Version 0.2.53

Merge pull request #2264 from ranaroussi/dev
sync dev -> main
2025-02-15 18:16:37 +00:00 · 2025-02-15 18:14:44 +00:00 · 2025-02-15 17:25:21 +00:00 · 2025-02-15 17:10:07 +00:00 · 2025-02-15 17:00:38 +00:00 · 2025-02-02 17:32:47 +00:00
86 changed files with 7249 additions and 4939 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/.github/workflows/pytest.yml.disabled
+++ b/.github/workflows/pytest.yml.disabled
@@ -0,0 +1,35 @@
+name: Pytest
+
+on:
+  pull_request:
+    branches:
+      - master
+      - main
+      - dev
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v3
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.12"
+        cache: 'pip'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt pytest
+
+    - name: Run non-cache tests
+      run: pytest tests/ --ignore tests/test_cache.py --ignore tests/test_price_repair.py
+
+    - name: Run cache tests
+      run: |
+        pytest tests/test_cache.py::TestCache
+        pytest tests/test_cache.py::TestCacheNoPermission
--- 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 +1,63 @@
 Change Log
 ===========

+0.2.53
+------
+Fixes:
+- Fix: Failed to parse holders JSON data  #2234
+- Fix: Bad data in Holders #2244
+- Stop CSRF-cookie-fetch fail killing yfinance #2249
+- Fix Market Docs #2250
+- Fix: Broken "See also" links in documentation #2253
+- Fix: Interval check and error message formatting in multi.py #2256
+Improve:
+- Add pre- / post-stock prices (and other useful information) #2212
+- Warn user when use download() without specifying auto_adjust #2230
+- Refactor: Earnings Dates – Switch to API Fetching #2247
+- Improve prices div repair #2260
+Maintenance:
+- Add GitHub Actions workflow and fix failing tests #2233
+
+0.2.52
+------
+Features:
+- Improve Screener & docs #2207
+- Add Market summary & status #2175
+- Support custom period in Ticker.history() #2192
+- raise YfRateLimitError if rate limited #2108
+- add more options to Search #2191
+Fixes:
+- remove hardcoded keys in Analysis #2194
+- handle Yahoo changed Search response #2202
+Maintenance:
+- add optional dependencies to requirements.txt #2199
+
+0.2.51
+------
+Features:
+- Screener tweaks #2168
+- Search #2160
+- get_news() expose count #2173
+Fixes:
+- earnings_dates #2169
+
+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
+
+0.2.47 and 0.2.48
+-----------------
+Add yf.download(multi_level_index)
+
 0.2.46
 ------
 Fix regression in 0.2.45 #2094
--- a/README.md
+++ b/README.md
@@ -32,328 +32,33 @@ 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
+- `Market`: get infomation about a market
+- `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 +67,6 @@ 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 +82,4 @@ 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 +1,46 @@
+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__',
+    'members': True,
+}
+
+# -- 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/market.py
+++ b/doc/source/reference/examples/market.py
@@ -0,0 +1,6 @@
+import yfinance as yf
+
+EUROPE = yf.Market("EUROPE")
+
+status = EUROPE.status
+summary = EUROPE.summary
--- 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 +1,10 @@
+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
+
+# get list of related research
+research = yf.Search("apple", include_research=True).research
--- 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 +1,47 @@
+=============
+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:`Market <yfinance.Market>`: Class for accessing market summary.
+- :attr:`download <yfinance.download>`: Function to download market data for 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:`Market <yfinance.Market>`: Class for accessing market status & summary.
+- :attr:`EquityQuery <yfinance.EquityQuery>`: Class to build equity query filters.
+- :attr:`FundQuery <yfinance.FundQuery>`: Class to build fund query filters.
+- :attr:`screen <yfinance.screen>`: Run equity/fund queries.
+- :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.market
+   yfinance.financials
+   yfinance.analysis
+   yfinance.market
+   yfinance.search
+   yfinance.sector_industry
+   yfinance.screener
+   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 +1,32 @@
+=========================
+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
+
+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.market.rst
+++ b/doc/source/reference/yfinance.market.rst
@@ -0,0 +1,41 @@
+=====================
+Market
+=====================
+
+.. currentmodule:: yfinance
+
+
+Class
+------------
+The `Market` class, allows you to access market data in a Pythonic way.
+
+.. autosummary::
+   :toctree: api/
+
+   Market
+
+Market Sample Code
+------------------
+
+.. literalinclude:: examples/market.py
+   :language: python
+
+
+Markets
+------------
+There are 8 different markets available in Yahoo Finance.
+
+* US
+* GB
+
+\ 
+
+* ASIA
+* EUROPE
+
+\ 
+
+* RATES
+* COMMODITIES
+* CURRENCIES
+* CRYPTOCURRENCIES
--- 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.screener.rst
+++ b/doc/source/reference/yfinance.screener.rst
@@ -0,0 +1,27 @@
+=========================
+Screener & Query
+=========================
+
+.. currentmodule:: yfinance
+
+Query Market Data
+~~~~~~~~~~~~~~~~~~~~~
+The `Sector` and `Industry` modules allow you to access the sector and industry information.
+
+.. autosummary:: 
+   :toctree: api/
+
+   EquityQuery
+   FundQuery
+   screen
+
+.. seealso::
+   :attr:`EquityQuery.valid_fields <yfinance.EquityQuery.valid_fields>`
+      supported operand values for query
+   :attr:`EquityQuery.valid_values <yfinance.EquityQuery.valid_values>`
+      supported `EQ query operand parameters`
+   :attr:`FundQuery.valid_fields <yfinance.FundQuery.valid_fields>`
+      supported operand values for query
+   :attr:`FundQuery.valid_values <yfinance.FundQuery.valid_values>`
+      supported `EQ query operand parameters`
+   
--- 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 = "0.2.46" %}
+{% set version = "0.2.53" %}

 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
@@ -2,10 +2,11 @@ pandas>=1.3.0
 numpy>=1.16.5
 requests>=2.31
 multitasking>=0.0.7
-lxml>=4.9.1
 platformdirs>=2.0.0
 pytz>=2022.5
 frozendict>=2.3.4
 beautifulsoup4>=4.11.1
-html5lib>=1.1
 peewee>=3.16.2
+requests_cache>=1.0
+requests_ratelimiter>=0.3.1
+scipy>=1.6.3
--- a/setup.py
+++ b/setup.py
@@ -61,9 +61,9 @@ setup(
    packages=find_packages(exclude=['contrib', 'docs', 'tests', 'examples']),
    install_requires=['pandas>=1.3.0', 'numpy>=1.16.5',
                      'requests>=2.31', 'multitasking>=0.0.7',
-                      'lxml>=4.9.1', 'platformdirs>=2.0.0', 'pytz>=2022.5',
+                      'platformdirs>=2.0.0', 'pytz>=2022.5',
                      'frozendict>=2.3.4', 'peewee>=3.16.2',
-                      'beautifulsoup4>=4.11.1', 'html5lib>=1.1'],
+                      'beautifulsoup4>=4.11.1'],
    extras_require={
        'nospam': ['requests_cache>=1.0', 'requests_ratelimiter>=0.3.1'],
        'repair': ['scipy>=1.6.3'],
--- a/tests/context.py
+++ b/tests/context.py
@@ -5,9 +5,7 @@ import datetime as _dt
 import sys
 import os
 import yfinance
-from requests import Session
-from requests_cache import CacheMixin, SQLiteCache
-from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
+from requests_ratelimiter import LimiterSession
 from pyrate_limiter import Duration, RequestRate, Limiter

 _parent_dp = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
@@ -27,19 +25,21 @@ if os.path.isdir(testing_cache_dirpath):
        import shutil
        shutil.rmtree(testing_cache_dirpath)

-
-# Setup a session to rate-limit and cache persistently:
-class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
-    pass
-history_rate = RequestRate(1, Duration.SECOND*2)
+# Setup a session to only rate-limit
+history_rate = RequestRate(1, Duration.SECOND)
 limiter = Limiter(history_rate)
-cache_fp = os.path.join(testing_cache_dirpath, "unittests-cache")
-session_gbl = CachedLimiterSession(
-    limiter=limiter,
-    bucket_class=MemoryQueueBucket,
-    backend=SQLiteCache(cache_fp, expire_after=_dt.timedelta(hours=1)),
-)
-# Use this instead if only want rate-limiting:
-# from requests_ratelimiter import LimiterSession
-# session_gbl = LimiterSession(limiter=limiter)
+session_gbl = LimiterSession(limiter=limiter)

+# Use this instead if you also want caching:
+# from requests_cache import CacheMixin, SQLiteCache
+# from requests_ratelimiter import LimiterMixin
+# from requests import Session
+# from pyrate_limiter import MemoryQueueBucket
+# class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
+#     pass
+# cache_fp = os.path.join(testing_cache_dirpath, "unittests-cache")
+# session_gbl = CachedLimiterSession(
+#     limiter=limiter,
+#     bucket_class=MemoryQueueBucket,
+#     backend=SQLiteCache(cache_fp, expire_after=_dt.timedelta(hours=1)),
+# )
--- 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
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+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
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 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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+2024-11-08 00:00:00+00:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0,False
+2024-11-11 00:00:00+00:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0,False
+2024-11-12 00:00:00+00:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0,False
+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
+2024-11-14 00:00:00+00:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0,False
+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
 2022-01-21 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.6084982681274415,653,0.0,0.0,0.0
 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
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 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
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 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
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@@ -234,91 +234,91 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gai
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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
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@@ -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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-2023-07-14 00:00:00+01:00,0.595,0.5700000000000001,0.5700000000000001,0.595,0.5624320983886719,1919,0.0,0.0,0.0
-2023-07-17 00:00:00+01:00,0.595,0.6025,0.6025,0.595,0.5624320983886719,3400,0.0,0.0,0.0
-2023-07-18 00:00:00+01:00,0.595,0.5650000000000001,0.5650000000000001,0.59,0.5577057647705078,14146,0.0,0.0,0.0
-2023-07-19 00:00:00+01:00,0.59,0.5970000076293945,0.5970000076293945,0.59,0.5577057647705078,1000,0.0,0.0,0.0
+2023-07-06 00:00:00+01:00,0.6,0.585,0.5802999877929688,0.595,0.5624321365356445,7299,0.0,0.0,0.0
+2023-07-07 00:00:00+01:00,0.595,0.5802999877929688,0.5802999877929688,0.595,0.5624321365356445,13000,0.0,0.0,0.0
+2023-07-10 00:00:00+01:00,0.595,0.595,0.595,0.595,0.5624321365356445,0,0.0,0.0,0.0
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+2023-07-12 00:00:00+01:00,0.595,0.595,0.595,0.595,0.5624321365356445,0,0.0,0.0,0.0
+2023-07-13 00:00:00+01:00,0.595,0.6025,0.585,0.595,0.5624321365356445,372468,0.0,0.0,0.0
+2023-07-14 00:00:00+01:00,0.595,0.5700000000000001,0.5700000000000001,0.595,0.5624321365356445,1919,0.0,0.0,0.0
+2023-07-17 00:00:00+01:00,0.595,0.6025,0.6025,0.595,0.5624321365356445,3400,0.0,0.0,0.0
+2023-07-18 00:00:00+01:00,0.595,0.5650000000000001,0.5650000000000001,0.59,0.5577058029174805,14146,0.0,0.0,0.0
+2023-07-19 00:00:00+01:00,0.59,0.5970000076293945,0.5970000076293945,0.59,0.5577058029174805,1000,0.0,0.0,0.0
 2023-07-20 00:00:00+01:00,0.5700000000000001,0.5700000000000001,0.5700000000000001,0.5700000000000001,0.5389831924438476,0,0.0002,0.0,0.0
 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
 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
@@ -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
 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
 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
-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
+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
 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
 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
 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
 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
 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
 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
+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
+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
+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
+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
+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
+2024-09-30 00:00:00+01:00,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0,0.0,0.0,0.0
+2024-10-01 00:00:00+01:00,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0,0.0,0.0,0.0
+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
+2024-10-03 00:00:00+01:00,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0,0.0,0.0,0.0
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+2024-10-07 00:00:00+01:00,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0.005550000071525574,0,0.0,0.0,0.0
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+2024-11-12 00:00:00+00:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0
+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
+2024-11-14 00:00:00+00:00,0.555,0.555,0.555,0.555,0.555,0,0.0,0.0,0.0
+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
--- a/tests/data/SCR-TO-1d-bad-div-fixed.csv
+++ b/tests/data/SCR-TO-1d-bad-div-fixed.csv
--- a/tests/data/SCR-TO-1d-bad-div.csv
+++ b/tests/data/SCR-TO-1d-bad-div.csv
@@ -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
+2022-01-06 00:00:00-05:00,4.150000095367432,4.179999828338623,4.050000190734863,4.090000152587891,2.3010199069976807,393300,0.0,0.0
+2022-01-07 00:00:00-05:00,4.130000114440918,4.230000019073486,4.070000171661377,4.159999847412109,2.3404018878936768,860600,0.0,0.0
 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
-2022-01-13 00:00:00-05:00,4.590000152587891,4.639999866485596,4.420000076293945,4.46999979019165,2.5148067474365234,373700,0.0,0.0
-2022-01-14 00:00:00-05:00,4.480000019073486,4.590000152587891,4.429999828338623,4.559999942779541,2.5654404163360596,339600,0.0,0.0
+2022-01-11 00:00:00-05:00,4.199999809265137,4.460000038146973,4.159999847412109,4.420000076293945,2.4866771697998047,695900,0.0,0.0
+2022-01-12 00:00:00-05:00,4.449999809265137,4.639999866485596,4.449999809265137,4.579999923706055,2.5766921043395996,452500,0.0,0.0
+2022-01-13 00:00:00-05:00,4.590000152587891,4.639999866485596,4.420000076293945,4.46999979019165,2.5148065090179443,373700,0.0,0.0
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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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-2022-01-19 00:00:00-05:00,4.71999979019165,4.730000019073486,4.489999771118164,4.519999980926514,2.5429365634918213,642600,0.0,0.0
-2022-01-20 00:00:00-05:00,4.400000095367432,4.639999866485596,4.320000171661377,4.539999961853027,2.5541882514953613,780900,0.0,0.0
-2022-01-21 00:00:00-05:00,4.460000038146973,4.579999923706055,4.369999885559082,4.420000076293945,2.4866766929626465,426200,0.0,0.0
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+2022-01-20 00:00:00-05:00,4.400000095367432,4.639999866485596,4.320000171661377,4.539999961853027,2.5541887283325195,780900,0.0,0.0
+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
-2022-01-25 00:00:00-05:00,4.400000095367432,4.639999866485596,4.309999942779541,4.579999923706055,2.5766923427581787,461500,0.0,0.0
+2022-01-25 00:00:00-05:00,4.400000095367432,4.639999866485596,4.309999942779541,4.579999923706055,2.5766921043395996,461500,0.0,0.0
 2022-01-26 00:00:00-05:00,4.699999809265137,4.75,4.550000190734863,4.570000171661377,2.571066379547119,229400,0.0,0.0
 2022-01-27 00:00:00-05:00,4.590000152587891,4.699999809265137,4.429999828338623,4.480000019073486,2.520432710647583,1782700,0.0,0.0
-2022-01-28 00:00:00-05:00,4.539999961853027,4.699999809265137,4.53000020980835,4.690000057220459,2.638578176498413,340300,0.0,0.0
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 2022-02-01 00:00:00-05:00,4.900000095367432,4.900000095367432,4.75,4.809999942779541,2.706089496612549,245000,0.0,0.0
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+2022-02-02 00:00:00-05:00,4.789999961853027,4.880000114440918,4.659999847412109,4.75,2.6723337173461914,335900,0.0,0.0
 2022-02-03 00:00:00-05:00,4.71999979019165,4.800000190734863,4.579999923706055,4.730000019073486,2.6610817909240723,567100,0.0,0.0
 2022-02-04 00:00:00-05:00,4.760000228881836,4.980000019073486,4.760000228881836,4.880000114440918,2.745471239089966,728600,0.0,0.0
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 2022-02-08 00:00:00-05:00,4.650000095367432,4.650000095367432,4.360000133514404,4.460000038146973,2.509180784225464,520500,0.0,0.0
 2022-02-09 00:00:00-05:00,4.46999979019165,4.619999885559082,4.449999809265137,4.550000190734863,2.559814453125,225400,0.0,0.0
 2022-02-10 00:00:00-05:00,4.519999980926514,4.610000133514404,4.449999809265137,4.510000228881836,2.537310838699341,225300,0.0,0.0
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 2022-02-22 00:00:00-05:00,4.460000038146973,4.599999904632568,4.429999828338623,4.53000020980835,2.548562526702881,1063700,0.0,0.0
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 2022-05-30 00:00:00-04:00,5.840000152587891,6.139999866485596,5.840000152587891,6.139999866485596,3.454342842102051,430100,0.0,0.0
 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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 2022-06-03 00:00:00-04:00,5.960000038146973,6.210000038146973,5.889999866485596,6.190000057220459,3.4824728965759277,758200,0.0,0.0
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 2022-06-07 00:00:00-04:00,6.369999885559082,6.679999828338623,6.269999980926514,6.570000171661377,3.6962597370147705,647300,0.0,0.0
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 2022-06-21 00:00:00-04:00,4.800000190734863,4.949999809265137,4.710000038146973,4.769999980926514,2.6835856437683105,592700,0.0,0.0
 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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 2022-07-20 00:00:00-04:00,3.819999933242798,3.890000104904175,3.7300000190734863,3.859999895095825,2.1716225147247314,397100,0.0,0.0
 2022-07-21 00:00:00-04:00,3.740000009536743,3.799999952316284,3.619999885559082,3.7100000381469727,2.087233304977417,481600,0.0,0.0
 2022-07-22 00:00:00-04:00,3.740000009536743,3.7899999618530273,3.630000114440918,3.630000114440918,2.0422255992889404,547400,0.0,0.0
 2022-07-25 00:00:00-04:00,3.6500000953674316,3.890000104904175,3.609999895095825,3.8399999141693115,2.1603708267211914,617400,0.0,0.0
 2022-07-26 00:00:00-04:00,3.9000000953674316,3.9800000190734863,3.7899999618530273,3.869999885559082,2.17724871635437,538400,0.0,0.0
 2022-07-27 00:00:00-04:00,3.8499999046325684,4.03000020980835,3.8499999046325684,4.0,2.2503862380981445,607300,0.0,0.0
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 2022-08-04 00:00:00-04:00,3.859999895095825,3.880000114440918,3.680000066757202,3.680000066757202,2.0703554153442383,809500,0.0,0.0
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 2022-08-08 00:00:00-04:00,3.799999952316284,3.940000057220459,3.759999990463257,3.940000057220459,2.216630458831787,429300,0.0,0.0
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 2022-08-10 00:00:00-04:00,4.039999961853027,4.039999961853027,3.9000000953674316,3.990000009536743,2.244760513305664,848100,0.0,0.0
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 2022-08-12 00:00:00-04:00,4.150000095367432,4.420000076293945,4.110000133514404,4.300000190734863,2.4191653728485107,1629200,0.0,0.0
 2022-08-15 00:00:00-04:00,4.110000133514404,4.369999885559082,4.03000020980835,4.300000190734863,2.4191653728485107,712300,0.0,0.0
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 2022-08-18 00:00:00-04:00,4.349999904632568,4.639999866485596,4.349999904632568,4.550000190734863,2.559814453125,1213700,0.0,0.0
 2022-08-19 00:00:00-04:00,4.610000133514404,4.650000095367432,4.489999771118164,4.5,2.531684637069702,348900,0.0,0.0
 2022-08-22 00:00:00-04:00,4.460000038146973,4.519999980926514,4.349999904632568,4.510000228881836,2.537310838699341,589300,0.0,0.0
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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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 2022-12-22 00:00:00-05:00,3.049999952316284,3.059999942779541,2.890000104904175,2.9200000762939453,1.6427820920944214,1115000,0.0,0.0
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 2022-12-28 00:00:00-05:00,3.109999895095825,3.119999885559082,2.9200000762939453,2.940000057220459,1.6540340185165405,1075500,0.0,0.0
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 2023-04-11 00:00:00-04:00,2.7699999809265137,2.8299999237060547,2.7699999809265137,2.809999942779541,1.864416241645813,345000,0.0,0.0
 2023-04-12 00:00:00-04:00,2.8299999237060547,2.8499999046325684,2.799999952316284,2.809999942779541,1.864416241645813,210200,0.0,0.0
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 2023-04-18 00:00:00-04:00,2.75,2.75,2.680000066757202,2.7100000381469727,1.7980668544769287,194200,0.0,0.0
 2023-04-19 00:00:00-04:00,2.7100000381469727,2.7100000381469727,2.619999885559082,2.6600000858306885,1.764892339706421,269500,0.0,0.0
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 2023-05-12 00:00:00-04:00,2.430000066757202,2.549999952316284,2.4000000953674316,2.5299999713897705,1.6786381006240845,510700,0.0,0.0
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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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 2023-06-12 00:00:00-04:00,2.4800000190734863,2.4800000190734863,2.390000104904175,2.4100000858306885,1.599018931388855,205900,0.0,0.0
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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
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+2023-07-19 00:00:00-04:00,2.299999952316284,2.4700000286102295,2.299999952316284,2.450000047683716,1.9877979755401611,443000,0.0,0.0
 2023-07-20 00:00:00-04:00,2.4800000190734863,2.569999933242798,2.4800000190734863,2.5199999809265137,2.0445921421051025,270100,0.0,0.0
-2023-07-21 00:00:00-04:00,2.5799999237060547,2.5799999237060547,2.450000047683716,2.4800000190734863,2.0121383666992188,222600,0.0,0.0
+2023-07-21 00:00:00-04:00,2.5799999237060547,2.5799999237060547,2.450000047683716,2.4800000190734863,2.0121381282806396,222600,0.0,0.0
 2023-07-24 00:00:00-04:00,2.5,2.5299999713897705,2.4700000286102295,2.490000009536743,2.020251750946045,197200,0.0,0.0
-2023-07-25 00:00:00-04:00,2.4700000286102295,2.490000009536743,2.4100000858306885,2.440000057220459,1.9796844720840454,188700,0.0,0.0
-2023-07-26 00:00:00-04:00,2.4000000953674316,2.450000047683716,2.4000000953674316,2.450000047683716,1.9877978563308716,128100,0.0,0.0
+2023-07-25 00:00:00-04:00,2.4700000286102295,2.490000009536743,2.4100000858306885,2.440000057220459,1.9796843528747559,188700,0.0,0.0
+2023-07-26 00:00:00-04:00,2.4000000953674316,2.450000047683716,2.4000000953674316,2.450000047683716,1.9877979755401611,128100,0.0,0.0
 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
-2023-08-01 00:00:00-04:00,2.740000009536743,2.759999990463257,2.3399999141693115,2.450000047683716,1.9877978563308716,3980500,0.0,0.0
-2023-08-02 00:00:00-04:00,2.4600000381469727,2.4600000381469727,2.3299999237060547,2.369999885559082,1.9228901863098145,2111700,0.0,0.0
-2023-08-03 00:00:00-04:00,2.359999895095825,2.450000047683716,2.359999895095825,2.440000057220459,1.9796844720840454,814300,0.0,0.0
+2023-08-01 00:00:00-04:00,2.740000009536743,2.759999990463257,2.3399999141693115,2.450000047683716,1.9877979755401611,3980500,0.0,0.0
+2023-08-02 00:00:00-04:00,2.4600000381469727,2.4600000381469727,2.3299999237060547,2.369999885559082,1.922890067100525,2111700,0.0,0.0
+2023-08-03 00:00:00-04:00,2.359999895095825,2.450000047683716,2.359999895095825,2.440000057220459,1.9796843528747559,814300,0.0,0.0
 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
-2023-08-14 00:00:00-04:00,2.509999990463257,2.509999990463257,2.4000000953674316,2.430000066757202,1.9715710878372192,361600,0.0,0.0
-2023-08-15 00:00:00-04:00,2.4200000762939453,2.440000057220459,2.2699999809265137,2.319999933242798,1.8823227882385254,1139100,0.0,0.0
+2023-08-14 00:00:00-04:00,2.509999990463257,2.509999990463257,2.4000000953674316,2.430000066757202,1.9715709686279297,361600,0.0,0.0
+2023-08-15 00:00:00-04:00,2.4200000762939453,2.440000057220459,2.2699999809265137,2.319999933242798,1.882322907447815,1139100,0.0,0.0
 2023-08-16 00:00:00-04:00,2.2899999618530273,2.359999895095825,2.2300000190734863,2.259999990463257,1.8336421251296997,474700,0.0,0.0
-2023-08-17 00:00:00-04:00,2.259999990463257,2.309999942779541,2.25,2.309999942779541,1.8742094039916992,1188900,0.0,0.0
-2023-08-18 00:00:00-04:00,2.2699999809265137,2.390000104904175,2.240000009536743,2.359999895095825,1.9147765636444092,554900,0.0,0.0
-2023-08-21 00:00:00-04:00,2.380000114440918,2.4000000953674316,2.3299999237060547,2.3299999237060547,1.8904362916946411,211200,0.0,0.0
-2023-08-22 00:00:00-04:00,2.3499999046325684,2.369999885559082,2.25,2.2699999809265137,1.8417555093765259,336200,0.0,0.0
-2023-08-23 00:00:00-04:00,2.240000009536743,2.259999990463257,2.200000047683716,2.2200000286102295,1.801188349723816,368100,0.0,0.0
-2023-08-24 00:00:00-04:00,2.2100000381469727,2.2100000381469727,2.130000114440918,2.1600000858306885,1.7525076866149902,270700,0.0,0.0
-2023-08-25 00:00:00-04:00,2.1600000858306885,2.190000057220459,2.0,2.109999895095825,1.7119401693344116,706100,0.0,0.0
-2023-08-28 00:00:00-04:00,2.1500000953674316,2.190000057220459,2.130000114440918,2.1500000953674316,1.744394063949585,655500,0.0,0.0
+2023-08-17 00:00:00-04:00,2.259999990463257,2.309999942779541,2.25,2.309999942779541,1.8742092847824097,1188900,0.0,0.0
+2023-08-18 00:00:00-04:00,2.2699999809265137,2.390000104904175,2.240000009536743,2.359999895095825,1.9147766828536987,554900,0.0,0.0
+2023-08-21 00:00:00-04:00,2.380000114440918,2.4000000953674316,2.3299999237060547,2.3299999237060547,1.8904361724853516,211200,0.0,0.0
+2023-08-22 00:00:00-04:00,2.3499999046325684,2.369999885559082,2.25,2.2699999809265137,1.8417556285858154,336200,0.0,0.0
+2023-08-23 00:00:00-04:00,2.240000009536743,2.259999990463257,2.200000047683716,2.2200000286102295,1.8011882305145264,368100,0.0,0.0
+2023-08-24 00:00:00-04:00,2.2100000381469727,2.2100000381469727,2.130000114440918,2.1600000858306885,1.7525074481964111,270700,0.0,0.0
+2023-08-25 00:00:00-04:00,2.1600000858306885,2.190000057220459,2.0,2.109999895095825,1.711940050125122,706100,0.0,0.0
+2023-08-28 00:00:00-04:00,2.1500000953674316,2.190000057220459,2.130000114440918,2.1500000953674316,1.7443941831588745,655500,0.0,0.0
 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
-2023-09-01 00:00:00-04:00,2.299999952316284,2.369999885559082,2.299999952316284,2.359999895095825,1.9147765636444092,761300,0.0,0.0
+2023-09-01 00:00:00-04:00,2.299999952316284,2.369999885559082,2.299999952316284,2.359999895095825,1.9147766828536987,761300,0.0,0.0
 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
-2023-09-08 00:00:00-04:00,2.3499999046325684,2.359999895095825,2.3299999237060547,2.359999895095825,1.9147765636444092,405100,0.0,0.0
+2023-09-08 00:00:00-04:00,2.3499999046325684,2.359999895095825,2.3299999237060547,2.359999895095825,1.9147766828536987,405100,0.0,0.0
 2023-09-11 00:00:00-04:00,2.390000104904175,2.4200000762939453,2.3299999237060547,2.3499999046325684,1.906663179397583,740800,0.0,0.0
-2023-09-12 00:00:00-04:00,2.3499999046325684,2.440000057220459,2.3499999046325684,2.430000066757202,1.9715710878372192,696100,0.0,0.0
-2023-09-13 00:00:00-04:00,2.440000057220459,2.4600000381469727,2.4000000953674316,2.430000066757202,1.9715710878372192,328600,0.0,0.0
+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
+2024-10-17 00:00:00-04:00,26.81999969482422,27.18000030517578,26.770000457763672,27.149999618530273,27.149999618530273,21700,0.0,0.0
+2024-10-18 00:00:00-04:00,27.0,27.09000015258789,26.799999237060547,27.040000915527344,27.040000915527344,24400,0.0,0.0
+2024-10-21 00:00:00-04:00,27.059999465942383,27.739999771118164,27.0,27.739999771118164,27.739999771118164,32400,0.0,0.0
+2024-10-22 00:00:00-04:00,27.8799991607666,28.350000381469727,27.639999389648438,28.290000915527344,28.290000915527344,23900,0.0,0.0
+2024-10-23 00:00:00-04:00,28.1200008392334,28.1200008392334,27.450000762939453,27.81999969482422,27.81999969482422,7200,0.0,0.0
+2024-10-24 00:00:00-04:00,27.719999313354492,27.979999542236328,27.469999313354492,27.979999542236328,27.979999542236328,9100,0.0,0.0
+2024-10-25 00:00:00-04:00,27.479999542236328,29.139999389648438,27.479999542236328,28.8799991607666,28.8799991607666,26900,0.0,0.0
+2024-10-28 00:00:00-04:00,27.950000762939453,28.06999969482422,27.299999237060547,27.739999771118164,27.739999771118164,75500,0.0,0.0
+2024-10-29 00:00:00-04:00,27.479999542236328,27.899999618530273,27.3799991607666,27.6299991607666,27.6299991607666,23100,0.0,0.0
+2024-10-30 00:00:00-04:00,27.290000915527344,28.06999969482422,27.290000915527344,28.06999969482422,28.06999969482422,11500,0.0,0.0
+2024-10-31 00:00:00-04:00,27.75,27.809999465942383,27.190000534057617,27.520000457763672,27.520000457763672,14500,0.0,0.0
+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
+2024-11-05 00:00:00-05:00,28.440000534057617,28.440000534057617,28.09000015258789,28.290000915527344,28.290000915527344,7100,0.0,0.0
+2024-11-06 00:00:00-05:00,27.329999923706055,28.479999542236328,27.329999923706055,28.15999984741211,28.15999984741211,35200,0.0,0.0
+2024-11-07 00:00:00-05:00,27.65999984741211,29.34000015258789,27.65999984741211,29.25,29.25,25200,0.0,0.0
+2024-11-08 00:00:00-05:00,29.43000030517578,29.43000030517578,28.0,28.06999969482422,28.06999969482422,27000,0.0,0.0
+2024-11-11 00:00:00-05:00,27.940000534057617,29.190000534057617,27.940000534057617,28.969999313354492,28.969999313354492,20600,0.0,0.0
+2024-11-12 00:00:00-05:00,29.139999389648438,29.329999923706055,28.15999984741211,28.68000030517578,28.68000030517578,31900,0.0,0.0
+2024-11-13 00:00:00-05:00,28.489999771118164,28.719999313354492,28.030000686645508,28.690000534057617,28.690000534057617,54300,0.0,0.0
+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
-2022-01-03 00:00:00+01:00,20.400468826293945,20.80828094482422,20.400468826293945,20.629241943359375,11.042257308959961,559352,0.0,0.0
+2022-01-03 00:00:00+01:00,20.400468826293945,20.80828094482422,20.400468826293945,20.629241943359375,11.042256355285645,559352,0.0,0.0
 2022-01-04 00:00:00+01:00,20.649133682250977,20.897798538208008,20.5098819732666,20.848066329956055,11.15938663482666,930594,0.0,0.0
 2022-01-05 00:00:00+01:00,20.897798538208008,21.126571655273438,20.838119506835938,21.126571655273438,11.308463096618652,572100,0.0,0.0
-2022-01-06 00:00:00+01:00,20.887853622436523,21.126571655273438,20.599401473999023,20.967424392700195,11.223275184631348,532086,0.0,0.0
+2022-01-06 00:00:00+01:00,20.887853622436523,21.126571655273438,20.599401473999023,20.967424392700195,11.22327709197998,532086,0.0,0.0
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@@ -402,123 +402,123 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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 2023-11-23 00:00:00+01:00,20.599401473999023,20.76849365234375,20.599401473999023,20.7386531829834,18.984848022460938,633558,0.0,0.0
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 2024-01-08 00:00:00+01:00,28.290000915527344,28.329999923706055,27.540000915527344,28.0,25.632125854492188,503233,0.0,0.0
 2024-01-09 00:00:00+01:00,27.190000534057617,27.549999237060547,27.079999923706055,27.299999237060547,24.991321563720703,592677,0.0,0.0
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 2024-01-12 00:00:00+01:00,26.639999389648438,26.860000610351562,26.06999969482422,26.139999389648438,23.929418563842773,803481,0.0,0.0
 2024-01-15 00:00:00+01:00,25.059999465942383,25.329999923706055,25.0,25.299999237060547,24.690631866455078,726646,1.62,0.0
 2024-01-16 00:00:00+01:00,25.149999618530273,25.200000762939453,24.579999923706055,24.989999771118164,24.388099670410156,493029,0.0,0.0
@@ -675,4 +675,67 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2024-08-19 00:00:00+02:00,31.030000686645508,31.299999237060547,30.670000076293945,31.030000686645508,31.030000686645508,180272,0.0,0.0
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+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_cache.py
+++ b/tests/test_cache.py
@@ -0,0 +1,93 @@
+"""
+Tests for cache
+
+To run all tests in suite from commandline:
+   python -m unittest tests.cache
+
+Specific test class:
+   python -m unittest tests.cache.TestCache
+
+"""
+from unittest import TestSuite
+
+from tests.context import yfinance as yf
+
+import unittest
+import tempfile
+import os
+
+
+class TestCache(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.tempCacheDir = tempfile.TemporaryDirectory()
+        yf.set_tz_cache_location(cls.tempCacheDir.name)
+
+    @classmethod
+    def tearDownClass(cls):
+        yf.cache._TzDBManager.close_db()
+        cls.tempCacheDir.cleanup()
+
+    def test_storeTzNoRaise(self):
+        # storing TZ to cache should never raise exception
+        tkr = 'AMZN'
+        tz1 = "America/New_York"
+        tz2 = "London/Europe"
+        cache = yf.cache.get_tz_cache()
+        cache.store(tkr, tz1)
+        cache.store(tkr, tz2)
+
+    def test_setTzCacheLocation(self):
+        self.assertEqual(yf.cache._TzDBManager.get_location(), self.tempCacheDir.name)
+
+        tkr = 'AMZN'
+        tz1 = "America/New_York"
+        cache = yf.cache.get_tz_cache()
+        cache.store(tkr, tz1)
+
+        self.assertTrue(os.path.exists(os.path.join(self.tempCacheDir.name, "tkr-tz.db")))
+
+
+class TestCacheNoPermission(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        if os.name == "nt":  # Windows
+            cls.cache_path = "C:\\Windows\\System32\\yf-cache"
+        else:  # Unix/Linux/MacOS
+            # Use a writable directory
+            cls.cache_path = "/yf-cache"
+        yf.set_tz_cache_location(cls.cache_path)
+
+    def test_tzCacheRootStore(self):
+        # Test that if cache path in read-only filesystem, no exception.
+        tkr = 'AMZN'
+        tz1 = "America/New_York"
+
+        # During attempt to store, will discover cannot write
+        yf.cache.get_tz_cache().store(tkr, tz1)
+
+        # Handling the store failure replaces cache with a dummy
+        cache = yf.cache.get_tz_cache()
+        self.assertTrue(cache.dummy)
+        cache.store(tkr, tz1)
+
+    def test_tzCacheRootLookup(self):
+        # Test that if cache path in read-only filesystem, no exception.
+        tkr = 'AMZN'
+        # During attempt to lookup, will discover cannot write
+        yf.cache.get_tz_cache().lookup(tkr)
+
+        # Handling the lookup failure replaces cache with a dummy
+        cache = yf.cache.get_tz_cache()
+        self.assertTrue(cache.dummy)
+        cache.lookup(tkr)
+
+def suite():
+    ts: TestSuite = unittest.TestSuite()
+    ts.addTest(TestCache('Test cache'))
+    ts.addTest(TestCacheNoPermission('Test cache no permission'))
+    return ts
+
+
+if __name__ == '__main__':
+    unittest.main()
--- a/tests/test_price_repair.py
+++ b/tests/test_price_repair.py
@@ -367,9 +367,9 @@ class TestPriceRepair(unittest.TestCase):
                                     "Close":     [103.03, 102.05, 102.08],
                                     "Adj Close": [102.03, 102.05, 102.08],
                                     "Volume": [560, 137, 117]},
-                               index=_pd.to_datetime([_dt.datetime(2022, 11, 1),
-                                                      _dt.datetime(2022, 10, 31),
-                                                      _dt.datetime(2022, 10, 30)]))
+                               index=_pd.to_datetime([_dt.datetime(2024, 11, 1),
+                                                      _dt.datetime(2024, 10, 31),
+                                                      _dt.datetime(2024, 10, 30)]))
        df_bad = df_bad.sort_index()
        df_bad.index.name = "Date"
        df_bad.index = df_bad.index.tz_localize(tz_exchange)
@@ -377,9 +377,9 @@ class TestPriceRepair(unittest.TestCase):
        repaired_df = hist._fix_zeroes(df_bad, "1d", tz_exchange, prepost=False)

        correct_df = df_bad.copy()
-        correct_df.loc["2022-11-01", "Open"] = 102.080002
-        correct_df.loc["2022-11-01", "Low"] = 102.032501
-        correct_df.loc["2022-11-01", "High"] = 102.080002
+        correct_df.loc["2024-11-01", "Open"] = 102.572729
+        correct_df.loc["2024-11-01", "Low"] = 102.309091
+        correct_df.loc["2024-11-01", "High"] = 102.572729
        for c in ["Open", "Low", "High", "Close"]:
            self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-8).all())

@@ -462,7 +462,7 @@ class TestPriceRepair(unittest.TestCase):
        # Stocks that split in 2022 but no problems in Yahoo data,
        # so repair should change nothing
        good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
-        good_tkrs += ['AEI', 'GHI', 'IRON', 'LXU', 'NUZE', 'RSLS', 'TISI']
+        good_tkrs += ['AEI', 'GHI', 'IRON', 'LXU', 'RSLS', 'TISI']
        good_tkrs += ['BOL.ST', 'TUI1.DE']
        intervals = ['1d', '1wk', '1mo', '3mo']
        for tkr in good_tkrs:
@@ -580,7 +580,6 @@ class TestPriceRepair(unittest.TestCase):
        # Div 100x
        bad_tkrs += ['ABDP.L']
        bad_tkrs += ['ELCO.L']
-        bad_tkrs += ['KWS.L']
        bad_tkrs += ['PSH.L']

        # Div 100x and adjust too big
--- a/tests/test_prices.py
+++ b/tests/test_prices.py
@@ -118,7 +118,7 @@ class TestPriceHistory(unittest.TestCase):
                continue
            test_run = True

-            df = dat.history(start=dt.date() - _dt.timedelta(days=7), interval="1wk")
+            df = dat.history(start=dt.date() - _dt.timedelta(days=13), interval="1wk")
            dt0 = df.index[-2]
            dt1 = df.index[-1]
            try:
@@ -401,7 +401,7 @@ class TestPriceHistory(unittest.TestCase):

        # Setup
        tkr = "AMZN"
-        special_day = _dt.date(2023, 11, 24)
+        special_day = _dt.date(2024, 11, 29)
        time_early_close = _dt.time(13)
        dat = yf.Ticker(tkr, session=self.session)

@@ -427,8 +427,8 @@ class TestPriceHistory(unittest.TestCase):
        dat = yf.Ticker(tkr, session=self.session)

        # Test no other afternoons (or mornings) were pruned
-        start_d = _dt.date(2023, 1, 1)
-        end_d = _dt.date(2023+1, 1, 1)
+        start_d = _dt.date(2024, 1, 1)
+        end_d = _dt.date(2024+1, 1, 1)
        df = dat.history(start=start_d, end=end_d, interval="1h", prepost=False, keepna=True)
        last_dts = _pd.Series(df.index).groupby(df.index.date).last()
        dfd = dat.history(start=start_d, end=end_d, interval='1d', prepost=False, keepna=True)
--- a/tests/test_screener.py
+++ b/tests/test_screener.py
@@ -1,133 +1,38 @@
 import unittest
 from unittest.mock import patch, MagicMock
-from yfinance.const import PREDEFINED_SCREENER_BODY_MAP
-from yfinance.screener.screener import Screener
-from yfinance.screener.screener_query import EquityQuery
+from yfinance.screener.screener import screen
+from yfinance.screener.query import EquityQuery


 class TestScreener(unittest.TestCase):

    @classmethod
    def setUpClass(self):
-        self.screener = Screener()
        self.query = EquityQuery('gt',['eodprice',3])
-
-    def test_set_default_body(self):
-        self.screener.set_default_body(self.query)
-
-        self.assertEqual(self.screener.body['offset'], 0)
-        self.assertEqual(self.screener.body['size'], 100)
-        self.assertEqual(self.screener.body['sortField'], 'ticker')
-        self.assertEqual(self.screener.body['sortType'], 'desc')
-        self.assertEqual(self.screener.body['quoteType'], 'equity')
-        self.assertEqual(self.screener.body['query'], self.query.to_dict())
-        self.assertEqual(self.screener.body['userId'], '')
-        self.assertEqual(self.screener.body['userIdType'], 'guid')
-
-    def test_set_predefined_body(self):
-        k = 'most_actives'
-        self.screener.set_predefined_body(k)
-        self.assertEqual(self.screener.body, PREDEFINED_SCREENER_BODY_MAP[k])
-
-    def test_set_predefined_body_invalid_key(self):
-        with self.assertRaises(ValueError):
-            self.screener.set_predefined_body('invalid_key')
-
-    def test_set_body(self):
-        body = {
-            "offset": 0,
-            "size": 100,
-            "sortField": "ticker",
-            "sortType": "desc",
-            "quoteType": "equity",
-            "query": self.query.to_dict(),
-            "userId": "",
-            "userIdType": "guid"
-        }
-        self.screener.set_body(body)
-
-        self.assertEqual(self.screener.body, body)
-
-    def test_set_body_missing_keys(self):
-        body = {
-            "offset": 0,
-            "size": 100,
-            "sortField": "ticker",
-            "sortType": "desc",
-            "quoteType": "equity"
-        }
-        with self.assertRaises(ValueError):
-            self.screener.set_body(body)
-
-    def test_set_body_extra_keys(self):
-        body = {
-            "offset": 0,
-            "size": 100,
-            "sortField": "ticker",
-            "sortType": "desc",
-            "quoteType": "equity",
-            "query": self.query.to_dict(),
-            "userId": "",
-            "userIdType": "guid",
-            "extraKey": "extraValue"
-        }
-        with self.assertRaises(ValueError):
-            self.screener.set_body(body)
-
-    def test_patch_body(self):
-        initial_body = {
-            "offset": 0,
-            "size": 100,
-            "sortField": "ticker",
-            "sortType": "desc",
-            "quoteType": "equity",
-            "query": self.query.to_dict(),
-            "userId": "",
-            "userIdType": "guid"
-        }
-        self.screener.set_body(initial_body)
-        patch_values = {"size": 50}
-        self.screener.patch_body(patch_values)
-
-        self.assertEqual(self.screener.body['size'], 50)
-        self.assertEqual(self.screener.body['query'], self.query.to_dict())
-
-    def test_patch_body_extra_keys(self):
-        initial_body = {
-            "offset": 0,
-            "size": 100,
-            "sortField": "ticker",
-            "sortType": "desc",
-            "quoteType": "equity",
-            "query": self.query.to_dict(),
-            "userId": "",
-            "userIdType": "guid"
-        }
-        self.screener.set_body(initial_body)
-        patch_values = {"extraKey": "extraValue"}
-        with self.assertRaises(ValueError):
-            self.screener.patch_body(patch_values)
+        self.predefined = 'aggressive_small_caps'

    @patch('yfinance.screener.screener.YfData.post')
-    def test_fetch(self, mock_post):
-        mock_response = MagicMock()
-        mock_response.json.return_value = {'finance': {'result': [{}]}}
-        mock_post.return_value = mock_response
+    def test_set_large_size_in_body(self, mock_post):
+        with self.assertRaises(ValueError):
+            screen(self.query, size=251)

-        self.screener.set_default_body(self.query)
-        response = self.screener._fetch()
-
-        self.assertEqual(response, {'finance': {'result': [{}]}})
-
-    @patch('yfinance.screener.screener.YfData.post')
-    def test_fetch_and_parse(self, mock_post):
+    @patch('yfinance.data.YfData.post')
+    def test_fetch_query(self, mock_post):
        mock_response = MagicMock()
        mock_response.json.return_value = {'finance': {'result': [{'key': 'value'}]}}
        mock_post.return_value = mock_response

-        self.screener.set_default_body(self.query)
-        self.screener._fetch_and_parse()
-        self.assertEqual(self.screener.response, {'key': 'value'})
+        response = screen(self.query)
+        self.assertEqual(response, {'key': 'value'})
+
+    @patch('yfinance.data.YfData.get')
+    def test_fetch_predefined(self, mock_get):
+        mock_response = MagicMock()
+        mock_response.json.return_value = {'finance': {'result': [{'key': 'value'}]}}
+        mock_get.return_value = mock_response
+
+        response = screen(self.predefined)
+        self.assertEqual(response, {'key': 'value'})

 if __name__ == '__main__':
    unittest.main()
--- a/tests/test_search.py
+++ b/tests/test_search.py
@@ -0,0 +1,42 @@
+import unittest
+
+from tests.context import yfinance as yf
+
+
+class TestSearch(unittest.TestCase):
+    def test_invalid_query(self):
+        search = yf.Search(query="XYZXYZ")
+
+        self.assertEqual(len(search.quotes), 0)
+        self.assertEqual(len(search.news), 0)
+        self.assertEqual(len(search.lists), 0)
+        self.assertEqual(len(search.nav), 0)
+        self.assertEqual(len(search.research), 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'])
+
+    def test_quotes(self):
+        search = yf.Search(query="AAPL", max_results=5)
+
+        self.assertEqual(len(search.quotes), 5)
+        self.assertIn("AAPL", search.quotes[0]['symbol'])
+
+    def test_news(self):
+        search = yf.Search(query="AAPL", news_count=3)
+
+        self.assertEqual(len(search.news), 3)
+
+    def test_research_reports(self):
+        search = yf.Search(query="AAPL", include_research=True)
+        self.assertEqual(len(search.research), 3)
--- a/tests/test_ticker.py
+++ b/tests/test_ticker.py
@@ -8,6 +8,8 @@ Specific test class:
   python -m unittest tests.ticker.TestTicker

 """
+from datetime import datetime, timedelta
+
 import pandas as pd

 from tests.context import yfinance as yf
@@ -133,8 +135,55 @@ class TestTicker(unittest.TestCase):
        with self.assertRaises(YFInvalidPeriodError):
            dat.history(period="2wks", interval="1d", raise_errors=True)
        with self.assertRaises(YFInvalidPeriodError):
-            dat.history(period="2mo", interval="1d", raise_errors=True)
+            dat.history(period="2mos", interval="1d", raise_errors=True)

+    def test_valid_custom_periods(self):
+        valid_periods = [
+            # Yahoo provided periods
+            ("1d", "1m"), ("5d", "15m"), ("1mo", "1d"), ("3mo", "1wk"),
+            ("6mo", "1d"), ("1y", "1mo"), ("5y", "1wk"), ("max", "1mo"),
+
+            # Custom periods
+            ("2d", "30m"), ("10mo", "1d"), ("1y", "1d"), ("3y", "1d"),
+            ("2wk", "15m"), ("6mo", "5d"), ("10y", "1wk")
+        ]
+
+        tkr = "AAPL"
+        dat = yf.Ticker(tkr, session=self.session)
+
+        for period, interval in valid_periods:
+            with self.subTest(period=period, interval=interval):
+                df = dat.history(period=period, interval=interval, raise_errors=True)
+                self.assertIsInstance(df, pd.DataFrame)
+                self.assertFalse(df.empty, f"No data returned for period={period}, interval={interval}")
+                self.assertIn("Close", df.columns, f"'Close' column missing for period={period}, interval={interval}")
+
+                # Validate date range
+                now = datetime.now()
+                if period != "max":  # Difficult to assert for "max", therefore we skip
+                    if period.endswith("d"):
+                        days = int(period[:-1])
+                        expected_start = now - timedelta(days=days)
+                    elif period.endswith("mo"):
+                        months = int(period[:-2])
+                        expected_start = now - timedelta(days=30 * months)
+                    elif period.endswith("y"):
+                        years = int(period[:-1])
+                        expected_start = now - timedelta(days=365 * years)
+                    elif period.endswith("wk"):
+                        weeks = int(period[:-2])
+                        expected_start = now - timedelta(weeks=weeks)
+                    else:
+                        continue
+
+                    actual_start = df.index[0].to_pydatetime().replace(tzinfo=None)
+                    expected_start = expected_start.replace(hour=0, minute=0, second=0, microsecond=0)
+
+                    # leeway added because of weekends
+                    self.assertGreaterEqual(actual_start, expected_start - timedelta(days=10),
+                                            f"Start date {actual_start} out of range for period={period}")
+                    self.assertLessEqual(df.index[-1].to_pydatetime().replace(tzinfo=None), now,
+                                         f"End date {df.index[-1]} out of range for period={period}")

    def test_prices_missing(self):
        # this test will need to be updated every time someone wants to run a test
@@ -216,11 +265,24 @@ 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):
        """
@@ -246,14 +308,13 @@ class TestTickerHistory(unittest.TestCase):
            actual_urls_called[i] = u
        actual_urls_called = tuple(actual_urls_called)

-        expected_urls = (
-            f"https://query2.finance.yahoo.com/v8/finance/chart/{symbol}?events=div%2Csplits%2CcapitalGains&includePrePost=False&interval=1d&range={period}",
-        )
-        self.assertEqual(
-            expected_urls,
-            actual_urls_called,
-            "Different than expected url used to fetch history."
-        )
+        expected_urls = [
+            f"https://query2.finance.yahoo.com/v8/finance/chart/{symbol}?interval=1d&range=1d",  # ticker's tz
+            f"https://query2.finance.yahoo.com/v8/finance/chart/{symbol}?events=div%2Csplits%2CcapitalGains&includePrePost=False&interval=1d&range={period}"
+        ]
+        for url in actual_urls_called:
+            self.assertTrue(url in expected_urls, f"Unexpected URL called: {url}")
+
    def test_dividends(self):
        data = self.ticker.dividends
        self.assertIsInstance(data, pd.Series, "data has wrong type")
@@ -296,7 +357,7 @@ class TestTickerEarnings(unittest.TestCase):
    def test_earnings_dates_with_limit(self):
        # use ticker with lots of historic earnings
        ticker = yf.Ticker("IBM")
-        limit = 110
+        limit = 100
        data = ticker.get_earnings_dates(limit=limit)
        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
        self.assertFalse(data.empty, "data is empty")
@@ -747,7 +808,7 @@ class TestTickerAnalysts(unittest.TestCase):
        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
        self.assertFalse(data.empty, "data is empty")
        self.assertTrue(len(data.columns) == 4, "data has wrong number of columns")
-        self.assertEqual(data.columns.values.tolist(), ['Firm', 'ToGrade', 'FromGrade', 'Action'], "data has wrong column names")
+        self.assertCountEqual(data.columns.values.tolist(), ['Firm', 'ToGrade', 'FromGrade', 'Action'], "data has wrong column names")
        self.assertIsInstance(data.index, pd.DatetimeIndex, "data has wrong index type")

        data_cached = self.ticker.upgrades_downgrades
@@ -757,9 +818,6 @@ class TestTickerAnalysts(unittest.TestCase):
        data = self.ticker.analyst_price_targets
        self.assertIsInstance(data, dict, "data has wrong type")

-        keys = {'current', 'low', 'high', 'mean', 'median'}
-        self.assertEqual(data.keys(), keys, "data has wrong keys")
-
        data_cached = self.ticker.analyst_price_targets
        self.assertIs(data, data_cached, "data not cached")

@@ -768,12 +826,6 @@ class TestTickerAnalysts(unittest.TestCase):
        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
        self.assertFalse(data.empty, "data is empty")

-        columns = ['numberOfAnalysts', 'avg', 'low', 'high', 'yearAgoEps', 'growth']
-        self.assertEqual(data.columns.values.tolist(), columns, "data has wrong column names")
-
-        index = ['0q', '+1q', '0y', '+1y']
-        self.assertEqual(data.index.values.tolist(), index, "data has wrong row names")
-
        data_cached = self.ticker.earnings_estimate
        self.assertIs(data, data_cached, "data not cached")

@@ -782,12 +834,6 @@ class TestTickerAnalysts(unittest.TestCase):
        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
        self.assertFalse(data.empty, "data is empty")

-        columns = ['numberOfAnalysts', 'avg', 'low', 'high', 'yearAgoRevenue', 'growth']
-        self.assertEqual(data.columns.values.tolist(), columns, "data has wrong column names")
-
-        index = ['0q', '+1q', '0y', '+1y']
-        self.assertEqual(data.index.values.tolist(), index, "data has wrong row names")
-
        data_cached = self.ticker.revenue_estimate
        self.assertIs(data, data_cached, "data not cached")

@@ -796,8 +842,6 @@ class TestTickerAnalysts(unittest.TestCase):
        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
        self.assertFalse(data.empty, "data is empty")

-        columns = ['epsEstimate', 'epsActual', 'epsDifference', 'surprisePercent']
-        self.assertEqual(data.columns.values.tolist(), columns, "data has wrong column names")
        self.assertIsInstance(data.index, pd.DatetimeIndex, "data has wrong index type")

        data_cached = self.ticker.earnings_history
@@ -808,12 +852,6 @@ class TestTickerAnalysts(unittest.TestCase):
        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
        self.assertFalse(data.empty, "data is empty")

-        columns = ['current', '7daysAgo', '30daysAgo', '60daysAgo', '90daysAgo']
-        self.assertEqual(data.columns.values.tolist(), columns, "data has wrong column names")
-
-        index = ['0q', '+1q', '0y', '+1y']
-        self.assertEqual(data.index.values.tolist(), index, "data has wrong row names")
-
        data_cached = self.ticker.eps_trend
        self.assertIs(data, data_cached, "data not cached")

@@ -822,12 +860,6 @@ class TestTickerAnalysts(unittest.TestCase):
        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
        self.assertFalse(data.empty, "data is empty")

-        columns = ['stock', 'industry', 'sector', 'index']
-        self.assertEqual(data.columns.values.tolist(), columns, "data has wrong column names")
-
-        index = ['0q', '+1q', '0y', '+1y', '+5y', '-5y']
-        self.assertEqual(data.index.values.tolist(), index, "data has wrong row names")
-
        data_cached = self.ticker.growth_estimates
        self.assertIs(data, data_cached, "data not cached")

--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -12,74 +12,10 @@ from datetime import datetime
 from unittest import TestSuite

 import pandas as pd
-# import numpy as np
-
-from tests.context import yfinance as yf

 import unittest
-# import requests_cache
-import tempfile
-import os

-
-class TestCache(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        cls.tempCacheDir = tempfile.TemporaryDirectory()
-        yf.set_tz_cache_location(cls.tempCacheDir.name)
-
-    @classmethod
-    def tearDownClass(cls):
-        cls.tempCacheDir.cleanup()
-
-    def test_storeTzNoRaise(self):
-        # storing TZ to cache should never raise exception
-        tkr = 'AMZN'
-        tz1 = "America/New_York"
-        tz2 = "London/Europe"
-        cache = yf.cache.get_tz_cache()
-        cache.store(tkr, tz1)
-        cache.store(tkr, tz2)
-
-    def test_setTzCacheLocation(self):
-        self.assertEqual(yf.cache._TzDBManager.get_location(), self.tempCacheDir.name)
-
-        tkr = 'AMZN'
-        tz1 = "America/New_York"
-        cache = yf.cache.get_tz_cache()
-        cache.store(tkr, tz1)
-
-        self.assertTrue(os.path.exists(os.path.join(self.tempCacheDir.name, "tkr-tz.db")))
-
-
-class TestCacheNoPermission(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        yf.set_tz_cache_location("/root/yf-cache")
-
-    def test_tzCacheRootStore(self):
-        # Test that if cache path in read-only filesystem, no exception.
-        tkr = 'AMZN'
-        tz1 = "America/New_York"
-
-        # During attempt to store, will discover cannot write
-        yf.cache.get_tz_cache().store(tkr, tz1)
-
-        # Handling the store failure replaces cache with a dummy
-        cache = yf.cache.get_tz_cache()
-        self.assertTrue(cache.dummy)
-        cache.store(tkr, tz1)
-
-    def test_tzCacheRootLookup(self):
-        # Test that if cache path in read-only filesystem, no exception.
-        tkr = 'AMZN'
-        # During attempt to lookup, will discover cannot write
-        yf.cache.get_tz_cache().lookup(tkr)
-
-        # Handling the lookup failure replaces cache with a dummy
-        cache = yf.cache.get_tz_cache()
-        self.assertTrue(cache.dummy)
-        cache.lookup(tkr)
+from yfinance.utils import is_valid_period_format


 class TestPandas(unittest.TestCase):
@@ -105,11 +41,30 @@ class TestPandas(unittest.TestCase):
            i += 1


+class TestUtils(unittest.TestCase):
+    def test_is_valid_period_format_valid(self):
+        self.assertTrue(is_valid_period_format("1d"))
+        self.assertTrue(is_valid_period_format("5wk"))
+        self.assertTrue(is_valid_period_format("12mo"))
+        self.assertTrue(is_valid_period_format("2y"))
+
+    def test_is_valid_period_format_invalid(self):
+        self.assertFalse(is_valid_period_format("1m"))    # Incorrect suffix
+        self.assertFalse(is_valid_period_format("2wks"))  # Incorrect suffix
+        self.assertFalse(is_valid_period_format("10"))    # Missing suffix
+        self.assertFalse(is_valid_period_format("abc"))   # Invalid string
+        self.assertFalse(is_valid_period_format(""))      # Empty string
+
+    def test_is_valid_period_format_edge_cases(self):
+        self.assertFalse(is_valid_period_format(None))    # None input
+        self.assertFalse(is_valid_period_format("0d"))    # Zero is invalid
+        self.assertTrue(is_valid_period_format("999mo"))  # Large number valid
+
+
 def suite():
    ts: TestSuite = unittest.TestSuite()
-    ts.addTest(TestCache('Test cache'))
-    ts.addTest(TestCacheNoPermission('Test cache no permission'))
    ts.addTest(TestPandas("Test pandas"))
+    ts.addTest(TestUtils("Test utils"))
    return ts


--- 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
@@ -27,8 +28,10 @@ from .utils import enable_debug_mode
 from .cache import set_tz_cache_location
 from .domain.sector import Sector
 from .domain.industry import Industry
-from .screener.screener import Screener
-from .screener.screener_query import EquityQuery
+from .domain.market import Market
+
+from .screener.query import EquityQuery, FundQuery
+from .screener.screener import screen, PREDEFINED_SCREENER_QUERIES

 __version__ = version.version
 __author__ = "Ran Aroussi"
@@ -36,5 +39,6 @@ __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', 'Market', 'Search', 'Ticker', 'Tickers', 'enable_debug_mode', 'set_tz_cache_location', 'Sector', 'Industry']
+# screener stuff:
+__all__ += ['EquityQuery', 'FundQuery', 'screen', 'PREDEFINED_SCREENER_QUERIES']
--- a/yfinance/base.py
+++ b/yfinance/base.py
@@ -21,18 +21,18 @@

 from __future__ import print_function

-from io import StringIO
 import json as _json
 import warnings
 from typing import Optional, Union
 from urllib.parse import quote as urlencode

+import numpy as np
 import pandas as pd
 import requests

 from . import utils, cache
 from .data import YfData
-from .exceptions import YFEarningsDateMissing
+from .exceptions import YFEarningsDateMissing, YFRateLimitError
 from .scrapers.analysis import Analysis
 from .scrapers.fundamentals import Fundamentals
 from .scrapers.holders import Holders
@@ -40,7 +40,7 @@ from .scrapers.quote import Quote, FastInfo
 from .scrapers.history import PriceHistory
 from .scrapers.funds import FundsData

-from .const import _BASE_URL_, _ROOT_URL_
+from .const import _BASE_URL_, _ROOT_URL_, _QUERY1_URL_


 class TickerBase:
@@ -124,6 +124,9 @@ class TickerBase:
        try:
            data = self._data.cache_get(url=url, params=params, proxy=proxy, timeout=timeout)
            data = data.json()
+        except YFRateLimitError:
+            # Must propagate this
+            raise
        except Exception as e:
            logger.error(f"Failed to get ticker '{self.ticker}' reason: {e}")
            return None
@@ -534,118 +537,117 @@ 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]
-
        logger = utils.get_yf_logger()
+        clamped_limit = min(limit, 100)  # YF caps at 100, don't go higher

-        page_size = min(limit, 100)  # YF caps at 100, don't go higher
-        page_offset = 0
-        dates = None
-        while True:
-            url = f"{_ROOT_URL_}/calendar/earnings?symbol={self.ticker}&offset={page_offset}&size={page_size}"
-            data = self._data.cache_get(url=url, proxy=proxy).text
+        if self._earnings_dates and clamped_limit in self._earnings_dates:
+            return self._earnings_dates[clamped_limit]

-            if "Will be right back" in data:
-                raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
-                                   "Our engineers are working quickly to resolve "
-                                   "the issue. Thank you for your patience.")
+        # Fetch data
+        url = f"{_QUERY1_URL_}/v1/finance/visualization"
+        params = {"lang": "en-US", "region": "US"}
+        body = {
+            "size": clamped_limit,
+            "query": {
+                "operator": "and",
+                "operands": [
+                    {"operator": "eq", "operands": ["ticker", self.ticker]},
+                    {"operator": "eq", "operands": ["eventtype", "2"]}
+                ]
+            },
+            "sortField": "startdatetime",
+            "sortType": "DESC",
+            "entityIdType": "earnings",
+            "includeFields": ["startdatetime", "timeZoneShortName", "epsestimate", "epsactual", "epssurprisepct"]
+        }
+        response = self._data.post(url, params=params, body=body, proxy=proxy)
+        json_data = response.json()

-            try:
-                data = pd.read_html(StringIO(data))[0]
-            except ValueError:
-                if page_offset == 0:
-                    # Should not fail on first page
-                    if "Showing Earnings for:" in data:
-                        # Actually YF was successful, problem is company doesn't have earnings history
-                        dates = utils.empty_earnings_dates_df()
-                break
-            if dates is None:
-                dates = data
-            else:
-                dates = pd.concat([dates, data], axis=0)
+        # Extract data
+        columns = [row['label'] for row in json_data['finance']['result'][0]['documents'][0]['columns']]
+        rows = json_data['finance']['result'][0]['documents'][0]['rows']
+        df = pd.DataFrame(rows, columns=columns)

-            page_offset += page_size
-            # got less data then we asked for or already fetched all we requested, no need to fetch more pages
-            if len(data) < page_size or len(dates) >= limit:
-                dates = dates.iloc[:limit]
-                break
-            else:
-                # do not fetch more than needed next time
-                page_size = min(limit - len(dates), page_size)
-
-        if dates is None or dates.shape[0] == 0:
+        if df.empty:
            _exception = YFEarningsDateMissing(self.ticker)
            err_msg = str(_exception)
            logger.error(f'{self.ticker}: {err_msg}')
            return None
-        dates = dates.reset_index(drop=True)

-        # Drop redundant columns
-        dates = dates.drop(["Symbol", "Company"], axis=1)
+        # Calculate earnings date
+        df['Earnings Date'] = pd.to_datetime(df['Event Start Date']).dt.normalize()
+        tz = self._get_ticker_tz(proxy=proxy, timeout=30)
+        if df['Earnings Date'].dt.tz is None:
+            df['Earnings Date'] = df['Earnings Date'].dt.tz_localize(tz)
+        else:
+            df['Earnings Date'] = df['Earnings Date'].dt.tz_convert(tz)

        # Convert types
-        for cn in ["EPS Estimate", "Reported EPS", "Surprise(%)"]:
-            dates.loc[dates[cn] == '-', cn] = float("nan")
-            dates[cn] = dates[cn].astype(float)
+        columns_to_update = ['Surprise (%)', 'EPS Estimate', 'Reported EPS']
+        df[columns_to_update] = df[columns_to_update].astype('float64').replace(0.0, np.nan)

-        # Convert % to range 0->1:
-        dates["Surprise(%)"] *= 0.01
+        # Format the dataframe
+        df.drop(['Event Start Date', 'Timezone short name'], axis=1, inplace=True)
+        df.set_index('Earnings Date', inplace=True)
+        df.rename(columns={'Surprise (%)': 'Surprise(%)'}, inplace=True)  # Compatibility

-        # 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)
-
-        dates = dates.set_index("Earnings Date")
-
-        self._earnings_dates[limit] = dates
-
-        return dates
+        self._earnings_dates[clamped_limit] = df
+        return df

    def get_history_metadata(self, proxy=None) -> dict:
        return self._lazy_load_price_history().get_history_metadata(proxy)
@@ -654,4 +656,4 @@ 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
@@ -305,21 +305,92 @@ SECTOR_INDUSTY_MAPPING = {
                    'utilities-independent-power-producers',
                    'utilities-regulated-water'}
 }
+
+def merge_two_level_dicts(dict1, dict2):
+    result = dict1.copy()
+    for key, value in dict2.items():
+        if key in result:
+            # If both are sets, merge them
+            if isinstance(value, set) and isinstance(result[key], set):
+                result[key] = result[key] | value
+            # If both are dicts, merge their contents
+            elif isinstance(value, dict) and isinstance(result[key], dict):
+                result[key] = {
+                    k: (result[key].get(k, set()) | v if isinstance(v, set) 
+                        else v) if k in result[key]
+                    else v
+                    for k, v in value.items()
+                }
+        else:
+            result[key] = value
+    return result
+
 EQUITY_SCREENER_EQ_MAP = {
-    "region": {
-        "za", "ve", "vn", "us", "tw", "th", "tr", "sr", "sg", "sa", "se", "ru", "ro", "qa", "pt", "pk", "pl",
-        "ph", "nz", "nl", "mx", "pe", "no", "my", "lv", "lt", "kw", "jp", "is", "il", "lk", "kr", "it", "in",
-        "ie", "hu", "id", "hk", "gb", "fi", "eg", "dk", "gr", "fr", "es", "ee", "de", "cz", "cl", "ca", "be",
-        "at", "cn", "br", "au", "ar", "ch"
+    "exchange": {
+        'ar': {'BUE'},
+        'at': {'VIE'},
+        'au': {'ASX'},
+        'be': {'BRU'},
+        'br': {'SAO'},
+        'ca': {'CNQ', 'NEO', 'TOR', 'VAN'},
+        'ch': {'EBS'},
+        'cl': {'SGO'},
+        'cn': {'SHH', 'SHZ'},
+        'co': {'BVC'},
+        'cz': {'PRA'},
+        'de': {'BER', 'DUS', 'FRA', 'HAM', 'GER', 'MUN', 'STU'},
+        'dk': {'CPH'},
+        'ee': {'TAL'},
+        'eg': {'CAI'},
+        'es': {'MCE'},
+        'fi': {'HEL'},
+        'fr': {'PAR'},
+        'gb': {'AQS', 'IOB', 'LSE'},
+        'gr': {'ATH'},
+        'hk': {'HKG'},
+        'hu': {'BUD'},
+        'id': {'JKT'},
+        'ie': {'ISE'},
+        'il': {'TLV'},
+        'in': {'BSE', 'NSI'},
+        'is': {'ICE'},
+        'it': {'MIL'},
+        'jp': {'FKA', 'JPX', 'SAP'},
+        'kr': {'KOE', 'KSC'},
+        'kw': {'KUW'},
+        'lk': {},
+        'lt': {'LIT'},
+        'lv': {'RIS'},
+        'mx': {'MEX'},
+        'my': {'KLS'},
+        'nl': {'AMS'},
+        'no': {'OSL'},
+        'nz': {'NZE'},
+        'pe': {},
+        'ph': {'PHP', 'PHS'},
+        'pk': {},
+        'pl': {'WSE'},
+        'pt': {'LIS'},
+        'qa': {'DOH'},
+        'ro': {'BVB'},
+        'ru': {},
+        'sa': {'SAU'},
+        'se': {'STO'},
+        'sg': {'SES'},
+        'sr': {},
+        'th': {'SET'},
+        'tr': {'IST'},
+        'tw': {'TAI', 'TWO'},
+        'us': {'ASE', 'BTS', 'CXI', 'NCM', 'NGM', 'NMS', 'NYQ', 'OEM', 'OQB', 'OQX', 'PCX', 'PNK', 'YHD'},
+        've': {'CCS'},
+        'vn': {},
+        'za': {'JNB'}
    },
    "sector": {
        "Basic Materials", "Industrials", "Communication Services", "Healthcare",
        "Real Estate", "Technology", "Energy", "Utilities", "Financial Services",
        "Consumer Defensive", "Consumer Cyclical"
    },
-    "exchanges": {
-        "NMS", "NAS", "YHD", "NYQ", "NGM", "NCM", "BSE"
-    },
    "peer_group": {
        "US Fund Equity Energy",
        "US CE Convertibles",
@@ -426,136 +497,130 @@ EQUITY_SCREENER_EQ_MAP = {
        "Banks"
    }
 }
-EQUITY_SCREENER_FIELDS = {
-    # EQ Fields
-    "region",
-    "sector",
-    "peer_group",
-    "exchanges",
-
-    # price
-    "eodprice",
-    "intradaypricechange",
-    "lastclosemarketcap.lasttwelvemonths",
-    "percentchange",
-    "lastclose52weekhigh.lasttwelvemonths",
-    "fiftytwowkpercentchange",
-    "intradayprice",
-    "lastclose52weeklow.lasttwelvemonths",
-    "intradaymarketcap",
-
-    # trading
-    "beta",
-    "avgdailyvol3m",
-    "pctheldinsider",
-    "pctheldinst",
-    "dayvolume",
-    "eodvolume",
-
-    # short interest
-    "short_percentage_of_shares_outstanding.value",
-    "short_interest.value",
-    "short_percentage_of_float.value",
-    "days_to_cover_short.value",
-    "short_interest_percentage_change.value",
-
-    # valuation
-    "bookvalueshare.lasttwelvemonths",
-    "lastclosemarketcaptotalrevenue.lasttwelvemonths",
-    "lastclosetevtotalrevenue.lasttwelvemonths",
-    "pricebookratio.quarterly",
-    "peratio.lasttwelvemonths",
-    "lastclosepricetangiblebookvalue.lasttwelvemonths",
-    "lastclosepriceearnings.lasttwelvemonths",
-    "pegratio_5y",
-
-    # profitability
-    "consecutive_years_of_dividend_growth_count",
-    "returnonassets.lasttwelvemonths",
-    "returnonequity.lasttwelvemonths",
-    "forward_dividend_per_share",
-    "forward_dividend_yield",
-    "returnontotalcapital.lasttwelvemonths",
-
-    # leverage
-    "lastclosetevebit.lasttwelvemonths",
-    "netdebtebitda.lasttwelvemonths",
-    "totaldebtequity.lasttwelvemonths",
-    "ltdebtequity.lasttwelvemonths",
-    "ebitinterestexpense.lasttwelvemonths",
-    "ebitdainterestexpense.lasttwelvemonths",
-    "lastclosetevebitda.lasttwelvemonths",
-    "totaldebtebitda.lasttwelvemonths",
-
-    # liquidity
-    "quickratio.lasttwelvemonths",
-    "altmanzscoreusingtheaveragestockinformationforaperiod.lasttwelvemonths",
-    "currentratio.lasttwelvemonths",
-    "operatingcashflowtocurrentliabilities.lasttwelvemonths",
-
-    # income statement
-    "totalrevenues.lasttwelvemonths",
-    "netincomemargin.lasttwelvemonths",
-    "grossprofit.lasttwelvemonths",
-    "ebitda1yrgrowth.lasttwelvemonths",
-    "dilutedepscontinuingoperations.lasttwelvemonths",
-    "quarterlyrevenuegrowth.quarterly",
-    "epsgrowth.lasttwelvemonths",
-    "netincomeis.lasttwelvemonths",
-    "ebitda.lasttwelvemonths",
-    "dilutedeps1yrgrowth.lasttwelvemonths",
-    "totalrevenues1yrgrowth.lasttwelvemonths",
-    "operatingincome.lasttwelvemonths",
-    "netincome1yrgrowth.lasttwelvemonths",
-    "grossprofitmargin.lasttwelvemonths",
-    "ebitdamargin.lasttwelvemonths",
-    "ebit.lasttwelvemonths",
-    "basicepscontinuingoperations.lasttwelvemonths",
-    "netepsbasic.lasttwelvemonths"
-    "netepsdiluted.lasttwelvemonths",
-
-    # balance sheet
-    "totalassets.lasttwelvemonths",
-    "totalcommonsharesoutstanding.lasttwelvemonths",
-    "totaldebt.lasttwelvemonths",
-    "totalequity.lasttwelvemonths",
-    "totalcurrentassets.lasttwelvemonths",
-    "totalcashandshortterminvestments.lasttwelvemonths",
-    "totalcommonequity.lasttwelvemonths",
-    "totalcurrentliabilities.lasttwelvemonths",
-    "totalsharesoutstanding",
-
-    # cash flow
-    "forward_dividend_yield",
-    "leveredfreecashflow.lasttwelvemonths",
-    "capitalexpenditure.lasttwelvemonths",
-    "cashfromoperations.lasttwelvemonths",
-    "leveredfreecashflow1yrgrowth.lasttwelvemonths",
-    "unleveredfreecashflow.lasttwelvemonths",
-    "cashfromoperations1yrgrowth.lasttwelvemonths",
-
-    # ESG
-    "esg_score",
-    "environmental_score",
-    "governance_score",
-    "social_score",
-    "highest_controversy"
+EQUITY_SCREENER_EQ_MAP['region'] = EQUITY_SCREENER_EQ_MAP['exchange'].keys()
+ordered_keys = ['region'] + [k for k in EQUITY_SCREENER_EQ_MAP.keys() if k != 'region']
+EQUITY_SCREENER_EQ_MAP = {k:EQUITY_SCREENER_EQ_MAP[k] for k in ordered_keys}
+FUND_SCREENER_EQ_MAP = {
+    "exchange": {
+        'us': {'NAS'}
+    }
 }
-
-PREDEFINED_SCREENER_BODY_MAP = {
-    'aggressive_small_caps': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]},{"operator":"or","operands":[{"operator":"LT","operands":["epsgrowth.lasttwelvemonths",15]}]}]},"userId":"","userIdType":"guid"},
-    'day_gainers': {"offset":0,"size":25,"sortField":"percentchange","sortType":"DESC","quoteType":"EQUITY","query":{"operator":"AND","operands":[{"operator":"gt","operands":["percentchange",3]},{"operator":"eq","operands":["region","us"]},{"operator":"or","operands":[{"operator":"BTWN","operands":["intradaymarketcap",2000000000,10000000000]},{"operator":"BTWN","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"GT","operands":["intradaymarketcap",100000000000]}]},{"operator":"gte","operands":["intradayprice",5]},{"operator":"gt","operands":["dayvolume",15000]}]},"userId":"","userIdType":"guid"},
-    'day_losers': {"offset":0,"size":25,"sortField":"percentchange","sortType":"ASC","quoteType":"EQUITY","query":{"operator":"AND","operands":[{"operator":"lt","operands":["percentchange",-2.5]},{"operator":"eq","operands":["region","us"]},{"operator":"or","operands":[{"operator":"BTWN","operands":["intradaymarketcap",2000000000,10000000000]},{"operator":"BTWN","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"GT","operands":["intradaymarketcap",100000000000]}]},{"operator":"gte","operands":["intradayprice",5]},{"operator":"gt","operands":["dayvolume",20000]}]},"userId":"","userIdType":"guid"},
-    'growth_technology_stocks': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"BTWN","operands":["quarterlyrevenuegrowth.quarterly",50,100]},{"operator":"GT","operands":["quarterlyrevenuegrowth.quarterly",100]},{"operator":"BTWN","operands":["quarterlyrevenuegrowth.quarterly",25,50]}]},{"operator":"or","operands":[{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",25,50]},{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",50,100]},{"operator":"GT","operands":["epsgrowth.lasttwelvemonths",100]}]},{"operator":"eq","operands":["sector","Technology"]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
-    'most_actives': {"offset":0,"size":25,"sortField":"dayvolume","sortType":"DESC","quoteType":"EQUITY","query":{"operator":"AND","operands":[{"operator":"eq","operands":["region","us"]},{"operator":"or","operands":[{"operator":"BTWN","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"GT","operands":["intradaymarketcap",100000000000]},{"operator":"BTWN","operands":["intradaymarketcap",2000000000,10000000000]}]},{"operator":"gt","operands":["dayvolume",5000000]}]},"userId":"","userIdType":"guid"},
-    'most_shorted_stocks': {"size":25,"offset":0,"sortField":"short_percentage_of_shares_outstanding.value","sortType":"DESC","quoteType":"EQUITY","topOperator":"AND","query":{"operator":"AND","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["region","us"]}]},{"operator":"gt","operands":["intradayprice",1]},{"operator":"gt","operands":["avgdailyvol3m",200000]}]},"userId":"","userIdType":"guid"},
-    'small_cap_gainers': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"lt","operands":["intradaymarketcap",2000000000]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
-    'undervalued_growth_stocks': {"offset":0,"size":25,"sortType":"DESC","sortField":"eodvolume","quoteType":"EQUITY","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"BTWN","operands":["peratio.lasttwelvemonths",0,20]}]},{"operator":"or","operands":[{"operator":"LT","operands":["pegratio_5y",1]}]},{"operator":"or","operands":[{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",25,50]},{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",50,100]},{"operator":"GT","operands":["epsgrowth.lasttwelvemonths",100]}]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
-    'undervalued_large_caps': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"BTWN","operands":["peratio.lasttwelvemonths",0,20]}]},{"operator":"lt","operands":["pegratio_5y",1]},{"operator":"btwn","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
-    'conservative_foreign_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Foreign Large Value"]},{"operator":"EQ","operands":["categoryname","Foreign Large Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Large Growth"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Growth"]},{"operator":"EQ","operands":["categoryname","Foreign Large Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",4]},{"operator":"EQ","operands":["performanceratingoverall",5]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"EQ","operands":["riskratingoverall",1]},{"operator":"EQ","operands":["riskratingoverall",3]},{"operator":"EQ","operands":["riskratingoverall",2]}]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
-    'high_yield_bond': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",4]},{"operator":"EQ","operands":["performanceratingoverall",5]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"EQ","operands":["riskratingoverall",1]},{"operator":"EQ","operands":["riskratingoverall",3]},{"operator":"EQ","operands":["riskratingoverall",2]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","High Yield Bond"]}]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
-    'portfolio_anchors': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Large Blend"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",4]},{"operator":"EQ","operands":["performanceratingoverall",5]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
-    'solid_large_growth_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Large Growth"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",5]},{"operator":"EQ","operands":["performanceratingoverall",4]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
-    'solid_midcap_growth_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Mid-Cap Growth"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",5]},{"operator":"EQ","operands":["performanceratingoverall",4]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
-    'top_mutual_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"percentchange","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"gt","operands":["intradayprice",15]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",5]},{"operator":"EQ","operands":["performanceratingoverall",4]}]},{"operator":"gt","operands":["initialinvestment",1000]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"}
-}
+COMMON_SCREENER_FIELDS = {
+    "price":{
+        "eodprice",
+        "intradaypricechange",
+        "intradayprice"
+    },
+    "eq_fields": {
+        "exchange"}, 
+}
+FUND_SCREENER_FIELDS = {
+    "eq_fields": {
+        "categoryname",
+        "performanceratingoverall",
+        "initialinvestment", 
+        "annualreturnnavy1categoryrank", 
+        "riskratingoverall"}
+}
+FUND_SCREENER_FIELDS = merge_two_level_dicts(FUND_SCREENER_FIELDS, COMMON_SCREENER_FIELDS)
+EQUITY_SCREENER_FIELDS = {
+    "eq_fields": {
+        "region",
+        "sector",
+        "peer_group"}, 
+    "price":{
+        "lastclosemarketcap.lasttwelvemonths",
+        "percentchange",
+        "lastclose52weekhigh.lasttwelvemonths",
+        "fiftytwowkpercentchange",
+        "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"}
+}
+EQUITY_SCREENER_FIELDS = merge_two_level_dicts(EQUITY_SCREENER_FIELDS, COMMON_SCREENER_FIELDS)
--- a/yfinance/data.py
+++ b/yfinance/data.py
@@ -10,6 +10,8 @@ from frozendict import frozendict
 from . import utils, cache
 import threading

+from .exceptions import YFRateLimitError
+
 cache_maxsize = 64


@@ -229,11 +231,16 @@ class YfData(metaclass=SingletonMeta):
            'timeout': timeout}

        get_args = {**base_args, 'url': 'https://guce.yahoo.com/consent'}
-        if self._session_is_caching:
-            get_args['expire_after'] = self._expire_after
-            response = self._session.get(**get_args)
-        else:
-            response = self._session.get(**get_args)
+        try:
+            if self._session_is_caching:
+                get_args['expire_after'] = self._expire_after
+                response = self._session.get(**get_args)
+            else:
+                response = self._session.get(**get_args)
+        except requests.exceptions.ChunkedEncodingError:
+            # No idea why happens, but handle nicely so can switch to other cookie method.
+            utils.get_yf_logger().debug('_get_cookie_csrf() encountering requests.exceptions.ChunkedEncodingError, aborting')
+            return False

        soup = BeautifulSoup(response.content, 'html.parser')
        csrfTokenInput = soup.find('input', attrs={'name': 'csrfToken'})
@@ -262,14 +269,18 @@ class YfData(metaclass=SingletonMeta):
        get_args = {**base_args,
            'url': f'https://guce.yahoo.com/copyConsent?sessionId={sessionId}',
            'data': data}
-        if self._session_is_caching:
-            post_args['expire_after'] = self._expire_after
-            get_args['expire_after'] = self._expire_after
-            self._session.post(**post_args)
-            self._session.get(**get_args)
-        else:
-            self._session.post(**post_args)
-            self._session.get(**get_args)
+        try:
+            if self._session_is_caching:
+                post_args['expire_after'] = self._expire_after
+                get_args['expire_after'] = self._expire_after
+                self._session.post(**post_args)
+                self._session.get(**get_args)
+            else:
+                self._session.post(**post_args)
+                self._session.get(**get_args)
+        except requests.exceptions.ChunkedEncodingError:
+            # No idea why happens, but handle nicely so can switch to other cookie method.
+            utils.get_yf_logger().debug('_get_cookie_csrf() encountering requests.exceptions.ChunkedEncodingError, aborting')
        self._cookie = True
        self._save_session_cookies()
        return True
@@ -390,6 +401,10 @@ class YfData(metaclass=SingletonMeta):
            response = request_method(**request_args)
            utils.get_yf_logger().debug(f'response code={response.status_code}')

+            # Raise exception if rate limited
+            if response.status_code == 429:
+                raise YFRateLimitError()
+
        return response

    @lru_cache_freezeargs
--- 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/market.py
+++ b/yfinance/domain/market.py
@@ -0,0 +1,100 @@
+import datetime as dt
+
+from ..data import YfData
+from ..data import utils
+from ..const import _QUERY1_URL_
+import json as _json
+
+class Market:
+    def __init__(self, market:'str',  session=None, proxy=None, timeout=30):
+        self.market = market
+        self.session = session
+        self.proxy = proxy
+        self.timeout = timeout
+
+        self._data = YfData(session=self.session)
+        self._logger = utils.get_yf_logger()
+        
+        self._status = None
+        self._summary = None
+
+    def _fetch_json(self, url, params):
+        data = self._data.cache_get(url=url, params=params, proxy=self.proxy, timeout=self.timeout)
+        if data is None or "Will be right back" in data.text:
+            raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
+                               "Our engineers are working quickly to resolve "
+                               "the issue. Thank you for your patience.")
+        try:
+            return data.json()
+        except _json.JSONDecodeError:
+            self._logger.error(f"{self.market}: Failed to retrieve market data and recieved faulty data.")
+            return {}
+        
+    def _parse_data(self):
+        # Fetch both to ensure they are at the same time
+        if (self._status is not None) and (self._summary is not None):
+            return
+        
+        self._logger.debug(f"{self.market}: Parsing market data")
+
+        # Summary
+
+        summary_url = f"{_QUERY1_URL_}/v6/finance/quote/marketSummary"
+        summary_fields = ["shortName", "regularMarketPrice", "regularMarketChange", "regularMarketChangePercent"]
+        summary_params = {
+            "fields": ",".join(summary_fields),
+            "formatted": False,
+            "lang": "en-US",
+            "market": self.market
+        }
+
+        status_url = f"{_QUERY1_URL_}/v6/finance/markettime"
+        status_params = {
+            "formatted": True,
+            "key": "finance",
+            "lang": "en-US",
+            "market": self.market
+        }
+
+        self._summary = self._fetch_json(summary_url, summary_params)
+        self._status = self._fetch_json(status_url, status_params)
+
+        try:
+            self._summary = self._summary['marketSummaryResponse']['result']
+            self._summary = {x['exchange']:x for x in self._summary}
+        except Exception as e:
+            self._logger.error(f"{self.market}: Failed to parse market summary")
+            self._logger.debug(f"{type(e)}: {e}")
+
+
+        try:
+            # Unpack
+            self._status = self._status['finance']['marketTimes'][0]['marketTime'][0]
+            self._status['timezone'] = self._status['timezone'][0]
+            del self._status['time']  # redundant
+            try:
+                self._status.update({
+                    "open": dt.datetime.fromisoformat(self._status["open"]),
+                    "close": dt.datetime.fromisoformat(self._status["close"]),
+                    "tz": dt.timezone(dt.timedelta(hours=int(self._status["timezone"]["gmtoffset"]))/1000, self._status["timezone"]["short"])
+                })
+            except Exception as e:
+                self._logger.error(f"{self.market}: Failed to update market status")
+                self._logger.debug(f"{type(e)}: {e}")
+        except Exception as e:
+            self._logger.error(f"{self.market}: Failed to parse market status")
+            self._logger.debug(f"{type(e)}: {e}")
+
+
+
+
+    @property
+    def status(self):
+        self._parse_data()
+        return self._status
+
+
+    @property
+    def summary(self):
+        self._parse_data()
+        return self._summary
--- 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/exceptions.py
+++ b/yfinance/exceptions.py
@@ -44,4 +44,10 @@ class YFInvalidPeriodError(YFException):
        self.ticker = ticker
        self.invalid_period = invalid_period
        self.valid_ranges = valid_ranges
-        super().__init__(f"{self.ticker}: Period '{invalid_period}' is invalid, must be one of {valid_ranges}")
+        super().__init__(f"{self.ticker}: Period '{invalid_period}' is invalid, "
+                         f"must be of the format {valid_ranges}, etc.")
+
+
+class YFRateLimitError(YFException):
+    def __init__(self):
+        super().__init__("Too Many Requests. Rate limited. Try after a while.")
--- a/yfinance/multi.py
+++ b/yfinance/multi.py
@@ -24,6 +24,7 @@ from __future__ import print_function
 import logging
 import time as _time
 import traceback
+from typing import Union

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


@utils.log_indent_decorator
-def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=None,
-             group_by='column', auto_adjust=False, back_adjust=False, repair=False, keepna=False,
-             progress=True, period="max", interval="1d", prepost=False,
-             proxy=None, rounding=False, timeout=10, session=None):
-    """Download yahoo tickers
+def download(tickers, start=None, end=None, actions=False, threads=True,
+             ignore_tz=None, group_by='column', auto_adjust=None, back_adjust=False,
+             repair=False, keepna=False, progress=True, period="max", interval="1d",
+             prepost=False, proxy=None, rounding=False, timeout=10, session=None,
+             multi_level_index=True) -> Union[_pd.DataFrame, None]:
+    """
+    Download yahoo tickers
    :Parameters:
        tickers : str, list
            List of tickers to download
@@ -62,7 +65,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
            Include Pre and Post market data in results?
            Default is False
        auto_adjust: bool
-            Adjust all OHLC automatically? Default is False
+            Adjust all OHLC automatically? Default is True
        repair: bool
            Detect currency unit 100x mixups and attempt repair
            Default is False
@@ -85,9 +88,16 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
            seconds. (Can also be a fraction of a second e.g. 0.01)
        session: None or Session
            Optional. Pass your own session object to be used for all requests
+        multi_level_index: bool
+            Optional. Always return a MultiIndex DataFrame? Default is True
    """
    logger = utils.get_yf_logger()

+    if auto_adjust is None:
+        # Warn users that default has changed to True
+        utils.print_once("YF.download() has changed argument auto_adjust default to True")
+        auto_adjust = True
+
    if logger.isEnabledFor(logging.DEBUG):
        if threads:
            # With DEBUG, each thread generates a lot of log messages.
@@ -101,7 +111,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_

    if ignore_tz is None:
        # Set default value depending on interval
-        if interval[1:] in ['m', 'h']:
+        if interval[-1] in ['m', 'h']:
            # Intraday
            ignore_tz = False
        else:
@@ -175,7 +185,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
        errors = {}
        for ticker in shared._ERRORS:
            err = shared._ERRORS[ticker]
-            err = err.replace(f'{ticker}', '%ticker%')
+            err = err.replace(f'${ticker}: ', '')
            if err not in errors:
                errors[err] = [ticker]
            else:
@@ -187,7 +197,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
        tbs = {}
        for ticker in shared._TRACEBACKS:
            tb = shared._TRACEBACKS[ticker]
-            tb = tb.replace(f'{ticker}', '%ticker%')
+            tb = tb.replace(f'${ticker}: ', '')
            if tb not in tbs:
                tbs[tb] = [ticker]
            else:
@@ -207,7 +217,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
        _realign_dfs()
        data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
                          keys=shared._DFS.keys(), names=['Ticker', 'Price'])
-    data.index = _pd.to_datetime(data.index, utc=True)
+    data.index = _pd.to_datetime(data.index, utc=not ignore_tz)
    # switch names back to isins if applicable
    data.rename(columns=shared._ISINS, inplace=True)

@@ -215,7 +225,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
        data.columns = data.columns.swaplevel(0, 1)
        data.sort_index(level=0, axis=1, inplace=True)

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

    return data
--- a/yfinance/scrapers/analysis.py
+++ b/yfinance/scrapers/analysis.py
@@ -28,6 +28,50 @@ class Analysis:
        self._eps_revisions = None
        self._growth_estimates = None

+    def _get_periodic_df(self, key) -> pd.DataFrame:
+        if self._earnings_trend is None:
+            self._fetch_earnings_trend()
+
+        data = []
+        for item in self._earnings_trend[:4]:
+            row = {'period': item['period']}
+            for k, v in item[key].items():
+                if not isinstance(v, dict) or len(v) == 0:
+                    continue
+                row[k] = v['raw']
+            data.append(row)
+        if len(data) == 0:
+            return pd.DataFrame()
+        return pd.DataFrame(data).set_index('period')
+
+    @property
+    def earnings_estimate(self) -> pd.DataFrame:
+        if self._earnings_estimate is not None:
+            return self._earnings_estimate
+        self._earnings_estimate = self._get_periodic_df('earningsEstimate')
+        return self._earnings_estimate
+
+    @property
+    def revenue_estimate(self) -> pd.DataFrame:
+        if self._revenue_estimate is not None:
+            return self._revenue_estimate
+        self._revenue_estimate = self._get_periodic_df('revenueEstimate')
+        return self._revenue_estimate
+
+    @property
+    def eps_trend(self) -> pd.DataFrame:
+        if self._eps_trend is not None:
+            return self._eps_trend
+        self._eps_trend = self._get_periodic_df('epsTrend')
+        return self._eps_trend
+
+    @property
+    def eps_revisions(self) -> pd.DataFrame:
+        if self._eps_revisions is not None:
+            return self._eps_revisions
+        self._eps_revisions = self._get_periodic_df('epsRevisions')
+        return self._eps_revisions
+
    @property
    def analyst_price_targets(self) -> dict:
        if self._analyst_price_targets is not None:
@@ -40,73 +84,17 @@ class Analysis:
            self._analyst_price_targets = {}
            return self._analyst_price_targets

-        keys = [
-            ('currentPrice', 'current'),
-            ('targetLowPrice', 'low'),
-            ('targetHighPrice', 'high'),
-            ('targetMeanPrice', 'mean'),
-            ('targetMedianPrice', 'median'),
-        ]
+        result = {}
+        for key, value in data.items():
+            if key.startswith('target'):
+                new_key = key.replace('target', '').lower().replace('price', '').strip()
+                result[new_key] = value
+            elif key == 'currentPrice':
+                result['current'] = value

-        self._analyst_price_targets = {newKey: data.get(oldKey, None) for oldKey, newKey in keys}
+        self._analyst_price_targets = result
        return self._analyst_price_targets

-    @property
-    def earnings_estimate(self) -> pd.DataFrame:
-        if self._earnings_estimate is not None:
-            return self._earnings_estimate
-
-        if self._earnings_trend is None:
-            self._fetch_earnings_trend()
-
-        data_dict = {
-            'numberOfAnalysts': [],
-            'avg': [],
-            'low': [],
-            'high': [],
-            'yearAgoEps': [],
-            'growth': []
-        }
-        periods = []
-
-        for item in self._earnings_trend[:4]:
-            periods.append(item['period'])
-            earnings_estimate = item.get('earningsEstimate', {})
-
-            for key in data_dict.keys():
-                data_dict[key].append(earnings_estimate.get(key, {}).get('raw', None))
-
-        self._earnings_estimate = pd.DataFrame(data_dict, index=periods)
-        return self._earnings_estimate
-
-    @property
-    def revenue_estimate(self) -> pd.DataFrame:
-        if self._revenue_estimate is not None:
-            return self._revenue_estimate
-
-        if self._earnings_trend is None:
-            self._fetch_earnings_trend()
-
-        data_dict = {
-            'numberOfAnalysts': [],
-            'avg': [],
-            'low': [],
-            'high': [],
-            'yearAgoRevenue': [],
-            'growth': []
-        }
-        periods = []
-
-        for item in self._earnings_trend[:4]:
-            periods.append(item['period'])
-            revenue_estimate = item.get('revenueEstimate', {})
-
-            for key in data_dict.keys():
-                data_dict[key].append(revenue_estimate.get(key, {}).get('raw', None))
-
-        self._revenue_estimate = pd.DataFrame(data_dict, index=periods)
-        return self._revenue_estimate
-
    @property
    def earnings_history(self) -> pd.DataFrame:
        if self._earnings_history is not None:
@@ -119,77 +107,27 @@ class Analysis:
            self._earnings_history = pd.DataFrame()
            return self._earnings_history

-        data_dict = {
-            'epsEstimate': [],
-            'epsActual': [],
-            'epsDifference': [],
-            'surprisePercent': []
-        }
-        quarters = []
-
+        rows = []
        for item in data:
-            quarters.append(item.get('quarter', {}).get('fmt', None))
+            row = {'quarter': item.get('quarter', {}).get('fmt', None)}
+            for k, v in item.items():
+                if k == 'quarter':
+                    continue
+                if not isinstance(v, dict) or len(v) == 0:
+                    continue
+                row[k] = v.get('raw', None)
+            rows.append(row)
+        if len(data) == 0:
+            return pd.DataFrame()

-            for key in data_dict.keys():
-                data_dict[key].append(item.get(key, {}).get('raw', None))
-        
-        datetime_index = pd.to_datetime(quarters, format='%Y-%m-%d')
-        self._earnings_history = pd.DataFrame(data_dict, index=datetime_index)
+        df = pd.DataFrame(rows)
+        if 'quarter' in df.columns:
+            df['quarter'] = pd.to_datetime(df['quarter'], format='%Y-%m-%d')
+            df.set_index('quarter', inplace=True)
+
+        self._earnings_history = df
        return self._earnings_history

-    @property
-    def eps_trend(self) -> pd.DataFrame:
-        if self._eps_trend is not None:
-            return self._eps_trend
-
-        if self._earnings_trend is None:
-            self._fetch_earnings_trend()
-
-        data_dict = {
-            'current': [],
-            '7daysAgo': [],
-            '30daysAgo': [],
-            '60daysAgo': [],
-            '90daysAgo': []
-        }
-        periods = []
-
-        for item in self._earnings_trend[:4]:
-            periods.append(item['period'])
-            eps_trend = item.get('epsTrend', {})
-
-            for key in data_dict.keys():
-                data_dict[key].append(eps_trend.get(key, {}).get('raw', None))
-
-        self._eps_trend = pd.DataFrame(data_dict, index=periods)
-        return self._eps_trend
-
-    @property
-    def eps_revisions(self) -> pd.DataFrame:
-        if self._eps_revisions is not None:
-            return self._eps_revisions
-
-        if self._earnings_trend is None:
-            self._fetch_earnings_trend()
-
-        data_dict = {
-            'upLast7days': [],
-            'upLast30days': [],
-            'downLast7days': [],
-            'downLast30days': []
-        }
-        periods = []
-
-        for item in self._earnings_trend[:4]:
-            periods.append(item['period'])
-            eps_revisions = item.get('epsRevisions', {})
-
-            for key in data_dict.keys():
-                data_dict[key].append(eps_revisions.get(key, {}).get('raw', None))
-
-        self._eps_revisions = pd.DataFrame(data_dict, index=periods)
-        return self._eps_revisions
-
    @property
    def growth_estimates(self) -> pd.DataFrame:
        if self._growth_estimates is not None:
@@ -205,39 +143,26 @@ class Analysis:
            self._growth_estimates = pd.DataFrame()
            return self._growth_estimates

-        data_dict = {
-            '0q': [],
-            '+1q': [],
-            '0y': [],
-            '+1y': [],
-            '+5y': [],
-            '-5y': []
-        }
-
-        # make sure no column is empty
-        dummy_trend = [{'period': key, 'growth': None} for key in data_dict.keys()]
-        industry_trend = trends['industryTrend']['estimates'] or dummy_trend
-        sector_trend = trends['sectorTrend']['estimates'] or dummy_trend
-        index_trend = trends['indexTrend']['estimates'] or dummy_trend
-
+        data = []
        for item in self._earnings_trend:
            period = item['period']
-            data_dict[period].append(item.get('growth', {}).get('raw', None))
+            row = {'period': period, 'stockTrend': item.get('growth', {}).get('raw', None)}
+            data.append(row)

-        for item in industry_trend:
-            period = item['period']
-            data_dict[period].append(item.get('growth', None))
+        for trend_name, trend_info in trends.items():
+            if trend_info.get('estimates'):
+                for estimate in trend_info['estimates']:
+                    period = estimate['period']
+                    existing_row = next((row for row in data if row['period'] == period), None)
+                    if existing_row:
+                        existing_row[trend_name] = estimate.get('growth')
+                    else:
+                        row = {'period': period, trend_name: estimate.get('growth')}
+                        data.append(row)
+        if len(data) == 0:
+            return pd.DataFrame()

-        for item in sector_trend:
-            period = item['period']
-            data_dict[period].append(item.get('growth', None))
-
-        for item in index_trend:
-            period = item['period']
-            data_dict[period].append(item.get('growth', None))
-
-        cols = ['stock', 'industry', 'sector', 'index']
-        self._growth_estimates = pd.DataFrame(data_dict, index=cols).T
+        self._growth_estimates = pd.DataFrame(data).set_index('period').dropna(how='all')
        return self._growth_estimates

    # modified version from quote.py
--- 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
@@ -9,7 +9,7 @@ import bisect

 from yfinance import shared, utils
 from yfinance.const import _BASE_URL_, _PRICE_COLNAMES_
-from yfinance.exceptions import YFInvalidPeriodError, YFPricesMissingError, YFTzMissingError
+from yfinance.exceptions import YFInvalidPeriodError, YFPricesMissingError, YFTzMissingError, YFRateLimitError

 class PriceHistory:
    def __init__(self, data, ticker, tz, session=None, proxy=None):
@@ -78,7 +78,7 @@ class PriceHistory:

        interval_user = interval
        period_user = period
-        if repair and interval in ['5d', '1wk', '1mo', '3mo']:
+        if repair and interval in ["5d", "1wk", "1mo", "3mo"]:
            # Yahoo's way of adjusting mutiday intervals is fundamentally broken.
            # Have to fetch 1d, adjust, then resample.
            if interval == '5d':
@@ -184,6 +184,9 @@ class PriceHistory:
                                   "the issue. Thank you for your patience.")

            data = data.json()
+        # Special case for rate limits
+        except YFRateLimitError:
+            raise
        except Exception:
            if raise_errors:
                raise
@@ -229,10 +232,9 @@ class PriceHistory:
        elif "chart" not in data or data["chart"]["result"] is None or not data["chart"]["result"] or not data["chart"]["result"][0]["indicators"]["quote"][0]:
            _exception = YFPricesMissingError(self.ticker, _price_data_debug)
            fail = True
-        elif period is not None and period not in self._history_metadata["validRanges"]:
-            # even if timestamp is in the data, the data doesn't encompass the period requested
-            # User provided a bad period. The minimum should be '1d', but sometimes Yahoo accepts '1h'.
-            _exception = YFInvalidPeriodError(self.ticker, period, self._history_metadata['validRanges'])
+        elif period and period not in self._history_metadata['validRanges'] and not utils.is_valid_period_format(period):
+            # User provided a bad period
+            _exception = YFInvalidPeriodError(self.ticker, period, ", ".join(self._history_metadata['validRanges']))
            fail = True

        if fail:
@@ -247,6 +249,13 @@ class PriceHistory:
                self._reconstruct_start_interval = None
            return utils.empty_df()

+        # Process custom periods
+        if period and period not in self._history_metadata.get("validRanges", []):
+            end = int(_time.time())
+            start = _datetime.date.fromtimestamp(end)
+            start -= utils._interval_to_timedelta(period)
+            start -= _datetime.timedelta(days=4)
+
        # parse quotes
        quotes = utils.parse_quotes(data["chart"]["result"][0])
        # Yahoo bug fix - it often appends latest price even if after end date
@@ -953,7 +962,7 @@ 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():
@@ -1295,7 +1304,7 @@ class PriceHistory:

        if df is None or df.empty:
            return df
-        if interval != '1d':
+        if interval in ['1wk', '1mo', '3mo', '1y']:
            return df

        logger = utils.get_yf_logger()
@@ -1426,7 +1435,7 @@ 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:
@@ -1605,9 +1614,9 @@ class PriceHistory:
        checks += ['adj_missing', 'adj_exceeds_div', 'div_exceeds_adj']

        div_status_df['phantom'] = False
-        phantom_proximity_threshold = _datetime.timedelta(days=7)
+        phantom_proximity_threshold = _datetime.timedelta(days=17)
        f = div_status_df[['div_too_big', 'div_exceeds_adj']].any(axis=1)
-        if f.any():
+        if f.any() and len(div_status_df) > 1:
            # One/some of these may be phantom dividends. Clue is if another correct dividend is very close
            indices = np.where(f)[0]
            dts_to_check = div_status_df.index[f]
@@ -1616,37 +1625,24 @@ class PriceHistory:
                div_dt = div.name
                phantom_dt = None
                if i > 0:
-                    prev_div = div_status_df.iloc[i-1]
-                    ratio1 = (div['div']/currency_divide) / prev_div['div']
-                    ratio2 = div['div'] / prev_div['div']
-                    divergence = min(abs(ratio1-1.0), abs(ratio2-1.0))
-                    if abs(div_dt-prev_div.name) <= phantom_proximity_threshold and not prev_div['phantom'] and divergence < 0.01:
-                        if prev_div.name in dts_to_check:
-                            # Both this and previous are anomalous, so mark smallest drop as phantom
-                            drop = div['drop']
-                            drop_prev = prev_div['drop']
-                            if drop > 1.5*drop_prev:
-                                phantom_dt = prev_div.name
-                            else:
-                                phantom_dt = div_dt
-                        else:
-                            phantom_dt = div_dt
-                elif i < len(div_status_df)-1:
-                    next_div = div_status_df.iloc[i+1]
-                    ratio1 = (div['div']/currency_divide) / next_div['div']
-                    ratio2 = div['div'] / next_div['div']
-                    divergence = min(abs(ratio1-1.0), abs(ratio2-1.0))
-                    if abs(div_dt-next_div.name) <= phantom_proximity_threshold and divergence < 0.01:
-                        if next_div.name in dts_to_check:
-                            # Both this and previous are anomalous, so mark smallest drop as phantom
-                            drop = div['drop']
-                            drop_next = next_div['drop']
-                            if drop > 1.5*drop_next:
-                                phantom_dt = next_div.name
-                            else:
-                                phantom_dt = div_dt
+                    other_div = div_status_df.iloc[i-1]
+                else:
+                    other_div = div_status_df.iloc[i+1]
+                ratio1 = (div['div']/currency_divide) / other_div['div']
+                ratio2 = div['div'] / other_div['div']
+                divergence = min(abs(ratio1-1.0), abs(ratio2-1.0))
+                if abs(div_dt-other_div.name) <= phantom_proximity_threshold and not other_div['phantom'] and divergence < 0.01:
+                    if other_div.name in dts_to_check:
+                        # Both this and previous are anomalous, so mark smallest drop as phantom
+                        drop = div['drop']
+                        drop_next = other_div['drop']
+                        if drop > 1.5*drop_next:
+                            phantom_dt = other_div.name
                        else:
                            phantom_dt = div_dt
+                    else:
+                        phantom_dt = div_dt
+
                if phantom_dt:
                    div_status_df.loc[phantom_dt, 'phantom'] = True
                    for c in checks:
@@ -1681,10 +1677,6 @@ 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 +1701,29 @@ 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]
@@ -1726,7 +1741,7 @@ class PriceHistory:
            lookahead_idx = bisect.bisect_left(df2.index, lookahead_date)
            lookahead_idx = min(lookahead_idx, len(df2)-1)
            # In rare cases, the price dropped 1 day before dividend (DVD.OL @ 2024-05-15)
-            lookback_idx = div_idx-2 if div_idx > 1 else div_idx-1
+            lookback_idx = max(0, div_idx-14)
            # Check for bad stock splits in the lookahead period - 
            # if present, reduce lookahead to before.
            future_changes = df2['Close'].iloc[div_idx:lookahead_idx+1].pct_change()
@@ -1748,8 +1763,6 @@ class PriceHistory:
                adjDeltas = x['Adj Low'].iloc[1:].to_numpy() - x['Adj Close'].iloc[:-1].to_numpy()
                adjDeltas = np.append([0.0], adjDeltas)
                x['adjDelta'] = adjDeltas
-                for i in np.where(x['Dividends']>0)[0]:
-                    x.loc[x.index[i], 'adjDelta'] += x['Dividends'].iloc[i]*x['Adj'].iloc[i]
                deltas = x[['delta', 'adjDelta']]
                if div_pct > 0.05 and div_pct < 1.0:
                    adjDiv = div * x['Adj'].iloc[0]
@@ -1789,7 +1802,8 @@ 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 +1857,6 @@ 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)
@@ -1887,20 +1885,34 @@ 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
+                    fals_threshold = 0.25

                    if 'div_date_wrong' in cluster.columns and (cluster[c] == cluster['div_date_wrong']).all():
                        continue

                    if 'adj_exceeds_prices' in cluster.columns and (cluster[c] == (cluster[c] & cluster['adj_exceeds_prices'])).all():
-                        # 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():
@@ -1964,7 +1976,7 @@ class PriceHistory:
                if c == 'div_date_wrong':
                    # Fine, these should be rare
                    continue
-                if c == 'div_pre_split':
+                if c in ['div_pre_split', 'div_too_big_and_pre_split']:
                    # Fine, these should be rare
                    continue

@@ -2200,6 +2212,26 @@ class PriceHistory:
                        df2_nan.loc[:enddt, 'Repaired?'] = True
                        cluster.loc[dt, 'Fixed?'] = True

+                elif n_failed_checks == 3:
+                    if div_too_big and div_exceeds_adj and div_pre_split:
+                        k = 'too-big div & pre-split'
+                        correction = (1.0/currency_divide) * (1.0/df2['Stock Splits'].loc[dt])
+                        correct_div = row['div'] * correction
+                        df2.loc[dt, 'Dividends'] = correct_div
+
+                        target_div_pct = row['%'] * correction
+                        target_adj = 1.0 - target_div_pct
+                        present_adj = row['present adj']
+                        # Also correct adjustment to match corrected dividend
+                        k += ' & div-adjust'
+                        adj_correction = target_adj / present_adj
+                        df2.loc[    :enddt, 'Adj Close'] *= adj_correction
+                        df2.loc[    :enddt, 'Repaired?'] = True
+                        df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
+                        df2_nan.loc[:enddt, 'Repaired?'] = True
+                        cluster.loc[dt, 'Fixed?'] = True
+                        div_repairs.setdefault(k, []).append(dt)
+
            if cluster.empty:
                continue

@@ -2455,14 +2487,14 @@ class PriceHistory:

        r = _1d_change_x / split_rcp
        f_down = _1d_change_x < 1.0 / threshold
-        if f_down.any():
-            # Discard where triggered by negative Adj Close after dividend
-            f_neg = _1d_change_x < 0.0
-            f_div = (df2['Dividends']>0).to_numpy()
-            f_div_before = np.roll(f_div, 1)
-            if f_down.ndim == 2:
-                f_div_before = f_div_before[:, np.newaxis].repeat(f_down.shape[1], axis=1)
-            f_down = f_down & ~(f_neg + f_div_before)
+        # if f_down.any():
+        #     # Discard where triggered by negative Adj Close after dividend
+        #     f_neg = _1d_change_x < 0.0
+        #     f_div = (df2['Dividends']>0).to_numpy()
+        #     f_div_before = np.roll(f_div, 1)
+        #     if f_down.ndim == 2:
+        #         f_div_before = f_div_before[:, np.newaxis].repeat(f_down.shape[1], axis=1)
+        #     f_down = f_down & ~(f_neg + f_div_before)
        f_up = _1d_change_x > threshold
        f_up_ndims = len(f_up.shape)
        f_up_shifts = f_up if f_up_ndims==1 else f_up.any(axis=1)
@@ -2485,7 +2517,7 @@ class PriceHistory:
                        # assume false positive
                        continue
                    avg_vol_after = df2['Volume'].iloc[lookback:i-1].mean()
-                    if not np.isnan(avg_vol_after) and v/avg_vol_after < 2.0:
+                    if not np.isnan(avg_vol_after) and avg_vol_after > 0 and v/avg_vol_after < 2.0:
                        # volume spike is actually a step-change, so 
                        # probably missing stock split
                        continue
--- a/yfinance/scrapers/holders.py
+++ b/yfinance/scrapers/holders.py
@@ -1,5 +1,3 @@
-# from io import StringIO
-
 import pandas as pd
 import requests

@@ -8,7 +6,7 @@ from yfinance.data import YfData
 from yfinance.const import _BASE_URL_
 from yfinance.exceptions import YFDataException

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


 class Holders:
@@ -31,42 +29,36 @@ class Holders:
    @property
    def major(self) -> pd.DataFrame:
        if self._major is None:
-            # self._scrape(self.proxy)
            self._fetch_and_parse()
        return self._major

    @property
    def institutional(self) -> pd.DataFrame:
        if self._institutional is None:
-            # self._scrape(self.proxy)
            self._fetch_and_parse()
        return self._institutional

    @property
    def mutualfund(self) -> pd.DataFrame:
        if self._mutualfund is None:
-            # self._scrape(self.proxy)
            self._fetch_and_parse()
        return self._mutualfund

    @property
    def insider_transactions(self) -> pd.DataFrame:
        if self._insider_transactions is None:
-            # self._scrape_insider_transactions(self.proxy)
            self._fetch_and_parse()
        return self._insider_transactions

    @property
    def insider_purchases(self) -> pd.DataFrame:
        if self._insider_purchases is None:
-            # self._scrape_insider_transactions(self.proxy)
            self._fetch_and_parse()
        return self._insider_purchases

    @property
    def insider_roster(self) -> pd.DataFrame:
        if self._insider_roster is None:
-            # self._scrape_insider_ros(self.proxy)
            self._fetch_and_parse()
        return self._insider_roster

@@ -187,8 +179,10 @@ class Holders:
            del owner["maxAge"]
        df = pd.DataFrame(holders)
        if not df.empty:
-            df["positionDirectDate"] = pd.to_datetime(df["positionDirectDate"], unit="s")
-            df["latestTransDate"] = pd.to_datetime(df["latestTransDate"], unit="s")
+            if "positionDirectDate" in df:
+                df["positionDirectDate"] = pd.to_datetime(df["positionDirectDate"], unit="s")
+            if "latestTransDate" in df:
+                df["latestTransDate"] = pd.to_datetime(df["latestTransDate"], unit="s")

            df.rename(columns={
                "name": "Name",
--- a/yfinance/scrapers/quote.py
+++ b/yfinance/scrapers/quote.py
@@ -7,7 +7,7 @@ import requests

 from yfinance import utils
 from yfinance.data import YfData
-from yfinance.const import quote_summary_valid_modules, _BASE_URL_
+from yfinance.const import quote_summary_valid_modules, _BASE_URL_, _QUERY1_URL_
 from yfinance.exceptions import YFDataException, YFException

 info_retired_keys_price = {"currentPrice", "dayHigh", "dayLow", "open", "previousClose", "volume", "volume24Hr"}
@@ -590,33 +590,56 @@ class Quote:
            return None
        return result

+    def _fetch_additional_info(self, proxy):
+        params_dict = {"symbols": self._symbol, "formatted": "false"}
+        try:
+            result = self._data.get_raw_json(f"{_QUERY1_URL_}/v7/finance/quote?",
+                                             user_agent_headers=self._data.user_agent_headers,
+                                             params=params_dict, proxy=proxy)
+        except requests.exceptions.HTTPError as e:
+            utils.get_yf_logger().error(str(e))
+            return None
+        return result
+
    def _fetch_info(self, proxy):
        if self._already_fetched:
            return
        self._already_fetched = True
        modules = ['financialData', 'quoteType', 'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
        result = self._fetch(proxy, modules=modules)
+        result.update(self._fetch_additional_info(proxy))
        if result is None:
            self._info = {}
            return

-        result["quoteSummary"]["result"][0]["symbol"] = self._symbol
-        query1_info = next(
-            (info for info in result.get("quoteSummary", {}).get("result", []) if info["symbol"] == self._symbol),
-            None,
-        )
-        # Most keys that appear in multiple dicts have same value. Except 'maxAge' because
-        # Yahoo not consistent with days vs seconds. Fix it here:
-        for k in query1_info:
-            if "maxAge" in query1_info[k] and query1_info[k]["maxAge"] == 1:
-                query1_info[k]["maxAge"] = 86400
-        query1_info = {
-            k1: v1
-            for k, v in query1_info.items()
-            if isinstance(v, dict)
-            for k1, v1 in v.items()
-            if v1
-        }
+        query1_info = {}
+        for quote in ["quoteSummary", "quoteResponse"]:
+            if quote in result:
+                result[quote]["result"][0]["symbol"] = self._symbol
+                query_info = next(
+                    (info for info in result.get(quote, {}).get("result", [])
+                     if info["symbol"] == self._symbol),
+                    None,
+                )
+                if query_info:
+                    query1_info.update(query_info)
+
+        # Normalize and flatten nested dictionaries while converting maxAge from days (1) to seconds (86400).
+        # This handles Yahoo Finance API inconsistency where maxAge is sometimes expressed in days instead of seconds.
+        processed_info = {}
+        for k, v in query1_info.items():
+
+            # Handle nested dictionary
+            if isinstance(v, dict):
+                for k1, v1 in v.items():
+                    if v1 is not None:
+                        processed_info[k1] = 86400 if k1 == "maxAge" and v1 == 1 else v1
+
+            elif v is not None:
+                processed_info[k] = v
+
+        query1_info = processed_info
+
        # recursively format but only because of 'companyOfficers'

        def _format(k, v):
@@ -631,9 +654,8 @@ class Quote:
            else:
                v2 = v
            return v2
-        for k, v in query1_info.items():
-            query1_info[k] = _format(k, v)
-        self._info = query1_info
+
+        self._info = {k: _format(k, v) for k, v in query1_info.items()}

    def _fetch_complementary(self, proxy):
        if self._already_fetched_complementary:
--- a/yfinance/screener/init.py
+++ b/yfinance/screener/init.py
@@ -1,4 +1,4 @@
-from .screener import Screener
-from .screener_query import EquityQuery
+from .query import EquityQuery
+from .screener import screen, PREDEFINED_SCREENER_QUERIES

-__all__ = ['EquityQuery', 'Screener']
+__all__ = ['EquityQuery', 'FundQuery', 'screen', 'PREDEFINED_SCREENER_QUERIES']
--- a/yfinance/screener/query.py
+++ b/yfinance/screener/query.py
@@ -0,0 +1,218 @@
+from abc import ABC, abstractmethod
+import numbers
+from typing import List, Union, Dict, TypeVar, Tuple
+
+from yfinance.const import EQUITY_SCREENER_EQ_MAP, EQUITY_SCREENER_FIELDS
+from yfinance.const import FUND_SCREENER_EQ_MAP, FUND_SCREENER_FIELDS
+from yfinance.exceptions import YFNotImplementedError
+from ..utils import dynamic_docstring, generate_list_table_from_dict_universal
+
+T = TypeVar('T', bound=Union[str, numbers.Real])
+
+class QueryBase(ABC):
+    def __init__(self, operator: str, operand: Union[ List['QueryBase'], Tuple[str, Tuple[Union[str, numbers.Real],  ...]] ]):
+        operator = operator.upper()
+
+        if not isinstance(operand, list):
+            raise TypeError('Invalid operand type')
+        if len(operand) <= 0:
+            raise ValueError('Invalid field for EquityQuery')
+            
+        if operator == 'IS-IN':
+            self._validate_isin_operand(operand)
+        elif operator in {'OR','AND'}: 
+            self._validate_or_and_operand(operand)
+        elif operator == 'EQ': 
+            self._validate_eq_operand(operand)
+        elif operator == 'BTWN': 
+            self._validate_btwn_operand(operand)
+        elif operator in {'GT','LT','GTE','LTE'}: 
+            self._validate_gt_lt(operand)
+        else: 
+            raise ValueError('Invalid Operator Value')
+
+        self.operator = operator
+        self.operands = operand
+
+    @property
+    @abstractmethod
+    def valid_fields(self) -> List:
+        raise YFNotImplementedError('valid_fields() needs to be implemented by child')
+
+    @property
+    @abstractmethod
+    def valid_values(self) -> Dict:
+        raise YFNotImplementedError('valid_values() needs to be implemented by child')
+
+    def _validate_or_and_operand(self, operand: List['QueryBase']) -> None:
+        if len(operand) <= 1: 
+            raise ValueError('Operand must be length longer than 1')
+        if all(isinstance(e, QueryBase) for e in operand) is False: 
+            raise TypeError(f'Operand must be type {type(self)} for OR/AND')
+
+    def _validate_eq_operand(self, operand: List[Union[str, numbers.Real]]) -> None:
+        if len(operand) != 2:
+            raise ValueError('Operand must be length 2 for EQ')
+        
+        if  not any(operand[0] in fields_by_type for fields_by_type in self.valid_fields.values()):
+            raise ValueError(f'Invalid field for {type(self)} "{operand[0]}"')
+        if operand[0] in self.valid_values:
+            vv = self.valid_values[operand[0]]
+            if isinstance(vv, dict):
+                # this data structure is slightly different to generate better docs, 
+                # need to unpack here.
+                vv = set().union(*[e for e in vv.values()])
+            if operand[1] not in vv:
+                raise ValueError(f'Invalid EQ value "{operand[1]}"')
+    
+    def _validate_btwn_operand(self, operand: List[Union[str, numbers.Real]]) -> None:
+        if len(operand) != 3: 
+            raise ValueError('Operand must be length 3 for BTWN')
+        if  not any(operand[0] in fields_by_type for fields_by_type in self.valid_fields.values()):
+            raise ValueError(f'Invalid field for {type(self)}')
+        if isinstance(operand[1], numbers.Real) is False:
+            raise TypeError('Invalid comparison type for BTWN')
+        if isinstance(operand[2], numbers.Real) is False:
+            raise TypeError('Invalid comparison type for BTWN')
+
+    def _validate_gt_lt(self, operand: List[Union[str, numbers.Real]]) -> None:
+        if len(operand) != 2:
+            raise ValueError('Operand must be length 2 for GT/LT')
+        if  not any(operand[0] in fields_by_type for fields_by_type in self.valid_fields.values()):
+            raise ValueError(f'Invalid field for {type(self)} "{operand[0]}"')
+        if isinstance(operand[1], numbers.Real) is False:
+            raise TypeError('Invalid comparison type for GT/LT')
+
+    def _validate_isin_operand(self, operand: List['QueryBase']) -> None:
+        if len(operand) < 2:
+            raise ValueError('Operand must be length 2+ for IS-IN')
+        
+        if  not any(operand[0] in fields_by_type for fields_by_type in self.valid_fields.values()):
+            raise ValueError(f'Invalid field for {type(self)} "{operand[0]}"')
+        if operand[0] in self.valid_values:
+            vv = self.valid_values[operand[0]]
+            if isinstance(vv, dict):
+                # this data structure is slightly different to generate better docs, 
+                # need to unpack here.
+                vv = set().union(*[e for e in vv.values()])
+            for i in range(1, len(operand)):
+                if operand[i] not in vv:
+                    raise ValueError(f'Invalid EQ value "{operand[i]}"')
+
+    def to_dict(self) -> Dict:
+        op = self.operator
+        ops = self.operands
+        if self.operator == 'IS-IN':
+            # Expand to OR of EQ queries
+            op = 'OR'
+            ops = [type(self)('EQ', [self.operands[0], v]) for v in self.operands[1:]]
+        return {
+            "operator": op,
+            "operands": [o.to_dict() if isinstance(o, QueryBase) else o for o in ops]
+        }
+
+    def __repr__(self, indent=0) -> str:
+        indent_str = "  " * indent
+        class_name = self.__class__.__name__
+
+        if isinstance(self.operands, list):
+            # For list operands, check if they contain any QueryBase objects
+            if any(isinstance(op, QueryBase) for op in self.operands):
+                # If there are nested queries, format them with newlines
+                operands_str = ",\n".join(
+                    f"{indent_str}  {op.__repr__(indent + 1) if isinstance(op, QueryBase) else repr(op)}"
+                    for op in self.operands
+                )
+                return f"{class_name}({self.operator}, [\n{operands_str}\n{indent_str}])"
+            else:
+                # For lists of simple types, keep them on one line
+                return f"{class_name}({self.operator}, {repr(self.operands)})"
+        else:
+            # Handle single operand
+            return f"{class_name}({self.operator}, {repr(self.operands)})"
+
+    def __str__(self) -> str:
+        return self.__repr__()
+
+
+class EquityQuery(QueryBase):
+    """
+    The `EquityQuery` class constructs filters for stocks based on specific criteria such as region, sector, exchange, and peer group.
+
+    Start with value operations: `EQ` (equals), `IS-IN` (is in), `BTWN` (between), `GT` (greater than), `LT` (less than), `GTE` (greater or equal), `LTE` (less or equal).
+
+    Combine them with logical operations: `AND`, `OR`.
+
+    Example:
+        Predefined Yahoo query `aggressive_small_caps`:
+        
+        .. code-block:: python
+
+            from yfinance import EquityQuery
+
+            EquityQuery('and', [
+                EquityQuery('is-in', ['exchange', 'NMS', 'NYQ']), 
+                EquityQuery('lt', ["epsgrowth.lasttwelvemonths", 15])
+            ])
+    """
+
+    @dynamic_docstring({"valid_operand_fields_table": generate_list_table_from_dict_universal(EQUITY_SCREENER_FIELDS)})
+    @property
+    def valid_fields(self) -> Dict:
+        """
+        Valid operands, grouped by category.
+        {valid_operand_fields_table}
+        """
+        return EQUITY_SCREENER_FIELDS
+    
+    @dynamic_docstring({"valid_values_table": generate_list_table_from_dict_universal(EQUITY_SCREENER_EQ_MAP, concat_keys=['exchange'])})
+    @property
+    def valid_values(self) -> Dict:
+        """
+        Most operands take number values, but some have a restricted set of valid values.
+        {valid_values_table}
+        """
+        return EQUITY_SCREENER_EQ_MAP
+
+
+class FundQuery(QueryBase):
+    """
+    The `FundQuery` class constructs filters for mutual funds based on specific criteria such as region, sector, exchange, and peer group.
+
+    Start with value operations: `EQ` (equals), `IS-IN` (is in), `BTWN` (between), `GT` (greater than), `LT` (less than), `GTE` (greater or equal), `LTE` (less or equal).
+
+    Combine them with logical operations: `AND`, `OR`.
+
+    Example:
+        Predefined Yahoo query `solid_large_growth_funds`:
+        
+        .. code-block:: python
+
+            from yfinance import FundQuery
+            
+            FundQuery('and', [
+                FundQuery('eq', ['categoryname', 'Large Growth']), 
+                FundQuery('is-in', ['performanceratingoverall', 4, 5]), 
+                FundQuery('lt', ['initialinvestment', 100001]), 
+                FundQuery('lt', ['annualreturnnavy1categoryrank', 50]), 
+                FundQuery('eq', ['exchange', 'NAS'])
+            ])
+    """
+    @dynamic_docstring({"valid_operand_fields_table": generate_list_table_from_dict_universal(FUND_SCREENER_FIELDS)})
+    @property
+    def valid_fields(self) -> Dict:
+        """
+        Valid operands, grouped by category.
+        {valid_operand_fields_table}
+        """
+        return FUND_SCREENER_FIELDS
+    
+    @dynamic_docstring({"valid_values_table": generate_list_table_from_dict_universal(FUND_SCREENER_EQ_MAP)})
+    @property
+    def valid_values(self) -> Dict:
+        """
+        Most operands take number values, but some have a restricted set of valid values.
+        {valid_values_table}
+        """
+        return FUND_SCREENER_EQ_MAP
+
--- a/yfinance/screener/screener.py
+++ b/yfinance/screener/screener.py
@@ -1,105 +1,180 @@
-from typing import Dict
+from .query import EquityQuery as EqyQy
+from .query import FundQuery as FndQy
+from .query import QueryBase, EquityQuery, FundQuery

-from yfinance import utils
+from yfinance.const import _BASE_URL_
 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_universal
+
+from typing import Union
+import requests

 _SCREENER_URL_ = f"{_BASE_URL_}/v1/finance/screener"
+_PREDEFINED_URL_ = f"{_SCREENER_URL_}/predefined/saved"

-class Screener:
-    def __init__(self, session=None, proxy=None):
-        self.proxy = proxy
-        self.session = session
+PREDEFINED_SCREENER_BODY_DEFAULTS = {
+    "offset":0, "size":25, "userId":"","userIdType":"guid"
+}

-        self._data: YfData = YfData(session=session)
-        self._body: Dict = {}
-        self._response: Dict = {}
-        self._body_updated = False
-        self._accepted_body_keys = {"offset","size","sortField","sortType","quoteType","query","userId","userIdType"}
-        self._predefined_bodies = PREDEFINED_SCREENER_BODY_MAP.keys()
+PREDEFINED_SCREENER_QUERIES = {
+    'aggressive_small_caps': {"sortField":"eodvolume", "sortType":"desc",
+                            "query": EqyQy('and', [EqyQy('is-in', ['exchange', 'NMS', 'NYQ']), EqyQy('lt', ["epsgrowth.lasttwelvemonths", 15])])},
+    'day_gainers': {"sortField":"percentchange", "sortType":"DESC",
+                    "query": EqyQy('and', [EqyQy('gt', ['percentchange', 3]), EqyQy('eq', ['region', 'us']), EqyQy('gte', ['intradaymarketcap', 2000000000]), EqyQy('gte', ['intradayprice', 5]), EqyQy('gt', ['dayvolume', 15000])])},
+    'day_losers': {"sortField":"percentchange", "sortType":"ASC",
+                    "query": EqyQy('and', [EqyQy('lt', ['percentchange', -2.5]), EqyQy('eq', ['region', 'us']), EqyQy('gte', ['intradaymarketcap', 2000000000]), EqyQy('gte', ['intradayprice', 5]), EqyQy('gt', ['dayvolume', 20000])])},
+    'growth_technology_stocks': {"sortField":"eodvolume", "sortType":"desc",
+                                "query": EqyQy('and', [EqyQy('gte', ['quarterlyrevenuegrowth.quarterly', 25]), EqyQy('gte', ['epsgrowth.lasttwelvemonths', 25]), EqyQy('eq', ['sector', 'Technology']), EqyQy('is-in', ['exchange', 'NMS', 'NYQ'])])},
+    'most_actives': {"sortField":"dayvolume", "sortType":"DESC",
+                    "query": EqyQy('and', [EqyQy('eq', ['region', 'us']), EqyQy('gte', ['intradaymarketcap', 2000000000]), EqyQy('gt', ['dayvolume', 5000000])])},
+    'most_shorted_stocks': {"size":25, "offset":0, "sortField":"short_percentage_of_shares_outstanding.value", "sortType":"DESC", 
+                            "query": EqyQy('and', [EqyQy('eq', ['region', 'us']), EqyQy('gt', ['intradayprice', 1]), EqyQy('gt', ['avgdailyvol3m', 200000])])},
+    'small_cap_gainers': {"sortField":"eodvolume", "sortType":"desc", 
+                        "query": EqyQy("and", [EqyQy("lt", ["intradaymarketcap",2000000000]), EqyQy("is-in", ["exchange", "NMS", "NYQ"])])},
+    'undervalued_growth_stocks': {"sortType":"DESC", "sortField":"eodvolume", 
+                                "query": EqyQy('and', [EqyQy('btwn', ['peratio.lasttwelvemonths', 0, 20]), EqyQy('lt', ['pegratio_5y', 1]), EqyQy('gte', ['epsgrowth.lasttwelvemonths', 25]), EqyQy('is-in', ['exchange', 'NMS', 'NYQ'])])},
+    'undervalued_large_caps': {"sortField":"eodvolume", "sortType":"desc", 
+                            "query": EqyQy('and', [EqyQy('btwn', ['peratio.lasttwelvemonths', 0, 20]), EqyQy('lt', ['pegratio_5y', 1]), EqyQy('btwn', ['intradaymarketcap', 10000000000, 100000000000]), EqyQy('is-in', ['exchange', 'NMS', 'NYQ'])])},
+    'conservative_foreign_funds': {"sortType":"DESC", "sortField":"fundnetassets",
+                                "query": FndQy('and', [FndQy('is-in', ['categoryname', 'Foreign Large Value', 'Foreign Large Blend', 'Foreign Large Growth', 'Foreign Small/Mid Growth', 'Foreign Small/Mid Blend', 'Foreign Small/Mid Value']), FndQy('is-in', ['performanceratingoverall', 4, 5]), FndQy('lt', ['initialinvestment', 100001]), FndQy('lt', ['annualreturnnavy1categoryrank', 50]), FndQy('is-in', ['riskratingoverall', 1, 2, 3]), FndQy('eq', ['exchange', 'NAS'])])},
+    'high_yield_bond': {"sortType":"DESC", "sortField":"fundnetassets",
+                        "query": FndQy('and', [FndQy('is-in', ['performanceratingoverall', 4, 5]), FndQy('lt', ['initialinvestment', 100001]), FndQy('lt', ['annualreturnnavy1categoryrank', 50]), FndQy('is-in', ['riskratingoverall', 1, 2, 3]), FndQy('eq', ['categoryname', 'High Yield Bond']), FndQy('eq', ['exchange', 'NAS'])])},
+    'portfolio_anchors': {"sortType":"DESC", "sortField":"fundnetassets",
+                        "query": FndQy('and', [FndQy('eq', ['categoryname', 'Large Blend']), FndQy('is-in', ['performanceratingoverall', 4, 5]), FndQy('lt', ['initialinvestment', 100001]), FndQy('lt', ['annualreturnnavy1categoryrank', 50]), FndQy('eq', ['exchange', 'NAS'])])},
+    'solid_large_growth_funds': {"sortType":"DESC", "sortField":"fundnetassets",
+                                "query": FndQy('and', [FndQy('eq', ['categoryname', 'Large Growth']), FndQy('is-in', ['performanceratingoverall', 4, 5]), FndQy('lt', ['initialinvestment', 100001]), FndQy('lt', ['annualreturnnavy1categoryrank', 50]), FndQy('eq', ['exchange', 'NAS'])])},
+    'solid_midcap_growth_funds': {"sortType":"DESC", "sortField":"fundnetassets",
+                                "query": FndQy('and', [FndQy('eq', ['categoryname', 'Mid-Cap Growth']), FndQy('is-in', ['performanceratingoverall', 4, 5]), FndQy('lt', ['initialinvestment', 100001]), FndQy('lt', ['annualreturnnavy1categoryrank', 50]), FndQy('eq', ['exchange', 'NAS'])])},
+    'top_mutual_funds': {"sortType":"DESC", "sortField":"percentchange",
+                        "query": FndQy('and', [FndQy('gt', ['intradayprice', 15]), FndQy('is-in', ['performanceratingoverall', 4, 5]), FndQy('gt', ['initialinvestment', 1000]), FndQy('eq', ['exchange', 'NAS'])])}
+}

-    @property
-    def body(self) -> Dict:
-        return self._body
-    
-    @property
-    def response(self) -> Dict:
-        if self._body_updated or self._response is None:
-            self._fetch_and_parse()
-        
-        self._body_updated = False
-        return self._response
-    
-    @property
-    def predefined_bodies(self) -> Dict:
-        return self._predefined_bodies
+@dynamic_docstring({"predefined_screeners": generate_list_table_from_dict_universal(PREDEFINED_SCREENER_QUERIES, bullets=True, title='Predefined queries (Dec-2024)')})
+def screen(query: Union[str, EquityQuery, FundQuery],
+            offset: int = None, 
+            size: int = None, 
+            sortField: str = None, 
+            sortAsc: bool = None,
+            userId: str = None, 
+            userIdType: str = None, 
+            session = None, proxy = None):
+    """
+    Run a screen: predefined query, or custom query.

-    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:
-        self._body_updated = True
+    :Parameters:
+        * Defaults only apply if query = EquityQuery or FundQuery
+        query : str | Query:
+            The query to execute, either name of predefined or custom query.
+            For predefined list run yf.PREDEFINED_SCREENER_QUERIES.keys()
+        offset : int
+            The offset for the results. Default 0.
+        size : int
+            number of results to return. Default 100, maximum 250 (Yahoo)
+        sortField : str
+            field to sort by. Default "ticker"
+        sortAsc : bool
+            Sort ascending? Default False
+        userId : str
+            The user ID. Default empty.
+        userIdType : str
+            Type of user ID (e.g., "guid"). Default "guid".

-        self._body = {
-            "offset": offset,
-            "size": size,
-            "sortField": sortField,
-            "sortType": sortType,
-            "quoteType": quoteType,
-            "query": query.to_dict(),
-            "userId": userId,
-            "userIdType": userIdType
-        }
+    Example: predefined query
+        .. code-block:: python

-    def set_predefined_body(self, k: str) -> None:
-        body = PREDEFINED_SCREENER_BODY_MAP.get(k, None)
-        if not body:
-            raise ValueError(f'Invalid key {k} provided for predefined screener')
-        
-        self._body_updated = True
-        self._body = body
+            import yfinance as yf
+            response = yf.screen("aggressive_small_caps")

-    def set_body(self, body: Dict) -> None:
-        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}")
+    Example: custom query
+        .. code-block:: python

-        extra_keys = [key for key in body if key not in self._accepted_body_keys]
-        if extra_keys:
-            raise ValueError(f"Body contains extra keys: {extra_keys}")
+            import yfinance as yf
+            from yfinance import EquityQuery
+            q = EquityQuery('and', [
+                   EquityQuery('gt', ['percentchange', 3]), 
+                   EquityQuery('eq', ['region', 'us'])
+            ])
+            response = yf.screen(q, sortField = 'percentchange', sortAsc = True)

-        self._body_updated = True
-        self._body = body
+    To access predefineds query code
+        .. code-block:: python

+            import yfinance as yf
+            query = yf.PREDEFINED_SCREENER_QUERIES['aggressive_small_caps']

-    def patch_body(self, values: Dict) -> None:
-        extra_keys = [key for key in values if key not in self._accepted_body_keys]
-        if extra_keys:
-            raise ValueError(f"Body contains extra keys: {extra_keys}")
-        
-        self._body_updated = True
-        for k in values:
-            self._body[k] = values[k]
+    {predefined_screeners}
+    """

-    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")
+    # Only use defaults when user NOT give a predefined, because
+    # Yahoo's predefined endpoint auto-applies defaults. Also,
+    # that endpoint might be ignoring these fields.
+    defaults = {
+        'offset': 0,
+        'size': 25,
+        'sortField': 'ticker',
+        'sortAsc': False,
+        'userId': "",
+        'userIdType': "guid"
+    }

-    def _fetch(self) -> Dict:
-        params_dict = {"corsDomain": "finance.yahoo.com", "formatted": "false", "lang": "en-US", "region": "US"}
-        response = self._data.post(_SCREENER_URL_, body=self.body, user_agent_headers=self._data.user_agent_headers, params=params_dict, proxy=self.proxy)
-        response.raise_for_status()
-        return response.json()
-    
-    def _fetch_and_parse(self) -> None:
-        response = None
-        self._validate_body()
-        
+    if size is not None and size > 250:
+        raise ValueError("Yahoo limits query size to 250, reduce size.")
+
+    fields = dict(locals())
+    for k in ['query', 'session', 'proxy']:
+        if k in fields:
+            del fields[k]
+
+    params_dict = {"corsDomain": "finance.yahoo.com", "formatted": "false", "lang": "en-US", "region": "US"}
+
+    post_query = None
+    if isinstance(query, str):
+        # post_query = PREDEFINED_SCREENER_QUERIES[query]
+        # Switch to Yahoo's predefined endpoint
+        _data = YfData(session=session)
+        params_dict['scrIds'] = query
+        for k,v in fields.items():
+            if v is not None:
+                params_dict[k] = v
+        resp = _data.get(url=_PREDEFINED_URL_, params=params_dict, proxy=proxy)
        try:
-            response = self._fetch()
-            self._response = response['finance']['result'][0]
-        except Exception as e:
-            logger = utils.get_yf_logger()
-            logger.error(f"Failed to get screener data for '{self._body.get('query', 'query not set')}' reason: {e}")
-            logger.debug("Got response: ")
-            logger.debug("-------------")
-            logger.debug(f" {response}")
-            logger.debug("-------------")
+            resp.raise_for_status()
+        except requests.exceptions.HTTPError:
+            if query not in PREDEFINED_SCREENER_QUERIES:
+                print(f"yfinance.screen: '{query}' is probably not a predefined query.")
+            raise
+        return resp.json()["finance"]["result"][0]
+
+    elif isinstance(query, QueryBase):
+        # Prepare other fields
+        for k in defaults:
+            if k not in fields or fields[k] is None:
+                fields[k] = defaults[k]
+        fields['sortType'] = 'ASC' if fields['sortAsc'] else 'DESC'
+        del fields['sortAsc']
+
+        post_query = fields
+        post_query['query'] = query
+
+    else:
+        raise ValueError(f'Query must be type str or QueryBase, not "{type(query)}"')
+
+    if query is None:
+        raise ValueError('No query provided')
+
+    if isinstance(post_query['query'], EqyQy):
+        post_query['quoteType'] = 'EQUITY'
+    elif isinstance(post_query['query'], FndQy):
+        post_query['quoteType'] = 'MUTUALFUND'
+    post_query['query'] = post_query['query'].to_dict()
+
+    # Fetch
+    _data = YfData(session=session)
+    response = _data.post(_SCREENER_URL_, 
+                            body=post_query, 
+                            user_agent_headers=_data.user_agent_headers, 
+                            params=params_dict, 
+                            proxy=proxy)
+    response.raise_for_status()
+    return response.json()['finance']['result'][0]
--- a/yfinance/screener/screener_query.py
+++ b/yfinance/screener/screener_query.py
@@ -1,86 +0,0 @@
-import numbers
-from typing import List, Union, Dict, Set
-
-from yfinance.const import EQUITY_SCREENER_EQ_MAP, EQUITY_SCREENER_FIELDS
-from yfinance.exceptions import YFNotImplementedError
-
-class Query:
-    def __init__(self, operator: str, operand: Union[numbers.Real, str, List['Query']]):
-        self.operator = operator
-        self.operands = operand
-    
-    def to_dict(self) -> Dict:
-        raise YFNotImplementedError('to_dict() needs to be implemented by children classes')
-
-class EquityQuery(Query):
-    def __init__(self, operator: str, operand: Union[numbers.Real, str, List['EquityQuery']]):
-        operator = operator.upper()
-
-        if not isinstance(operand, list):
-            raise TypeError('Invalid operand type')
-        if len(operand) <= 0:
-            raise ValueError('Invalid field for Screener')
-            
-        if operator in {'OR','AND'}: 
-            self._validate_or_and_operand(operand)
-        elif operator == 'EQ': 
-            self._validate_eq_operand(operand)
-        elif operator == 'BTWN': 
-            self._validate_btwn_operand(operand)
-        elif operator in {'GT','LT'}: 
-            self._validate_gt_lt(operand)
-        else: 
-            raise ValueError('Invalid Operator Value')
-
-        self.operator = operator
-        self.operands = operand
-        self._valid_eq_map = EQUITY_SCREENER_EQ_MAP
-        self._valid_fields = EQUITY_SCREENER_FIELDS
-        
-    @property
-    def valid_eq_map(self) -> Dict:
-        return self._valid_eq_map
-
-    @property
-    def valid_fields(self) -> Set:
-        return self._valid_fields
-    
-    def _validate_or_and_operand(self, operand: List['EquityQuery']) -> None:
-        if len(operand) <= 1: 
-            raise ValueError('Operand must be length longer than 1')
-        if all(isinstance(e, EquityQuery) for e in operand) is False: 
-            raise TypeError('Operand must be type EquityQuery for OR/AND')
-
-    def _validate_eq_operand(self, operand: List[Union[str, numbers.Real]]) -> None:
-        if len(operand) != 2:
-            raise ValueError('Operand must be length 2 for EQ')
-        if operand[0] not in EQUITY_SCREENER_FIELDS:
-            raise ValueError('Invalid field for Screener')
-        if operand[0] not in EQUITY_SCREENER_EQ_MAP:
-            raise ValueError('Invalid EQ key')
-        if operand[1] not in EQUITY_SCREENER_EQ_MAP[operand[0]]:
-            raise ValueError('Invalid EQ value')
-    
-    def _validate_btwn_operand(self, operand: List[Union[str, numbers.Real]]) -> None:
-        if len(operand) != 3: 
-            raise ValueError('Operand must be length 3 for BTWN')
-        if operand[0] not in EQUITY_SCREENER_FIELDS:
-            raise ValueError('Invalid field for Screener')
-        if isinstance(operand[1], numbers.Real) is False:
-            raise TypeError('Invalid comparison type for BTWN')
-        if isinstance(operand[2], numbers.Real) is False:
-            raise TypeError('Invalid comparison type for BTWN')
-
-    def _validate_gt_lt(self, operand: List[Union[str, numbers.Real]]) -> None:
-        if len(operand) != 2:
-            raise ValueError('Operand must be length 2 for GT/LT')
-        if operand[0] not in EQUITY_SCREENER_FIELDS:
-            raise ValueError('Invalid field for Screener')
-        if isinstance(operand[1], numbers.Real) is False:
-            raise TypeError('Invalid comparison type for GT/LT')
-
-    def to_dict(self) -> Dict:
-        return {
-            "operator": self.operator,
-            "operands": [operand.to_dict() if isinstance(operand, EquityQuery) else operand for operand in self.operands]
-        }
--- a/yfinance/search.py
+++ b/yfinance/search.py
@@ -0,0 +1,158 @@
+#!/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, lists_count=8, include_cb=True, include_nav_links=False,
+                 include_research=False, include_cultural_assets=False, enable_fuzzy_query=False, recommended=8,
+                 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).
+            lists_count: Number of lists to include (default 8).
+            include_cb: Include the company breakdown (default True).
+            include_nav_links: Include the navigation links (default False).
+            include_research: Include the research reports (default False).
+            include_cultural_assets: Include the cultural assets (default False).
+            enable_fuzzy_query: Enable fuzzy search for typos (default False).
+            recommended: Recommended number of results to return (default 8).
+            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.lists_count = lists_count
+        self.include_cb = include_cb
+        self.nav_links = include_nav_links
+        self.enable_research = include_research
+        self.enable_cultural_assets = include_cultural_assets
+        self.recommended = recommended
+
+        self._data = YfData(session=self.session)
+        self._logger = utils.get_yf_logger()
+
+        self._response = {}
+        self._all = {}
+        self._quotes = []
+        self._news = []
+        self._lists = []
+        self._research = []
+        self._nav = []
+
+        self.search()
+
+    def search(self) -> 'Search':
+        """Search using the query parameters defined in the constructor."""
+        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",
+            "listsCount": self.lists_count,
+            "enableCb": self.include_cb,
+            "enableNavLinks": self.nav_links,
+            "enableResearchReports": self.enable_research,
+            "enableCulturalAssets": self.enable_cultural_assets,
+            "recommendedCount": self.recommended
+        }
+
+        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 search results and received faulty response instead.")
+            data = {}
+
+        self._response = data
+        # Filter quotes to only include symbols
+        self._quotes = [quote for quote in data.get("quotes", []) if "symbol" in quote]
+        self._news = data.get("news", [])
+        self._lists = data.get("lists", [])
+        self._research = data.get("researchReports", [])
+        self._nav = data.get("nav", [])
+
+        self._all = {"quotes": self._quotes, "news": self._news, "lists": self._lists, "research": self._research,
+                     "nav": self._nav}
+
+        return self
+
+    @property
+    def quotes(self) -> 'list':
+        """Get the quotes from the search results."""
+        return self._quotes
+
+    @property
+    def news(self) -> 'list':
+        """Get the news from the search results."""
+        return self._news
+
+    @property
+    def lists(self) -> 'list':
+        """Get the lists from the search results."""
+        return self._lists
+
+    @property
+    def research(self) -> 'list':
+        """Get the research reports from the search results."""
+        return self._research
+
+    @property
+    def nav(self) -> 'list':
+        """Get the navigation links from the search results."""
+        return self._nav
+
+    @property
+    def all(self) -> 'dict[str,list]':
+        """Get all the results from the search results: filtered down version of response."""
+        return self._all
+
+    @property
+    def response(self) -> 'dict':
+        """Get the raw response from the search results."""
+        return self._response
--- a/yfinance/utils.py
+++ b/yfinance/utils.py
@@ -23,6 +23,7 @@ from __future__ import print_function

 import datetime as _datetime
 import logging
+import re
 import re as _re
 import sys as _sys
 import threading
@@ -39,7 +40,6 @@ 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 +189,27 @@ 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):
@@ -427,26 +430,28 @@ def _parse_user_dt(dt, exchange_tz):


 def _interval_to_timedelta(interval):
-    if interval == "1mo":
-        return relativedelta(months=1)
-    elif interval == "3mo":
-        return relativedelta(months=3)
-    elif interval == "6mo":
-        return relativedelta(months=6)
-    elif interval == "1y":
-        return relativedelta(years=1)
-    elif interval == "2y":
-        return relativedelta(years=2)
-    elif interval == "5y":
-        return relativedelta(years=5)
-    elif interval == "10y":
-        return relativedelta(years=10)
-    elif interval == "1wk":
-        return _pd.Timedelta(days=7)
+    if interval[-1] == "d":
+        return relativedelta(days=int(interval[:-1]))
+    elif interval[-2:] == "wk":
+        return relativedelta(weeks=int(interval[:-2]))
+    elif interval[-2:] == "mo":
+        return relativedelta(months=int(interval[:-2]))
+    elif interval[-1] == "y":
+        return relativedelta(years=int(interval[:-1]))
    else:
        return _pd.Timedelta(interval)


+def is_valid_period_format(period):
+    """Check if the provided period has a valid format."""
+    if period is None:
+        return False
+
+    # Regex pattern to match valid period formats like '1d', '2wk', '3mo', '1y'
+    valid_pattern = r"^[1-9]\d*(d|wk|mo|y)$"
+    return bool(re.match(valid_pattern, period))
+
+
 def auto_adjust(data):
    col_order = data.columns
    df = data.copy()
@@ -613,7 +618,7 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange, repair=Fals
            # - exception is volume, *slightly* greater on final row (and matches website)
            if dt1.date() == dt2.date():
                # Last two rows are on same day. Drop second-to-last row
-                quotes = quotes.drop(quotes.index[n - 2])
+                quotes = _pd.concat([quotes.iloc[:-2], quotes.iloc[-1:]])
        else:
            if interval == "1wk":
                last_rows_same_interval = dt1.year == dt2.year and dt1.week == dt2.week
@@ -932,3 +937,166 @@ 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(title = None) -> str:
+    import textwrap
+    if title is None:
+        title = "Permitted Keys/Values"
+    table = textwrap.dedent(f"""
+    .. list-table:: {title}
+       :widths: 25 75
+       :header-rows: 1
+
+       * - Key
+         - Values
+    """)
+
+    return table
+
+def generate_list_table_from_dict(data: dict, bullets: bool=True, title: str=None) -> str:
+    """
+    Generate a list-table for the docstring showing permitted keys/values.
+    """
+    table = _generate_table_configurations(title)
+    for k in sorted(data.keys()):
+        values = data[k]
+        table += ' '*3 + f"* - {k}\n"
+        lengths = [len(str(v)) for v in values]
+        if bullets and max(lengths) > 5:
+            table += ' '*5 + "-\n"
+            for value in sorted(values):
+                table += ' '*7 + f"- {value}\n"
+        else:
+            value_str = ', '.join(sorted(values))
+            table += ' '*5 + f"- {value_str}\n"
+    return table
+
+# def generate_list_table_from_dict_of_dict(data: dict, bullets: bool=True, title: str=None) -> str:
+#     """
+#     Generate a list-table for the docstring showing permitted keys/values.
+#     """
+#     table = _generate_table_configurations(title)
+#     for k in sorted(data.keys()):
+#         values = data[k]
+#         table += ' '*3 + f"* - {k}\n"
+#         if bullets:
+#             table += ' '*5 + "-\n"
+#             for value in sorted(values):
+#                 table += ' '*7 + f"- {value}\n"
+#         else:
+#             table += ' '*5 + f"- {values}\n"
+#     return table
+
+
+def generate_list_table_from_dict_universal(data: dict, bullets: bool=True, title: str=None, concat_keys=[]) -> str:
+    """
+    Generate a list-table for the docstring showing permitted keys/values.
+    """
+    table = _generate_table_configurations(title)
+    for k in data.keys():
+        values = data[k]
+
+        table += ' '*3 + f"* - {k}\n"
+        if isinstance(values, dict):
+            table_add = ''
+
+            concat_short_lines = k in concat_keys
+
+            if bullets:
+                k_keys = sorted(list(values.keys()))
+                current_line = ''
+                block_format = 'query' in k_keys
+                for i in range(len(k_keys)):
+                    k2 = k_keys[i]
+                    k2_values = values[k2]
+                    k2_values_str = None
+                    if isinstance(k2_values, set):
+                        k2_values = list(k2_values)
+                    elif isinstance(k2_values, dict) and len(k2_values) == 0:
+                        k2_values = []
+                    if isinstance(k2_values, list):
+                        k2_values = sorted(k2_values)
+                        all_scalar = all(isinstance(k2v, (int, float, str)) for k2v in k2_values)
+                        if all_scalar:
+                            k2_values_str = _re.sub(r"[{}\[\]']", "", str(k2_values))
+
+                    if k2_values_str is None:
+                        k2_values_str = str(k2_values)
+
+                    if len(current_line) > 0 and (len(current_line) + len(k2_values_str) > 40):
+                        # new line
+                        table_add += current_line + '\n'
+                        current_line = ''
+
+                    if concat_short_lines:
+                        if current_line == '':
+                            current_line += ' '*5
+                            if i == 0:
+                                # Only add dash to first
+                                current_line += "- "
+                            else:
+                                current_line += "  "
+                            # Don't draw bullet points:
+                            current_line += '| '
+                        else:
+                            current_line += '.  '
+                        current_line += f"{k2}: " + k2_values_str
+                    else:
+                        table_add += ' '*5
+                        if i == 0:
+                            # Only add dash to first
+                            table_add += "- "
+                        else:
+                            table_add += "  "
+
+                        if '\n' in k2_values_str:
+                            # Block format multiple lines
+                            table_add += '| ' + f"{k2}: " + "\n"
+                            k2_values_str_lines = k2_values_str.split('\n')
+                            for j in range(len(k2_values_str_lines)):
+                                line = k2_values_str_lines[j]
+                                table_add += ' '*7 + '|' + ' '*5 + line
+                                if j < len(k2_values_str_lines)-1:
+                                    table_add += "\n"
+                        else:
+                            if block_format:
+                                table_add += '| '
+                            else:
+                                table_add += '* '
+                            table_add += f"{k2}: " + k2_values_str
+
+                        table_add += "\n"
+                if current_line != '':
+                    table_add += current_line + '\n'
+            else:
+                table_add += ' '*5 + f"- {values}\n"
+
+            table += table_add
+
+        else:
+            lengths = [len(str(v)) for v in values]
+            if bullets and max(lengths) > 5:
+                table += ' '*5 + "-\n"
+                for value in sorted(values):
+                    table += ' '*7 + f"- {value}\n"
+            else:
+                value_str = ', '.join(sorted(values))
+                table += ' '*5 + f"- {value_str}\n"
+
+    return table
--- a/yfinance/version.py
+++ b/yfinance/version.py
@@ -1 +1 @@
-version = "0.2.47"
+version = "0.2.53"
Author	SHA1	Message	Date
ValueRaider	1ddc38c04f	Version 0.2.53	2025-02-15 18:16:37 +00:00
ValueRaider	fcab4ea37e	Merge pull request #2264 from ranaroussi/dev sync dev -> main	2025-02-15 18:14:44 +00:00
ValueRaider	d40ad6282a	Merge pull request #2260 from ranaroussi/fix/price-div-repair-tweaks Improve prices div repair	2025-02-15 17:25:21 +00:00
ValueRaider	c95c02fdfc	Improve prices div repair Extend div-repair to intraday Increase lookbacks for 'div date wrong' and 'phantom div' Slightly loosen default 'false threshold' for cluster of mixed False/True Fix bugs in: - detect 'phantom div' - handle div-too-big-and-missing-split Stop checking for down-changes after negative adj-close Handle avg-volume=0%	2025-02-15 17:10:07 +00:00
ValueRaider	5fb9cab817	Disable pytest.yml, need to mock all GETs to restore	2025-02-15 17:00:38 +00:00
ValueRaider	1c39ca4762	Merge pull request #2256 from dhruvan2006/fix/multi-bugs Fix: Interval check and error message formatting in `multi.py`	2025-02-02 17:32:47 +00:00
Dhruvan Gnanadhandayuthapani	27868a4677	Fix interval check and error message formatting in multi.py	2025-02-02 14:15:50 +01:00
ValueRaider	0da75493dc	Merge pull request #2233 from dhruvan2006/fix/tests Add GitHub Actions workflow and fix failing tests	2025-02-01 14:59:14 +00:00
ValueRaider	14c6d05854	Merge pull request #2253 from dhruvan2006/fix/doc-see-also-links Fix: Broken "See also" links in documentation	2025-02-01 13:31:23 +00:00
ValueRaider	8e369a2736	Merge pull request #2250 from R5dan/fix-market-doc Fix Market Docs	2025-02-01 13:25:38 +00:00
R5dan	e09f77eb06	Fix	2025-01-31 22:45:08 +00:00
R5dan	7db798e900	Fix docs	2025-01-31 22:45:08 +00:00
ValueRaider	3b7758b3ab	Version 0.2.52	2025-01-31 22:42:19 +00:00
Dhruvan Gnanadhandayuthapani	b729bcb1df	Fix broken 'See also' links in docs	2025-01-31 15:12:36 +01:00
ValueRaider	6d7c5b6f4c	Merge pull request #2249 from ranaroussi/fix/cookie-csrf-exception Stop CSRF-cookie-fetch fail killing yfinance	2025-01-30 20:25:32 +00:00
ValueRaider	45568fd765	Stop CSRF-cookie-fetch fail killing yfinance	2025-01-30 20:20:43 +00:00
ValueRaider	fc020d953a	Merge pull request #2247 from dhruvan2006/refactor/earnings-dates Refactor: Earnings Dates – Switch to API Fetching	2025-01-29 21:55:27 +00:00
Dhruvan Gnanadhandayuthapani	bc645bb7dd	Refactor earnings dates to use API instead of HTML	2025-01-29 21:53:58 +01:00
Dhruvan Gnanadhandayuthapani	74198aeed4	Add new test workflow	2025-01-27 23:53:36 +01:00
Dhruvan Gnanadhandayuthapani	890922f68b	Fix tests	2025-01-27 23:53:29 +01:00
ValueRaider	44ef4a73ac	Merge pull request #2244 from dhruvan2006/fix/holders-bad-data Fix: Bad data in Holders	2025-01-26 21:23:19 +00:00
ValueRaider	c745df183a	Merge pull request #2212 from ocp1006/ocp1006/fix_pre_post_fetch Add pre- / post-stock prices (and other useful information)	2025-01-26 21:19:54 +00:00
Dhruvan Gnanadhandayuthapani	0e069dbbff	Fix bad data in Holders	2025-01-26 21:27:43 +01:00
ValueRaider	25144351a4	Merge pull request #2230 from ranaroussi/feature/download-warn-autoadjust Warn user when use download() without specifying auto_adjust	2025-01-25 20:59:14 +00:00
ValueRaider	3dd7e40df2	Merge pull request #2234 from dhruvan2006/fix/holders Fix: Failed to parse holders JSON data	2025-01-22 21:50:05 +00:00
Dhruvan Gnanadhandayuthapani	5bbcedfe2e	Fix failed to parse holders JSON data	2025-01-22 19:31:16 +01:00
ValueRaider	fc049e1e0d	Warn user when use download() without specifying auto_adjust	2025-01-19 20:30:35 +00:00
ValueRaider	00a44a8132	Fix dead links in docs API index.rst	2025-01-18 16:28:23 +00:00
ValueRaider	4ec93d90f1	Version 0.2.52	2025-01-18 16:14:36 +00:00
ValueRaider	1081f1e809	Merge pull request #2226 from ranaroussi/dev sync dev -> main	2025-01-18 16:09:49 +00:00
ValueRaider	fed35b175f	Fix typo in screener RST doc file	2025-01-18 16:08:48 +00:00
ValueRaider	3f6a46d77d	Merge pull request #2207 from ranaroussi/feature/improve-screener-docs Improve Screener docs	2025-01-18 11:02:35 +00:00
ValueRaider	dee9a55e43	Simplify Screener & improve its docs - screener now just a function, not a class - add 'FundQuery' query class - add 'IS-IN' operator - fix 'GTE' & 'LTE' operators - more exchanges Predefined tweaks: - convert predefined query strings to use EquityQuery (better docs) - send predefined queries to Yahoo's predefined endpoint - expose PREDEFINED_SCREENER_QUERIES via __init__.py - screen() argument defaults don't apply to predefineds	2025-01-17 22:15:18 +00:00
ValueRaider	20b3b438e2	Merge pull request #2211 from dhruvan2006/fix/custom-period Fix: Restore dev branch regression with Ticker.history()	2025-01-11 20:57:26 +00:00
ocp1006	0be2ce1fd5	Add pre- / post-stock prices (and other useful information)	2025-01-08 20:11:26 +02:00
Dhruvan Gnanadhandayuthapani	2d60f9ad2a	Fix custom periods	2025-01-07 13:54:50 +01:00
Dhruvan Gnanadhandayuthapani	2919ccb732	Add utils method for validating period	2025-01-07 13:52:40 +01:00
ValueRaider	ffa697cea1	Merge pull request #2202 from dhruvan2006/fix/search-quotes Fix: Extra key in Search quotes	2025-01-05 15:09:31 +00:00
Dhruvan Gnanadhandayuthapani	e3aae8a8ba	Expand Search test suite	2025-01-03 00:35:53 +01:00
Dhruvan Gnanadhandayuthapani	4ded15740e	Fix extra row in search quotes	2025-01-03 00:33:50 +01:00
ValueRaider	506f792772	Merge pull request #2199 from dhruvan2006/fix/requirements Added missing extra dependencies to requirements.txt	2025-01-02 21:23:27 +00:00
Dhruvan Gnanadhandayuthapani	5a342cf8b0	Add missing dependencies to requirements.txt	2025-01-02 15:06:22 +01:00
ValueRaider	52f6462d20	Merge pull request #2194 from dhruvan2006/fix/eps-revisions Fix: Remove hardcoded keys in Analysis	2025-01-01 21:00:33 +00:00
ValueRaider	508afcae23	Analysis: refactor some fetchers, fix tests Refactor some fetch functions (duplicate logic). Fix failing unit tests.	2025-01-01 20:57:55 +00:00
Dhruvan Gnanadhandayuthapani	a9859e2f9f	Remove hardcoded keys in Analysis	2024-12-31 17:42:03 +01:00
ValueRaider	d0e59203cc	Merge pull request #2191 from R5dan/Update-Search Update Search class to allow more options	2024-12-30 15:09:08 +00:00
R5dan	b94173bddf	Update Search class to allow more options	2024-12-30 15:08:19 +00:00
ValueRaider	a928eb45fd	Merge pull request #2192 from R5dan/custom-period Custom period	2024-12-30 15:03:54 +00:00
R5dan	60c7477a03	Custom period	2024-12-30 15:02:37 +00:00
ValueRaider	42d946cee1	Fix TZ-fetch hiding 429 rate-limit error	2024-12-29 18:34:41 +00:00
ValueRaider	24958d5442	Merge pull request #2175 from R5dan/market-summary-v2 Add market Summary	2024-12-29 17:49:05 +00:00
R5dan	966473f7c7	Add Market summary & status	2024-12-29 17:33:51 +00:00
ValueRaider	ae7f371ada	Merge pull request #2180 from dhruvan2006/rate-limit-exception Raise Exception if Rate Limited	2024-12-22 13:12:11 +00:00
Dhruvan Gnanadhandayuthapani	af7806640f	Raise error if rate limited	2024-12-22 11:12:19 +01:00
ValueRaider	64b4726c4e	Merge pull request #2184 from ranaroussi/main sync main -> dev	2024-12-20 21:39:14 +00:00
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	dfe3ec8df7	Version 0.2.49	2024-11-10 21:37:06 +00:00
ValueRaider	bc52389135	Merge pull request #2122 from ranaroussi/hotfix/prices-missing-latest-day Fix prices-clean discarding both duplicate last rows instead of 1/2	2024-11-10 21:32:22 +00:00
ValueRaider	e077a2b50c	Fix prices-clean discarding both duplicate last rows instead of 1/2	2024-11-10 11:14:49 +00:00
ValueRaider	5d8b444281	Action deploy_doc: enable manual trigger	2024-11-05 22:05:45 +00:00
ValueRaider	e27b90d875	Merge pull request #2113 from ericpien/docs Add Sphinx Documentation	2024-11-05 18:56:01 +00:00
Eric Pien	deafc88520	Add Sphinx Documentation - Initial draft of Sphinx documentation using rst - update docstrings - add deploy_doc.yml for automated deployment through GitHub Actions	2024-11-04 13:59:35 -08:00
ValueRaider	c012ceac01	Merge pull request #2111 from ranaroussi/fix/price-repair-fx Price repair: when fix currency, update metadata	2024-11-03 18:25:25 +00:00
ValueRaider	13d006827f	Merge pull request #2109 from ranaroussi/fix/download-tz Fix times appearing in output from yf.download(ignore_tz=True)	2024-11-03 18:24:46 +00:00
ValueRaider	2d5d752b87	Price repair: when fix currency, update metadata	2024-11-03 18:23:36 +00:00
ValueRaider	f05f99c2b8	Merge pull request #2110 from dhruvan2006/fix/multi-level-index-typo Fix typo in multi.py download() docs	2024-11-03 12:04:30 +00:00
Dhruvan Gnanadhandayuthapani	ca7f11bb41	Fix typo in multi.py download()	2024-11-03 12:45:42 +01:00
ValueRaider	4157b9e170	Fix times appearing in output from yf.download(ignore_tz=True)	2024-11-02 12:53:14 +00:00
ValueRaider	0bc22f82bb	Merge pull request #2108 from ranaroussi/main sync main -> dev	2024-11-02 12:38:00 +00:00
Ran Aroussi	5e942fdec4	Refactor multi.py to handle single tickers and rename columns	2024-10-25 19:02:07 +01:00
Ran Aroussi	16072e87df	Bump version to 0.2.48	2024-10-25 18:59:19 +01:00
Ran Aroussi	6946f99e30	Refactor multi.py to handle single tickers and rename columns	2024-10-25 18:59:03 +01:00