diff --git a/README.md b/README.md
index c89d78ef3..9fa7a793e 100644
--- a/README.md
+++ b/README.md
@@ -156,6 +156,31 @@ 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
@@ -198,6 +223,55 @@ 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()
+```
+
 ### 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.
diff --git a/tests/data/4063-T-1d-bad-div-fixed.csv b/tests/data/4063-T-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..c7bae7f79
--- /dev/null
+++ b/tests/data/4063-T-1d-bad-div-fixed.csv
@@ -0,0 +1,668 @@
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diff --git a/tests/data/4063-T-1d-bad-div.csv b/tests/data/4063-T-1d-bad-div.csv
new file mode 100644
index 000000000..1d17ba431
--- /dev/null
+++ b/tests/data/4063-T-1d-bad-div.csv
@@ -0,0 +1,668 @@
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diff --git a/tests/data/AET-L-1d-100x-error-fixed.csv b/tests/data/AET-L-1d-100x-error-fixed.csv
index b1fa16e28..ed6c8e520 100644
--- a/tests/data/AET-L-1d-100x-error-fixed.csv
+++ b/tests/data/AET-L-1d-100x-error-fixed.csv
@@ -1,24 +1,24 @@
 Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
-2022-06-06 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
-2022-06-01 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
-2022-05-31 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
-2022-05-30 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
-2022-05-27 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
-2022-05-26 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
-2022-05-25 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
-2022-05-24 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
-2022-05-23 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
-2022-05-20 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
-2022-05-19 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
-2022-05-18 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,532454,0,0
-2022-05-17 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
-2022-05-16 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
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-2022-05-09 00:00:00+01:00,0.1455,0.1455,0.1455,0.1455,0.1455,0,0,0
-2022-05-06 00:00:00+01:00,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0.145500004291534,0,0,0
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+2022-06-01 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-31 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-30 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-27 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-26 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
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+2022-05-24 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-23 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-20 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-19 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-18 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,532454,0,0
+2022-05-17 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-16 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-13 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
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+2022-05-11 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-10 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-09 00:00:00+01:00,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,14.5500001907349,0,0,0
+2022-05-06 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-05 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
+2022-05-04 00:00:00+01:00,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,14.5500004291534,0,0,0
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diff --git a/tests/data/AET-L-1wk-100x-error-fixed.csv.old b/tests/data/AET-L-1wk-100x-error-fixed.csv.old
deleted file mode 100644
index 4e661b0bd..000000000
--- a/tests/data/AET-L-1wk-100x-error-fixed.csv.old
+++ /dev/null
@@ -1,25 +0,0 @@
-Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
-2022-08-15 00:00:00+01:00,27.6000,28.2000,26.2000,27.6000,27.6000,3535668,0,0
-2022-08-12 00:00:00+01:00,27.3000,29.8000,26.4030,27.0000,27.0000,7223353,0,0
-2022-08-11 00:00:00+01:00,26.0000,29.8000,24.2000,27.1000,27.1000,12887933,0,0
-2022-08-10 00:00:00+01:00,25.0000,29.2000,22.5000,25.0000,25.0000,26572680,0,0
-2022-08-09 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-08-08 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-08-05 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-08-04 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-08-03 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-08-02 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-08-01 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-29 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-28 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-27 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-26 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-25 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-22 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-21 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-20 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-19 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-18 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-15 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-14 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-07-13 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
diff --git a/tests/data/AET-L-1wk-100x-error.csv.old b/tests/data/AET-L-1wk-100x-error.csv.old
deleted file mode 100644
index e629e2644..000000000
--- a/tests/data/AET-L-1wk-100x-error.csv.old
+++ /dev/null
@@ -1,25 +0,0 @@
-Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
-2022-08-15 00:00:00+01:00,27.600000381469727,28.200000762939453,26.200000762939453,27.600000381469727,27.600000381469727,3535668,0.0,0.0
-2022-08-12 00:00:00+01:00,27.299999237060547,29.799999237060547,26.402999877929688,27.0,27.0,7223353,0.0,0.0
-2022-08-11 00:00:00+01:00,26.0,29.799999237060547,24.200000762939453,27.100000381469727,27.100000381469727,12887933,0.0,0.0
-2022-08-10 00:00:00+01:00,25.0,29.200000762939453,22.5,25.0,25.0,26572680,0.0,0.0
-2022-08-09 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
-2022-08-08 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
-2022-08-05 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
-2022-08-04 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
-2022-08-03 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
-2022-08-02 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
-2022-08-01 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
-2022-07-29 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-28 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-27 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-26 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-25 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-22 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-21 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-20 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-19 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-18 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-15 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-14 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-07-13 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
diff --git a/tests/data/LSC-L-1d-bad-div-fixed.csv b/tests/data/LSC-L-1d-bad-div-fixed.csv
index a8e3d1ac6..114741dbd 100644
--- a/tests/data/LSC-L-1d-bad-div-fixed.csv
+++ b/tests/data/LSC-L-1d-bad-div-fixed.csv
@@ -12,70 +12,70 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
 2022-01-18 00:00:00+00:00,0.275,0.275,0.275,0.275,0.2529500382534256,0,0.0,0.0,True
 2022-01-19 00:00:00+00:00,0.275,0.275,0.275,0.275,0.2529500382534256,0,0.0,0.0,True
 2022-01-20 00:00:00+00:00,0.275,0.27299999237060546,0.27299999237060546,0.275,0.2529500382534256,36,0.0,0.0,True
-2022-01-21 00:00:00+00:00,0.275,0.29,0.29,0.28500000000000003,0.2621482159594605,206,0.0,0.0,True
-2022-01-24 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-01-25 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-01-26 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-01-27 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-01-28 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-01-31 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-01 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-02 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-03 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-04 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-07 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-08 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-09 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-10 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.2621482159594605,0,0.0,0.0,True
-2022-02-11 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True
-2022-02-14 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True
-2022-02-15 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True
-2022-02-16 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True
-2022-02-17 00:00:00+00:00,0.29,0.29,0.29,0.29,0.2667473048124779,0,0.0,0.0,True
-2022-02-18 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-21 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-22 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-23 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-24 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-25 00:00:00+00:00,0.295,0.295,0.295,0.295,0.27134641258267866,0,0.0,0.0,True
-2022-02-28 00:00:00+00:00,0.295,0.3,0.29,0.305,0.28054459028871354,119,0.0,0.0,True
-2022-03-01 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28054459028871354,0,0.0,0.0,True
-2022-03-02 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28054459028871354,0,0.0,0.0,True
-2022-03-03 00:00:00+00:00,0.305,0.32,0.32,0.315,0.28974278691193167,100,0.0,0.0,True
-2022-03-04 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-07 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-08 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-09 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-10 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-11 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-14 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-15 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
-2022-03-16 00:00:00+00:00,0.315,0.315,0.315,0.315,0.28974278691193167,0,0.0,0.0,True
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@@ -122,89 +122,89 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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@@ -226,7 +226,7 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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 2022-12-01 00:00:00+00:00,0.26,0.26,0.26,0.26,0.24591193824852078,0,0.0,0.0,True
@@ -242,8 +242,8 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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@@ -251,77 +251,77 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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+2023-03-14 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-03-15 00:00:00+00:00,0.305,0.2929999923706055,0.2929999923706055,0.305,0.28847362498050666,25,0.0,0.0,True
+2023-03-16 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-03-17 00:00:00+00:00,0.315,0.31,0.31,0.305,0.28847362498050666,15,0.0,0.0,True
+2023-03-20 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-03-21 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-03-22 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-03-23 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-03-24 00:00:00+00:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-03-27 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-03-28 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-03-29 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-03-30 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-03-31 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-04-03 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-04-04 00:00:00+01:00,0.305,0.31,0.31,0.305,0.28847362498050666,4,0.0,0.0,True
+2023-04-05 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-04-06 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-04-11 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-04-12 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-04-13 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-04-14 00:00:00+01:00,0.305,0.305,0.305,0.305,0.28847362498050666,0,0.0,0.0,True
+2023-04-17 00:00:00+01:00,0.315,0.31,0.2929999923706055,0.305,0.28847362498050666,35,0.0,0.0,True
 2023-04-18 00:00:00+01:00,0.305,0.30950000762939456,0.30950000762939456,0.31,0.2932027039691775,24,0.0,0.0,True
 2023-04-19 00:00:00+01:00,0.31,0.31,0.31,0.31,0.2932027039691775,0,0.0,0.0,True
 2023-04-20 00:00:00+01:00,0.31,0.31,0.31,0.31,0.2932027039691775,0,0.0,0.0,True
@@ -343,12 +343,12 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
 2023-05-16 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2600991752145336,0,0.0,0.0,True
 2023-05-17 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2600991752145336,0,0.0,0.0,True
 2023-05-18 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2600991752145336,0,0.0,0.0,True
-2023-05-19 00:00:00+01:00,0.275,0.29649999618530276,0.29649999618530276,0.28,0.264828230037152,33,0.0,0.0,True
-2023-05-22 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True
-2023-05-23 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True
-2023-05-24 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True
-2023-05-25 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True
-2023-05-26 00:00:00+01:00,0.28,0.28,0.28,0.28,0.264828230037152,0,0.0,0.0,True
+2023-05-19 00:00:00+01:00,0.275,0.29649999618530276,0.29649999618530276,0.28,0.26482825420320455,33,0.0,0.0,True
+2023-05-22 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True
+2023-05-23 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True
+2023-05-24 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True
+2023-05-25 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True
+2023-05-26 00:00:00+01:00,0.28,0.28,0.28,0.28,0.26482825420320455,0,0.0,0.0,True
 2023-05-30 00:00:00+01:00,0.29,0.29,0.29,0.29,0.27428638801449384,0,0.0,0.0,True
 2023-05-31 00:00:00+01:00,0.29,0.29,0.29,0.29,0.27428638801449384,0,0.0,0.0,True
 2023-06-01 00:00:00+01:00,0.29,0.29,0.29,0.29,0.27428638801449384,0,0.0,0.0,True
@@ -662,4 +662,10 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
 2024-08-16 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0,False
 2024-08-19 00:00:00+01:00,37.5,38.1,38.1,39.0,39.0,14,0.0,0.0,False
 2024-08-20 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0,False
-2024-08-21 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0,False
+2024-08-21 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-22 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-23 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-27 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-28 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-29 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
+2024-08-30 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0,False
diff --git a/tests/data/LSC-L-1d-bad-div.csv b/tests/data/LSC-L-1d-bad-div.csv
index dc5f5bcc1..2e0923a74 100644
--- a/tests/data/LSC-L-1d-bad-div.csv
+++ b/tests/data/LSC-L-1d-bad-div.csv
@@ -12,70 +12,70 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2022-01-18 00:00:00+00:00,0.275,0.275,0.275,0.275,0.01594000220298767,0,0.0,0.0
 2022-01-19 00:00:00+00:00,0.275,0.275,0.275,0.275,0.01594000220298767,0,0.0,0.0
 2022-01-20 00:00:00+00:00,0.275,0.27299999237060546,0.27299999237060546,0.275,0.01594000220298767,36,0.0,0.0
-2022-01-21 00:00:00+00:00,0.275,0.29,0.29,0.28500000000000003,0.016519638299942015,206,0.0,0.0
-2022-01-24 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
-2022-01-25 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
-2022-01-26 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
-2022-01-27 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
-2022-01-28 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
-2022-01-31 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
-2022-02-01 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
-2022-02-02 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
-2022-02-03 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
-2022-02-04 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
-2022-02-07 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
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-2022-02-10 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.016519638299942015,0,0.0,0.0
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-2022-02-16 00:00:00+00:00,0.29,0.29,0.29,0.29,0.01680945634841919,0,0.0,0.0
-2022-02-17 00:00:00+00:00,0.29,0.29,0.29,0.29,0.01680945634841919,0,0.0,0.0
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@@ -122,89 +122,89 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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 2022-10-28 00:00:00+01:00,0.245,0.26,0.26,0.26,-0.12558789253234864,200,0.0,0.0
 2022-10-31 00:00:00+00:00,0.26,0.26,0.26,0.26,-0.12558789253234864,0,0.0,0.0
 2022-11-01 00:00:00+00:00,0.26,0.26,0.26,0.26,-0.12558789253234864,0,0.0,0.0
@@ -226,7 +226,7 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2022-11-23 00:00:00+00:00,0.255,0.22670000076293945,0.215,0.23,0.17169532775878907,1258,0.0,0.0
 2022-11-24 00:00:00+00:00,0.225,0.225,0.225,0.225,0.16796281814575195,0,0.0,0.0
 2022-11-25 00:00:00+00:00,0.225,0.225,0.225,0.225,0.16796281814575195,0,0.0,0.0
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+2022-11-28 00:00:00+00:00,0.225,0.24760000228881837,0.23450000762939455,0.24700000762939453,0.18438585281372072,912,0.0,0.0
 2022-11-29 00:00:00+00:00,0.24700000762939453,0.2670000076293945,0.24,0.26,0.1940903663635254,201,0.0,0.0
 2022-11-30 00:00:00+00:00,0.26,0.26,0.26,0.26,0.1940903663635254,0,0.0,0.0
 2022-12-01 00:00:00+00:00,0.26,0.26,0.26,0.26,0.1940903663635254,0,0.0,0.0
@@ -242,8 +242,8 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2022-12-15 00:00:00+00:00,0.27,0.27,0.27,0.27,0.20155538558959962,0,0.0,0.0
 2022-12-16 00:00:00+00:00,0.27,0.27,0.27,0.27,0.20155538558959962,0,0.0,0.0
 2022-12-19 00:00:00+00:00,0.27,0.27,0.27,0.27,0.20155538558959962,0,0.0,0.0
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+2022-12-21 00:00:00+00:00,0.28,0.28,0.28,0.28,0.20902040481567383,0,0.0,0.0
 2022-12-22 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0
 2022-12-23 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0
 2022-12-28 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0
@@ -251,77 +251,77 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2022-12-30 00:00:00+00:00,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.28500000000000003,0.21275291442871094,0,0.0,0.0
 2023-01-03 00:00:00+00:00,0.28500000000000003,0.2720000076293945,0.2720000076293945,0.28500000000000003,0.21275291442871094,15,0.0,0.0
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@@ -343,12 +343,12 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2023-05-16 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2052878952026367,0,0.0,0.0
 2023-05-17 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2052878952026367,0,0.0,0.0
 2023-05-18 00:00:00+01:00,0.275,0.275,0.275,0.275,0.2052878952026367,0,0.0,0.0
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 2023-05-30 00:00:00+01:00,0.29,0.29,0.29,0.29,0.2164854049682617,0,0.0,0.0
 2023-05-31 00:00:00+01:00,0.29,0.29,0.29,0.29,0.2164854049682617,0,0.0,0.0
 2023-06-01 00:00:00+01:00,0.29,0.29,0.29,0.29,0.2164854049682617,0,0.0,0.0
@@ -662,4 +662,10 @@ Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
 2024-08-16 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0
 2024-08-19 00:00:00+01:00,37.5,38.1,38.1,39.0,39.0,14,0.0,0.0
 2024-08-20 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0
-2024-08-21 00:00:00+01:00,39.0,39.0,39.0,39.0,39.0,0,0.0,0.0
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+2024-08-29 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0
+2024-08-30 00:00:00+01:00,0.39,0.39,0.39,0.39,0.39,0,0.0,0.0
diff --git a/tests/data/NPK-1d-no-bad-divs.csv b/tests/data/NPK-1d-no-bad-divs.csv
deleted file mode 100644
index 294cdd0d8..000000000
--- a/tests/data/NPK-1d-no-bad-divs.csv
+++ /dev/null
@@ -1,663 +0,0 @@
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diff --git a/tests/data/NVT-L-1d-bad-div-fixed.csv b/tests/data/NVT-L-1d-bad-div-fixed.csv
index c86b08d88..6af186ea3 100644
--- a/tests/data/NVT-L-1d-bad-div-fixed.csv
+++ b/tests/data/NVT-L-1d-bad-div-fixed.csv
@@ -1,666 +1,686 @@
-Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
-2022-01-04 00:00:00+00:00,0.6875,0.6940000152587891,0.675,0.6875,0.5880745263310649,14877,0.0,0.0,True
-2022-01-05 00:00:00+00:00,0.6875,0.6751000213623047,0.675,0.6875,0.5880745263310649,15180,0.0,0.0,True
-2022-01-06 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-01-07 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-01-10 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-01-11 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-01-12 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-01-13 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-01-14 00:00:00+00:00,0.6875,0.6940000152587891,0.675,0.6875,0.5880745263310649,10268,0.0,0.0,True
-2022-01-17 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880745263310649,1004,0.0,0.0,True
-2022-01-18 00:00:00+00:00,0.6875,0.6940000152587891,0.675,0.6875,0.5880745263310649,15202,0.0,0.0,True
-2022-01-19 00:00:00+00:00,0.6875,0.675,0.67,0.6875,0.5880745263310649,26258,0.0,0.0,True
-2022-01-20 00:00:00+00:00,0.6875,0.675,0.675,0.6875,0.5880745263310649,11286,0.0,0.0,True
-2022-01-21 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880745263310649,653,0.0,0.0,True
-2022-01-24 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880745263310649,300,0.0,0.0,True
-2022-01-25 00:00:00+00:00,0.6875,0.67,0.67,0.6825,0.5837975216144157,18000,0.0,0.0,True
-2022-01-26 00:00:00+00:00,0.6825,0.6940000152587891,0.6940000152587891,0.6825,0.5837975216144157,1560,0.0,0.0,True
-2022-01-27 00:00:00+00:00,0.6825,0.67,0.67,0.6825,0.5837975216144157,1000,0.0,0.0,True
-2022-01-28 00:00:00+00:00,0.6825,0.67,0.67,0.6825,0.5837975216144157,5588,0.0,0.0,True
-2022-01-31 00:00:00+00:00,0.6825,0.6825,0.6825,0.6825,0.5837975216144157,0,0.0,0.0,True
-2022-02-01 00:00:00+00:00,0.6825,0.66,0.65,0.66,0.5645515349551748,30612,0.0,0.0,True
-2022-02-02 00:00:00+00:00,0.66,0.65,0.65,0.66,0.5645515349551748,9000,0.0,0.0,True
-2022-02-03 00:00:00+00:00,0.67,0.68,0.67,0.6775,0.579520664364161,19301,0.0,0.0,True
-2022-02-04 00:00:00+00:00,0.6775,0.67,0.67,0.6775,0.579520664364161,29455,0.0,0.0,True
-2022-02-07 00:00:00+00:00,0.6775,0.67,0.67,0.6775,0.579520664364161,3125,0.0,0.0,True
-2022-02-08 00:00:00+00:00,0.6775,0.685,0.67,0.6775,0.579520664364161,15202,0.0,0.0,True
-2022-02-09 00:00:00+00:00,0.6775,0.6775,0.6775,0.6775,0.579520664364161,0,0.0,0.0,True
-2022-02-10 00:00:00+00:00,0.6775,0.67,0.67,0.6775,0.579520664364161,3506,0.0,0.0,True
-2022-02-11 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-02-14 00:00:00+00:00,0.6875,0.68,0.68,0.6875,0.5880745263310649,2461,0.0,0.0,True
-2022-02-15 00:00:00+00:00,0.6875,0.68,0.68,0.6875,0.5880745263310649,19756,0.0,0.0,True
-2022-02-16 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-02-17 00:00:00+00:00,0.6875,0.6825,0.6825,0.6875,0.5880745263310649,7275,0.0,0.0,True
-2022-02-18 00:00:00+00:00,0.6875,0.68,0.675,0.6875,0.5880745263310649,45627,0.0,0.0,True
-2022-02-21 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-02-22 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-02-23 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-02-24 00:00:00+00:00,0.6875,0.6940000152587891,0.6940000152587891,0.6875,0.5880745263310649,14323,0.0,0.0,True
-2022-02-25 00:00:00+00:00,0.6875,0.6827999877929688,0.6827999877929688,0.6875,0.5880745263310649,28473,0.0,0.0,True
-2022-02-28 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-03-01 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-03-02 00:00:00+00:00,0.6875,0.6825,0.6825,0.6875,0.5880745263310649,1559,0.0,0.0,True
-2022-03-03 00:00:00+00:00,0.6875,0.67,0.67,0.6875,0.5880745263310649,1524,0.0,0.0,True
-2022-03-04 00:00:00+00:00,0.6875,0.6809999847412109,0.6809999847412109,0.6875,0.5880745263310649,718,0.0,0.0,True
-2022-03-07 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-03-08 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-03-09 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-03-10 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-03-11 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-03-14 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-03-15 00:00:00+00:00,0.6875,0.68,0.65,0.6875,0.5880745263310649,1430,0.0,0.0,True
-2022-03-16 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-03-17 00:00:00+00:00,0.6875,0.6875,0.6875,0.6875,0.5880745263310649,0,0.0,0.0,True
-2022-03-18 00:00:00+00:00,0.665,0.655,0.655,0.66,0.5645515349551748,2500,0.0,0.0,True
-2022-03-21 00:00:00+00:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-03-22 00:00:00+00:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-03-23 00:00:00+00:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-03-24 00:00:00+00:00,0.66,0.65,0.65,0.66,0.5645515349551748,24314,0.0,0.0,True
-2022-03-25 00:00:00+00:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-03-28 00:00:00+01:00,0.66,0.6552999877929687,0.6552999877929687,0.66,0.5645515349551748,16749,0.0,0.0,True
-2022-03-29 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-03-30 00:00:00+01:00,0.66,0.665,0.65,0.66,0.5645515349551748,38438,0.0,0.0,True
-2022-03-31 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-04-01 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-04-04 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-04-05 00:00:00+01:00,0.66,0.66,0.65,0.66,0.5645515349551748,16181,0.0,0.0,True
-2022-04-06 00:00:00+01:00,0.66,0.65,0.65,0.66,0.5645515349551748,672,0.0,0.0,True
-2022-04-07 00:00:00+01:00,0.66,0.66,0.6501000213623047,0.66,0.5645515349551748,55267,0.0,0.0,True
-2022-04-08 00:00:00+01:00,0.66,0.6598000335693359,0.6598000335693359,0.66,0.5645515349551748,1496,0.0,0.0,True
-2022-04-11 00:00:00+01:00,0.66,0.659000015258789,0.65,0.66,0.5645515349551748,10068,0.0,0.0,True
-2022-04-12 00:00:00+01:00,0.66,0.6588999938964843,0.6588999938964843,0.66,0.5645515349551748,7588,0.0,0.0,True
-2022-04-13 00:00:00+01:00,0.66,0.658499984741211,0.658499984741211,0.66,0.5645515349551748,1511,0.0,0.0,True
-2022-04-14 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-04-19 00:00:00+01:00,0.66,0.6583999633789063,0.65,0.66,0.5645515349551748,12524,0.0,0.0,True
-2022-04-20 00:00:00+01:00,0.66,0.6583999633789063,0.6583999633789063,0.66,0.5645515349551748,580,0.0,0.0,True
-2022-04-21 00:00:00+01:00,0.66,0.6583999633789063,0.64,0.66,0.5645515349551748,22992,0.0,0.0,True
-2022-04-22 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-04-25 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-04-26 00:00:00+01:00,0.66,0.6580000305175782,0.6580000305175782,0.66,0.5645515349551748,1500,0.0,0.0,True
-2022-04-27 00:00:00+01:00,0.66,0.64,0.64,0.66,0.5645515349551748,17000,0.0,0.0,True
-2022-04-28 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-04-29 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-05-03 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-05-04 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-05-05 00:00:00+01:00,0.66,0.6576000213623047,0.6576000213623047,0.66,0.5645515349551748,5000,0.0,0.0,True
-2022-05-06 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-05-09 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-05-10 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-05-11 00:00:00+01:00,0.66,0.6576000213623047,0.6576000213623047,0.66,0.5645515349551748,20230,0.0,0.0,True
-2022-05-12 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-05-13 00:00:00+01:00,0.66,0.6573999786376953,0.6573999786376953,0.66,0.5645515349551748,7567,0.0,0.0,True
-2022-05-16 00:00:00+01:00,0.66,0.65,0.65,0.66,0.5645515349551748,5842,0.0,0.0,True
-2022-05-17 00:00:00+01:00,0.66,0.65,0.65,0.66,0.5645515349551748,27137,0.0,0.0,True
-2022-05-18 00:00:00+01:00,0.66,0.65,0.65,0.66,0.5645515349551748,24000,0.0,0.0,True
-2022-05-19 00:00:00+01:00,0.66,0.6569999694824219,0.635,0.66,0.5645515349551748,10757,0.0,0.0,True
-2022-05-20 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-05-23 00:00:00+01:00,0.66,0.6558000183105469,0.6558000183105469,0.66,0.5645515349551748,1517,0.0,0.0,True
-2022-05-24 00:00:00+01:00,0.66,0.635,0.635,0.66,0.5645515349551748,13072,0.0,0.0,True
-2022-05-25 00:00:00+01:00,0.66,0.66,0.66,0.66,0.5645515349551748,0,0.0,0.0,True
-2022-05-26 00:00:00+01:00,0.66,0.64,0.64,0.655,0.560274567105124,7206,0.0,0.0,True
-2022-05-27 00:00:00+01:00,0.655,0.655,0.655,0.655,0.560274567105124,0,0.0,0.0,True
-2022-05-30 00:00:00+01:00,0.655,0.65,0.65,0.655,0.560274567105124,5000,0.0,0.0,True
-2022-05-31 00:00:00+01:00,0.655,0.65,0.65,0.655,0.560274567105124,1518,0.0,0.0,True
-2022-06-01 00:00:00+01:00,0.655,0.65,0.65,0.645,0.5517207788714176,1,0.0,0.0,True
-2022-06-06 00:00:00+01:00,0.645,0.635,0.635,0.645,0.5517207788714176,15500,0.0,0.0,True
-2022-06-07 00:00:00+01:00,0.645,0.635,0.635,0.645,0.5517207788714176,1004,0.0,0.0,True
-2022-06-08 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-09 00:00:00+01:00,0.645,0.635,0.635,0.645,0.5517207788714176,8341,0.0,0.0,True
-2022-06-10 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-13 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-14 00:00:00+01:00,0.645,0.64,0.64,0.645,0.5517207788714176,31092,0.0,0.0,True
-2022-06-15 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-16 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-17 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-20 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-21 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-22 00:00:00+01:00,0.645,0.6498000335693359,0.64,0.645,0.5517207788714176,189418,0.0,0.0,True
-2022-06-23 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-24 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-27 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-28 00:00:00+01:00,0.645,0.64,0.64,0.645,0.5517207788714176,2500,0.0,0.0,True
-2022-06-29 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-06-30 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-07-01 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-07-04 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-07-05 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-07-06 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-07-07 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-07-08 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-07-11 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-07-12 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-07-13 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-07-14 00:00:00+01:00,0.645,0.645,0.645,0.645,0.5517207788714176,0,0.0,0.0,True
-2022-07-15 00:00:00+01:00,0.6375000000000001,0.6375000000000001,0.6375000000000001,0.6375000000000001,0.5453054376961378,0,0.0,0.0,True
-2022-07-18 00:00:00+01:00,0.6375000000000001,0.64,0.6388000106811523,0.6375000000000001,0.5453054376961378,31351,0.0,0.0,True
-2022-07-19 00:00:00+01:00,0.6375000000000001,0.65,0.63,0.6375000000000001,0.5453054376961378,38011,0.0,0.0,True
-2022-07-20 00:00:00+01:00,0.6375000000000001,0.6375000000000001,0.6375000000000001,0.6375000000000001,0.5453054376961378,0,0.0,0.0,True
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diff --git a/tests/data/NVT-L-1d-bad-div.csv b/tests/data/NVT-L-1d-bad-div.csv
index 701299180..64fc266a1 100644
--- a/tests/data/NVT-L-1d-bad-div.csv
+++ b/tests/data/NVT-L-1d-bad-div.csv
@@ -1,666 +1,686 @@
-Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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+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
diff --git a/tests/data/SCR-TO-1d-bad-div-fixed.csv b/tests/data/SCR-TO-1d-bad-div-fixed.csv
index 210b01d25..ca77be86c 100644
--- a/tests/data/SCR-TO-1d-bad-div-fixed.csv
+++ b/tests/data/SCR-TO-1d-bad-div-fixed.csv
@@ -1,663 +1,689 @@
 Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
-2022-01-04 00:00:00-05:00,3.8299999237060547,4.269999980926514,3.8299999237060547,4.0,3.979404256584549,559600,0.0,0.0,True
-2022-01-05 00:00:00-05:00,4.050000190734863,4.199999809265137,4.039999961853027,4.050000190734863,4.029146783672725,467700,0.0,0.0,True
-2022-01-06 00:00:00-05:00,4.150000095367432,4.179999828338623,4.050000190734863,4.090000152587891,4.068941056086924,393300,0.0,0.0,True
-2022-01-07 00:00:00-05:00,4.130000114440918,4.230000019073486,4.070000171661377,4.159999847412109,4.13858051042915,860600,0.0,0.0,True
-2022-01-10 00:00:00-05:00,4.199999809265137,4.199999809265137,4.119999885559082,4.199999809265137,4.178374364937252,281900,0.0,0.0,True
-2022-01-11 00:00:00-05:00,4.199999809265137,4.460000038146973,4.159999847412109,4.420000076293945,4.397241818450103,695900,0.0,0.0,True
-2022-01-12 00:00:00-05:00,4.449999809265137,4.639999866485596,4.449999809265137,4.579999923706055,4.556417654388606,452500,0.0,0.0,True
-2022-01-13 00:00:00-05:00,4.590000152587891,4.639999866485596,4.420000076293945,4.46999979019165,4.44698392763218,373700,0.0,0.0,True
-2022-01-14 00:00:00-05:00,4.480000019073486,4.590000152587891,4.429999828338623,4.559999942779541,4.5365207271345565,339600,0.0,0.0,True
-2022-01-17 00:00:00-05:00,4.570000171661377,4.760000228881836,4.519999980926514,4.679999828338623,4.655902708564958,317900,0.0,0.0,True
-2022-01-18 00:00:00-05:00,4.75,4.78000020980835,4.650000095367432,4.659999847412109,4.6360057813109075,317400,0.0,0.0,True
-2022-01-19 00:00:00-05:00,4.71999979019165,4.730000019073486,4.489999771118164,4.519999980926514,4.496726454720356,642600,0.0,0.0,True
-2022-01-20 00:00:00-05:00,4.400000095367432,4.639999866485596,4.320000171661377,4.539999961853027,4.516623799880505,780900,0.0,0.0,True
-2022-01-21 00:00:00-05:00,4.460000038146973,4.579999923706055,4.369999885559082,4.420000076293945,4.397241818450103,426200,0.0,0.0,True
-2022-01-24 00:00:00-05:00,4.309999942779541,4.400000095367432,4.070000171661377,4.380000114440918,4.357447963942002,391900,0.0,0.0,True
-2022-01-25 00:00:00-05:00,4.400000095367432,4.639999866485596,4.309999942779541,4.579999923706055,4.556417654388606,461500,0.0,0.0,True
-2022-01-26 00:00:00-05:00,4.699999809265137,4.75,4.550000190734863,4.570000171661377,4.54646939971463,229400,0.0,0.0,True
-2022-01-27 00:00:00-05:00,4.590000152587891,4.699999809265137,4.429999828338623,4.480000019073486,4.456932600212255,1782700,0.0,0.0,True
-2022-01-28 00:00:00-05:00,4.539999961853027,4.699999809265137,4.53000020980835,4.690000057220459,4.665851381145033,340300,0.0,0.0,True
-2022-01-31 00:00:00-05:00,4.670000076293945,4.960000038146973,4.670000076293945,4.889999866485596,4.864821489497735,341600,0.0,0.0,True
-2022-02-01 00:00:00-05:00,4.900000095367432,4.900000095367432,4.75,4.809999942779541,4.7852337804815335,245000,0.0,0.0,True
-2022-02-02 00:00:00-05:00,4.789999961853027,4.880000114440918,4.659999847412109,4.75,4.725542580813283,335900,0.0,0.0,True
-2022-02-03 00:00:00-05:00,4.71999979019165,4.800000190734863,4.579999923706055,4.730000019073486,4.705645235653133,567100,0.0,0.0,True
-2022-02-04 00:00:00-05:00,4.760000228881836,4.980000019073486,4.760000228881836,4.880000114440918,4.854873234823759,728600,0.0,0.0,True
-2022-02-07 00:00:00-05:00,4.800000190734863,4.849999904632568,4.639999866485596,4.670000076293945,4.645954453890982,509100,0.0,0.0,True
-2022-02-08 00:00:00-05:00,4.650000095367432,4.650000095367432,4.360000133514404,4.460000038146973,4.437035672958205,520500,0.0,0.0,True
-2022-02-09 00:00:00-05:00,4.46999979019165,4.619999885559082,4.449999809265137,4.550000190734863,4.52657247246058,225400,0.0,0.0,True
-2022-02-10 00:00:00-05:00,4.519999980926514,4.610000133514404,4.449999809265137,4.510000228881836,4.4867782000463805,225300,0.0,0.0,True
-2022-02-11 00:00:00-05:00,4.5,4.630000114440918,4.489999771118164,4.630000114440918,4.60616059938288,380600,0.0,0.0,True
-2022-02-14 00:00:00-05:00,4.5,4.550000190734863,4.400000095367432,4.510000228881836,4.4867782000463805,507600,0.0,0.0,True
-2022-02-15 00:00:00-05:00,4.420000076293945,4.71999979019165,4.420000076293945,4.690000057220459,4.665851381145033,342800,0.0,0.0,True
-2022-02-16 00:00:00-05:00,4.699999809265137,4.800000190734863,4.539999961853027,4.579999923706055,4.556417654388606,508700,0.0,0.0,True
-2022-02-17 00:00:00-05:00,4.599999904632568,4.659999847412109,4.519999980926514,4.570000171661377,4.54646939971463,309900,0.0,0.0,True
-2022-02-18 00:00:00-05:00,4.510000228881836,4.559999942779541,4.380000114440918,4.420000076293945,4.397241818450103,191700,0.0,0.0,True
-2022-02-22 00:00:00-05:00,4.460000038146973,4.599999904632568,4.429999828338623,4.53000020980835,4.506675545206529,1063700,0.0,0.0,True
-2022-02-23 00:00:00-05:00,4.590000152587891,4.739999771118164,4.559999942779541,4.679999828338623,4.655902708564958,256600,0.0,0.0,True
-2022-02-24 00:00:00-05:00,4.699999809265137,4.820000171661377,4.559999942779541,4.670000076293945,4.645954453890982,392200,0.0,0.0,True
-2022-02-25 00:00:00-05:00,4.659999847412109,4.880000114440918,4.630000114440918,4.849999904632568,4.8250272170835355,264400,0.0,0.0,True
-2022-02-28 00:00:00-05:00,4.880000114440918,5.099999904632568,4.869999885559082,5.079999923706055,5.05384376108256,543200,0.0,0.0,True
-2022-03-01 00:00:00-05:00,5.159999847412109,5.28000020980835,5.03000020980835,5.179999828338623,5.153328397352814,481700,0.0,0.0,True
-2022-03-02 00:00:00-05:00,5.239999771118164,5.309999942779541,5.079999923706055,5.179999828338623,5.153328397352814,232200,0.0,0.0,True
-2022-03-03 00:00:00-05:00,5.21999979019165,5.230000019073486,4.949999809265137,4.96999979019165,4.944409616420036,234500,0.0,0.0,True
-2022-03-04 00:00:00-05:00,5.03000020980835,5.389999866485596,5.03000020980835,5.360000133514404,5.332401996357564,1110400,0.0,0.0,True
-2022-03-07 00:00:00-05:00,5.570000171661377,5.849999904632568,5.440000057220459,5.650000095367432,5.620908068400445,738400,0.0,0.0,True
-2022-03-08 00:00:00-05:00,5.829999923706055,5.889999866485596,5.46999979019165,5.679999828338623,5.650754086140669,733300,0.0,0.0,True
-2022-03-09 00:00:00-05:00,5.300000190734863,5.639999866485596,5.199999809265137,5.21999979019165,5.193121833954816,796500,0.0,0.0,True
-2022-03-10 00:00:00-05:00,5.360000133514404,5.449999809265137,5.239999771118164,5.309999942779541,5.282658633457191,454800,0.0,0.0,True
-2022-03-11 00:00:00-05:00,5.289999961853027,5.329999923706055,5.110000133514404,5.25,5.22296785169504,222100,0.0,0.0,True
-2022-03-14 00:00:00-04:00,5.050000190734863,5.099999904632568,4.559999942779541,4.789999961853027,4.765336435321384,642400,0.0,0.0,True
-2022-03-15 00:00:00-04:00,4.53000020980835,4.889999866485596,4.420000076293945,4.610000133514404,4.5862632542227315,594600,0.0,0.0,True
-2022-03-16 00:00:00-04:00,4.579999923706055,4.829999923706055,4.579999923706055,4.679999828338623,4.655902708564958,583800,0.0,0.0,True
-2022-03-17 00:00:00-04:00,4.789999961853027,4.960000038146973,4.739999771118164,4.800000190734863,4.775285525807557,654300,0.0,0.0,True
-2022-03-18 00:00:00-04:00,4.71999979019165,4.920000076293945,4.710000038146973,4.71999979019165,4.695696980979157,284100,0.0,0.0,True
-2022-03-21 00:00:00-04:00,4.829999923706055,5.010000228881836,4.820000171661377,4.980000019073486,4.954357871094012,344500,0.0,0.0,True
-2022-03-22 00:00:00-04:00,4.96999979019165,4.96999979019165,4.820000171661377,4.940000057220459,4.914564016585911,374000,0.0,0.0,True
-2022-03-23 00:00:00-04:00,5.010000228881836,5.099999904632568,4.940000057220459,4.940000057220459,4.914564016585911,535800,0.0,0.0,True
-2022-03-24 00:00:00-04:00,5.0,5.0,4.840000152587891,4.900000095367432,4.874770579983908,385800,0.0,0.0,True
-2022-03-25 00:00:00-04:00,4.849999904632568,5.21999979019165,4.840000152587891,5.179999828338623,5.153328397352814,821200,0.0,0.0,True
-2022-03-28 00:00:00-04:00,4.900000095367432,5.110000133514404,4.900000095367432,5.070000171661377,5.043894670596387,338100,0.0,0.0,True
-2022-03-29 00:00:00-04:00,4.940000057220459,5.230000019073486,4.809999942779541,5.210000038146973,5.183173997186938,628200,0.0,0.0,True
-2022-03-30 00:00:00-04:00,5.269999980926514,5.400000095367432,5.269999980926514,5.369999885559082,5.342349833125442,448200,0.0,0.0,True
-2022-03-31 00:00:00-04:00,5.300000190734863,5.409999847412109,5.260000228881836,5.309999942779541,5.282658633457191,308000,0.0,0.0,True
-2022-04-01 00:00:00-04:00,5.210000038146973,5.400000095367432,5.210000038146973,5.28000020980835,5.252813451529165,279000,0.0,0.0,True
-2022-04-04 00:00:00-04:00,5.349999904632568,5.429999828338623,5.260000228881836,5.300000190734863,5.2727107966893145,298100,0.0,0.0,True
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-2022-04-06 00:00:00-04:00,5.179999828338623,5.309999942779541,5.090000152587891,5.119999885559082,5.093637197684563,395100,0.0,0.0,True
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-2022-04-08 00:00:00-04:00,5.230000019073486,5.400000095367432,5.190000057220459,5.349999904632568,5.322453323777489,281000,0.0,0.0,True
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-2022-04-12 00:00:00-04:00,5.400000095367432,5.5,5.300000190734863,5.329999923706055,5.302556396523439,440400,0.0,0.0,True
-2022-04-13 00:00:00-04:00,5.400000095367432,5.46999979019165,5.309999942779541,5.360000133514404,5.332401996357564,553200,0.0,0.0,True
-2022-04-14 00:00:00-04:00,5.369999885559082,5.510000228881836,5.349999904632568,5.429999828338623,5.402041032793692,399900,0.0,0.0,True
-2022-04-18 00:00:00-04:00,5.460000038146973,5.639999866485596,5.400000095367432,5.550000190734863,5.5214234321301925,412700,0.0,0.0,True
-2022-04-19 00:00:00-04:00,5.489999771118164,5.489999771118164,5.21999979019165,5.329999923706055,5.302556396523439,375600,0.0,0.0,True
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-2022-04-21 00:00:00-04:00,5.289999961853027,5.389999866485596,5.0,5.059999942779541,5.033945998016313,441300,0.0,0.0,True
-2022-04-22 00:00:00-04:00,5.059999942779541,5.059999942779541,4.820000171661377,4.829999923706055,4.805130707735584,444800,0.0,0.0,True
-2022-04-25 00:00:00-04:00,4.610000133514404,4.800000190734863,4.5,4.739999771118164,4.715593908233208,598100,0.0,0.0,True
-2022-04-26 00:00:00-04:00,4.78000020980835,4.929999828338623,4.730000019073486,4.820000171661377,4.795182035155509,362000,0.0,0.0,True
-2022-04-27 00:00:00-04:00,4.820000171661377,4.909999847412109,4.710000038146973,4.880000114440918,4.854873234823759,306800,0.0,0.0,True
-2022-04-28 00:00:00-04:00,4.920000076293945,4.989999771118164,4.800000190734863,4.949999809265137,4.924512271259887,337000,0.0,0.0,True
-2022-04-29 00:00:00-04:00,4.960000038146973,5.0,4.769999980926514,4.820000171661377,4.795182035155509,312400,0.0,0.0,True
-2022-05-02 00:00:00-04:00,4.710000038146973,4.829999923706055,4.630000114440918,4.730000019073486,4.705645235653133,438800,0.0,0.0,True
-2022-05-03 00:00:00-04:00,4.710000038146973,5.03000020980835,4.710000038146973,4.96999979019165,4.944409616420036,675000,0.0,0.0,True
-2022-05-04 00:00:00-04:00,5.0,5.079999923706055,4.900000095367432,5.050000190734863,5.023998579154534,1268500,0.0,0.0,True
-2022-05-05 00:00:00-04:00,5.099999904632568,5.150000095367432,4.860000133514404,5.03000020980835,5.004100816088287,356000,0.0,0.0,True
-2022-05-06 00:00:00-04:00,4.96999979019165,5.139999866485596,4.880000114440918,4.940000057220459,4.914564016585911,250600,0.0,0.0,True
-2022-05-09 00:00:00-04:00,4.78000020980835,4.78000020980835,4.460000038146973,4.579999923706055,4.556417654388606,587200,0.0,0.0,True
-2022-05-10 00:00:00-04:00,4.650000095367432,4.75,4.440000057220459,4.539999961853027,4.516623799880505,359400,0.0,0.0,True
-2022-05-11 00:00:00-04:00,4.670000076293945,4.670000076293945,4.21999979019165,4.329999923706055,4.307705018947728,709200,0.0,0.0,True
-2022-05-12 00:00:00-04:00,4.349999904632568,4.489999771118164,4.25,4.380000114440918,4.357447963942002,564300,0.0,0.0,True
-2022-05-13 00:00:00-04:00,4.429999828338623,4.840000152587891,4.429999828338623,4.769999980926514,4.745439508067333,800600,0.0,0.0,True
-2022-05-16 00:00:00-04:00,4.769999980926514,5.010000228881836,4.760000228881836,4.920000076293945,4.89466708933186,430900,0.0,0.0,True
-2022-05-17 00:00:00-04:00,5.0,5.159999847412109,4.989999771118164,5.130000114440918,5.103585870264637,491800,0.0,0.0,True
-2022-05-18 00:00:00-04:00,5.239999771118164,5.28000020980835,4.949999809265137,5.059999942779541,5.033945998016313,526000,0.0,0.0,True
-2022-05-19 00:00:00-04:00,4.940000057220459,5.28000020980835,4.940000057220459,5.230000019073486,5.203071342347087,440200,0.0,0.0,True
-2022-05-20 00:00:00-04:00,5.179999828338623,5.400000095367432,5.179999828338623,5.349999904632568,5.322453323777489,510600,0.0,0.0,True
-2022-05-24 00:00:00-04:00,5.320000171661377,5.579999923706055,5.300000190734863,5.570000171661377,5.541320777290341,522100,0.0,0.0,True
-2022-05-25 00:00:00-04:00,5.599999904632568,5.760000228881836,5.550000190734863,5.690000057220459,5.660702758720744,634300,0.0,0.0,True
-2022-05-26 00:00:00-04:00,5.849999904632568,5.849999904632568,5.610000133514404,5.610000133514404,5.581114213892345,492900,0.0,0.0,True
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diff --git a/tests/data/SCR-TO-1d-bad-div.csv b/tests/data/SCR-TO-1d-bad-div.csv
index 0daff1558..211d1fcce 100644
--- a/tests/data/SCR-TO-1d-bad-div.csv
+++ b/tests/data/SCR-TO-1d-bad-div.csv
@@ -1,663 +1,689 @@
 Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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-2022-01-10 00:00:00-05:00,4.199999809265137,4.199999809265137,4.119999885559082,4.199999809265137,2.383794069290161,281900,0.0,0.0
-2022-01-11 00:00:00-05:00,4.199999809265137,4.460000038146973,4.159999847412109,4.420000076293945,2.508659601211548,695900,0.0,0.0
-2022-01-12 00:00:00-05:00,4.449999809265137,4.639999866485596,4.449999809265137,4.579999923706055,2.599470615386963,452500,0.0,0.0
-2022-01-13 00:00:00-05:00,4.590000152587891,4.639999866485596,4.420000076293945,4.46999979019165,2.5370378494262695,373700,0.0,0.0
-2022-01-14 00:00:00-05:00,4.480000019073486,4.590000152587891,4.429999828338623,4.559999942779541,2.5881192684173584,339600,0.0,0.0
-2022-01-17 00:00:00-05:00,4.570000171661377,4.760000228881836,4.519999980926514,4.679999828338623,2.6562275886535645,317900,0.0,0.0
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-2022-01-31 00:00:00-05:00,4.670000076293945,4.960000038146973,4.670000076293945,4.889999866485596,2.7754173278808594,341600,0.0,0.0
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diff --git a/tests/data/TENT-L-1d-bad-div-fixed.csv b/tests/data/TENT-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..88cb1d6e8
--- /dev/null
+++ b/tests/data/TENT-L-1d-bad-div-fixed.csv
@@ -0,0 +1,686 @@
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diff --git a/tests/data/TENT-L-1d-bad-div.csv b/tests/data/TENT-L-1d-bad-div.csv
new file mode 100644
index 000000000..e8645439a
--- /dev/null
+++ b/tests/data/TENT-L-1d-bad-div.csv
@@ -0,0 +1,686 @@
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+2024-09-05 00:00:00+01:00,0.7000000000000001,0.7040000152587891,0.6859999847412109,0.6919999694824219,0.45105289459228515,222524,0.0001375,0.0
+2024-09-06 00:00:00+01:00,0.7180000305175781,0.75,0.71,0.715999984741211,0.4666963577270508,1764695,0.0,0.0
+2024-09-09 00:00:00+01:00,0.72,0.7206800079345703,0.71,0.715,0.4660445785522461,450503,0.0,0.0
+2024-09-10 00:00:00+01:00,0.7180000305175781,0.72,0.7119999694824218,0.715999984741211,0.4666963577270508,115944,0.0,0.0
+2024-09-11 00:00:00+01:00,0.7180000305175781,0.72,0.71,0.7130000305175781,0.46474098205566405,237914,0.0,0.0
+2024-09-12 00:00:00+01:00,0.72,0.7219999694824218,0.7119999694824218,0.715999984741211,0.4666963577270508,371034,0.0,0.0
+2024-09-13 00:00:00+01:00,0.72,0.72,0.712979965209961,0.7180000305175781,0.46800003051757816,634981,0.0,0.0
+2024-09-16 00:00:00+01:00,0.7219999694824218,0.7219999694824218,0.7140000152587891,0.7169999694824218,0.46734817504882814,1095043,0.0,0.0
+2024-09-17 00:00:00+01:00,0.7180000305175781,0.7219999694824218,0.7138200378417969,0.7190000152587891,0.46865180969238285,667418,0.0,0.0
+2024-09-18 00:00:00+01:00,0.7219999694824218,0.7219999694824218,0.7140000152587891,0.7180000305175781,0.46800003051757816,1771156,0.0,0.0
+2024-09-19 00:00:00+01:00,0.4640000152587891,0.4909999847412109,0.4520000076293945,0.4640000152587891,0.4640000152587891,794932,0.25,0.0
+2024-09-20 00:00:00+01:00,0.4458200073242188,0.480999984741211,0.4458200073242188,0.46950000762939453,0.46950000762939453,328898,0.0,0.0
diff --git a/tests/test_price_repair.py b/tests/test_price_repair.py
index 514230a1a..2b78766d1 100644
--- a/tests/test_price_repair.py
+++ b/tests/test_price_repair.py
@@ -35,7 +35,6 @@ def test_resampling(self):
                     continue
                 for j in range(i, len(periods)):
                     period = periods[j]
-                    print(f"- interval={interval} period={period}")
 
                     df_truth = dat.history(interval=interval, period=period)
                     # df_1d = dat.history(interval='1d', period=period)
@@ -115,7 +114,6 @@ def test_types(self):
         self.assertIsInstance(reconstructed, _pd.DataFrame, "data has wrong type")
         self.assertFalse(data.empty, "data is empty")
 
-    # pass
     def test_reconstruct_2m(self):
         # 2m repair requires 1m data.
         # Yahoo restricts 1m fetches to 7 days max within last 30 days.
@@ -135,7 +133,6 @@ def test_reconstruct_2m(self):
             start_dt = end_dt - td_60d
             dat.history(start=start_dt, end=end_dt, interval="2m", repair=True)
 
-    # pass
     def test_repair_100x_random_weekly(self):
         # Setup:
         tkr = "PNL.L"
@@ -190,7 +187,6 @@ def test_repair_100x_random_weekly(self):
         self.assertTrue("Repaired?" in df_repaired.columns)
         self.assertFalse(df_repaired["Repaired?"].isna().any())
 
-    # pass
     def test_repair_100x_random_weekly_preSplit(self):
         # PNL.L has a stock-split in 2022. Sometimes requesting data before 2022 is not split-adjusted.
 
@@ -252,7 +248,6 @@ def test_repair_100x_random_weekly_preSplit(self):
         self.assertTrue("Repaired?" in df_repaired.columns)
         self.assertFalse(df_repaired["Repaired?"].isna().any())
 
-    # pass
     def test_repair_100x_random_daily(self):
         tkr = "PNL.L"
         dat = yf.Ticker(tkr, session=self.session)
@@ -301,7 +296,6 @@ def test_repair_100x_random_daily(self):
         self.assertTrue("Repaired?" in df_repaired.columns)
         self.assertFalse(df_repaired["Repaired?"].isna().any())
 
-    # pass
     def test_repair_100x_block_daily(self):
         # Some 100x errors are not sporadic.
         # Sometimes Yahoo suddenly shifts from cents->$ from some recent date.
@@ -361,7 +355,6 @@ def test_repair_100x_block_daily(self):
                 self.assertTrue("Repaired?" in df_repaired.columns)
                 self.assertFalse(df_repaired["Repaired?"].isna().any())
 
-    # pass
     def test_repair_zeroes_daily(self):
         tkr = "BBIL.L"
         dat = yf.Ticker(tkr, session=self.session)
@@ -393,7 +386,6 @@ def test_repair_zeroes_daily(self):
         self.assertTrue("Repaired?" in repaired_df.columns)
         self.assertFalse(repaired_df["Repaired?"].isna().any())
 
-    # pass
     def test_repair_zeroes_daily_adjClose(self):
         # Test that 'Adj Close' is reconstructed correctly,
         # particularly when a dividend occurred within 1 day.
@@ -431,7 +423,6 @@ def test_repair_zeroes_daily_adjClose(self):
                 self.assertTrue("Repaired?" in df_slice_bad_repaired.columns)
                 self.assertFalse(df_slice_bad_repaired["Repaired?"].isna().any())
 
-    # pass
     def test_repair_zeroes_hourly(self):
         tkr = "INTC"
         dat = yf.Ticker(tkr, session=self.session)
@@ -467,7 +458,6 @@ def test_repair_zeroes_hourly(self):
         self.assertTrue("Repaired?" in repaired_df.columns)
         self.assertFalse(repaired_df["Repaired?"].isna().any())
 
-    # pass
     def test_repair_bad_stock_splits(self):
         # Stocks that split in 2022 but no problems in Yahoo data,
         # so repair should change nothing
@@ -562,37 +552,6 @@ def test_repair_bad_stock_splits(self):
                         print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
                         raise
 
-    # pass
-    def test_repair_missing_div_adjust(self):
-        tkr = '8TRA.DE'
-
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        df_bad = _pd.read_csv(os.path.join(self.dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust.csv"), index_col="Date")
-        df_bad.index = _pd.to_datetime(df_bad.index)
-
-        repaired_df = hist._fix_missing_div_adjust(df_bad, "1d", tz_exchange)
-
-        correct_df = _pd.read_csv(os.path.join(self.dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust-fixed.csv"), index_col="Date")
-        correct_df.index = _pd.to_datetime(correct_df.index)
-
-        repaired_df = repaired_df.sort_index()
-        correct_df = correct_df.sort_index()
-        for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
-            try:
-                self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
-            except Exception:
-                print(f"tkr={tkr} COLUMN={c}")
-                print("- repaired_df")
-                print(repaired_df)
-                print("- correct_df[c]:")
-                print(correct_df[c])
-                print("- diff:")
-                print(repaired_df[c] - correct_df[c])
-                raise
-
     def test_repair_bad_div_adjusts(self):
         interval = '1d'
         bad_tkrs = []
@@ -606,11 +565,11 @@ def test_repair_bad_div_adjusts(self):
         # These tickers were exceptionally bad
         bad_tkrs += ['LSC.L']
         bad_tkrs += ['TEM.L']
-        bad_tkrs += ['CLC.L']
 
         # Other special sits
         bad_tkrs += ['KME.MI']  # 2023 dividend paid to savings share, not common/preferred
         bad_tkrs += ['REL.L']  # 100x div also missing adjust
+        bad_tkrs.append('4063.T')  # Div with same-day split not split adjusted
 
         # Adj too small
         bad_tkrs += ['ADIG.L']
@@ -623,8 +582,14 @@ def test_repair_bad_div_adjusts(self):
         bad_tkrs += ['ELCO.L']
         bad_tkrs += ['KWS.L']
         bad_tkrs += ['PSH.L']
+
+        # Div 100x and adjust too big
         bad_tkrs += ['SCR.TO']
 
+        # Div 0.01x
+        bad_tkrs += ['NVT.L']
+        bad_tkrs += ['TENT.L']
+
         # Missing div adjusts:
         bad_tkrs += ['1398.HK']
         bad_tkrs += ['3988.HK']
@@ -652,12 +617,13 @@ def test_repair_bad_div_adjusts(self):
             hist = dat._lazy_load_price_history()
             hist.history(period='1mo')  # init metadata for currency
             currency = hist._history_metadata['currency']
+            tz = hist._history_metadata['exchangeTimezoneName']
 
             fp = os.path.join(self.dp, "data", tkr.replace('.','-') + '-' + interval + "-no-bad-divs.csv")
             if not os.path.isfile(fp):
                 continue
             df = _pd.read_csv(fp, index_col='Datetime')
-            df.index = _pd.to_datetime(df.index, utc=True)
+            df.index = _pd.to_datetime(df.index, utc=True).tz_convert(tz)
 
             repaired_df = hist._fix_bad_div_adjust(df, interval, currency)
 
@@ -683,15 +649,16 @@ def test_repair_bad_div_adjusts(self):
             hist = dat._lazy_load_price_history()
             hist.history(period='1mo')  # init metadata for currency
             currency = hist._history_metadata['currency']
+            tz = hist._history_metadata['exchangeTimezoneName']
 
             fp = os.path.join(self.dp, "data", tkr.replace('.','-') + '-' + interval + "-bad-div.csv")
             if not os.path.isfile(fp):
                 continue
             df_bad = _pd.read_csv(fp, index_col='Datetime')
-            df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
+            df_bad.index = _pd.to_datetime(df_bad.index, utc=True).tz_convert(tz)
             fp = os.path.join(self.dp, "data", tkr.replace('.','-') + '-' + interval + "-bad-div-fixed.csv")
             correct_df = _pd.read_csv(fp, index_col='Datetime')
-            correct_df.index = _pd.to_datetime(correct_df.index, utc=True)
+            correct_df.index = _pd.to_datetime(correct_df.index, utc=True).tz_convert(tz)
 
             repaired_df = hist._fix_bad_div_adjust(df_bad, interval, currency)
 
diff --git a/tests/test_ticker.py b/tests/test_ticker.py
index 6a04661fa..c851c6b37 100644
--- a/tests/test_ticker.py
+++ b/tests/test_ticker.py
@@ -12,7 +12,7 @@
 
 from .context import yfinance as yf
 from .context import session_gbl
-from yfinance.exceptions import YFChartError, YFInvalidPeriodError, YFNotImplementedError, YFTickerMissingError, YFTzMissingError
+from yfinance.exceptions import YFPricesMissingError, YFInvalidPeriodError, YFNotImplementedError, YFTickerMissingError, YFTzMissingError, YFDataException
 
 
 import unittest
@@ -142,14 +142,14 @@ def test_prices_missing(self):
         # META call option, 2024 April 26th @ strike of 180000
         tkr = 'META240426C00180000'
         dat = yf.Ticker(tkr, session=self.session)
-        with self.assertRaises(YFChartError):
+        with self.assertRaises(YFPricesMissingError):
             dat.history(period="5d", interval="1m", raise_errors=True)
 
     def test_ticker_missing(self):
         tkr = 'ATVI'
         dat = yf.Ticker(tkr, session=self.session)
         # A missing ticker can trigger either a niche error or the generalized error
-        with self.assertRaises((YFTickerMissingError, YFTzMissingError, YFChartError)):
+        with self.assertRaises((YFTickerMissingError, YFTzMissingError, YFPricesMissingError)):
             dat.history(period="3mo", interval="1d", raise_errors=True)
 
     def test_goodTicker(self):
@@ -997,7 +997,84 @@ def test_complementary_info(self):
     #                     else:
     #                         raise
 
+class TestTickerFundsData(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def setUp(self):
+        self.test_tickers = [yf.Ticker("SPY", session=self.session),    # equity etf
+                            yf.Ticker("JNK", session=self.session),     # bonds etf
+                            yf.Ticker("VTSAX", session=self.session)]   # mutual fund
+
+    def tearDown(self):
+        self.ticker = None
 
+    def test_fetch_and_parse(self):
+        try:
+            for ticker in self.test_tickers:
+                ticker.funds_data._fetch_and_parse()
+
+        except Exception as e:
+            self.fail(f"_fetch_and_parse raised an exception unexpectedly: {e}")
+
+        with self.assertRaises(YFDataException):
+            ticker = yf.Ticker("AAPL", session=self.session) # stock, not funds
+            ticker.funds_data._fetch_and_parse()
+            self.fail("_fetch_and_parse should have failed when calling for non-funds data")
+
+    def test_description(self):
+        for ticker in self.test_tickers:
+            description = ticker.funds_data.description
+            self.assertIsInstance(description, str)
+            self.assertTrue(len(description) > 0)
+
+    def test_fund_overview(self):
+        for ticker in self.test_tickers:
+            fund_overview = ticker.funds_data.fund_overview
+            self.assertIsInstance(fund_overview, dict)
+
+    def test_fund_operations(self):
+        for ticker in self.test_tickers:
+            fund_operations = ticker.funds_data.fund_operations
+            self.assertIsInstance(fund_operations, pd.DataFrame)
+
+    def test_asset_classes(self):
+        for ticker in self.test_tickers:
+            asset_classes = ticker.funds_data.asset_classes
+            self.assertIsInstance(asset_classes, dict)
+
+    def test_top_holdings(self):
+        for ticker in self.test_tickers:
+            top_holdings = ticker.funds_data.top_holdings
+            self.assertIsInstance(top_holdings, pd.DataFrame)
+
+    def test_equity_holdings(self):
+        for ticker in self.test_tickers:
+            equity_holdings = ticker.funds_data.equity_holdings
+            self.assertIsInstance(equity_holdings, pd.DataFrame)
+
+    def test_bond_holdings(self):
+        for ticker in self.test_tickers:
+            bond_holdings = ticker.funds_data.bond_holdings
+            self.assertIsInstance(bond_holdings, pd.DataFrame)
+
+    def test_bond_ratings(self):
+        for ticker in self.test_tickers:
+            bond_ratings = ticker.funds_data.bond_ratings
+            self.assertIsInstance(bond_ratings, dict)
+
+    def test_sector_weightings(self):
+        for ticker in self.test_tickers:
+            sector_weightings = ticker.funds_data.sector_weightings
+            self.assertIsInstance(sector_weightings, dict)
 
 def suite():
     suite = unittest.TestSuite()
@@ -1007,6 +1084,7 @@ def suite():
     suite.addTest(TestTickerHistory('Test Ticker history'))
     suite.addTest(TestTickerMiscFinancials('Test misc financials'))
     suite.addTest(TestTickerInfo('Test info & fast_info'))
+    suite.addTest(TestTickerFundsData('Test Funds Data'))
     return suite
 
 
diff --git a/yfinance/__init__.py b/yfinance/__init__.py
index 0270e0099..38930e09f 100644
--- a/yfinance/__init__.py
+++ b/yfinance/__init__.py
@@ -25,6 +25,8 @@
 from .multi import download
 from .utils import enable_debug_mode
 from .cache import set_tz_cache_location
+from .domain.sector import Sector
+from .domain.industry import Industry
 
 __version__ = version.version
 __author__ = "Ran Aroussi"
@@ -32,4 +34,4 @@
 import warnings
 warnings.filterwarnings('default', category=DeprecationWarning, module='^yfinance')
 
-__all__ = ['download', 'Ticker', 'Tickers', 'enable_debug_mode', 'set_tz_cache_location']
+__all__ = ['download', 'Ticker', 'Tickers', 'enable_debug_mode', 'set_tz_cache_location', 'Sector', 'Industry']
diff --git a/yfinance/base.py b/yfinance/base.py
index 1a37c845e..96805d317 100644
--- a/yfinance/base.py
+++ b/yfinance/base.py
@@ -38,6 +38,7 @@
 from .scrapers.holders import Holders
 from .scrapers.quote import Quote, FastInfo
 from .scrapers.history import PriceHistory
+from .scrapers.funds import FundsData
 
 from .const import _BASE_URL_, _ROOT_URL_
 
@@ -70,6 +71,7 @@ def __init__(self, ticker, session=None, proxy=None):
         self._holders = Holders(self._data, self.ticker)
         self._quote = Quote(self._data, self.ticker)
         self._fundamentals = Fundamentals(self._data, self.ticker)
+        self._funds_data = None
 
         self._fast_info = None
 
@@ -647,3 +649,9 @@ def get_earnings_dates(self, limit=12, proxy=None) -> Optional[pd.DataFrame]:
 
     def get_history_metadata(self, proxy=None) -> dict:
         return self._lazy_load_price_history().get_history_metadata(proxy)
+
+    def get_funds_data(self, proxy=None) -> Optional[FundsData]:
+        if not self._funds_data:
+            self._funds_data = FundsData(self._data, self.ticker)
+        
+        return self._funds_data
\ No newline at end of file
diff --git a/yfinance/const.py b/yfinance/const.py
index b88558b82..9265397a3 100644
--- a/yfinance/const.py
+++ b/yfinance/const.py
@@ -1,3 +1,4 @@
+_QUERY1_URL_ = 'https://query1.finance.yahoo.com'
 _BASE_URL_ = 'https://query2.finance.yahoo.com'
 _ROOT_URL_ = 'https://finance.yahoo.com'
 
@@ -155,3 +156,152 @@
     "recommendationTrend",
     "futuresChain",
 )
+
+# map last updated as of 2024.09.18
+SECTOR_INDUSTY_MAPPING = {
+    'basic-materials': {'specialty-chemicals',
+                        'gold',
+                        'building-materials',
+                        'copper',
+                        'steel',
+                        'agricultural-inputs',
+                        'chemicals',
+                        'other-industrial-metals-mining',
+                        'lumber-wood-production',
+                        'aluminum',
+                        'other-precious-metals-mining',
+                        'coking-coal',
+                        'paper-paper-products',
+                        'silver'},
+    'communication-services': {'internet-content-information',
+                                'telecom-services',
+                                'entertainment',
+                                'electronic-gaming-multimedia',
+                                'advertising-agencies',
+                                'broadcasting',
+                                'publishing'},
+    'consumer-cyclical': {'internet-retail',
+                            'auto-manufacturers',
+                            'restaurants',
+                            'home-improvement-retail',
+                            'travel-services',
+                            'specialty-retail',
+                            'apparel-retail',
+                            'residential-construction',
+                            'footwear-accessories',
+                            'packaging-containers',
+                            'lodging',
+                            'auto-parts',
+                            'auto-truck-dealerships',
+                            'gambling',
+                            'resorts-casinos',
+                            'leisure',
+                            'apparel-manufacturing',
+                            'personal-services',
+                            'furnishings-fixtures-appliances',
+                            'recreational-vehicles',
+                            'luxury-goods',
+                            'department-stores',
+                            'textile-manufacturing'},
+    'consumer-defensive': {'discount-stores',
+                            'beverages-non-alcoholic',
+                            'household-personal-products',
+                            'packaged-foods',
+                            'tobacco',
+                            'confectioners',
+                            'farm-products',
+                            'food-distribution',
+                            'grocery-stores',
+                            'beverages-brewers',
+                            'education-training-services',
+                            'beverages-wineries-distilleries'},
+    'energy': {'oil-gas-integrated',
+                'oil-gas-midstream',
+                'oil-gas-e-p',
+                'oil-gas-equipment-services',
+                'oil-gas-refining-marketing',
+                'uranium',
+                'oil-gas-drilling',
+                'thermal-coal'},
+    'financial-services': {'banks-diversified',
+                            'credit-services',
+                            'asset-management',
+                            'insurance-diversified',
+                            'banks-regional',
+                            'capital-markets',
+                            'financial-data-stock-exchanges',
+                            'insurance-property-casualty',
+                            'insurance-brokers',
+                            'insurance-life',
+                            'insurance-specialty',
+                            'mortgage-finance',
+                            'insurance-reinsurance',
+                            'shell-companies',
+                            'financial-conglomerates'},
+    'healthcare': {'drug-manufacturers-general',
+                    'healthcare-plans',
+                    'biotechnology',
+                    'medical-devices',
+                    'diagnostics-research',
+                    'medical-instruments-supplies',
+                    'medical-care-facilities',
+                    'drug-manufacturers-specialty-generic',
+                    'health-information-services',
+                    'medical-distribution',
+                    'pharmaceutical-retailers'},
+    'industrials': {'aerospace-defense',
+                    'specialty-industrial-machinery',
+                    'railroads',
+                    'building-products-equipment',
+                    'farm-heavy-construction-machinery',
+                    'specialty-business-services',
+                    'integrated-freight-logistics',
+                    'waste-management',
+                    'conglomerates',
+                    'industrial-distribution',
+                    'engineering-construction',
+                    'rental-leasing-services',
+                    'consulting-services',
+                    'trucking',
+                    'electrical-equipment-parts',
+                    'airlines',
+                    'tools-accessories',
+                    'pollution-treatment-controls',
+                    'security-protection-services',
+                    'marine-shipping',
+                    'metal-fabrication',
+                    'infrastructure-operations',
+                    'staffing-employment-services',
+                    'airports-air-services',
+                    'business-equipment-supplies'},
+    'real-estate': {'reit-specialty',
+                    'reit-industrial',
+                    'reit-retail',
+                    'reit-residential',
+                    'reit-healthcare-facilities',
+                    'real-estate-services',
+                    'reit-office',
+                    'reit-diversified',
+                    'reit-mortgage',
+                    'reit-hotel-motel',
+                    'real-estate-development',
+                    'real-estate-diversified'},
+    'technology': {'software-infrastructure',
+                    'semiconductors',
+                    'consumer-electronics',
+                    'software-application',
+                    'information-technology-services',
+                    'semiconductor-equipment-materials',
+                    'communication-equipment',
+                    'computer-hardware',
+                    'electronic-components',
+                    'scientific-technical-instruments',
+                    'solar',
+                    'electronics-computer-distribution'},
+    'utilities': {'utilities-regulated-electric',
+                    'utilities-renewable',
+                    'utilities-diversified',
+                    'utilities-regulated-gas',
+                    'utilities-independent-power-producers',
+                    'utilities-regulated-water'}
+}
\ No newline at end of file
diff --git a/yfinance/domain/__init__.py b/yfinance/domain/__init__.py
new file mode 100644
index 000000000..304d92f50
--- /dev/null
+++ b/yfinance/domain/__init__.py
@@ -0,0 +1,5 @@
+# domain/__init__.py
+from .sector import Sector
+from .industry import Industry
+
+__all__ = ['Sector', 'Industry']
\ No newline at end of file
diff --git a/yfinance/domain/domain.py b/yfinance/domain/domain.py
new file mode 100644
index 000000000..5ad1ad084
--- /dev/null
+++ b/yfinance/domain/domain.py
@@ -0,0 +1,97 @@
+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:
+    def __init__(self, key: str, session=None, proxy=None):
+        self._key: str = key
+        self.proxy = proxy
+        self.session = session
+        self._data: YfData = YfData(session=session)
+        
+        self._name: Optional[str] = None
+        self._symbol: Optional[str] = None
+        self._overview: Optional[Dict] = None
+        self._top_companies: Optional[_pd.DataFrame] = None
+        self._research_reports: Optional[List[Dict[str, str]]] = None
+    
+    @property
+    def key(self) -> str:
+        return self._key
+    
+    @property
+    def name(self) -> str:
+        self._ensure_fetched(self._name)
+        return self._name
+    
+    @property
+    def symbol(self) -> str:
+        self._ensure_fetched(self._symbol)
+        return self._symbol
+    
+    @property
+    def ticker(self) -> Ticker:
+        self._ensure_fetched(self._symbol)
+        return Ticker(self._symbol)
+    
+    @property
+    def overview(self) -> Dict:
+        self._ensure_fetched(self._overview)
+        return self._overview
+    
+    @property
+    def top_companies(self) -> Optional[_pd.DataFrame]:
+        self._ensure_fetched(self._top_companies)
+        return self._top_companies 
+    
+    @property
+    def research_reports(self) -> List[Dict[str, str]]:
+        self._ensure_fetched(self._research_reports)
+        return self._research_reports
+    
+    def _fetch(self, query_url, proxy) -> Dict:
+        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:
+        self._name = data.get('name')
+        self._symbol = data.get('symbol')
+        self._overview = self._parse_overview(data.get('overview', {}))
+        self._top_companies = self._parse_top_companies(data.get('topCompanies', {}))
+        self._research_reports = data.get('researchReports')
+
+    def _parse_overview(self, overview) -> Dict:
+        return {
+            "companies_count": overview.get('companiesCount', None),
+            "market_cap": overview.get('marketCap', {}).get('raw', None),
+            "message_board_id": overview.get('messageBoardId', None),
+            "description": overview.get('description', None),
+            "industries_count": overview.get('industriesCount', None),
+            "market_weight": overview.get('marketWeight', {}).get('raw', None),
+            "employee_count": overview.get('employeeCount', {}).get('raw', None)
+        }
+
+    def _parse_top_companies(self, top_companies) -> Optional[_pd.DataFrame]:
+        top_companies_column = ['symbol', 'name', 'rating', 'market weight']
+        top_companies_values = [(c.get('symbol'), 
+                                c.get('name'), 
+                                c.get('rating'), 
+                                c.get('marketWeight',{}).get('raw',None)) for c in top_companies]
+
+        if not top_companies_values: 
+            return None
+        
+        return _pd.DataFrame(top_companies_values, columns = top_companies_column).set_index('symbol')
+
+    def _fetch_and_parse(self) -> None:
+        raise NotImplementedError("_fetch_and_parse() needs to be implemented by children classes")
+
+    def _ensure_fetched(self, attribute) -> None:
+        if attribute is None:
+            self._fetch_and_parse()
\ No newline at end of file
diff --git a/yfinance/domain/industry.py b/yfinance/domain/industry.py
new file mode 100644
index 000000000..698bce0d8
--- /dev/null
+++ b/yfinance/domain/industry.py
@@ -0,0 +1,87 @@
+from __future__ import print_function
+from typing import Dict, Optional
+
+import pandas as _pd
+
+from .domain import Domain, _QUERY_URL_
+from .. import utils
+
+class Industry(Domain):
+    def __init__(self, key, session=None, proxy=None):
+        super(Industry, self).__init__(key, session, proxy)
+        self._query_url = f'{_QUERY_URL_}/industries/{self._key}'
+
+        self._sector_key = None
+        self._sector_name = None
+        self._top_performing_companies = None
+        self._top_growth_companies = None
+
+    def __repr__(self):
+        return f'yfinance.Industry object <{self._key}>'
+    
+    @property
+    def sector_key(self) -> str:
+        self._ensure_fetched(self._sector_key)
+        return self._sector_key
+    
+    @property
+    def sector_name(self) -> str:
+        self._ensure_fetched(self._sector_name)
+        return self._sector_name
+    
+    @property
+    def top_performing_companies(self) -> Optional[_pd.DataFrame]:
+        self._ensure_fetched(self._top_performing_companies)
+        return self._top_performing_companies
+    
+    @property
+    def top_growth_companies(self) -> Optional[_pd.DataFrame]:
+        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]:
+        compnaies_column = ['symbol','name','ytd return',' last price','target price']
+        compnaies_values = [(c.get('symbol', None),
+                             c.get('name', None),
+                             c.get('ytdReturn',{}).get('raw', None),
+                             c.get('lastPrice',{}).get('raw', None),
+                             c.get('targetPrice',{}).get('raw', None),) for c in top_performing_companies]
+        
+        if not compnaies_values: 
+            return None
+
+        return _pd.DataFrame(compnaies_values, columns = compnaies_column).set_index('symbol')
+    
+    def _parse_top_growth_companies(self, top_growth_companies: Dict) -> Optional[_pd.DataFrame]:
+        compnaies_column = ['symbol','name','ytd return',' growth estimate']
+        compnaies_values = [(c.get('symbol', None),
+                             c.get('name', None),
+                             c.get('ytdReturn',{}).get('raw', None),
+                             c.get('growthEstimate',{}).get('raw', None),) for c in top_growth_companies]
+        
+        if not compnaies_values: 
+            return None
+
+        return _pd.DataFrame(compnaies_values, columns = compnaies_column).set_index('symbol')
+
+    def _fetch_and_parse(self) -> None:
+        result = None
+        
+        try:
+            result = self._fetch(self._query_url, self.proxy)
+            data = result['data']
+            self._parse_and_assign_common(data)
+
+            self._sector_key = data.get('sectorKey')
+            self._sector_name = data.get('sectorName')
+            self._top_performing_companies = self._parse_top_performing_companies(data.get('topPerformingCompanies'))
+            self._top_growth_companies = self._parse_top_growth_companies(data.get('topGrowthCompanies'))
+
+            return result
+        except Exception as e:
+            logger = utils.get_yf_logger()
+            logger.error(f"Failed to get industry data for '{self._key}' reason: {e}")
+            logger.debug("Got response: ")
+            logger.debug("-------------")
+            logger.debug(f" {result}")
+            logger.debug("-------------")
\ No newline at end of file
diff --git a/yfinance/domain/sector.py b/yfinance/domain/sector.py
new file mode 100644
index 000000000..2ae3a1137
--- /dev/null
+++ b/yfinance/domain/sector.py
@@ -0,0 +1,69 @@
+from __future__ import print_function
+from typing import Dict, Optional
+
+import pandas as _pd
+
+from .domain import Domain, _QUERY_URL_
+from .. import utils
+
+class Sector(Domain):
+    def __init__(self, key, session=None, proxy=None):
+        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):
+        return f'yfinance.Sector object <{self._key}>'
+    
+    @property
+    def top_etfs(self) -> Dict[str, str]:
+        self._ensure_fetched(self._top_etfs)
+        return self._top_etfs
+
+    @property
+    def top_mutual_funds(self) -> Dict[str, str]:
+        self._ensure_fetched(self._top_mutual_funds)
+        return self._top_mutual_funds
+
+    @property
+    def industries(self) -> _pd.DataFrame:
+        self._ensure_fetched(self._industries)
+        return self._industries
+    
+    def _parse_top_etfs(self, top_etfs: Dict) -> Dict[str, str]:
+        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]:
+        return {e.get('symbol'): e.get('name') for e in top_mutual_funds}
+    
+    def _parse_industries(self, industries: Dict) -> _pd.DataFrame:
+        industries_column = ['key','name','symbol','market weight']
+        industries_values = [(i.get('key'),
+                              i.get('name'),
+                              i.get('symbol'),
+                              i.get('marketWeight',{}).get('raw', None)
+                              ) for i in industries if i.get('name') != 'All Industries']
+        return _pd.DataFrame(industries_values, columns = industries_column).set_index('key')
+
+    def _fetch_and_parse(self) -> None:
+        result = None
+        
+        try:
+            result = self._fetch(self._query_url, self.proxy)
+            data = result['data']
+            self._parse_and_assign_common(data)
+
+            self._top_etfs = self._parse_top_etfs(data.get('topETFs', {}))
+            self._top_mutual_funds = self._parse_top_mutual_funds(data.get('topMutualFunds', {}))
+            self._industries = self._parse_industries(data.get('industries', {}))
+
+        except Exception as e:
+            logger = utils.get_yf_logger()
+            logger.error(f"Failed to get sector data for '{self._key}' reason: {e}")
+            logger.debug("Got response: ")
+            logger.debug("-------------")
+            logger.debug(f" {result}")
+            logger.debug("-------------")
\ No newline at end of file
diff --git a/yfinance/scrapers/funds.py b/yfinance/scrapers/funds.py
new file mode 100644
index 000000000..874bed856
--- /dev/null
+++ b/yfinance/scrapers/funds.py
@@ -0,0 +1,231 @@
+import pandas as pd
+
+from yfinance.data import YfData
+from yfinance.const import _BASE_URL_
+from yfinance.exceptions import YFDataException
+from yfinance import utils
+
+from typing import Dict, Optional
+
+_QUOTE_SUMMARY_URL_ = f"{_BASE_URL_}/v10/finance/quoteSummary/"
+
+'''
+Supports ETF and Mutual Funds Data
+Queried Modules: quoteType, summaryProfile, fundProfile, topHoldings
+
+Notes: 
+- fundPerformance module is not implemented as better data is queriable using history
+'''
+class FundsData:
+    def __init__(self, data: YfData, symbol: str, proxy=None):
+        self._data = data
+        self._symbol = symbol
+        self.proxy = proxy
+        
+        # quoteType
+        self._quote_type = None
+
+        # summaryProfile
+        self._description = None
+
+        # fundProfile
+        self._fund_overview = None
+        self._fund_operations = None
+
+        # topHoldings
+        self._asset_classes = None
+        self._top_holdings = None
+        self._equity_holdings = None
+        self._bond_holdings = None
+        self._bond_ratings = None
+        self._sector_weightings = None
+
+    def quote_type(self) -> str:
+        if self._quote_type is None:
+            self._fetch_and_parse()
+        return self._quote_type
+    
+    @property
+    def description(self) -> str:
+        if self._description is None:
+            self._fetch_and_parse()
+        return self._description
+    
+    @property
+    def fund_overview(self) -> Dict[str, Optional[str]]:
+        if self._fund_overview is None:
+            self._fetch_and_parse()
+        return self._fund_overview
+
+    @property
+    def fund_operations(self) -> pd.DataFrame:
+        if self._fund_operations is None:
+            self._fetch_and_parse()
+        return self._fund_operations
+
+    @property
+    def asset_classes(self) -> Dict[str, float]:
+        if self._asset_classes is None:
+            self._fetch_and_parse()
+        return self._asset_classes
+
+    @property
+    def top_holdings(self) -> pd.DataFrame:
+        if self._top_holdings is None:
+            self._fetch_and_parse()
+        return self._top_holdings
+
+    @property
+    def equity_holdings(self) -> pd.DataFrame:
+        if self._equity_holdings is None:
+            self._fetch_and_parse()
+        return self._equity_holdings
+
+    @property
+    def bond_holdings(self) -> pd.DataFrame:
+        if self._bond_holdings is None:
+            self._fetch_and_parse()
+        return self._bond_holdings
+
+    @property
+    def bond_ratings(self) -> Dict[str, float]:
+        if self._bond_ratings is None:
+            self._fetch_and_parse()
+        return self._bond_ratings
+
+    @property
+    def sector_weightings(self) -> Dict[str,float]:
+        if self._sector_weightings is None:
+            self._fetch_and_parse()
+        return self._sector_weightings
+
+    def _fetch(self, proxy):
+        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:
+        result = self._fetch(self.proxy)
+        try:
+            data = result["quoteSummary"]["result"][0]
+            # check quote type
+            self._quote_type = data["quoteType"]["quoteType"]
+            
+            # parse "summaryProfile", "topHoldings", "fundProfile"
+            self._parse_description(data["summaryProfile"])
+            self._parse_top_holdings(data["topHoldings"])
+            self._parse_fund_profile(data["fundProfile"])
+        except KeyError:
+            raise YFDataException("No Fund data found.")
+        except Exception as e:
+            logger = utils.get_yf_logger()
+            logger.error(f"Failed to get fund data for '{self._symbol}' reason: {e}")
+            logger.debug("Got response: ")
+            logger.debug("-------------")
+            logger.debug(f" {data}")
+            logger.debug("-------------")
+
+    @staticmethod
+    def _parse_raw_values(data, default=None):
+        if not isinstance(data, dict):
+            return data
+        
+        return data.get("raw", default)
+
+    def _parse_description(self, data) -> None:
+        self._description = data.get("longBusinessSummary", "")
+
+    def _parse_top_holdings(self, data) -> None:  # done
+        # asset classes
+        self._asset_classes = {
+            "cashPosition": self._parse_raw_values(data.get("cashPosition", None)),
+            "stockPosition": self._parse_raw_values(data.get("stockPosition", None)),
+            "bondPosition": self._parse_raw_values(data.get("bondPosition", None)),
+            "preferredPosition": self._parse_raw_values(data.get("preferredPosition", None)),
+            "convertiblePosition": self._parse_raw_values(data.get("convertiblePosition", None)),
+            "otherPosition": self._parse_raw_values(data.get("otherPosition", None))
+        }
+
+        # top holdings
+        _holdings = data.get("holdings", [])
+        _symbol, _name, _holding_percent = [], [], []
+
+        for item in _holdings:
+            _symbol.append(item["symbol"])
+            _name.append(item["holdingName"])
+            _holding_percent.append(item["holdingPercent"])
+        
+        self._top_holdings = pd.DataFrame({
+            "Symbol": _symbol,
+            "Name": _name,
+            "Holding Percent": _holding_percent
+        }).set_index("Symbol")
+
+        # equity holdings
+        _equity_holdings = data.get("equityHoldings", {})
+        self._equity_holdings = pd.DataFrame({
+            "Average": ["Price/Earnings", "Price/Book", "Price/Sales", "Price/Cashflow", "Median Market Cap", "3 Year Earnings Growth"],
+            self._symbol: [
+                self._parse_raw_values(_equity_holdings.get("priceToEarnings", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToBook", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToSales", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToCashflow", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("medianMarketCap", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("threeYearEarningsGrowth", pd.NA)),
+            ],
+            "Category Average": [
+                self._parse_raw_values(_equity_holdings.get("priceToEarningsCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToBookCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToSalesCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("priceToCashflowCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("medianMarketCapCat", pd.NA)),
+                self._parse_raw_values(_equity_holdings.get("threeYearEarningsGrowthCat", pd.NA)),
+            ]
+        }).set_index("Average")
+        
+        # bond holdings
+        _bond_holdings = data.get("bondHoldings", {})
+        self._bond_holdings = pd.DataFrame({
+            "Average": ["Duration", "Maturity", "Credit Quality"],
+            self._symbol: [
+                self._parse_raw_values(_bond_holdings.get("duration", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("maturity", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("creditQuality", pd.NA)),
+            ],
+            "Category Average": [
+                self._parse_raw_values(_bond_holdings.get("durationCat", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("maturityCat", pd.NA)),
+                self._parse_raw_values(_bond_holdings.get("creditQualityCat", pd.NA)),
+            ]
+        }).set_index("Average")
+
+        # bond ratings
+        self._bond_ratings = dict((key, d[key]) for d in data.get("bondRatings", []) for key in d)
+
+        # sector weightings
+        self._sector_weightings = dict((key, d[key]) for d in data.get("sectorWeightings", []) for key in d)
+        
+    def _parse_fund_profile(self, data):
+        self._fund_overview = {
+            "categoryName": data.get("categoryName", None), 
+            "family":       data.get("family", None), 
+            "legalType":    data.get("legalType", None)
+        }
+        
+        _fund_operations = data.get("feesExpensesInvestment", {})
+        _fund_operations_cat = data.get("feesExpensesInvestmentCat", {})
+
+        self._fund_operations = pd.DataFrame({
+            "Attributes": ["Annual Report Expense Ratio", "Annual Holdings Turnover", "Total Net Assets"],
+            self._symbol: [
+                self._parse_raw_values(_fund_operations.get("annualReportExpenseRatio", pd.NA)),
+                self._parse_raw_values(_fund_operations.get("annualHoldingsTurnover", pd.NA)),
+                self._parse_raw_values(_fund_operations.get("totalNetAssets", pd.NA))
+            ],
+            "Category Average": [
+                self._parse_raw_values(_fund_operations_cat.get("annualReportExpenseRatio", pd.NA)),
+                self._parse_raw_values(_fund_operations_cat.get("annualHoldingsTurnover", pd.NA)),
+                self._parse_raw_values(_fund_operations_cat.get("totalNetAssets", pd.NA))
+            ]
+        }).set_index("Attributes")
\ No newline at end of file
diff --git a/yfinance/scrapers/history.py b/yfinance/scrapers/history.py
index 00f454f38..1131469eb 100644
--- a/yfinance/scrapers/history.py
+++ b/yfinance/scrapers/history.py
@@ -1297,14 +1297,6 @@ def _fix_bad_div_adjust(self, df, interval, currency):
 
         # Split df2 into: nan data, and non-nan data
         f_nan = df2['Close'].isna().to_numpy()
-        f_no_change_inter = df2['Close'].iloc[1:].to_numpy() == df2['Close'].iloc[:-1].to_numpy()
-        f_no_change_inter = np.append([False], f_no_change_inter)
-        f_no_change_intra = (df2['High'] == df2['Low']).to_numpy()
-        f_no_change = f_no_change_intra & f_no_change_inter
-        f_nan = f_nan | f_no_change
-        # Sometimes a dividend occurs on day with zero volume, and sometimes they are bad dividends that need correction!
-        # So don't discard volume=0
-        f_nan = f_nan & (df2['Dividends']==0.0).to_numpy()
         df2_nan = df2[f_nan].copy()
         df2 = df2[~f_nan].copy()
 
@@ -1325,8 +1317,11 @@ def _fix_bad_div_adjust(self, df, interval, currency):
             if div_idx == 0:
                 continue
             prices_before = df2.iloc[div_idx-1]
-            if prices_before['Close'] < prices_before['Low']:
-                div = df2['Dividends'].iloc[div_idx]
+            diff = prices_before['Low'] - prices_before['Close']
+            div = df2['Dividends'].iloc[div_idx]
+            if diff > 0 and (diff/div-1)<0.01:
+                # Close<Low and the difference not bigger than the dividend, 
+                # because if diff > dividend then something else caused problem.
                 dt_before = df2.index[div_idx-1]
                 new_close = prices_before['Close'] + div
                 if new_close >= prices_before['Low'] and new_close <= prices_before['High']:
@@ -1353,8 +1348,6 @@ def _fix_bad_div_adjust(self, df, interval, currency):
             div_pct = div / df2['Close'].iloc[div_idx-1]
 
             # Check if dividend is 100x market movement.
-            div_too_big = False
-            div_too_small = False
             div_too_small_improvement_threshold = 1
             # div_too_big_improvement_threshold = 1
             div_too_big_improvement_threshold = 2
@@ -1375,6 +1368,7 @@ def _fix_bad_div_adjust(self, df, interval, currency):
                 drops = df2['Close'].iloc[div_idx-1:div_idx+1].to_numpy() - df2['Low'].iloc[div_idx:div_idx+2].to_numpy()
                 drop_2Dmax = np.max(drops)
             else:
+                drops = np.array([drop])
                 drop_2Dmax = drop
 
             if (len(df2)-div_idx) < 4:
@@ -1390,42 +1384,93 @@ def _fix_bad_div_adjust(self, df, interval, currency):
                 diffs = df2['Close'].iloc[start:end-1].to_numpy() - df2['Low'].iloc[start+1:end].to_numpy()
                 typical_volatility = np.median(np.abs(diffs))
 
-            if drop == 0.0 and df2['Volume'].iloc[div_idx]==0:
+            possibilities = []
+            if (drops==0.0).all() and df2['Volume'].iloc[div_idx]==0:
                 # Can't analyse price action so use crude heuristics
-                # if div_pct*100 < 0.1:
-                if div_pct*100 < 0.08:  # lowered for $AG and their tiny divs
-                    div_too_small = True  # Could be a 0.01x error
-                elif div_pct > 1.0:
-                    div_too_big = True  # Could be a 100x error
-            if not (div_too_small or div_too_big):
+                pct_zero_vol = np.sum(df2['Volume']==0.0)/len(df2)
+                if div_pct*100 < 0.1:
+                    # Could be a 0.01x error
+                    possibilities.append({'state':'div-too-small', 'diff':0.0})
+                # elif div_pct > 1.0:
+                # Update: lower threshold for illiquid stocks, because why paying mega dividends?
+                elif (pct_zero_vol > 0.75 and div_pct > 0.25) or (div_pct > 1.0):
+                    # Could be a 100x error
+                    possibilities.append({'state':'div-too-big', 'diff':0.0})
+            else:
+                split = df2['Stock Splits'].loc[dt]
+                if split == 0.0:
+                    div_postSplit = None
+                else:
+                    # Maybe Yahoo has not applied coincident split to dividend
+                    div_postSplit = div / split
+
+                    if div_postSplit > div:
+                        # Use volatility-adjusted drop
+                        _drop = drop - typical_volatility
+                    else:
+                        _drop = drop_2Dmax
+                    if _drop > 0:
+                        diff = abs(div-_drop)
+                        diff_postSplit = abs(div_postSplit-_drop)
+                        if (diff_postSplit * div_too_big_improvement_threshold) <= diff:
+                            possibilities.append({'state':'div-pre-split', 'diff':diff_postSplit})
+
                 # Check for div-too-big
                 if div_pct > too_big_check_threshold:
                     if drop_2Dmax <= 0.0:
-                        div_too_big = True
+                        possibilities.append({'state':'div-too-big', 'diff':0.0})
                     else:
                         diff = abs(div-drop_2Dmax)
-                        diff2 = abs((div/currency_divide)-drop_2Dmax)
-                        if (diff2 * div_too_big_improvement_threshold) <= diff:
-                            # div exceeds market move AND 0.01x div fits drop better
-                            div_too_big = True
+                        diff_fx = abs((div/currency_divide)-drop_2Dmax)
+                        if div_postSplit is None:
+                            if (diff_fx * div_too_big_improvement_threshold) <= diff:
+                                possibilities.append({'state':'div-too-big', 'diff':diff_fx})
+                        else:
+                            diff_fxPostSplit = abs((div_postSplit/currency_divide)-drop_2Dmax)
+                            if diff_fx < diff_fxPostSplit:
+                                if (diff_fx * div_too_big_improvement_threshold) <= diff:
+                                    possibilities.append({'state':'div-too-big', 'diff':diff_fx})
+                            else:
+                                if (diff_fxPostSplit * div_too_big_improvement_threshold) <= diff:
+                                    possibilities.append({'state':'div-too-big-and-pre-split', 'diff':diff_fxPostSplit})
 
                 # Check for div-too-small - can be tricked by normal price volatility
-                if not div_too_big and not np.isnan(typical_volatility):
-                    drop_wo_vol = drop_2Dmax - typical_volatility
+                if not np.isnan(typical_volatility):
+                    # drop_wo_vol = drop_2Dmax - typical_volatility
+                    # Update: only use same-day change for too-small, to reduce false-positives
+                    drop_wo_vol = drop - typical_volatility
                     if drop_wo_vol > 0:
                         diff = abs(div-drop_wo_vol)
-                        diff2 = abs((div*currency_divide)-drop_wo_vol)
-                        if (diff2 * div_too_small_improvement_threshold) <= diff:
-                            # market move exceeds div AND 100x div fits drop better
-                            div_too_small = True
+                        diff_fx = abs((div*currency_divide)-drop_wo_vol)
+                        if div_postSplit is None:
+                            if (diff_fx * div_too_small_improvement_threshold) <= diff:
+                                possibilities.append({'state':'div-too-small', 'diff':diff_fx})
+                        else:
+                            diff_fxPostSplit = abs((div_postSplit*currency_divide)-drop_wo_vol)
+                            if diff_fx < diff_fxPostSplit:
+                                if (diff_fx * div_too_big_improvement_threshold) <= diff:
+                                    possibilities.append({'state':'div-too-small', 'diff':diff_fx})
+                            else:
+                                if (diff_fxPostSplit * div_too_big_improvement_threshold) <= diff:
+                                    possibilities.append({'state':'div-too-small-and-pre-split', 'diff':diff_fxPostSplit})
 
             div_status = {'date': dt, 'idx':div_idx, 'div': div, '%': div_pct}
             div_status['drop'] = drop
             div_status['drop_2Dmax'] = drop_2Dmax
             div_status['volume'] = df2['Volume'].iloc[div_idx]
             div_status['vol'] = typical_volatility
-            div_status['div_too_big'] = div_too_big
-            div_status['div_too_small'] = div_too_small
+
+            div_status['div_too_big'] = False
+            div_status['div_too_small'] = False
+            div_status['div_pre_split'] = False
+            div_status['div_too_big_and_pre_split'] = False
+            div_status['div_too_small_and_pre_split'] = False
+            if len(possibilities) > 0:
+                # Something is wrong with dividend - pick the best correction
+                possibilities = sorted(possibilities, key=lambda k: k['diff'])
+                p = possibilities[0]
+                div_status[p['state'].replace('-', '_')] = True
+
             row = pd.DataFrame([div_status]).set_index('date')
             if div_status_df is None:
                 div_status_df = row
@@ -1434,9 +1479,10 @@ def _fix_bad_div_adjust(self, df, interval, currency):
 
         if div_status_df is None and not df_modified:
             return df
+        checks = [c for c in div_status_df.columns if c.startswith('div_')]
         div_status_df = div_status_df.sort_index()
 
-        def cluster_dividends(df, column='div', threshold=10):
+        def cluster_dividends(df, column='div', threshold=7):
             n = len(df)
             sorted_df = df.sort_values(column)
             clusters = []
@@ -1465,6 +1511,8 @@ def cluster_dividends(df, column='div', threshold=10):
             return cluster_labels
 
         # Check if the present div-adjustment is too big/small, or missing
+        # - too-big determined from Adj Close movement vs Close
+        # - too-small compares Adj Close vs dividends
         for i in range(len(div_status_df)):
             div_idx = div_status_df['idx'].iloc[i]
             dt = div_status_df.index[i]
@@ -1480,28 +1528,6 @@ def cluster_dividends(df, column='div', threshold=10):
             post_adj = df2['Adj Close'].iloc[div_idx] / df2['Close'].iloc[div_idx]
             div_missing_from_adjclose = post_adj == pre_adj
 
-            # Adj Close should drop by LESS than Close on ex-div, at least for big dividends.
-            # Update: Yahoo might be reporting dividend slightly early, meaning
-            # Mr Market's price drop happens tomorrow e.g. UNTC in december 2023
-            lookahead_date = dt+_datetime.timedelta(days=20)
-            lookahead_idx = bisect.bisect_left(df2.index, lookahead_date)
-            lookahead_idx = min(lookahead_idx, len(df2)-1)
-            # In rare cases, the price dropped 1 day before dividend (DVD.OL @ 2024-05-15)
-            lookback_idx = div_idx-2 if div_idx > 1 else div_idx-1
-            div_adj_exceeds_prices = False
-            if lookahead_idx > lookback_idx:
-                close_deltas = np.diff(df2['Close'].iloc[lookback_idx:lookahead_idx+1].to_numpy())
-                adjClose_deltas = np.diff(df2['Adj Close'].iloc[lookback_idx:lookahead_idx+1].to_numpy())
-                close_chgs_pct = close_deltas / df2['Close'].iloc[lookback_idx:lookahead_idx].to_numpy()
-                adjClose_chgs_pct = adjClose_deltas / df2['Adj Close'].iloc[lookback_idx:lookahead_idx].to_numpy()
-                # Check if adjustment is too much. Need a big dividend % to be sure.
-                if div_pct > 0.15:
-                    adjClose_chg_pct = np.max(adjClose_chgs_pct)
-                    close_chg_pct = np.max(close_chgs_pct)
-                    if adjClose_chg_pct > 0.1 and adjClose_chg_pct > 1.0001*close_chg_pct:
-                        # Bigger drop
-                        div_adj_exceeds_prices = True
-
             # Check if adjustment too small
             present_adj = pre_adj / post_adj
             implied_div_yield = 1.0 - present_adj
@@ -1510,39 +1536,33 @@ def cluster_dividends(df, column='div', threshold=10):
             # ... and use same method for adjustment too big:
             div_adj_exceeds_div = implied_div_yield > (10*div_pct)
 
-            # Have done 4 different checks that can interact, so handle very carefully.
             # Can prune the space:
             if div_missing_from_adjclose:
                 div_adj_is_too_small = False  # redundant information
 
-            if div_adj_exceeds_prices and div_adj_is_too_small:
-                # Contradiction. Assume former tricked by low-liquidity price action
-                div_adj_exceeds_prices = False
-
             div_status = {'present adj': present_adj}
             div_status['adj_missing'] = div_missing_from_adjclose
-            div_status['adj_exceeds_prices'] = div_adj_exceeds_prices
             div_status['adj_exceeds_div'] = div_adj_exceeds_div
             div_status['div_exceeds_adj'] = div_adj_is_too_small
 
             for k,v in div_status.items():
                 if k not in div_status_df:
-                    if isinstance(v, (bool, np.bool)):
+                    if isinstance(v, (bool, np.bool_)):
                         div_status_df[k] = False
                     elif isinstance(v, int):
                         div_status_df[k] = 0
                     elif isinstance(v, float):
                         div_status_df[k] = 0.0
+                    # elif k == 'div_true_date':
+                    #     div_status_df[k] = pd.Series(dtype='datetime64[ns, UTC]')
                     else:
                         raise Exception(k,v,type(v))
                 div_status_df.loc[dt, k] = v
-
-        checks = ['adj_missing', 'adj_exceeds_div', 'adj_exceeds_prices', 'div_exceeds_adj', 'div_too_big', 'div_too_small', 'fx_mixup']
-        checks = [c for c in checks if c in div_status_df.columns]
+        checks += ['adj_missing', 'adj_exceeds_div', 'div_exceeds_adj']
 
         div_status_df['phantom'] = False
         phantom_proximity_threshold = _datetime.timedelta(days=7)
-        f = div_status_df['div_too_big'] | div_status_df['div_exceeds_adj']
+        f = div_status_df[['div_too_big', 'div_exceeds_adj']].any(axis=1)
         if f.any():
             # One/some of these may be phantom dividends. Clue is if another correct dividend is very close
             indices = np.where(f)[0]
@@ -1617,13 +1637,146 @@ def cluster_dividends(df, column='div', threshold=10):
                         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']
+            # ... but only if no other problems
+            f_phantom = f_phantom & (~div_status_df[[c for c in checks if c != 'phantom']].any(axis=1))
+            if f_phantom.any():
+                div_dts = div_status_df.index[f_phantom]
+                msg = f'Removing phantom div(s): {[str(dt.date()) for dt in div_dts]}'
+                logger.info(msg, extra=log_extras)
+                phantom_div_dts = div_status_df.index[f_phantom]
+                for dt in phantom_div_dts:
+                    enddt = dt-_datetime.timedelta(seconds=1)
+                    df2.loc[    :enddt, 'Adj Close'] /= div_status_df['present adj'].loc[dt]
+                    df2.loc[    :enddt, 'Repaired?'] = True
+                    df2_nan.loc[:enddt, 'Adj Close'] /= div_status_df['present adj'].loc[dt]
+                    df2_nan.loc[:enddt, 'Repaired?'] = True
+                    df2.loc[dt, 'Dividends'] = 0
+                    df_modified = True
+                    div_status_df = div_status_df.drop(dt)
+                div_status_df.loc[f_phantom, 'phantom'] = False
+            div_status_df = div_status_df.drop('phantom', axis=1)
+            if 'phantom' in checks:
+                checks.remove('phantom')
+
+        # Check if the present div-adjustment contradicts price action
+        for i in range(len(div_status_df)):
+            div_idx = div_status_df['idx'].iloc[i]
+            dt = div_status_df.index[i]
+            div = div_status_df['div'].iloc[i]
+            if div_idx == 0:
+                continue
+            div_pct = div / df2['Close'].iloc[div_idx-1]
+
+            # Adj Close should drop by LESS than Close on ex-div, at least for big dividends.
+            # Update: Yahoo might be reporting dividend slightly early, meaning
+            # Mr Market's price drop happens tomorrow e.g. UNTC in december 2023.
+            # Or worse, Yahoo is 1 month early e.g. GWI.L ex-div was mid-April not mid-March
+            lookahead_date = dt+_datetime.timedelta(days=35)
+            lookahead_idx = bisect.bisect_left(df2.index, lookahead_date)
+            lookahead_idx = min(lookahead_idx, len(df2)-1)
+            # In rare cases, the price dropped 1 day before dividend (DVD.OL @ 2024-05-15)
+            lookback_idx = div_idx-2 if div_idx > 1 else div_idx-1
+            # Check for bad stock splits in the lookahead period - 
+            # if present, reduce lookahead to before.
+            future_changes = df2['Close'].iloc[div_idx:lookahead_idx+1].pct_change()
+            f_big_change = (future_changes > 2).to_numpy() | (future_changes < -0.9).to_numpy()
+            if f_big_change.any():
+                lookahead_idx = div_idx + np.where(f_big_change)[0][0]-1
+                lookahead_date = df2.index[lookahead_idx]
+            
+            div_adj_exceeds_prices = False
+            div_date_wrong = False
+            div_true_date = pd.NaT
+            if lookahead_idx > lookback_idx:
+                x = df2.iloc[lookback_idx:lookahead_idx+1].copy()
+                x['Adj'] = x['Adj Close'] / x['Close']
+                x['Adj Low'] = x['Adj'] * x['Low']
+                deltas = x['Low'].iloc[1:].to_numpy() - x['Close'].iloc[:-1].to_numpy()
+                deltas = np.append([0.0], deltas)
+                x['delta'] = deltas
+                adjDeltas = x['Adj Low'].iloc[1:].to_numpy() - x['Adj Close'].iloc[:-1].to_numpy()
+                adjDeltas = np.append([0.0], adjDeltas)
+                x['adjDelta'] = adjDeltas
+                deltas = x[['delta', 'adjDelta']]
+                if div_pct > 0.15 and div_pct < 1.0:  # avoid analysing impossibly-big dividends here
+                    adjDiv = div * x['Adj'].iloc[0]
+                    f = deltas['adjDelta'] > (adjDiv*0.6)
+                    if f.any():
+                        for idx in np.where(f)[0]:
+                            adjDelta_max_drop_idx = idx
+                            adjDelta_max_drop = deltas['adjDelta'].iloc[idx]
+                            if adjDelta_max_drop > 1.001*deltas['delta'].iloc[adjDelta_max_drop_idx]:
+                                # Adjusted price has risen by more than unadjusted, should not happen.
+                                # See if Adjusted price later falls by a similar amount. This would mean
+                                # dividend has been applied too early.
+                                ratios = (-1*deltas['adjDelta'])/adjDelta_max_drop
+                                f_near1_or_above = ratios>=0.8
+                                if f_near1_or_above.any():
+                                    near_indices = np.where(f_near1_or_above)[0]
+                                    if len(near_indices) > 1:
+                                        penalties = np.zeros(len(near_indices))
+                                        for i in range(len(near_indices)):
+                                            idx = near_indices[i]
+                                            dti = ratios.index[idx]
+                                            if dti < dt:
+                                                penalties[i] += (dt-dti).days
+                                            else:
+                                                penalties[i] += 0.1*(dti-dt).days
+                                        i = np.argmin(penalties)
+                                        reversal_idx = near_indices[i]
+                                    else:
+                                        reversal_idx = near_indices[0]
+                                    div_date_wrong = True
+                                    div_true_date = ratios.index[reversal_idx]
+                                    break
+                                elif adjDelta_max_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
+                                    break
+
+            # Can prune the space:
+            div_adj_is_too_small = div_status_df.loc[dt, 'div_exceeds_adj']
+            if div_adj_exceeds_prices and div_adj_is_too_small:
+                # Contradiction. Assume former tricked by low-liquidity price action
+                div_adj_exceeds_prices = False
+
+            div_status = {}
+            div_status['adj_exceeds_prices'] = div_adj_exceeds_prices
+            div_status['div_date_wrong'] = div_date_wrong
+            div_status['div_true_date'] = div_true_date
+
+            for k,v in div_status.items():
+                if k not in div_status_df:
+                    if isinstance(v, (bool, np.bool_)):
+                        div_status_df[k] = False
+                    elif isinstance(v, int):
+                        div_status_df[k] = 0
+                    elif isinstance(v, float):
+                        div_status_df[k] = 0.0
+                    elif k == 'div_true_date':
+                        div_status_df[k] = pd.Series(dtype='datetime64[ns, UTC]')
+                    else:
+                        raise Exception(k,v,type(v))
+                div_status_df.loc[dt, k] = v
+            if 'div_too_big' in div_status_df.columns and 'div_date_wrong' in div_status_df.columns:
+                # Where div_date_wrong = True, discard div_too_big. Helps with false-positive handling later.
+                div_status_df.loc[div_status_df['div_date_wrong'].to_numpy(), 'div_too_big'] = False
+
+        checks += ['adj_exceeds_prices', 'div_date_wrong']
+
         if not div_status_df[checks].any().any():
             # Maybe failed to detect a too-small div. If div is ~0.01x of previous and next, then
             # treat as a 0.01x error
             if len(div_status_df) > 1:
                 for i in range(0, len(div_status_df)):
-                    if div_status_df['phantom'].iloc[i]:
-                        continue
                     r_pre, r_post = None, None
                     if i > 0:
                         r_pre = div_status_df['%'].iloc[i-1] / div_status_df['%'].iloc[i]
@@ -1635,39 +1788,26 @@ def cluster_dividends(df, column='div', threshold=10):
                         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
-            return df
         for c in checks:
             if not div_status_df[c].any():
                 div_status_df = div_status_df.drop(c, axis=1)
+        c = 'div_true_date'
+        if c in div_status_df.columns and div_status_df[c].isna().all():
+            div_status_df = div_status_df.drop(c, axis=1)
         checks = [c for c in checks if c in div_status_df.columns]
 
         # With small dividends e.g. < 10%, my error detecting logic can be tricked by price volatility.
         # But by looking at all the dividends, can find errors that previous logic missed.
 
         div_status_df = div_status_df.sort_values('%')
-        div_status_df['cluster'] = cluster_dividends(div_status_df, column='%', )
-
-        # Discard columns with no problems
-        for c in checks:
-            if (~div_status_df[c]).all():
-                div_status_df = div_status_df.drop(c, axis=1)
-        checks = [c for c in checks if c in div_status_df.columns]
-        if len(checks) == 0:
-            return df
+        div_status_df['cluster'] = cluster_dividends(div_status_df, column='%')
 
+        # Check for inconsistencies
         cluster_ids = div_status_df['cluster'].unique()
         for cid in cluster_ids:
             fc = div_status_df['cluster'] == cid
             cluster = div_status_df[fc].sort_index()
-
-            if 'phantom' in cluster.columns:
-                cluster = cluster[~cluster['phantom']]
             n = len(cluster)
-            if n == 0:
-                # this cluster is just phantom
-                continue
             div_pcts = cluster[['%']].copy()
             if len(div_pcts) > 1:
                 time_diffs = div_pcts['%'].index.to_series().diff().dt.total_seconds() / (365.25 * 24 * 60 * 60)
@@ -1685,6 +1825,13 @@ def cluster_dividends(df, column='div', threshold=10):
                     true_threshold = 1.0
                     fals_threshold = 0.2
 
+                    if 'div_date_wrong' in cluster.columns and (cluster[c] == cluster['div_date_wrong']).all():
+                        continue
+
+                    if 'adj_exceeds_prices' in cluster.columns and (cluster[c] == cluster['adj_exceeds_prices']).all():
+                        # More likely that true-positive. Maybe the div never happened
+                        continue
+
                     if 'div_exceeds_adj' in cluster.columns and cluster['div_exceeds_adj'].all():
                         # Dividend too big for prices AND the present adjustment,
                         # more likely the dividends are too big.
@@ -1693,15 +1840,14 @@ def cluster_dividends(df, column='div', threshold=10):
                             fals_threshold = 2/3
                         else:
                             # Relax thresholds
-                            true_threshold = 2/5
+                            true_threshold = 0.25
 
-                    elif 'adj_exceeds_prices' in cluster.columns and cluster['adj_exceeds_prices'].all():
+                    elif 'adj_exceeds_prices' in cluster.columns and (cluster[c]==cluster['adj_exceeds_prices']).all():
                         # Both dividend and present adjust too big for prices,
                         # more likely the dividends are too big.
                         true_threshold = 1/2
 
-                    elif not ('div_exceeds_adj' in cluster.columns or 'adj_exceeds_prices' in cluster.columns):
-                        # Present adjustment seems correct, so more likely that 'div_too_big' are false positives: NOT too big
+                    else:
                         fals_threshold = 1/2
 
                     if pct_fail >= true_threshold:
@@ -1716,7 +1862,8 @@ def cluster_dividends(df, column='div', threshold=10):
                     fals_threshold = 0.1
                     if 'adj_exceeds_div' not in cluster.columns:
                         # Adjustment confirms dividends => more likely that 'div_too_small' are false positives: NOT too small
-                        fals_threshold = 2/3
+                        true_threshold = 6/11
+                        fals_threshold = 1/2
                     if pct_fail >= true_threshold:
                         div_status_df.loc[fc, c] = True
                         continue
@@ -1731,53 +1878,40 @@ def cluster_dividends(df, column='div', threshold=10):
                 if c == 'div_exceeds_adj':
                     continue
 
+                if c == 'adj_exceeds_prices':
+                    continue
+
                 if c == 'phantom' and self.ticker in ['KAP.IL', 'SAND']:
                     # Manually approve, but these are probably safe to assume ok
                     continue
 
+                if c == 'div_date_wrong':
+                    # Fine, these should be rare
+                    continue
+                if c == 'div_pre_split':
+                    # Fine, these should be rare
+                    continue
+
         div_status_df = div_status_df.sort_index()
 
         # Discard dividends with no problems
         div_status_df = div_status_df[div_status_df[checks].any(axis=1)]
         if div_status_df.empty:
-            return df
-
-        # Process phantoms first 
-        if 'phantom' in div_status_df.columns:
-            f_phantom = div_status_df['phantom']
-            # ... but only if no other problems
-            f_phantom = f_phantom & (~div_status_df[[c for c in checks if c != 'phantom']].any(axis=1))
-            if f_phantom.any():
-                div_dts = div_status_df.index[f_phantom]
-                msg = f'Removing phantom div(s): {[str(dt.date()) for dt in div_dts]}'
-                logger.info(msg, extra=log_extras)
-                for i in np.where(f_phantom)[0]:
-                    dt = div_status_df.index[i]
-                    enddt = dt-_datetime.timedelta(seconds=1)
-                    df2.loc[    :enddt, 'Adj Close'] /= div_status_df['present adj'].iloc[i]
-                    df2.loc[    :enddt, 'Repaired?'] = True
-                    df2_nan.loc[:enddt, 'Adj Close'] /= div_status_df['present adj'].iloc[i]
-                    df2_nan.loc[:enddt, 'Repaired?'] = True
-                    df2.loc[dt, 'Dividends'] = 0
-                    df_modified = True
-                div_status_df.loc[f_phantom, 'phantom'] = False
-            # Discard dividends with no problems
-            div_status_df = div_status_df[div_status_df[checks].any(axis=1)]
-            
-            if div_status_df.empty:
-                if not df2_nan.empty:
-                    df2 = pd.concat([df2, df2_nan]).sort_index()
-                return df2
+            if not df2_nan.empty:
+                df2 = pd.concat([df2, df2_nan]).sort_index()
+            return df2
 
         # These arrays track changes for constructing compact log messages
         div_repairs = {}
-        for cid in div_status_df['cluster'].unique():
+        for cid in list(div_status_df['cluster'].unique()):
             cluster = div_status_df[div_status_df['cluster']==cid]
             cluster = cluster.sort_index(ascending=False)
             cluster['Fixed?'] = False
-            for i in range(len(cluster)):
+            # Reverse order because may delete false-positives
+            for i in range(len(cluster)-1, -1, -1):
                 row = cluster.iloc[i]
                 dt = row.name
+                enddt = dt-_datetime.timedelta(seconds=1)
 
                 adj_missing = 'adj_missing' in row and row['adj_missing']
                 div_exceeds_adj = 'div_exceeds_adj' in row and row['div_exceeds_adj']
@@ -1785,15 +1919,38 @@ def cluster_dividends(df, column='div', threshold=10):
                 adj_exceeds_prices = 'adj_exceeds_prices' in row and row['adj_exceeds_prices']
                 div_too_small = 'div_too_small' in row and row['div_too_small']
                 div_too_big = 'div_too_big' in row and row['div_too_big']
-
+                div_pre_split = 'div_pre_split' in row and row['div_pre_split']
+                # div_too_small_and_pre_split = 'div_too_small_and_pre_split' in row and row['div_too_small_and_pre_split']  # not happened yet
+                # div_too_big_and_pre_split = 'div_too_big_and_pre_split' in row and row['div_too_big_and_pre_split']  # not happened yet
+                div_date_wrong = 'div_date_wrong' in row and row['div_date_wrong']
                 n_failed_checks = np.sum([row[c] for c in checks if c in row])
+
+                if div_too_big and adj_exceeds_prices and n_failed_checks == 2:
+                    # adj_exceeds_prices is redundant information, fixing div-too-big
+                    # will fix adjustment
+                    adj_exceeds_prices = False
+                    n_failed_checks -= 1
+
+                if div_date_wrong:
+                    if div_too_big:
+                        # redundant information
+                        div_too_big = False
+                        cluster.loc[dt, 'div_too_big'] = False
+                        n_failed_checks -= 1
+
+                if div_pre_split:
+                    if adj_exceeds_prices:
+                        # redundant information
+                        adj_exceeds_prices = False
+                        cluster.loc[dt, 'adj_exceeds_prices'] = False
+                        n_failed_checks -= 1
+
                 if n_failed_checks == 1:
                     if div_exceeds_adj or adj_exceeds_div:
                         # Simply recalculate Adj Close
                         k = 'too-small div-adjust' if div_exceeds_adj else 'too-big div-adjust'
                         div_repairs.setdefault(k, []).append(dt)
                         adj_correction = (1.0 - row['%']) / row['present adj']
-                        enddt = dt-_datetime.timedelta(seconds=1)
                         df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                         df2.loc[    :enddt, 'Repaired?'] = True
                         df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
@@ -1812,7 +1969,6 @@ def cluster_dividends(df, column='div', threshold=10):
                         k += ' & div-adjust'
                         target_adj = 1.0 - ((1.0 - row['present adj']) * correction)
                         adj_correction = target_adj / row['present adj']
-                        enddt = dt-_datetime.timedelta(seconds=1)
                         df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                         df2.loc[    :enddt, 'Repaired?'] = True
                         df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
@@ -1827,13 +1983,11 @@ def cluster_dividends(df, column='div', threshold=10):
                         correct_div = row['div'] * correction
                         df2.loc[dt, 'Dividends'] = correct_div
 
-                        # Also correct adjustment to match corrected dividend
-                        target_div_pct = row['%']/currency_divide
+                        target_div_pct = row['%'] * correction
                         target_adj = 1.0 - target_div_pct
                         present_adj = row['present adj']
                         k += ' & div-adjust'
-                        adj_correction = target_adj / row['present adj']
-                        enddt = dt-_datetime.timedelta(seconds=1)
+                        adj_correction = target_adj / present_adj
                         df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                         df2.loc[    :enddt, 'Repaired?'] = True
                         df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
@@ -1845,7 +1999,6 @@ def cluster_dividends(df, column='div', threshold=10):
                     elif adj_missing:
                         k = 'missing div-adjust'
                         div_repairs.setdefault(k, []).append(dt)
-                        enddt = dt-_datetime.timedelta(seconds=1)
                         adj_correction = 1.0-row['%']
                         df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                         df2.loc[    :enddt, 'Repaired?'] = True
@@ -1853,6 +2006,69 @@ def cluster_dividends(df, column='div', threshold=10):
                         df2_nan.loc[:enddt, 'Repaired?'] = True
                         cluster.loc[dt, 'Fixed?'] = True
 
+                    elif div_date_wrong:
+                        k = 'wrong ex-div date'
+                        div_repairs.setdefault(k, []).append(dt)
+
+                        # First rollback the present adj
+                        adj_correction = 1.0/row['present adj']
+                        df2.loc[    :enddt, 'Adj Close'] *= adj_correction
+                        df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
+
+                        # Apply correct adj from correct date
+                        div_true_date = row['div_true_date']
+                        close_before = df2['Close'].iloc[row['idx']]
+                        div = row['div']
+                        true_adj = 1.0 - div/close_before
+                        enddt2 = div_true_date-_datetime.timedelta(seconds=1)
+                        df2.loc[    :enddt2, 'Adj Close'] *= true_adj
+                        df2_nan.loc[:enddt2, 'Adj Close'] *= true_adj
+
+                        # Move div to correct date
+                        df2.loc[div_true_date, 'Dividends'] += div
+                        df2.loc[dt, 'Dividends'] = 0
+
+                        df2.loc[    :enddt, 'Repaired?'] = True
+                        df2_nan.loc[:enddt, 'Repaired?'] = True
+                        cluster.loc[dt, 'Fixed?'] = True
+
+                    elif adj_exceeds_prices:
+                        # Nothing else wrong => probably false positive, 
+                        # but no harm checking the adjustment
+                        target_adj = 1.0 - row['%']
+                        present_adj = row['present adj']
+                        if abs((target_adj/present_adj)-1) > 0.05:
+                            # Also correct adjustment to match corrected dividend
+                            k += ' & div-adjust'
+                            adj_correction = target_adj / present_adj
+                            df2.loc[    :enddt, 'Adj Close'] *= adj_correction
+                            df2.loc[    :enddt, 'Repaired?'] = True
+                            df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
+                            df2_nan.loc[:enddt, 'Repaired?'] = True
+                            cluster.loc[dt, 'Fixed?'] = True
+                        else:
+                            div_status_df = div_status_df.drop(dt)
+                            cluster = cluster.drop(dt)
+
+                    elif div_pre_split:
+                        k = 'pre-split div'
+                        correction = 1.0/df2['Stock Splits'].loc[dt]
+                        correct_div = row['div'] * correction
+                        df2.loc[dt, 'Dividends'] = correct_div
+
+                        target_div_pct = row['%'] * correction
+                        target_adj = 1.0 - target_div_pct
+                        present_adj = row['present adj']
+                        # Also correct adjustment to match corrected dividend
+                        k += ' & div-adjust'
+                        adj_correction = target_adj / present_adj
+                        df2.loc[    :enddt, 'Adj Close'] *= adj_correction
+                        df2.loc[    :enddt, 'Repaired?'] = True
+                        df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
+                        df2_nan.loc[:enddt, 'Repaired?'] = True
+                        cluster.loc[dt, 'Fixed?'] = True
+                        div_repairs.setdefault(k, []).append(dt)
+
                 elif n_failed_checks == 2:
                     if div_too_big and adj_missing:
                         # A currency unit mixup AND adjustment missing
@@ -1860,7 +2076,6 @@ def cluster_dividends(df, column='div', threshold=10):
                         div_repairs.setdefault(k, []).append(dt)
                         adj_correction = 1.0 - row['%']/currency_divide
                         df2.loc[dt, 'Dividends'] /= currency_divide
-                        enddt = dt-_datetime.timedelta(seconds=1)
                         df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                         df2.loc[    :enddt, 'Repaired?'] = True
                         df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
@@ -1879,7 +2094,6 @@ def cluster_dividends(df, column='div', threshold=10):
                             msg = None
                             div_adj = 1.0 - (row['%']/currency_divide)
                             adj_correction = div_adj / row['present adj']
-                            enddt = dt-_datetime.timedelta(seconds=1)
                             df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                             df2.loc[    :enddt, 'Repaired?'] = True
                             df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
@@ -1899,13 +2113,15 @@ def cluster_dividends(df, column='div', threshold=10):
                         target_adj = 1.0 - target_div_pct
                         adj_correction = target_adj / row['present adj']
                         df2.loc[dt, 'Dividends'] /= currency_divide
-                        enddt = dt-_datetime.timedelta(seconds=1)
                         df2.loc[    :enddt, 'Adj Close'] *= adj_correction
                         df2.loc[    :enddt, 'Repaired?'] = True
                         df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
                         df2_nan.loc[:enddt, 'Repaired?'] = True
                         cluster.loc[dt, 'Fixed?'] = True
 
+            if cluster.empty:
+                continue
+
         for k in div_repairs:
             msg = f"Repaired {k}: {[str(dt.date()) for dt in sorted(div_repairs[k])]}"
             logger.info(msg, extra=log_extras)
@@ -2158,6 +2374,14 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
 
         r = _1d_change_x / split_rcp
         f_down = _1d_change_x < 1.0 / threshold
+        if f_down.any():
+            # Discard where triggered by negative Adj Close after dividend
+            f_neg = _1d_change_x < 0.0
+            f_div = (df2['Dividends']>0).to_numpy()
+            f_div_before = np.roll(f_div, 1)
+            if f_down.ndim == 2:
+                f_div_before = f_div_before[:, np.newaxis].repeat(f_down.shape[1], axis=1)
+            f_down = f_down & ~(f_neg + f_div_before)
         f_up = _1d_change_x > threshold
         f_up_ndims = len(f_up.shape)
         f_up_shifts = f_up if f_up_ndims==1 else f_up.any(axis=1)
@@ -2166,12 +2390,24 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
             for i in np.where(f_up_shifts)[0]:
                 v = df2['Volume'].iloc[i]
                 vol_change_pct = 0 if v == 0 else df2['Volume'].iloc[i-1] / v
-                if multiday:
-                    v = df2['Volume'].iloc[i+1]
-                    if v > 0:
-                        vol_change_pct = max(vol_change_pct, df2['Volume'].iloc[i] / v)
+                if multiday and (i+1 < len(df2)):
+                    next_v = df2['Volume'].iloc[i+1]
+                    if next_v > 0:
+                        vol_change_pct = max(vol_change_pct, df2['Volume'].iloc[i] / next_v)
                 if vol_change_pct > 5:
-                    # big volume change +500% = false positive
+                    # big volume change +500%
+                    # Could be false-positive, but need some more checks
+                    lookback = max(0, i-10)
+                    lookahead = min(len(df2), i+10)
+                    if (df2['Stock Splits'].iloc[lookback:lookahead]!=0.0).any():
+                        # There's a stock split near the volume spike, so 
+                        # assume false positive
+                        continue
+                    avg_vol_after = df2['Volume'].iloc[lookback:i-1].mean()
+                    if not np.isnan(avg_vol_after) and v/avg_vol_after < 2.0:
+                        # volume spike is actually a step-change, so 
+                        # probably missing stock split
+                        continue
                     if f_up_ndims == 1:
                         f_up[i] = False
                     else:
@@ -2219,13 +2455,13 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
                     logger.info('100x changes are too soon after stock split events, aborting', extra=log_extras)
                     return df
 
-        # if logger.isEnabledFor(logging.DEBUG):
+        if logger.isEnabledFor(logging.DEBUG):
             df_debug['i'] = list(range(0, df_debug.shape[0]))
             df_debug['i_rev'] = df_debug.shape[0]-1 - df_debug['i']
             if correct_columns_individually:
                 f_change = df_debug[[c+'_down' for c in debug_cols]].any(axis=1) | df_debug[[c+'_up' for c in debug_cols]].any(axis=1)
             else:
-                f_change = df_debug['f_down'] | df_debug['f_up']
+                f_change = df_debug['down'] | df_debug['up']
             f_change = f_change | np.roll(f_change, -1) | np.roll(f_change, 1) | np.roll(f_change, -2) | np.roll(f_change, 2)
             with pd.option_context('display.max_rows', None, 'display.max_columns', 10, 'display.width', 1000):  # more options can be specified also
                 logger.debug("price-repair-split: my workings:" + '\n' + str(df_debug[f_change]))
@@ -2457,7 +2693,12 @@ def map_signals_to_ranges(f, f_up, f_down):
                 logger.debug(msg, extra=log_extras)
                 n_corrected += r[1] - r[0]
 
-            msg = f"Corrected: {n_corrected}x"
+            if len(ranges) <= 2:
+                msg = "Corrected:"
+                for r in ranges:
+                    msg += f" {df2.index[r[1]-1].date()} -> {df2.index[r[0]].date()}"
+            else:
+                msg = f"Corrected: {n_corrected}x"
             logger.info(msg, extra=log_extras)
 
         if correct_volume:
diff --git a/yfinance/ticker.py b/yfinance/ticker.py
index 535eab10d..a10c7980d 100644
--- a/yfinance/ticker.py
+++ b/yfinance/ticker.py
@@ -22,6 +22,7 @@
 from __future__ import print_function
 
 from collections import namedtuple as _namedtuple
+from .scrapers.funds import FundsData
 
 import pandas as _pd
 
@@ -297,3 +298,7 @@ def earnings_dates(self) -> _pd.DataFrame:
     @property
     def history_metadata(self) -> dict:
         return self.get_history_metadata()
+
+    @property
+    def funds_data(self) -> FundsData:
+        return self.get_funds_data()
\ No newline at end of file