,0.675,0.6875,0.5880703675715848,14877,0.0,0.0,0.0,True
-2022-01-05 00:00:00+00:00,0.6875,0.6751000213623047,0.675,0.6875,0.5880703675715848,15180,0.0,0.0,0.0,True
-2022-01-06 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-01-07 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-01-10 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-01-11 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-01-12 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-01-13 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-01-14 00:00:00+00:00,0.6875,0.6940000152587891,0.675,0.6875,0.5880703675715848,10268,0.0,0.0,0.0,True
-2022-01-17 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880703675715848,1004,0.0,0.0,0.0,True
-2022-01-18 00:00:00+00:00,0.6875,0.6940000152587891,0.675,0.6875,0.5880703675715848,15202,0.0,0.0,0.0,True
-2022-01-19 00:00:00+00:00,0.6875,0.675,0.67,0.6875,0.5880703675715848,26258,0.0,0.0,0.0,True
-2022-01-20 00:00:00+00:00,0.6875,0.675,0.675,0.6875,0.5880703675715848,11286,0.0,0.0,0.0,True
-2022-01-21 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880703675715848,653,0.0,0.0,0.0,True
-2022-01-24 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880703675715848,300,0.0,0.0,0.0,True
-2022-01-25 00:00:00+00:00,0.6875,0.67,0.67,0.6825,0.5837933928330346,18000,0.0,0.0,0.0,True
-2022-01-26 00:00:00+00:00,0.6825,0.6940000152587891,0.6940000152587891,0.6825,0.5837933928330346,1560,0.0,0.0,0.0,True
-2022-01-27 00:00:00+00:00,0.6825,0.67,0.67,0.6825,0.5837933928330346,1000,0.0,0.0,0.0,True
-2022-01-28 00:00:00+00:00,0.6825,0.67,0.67,0.6825,0.5837933928330346,5588,0.0,0.0,0.0,True
-2022-01-31 00:00:00+00:00,0.6825,0.6825,0.6825,0.6825,0.5837933928330346,0,0.0,0.0,0.0,True
-2022-02-01 00:00:00+00:00,0.6825,0.66,0.65,0.66,0.564547577937833,30612,0.0,0.0,0.0,True
-2022-02-02 00:00:00+00:00,0.66,0.65,0.65,0.66,0.564547577937833,9000,0.0,0.0,0.0,True
-2022-02-03 00:00:00+00:00,0.67,0.68,0.67,0.6775,0.5795166024261857,19301,0.0,0.0,0.0,True
-2022-02-04 00:00:00+00:00,0.6775,0.67,0.67,0.6775,0.5795166024261857,29455,0.0,0.0,0.0,True
-2022-02-07 00:00:00+00:00,0.6775,0.67,0.67,0.6775,0.5795166024261857,3125,0.0,0.0,0.0,True
-2022-02-08 00:00:00+00:00,0.6775,0.685,0.67,0.6775,0.5795166024261857,15202,0.0,0.0,0.0,True
-2022-02-09 00:00:00+00:00,0.6775,0.6775,0.6775,0.6775,0.5795166024261857,0,0.0,0.0,0.0,True
-2022-02-10 00:00:00+00:00,0.6775,0.67,0.67,0.6775,0.5795166024261857,3506,0.0,0.0,0.0,True
-2022-02-11 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-02-14 00:00:00+00:00,0.6875,0.68,0.68,0.6875,0.5880703675715848,2461,0.0,0.0,0.0,True
-2022-02-15 00:00:00+00:00,0.6875,0.68,0.68,0.6875,0.5880703675715848,19756,0.0,0.0,0.0,True
-2022-02-16 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-02-17 00:00:00+00:00,0.6875,0.6825,0.6825,0.6875,0.5880703675715848,7275,0.0,0.0,0.0,True
-2022-02-18 00:00:00+00:00,0.6875,0.68,0.675,0.6875,0.5880703675715848,45627,0.0,0.0,0.0,True
-2022-02-21 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-02-22 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-02-23 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-02-24 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880703675715848,14323,0.0,0.0,0.0,True
-2022-02-25 00:00:00+00:00,0.6875,0.6827999877929688,0.6827999877929688,0.6875,0.5880703675715848,28473,0.0,0.0,0.0,True
-2022-02-28 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-03-01 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-03-02 00:00:00+00:00,0.6875,0.6825,0.6825,0.6875,0.5880703675715848,1559,0.0,0.0,0.0,True
-2022-03-03 00:00:00+00:00,0.6875,0.67,0.67,0.6875,0.5880703675715848,1524,0.0,0.0,0.0,True
-2022-03-04 00:00:00+00:00,0.6875,0.6809999847412109,0.6809999847412109,0.6875,0.5880703675715848,718,0.0,0.0,0.0,True
-2022-03-07 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-03-08 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-03-09 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-03-10 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-03-11 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-03-14 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-03-15 00:00:00+00:00,0.6875,0.68,0.65,0.6875,0.5880703675715848,1430,0.0,0.0,0.0,True
-2022-03-16 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-03-17 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880703675715848,0,0.0,0.0,0.0,True
-2022-03-18 00:00:00+00:00,0.665,0.655,0.655,0.66,0.564547577937833,2500,0.0,0.0,0.0,True
-2022-03-21 00:00:00+00:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-03-22 00:00:00+00:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-03-23 00:00:00+00:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-03-24 00:00:00+00:00,0.66,0.65,0.65,0.66,0.564547577937833,24314,0.0,0.0,0.0,True
-2022-03-25 00:00:00+00:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-03-28 00:00:00+01:00,0.66,0.6552999877929687,0.6552999877929687,0.66,0.564547577937833,16749,0.0,0.0,0.0,True
-2022-03-29 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-03-30 00:00:00+01:00,0.66,0.665,0.65,0.66,0.564547577937833,38438,0.0,0.0,0.0,True
-2022-03-31 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-04-01 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-04-04 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-04-05 00:00:00+01:00,0.66,0.66,0.65,0.66,0.564547577937833,16181,0.0,0.0,0.0,True
-2022-04-06 00:00:00+01:00,0.66,0.65,0.65,0.66,0.564547577937833,672,0.0,0.0,0.0,True
-2022-04-07 00:00:00+01:00,0.66,0.66,0.6501000213623047,0.66,0.564547577937833,55267,0.0,0.0,0.0,True
-2022-04-08 00:00:00+01:00,0.66,0.6598000335693359,0.6598000335693359,0.66,0.564547577937833,1496,0.0,0.0,0.0,True
-2022-04-11 00:00:00+01:00,0.66,0.659000015258789,0.65,0.66,0.564547577937833,10068,0.0,0.0,0.0,True
-2022-04-12 00:00:00+01:00,0.66,0.6588999938964843,0.6588999938964843,0.66,0.564547577937833,7588,0.0,0.0,0.0,True
-2022-04-13 00:00:00+01:00,0.66,0.658499984741211,0.658499984741211,0.66,0.564547577937833,1511,0.0,0.0,0.0,True
-2022-04-14 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-04-19 00:00:00+01:00,0.66,0.6583999633789063,0.65,0.66,0.564547577937833,12524,0.0,0.0,0.0,True
-2022-04-20 00:00:00+01:00,0.66,0.6583999633789063,0.6583999633789063,0.66,0.564547577937833,580,0.0,0.0,0.0,True
-2022-04-21 00:00:00+01:00,0.66,0.6583999633789063,0.64,0.66,0.564547577937833,22992,0.0,0.0,0.0,True
-2022-04-22 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-04-25 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-04-26 00:00:00+01:00,0.66,0.6580000305175782,0.6580000305175782,0.66,0.564547577937833,1500,0.0,0.0,0.0,True
-2022-04-27 00:00:00+01:00,0.66,0.64,0.64,0.66,0.564547577937833,17000,0.0,0.0,0.0,True
-2022-04-28 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-04-29 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-05-03 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-05-04 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-05-05 00:00:00+01:00,0.66,0.6576000213623047,0.6576000213623047,0.66,0.564547577937833,5000,0.0,0.0,0.0,True
-2022-05-06 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-05-09 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-05-10 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-05-11 00:00:00+01:00,0.66,0.6576000213623047,0.6576000213623047,0.66,0.564547577937833,20230,0.0,0.0,0.0,True
-2022-05-12 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-05-13 00:00:00+01:00,0.66,0.6573999786376953,0.6573999786376953,0.66,0.564547577937833,7567,0.0,0.0,0.0,True
-2022-05-16 00:00:00+01:00,0.66,0.65,0.65,0.66,0.564547577937833,5842,0.0,0.0,0.0,True
-2022-05-17 00:00:00+01:00,0.66,0.65,0.65,0.66,0.564547577937833,27137,0.0,0.0,0.0,True
-2022-05-18 00:00:00+01:00,0.66,0.65,0.65,0.66,0.564547577937833,24000,0.0,0.0,0.0,True
-2022-05-19 00:00:00+01:00,0.66,0.6569999694824219,0.635,0.66,0.564547577937833,10757,0.0,0.0,0.0,True
-2022-05-20 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-05-23 00:00:00+01:00,0.66,0.6558000183105469,0.6558000183105469,0.66,0.564547577937833,1517,0.0,0.0,0.0,True
-2022-05-24 00:00:00+01:00,0.66,0.635,0.635,0.66,0.564547577937833,13072,0.0,0.0,0.0,True
-2022-05-25 00:00:00+01:00,0.66,0.66,0.66,0.66,0.564547577937833,0,0.0,0.0,0.0,True
-2022-05-26 00:00:00+01:00,0.66,0.64,0.64,0.655,0.5602706400656228,7206,0.0,0.0,0.0,True
-2022-05-27 00:00:00+01:00,0.655,0.655,0.655,0.655,0.5602706400656228,0,0.0,0.0,0.0,True
-2022-05-30 00:00:00+01:00,0.655,0.65,0.65,0.655,0.5602706400656228,5000,0.0,0.0,0.0,True
-2022-05-31 00:00:00+01:00,0.655,0.65,0.65,0.655,0.5602706400656228,1518,0.0,0.0,0.0,True
-2022-06-01 00:00:00+01:00,0.655,0.65,0.65,0.645,0.5517169486529044,1,0.0,0.0,0.0,True
-2022-06-06 00:00:00+01:00,0.645,0.635,0.635,0.645,0.5517169486529044,15500,0.0,0.0,0.0,True
-2022-06-07 00:00:00+01:00,0.645,0.635,0.635,0.645,0.5517169486529044,1004,0.0,0.0,0.0,True
-2022-06-08 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-09 00:00:00+01:00,0.645,0.635,0.635,0.645,0.5517169486529044,8341,0.0,0.0,0.0,True
-2022-06-10 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-13 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-14 00:00:00+01:00,0.645,0.64,0.64,0.645,0.5517169486529044,31092,0.0,0.0,0.0,True
-2022-06-15 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-16 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-17 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-20 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-21 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-22 00:00:00+01:00,0.645,0.6498000335693359,0.64,0.645,0.5517169486529044,189418,0.0,0.0,0.0,True
-2022-06-23 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-24 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-27 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-28 00:00:00+01:00,0.645,0.64,0.64,0.645,0.5517169486529044,2500,0.0,0.0,0.0,True
-2022-06-29 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-06-30 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-07-01 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-07-04 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-07-05 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-07-06 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-07-07 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
-2022-07-08 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517169486529044,0,0.0,0.0,0.0,True
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+2023-07-17 00:00:00+01:00,0.595,0.6025,0.6025,0.595,0.5435545323233965,3400,0.0,0.0,0.0,True
+2023-07-18 00:00:00+01:00,0.595,0.5650000000000001,0.5650000000000001,0.59,0.5389868344758844,14146,0.0,0.0,0.0,True
+2023-07-19 00:00:00+01:00,0.59,0.5970000076293945,0.5970000076293945,0.59,0.5389868344758844,1000,0.0,0.0,0.0,True
 2023-07-20 00:00:00+01:00,0.5700000000000001,0.5700000000000001,0.5700000000000001,0.5700000000000001,0.5389831924438476,0,0.02,0.0,0.0,False
 2023-07-21 00:00:00+01:00,0.5700000000000001,0.5770000076293945,0.5770000076293945,0.5700000000000001,0.5389831924438476,344,0.0,0.0,0.0,False
 2023-07-24 00:00:00+01:00,0.5700000000000001,0.5700000000000001,0.5700000000000001,0.5700000000000001,0.5389831924438476,0,0.0,0.0,0.0,False
@@ -396,7 +396,7 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gai
 2023-07-31 00:00:00+01:00,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.534255256652832,0,0.0,0.0,0.0,False
 2023-08-01 00:00:00+01:00,0.5650000000000001,0.5720000076293945,0.5720000076293945,0.5650000000000001,0.534255256652832,600,0.0,0.0,0.0,False
 2023-08-02 00:00:00+01:00,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.534255256652832,0,0.0,0.0,0.0,False
-2023-08-03 00:00:00+01:00,0.5650000000000001,0.54,0.54,0.54,0.5106156539916993,1731,0.0,0.0,0.0,False
+2023-08-03 00:00:00+01:00,0.5650000000000001,0.54,0.54,0.54,0.5106156921386719,1731,0.0,0.0,0.0,False
 2023-08-04 00:00:00+01:00,0.5650000000000001,0.54,0.54,0.5650000000000001,0.534255256652832,1800,0.0,0.0,0.0,False
 2023-08-07 00:00:00+01:00,0.5650000000000001,0.5720000076293945,0.5720000076293945,0.5650000000000001,0.534255256652832,8612,0.0,0.0,0.0,False
 2023-08-08 00:00:00+01:00,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.534255256652832,0,0.0,0.0,0.0,False
@@ -684,3 +684,43 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gai
 2024-09-18 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,9547,0.0,0.0,0.0,False
 2024-09-19 00:00:00+01:00,0.555,0.54,0.54,0.555,0.555,10580,0.0,0.0,0.0,False
 2024-09-20 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0,False
+2024-09-23 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0,False
+2024-09-24 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0,False
+2024-09-25 00:00:00+01:00,0.555,0.545099983215332,0.545099983215332,0.555,0.555,223787,0.0,0.0,0.0,False
+2024-09-26 00:00:00+01:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0,False
+2024-09-27 00:00:00+01:00,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0,0.0,0.0,0.0,False
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+2024-10-02 00:00:00+01:00,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0,0.0,0.0,0.0,False
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+2024-10-04 00:00:00+01:00,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0,0.0,0.0,0.0,False
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+2024-11-13 00:00:00+00:00,0.555,0.54,0.54,0.555,0.555,1180,0.0,0.0,0.0,False
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+2024-11-15 00:00:00+00:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0,False
--- a/tests/data/NVT-L-1d-bad-div.csv
+++ b/tests/data/NVT-L-1d-bad-div.csv
@@ -14,19 +14,19 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gai
 2022-01-20 00:00:00+00:00,0.6875,0.675,0.675,0.6875,0.6084982681274415,11286,0.0,0.0,0.0
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 2022-01-24 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.6084982681274415,300,0.0,0.0,0.0
-2022-01-25 00:00:00+00:00,0.6875,0.67,0.67,0.6825,0.6040727233886719,18000,0.0,0.0,0.0
-2022-01-26 00:00:00+00:00,0.6825,0.6940000152587891,0.6940000152587891,0.6825,0.6040727233886719,1560,0.0,0.0,0.0
-2022-01-27 00:00:00+00:00,0.6825,0.67,0.67,0.6825,0.6040727233886719,1000,0.0,0.0,0.0
-2022-01-28 00:00:00+00:00,0.6825,0.67,0.67,0.6825,0.6040727233886719,5588,0.0,0.0,0.0
-2022-01-31 00:00:00+00:00,0.6825,0.6825,0.6825,0.6825,0.6040727233886719,0,0.0,0.0,0.0
-2022-02-01 00:00:00+00:00,0.6825,0.66,0.65,0.66,0.5841583633422852,30612,0.0,0.0,0.0
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+2022-02-10 00:00:00+00:00,0.6775,0.67,0.67,0.6775,0.599647445678711,3506,0.0,0.0,0.0
 2022-02-11 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.6084982681274415,0,0.0,0.0,0.0
 2022-02-14 00:00:00+00:00,0.6875,0.68,0.68,0.6875,0.6084982681274415,2461,0.0,0.0,0.0
 2022-02-15 00:00:00+00:00,0.6875,0.68,0.68,0.6875,0.6084982681274415,19756,0.0,0.0,0.0
@@ -52,148 +52,148 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gai
 2022-03-15 00:00:00+00:00,0.6875,0.68,0.65,0.6875,0.6084982681274415,1430,0.0,0.0,0.0
 2022-03-16 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.6084982681274415,0,0.0,0.0,0.0
 2022-03-17 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.6084982681274415,0,0.0,0.0,0.0
-2022-03-18 00:00:00+00:00,0.665,0.655,0.655,0.66,0.5841583633422852,2500,0.0,0.0,0.0
-2022-03-21 00:00:00+00:00,0.66,0.66,0.66,0.66,0.5841583633422852,0,0.0,0.0,0.0
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@@ -234,91 +234,91 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gai
 2022-12-05 00:00:00+00:00,0.6,0.6,0.59,0.6,0.5482531356811523,67809,0.0,0.0,0.0
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 2023-04-13 00:00:00+01:00,0.5725,0.56,0.56,0.5700000000000001,0.5388005065917969,1459,0.0,0.0,0.0
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@@ -362,9 +362,9 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gai
 2023-06-13 00:00:00+01:00,0.5650000000000001,0.54,0.54,0.5650000000000001,0.5340741729736328,4006,0.0,0.0,0.0
 2023-06-14 00:00:00+01:00,0.5650000000000001,0.5608000183105469,0.54,0.5650000000000001,0.5340741729736328,3597,0.0,0.0,0.0
 2023-06-15 00:00:00+01:00,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.5340741729736328,0,0.0,0.0,0.0
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 2023-06-21 00:00:00+01:00,0.6,0.6,0.6,0.6,0.5671584320068359,0,0.0,0.0,0.0
 2023-06-22 00:00:00+01:00,0.6,0.6,0.6,0.6,0.5671584320068359,0,0.0,0.0,0.0
 2023-06-23 00:00:00+01:00,0.6,0.585,0.585,0.6,0.5671584320068359,6482,0.0,0.0,0.0
@@ -376,16 +376,16 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gai
 2023-07-03 00:00:00+01:00,0.6,0.6,0.6,0.6,0.5671584320068359,0,0.0,0.0,0.0
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 2023-07-05 00:00:00+01:00,0.6,0.6,0.6,0.6,0.5671584320068359,0,0.0,0.0,0.0
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@@ -396,7 +396,7 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gai
 2023-07-31 00:00:00+01:00,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.534255256652832,0,0.0,0.0,0.0
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 2023-08-04 00:00:00+01:00,0.5650000000000001,0.54,0.54,0.5650000000000001,0.534255256652832,1800,0.0,0.0,0.0
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 2023-08-08 00:00:00+01:00,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.5650000000000001,0.534255256652832,0,0.0,0.0,0.0
@@ -684,3 +684,43 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gai
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--- a/tests/data/SCR-TO-1d-bad-div-fixed.csv
+++ b/tests/data/SCR-TO-1d-bad-div-fixed.csv
@@ -1,430 +1,430 @@
 Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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-2022-01-12 00:00:00-05:00,4.449999809265137,4.639999866485596,4.449999809265137,4.579999923706055,3.8307288324959408,452500,0.0,0.0,True
-2022-01-13 00:00:00-05:00,4.590000152587891,4.639999866485596,4.420000076293945,4.46999979019165,3.738724470787365,373700,0.0,0.0,True
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-2022-02-25 00:00:00-05:00,4.659999847412109,4.880000114440918,4.630000114440918,4.849999904632568,4.056557365872851,264400,0.0,0.0,True
-2022-02-28 00:00:00-05:00,4.880000114440918,5.099999904632568,4.869999885559082,5.079999923706055,4.248930476625831,543200,0.0,0.0,True
-2022-03-01 00:00:00-05:00,5.159999847412109,5.28000020980835,5.03000020980835,5.179999828338623,4.332570805451809,481700,0.0,0.0,True
-2022-03-02 00:00:00-05:00,5.239999771118164,5.309999942779541,5.079999923706055,5.179999828338623,4.332570805451809,232200,0.0,0.0,True
-2022-03-03 00:00:00-05:00,5.21999979019165,5.230000019073486,4.949999809265137,4.96999979019165,4.156926469370485,234500,0.0,0.0,True
-2022-03-04 00:00:00-05:00,5.03000020980835,5.389999866485596,5.03000020980835,5.360000133514404,4.483123751791799,1110400,0.0,0.0,True
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+2023-07-31 00:00:00-04:00,2.5,2.7300000190734863,2.5,2.7200000286102295,2.6912723060742505,516500,0.0,0.0,True
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+2023-08-03 00:00:00-04:00,2.359999895095825,2.450000047683716,2.359999895095825,2.440000057220459,2.4142295558569598,814300,0.0,0.0,True
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+2023-08-10 00:00:00-04:00,2.5199999809265137,2.5299999713897705,2.4700000286102295,2.490000009536743,2.463701589762618,389700,0.0,0.0,True
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+2023-09-11 00:00:00-04:00,2.390000104904175,2.4200000762939453,2.3299999237060547,2.3499999046325684,2.325180069277974,740800,0.0,0.0,True
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 2023-09-14 00:00:00-04:00,2.450000047683716,2.5199999809265137,2.430000066757202,2.5199999809265137,2.497917890548706,553500,0.00441,0.0,False
 2023-09-15 00:00:00-04:00,2.5,2.640000104904175,2.4800000190734863,2.569999933242798,2.5474798679351807,770400,0.0,0.0,False
 2023-09-18 00:00:00-04:00,2.569999933242798,2.640000104904175,2.5299999713897705,2.559999942779541,2.537567377090454,753200,0.0,0.0,False
@@ -687,3 +687,37 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
 2024-09-25 00:00:00-04:00,27.8700008392334,27.8700008392334,27.0,27.489999771118164,27.489999771118164,30200,0.0,0.0,False
 2024-09-26 00:00:00-04:00,27.299999237060547,27.479999542236328,26.559999465942383,26.610000610351562,26.610000610351562,51800,0.0,0.0,False
 2024-09-27 00:00:00-04:00,26.989999771118164,26.989999771118164,26.25,26.579999923706055,26.579999923706055,20600,0.0,0.0,False
+2024-09-30 00:00:00-04:00,26.610000610351562,27.0,26.5,26.950000762939453,26.950000762939453,25200,0.0,0.0,False
+2024-10-01 00:00:00-04:00,26.969999313354492,27.690000534057617,26.93000030517578,27.3799991607666,27.3799991607666,42600,0.0,0.0,False
+2024-10-02 00:00:00-04:00,27.790000915527344,27.799999237060547,26.290000915527344,26.610000610351562,26.610000610351562,78000,0.0,0.0,False
+2024-10-03 00:00:00-04:00,26.84000015258789,28.489999771118164,26.84000015258789,28.15999984741211,28.15999984741211,84200,0.0,0.0,False
+2024-10-04 00:00:00-04:00,28.190000534057617,28.549999237060547,27.790000915527344,28.049999237060547,28.049999237060547,65200,0.0,0.0,False
+2024-10-07 00:00:00-04:00,28.079999923706055,29.15999984741211,27.760000228881836,28.010000228881836,28.010000228881836,44900,0.0,0.0,False
+2024-10-08 00:00:00-04:00,27.989999771118164,28.110000610351562,26.850000381469727,27.709999084472656,27.709999084472656,26300,0.0,0.0,False
+2024-10-09 00:00:00-04:00,27.030000686645508,28.360000610351562,27.030000686645508,28.360000610351562,28.360000610351562,23000,0.0,0.0,False
+2024-10-10 00:00:00-04:00,28.020000457763672,28.799999237060547,27.969999313354492,28.360000610351562,28.360000610351562,24300,0.0,0.0,False
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+2024-10-16 00:00:00-04:00,26.81999969482422,27.110000610351562,26.700000762939453,26.8700008392334,26.8700008392334,12500,0.0,0.0,False
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+2024-11-01 00:00:00-04:00,27.75,28.229999542236328,27.079999923706055,27.219999313354492,27.219999313354492,20300,0.0,0.0,False
+2024-11-04 00:00:00-05:00,27.81999969482422,28.510000228881836,27.59000015258789,28.219999313354492,28.219999313354492,21300,0.0,0.0,False
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+2024-11-07 00:00:00-05:00,27.65999984741211,29.34000015258789,27.65999984741211,29.25,29.25,25200,0.0,0.0,False
+2024-11-08 00:00:00-05:00,29.43000030517578,29.43000030517578,28.0,28.06999969482422,28.06999969482422,27000,0.0,0.0,False
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+2024-11-14 00:00:00-05:00,31.489999771118164,32.540000915527344,30.0,32.5,32.5,150800,0.0,0.0,False
+2024-11-15 00:00:00-05:00,32.16999816894531,33.119998931884766,32.0,32.209999084472656,32.209999084472656,51300,0.0,0.0,False
--- a/tests/data/SCR-TO-1d-bad-div.csv
+++ b/tests/data/SCR-TO-1d-bad-div.csv
@@ -1,128 +1,128 @@
 Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2022-01-04 00:00:00-05:00,3.8299999237060547,4.269999980926514,3.8299999237060547,4.0,2.2503862380981445,559600,0.0,0.0
 2022-01-05 00:00:00-05:00,4.050000190734863,4.199999809265137,4.039999961853027,4.050000190734863,2.2785160541534424,467700,0.0,0.0
-2022-01-06 00:00:00-05:00,4.150000095367432,4.179999828338623,4.050000190734863,4.090000152587891,2.3010201454162598,393300,0.0,0.0
-2022-01-07 00:00:00-05:00,4.130000114440918,4.230000019073486,4.070000171661377,4.159999847412109,2.3404016494750977,860600,0.0,0.0
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 2022-01-10 00:00:00-05:00,4.199999809265137,4.199999809265137,4.119999885559082,4.199999809265137,2.362905502319336,281900,0.0,0.0
-2022-01-11 00:00:00-05:00,4.199999809265137,4.460000038146973,4.159999847412109,4.420000076293945,2.4866766929626465,695900,0.0,0.0
-2022-01-12 00:00:00-05:00,4.449999809265137,4.639999866485596,4.449999809265137,4.579999923706055,2.5766923427581787,452500,0.0,0.0
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 2022-01-17 00:00:00-05:00,4.570000171661377,4.760000228881836,4.519999980926514,4.679999828338623,2.6329519748687744,317900,0.0,0.0
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+2022-01-21 00:00:00-05:00,4.460000038146973,4.579999923706055,4.369999885559082,4.420000076293945,2.4866771697998047,426200,0.0,0.0
 2022-01-24 00:00:00-05:00,4.309999942779541,4.400000095367432,4.070000171661377,4.380000114440918,2.4641730785369873,391900,0.0,0.0
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 2022-05-13 00:00:00-04:00,4.429999828338623,4.840000152587891,4.429999828338623,4.769999980926514,2.6835856437683105,800600,0.0,0.0
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 2022-05-31 00:00:00-04:00,6.150000095367432,6.170000076293945,5.710000038146973,5.840000152587891,3.2855641841888428,3694200,0.0,0.0
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 2022-06-22 00:00:00-04:00,4.449999809265137,4.460000038146973,4.300000190734863,4.309999942779541,2.424791097640991,809900,0.0,0.0
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 2022-06-24 00:00:00-04:00,3.9100000858306885,4.170000076293945,3.880000114440918,3.9700000286102295,2.233508348464966,708700,0.0,0.0
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 2022-06-28 00:00:00-04:00,4.199999809265137,4.400000095367432,4.179999828338623,4.309999942779541,2.424791097640991,998100,0.0,0.0
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 2022-06-30 00:00:00-04:00,4.0,4.110000133514404,3.8499999046325684,3.9800000190734863,2.2391343116760254,788400,0.0,0.0
 2022-07-04 00:00:00-04:00,4.019999980926514,4.050000190734863,3.890000104904175,4.03000020980835,2.2672643661499023,501500,0.0,0.0
 2022-07-05 00:00:00-04:00,3.9000000953674316,3.930000066757202,3.680000066757202,3.799999952316284,2.137866973876953,1068800,0.0,0.0
@@ -130,57 +130,57 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2022-07-07 00:00:00-04:00,3.640000104904175,3.7899999618530273,3.640000104904175,3.7300000190734863,2.098485231399536,537500,0.0,0.0
 2022-07-08 00:00:00-04:00,3.75,3.880000114440918,3.640000104904175,3.799999952316284,2.137866973876953,380000,0.0,0.0
 2022-07-11 00:00:00-04:00,3.740000009536743,3.890000104904175,3.640000104904175,3.8299999237060547,2.154744863510132,1113200,0.0,0.0
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 2022-07-13 00:00:00-04:00,3.4600000381469727,3.5799999237060547,3.440000057220459,3.509999990463257,1.974713921546936,537100,0.0,0.0
 2022-07-14 00:00:00-04:00,3.380000114440918,3.4600000381469727,3.259999990463257,3.440000057220459,1.9353322982788086,877700,0.0,0.0
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 2022-07-18 00:00:00-04:00,3.5799999237060547,3.809999942779541,3.559999942779541,3.7699999809265137,2.1209890842437744,1215000,0.0,0.0
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 2022-07-26 00:00:00-04:00,3.9000000953674316,3.9800000190734863,3.7899999618530273,3.869999885559082,2.17724871635437,538400,0.0,0.0
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 2022-08-26 00:00:00-04:00,5.059999942779541,5.170000076293945,5.010000228881836,5.010000228881836,2.8186089992523193,342300,0.0,0.0
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 2022-08-30 00:00:00-04:00,5.070000171661377,5.070000171661377,4.900000095367432,4.940000057220459,2.7792270183563232,335300,0.0,0.0
 2022-08-31 00:00:00-04:00,4.849999904632568,5.050000190734863,4.78000020980835,4.880000114440918,2.745471239089966,517200,0.0,0.0
 2022-09-01 00:00:00-04:00,4.880000114440918,4.880000114440918,4.570000171661377,4.619999885559082,2.599195957183838,587000,0.0,0.0
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 2022-09-08 00:00:00-04:00,4.449999809265137,4.539999961853027,4.409999847412109,4.480000019073486,2.520432710647583,369200,0.0,0.0
 2022-09-09 00:00:00-04:00,4.559999942779541,4.809999942779541,4.559999942779541,4.78000020980835,2.689211845397949,513000,0.0,0.0
 2022-09-12 00:00:00-04:00,4.789999961853027,4.880000114440918,4.739999771118164,4.829999923706055,2.717341423034668,285000,0.0,0.0
 2022-09-13 00:00:00-04:00,4.800000190734863,4.849999904632568,4.71999979019165,4.760000228881836,2.67795991897583,225800,0.0,0.0
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 2022-09-19 00:00:00-04:00,4.5,4.659999847412109,4.389999866485596,4.639999866485596,2.610448122024536,537500,0.0,0.0
 2022-09-20 00:00:00-04:00,4.639999866485596,4.639999866485596,4.429999828338623,4.480000019073486,2.520432710647583,400900,0.0,0.0
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 2022-09-23 00:00:00-04:00,4.0,4.0,3.5999999046325684,3.700000047683716,2.0816073417663574,960400,0.0,0.0
 2022-09-26 00:00:00-04:00,3.690000057220459,3.75,3.3299999237060547,3.369999885559082,1.895950436592102,683500,0.0,0.0
 2022-09-27 00:00:00-04:00,3.440000057220459,3.4700000286102295,3.3399999141693115,3.380000114440918,1.9015765190124512,931200,0.0,0.0
@@ -188,243 +188,243 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2022-09-29 00:00:00-04:00,3.5899999141693115,3.75,3.4800000190734863,3.740000009536743,2.1041111946105957,640300,0.0,0.0
 2022-09-30 00:00:00-04:00,3.6700000762939453,3.7699999809265137,3.569999933242798,3.700000047683716,2.0816073417663574,536300,0.0,0.0
 2022-10-03 00:00:00-04:00,3.809999942779541,3.940000057220459,3.7699999809265137,3.9000000953674316,2.194126844406128,656400,0.0,0.0
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 2022-10-06 00:00:00-04:00,4.139999866485596,4.409999847412109,4.139999866485596,4.380000114440918,2.4641730785369873,418100,0.0,0.0
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 2022-10-11 00:00:00-04:00,4.090000152587891,4.099999904632568,3.859999895095825,3.990000009536743,2.244760513305664,307800,0.0,0.0
 2022-10-12 00:00:00-04:00,3.940000057220459,3.9800000190734863,3.8399999141693115,3.9700000286102295,2.233508348464966,371100,0.0,0.0
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 2022-10-14 00:00:00-04:00,4.010000228881836,4.070000171661377,3.8299999237060547,3.8399999141693115,2.1603708267211914,353500,0.0,0.0
 2022-10-17 00:00:00-04:00,3.890000104904175,3.9700000286102295,3.8499999046325684,3.9000000953674316,2.194126844406128,546400,0.0,0.0
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 2022-10-19 00:00:00-04:00,3.9000000953674316,4.0,3.869999885559082,3.990000009536743,2.244760513305664,379500,0.0,0.0
 2022-10-20 00:00:00-04:00,4.0,4.130000114440918,3.9600000381469727,3.990000009536743,2.244760513305664,453900,0.0,0.0
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 2022-10-24 00:00:00-04:00,3.9800000190734863,4.059999942779541,3.9100000858306885,4.0,2.2503862380981445,496100,0.0,0.0
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 2022-10-26 00:00:00-04:00,4.050000190734863,4.230000019073486,4.050000190734863,4.210000038146973,2.3685317039489746,877200,0.0,0.0
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 2022-11-01 00:00:00-04:00,4.230000019073486,4.25,4.139999866485596,4.179999828338623,2.351653575897217,319700,0.0,0.0
 2022-11-02 00:00:00-04:00,4.170000076293945,4.340000152587891,4.110000133514404,4.21999979019165,2.374157428741455,481300,0.0,0.0
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 2022-11-04 00:00:00-04:00,4.360000133514404,4.590000152587891,4.340000152587891,4.550000190734863,2.559814453125,741800,0.0,0.0
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 2022-11-08 00:00:00-05:00,4.590000152587891,4.599999904632568,4.460000038146973,4.510000228881836,2.537310838699341,426500,0.0,0.0
 2022-11-09 00:00:00-05:00,4.099999904632568,4.130000114440918,3.509999990463257,3.5799999237060547,2.0140955448150635,3261400,0.0,0.0
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 2022-11-18 00:00:00-05:00,3.299999952316284,3.3499999046325684,3.2300000190734863,3.3299999237060547,1.8734464645385742,1005000,0.0,0.0
 2022-11-21 00:00:00-05:00,3.2899999618530273,3.3299999237060547,3.130000114440918,3.2899999618530273,1.8509427309036255,1701700,0.0,0.0
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 2022-11-24 00:00:00-05:00,3.450000047683716,3.4600000381469727,3.380000114440918,3.430000066757202,1.9297062158584595,213400,0.0,0.0
 2022-11-25 00:00:00-05:00,3.450000047683716,3.4800000190734863,3.369999885559082,3.380000114440918,1.9015765190124512,404100,0.0,0.0
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 2022-11-29 00:00:00-05:00,3.3299999237060547,3.4800000190734863,3.309999942779541,3.4000000953674316,1.9128283262252808,782700,0.0,0.0
 2022-11-30 00:00:00-05:00,3.5899999141693115,3.5899999141693115,3.4000000953674316,3.4000000953674316,1.9128283262252808,1510300,0.0,0.0
 2022-12-01 00:00:00-05:00,3.4600000381469727,3.549999952316284,3.359999895095825,3.369999885559082,1.895950436592102,570700,0.0,0.0
 2022-12-02 00:00:00-05:00,3.369999885559082,3.450000047683716,3.3299999237060547,3.3499999046325684,1.8846986293792725,319600,0.0,0.0
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 2022-12-06 00:00:00-05:00,3.2899999618530273,3.3499999046325684,3.0799999237060547,3.0799999237060547,1.732797384262085,1268000,0.0,0.0
 2022-12-07 00:00:00-05:00,3.0799999237060547,3.180000066757202,2.9800000190734863,3.0899999141693115,1.738423466682434,769100,0.0,0.0
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 2022-12-09 00:00:00-05:00,3.0199999809265137,3.059999942779541,2.950000047683716,3.059999942779541,1.7215455770492554,779100,0.0,0.0
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 2023-05-25 00:00:00-04:00,2.5,2.5,2.380000114440918,2.4000000953674316,1.5923840999603271,287100,0.0,0.0
 2023-05-26 00:00:00-04:00,2.4000000953674316,2.4700000286102295,2.369999885559082,2.4000000953674316,1.5923840999603271,150100,0.0,0.0
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 2023-07-10 00:00:00-04:00,2.309999942779541,2.3499999046325684,2.299999952316284,2.299999952316284,1.8660959005355835,55700,0.0,0.0
 2023-07-11 00:00:00-04:00,2.2899999618530273,2.4000000953674316,2.2799999713897705,2.4000000953674316,1.9472306966781616,238500,0.0,0.0
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-2023-07-14 00:00:00-04:00,2.3499999046325684,2.390000104904175,2.2799999713897705,2.2899999618530273,1.8579825162887573,232100,0.0,0.0
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 2023-07-17 00:00:00-04:00,2.2899999618530273,2.309999942779541,2.2200000286102295,2.240000009536743,1.8174152374267578,144600,0.0,0.0
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 2023-07-20 00:00:00-04:00,2.4800000190734863,2.569999933242798,2.4800000190734863,2.5199999809265137,2.0445921421051025,270100,0.0,0.0
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 2023-07-24 00:00:00-04:00,2.5,2.5299999713897705,2.4700000286102295,2.490000009536743,2.020251750946045,197200,0.0,0.0
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 2023-07-27 00:00:00-04:00,2.4800000190734863,2.4800000190734863,2.4000000953674316,2.4100000858306885,1.9553440809249878,381600,0.0,0.0
 2023-07-28 00:00:00-04:00,2.450000047683716,2.549999952316284,2.380000114440918,2.5299999713897705,2.0527055263519287,424500,0.0,0.0
 2023-07-31 00:00:00-04:00,2.5,2.7300000190734863,2.5,2.7200000286102295,2.2068612575531006,516500,0.0,0.0
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 2023-08-04 00:00:00-04:00,2.4700000286102295,2.5399999618530273,2.4200000762939453,2.5399999618530273,2.060818910598755,1363900,0.0,0.0
 2023-08-08 00:00:00-04:00,2.509999990463257,2.549999952316284,2.4700000286102295,2.5299999713897705,2.0527055263519287,776900,0.0,0.0
 2023-08-09 00:00:00-04:00,2.549999952316284,2.559999942779541,2.5,2.5199999809265137,2.0445921421051025,932100,0.0,0.0
 2023-08-10 00:00:00-04:00,2.5199999809265137,2.5299999713897705,2.4700000286102295,2.490000009536743,2.020251750946045,389700,0.0,0.0
 2023-08-11 00:00:00-04:00,2.4800000190734863,2.509999990463257,2.4800000190734863,2.509999990463257,2.0364787578582764,280800,0.0,0.0
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 2023-08-16 00:00:00-04:00,2.2899999618530273,2.359999895095825,2.2300000190734863,2.259999990463257,1.8336421251296997,474700,0.0,0.0
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 2023-08-29 00:00:00-04:00,2.1600000858306885,2.200000047683716,2.1500000953674316,2.190000057220459,1.7768479585647583,245600,0.0,0.0
 2023-08-30 00:00:00-04:00,2.180000066757202,2.2200000286102295,2.1700000762939453,2.180000066757202,1.7687344551086426,211200,0.0,0.0
 2023-08-31 00:00:00-04:00,2.190000057220459,2.299999952316284,2.190000057220459,2.2799999713897705,1.8498690128326416,1133800,0.0,0.0
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 2023-09-05 00:00:00-04:00,2.4200000762939453,2.4200000762939453,2.2699999809265137,2.390000104904175,1.939117193222046,1434000,0.0,0.0
 2023-09-06 00:00:00-04:00,2.380000114440918,2.4000000953674316,2.3299999237060547,2.4000000953674316,1.9472306966781616,352700,0.0,0.0
 2023-09-07 00:00:00-04:00,2.359999895095825,2.390000104904175,2.319999933242798,2.3499999046325684,1.906663179397583,501700,0.0,0.0
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 2023-09-11 00:00:00-04:00,2.390000104904175,2.4200000762939453,2.3299999237060547,2.3499999046325684,1.906663179397583,740800,0.0,0.0
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+2023-09-12 00:00:00-04:00,2.3499999046325684,2.440000057220459,2.3499999046325684,2.430000066757202,1.9715709686279297,696100,0.0,0.0
+2023-09-13 00:00:00-04:00,2.440000057220459,2.4600000381469727,2.4000000953674316,2.430000066757202,1.9715709686279297,328600,0.0,0.0
 2023-09-14 00:00:00-04:00,2.450000047683716,2.5199999809265137,2.430000066757202,2.5199999809265137,2.497917890548706,553500,0.441,0.0
 2023-09-15 00:00:00-04:00,2.5,2.640000104904175,2.4800000190734863,2.569999933242798,2.5474798679351807,770400,0.0,0.0
 2023-09-18 00:00:00-04:00,2.569999933242798,2.640000104904175,2.5299999713897705,2.559999942779541,2.537567377090454,753200,0.0,0.0
@@ -687,3 +687,37 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2024-09-25 00:00:00-04:00,27.8700008392334,27.8700008392334,27.0,27.489999771118164,27.489999771118164,30200,0.0,0.0
 2024-09-26 00:00:00-04:00,27.299999237060547,27.479999542236328,26.559999465942383,26.610000610351562,26.610000610351562,51800,0.0,0.0
 2024-09-27 00:00:00-04:00,26.989999771118164,26.989999771118164,26.25,26.579999923706055,26.579999923706055,20600,0.0,0.0
+2024-09-30 00:00:00-04:00,26.610000610351562,27.0,26.5,26.950000762939453,26.950000762939453,25200,0.0,0.0
+2024-10-01 00:00:00-04:00,26.969999313354492,27.690000534057617,26.93000030517578,27.3799991607666,27.3799991607666,42600,0.0,0.0
+2024-10-02 00:00:00-04:00,27.790000915527344,27.799999237060547,26.290000915527344,26.610000610351562,26.610000610351562,78000,0.0,0.0
+2024-10-03 00:00:00-04:00,26.84000015258789,28.489999771118164,26.84000015258789,28.15999984741211,28.15999984741211,84200,0.0,0.0
+2024-10-04 00:00:00-04:00,28.190000534057617,28.549999237060547,27.790000915527344,28.049999237060547,28.049999237060547,65200,0.0,0.0
+2024-10-07 00:00:00-04:00,28.079999923706055,29.15999984741211,27.760000228881836,28.010000228881836,28.010000228881836,44900,0.0,0.0
+2024-10-08 00:00:00-04:00,27.989999771118164,28.110000610351562,26.850000381469727,27.709999084472656,27.709999084472656,26300,0.0,0.0
+2024-10-09 00:00:00-04:00,27.030000686645508,28.360000610351562,27.030000686645508,28.360000610351562,28.360000610351562,23000,0.0,0.0
+2024-10-10 00:00:00-04:00,28.020000457763672,28.799999237060547,27.969999313354492,28.360000610351562,28.360000610351562,24300,0.0,0.0
+2024-10-11 00:00:00-04:00,28.780000686645508,28.780000686645508,28.06999969482422,28.520000457763672,28.520000457763672,12100,0.0,0.0
+2024-10-15 00:00:00-04:00,28.350000381469727,28.350000381469727,26.540000915527344,26.690000534057617,26.690000534057617,77300,0.0,0.0
+2024-10-16 00:00:00-04:00,26.81999969482422,27.110000610351562,26.700000762939453,26.8700008392334,26.8700008392334,12500,0.0,0.0
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+2024-10-21 00:00:00-04:00,27.059999465942383,27.739999771118164,27.0,27.739999771118164,27.739999771118164,32400,0.0,0.0
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+2024-11-01 00:00:00-04:00,27.75,28.229999542236328,27.079999923706055,27.219999313354492,27.219999313354492,20300,0.0,0.0
+2024-11-04 00:00:00-05:00,27.81999969482422,28.510000228881836,27.59000015258789,28.219999313354492,28.219999313354492,21300,0.0,0.0
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+2024-11-14 00:00:00-05:00,31.489999771118164,32.540000915527344,30.0,32.5,32.5,150800,0.0,0.0
+2024-11-15 00:00:00-05:00,32.16999816894531,33.119998931884766,32.0,32.209999084472656,32.209999084472656,51300,0.0,0.0
--- 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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 2022-01-04 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
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 2022-01-07 00:00:00+01:00,21.007211685180664,21.186250686645508,20.867958068847656,21.126571655273438,11.308463096618652,698666,0.0,0.0
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@@ -402,123 +402,123 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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@@ -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
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+2024-10-15 00:00:00+02:00,38.0,38.72999954223633,37.7400016784668,38.43000030517578,38.43000030517578,289899,0.0,0.0
+2024-10-16 00:00:00+02:00,38.5,39.31999969482422,38.310001373291016,39.31999969482422,39.31999969482422,351315,0.0,0.0
+2024-10-17 00:00:00+02:00,39.27000045776367,39.310001373291016,37.95000076293945,38.439998626708984,38.439998626708984,336837,0.0,0.0
+2024-10-18 00:00:00+02:00,38.209999084472656,39.369998931884766,37.939998626708984,39.349998474121094,39.349998474121094,284589,0.0,0.0
+2024-10-21 00:00:00+02:00,39.11000061035156,39.13999938964844,38.43000030517578,39.099998474121094,39.099998474121094,176237,0.0,0.0
+2024-10-22 00:00:00+02:00,39.119998931884766,39.369998931884766,38.63999938964844,38.900001525878906,38.900001525878906,219984,0.0,0.0
+2024-10-23 00:00:00+02:00,38.70000076293945,38.86000061035156,37.790000915527344,38.79999923706055,38.79999923706055,212351,0.0,0.0
+2024-10-24 00:00:00+02:00,38.70000076293945,38.939998626708984,38.36000061035156,38.68000030517578,38.68000030517578,118090,0.0,0.0
+2024-10-25 00:00:00+02:00,38.630001068115234,38.849998474121094,38.04999923706055,38.560001373291016,38.560001373291016,164039,0.0,0.0
+2024-10-28 00:00:00+01:00,38.560001373291016,38.81999969482422,37.84000015258789,38.25,38.25,156263,0.0,0.0
+2024-10-29 00:00:00+01:00,38.25,38.88999938964844,38.029998779296875,38.31999969482422,38.31999969482422,213012,0.0,0.0
+2024-10-30 00:00:00+01:00,37.75,38.04999923706055,37.029998779296875,37.029998779296875,37.029998779296875,397551,0.0,0.0
+2024-10-31 00:00:00+01:00,37.0,37.310001373291016,36.34000015258789,37.060001373291016,37.060001373291016,281687,0.0,0.0
+2024-11-01 00:00:00+01:00,36.84000015258789,37.43000030517578,36.84000015258789,37.2400016784668,37.2400016784668,81048,0.0,0.0
+2024-11-04 00:00:00+01:00,37.060001373291016,37.79999923706055,37.040000915527344,37.560001373291016,37.560001373291016,82632,0.0,0.0
+2024-11-05 00:00:00+01:00,37.560001373291016,37.95000076293945,37.369998931884766,37.95000076293945,37.95000076293945,199518,0.0,0.0
+2024-11-06 00:00:00+01:00,38.20000076293945,38.900001525878906,34.91999816894531,34.91999816894531,34.91999816894531,469899,0.0,0.0
+2024-11-07 00:00:00+01:00,34.08000183105469,34.900001525878906,33.72999954223633,33.72999954223633,33.72999954223633,380050,0.0,0.0
+2024-11-08 00:00:00+01:00,33.61000061035156,33.91999816894531,33.29999923706055,33.54999923706055,33.54999923706055,389420,0.0,0.0
+2024-11-11 00:00:00+01:00,33.810001373291016,34.31999969482422,33.130001068115234,33.130001068115234,33.130001068115234,233145,0.0,0.0
+2024-11-12 00:00:00+01:00,32.79999923706055,33.029998779296875,31.670000076293945,31.709999084472656,31.709999084472656,284709,0.0,0.0
+2024-11-13 00:00:00+01:00,31.780000686645508,32.11000061035156,31.360000610351562,31.510000228881836,31.510000228881836,208626,0.0,0.0
+2024-11-14 00:00:00+01:00,31.600000381469727,31.8799991607666,31.34000015258789,31.40999984741211,31.40999984741211,193374,0.0,0.0
+2024-11-15 00:00:00+01:00,31.309999465942383,32.5,31.309999465942383,32.22999954223633,32.22999954223633,130866,0.0,0.0
+2024-11-18 00:00:00+01:00,32.20000076293945,32.72999954223633,32.08000183105469,32.130001068115234,32.130001068115234,94641,0.0,0.0
+2024-11-19 00:00:00+01:00,32.099998474121094,32.650001525878906,31.06999969482422,31.920000076293945,31.920000076293945,159142,0.0,0.0
--- a/tests/test_screener.py
+++ b/tests/test_screener.py
@@ -13,7 +13,7 @@ class TestScreener(unittest.TestCase):
        self.query = EquityQuery('gt',['eodprice',3])

    def test_set_default_body(self):
-        self.screener.set_default_body(self.query)
+        result = self.screener.set_default_body(self.query)

        self.assertEqual(self.screener.body['offset'], 0)
        self.assertEqual(self.screener.body['size'], 100)
@@ -23,11 +23,13 @@ class TestScreener(unittest.TestCase):
        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'
-        self.screener.set_predefined_body(k)
+        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):
@@ -44,9 +46,10 @@ class TestScreener(unittest.TestCase):
            "userId": "",
            "userIdType": "guid"
        }
-        self.screener.set_body(body)
+        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 = {
@@ -87,10 +90,11 @@ class TestScreener(unittest.TestCase):
        }
        self.screener.set_body(initial_body)
        patch_values = {"size": 50}
-        self.screener.patch_body(patch_values)
+        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 = {
@@ -108,6 +112,22 @@ class TestScreener(unittest.TestCase):
        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()
--- 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
@@ -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
--- a/yfinance/init.py
+++ b/yfinance/init.py
@@ -20,6 +20,7 @@
 #

 from . import version
+from .search import Search
 from .ticker import Ticker
 from .tickers import Tickers
 from .multi import download
@@ -36,5 +37,5 @@ __author__ = "Ran Aroussi"
 import warnings
 warnings.filterwarnings('default', category=DeprecationWarning, module='^yfinance')

-__all__ = ['download', 'Ticker', 'Tickers', 'enable_debug_mode', 'set_tz_cache_location', 'Sector', 'Industry',
-           'EquityQuery','Screener']
+__all__ = ['download', 'Search', 'Ticker', 'Tickers', 'enable_debug_mode', 'set_tz_cache_location', 'Sector',
+           'Industry', 'EquityQuery', 'Screener']
--- a/yfinance/base.py
+++ b/yfinance/base.py
@@ -29,6 +29,7 @@ from urllib.parse import quote as urlencode

 import pandas as pd
 import requests
+from datetime import date

 from . import utils, cache
 from .data import YfData
@@ -534,38 +535,60 @@ 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]
@@ -576,7 +599,7 @@ class TickerBase:
        page_offset = 0
        dates = None
        while True:
-            url = f"{_ROOT_URL_}/calendar/earnings?symbol={self.ticker}&offset={page_offset}&size={page_size}"
+            url = f"{_ROOT_URL_}/calendar/earnings?day={date.today()}&symbol={self.ticker}&offset={page_offset}&size={page_size}"
            data = self._data.cache_get(url=url, proxy=proxy).text

            if "Will be right back" in data:
@@ -617,31 +640,40 @@ class TickerBase:
        # Drop redundant columns
        dates = dates.drop(["Symbol", "Company"], axis=1)

+        # Compatibility
+        dates = dates.rename(columns={'Surprise (%)': 'Surprise(%)'})
+
+        # Drop empty rows
+        for i in range(len(dates)-1, -1, -1):
+            if dates.iloc[i].isna().all():
+                dates = dates.drop(i)
+
        # Convert types
        for cn in ["EPS Estimate", "Reported EPS", "Surprise(%)"]:
            dates.loc[dates[cn] == '-', cn] = float("nan")
            dates[cn] = dates[cn].astype(float)

-        # Convert % to range 0->1:
-        dates["Surprise(%)"] *= 0.01
-
        # Parse earnings date string
        cn = "Earnings Date"
-        # - remove AM/PM and timezone from date string
-        tzinfo = dates[cn].str.extract('([AP]M[a-zA-Z]*)$')
-        dates[cn] = dates[cn].replace(' [AP]M[a-zA-Z]*$', '', regex=True)
-        # - split AM/PM from timezone
-        tzinfo = tzinfo[0].str.extract('([AP]M)([a-zA-Z]*)', expand=True)
-        tzinfo.columns = ["AM/PM", "TZ"]
-        # - combine and parse
-        dates[cn] = dates[cn] + ' ' + tzinfo["AM/PM"]
-        dates[cn] = pd.to_datetime(dates[cn], format="%b %d, %Y, %I %p")
-        # - instead of attempting decoding of ambiguous timezone abbreviation, just use 'info':
-        self._quote.proxy = proxy or self.proxy
-        tz = self._get_ticker_tz(proxy=proxy, timeout=30)
-        dates[cn] = dates[cn].dt.tz_localize(tz)
+        try:
+            dates_backup = dates.copy()
+            # - extract timezone because Yahoo stopped returning in UTC
+            tzy = dates[cn].str.split(' ').str.get(-1)
+            tzy[tzy.isin(['EDT', 'EST'])] = 'US/Eastern'
+            # - tidy date string
+            dates[cn] = dates[cn].str.split(' ').str[:-1].str.join(' ')
+            dates[cn] = dates[cn].replace(' at', ',', regex=True)
+            # - parse
+            dates[cn] = pd.to_datetime(dates[cn], format="%B %d, %Y, %I %p")
+            # - convert to exchange timezone
+            self._quote.proxy = proxy or self.proxy
+            tz = self._get_ticker_tz(proxy=proxy, timeout=30)
+            dates[cn] = [dates[cn].iloc[i].tz_localize(tzy.iloc[i], ambiguous=True).tz_convert(tz) for i in range(len(dates))]

-        dates = dates.set_index("Earnings Date")
+            dates = dates.set_index("Earnings Date")
+        except Exception as e:
+            utils.get_yf_logger().info(f"{self.ticker}: Problem parsing earnings_dates: {str(e)}")
+            dates = dates_backup

        self._earnings_dates[limit] = dates

@@ -654,4 +686,4 @@ class TickerBase:
        if not self._funds_data:
            self._funds_data = FundsData(self._data, self.ticker)
        
-        return self._funds_data
+        return self._funds_data
--- a/yfinance/const.py
+++ b/yfinance/const.py
@@ -427,119 +427,108 @@ EQUITY_SCREENER_EQ_MAP = {
    }
 }
 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"
+    "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 = {
--- a/yfinance/domain/domain.py
+++ b/yfinance/domain/domain.py
@@ -1,14 +1,27 @@
+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:
+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
@@ -19,47 +32,105 @@ class Domain:
        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', {}))
@@ -67,6 +138,15 @@ class Domain:
        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),
@@ -78,6 +158,15 @@ class Domain:
        }

    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'), 
@@ -87,11 +176,22 @@ class Domain:
        if not top_companies_values: 
            return None
        
-        return _pd.DataFrame(top_companies_values, columns = top_companies_column).set_index('symbol')
+        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
@@ -7,7 +7,17 @@ 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}'

@@ -17,29 +27,68 @@ class Industry(Domain):
        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),
@@ -53,6 +102,15 @@ class Industry(Domain):
        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),
@@ -65,6 +123,9 @@ class Industry(Domain):
        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:
--- a/yfinance/domain/sector.py
+++ b/yfinance/domain/sector.py
@@ -1,5 +1,7 @@
 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

@@ -7,48 +9,127 @@ 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')
+        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:
--- 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
@@ -35,11 +36,12 @@ 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,
+             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):
-    """Download yahoo tickers
+             multi_level_index=True) -> Union[_pd.DataFrame, None]:
+    """
+    Download yahoo tickers
    :Parameters:
        tickers : str, list
            List of tickers to download
@@ -63,7 +65,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True,
            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
@@ -210,7 +212,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True,
        _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)

--- 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
@@ -9,15 +9,21 @@ from typing import Dict, Optional

 _QUOTE_SUMMARY_URL_ = f"{_BASE_URL_}/v10/finance/quoteSummary/"

-'''
-Supports ETF and Mutual Funds Data
-Queried Modules: quoteType, summaryProfile, fundProfile, topHoldings
-
-Notes: 
- fundPerformance module is not implemented as better data is queryable using history
-'''
 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
@@ -41,71 +47,143 @@ class FundsData:
        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]
@@ -128,15 +206,37 @@ class FundsData:

    @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:  # done
+    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)),
@@ -207,6 +307,12 @@ class FundsData:
        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), 
--- a/yfinance/scrapers/history.py
+++ b/yfinance/scrapers/history.py
@@ -953,7 +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():
@@ -1426,7 +1426,7 @@ 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))

            possibilities = []
            if (drops==0.0).all() and df2['Volume'].iloc[div_idx]==0:
@@ -1681,10 +1681,6 @@ class PriceHistory:
                        div_status_df.loc[phantom_div_dt, c] = False
        checks.append('phantom')

-        if not div_status_df[checks].any().any():
-            # Perfect
-            return df
-
        # Remove phantoms early
        if 'phantom' in div_status_df.columns:
            f_phantom = div_status_df['phantom']
@@ -1709,6 +1705,29 @@ class PriceHistory:
            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)):
+                    r_pre, r_post = None, None
+                    if i > 0:
+                        r_pre = div_status_df['%'].iloc[i-1] / div_status_df['%'].iloc[i]
+                    if i < (len(div_status_df)-1):
+                        r_post = div_status_df['%'].iloc[i+1] / div_status_df['%'].iloc[i]
+                    r_pre = r_pre or r_post
+                    r_post = r_post or r_pre
+                    if abs(r_pre-currency_divide)<20 and abs(r_post-currency_divide)<20:
+                        div_dt = div_status_df.index[i]
+                        div_status_df.loc[div_dt, 'div_too_small'] = True
+
+        if not div_status_df[checks].any().any():
+            # Perfect
+            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]
@@ -1789,7 +1808,8 @@ class PriceHistory:
                                elif adjDelta_drop > 0.39*adjDiv:
                                    # Still true that applied adjustment exceeds price action, 
                                    # just not clear what solution is (if any).
-                                    div_adj_exceeds_prices = True
+                                    if (x['Adj']<1.0).any():
+                                        div_adj_exceeds_prices = True
                                    break

            # Can prune the space:
@@ -1843,22 +1863,6 @@ class PriceHistory:

        checks += ['adj_exceeds_prices', 'div_date_wrong']

-        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)):
-                    r_pre, r_post = None, None
-                    if i > 0:
-                        r_pre = div_status_df['%'].iloc[i-1] / div_status_df['%'].iloc[i]
-                    if i < (len(div_status_df)-1):
-                        r_post = div_status_df['%'].iloc[i+1] / div_status_df['%'].iloc[i]
-                    r_pre = r_pre or r_post
-                    r_post = r_post or r_pre
-                    if abs(r_pre-currency_divide)<20 and abs(r_post-currency_divide)<20:
-                        div_dt = div_status_df.index[i]
-                        div_status_df.loc[div_dt, 'div_too_small'] = True
-
        for c in checks:
            if not div_status_df[c].any():
                div_status_df = div_status_df.drop(c, axis=1)
@@ -1887,11 +1891,16 @@ 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
@@ -1900,7 +1909,16 @@ class PriceHistory:
                        continue

                    if 'adj_exceeds_prices' in cluster.columns and (cluster[c] == (cluster[c] & cluster['adj_exceeds_prices'])).all():
-                        # More likely that true-positive. Maybe the div never happened
+                        # 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():
--- a/yfinance/screener/screener.py
+++ b/yfinance/screener/screener.py
@@ -4,11 +4,28 @@ 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

@@ -25,17 +42,54 @@ class Screener:
    
    @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") -> None:
+    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 = {
@@ -48,16 +102,63 @@ class Screener:
            "userId": userId,
            "userIdType": userIdType
        }
+        return self

-    def set_predefined_body(self, k: str) -> None:
-        body = PREDEFINED_SCREENER_BODY_MAP.get(k, None)
+    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 {k} provided for predefined screener')
+            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) -> None:
+    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}")
@@ -68,9 +169,24 @@ class Screener:

        self._body_updated = True
        self._body = body
+        return self

+    def patch_body(self, values: Dict) -> 'Screener':
+        """
+        Patch parts of the body using dictionary input

-    def patch_body(self, values: Dict) -> None:
+        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}")
@@ -78,10 +194,14 @@ class Screener:
        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"}
--- a/yfinance/screener/screener_query.py
+++ b/yfinance/screener/screener_query.py
@@ -1,19 +1,67 @@
+from abc import ABC, abstractmethod
 import numbers
-from typing import List, Union, Dict, Set
+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:
+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):
@@ -34,16 +82,26 @@ class EquityQuery(Query):

        self.operator = operator
        self.operands = operand
-        self._valid_eq_map = EQUITY_SCREENER_EQ_MAP
-        self._valid_fields = EQUITY_SCREENER_FIELDS
-        
+        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_map(self) -> Dict:
-        return self._valid_eq_map
+    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_fields(self) -> Set:
-        return self._valid_fields
+    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: 
@@ -54,7 +112,8 @@ class EquityQuery(Query):
    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 operand[0] not in EQUITY_SCREENER_FIELDS:
+        
+        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')
@@ -64,7 +123,7 @@ class EquityQuery(Query):
    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 operand[0] not in EQUITY_SCREENER_FIELDS:
+        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')
@@ -74,7 +133,7 @@ class EquityQuery(Query):
    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 operand[0] not in EQUITY_SCREENER_FIELDS:
+        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')
--- 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/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):
@@ -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 = "0.2.49"
+version = "0.2.51"
Author	SHA1	Message	Date
ValueRaider	5adddf3e88	Version 0.2.51	2024-12-19 22:46:16 +00:00
ValueRaider	5bbe358ad7	Merge pull request #2182 from ranaroussi/dev dev -> main	2024-12-19 22:42:48 +00:00
ValueRaider	38c1323313	Merge pull request #2169 from hjlgood/main Fix get_earnings_dates method	2024-12-19 22:42:36 +00:00
ValueRaider	70243657a7	Earnings dates: fix time-shift, remove 'add_cokies'	2024-12-18 21:07:30 +00:00
jeong	e242617237	Fix Ticker.earnings_dates	2024-12-18 21:04:21 +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	3f911745c4	Merge pull request #2171 from R5dan/remove-mkdocs Remove mkdocs	2024-12-08 18:23:22 +00:00
R5dan	f95eef6489	Remove mkdocs	2024-12-08 17:28:41 +00:00
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	0c89fcd8f7	Version 0.2.50	2024-11-19 21:41:42 +00:00
ValueRaider	e797a30012	Merge pull request #2140 from ranaroussi/dev dev -> main	2024-11-19 21:38:56 +00:00
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	b1d9f54291	Merge pull request #2134 from ranaroussi/dev dev -> main	2024-11-17 21:02:14 +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	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	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