From d70c99ccfabfe536cee42f4fd1e23f11831ddea0 Mon Sep 17 00:00:00 2001
From: Steven Bischoff <61480100+stevenbischoff@users.noreply.github.com>
Date: Fri, 19 Jul 2024 15:04:26 -0500
Subject: [PATCH 01/16] Fix error on empty options chain

Signed-off-by: Steven Bischoff <61480100+stevenbischoff@users.noreply.github.com>
---
 yfinance/ticker.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/yfinance/ticker.py b/yfinance/ticker.py
index 837739700..d5824bf8c 100644
--- a/yfinance/ticker.py
+++ b/yfinance/ticker.py
@@ -92,6 +92,11 @@ def option_chain(self, date=None, tz=None):
             date = self._expirations[date]
             options = self._download_options(date)
 
+        if not options:
+            return _namedtuple('Options', ['calls', 'puts', 'underlying'])(**{
+                "calls": None, "puts": None, "underlying": None
+            })
+
         return _namedtuple('Options', ['calls', 'puts', 'underlying'])(**{
             "calls": self._options2df(options['calls'], tz=tz),
             "puts": self._options2df(options['puts'], tz=tz),

From ce6bf9378ce1321461196ddf042ccd6e825d6425 Mon Sep 17 00:00:00 2001
From: Aaron Jencks <32805004+aaron-jencks@users.noreply.github.com>
Date: Mon, 22 Jul 2024 11:29:31 -0500
Subject: [PATCH 02/16] Remove erroneous print statement

---
 yfinance/scrapers/history.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/yfinance/scrapers/history.py b/yfinance/scrapers/history.py
index e4afce5f2..95ff4bde8 100644
--- a/yfinance/scrapers/history.py
+++ b/yfinance/scrapers/history.py
@@ -225,7 +225,6 @@ def history(self, period="1mo", interval="1d",
                     quotes = quotes.iloc[0:quotes.shape[0] - 1]
         except Exception:
             shared._DFS[self.ticker] = utils.empty_df()
-            print(err_msg)
             shared._ERRORS[self.ticker] = err_msg.split(': ', 1)[1]
             if raise_errors:
                 raise _exception

From 614906cc50e2bb88f9b4512714a009f61c6565f4 Mon Sep 17 00:00:00 2001
From: ValueRaider <ValueRaider@protonmail.com>
Date: Sat, 3 Aug 2024 15:14:13 +0100
Subject: [PATCH 03/16] Implement fetch sec-filings

---
 README.md                  |  2 ++
 yfinance/base.py           |  4 ++++
 yfinance/scrapers/quote.py | 40 ++++++++++++++++++++++++++++++++++++++
 yfinance/ticker.py         |  4 ++++
 4 files changed, 50 insertions(+)

diff --git a/README.md b/README.md
index edc668d13..305cbc067 100644
--- a/README.md
+++ b/README.md
@@ -100,6 +100,8 @@ msft.capital_gains  # only for mutual funds & etfs
 msft.get_shares_full(start="2022-01-01", end=None)
 
 # show financials:
+msft.calendar
+msft.sec_filings
 # - income statement
 msft.income_stmt
 msft.quarterly_income_stmt
diff --git a/yfinance/base.py b/yfinance/base.py
index bf258a3f8..14f58f513 100644
--- a/yfinance/base.py
+++ b/yfinance/base.py
@@ -171,6 +171,10 @@ def get_calendar(self, proxy=None) -> dict:
         self._quote.proxy = proxy or self.proxy
         return self._quote.calendar
 
+    def get_sec_filings(self, proxy=None) -> dict:
+        self._quote.proxy = proxy or self.proxy
+        return self._quote.sec_filings
+
     def get_major_holders(self, proxy=None, as_dict=False):
         self._holders.proxy = proxy or self.proxy
         data = self._holders.major
diff --git a/yfinance/scrapers/quote.py b/yfinance/scrapers/quote.py
index d25c77696..a155c6209 100644
--- a/yfinance/scrapers/quote.py
+++ b/yfinance/scrapers/quote.py
@@ -496,6 +496,7 @@ def __init__(self, data: YfData, symbol: str, proxy=None):
         self._recommendations = None
         self._upgrades_downgrades = None
         self._calendar = None
+        self._sec_filings = None
 
         self._already_scraped = False
         self._already_fetched = False
@@ -563,6 +564,13 @@ def calendar(self) -> dict:
             self._fetch_calendar()
         return self._calendar
 
+    @property
+    def sec_filings(self) -> dict:
+        if self._sec_filings is None:
+            f = self._fetch_sec_filings()
+            self._sec_filings = {} if f is None else f
+        return self._sec_filings
+
     @staticmethod
     def valid_modules():
         return quote_summary_valid_modules
@@ -710,3 +718,35 @@ def _fetch_calendar(self):
                 self._calendar['Revenue Average'] = earnings.get('revenueAverage', None)
         except (KeyError, IndexError):
             raise YFDataException(f"Failed to parse json response from Yahoo Finance: {result}")
+
+
+    def _fetch_sec_filings(self):
+        result = self._fetch(self.proxy, modules=['secFilings'])
+        if result is None:
+            return None
+
+        filings = result["quoteSummary"]["result"][0]["secFilings"]["filings"]
+
+        # Improve structure
+        for f in filings:
+            if 'exhibits' in f:
+                f['exhibits'] = {e['type']:e['url'] for e in f['exhibits']}
+            f['date'] = datetime.datetime.strptime(f['date'], '%Y-%m-%d').date()
+
+        # Experimental: convert to pandas
+        # for i in range(len(filings)):
+        #     f = filings[i]
+        #     if 'exhibits' in f:
+        #         for e in f['exhibits']:
+        #             f[e['type']] = e['url']
+        #         del f['exhibits']
+        #     filings[i] = f
+        # filings = pd.DataFrame(filings)
+        # for c in filings.columns:
+        #     if c.startswith('EX-'):
+        #         filings[c] = filings[c].astype(str)
+        #         filings.loc[filings[c]=='nan', c] = ''
+        # filings = filings.drop('epochDate', axis=1)
+        # filings = filings.set_index('date')
+
+        return filings
diff --git a/yfinance/ticker.py b/yfinance/ticker.py
index d5824bf8c..73f14dece 100644
--- a/yfinance/ticker.py
+++ b/yfinance/ticker.py
@@ -168,6 +168,10 @@ def calendar(self) -> dict:
         """
         return self.get_calendar()
 
+    @property
+    def sec_filings(self) -> dict:
+        return self.get_sec_filings()
+
     @property
     def recommendations(self):
         return self.get_recommendations()

From d490fa3163e58cc682efd6130bbc307efd53f64f Mon Sep 17 00:00:00 2001
From: ThetisLake <68162082+ericpien@users.noreply.github.com>
Date: Mon, 5 Aug 2024 17:37:31 -0700
Subject: [PATCH 04/16] use dict.get() to safely access key

using .get() is safer than [key] and allows effective way to return non-None variable
---
 yfinance/scrapers/holders.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/yfinance/scrapers/holders.py b/yfinance/scrapers/holders.py
index d72553796..f5d7457c2 100644
--- a/yfinance/scrapers/holders.py
+++ b/yfinance/scrapers/holders.py
@@ -96,13 +96,13 @@ def _fetch_and_parse(self):
         try:
             data = result["quoteSummary"]["result"][0]
             # parse "institutionOwnership", "fundOwnership", "majorDirectHolders", "majorHoldersBreakdown", "insiderTransactions", "insiderHolders", "netSharePurchaseActivity"
-            self._parse_institution_ownership(data["institutionOwnership"])
-            self._parse_fund_ownership(data["fundOwnership"])
-            # self._parse_major_direct_holders(data["majorDirectHolders"])  # need more data to investigate
-            self._parse_major_holders_breakdown(data["majorHoldersBreakdown"])
-            self._parse_insider_transactions(data["insiderTransactions"])
-            self._parse_insider_holders(data["insiderHolders"])
-            self._parse_net_share_purchase_activity(data["netSharePurchaseActivity"])
+            self._parse_institution_ownership(data.get("institutionOwnership", {}))
+            self._parse_fund_ownership(data.get("fundOwnership", {}))
+            # self._parse_major_direct_holders(data.get("majorDirectHolders", {}))  # need more data to investigate
+            self._parse_major_holders_breakdown(data.get("majorHoldersBreakdown", {}))
+            self._parse_insider_transactions(data.get("insiderTransactions", {}))
+            self._parse_insider_holders(data.get("insiderHolders", {}))
+            self._parse_net_share_purchase_activity(data.get("netSharePurchaseActivity", {}))
         except (KeyError, IndexError):
             raise YFDataException("Failed to parse holders json data.")
 
@@ -113,7 +113,7 @@ def _parse_raw_values(data):
         return data
 
     def _parse_institution_ownership(self, data):
-        holders = data["ownershipList"]
+        holders = data.get("ownershipList", {})
         for owner in holders:
             for k, v in owner.items():
                 owner[k] = self._parse_raw_values(v)
@@ -125,7 +125,7 @@ def _parse_institution_ownership(self, data):
         self._institutional = df
 
     def _parse_fund_ownership(self, data):
-        holders = data["ownershipList"]
+        holders = data.get("ownershipList", {})
         for owner in holders:
             for k, v in owner.items():
                 owner[k] = self._parse_raw_values(v)
@@ -137,7 +137,7 @@ def _parse_fund_ownership(self, data):
         self._mutualfund = df
 
     def _parse_major_direct_holders(self, data):
-        holders = data["holders"]
+        holders = data.get("holders", {})
         for owner in holders:
             for k, v in owner.items():
                 owner[k] = self._parse_raw_values(v)
@@ -158,7 +158,7 @@ def _parse_major_holders_breakdown(self, data):
         self._major = df
 
     def _parse_insider_transactions(self, data):
-        holders = data["transactions"]
+        holders = data.get("transactions", {})
         for owner in holders:
             for k, v in owner.items():
                 owner[k] = self._parse_raw_values(v)
@@ -180,7 +180,7 @@ def _parse_insider_transactions(self, data):
         self._insider_transactions = df
 
     def _parse_insider_holders(self, data):
-        holders = data["holders"]
+        holders = data.get("holders", {})
         for owner in holders:
             for k, v in owner.items():
                 owner[k] = self._parse_raw_values(v)

From 76062dda22ba6ca73f361800d0b8d1c758d1d345 Mon Sep 17 00:00:00 2001
From: Eric Pien <eric.hs.pien@gmail.com>
Date: Sat, 10 Aug 2024 00:29:07 -0700
Subject: [PATCH 05/16] fixing minor typo

%% -> %
---
 yfinance/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/yfinance/utils.py b/yfinance/utils.py
index e51952075..170e46c38 100644
--- a/yfinance/utils.py
+++ b/yfinance/utils.py
@@ -885,7 +885,7 @@ def __update_amount(self, new_amount):
         num_hashes = int(round((percent_done / 100.0) * all_full))
         self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'
         pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
-        pct_string = f'{percent_done}%%'
+        pct_string = f'{percent_done}%'
         self.prog_bar = self.prog_bar[0:pct_place] + (pct_string + self.prog_bar[pct_place + len(pct_string):])
 
     def __str__(self):

From ffe364268cb60be1d994d67bba217323295a7c4b Mon Sep 17 00:00:00 2001
From: ValueRaider <ValueRaider@protonmail.com>
Date: Mon, 22 Jul 2024 20:46:41 +0100
Subject: [PATCH 06/16] Prices: improve exceptions & logging

Tidy messages sent to logging.INFO
Use logging's 'extra' argument to make YF log formatting programmatic.
Reclassify some YF price-repair log messages, so that repairs (or repair fails) are level INFO.
---
 yfinance/exceptions.py       |  15 +--
 yfinance/scrapers/history.py | 217 ++++++++++++++++++-----------------
 yfinance/utils.py            |  38 +++---
 3 files changed, 141 insertions(+), 129 deletions(-)

diff --git a/yfinance/exceptions.py b/yfinance/exceptions.py
index a44dc2d0e..46ef4fd4d 100644
--- a/yfinance/exceptions.py
+++ b/yfinance/exceptions.py
@@ -7,12 +7,6 @@ class YFDataException(YFException):
     pass
 
 
-class YFChartError(YFException):
-    def __init__(self, ticker, description):
-        self.ticker = ticker
-        super().__init__(f"{self.ticker}: {description}")
-
-
 class YFNotImplementedError(NotImplementedError):
     def __init__(self, method_name):
         super().__init__(f"Have not implemented fetching '{method_name}' from Yahoo API")
@@ -27,19 +21,22 @@ def __init__(self, ticker, rationale):
 
 class YFTzMissingError(YFTickerMissingError):
     def __init__(self, ticker):
-        super().__init__(ticker, "No timezone found")
+        super().__init__(ticker, "no timezone found")
 
 
 class YFPricesMissingError(YFTickerMissingError):
     def __init__(self, ticker, debug_info):
         self.debug_info = debug_info
-        super().__init__(ticker, f"No price data found {debug_info}")
+        if debug_info != '':
+            super().__init__(ticker, f"no price data found {debug_info}")
+        else:
+            super().__init__(ticker, "no price data found")
 
 
 class YFEarningsDateMissing(YFTickerMissingError):
     # note that this does not get raised. Added in case of raising it in the future
     def __init__(self, ticker):
-        super().__init__(ticker, "No earnings dates found")
+        super().__init__(ticker, "no earnings dates found")
 
 
 class YFInvalidPeriodError(YFException):
diff --git a/yfinance/scrapers/history.py b/yfinance/scrapers/history.py
index 95ff4bde8..9de625699 100644
--- a/yfinance/scrapers/history.py
+++ b/yfinance/scrapers/history.py
@@ -7,7 +7,7 @@
 
 from yfinance import shared, utils
 from yfinance.const import _BASE_URL_, _PRICE_COLNAMES_
-from yfinance.exceptions import YFChartError, YFInvalidPeriodError, YFPricesMissingError, YFTzMissingError
+from yfinance.exceptions import YFInvalidPeriodError, YFPricesMissingError, YFTzMissingError
 
 class PriceHistory:
     def __init__(self, data, ticker, tz, session=None, proxy=None):
@@ -164,7 +164,6 @@ def history(self, period="1mo", interval="1d",
 
         intraday = params["interval"][-1] in ("m", 'h')
         _price_data_debug = ''
-        _exception = YFPricesMissingError(self.ticker, '')
         if start or period is None or period.lower() == "max":
             _price_data_debug += f' ({params["interval"]} '
             if start_user is not None:
@@ -185,14 +184,18 @@ def history(self, period="1mo", interval="1d",
 
         fail = False
         if data is None or not isinstance(data, dict):
+            _exception = YFPricesMissingError(self.ticker, _price_data_debug)
             fail = True
         elif isinstance(data, dict) and 'status_code' in data:
             _price_data_debug += f"(Yahoo status_code = {data['status_code']})"
+            _exception = YFPricesMissingError(self.ticker, _price_data_debug)
             fail = True
         elif "chart" in data and data["chart"]["error"]:
-            _exception = YFChartError(self.ticker, data["chart"]["error"]["description"])
+            _price_data_debug += ' (Yahoo error = "' + data["chart"]["error"]["description"] + '")'
+            _exception = YFPricesMissingError(self.ticker, _price_data_debug)
             fail = True
-        elif "chart" not in data or data["chart"]["result"] is None or not data["chart"]["result"]:
+        elif "chart" not in data or data["chart"]["result"] is None or not data["chart"]["result"] or not data["chart"]["result"][0]["indicators"]["quote"][0]:
+            _exception = YFPricesMissingError(self.ticker, _price_data_debug)
             fail = True
         elif period is not None and period not in self._history_metadata["validRanges"]:
             # even if timestamp is in the data, the data doesn't encompass the period requested
@@ -200,11 +203,8 @@ def history(self, period="1mo", interval="1d",
             _exception = YFInvalidPeriodError(self.ticker, period, self._history_metadata['validRanges'])
             fail = True
 
-        if isinstance(_exception, YFPricesMissingError):
-            _exception = YFPricesMissingError(self.ticker, _price_data_debug)
-
-        err_msg = str(_exception)
         if fail:
+            err_msg = str(_exception)
             shared._DFS[self.ticker] = utils.empty_df()
             shared._ERRORS[self.ticker] = err_msg.split(': ', 1)[1]
             if raise_errors:
@@ -216,23 +216,12 @@ def history(self, period="1mo", interval="1d",
             return utils.empty_df()
 
         # parse quotes
-        try:
-            quotes = utils.parse_quotes(data["chart"]["result"][0])
-            # Yahoo bug fix - it often appends latest price even if after end date
-            if end and not quotes.empty:
-                endDt = pd.to_datetime(end, unit='s')
-                if quotes.index[quotes.shape[0] - 1] >= endDt:
-                    quotes = quotes.iloc[0:quotes.shape[0] - 1]
-        except Exception:
-            shared._DFS[self.ticker] = utils.empty_df()
-            shared._ERRORS[self.ticker] = err_msg.split(': ', 1)[1]
-            if raise_errors:
-                raise _exception
-            else:
-                logger.error(err_msg)
-            if self._reconstruct_start_interval is not None and self._reconstruct_start_interval == interval:
-                self._reconstruct_start_interval = None
-            return shared._DFS[self.ticker]
+        quotes = utils.parse_quotes(data["chart"]["result"][0])
+        # Yahoo bug fix - it often appends latest price even if after end date
+        if end and not quotes.empty:
+            endDt = pd.to_datetime(end, unit='s')
+            if quotes.index[quotes.shape[0] - 1] >= endDt:
+                quotes = quotes.iloc[0:quotes.shape[0] - 1]
         logger.debug(f'{self.ticker}: yfinance received OHLC data: {quotes.index[0]} -> {quotes.index[-1]}')
 
         # 2) fix weired bug with Yahoo! - returning 60m for 30m bars
@@ -441,6 +430,7 @@ def get_actions(self, proxy=None) -> pd.Series:
     def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
         # Reconstruct values in df using finer-grained price data. Delimiter marks what to reconstruct
         logger = utils.get_yf_logger()
+        log_extras = {'yf_cat': 'price-reconstruct', 'yf_interval': interval, 'yf_symbol': self.ticker}
 
         if not isinstance(df, pd.DataFrame):
             raise Exception("'df' must be a Pandas DataFrame not", type(df))
@@ -471,7 +461,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
             sub_interval = nexts[interval]
             td_range = itds[interval]
         else:
-            logger.warning(f"Have not implemented price repair for '{interval}' interval. Contact developers")
+            logger.warning(f"Have not implemented price reconstruct for '{interval}' interval. Contact developers")
             if "Repaired?" not in df.columns:
                 df["Repaired?"] = False
             return df
@@ -480,7 +470,8 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
         if self._reconstruct_start_interval is None:
             self._reconstruct_start_interval = interval
         if interval != self._reconstruct_start_interval and interval != nexts[self._reconstruct_start_interval]:
-            logger.debug(f"{self.ticker}: Price repair has hit max depth of 2 ('%s'->'%s'->'%s')", self._reconstruct_start_interval, nexts[self._reconstruct_start_interval], interval)
+            msg = "Hit max depth of 2 ('%s'->'%s'->'%s')", self._reconstruct_start_interval, nexts[self._reconstruct_start_interval], interval
+            logger.info(msg, extra=log_extras)
             return df
 
         df = df.sort_index()
@@ -496,12 +487,13 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
             m -= _datetime.timedelta(days=1)  # allow space for 1-day padding
             min_dt = pd.Timestamp.utcnow() - m
             min_dt = min_dt.tz_convert(df.index.tz).ceil("D")
-        logger.debug(f"min_dt={min_dt} interval={interval} sub_interval={sub_interval}")
+        logger.debug(f"min_dt={min_dt} interval={interval} sub_interval={sub_interval}", extra=log_extras)
         if min_dt is not None:
             f_recent = df.index >= min_dt
             f_repair_rows = f_repair_rows & f_recent
             if not f_repair_rows.any():
-                logger.info("Data too old to repair")
+                msg = f"Too old ({np.sum(f_repair.any(axis=1))} rows tagged)"
+                logger.info(msg, extra=log_extras)
                 if "Repaired?" not in df.columns:
                     df["Repaired?"] = False
                 return df
@@ -509,7 +501,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
         dts_to_repair = df.index[f_repair_rows]
 
         if len(dts_to_repair) == 0:
-            logger.info("Nothing needs repairing (dts_to_repair[] empty)")
+            logger.debug("Nothing needs repairing (dts_to_repair[] empty)", extra=log_extras)
             if "Repaired?" not in df.columns:
                 df["Repaired?"] = False
             return df
@@ -536,7 +528,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
             grp_max_size = _datetime.timedelta(days=5)  # allow 2 days for buffer below
         else:
             grp_max_size = _datetime.timedelta(days=30)
-        logger.debug(f"grp_max_size = {grp_max_size}")
+        logger.debug(f"grp_max_size = {grp_max_size}", extra=log_extras)
         for i in range(1, len(dts_to_repair)):
             dt = dts_to_repair[i]
             if dt.date() < dts_groups[-1][0].date() + grp_max_size:
@@ -544,7 +536,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
             else:
                 dts_groups.append([dt])
 
-        logger.debug("Repair groups:")
+        logger.debug("Repair groups:", extra=log_extras)
         for g in dts_groups:
             logger.debug(f"- {g[0]} -> {g[-1]}")
 
@@ -580,18 +572,11 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
                 reject = True
             if reject:
                 # Don't bother requesting more price data, Yahoo will reject
-                msg = f"Cannot reconstruct {interval} block starting"
-                if intraday:
-                    msg += f" {start_dt}"
-                else:
-                    msg += f" {start_d}"
-                msg += ", too old, Yahoo will reject request for finer-grain data"
-                logger.info(msg)
+                msg = f"Cannot reconstruct block starting {start_dt if intraday else start_d}, too old, Yahoo will reject request for finer-grain data"
+                logger.info(msg, extra=log_extras)
                 continue
 
             td_1d = _datetime.timedelta(days=1)
-            end_dt = g[-1]
-            end_d = end_dt.date() + td_1d
             if interval in "1wk":
                 fetch_start = start_d - td_range  # need previous week too
                 fetch_end = g[-1].date() + td_range
@@ -610,27 +595,25 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
                 fetch_end = fetch_end.date() + td_1d
             if min_dt is not None:
                 fetch_start = max(min_dt.date(), fetch_start)
-            logger.debug(f"Fetching {sub_interval} prepost={prepost} {fetch_start}->{fetch_end}")
+            logger.debug(f"Fetching {sub_interval} prepost={prepost} {fetch_start}->{fetch_end}", extra=log_extras)
+            # Temp disable erors printing
+            logger = utils.get_yf_logger()
+            if hasattr(logger, 'level'):
+                # YF's custom indented logger doesn't expose level
+                log_level = logger.level
+                logger.setLevel(logging.CRITICAL)
             df_fine = self.history(start=fetch_start, end=fetch_end, interval=sub_interval, auto_adjust=False, actions=True, prepost=prepost, repair=True, keepna=True)
+            if hasattr(logger, 'level'):
+                logger.setLevel(log_level)
             if df_fine is None or df_fine.empty:
-                msg = f"Cannot reconstruct {interval} block starting"
-                if intraday:
-                    msg += f" {start_dt}"
-                else:
-                    msg += f" {start_d}"
-                msg += ", too old, Yahoo is rejecting request for finer-grain data"
-                logger.debug(msg)
+                msg = f"Cannot reconstruct block starting {start_dt if intraday else start_d}, too old, Yahoo will reject request for finer-grain data"
+                logger.info(msg, extra=log_extras)
                 continue
             # Discard the buffer
             df_fine = df_fine.loc[g[0]: g[-1] + itds[sub_interval] - _datetime.timedelta(milliseconds=1)].copy()
             if df_fine.empty:
-                msg = f"Cannot reconstruct {interval} block range"
-                if intraday:
-                    msg += f" {start_dt}->{end_dt}"
-                else:
-                    msg += f" {start_d}->{end_d}"
-                msg += ", Yahoo not returning finer-grain data within range"
-                logger.debug(msg)
+                msg = f"Cannot reconstruct {interval} block range {start_dt if intraday else start_d}, Yahoo not returning finer-grain data within range"
+                logger.info(msg, extra=log_extras)
                 continue
 
             df_fine["ctr"] = 0
@@ -670,7 +653,8 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
             common_index = np.intersect1d(df_block.index, df_new.index)
             if len(common_index) == 0:
                 # Can't calibrate so don't attempt repair
-                logger.info(f"Can't calibrate {interval} block starting {start_d} so aborting repair")
+                msg = f"Can't calibrate {interval} block starting {start_d} so aborting repair"
+                logger.info(msg, extra=log_extras)
                 continue
             # First, attempt to calibrate the 'Adj Close' column. OK if cannot.
             # Only necessary for 1d interval, because the 1h data is not div-adjusted.
@@ -725,7 +709,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
             calib_filter = (df_block_calib != tag)
             if not calib_filter.any():
                 # Can't calibrate so don't attempt repair
-                logger.info(f"Can't calibrate {interval} block starting {start_d} so aborting repair")
+                logger.info(f"Can't calibrate block starting {start_d} so aborting repair", extra=log_extras)
                 continue
             # Avoid divide-by-zero warnings:
             for j in range(len(calib_cols)):
@@ -746,7 +730,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
                 ratio = 1.0
             else:
                 ratio = np.average(ratios, weights=weights)
-            logger.debug(f"Price calibration ratio (raw) = {ratio:6f}")
+            logger.debug(f"Price calibration ratio (raw) = {ratio:6f}", extra=log_extras)
             ratio_rcp = round(1.0 / ratio, 1)
             ratio = round(ratio, 1)
             if ratio == 1 and ratio_rcp == 1:
@@ -774,7 +758,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
                     # so probably no trading happened.
                     no_fine_data_dts.append(idx)
             if len(no_fine_data_dts) > 0:
-                logger.debug("Yahoo didn't return finer-grain data for these intervals: " + str(no_fine_data_dts))
+                logger.debug("Yahoo didn't return finer-grain data for these intervals: " + str(no_fine_data_dts), extra=log_extras)
             for idx in bad_dts:
                 if idx not in df_new.index:
                     # Yahoo didn't return finer-grain data for this interval,
@@ -810,6 +794,8 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
                 df_v2.loc[idx, "Repaired?"] = True
                 n_fixed += 1
 
+            # Not logging these reconstructions - that's job of calling function as it has context.
+
         return df_v2
 
     @utils.log_indent_decorator
@@ -834,6 +820,7 @@ def _fix_unit_random_mixups(self, df, interval, tz_exchange, prepost):
 
         # Easy to detect and fix, just look for outliers = ~100x local median
         logger = utils.get_yf_logger()
+        log_extras = {'yf_cat': 'price-repair-100x', 'yf_interval': interval, 'yf_symbol': self.ticker}
 
         if df.shape[0] == 0:
             if "Repaired?" not in df.columns:
@@ -841,7 +828,7 @@ def _fix_unit_random_mixups(self, df, interval, tz_exchange, prepost):
             return df
         if df.shape[0] == 1:
             # Need multiple rows to confidently identify outliers
-            logger.info("price-repair-100x: Cannot check single-row table for 100x price errors")
+            logger.debug("Cannot check single-row table for 100x price errors", extra=log_extras)
             if "Repaired?" not in df.columns:
                 df["Repaired?"] = False
             return df
@@ -868,7 +855,7 @@ def _fix_unit_random_mixups(self, df, interval, tz_exchange, prepost):
             df2_zeroes = None
             df_orig = df
         if df2.shape[0] <= 1:
-            logger.info("price-repair-100x: Insufficient good data for detecting 100x price errors")
+            logger.info("Insufficient good data for detecting 100x price errors", extra=log_extras)
             if "Repaired?" not in df.columns:
                 df["Repaired?"] = False
             return df
@@ -882,7 +869,7 @@ def _fix_unit_random_mixups(self, df, interval, tz_exchange, prepost):
         f_rcp = (ratio_rounded == 100) | (ratio_rcp_rounded == 100)
         f_either = f | f_rcp
         if not f_either.any():
-            logger.info("price-repair-100x: No sporadic 100x errors")
+            logger.debug("No sporadic 100x errors", extra=log_extras)
             if "Repaired?" not in df.columns:
                 df["Repaired?"] = False
             return df
@@ -954,11 +941,10 @@ def _fix_unit_random_mixups(self, df, interval, tz_exchange, prepost):
         n_fixed = n_before - n_after_crude
         n_fixed_crudely = n_after - n_after_crude
         if n_fixed > 0:
-            report_msg = f"{self.ticker}: fixed {n_fixed}/{n_before} currency unit mixups "
+            report_msg = f"fixed {n_fixed}/{n_before} currency unit mixups "
             if n_fixed_crudely > 0:
-                report_msg += f"({n_fixed_crudely} crudely) "
-            report_msg += f"in {interval} price data"
-            logger.info('price-repair-100x: ' + report_msg)
+                report_msg += f"({n_fixed_crudely} crudely)"
+            logger.info(report_msg, extra=log_extras)
 
         # Restore original values where repair failed
         f_either = df2[data_cols].to_numpy() == tag
@@ -1002,6 +988,7 @@ def _fix_zeroes(self, df, interval, tz_exchange, prepost):
             return df
 
         logger = utils.get_yf_logger()
+        log_extras = {'yf_cat': 'price-repair-zeroes', 'yf_interval': interval, 'yf_symbol': self.ticker}
 
         if df.shape[0] == 0:
             if "Repaired?" not in df.columns:
@@ -1070,13 +1057,13 @@ def _fix_zeroes(self, df, interval, tz_exchange, prepost):
         if f_vol_bad is not None:
             f_bad_rows = f_bad_rows | f_vol_bad
         if not f_bad_rows.any():
-            logger.info("price-repair-missing: No price=0 errors to repair")
+            logger.debug("No price=0 errors to repair", extra=log_extras)
             if "Repaired?" not in df.columns:
                 df["Repaired?"] = False
             return df
         if f_prices_bad.sum() == len(price_cols) * len(df2):
             # Need some good data to calibrate
-            logger.info("price-repair-missing: No good data for calibration so cannot fix price=0 bad data")
+            logger.debug("No good data for calibration so cannot fix price=0 bad data", extra=log_extras)
             if "Repaired?" not in df.columns:
                 df["Repaired?"] = False
             return df
@@ -1109,7 +1096,7 @@ def _fix_zeroes(self, df, interval, tz_exchange, prepost):
             if n_fixed < 4:
                 dts_repaired = sorted(list(set(dts_tagged).difference(dts_not_repaired)))
                 msg += f": {dts_repaired}"
-            logger.info('price-repair-missing: ' + msg)
+            logger.debug(msg, extra=log_extras)
 
         if df2_reserve is not None:
             if "Repaired?" not in df2_reserve.columns:
@@ -1136,6 +1123,7 @@ def _fix_missing_div_adjust(self, df, interval, tz_exchange):
             return df
 
         logger = utils.get_yf_logger()
+        log_extras = {'yf_cat': 'div-adjust-repair', 'yf_interval': interval, 'yf_symbol': self.ticker}
 
         if df is None or df.empty:
             return df
@@ -1147,7 +1135,7 @@ def _fix_missing_div_adjust(self, df, interval, tz_exchange):
 
         f_div = (df["Dividends"] != 0.0).to_numpy()
         if not f_div.any():
-            logger.debug('div-adjust-repair: No dividends to check')
+            logger.debug('No dividends to check', extra=log_extras)
             return df
 
         df2 = df.copy()
@@ -1161,7 +1149,7 @@ def _fix_missing_div_adjust(self, df, interval, tz_exchange):
         if last_div_idx == 0:
             # Not enough data to recalculate the div-adjustment,
             # because need close day before
-            logger.debug('div-adjust-repair: Insufficient data to recalculate div-adjustment')
+            logger.debug('Insufficient data to recalculate div-adjustment', extra=log_extras)
             return df
 
         # To determine if Yahoo messed up, analyse price data between today's dividend and
@@ -1184,8 +1172,9 @@ def _fix_missing_div_adjust(self, df, interval, tz_exchange):
             close_day_before = df2['Close'].iloc[last_div_idx - 1]
             adj = 1.0 - df2['Dividends'].iloc[last_div_idx] / close_day_before
             div = last_div_row['Dividends']
-            msg = f'Correcting missing div-adjustment preceding div = {div} @ {last_div_dt.date()} (prev_dt={prev_dt})'
-            logger.debug('div-adjust-repair: ' + msg)
+            msg = f'Correcting missing div-adjustment preceding div = {div} @ {last_div_dt.date()}'
+            logger.info(msg, extra=log_extras)
+            logger.debug(f'prev_dt={prev_dt}', extra=log_extras)
 
             if interval == '1d':
                 # exclusive
@@ -1214,6 +1203,7 @@ def _fix_bad_stock_splits(self, df, interval, tz_exchange):
             return df
 
         logger = utils.get_yf_logger()
+        log_extras = {'yf_cat': 'split-repair', 'yf_interval': interval, 'yf_symbol': self.ticker}
 
         interday = interval in ['1d', '1wk', '1mo', '3mo']
         if not interday:
@@ -1225,7 +1215,7 @@ def _fix_bad_stock_splits(self, df, interval, tz_exchange):
             logger.debug('price-repair-split: No splits in data')
             return df
 
-        logger.debug(f'price-repair-split: Splits: {str(df["Stock Splits"][split_f].to_dict())}')
+        logger.debug(f'Splits: {str(df["Stock Splits"][split_f].to_dict())}', extra=log_extras)
 
         if 'Repaired?' not in df.columns:
             df['Repaired?'] = False
@@ -1242,8 +1232,8 @@ def _fix_bad_stock_splits(self, df, interval, tz_exchange):
                 split_idx += 5
             cutoff_idx = min(df.shape[0], split_idx)  # add one row after to detect big change
             df_pre_split = df.iloc[0:cutoff_idx+1]
-            logger.debug(f'price-repair-split: split_idx={split_idx} split_dt={split_dt}')
-            logger.debug(f'price-repair-split: df dt range: {df_pre_split.index[0].date()} -> {df_pre_split.index[-1].date()}')
+            logger.debug(f'split_idx={split_idx} split_dt={split_dt}', extra=log_extras)
+            logger.debug(f'df dt range: {df_pre_split.index[0].date()} -> {df_pre_split.index[-1].date()}', extra=log_extras)
 
             df_pre_split_repaired = self._fix_prices_sudden_change(df_pre_split, interval, tz_exchange, split, correct_volume=True)
             # Merge back in:
@@ -1263,6 +1253,7 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
             return df
 
         logger = utils.get_yf_logger()
+        log_extras = {'yf_cat': 'price-change-repair', 'yf_interval': interval, 'yf_symbol': self.ticker}
 
         split = change
         split_rcp = 1.0 / split
@@ -1270,20 +1261,22 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
 
         if change in [100.0, 0.01]:
             fix_type = '100x error'
+            log_extras['yf_cat'] = 'price-repair-100x'
             start_min = None
         else:
             fix_type = 'bad split'
+            log_extras['yf_cat'] = 'price-repair-split'
             # start_min = 1 year before oldest split
             f = df['Stock Splits'].to_numpy() != 0.0
             start_min = (df.index[f].min() - _dateutil.relativedelta.relativedelta(years=1)).date()
-        logger.debug(f'price-repair-split: start_min={start_min} change={change}')
+        logger.debug(f'start_min={start_min} change={change}', extra=log_extras)
 
         OHLC = ['Open', 'High', 'Low', 'Close']
 
         # Do not attempt repair of the split is small,
         # could be mistaken for normal price variance
         if 0.8 < split < 1.25:
-            logger.info("price-repair-split: Split ratio too close to 1. Won't repair")
+            logger.debug("Split ratio too close to 1. Won't repair", extra=log_extras)
             return df
 
         df2 = df.copy().sort_index(ascending=False)
@@ -1305,10 +1298,10 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
             idx_latest_active = None
         else:
             idx_latest_active = int(idx_latest_active[0])
-        log_msg = f'price-repair-split: appears_suspended={appears_suspended}, idx_latest_active={idx_latest_active}'
+        log_msg = f'appears_suspended={appears_suspended}, idx_latest_active={idx_latest_active}'
         if idx_latest_active is not None:
             log_msg += f' ({df2.index[idx_latest_active].date()})'
-        logger.debug(log_msg)
+        logger.debug(log_msg, extra=log_extras)
 
         if logger.isEnabledFor(logging.DEBUG):
             df_debug = df2.copy()
@@ -1369,7 +1362,7 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
         # If all 1D changes are closer to 1.0 than split, exit
         split_max = max(split, split_rcp)
         if np.max(_1d_change_minx) < (split_max - 1) * 0.5 + 1 and np.min(_1d_change_minx) > 1.0 / ((split_max - 1) * 0.5 + 1):
-            logger.info(f"price-repair-split: No {fix_type}s detected")
+            logger.debug(f'No {fix_type}s detected', extra=log_extras)
             return df
 
         # Calculate the true price variance, i.e. remove effect of bad split-adjustments.
@@ -1383,7 +1376,7 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
         sd = np.std(_1d_change_minx[f])
         # Now can calculate SD as % of mean
         sd_pct = sd / avg
-        logger.debug(f"price-repair-split: Estimation of true 1D change stats: mean = {avg:.2f}, StdDev = {sd:.4f} ({sd_pct*100.0:.1f}% of mean)")
+        logger.debug(f"Estimation of true 1D change stats: mean = {avg:.2f}, StdDev = {sd:.4f} ({sd_pct*100.0:.1f}% of mean)", extra=log_extras)
 
         # Only proceed if split adjustment far exceeds normal 1D changes
         largest_change_pct = 5 * sd_pct
@@ -1392,19 +1385,17 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
             if interval in ['1mo', '3mo']:
                 largest_change_pct *= 2
         if max(split, split_rcp) < 1.0 + largest_change_pct:
-            logger.info("price-repair-split: Split ratio too close to normal price volatility. Won't repair")
-            logger.debug(f"sd_pct = {sd_pct:.4f}  largest_change_pct = {largest_change_pct:.4f}")
-            if logger.isEnabledFor(logging.DEBUG):
-                logger.debug(f"sd_pct = {sd_pct:.4f}  largest_change_pct = {largest_change_pct:.4f}")
+            logger.debug("Split ratio too close to normal price volatility. Won't repair", extra=log_extras)
+            logger.debug(f"sd_pct = {sd_pct:.4f}  largest_change_pct = {largest_change_pct:.4f}", extra=log_extras)
             return df
 
         # Now can detect bad split adjustments
         # Set threshold to halfway between split ratio and largest expected normal price change
         r = _1d_change_minx / split_rcp
         split_max = max(split, split_rcp)
-        logger.debug(f"price-repair-split: split_max={split_max:.3f} largest_change_pct={largest_change_pct:.4f}")
+        logger.debug(f"split_max={split_max:.3f} largest_change_pct={largest_change_pct:.4f}", extra=log_extras)
         threshold = (split_max + 1.0 + largest_change_pct) * 0.5
-        logger.debug(f"price-repair-split: threshold={threshold:.3f}")
+        logger.debug(f"threshold={threshold:.3f}", extra=log_extras)
 
         if 'Repaired?' not in df2.columns:
             df2['Repaired?'] = False
@@ -1445,7 +1436,7 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
 
 
         if not f.any():
-            logger.info(f'price-repair-split: No {fix_type}s detected')
+            logger.debug(f'No {fix_type}s detected', extra=log_extras)
             return df
 
         # Update: if any 100x changes are soon after a stock split, so could be confused with split error, then abort
@@ -1468,7 +1459,7 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
                 else:
                     threshold = _datetime.timedelta(days=threshold_days)
                 if gap_td < threshold:
-                    logger.info('price-repair-split: 100x changes are too soon after stock split events, aborting')
+                    logger.info('100x changes are too soon after stock split events, aborting', extra=log_extras)
                     return df
 
         # if logger.isEnabledFor(logging.DEBUG):
@@ -1517,7 +1508,7 @@ def map_signals_to_ranges(f, f_up, f_down):
 
         if idx_latest_active is not None:
             idx_rev_latest_active = df.shape[0] - 1 - idx_latest_active
-            logger.debug(f'price-repair-split: idx_latest_active={idx_latest_active}, idx_rev_latest_active={idx_rev_latest_active}')
+            logger.debug(f'idx_latest_active={idx_latest_active}, idx_rev_latest_active={idx_rev_latest_active}', extra=log_extras)
         if correct_columns_individually:
             f_corrected = np.full(n, False)
             if correct_volume:
@@ -1566,13 +1557,13 @@ def map_signals_to_ranges(f, f_up, f_down):
                     ranges.extend(ranges_after)
                 else:
                     ranges = map_signals_to_ranges(f[:, j], f_up[:, j], f_down[:, j])
-                logger.debug(f"column '{c}' ranges: {ranges}")
+                logger.debug(f"column '{c}' ranges: {ranges}", extra=log_extras)
                 if start_min is not None:
                     # Prune ranges that are older than start_min
                     for i in range(len(ranges)-1, -1, -1):
                         r = ranges[i]
                         if df2.index[r[0]].date() < start_min:
-                            logger.debug(f'price-repair-split: Pruning {c} range {df2.index[r[0]]}->{df2.index[r[1]-1]} because too old.')
+                            logger.debug(f'Pruning {c} range {df2.index[r[0]]}->{df2.index[r[1]-1]} because too old.', extra=log_extras)
                             del ranges[i]
 
                 if len(ranges) > 0:
@@ -1586,9 +1577,10 @@ def map_signals_to_ranges(f, f_up, f_down):
                 idxs = [i if OHLC_correct_ranges[i] else -1 for i in range(len(OHLC))]
                 idx = np.where(np.array(idxs) != -1)[0][0]
                 col = OHLC[idx]
-                logger.debug(f'price-repair-split: Potential {fix_type} detected only in column {col}, so treating as false positive (ignore)')
+                logger.debug(f'Potential {fix_type} detected only in column {col}, so treating as false positive (ignore)', extra=log_extras)
             else:
                 # Only correct if at least 2 columns require correction.
+                n_corrected = [0,0,0,0]
                 for j in range(len(OHLC)):
                     c = OHLC[j]
                     ranges = OHLC_correct_ranges[j]
@@ -1602,9 +1594,12 @@ def map_signals_to_ranges(f, f_up, f_down):
                             m = split_rcp
                             m_rcp = split
                         if interday:
-                            logger.info(f"price-repair-split: Corrected {fix_type} on col={c} range=[{df2.index[r[1]-1].date()}:{df2.index[r[0]].date()}] m={m:.4f}")
+                            msg = f"Corrected {fix_type} on col={c} range=[{df2.index[r[1]-1].date()}:{df2.index[r[0]].date()}] m={m:.4f}"
                         else:
-                            logger.info(f"price-repair-split: Corrected {fix_type} on col={c} range=[{df2.index[r[1]-1]}:{df2.index[r[0]]}] m={m:.4f}")
+                            msg = f"Corrected {fix_type} on col={c} range=[{df2.index[r[1]-1]}:{df2.index[r[0]]}] m={m:.4f}"
+                        logger.debug(msg, extra=log_extras)
+                        # Instead of logging here, just count
+                        n_corrected[j] += r[1]-r[0]
                         df2.iloc[r[0]:r[1], df2.columns.get_loc(c)] *= m
                         if c == 'Close':
                             df2.iloc[r[0]:r[1], df2.columns.get_loc('Adj Close')] *= m
@@ -1614,6 +1609,15 @@ def map_signals_to_ranges(f, f_up, f_down):
                             elif c == 'Close':
                                 f_close_fixed[r[0]:r[1]] = True
                         f_corrected[r[0]:r[1]] = True
+                if sum(n_corrected) > 0:
+                    counts_pretty = ''
+                    for j in range(len(OHLC)):
+                        if n_corrected[j] != 0:
+                            if counts_pretty != '':
+                                counts_pretty += ', '
+                            counts_pretty += f'{OHLC[j]}={n_corrected[j]}x'
+                    msg = f"Corrected: {counts_pretty}"
+                    logger.info(msg, extra=log_extras)
 
             if correct_volume:
                 f_open_and_closed_fixed = f_open_fixed & f_close_fixed
@@ -1626,6 +1630,7 @@ def map_signals_to_ranges(f, f_up, f_down):
             df2.loc[f_corrected, 'Repaired?'] = True
 
         else:
+            n_corrected = 0
             idx_first_f = np.where(f)[0][0]
             if appears_suspended and (idx_latest_active is not None and idx_latest_active >= idx_first_f):
                 # Suspended midway during data date range.
@@ -1661,7 +1666,7 @@ def map_signals_to_ranges(f, f_up, f_down):
                 for i in range(len(ranges)-1, -1, -1):
                     r = ranges[i]
                     if df2.index[r[0]].date() < start_min:
-                        logger.debug(f'price-repair-split: Pruning range {df2.index[r[0]]}->{df2.index[r[1]-1]} because too old.')
+                        logger.debug(f'Pruning range {df2.index[r[0]]}->{df2.index[r[1]-1]} because too old.', extra=log_extras)
                         del ranges[i]
             for r in ranges:
                 if r[2] == 'split':
@@ -1670,7 +1675,7 @@ def map_signals_to_ranges(f, f_up, f_down):
                 else:
                     m = split_rcp
                     m_rcp = split
-                logger.debug(f"price-repair-split: range={r} m={m}")
+                logger.debug(f"range={r} m={m}", extra=log_extras)
                 for c in ['Open', 'High', 'Low', 'Close', 'Adj Close']:
                     df2.iloc[r[0]:r[1], df2.columns.get_loc(c)] *= m
                 if correct_volume:
@@ -1679,18 +1684,22 @@ def map_signals_to_ranges(f, f_up, f_down):
                 df2.iloc[r[0]:r[1], df2.columns.get_loc('Repaired?')] = True
                 if r[0] == r[1] - 1:
                     if interday:
-                        msg = f"price-repair-split: Corrected {fix_type} on interval {df2.index[r[0]].date()}"
+                        msg = f"Corrected {fix_type} on interval {df2.index[r[0]].date()}"
                     else:
-                        msg = f"price-repair-split: Corrected {fix_type} on interval {df2.index[r[0]]}"
+                        msg = f"Corrected {fix_type} on interval {df2.index[r[0]]}"
                 else:
                     # Note: df2 sorted with index descending
                     start = df2.index[r[1] - 1]
                     end = df2.index[r[0]]
                     if interday:
-                        msg = f"price-repair-split: Corrected {fix_type} across intervals {start.date()} -> {end.date()} (inclusive)"
+                        msg = f"Corrected {fix_type} across intervals {start.date()} -> {end.date()} (inclusive)"
                     else:
-                        msg = f"price-repair-split: Corrected {fix_type} across intervals {start} -> {end} (inclusive)"
-                logger.info(msg)
+                        msg = f"Corrected {fix_type} across intervals {start} -> {end} (inclusive)"
+                logger.debug(msg, extra=log_extras)
+                n_corrected += r[1] - r[0]
+
+            msg = f"Corrected: {n_corrected}x"
+            logger.info(msg, extra=log_extras)
 
         if correct_volume:
             f_na = df2['Volume'].isna()
diff --git a/yfinance/utils.py b/yfinance/utils.py
index e51952075..7e8462ff4 100644
--- a/yfinance/utils.py
+++ b/yfinance/utils.py
@@ -141,39 +141,45 @@ def format(self, record):
 yf_log_indented = False
 
 
+class YFLogFormatter(logging.Filter):
+    # Help be consistent with structuring YF log messages
+    def filter(self, record):
+        msg = record.msg
+        if hasattr(record, 'yf_cat'):
+            msg = f"{record.yf_cat}: {msg}"
+        if hasattr(record, 'yf_interval'):
+            msg = f"{record.yf_interval}: {msg}"
+        if hasattr(record, 'yf_symbol'):
+            msg = f"{record.yf_symbol}: {msg}"
+        record.msg = msg
+        return True
+
+
 def get_yf_logger():
     global yf_logger
-    if yf_logger is None:
-        yf_logger = logging.getLogger('yfinance')
     global yf_log_indented
     if yf_log_indented:
         yf_logger = get_indented_logger('yfinance')
+    elif yf_logger is None:
+        yf_logger = logging.getLogger('yfinance')
+        yf_logger.addFilter(YFLogFormatter())
     return yf_logger
 
 
-def setup_debug_formatting():
+def enable_debug_mode():
     global yf_logger
-    yf_logger = get_yf_logger()
-
-    if not yf_logger.isEnabledFor(logging.DEBUG):
-        yf_logger.warning("logging mode not set to 'DEBUG', so not setting up debug formatting")
-        return
-
     global yf_log_indented
     if not yf_log_indented:
+        yf_logger = logging.getLogger('yfinance')
+        yf_logger.setLevel(logging.DEBUG)
         if yf_logger.handlers is None or len(yf_logger.handlers) == 0:
             h = logging.StreamHandler()
             # Ensure different level strings don't interfere with indentation
             formatter = MultiLineFormatter(fmt='%(levelname)-8s %(message)s')
             h.setFormatter(formatter)
             yf_logger.addHandler(h)
-
-    yf_log_indented = True
-
-
-def enable_debug_mode():
-    get_yf_logger().setLevel(logging.DEBUG)
-    setup_debug_formatting()
+        yf_logger = get_indented_logger()
+        yf_log_indented = True
 
 
 def is_isin(string):

From 447ec68eff27cf32a0ce46728e158e6687ff2eec Mon Sep 17 00:00:00 2001
From: Eric Pien <eric.hs.pien@gmail.com>
Date: Sat, 10 Aug 2024 01:29:55 -0700
Subject: [PATCH 07/16] add try except for when requests.response.json() fails

- return empty set rather than raising an exception
---
 yfinance/base.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/yfinance/base.py b/yfinance/base.py
index 14f58f513..fa40912de 100644
--- a/yfinance/base.py
+++ b/yfinance/base.py
@@ -512,11 +512,16 @@ def get_news(self, proxy=None) -> list:
         # Getting data from json
         url = f"{_BASE_URL_}/v1/finance/search?q={self.ticker}"
         data = self._data.cache_get(url=url, proxy=proxy)
-        if "Will be right back" in data.text:
+        if data is None or "Will be right back" in data.text:
             raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
                                "Our engineers are working quickly to resolve "
                                "the issue. Thank you for your patience.")
-        data = data.json()
+        try:
+            data = data.json()
+        except (_json.JSONDecodeError): 
+            logger = utils.get_yf_logger()
+            logger.error(f"{self.ticker}: Failed to retrieve the news and received faulty response instead.")
+            data = {}
 
         # parse news
         self._news = data.get("news", [])

From 1037ec5b059c9ebab8a68fd25aca4fab7f4579b1 Mon Sep 17 00:00:00 2001
From: Aleks Fasting <aleks.fasting@gmail.com>
Date: Thu, 15 Aug 2024 17:11:57 +0200
Subject: [PATCH 08/16] solves #2026

Solution that checks the offset of the opening time of the stock exchange (mod 30 minutes) and adds it to the quotes
---
 yfinance/scrapers/history.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/yfinance/scrapers/history.py b/yfinance/scrapers/history.py
index 9de625699..12cf88e77 100644
--- a/yfinance/scrapers/history.py
+++ b/yfinance/scrapers/history.py
@@ -227,7 +227,9 @@ def history(self, period="1mo", interval="1d",
         # 2) fix weired bug with Yahoo! - returning 60m for 30m bars
         if interval.lower() == "30m":
             logger.debug(f'{self.ticker}: resampling 30m OHLC from 15m')
-            quotes2 = quotes.resample('30min')
+            exchangeStartTime = pd.Timestamp(self._history_metadata["tradingPeriods"][0][0]["start"], unit='s')
+            offset = str(exchangeStartTime.minute % 30)+"min"
+            quotes2 = quotes.resample('30min', offset=offset)
             quotes = pd.DataFrame(index=quotes2.last().index, data={
                 'Open': quotes2['Open'].first(),
                 'High': quotes2['High'].max(),

From 408d0659e534c8ccdf296cf14dd35d225ea49c3f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Filip=20Kr=C3=A1sa?=
 <102549834+Fidasek009@users.noreply.github.com>
Date: Sun, 11 Aug 2024 10:53:09 +0200
Subject: [PATCH 09/16] Implement Analysis

---
 yfinance/base.py              |  75 +++++++---
 yfinance/scrapers/analysis.py | 265 ++++++++++++++++++++++++++++++----
 yfinance/ticker.py            |  40 +++--
 3 files changed, 315 insertions(+), 65 deletions(-)

diff --git a/yfinance/base.py b/yfinance/base.py
index bf258a3f8..b614b56ac 100644
--- a/yfinance/base.py
+++ b/yfinance/base.py
@@ -240,40 +240,67 @@ def get_sustainability(self, proxy=None, as_dict=False):
             return data.to_dict()
         return data
 
-    def get_analyst_price_target(self, proxy=None, as_dict=False):
+    def get_analyst_price_targets(self, proxy=None) -> dict:
+        """
+        Keys:   current  low  high  mean  median
+        """
         self._analysis.proxy = proxy or self.proxy
-        data = self._analysis.analyst_price_target
-        if as_dict:
-            return data.to_dict()
+        data = self._analysis.analyst_price_targets
         return data
 
-    def get_rev_forecast(self, proxy=None, as_dict=False):
+    def get_earnings_estimate(self, proxy=None, as_dict=False):
+        """
+        Index:      0q  +1q  0y  +1y
+        Columns:    numberOfAnalysts  avg  low  high  yearAgoEps  growth
+        """
         self._analysis.proxy = proxy or self.proxy
-        data = self._analysis.rev_est
-        if as_dict:
-            return data.to_dict()
-        return data
+        data = self._analysis.earnings_estimate
+        return data.to_dict() if as_dict else data
 
-    def get_earnings_forecast(self, proxy=None, as_dict=False):
+    def get_revenue_estimate(self, proxy=None, as_dict=False):
+        """
+        Index:      0q  +1q  0y  +1y
+        Columns:    numberOfAnalysts  avg  low  high  yearAgoRevenue  growth
+        """
         self._analysis.proxy = proxy or self.proxy
-        data = self._analysis.eps_est
-        if as_dict:
-            return data.to_dict()
-        return data
+        data = self._analysis.revenue_estimate
+        return data.to_dict() if as_dict else data
+
+    def get_earnings_history(self, proxy=None, as_dict=False):
+        """
+        Index:      pd.DatetimeIndex
+        Columns:    epsEstimate  epsActual  epsDifference  surprisePercent
+        """
+        self._analysis.proxy = proxy or self.proxy
+        data = self._analysis.earnings_history
+        return data.to_dict() if as_dict else data
 
-    def get_trend_details(self, proxy=None, as_dict=False):
+    def get_eps_trend(self, proxy=None, as_dict=False):
+        """
+        Index:      0q  +1q  0y  +1y
+        Columns:    current  7daysAgo  30daysAgo  60daysAgo  90daysAgo
+        """
         self._analysis.proxy = proxy or self.proxy
-        data = self._analysis.analyst_trend_details
-        if as_dict:
-            return data.to_dict()
-        return data
+        data = self._analysis.eps_trend
+        return data.to_dict() if as_dict else data
+
+    def get_eps_revisions(self, proxy=None, as_dict=False):
+        """
+        Index:      0q  +1q  0y  +1y
+        Columns:    upLast7days  upLast30days  downLast7days  downLast30days
+        """
+        self._analysis.proxy = proxy or self.proxy
+        data = self._analysis.eps_revisions
+        return data.to_dict() if as_dict else data
 
-    def get_earnings_trend(self, proxy=None, as_dict=False):
+    def get_growth_estimates(self, proxy=None, as_dict=False):
+        """
+        Index:      0q  +1q  0y  +1y +5y -5y
+        Columns:    stock  industry  sector  index
+        """
         self._analysis.proxy = proxy or self.proxy
-        data = self._analysis.earnings_trend
-        if as_dict:
-            return data.to_dict()
-        return data
+        data = self._analysis.growth_estimates
+        return data.to_dict() if as_dict else data
 
     def get_earnings(self, proxy=None, as_dict=False, freq="yearly"):
         """
diff --git a/yfinance/scrapers/analysis.py b/yfinance/scrapers/analysis.py
index 0f6cd5f15..ab74db4de 100644
--- a/yfinance/scrapers/analysis.py
+++ b/yfinance/scrapers/analysis.py
@@ -1,7 +1,11 @@
 import pandas as pd
+import requests
 
+from yfinance import utils
 from yfinance.data import YfData
-from yfinance.exceptions import YFNotImplementedError
+from yfinance.const import quote_summary_valid_modules
+from yfinance.scrapers.quote import _QUOTE_SUMMARY_URL_
+from yfinance.exceptions import YFException
 
 
 class Analysis:
@@ -11,39 +15,250 @@ def __init__(self, data: YfData, symbol: str, proxy=None):
         self._symbol = symbol
         self.proxy = proxy
 
+        # In quoteSummary the 'earningsTrend' module contains most of the data below.
+        # The format of data is not optimal so each function will process it's part of the data.
+        # This variable works as a cache.
         self._earnings_trend = None
-        self._analyst_trend_details = None
-        self._analyst_price_target = None
-        self._rev_est = None
-        self._eps_est = None
-        self._already_scraped = False
+
+        self._analyst_price_targets = None
+        self._earnings_estimate = None
+        self._revenue_estimate = None
+        self._earnings_history = None
+        self._eps_trend = None
+        self._eps_revisions = None
+        self._growth_estimates = None
+
+    @property
+    def analyst_price_targets(self) -> dict:
+        if self._analyst_price_targets is not None:
+            return self._analyst_price_targets
+
+        try:
+            data = self._fetch(['financialData'])
+            data = data['quoteSummary']['result'][0]['financialData']
+        except:
+            self._analyst_price_targets = {}
+            return self._analyst_price_targets
+
+        keys = [
+            ('currentPrice', 'current'),
+            ('targetLowPrice', 'low'),
+            ('targetHighPrice', 'high'),
+            ('targetMeanPrice', 'mean'),
+            ('targetMedianPrice', 'median'),
+        ]
+
+        self._analyst_price_targets = {newKey: data.get(oldKey, None) for oldKey, newKey in keys}
+        return self._analyst_price_targets
+
+    @property
+    def earnings_estimate(self) -> pd.DataFrame:
+        if self._earnings_estimate is not None:
+            return self._earnings_estimate
+
+        if self._earnings_trend is None:
+            self._fetch_earnings_trend()
+
+        data_dict = {
+            'numberOfAnalysts': [],
+            'avg': [],
+            'low': [],
+            'high': [],
+            'yearAgoEps': [],
+            'growth': []
+        }
+        periods = []
+
+        for item in self._earnings_trend[:4]:
+            periods.append(item['period'])
+            earnings_estimate = item.get('earningsEstimate', {})
+
+            for key in data_dict.keys():
+                data_dict[key].append(earnings_estimate.get(key, {}).get('raw', None))
+
+        self._earnings_estimate = pd.DataFrame(data_dict, index=periods)
+        return self._earnings_estimate
 
     @property
-    def earnings_trend(self) -> pd.DataFrame:
+    def revenue_estimate(self) -> pd.DataFrame:
+        if self._revenue_estimate is not None:
+            return self._revenue_estimate
+
         if self._earnings_trend is None:
-            raise YFNotImplementedError('earnings_trend')
-        return self._earnings_trend
+            self._fetch_earnings_trend()
+
+        data_dict = {
+            'numberOfAnalysts': [],
+            'avg': [],
+            'low': [],
+            'high': [],
+            'yearAgoRevenue': [],
+            'growth': []
+        }
+        periods = []
+
+        for item in self._earnings_trend[:4]:
+            periods.append(item['period'])
+            revenue_estimate = item.get('revenueEstimate', {})
+
+            for key in data_dict.keys():
+                data_dict[key].append(revenue_estimate.get(key, {}).get('raw', None))
+
+        self._revenue_estimate = pd.DataFrame(data_dict, index=periods)
+        return self._revenue_estimate
 
     @property
-    def analyst_trend_details(self) -> pd.DataFrame:
-        if self._analyst_trend_details is None:
-            raise YFNotImplementedError('analyst_trend_details')
-        return self._analyst_trend_details
+    def earnings_history(self) -> pd.DataFrame:
+        if self._earnings_history is not None:
+            return self._earnings_history
+
+        try:
+            data = self._fetch(['earningsHistory'])
+            data = data['quoteSummary']['result'][0]['earningsHistory']['history']
+        except:
+            self._earnings_history = pd.DataFrame()
+            return self._earnings_history
+
+        data_dict = {
+            'epsEstimate': [],
+            'epsActual': [],
+            'epsDifference': [],
+            'surprisePercent': []
+        }
+        quarters = []
+
+        for item in data:
+            quarters.append(item.get('quarter', {}).get('fmt', None))
+
+            for key in data_dict.keys():
+                data_dict[key].append(item.get(key, {}).get('raw', None))
+        
+        datetime_index = pd.to_datetime(quarters, format='%Y-%m-%d')
+        self._earnings_history = pd.DataFrame(data_dict, index=datetime_index)
+        return self._earnings_history
 
     @property
-    def analyst_price_target(self) -> pd.DataFrame:
-        if self._analyst_price_target is None:
-            raise YFNotImplementedError('analyst_price_target')
-        return self._analyst_price_target
+    def eps_trend(self) -> pd.DataFrame:
+        if self._eps_trend is not None:
+            return self._eps_trend
+
+        if self._earnings_trend is None:
+            self._fetch_earnings_trend()
+
+        data_dict = {
+            'current': [],
+            '7daysAgo': [],
+            '30daysAgo': [],
+            '60daysAgo': [],
+            '90daysAgo': []
+        }
+        periods = []
+
+        for item in self._earnings_trend[:4]:
+            periods.append(item['period'])
+            eps_trend = item.get('epsTrend', {})
+
+            for key in data_dict.keys():
+                data_dict[key].append(eps_trend.get(key, {}).get('raw', None))
+
+        self._eps_trend = pd.DataFrame(data_dict, index=periods)
+        return self._eps_trend
 
     @property
-    def rev_est(self) -> pd.DataFrame:
-        if self._rev_est is None:
-            raise YFNotImplementedError('rev_est')
-        return self._rev_est
+    def eps_revisions(self) -> pd.DataFrame:
+        if self._eps_revisions is not None:
+            return self._eps_revisions
+
+        if self._earnings_trend is None:
+            self._fetch_earnings_trend()
+
+        data_dict = {
+            'upLast7days': [],
+            'upLast30days': [],
+            'downLast7days': [],
+            'downLast30days': []
+        }
+        periods = []
+
+        for item in self._earnings_trend[:4]:
+            periods.append(item['period'])
+            eps_revisions = item.get('epsRevisions', {})
+
+            for key in data_dict.keys():
+                data_dict[key].append(eps_revisions.get(key, {}).get('raw', None))
+
+        self._eps_revisions = pd.DataFrame(data_dict, index=periods)
+        return self._eps_revisions
 
     @property
-    def eps_est(self) -> pd.DataFrame:
-        if self._eps_est is None:
-            raise YFNotImplementedError('eps_est')
-        return self._eps_est
+    def growth_estimates(self) -> pd.DataFrame:
+        if self._growth_estimates is not None:
+            return self._growth_estimates
+
+        if self._earnings_trend is None:
+            self._fetch_earnings_trend()
+
+        try:
+            trends = self._fetch(['industryTrend', 'sectorTrend', 'indexTrend'])
+            trends = trends['quoteSummary']['result'][0]
+        except:
+            self._growth_estimates = pd.DataFrame()
+            return self._growth_estimates
+
+        data_dict = {
+            '0q': [],
+            '+1q': [],
+            '0y': [],
+            '+1y': [],
+            '+5y': [],
+            '-5y': []
+        }
+
+        # make sure no column is empty
+        dummy_trend = [{'period': key, 'growth': None} for key in data_dict.keys()]
+        industry_trend = trends['industryTrend']['estimates'] or dummy_trend
+        sector_trend = trends['sectorTrend']['estimates'] or dummy_trend
+        index_trend = trends['indexTrend']['estimates'] or dummy_trend
+
+        for item in self._earnings_trend:
+            period = item['period']
+            data_dict[period].append(item.get('growth', {}).get('raw', None))
+
+        for item in industry_trend:
+            period = item['period']
+            data_dict[period].append(item.get('growth', None))
+
+        for item in sector_trend:
+            period = item['period']
+            data_dict[period].append(item.get('growth', None))
+
+        for item in index_trend:
+            period = item['period']
+            data_dict[period].append(item.get('growth', None))
+
+        cols = ['stock', 'industry', 'sector', 'index']
+        self._growth_estimates = pd.DataFrame(data_dict, index=cols).T
+        return self._growth_estimates
+
+    # modified version from quote.py
+    def _fetch(self, modules: list):
+        if not isinstance(modules, list):
+            raise YFException("Should provide a list of modules, see available modules using `valid_modules`")
+
+        modules = ','.join([m for m in modules if m in quote_summary_valid_modules])
+        if len(modules) == 0:
+            raise YFException("No valid modules provided, see available modules using `valid_modules`")
+        params_dict = {"modules": modules, "corsDomain": "finance.yahoo.com", "formatted": "false", "symbol": self._symbol}
+        try:
+            result = self._data.get_raw_json(_QUOTE_SUMMARY_URL_ + f"/{self._symbol}", user_agent_headers=self._data.user_agent_headers, params=params_dict, proxy=self.proxy)
+        except requests.exceptions.HTTPError as e:
+            utils.get_yf_logger().error(str(e))
+            return None
+        return result
+
+    def _fetch_earnings_trend(self) -> None:
+        try:
+            data = self._fetch(['earningsTrend'])
+            self._earnings_trend = data['quoteSummary']['result'][0]['earningsTrend']['trend']
+        except:
+            self._earnings_trend = []
diff --git a/yfinance/ticker.py b/yfinance/ticker.py
index 837739700..2cbdac499 100644
--- a/yfinance/ticker.py
+++ b/yfinance/ticker.py
@@ -240,12 +240,32 @@ def quarterly_cashflow(self) -> _pd.DataFrame:
         return self.quarterly_cash_flow
 
     @property
-    def analyst_price_target(self) -> _pd.DataFrame:
-        return self.get_analyst_price_target()
+    def analyst_price_targets(self) -> dict:
+        return self.get_analyst_price_targets()
 
     @property
-    def revenue_forecasts(self) -> _pd.DataFrame:
-        return self.get_rev_forecast()
+    def earnings_estimate(self) -> _pd.DataFrame:
+        return self.get_earnings_estimate()
+
+    @property
+    def revenue_estimate(self) -> _pd.DataFrame:
+        return self.get_revenue_estimate()
+
+    @property
+    def earnings_history(self) -> _pd.DataFrame:
+        return self.get_earnings_history()
+
+    @property
+    def eps_trend(self) -> _pd.DataFrame:
+        return self.get_eps_trend()
+
+    @property
+    def eps_revisions(self) -> _pd.DataFrame:
+        return self.get_eps_revisions()
+
+    @property
+    def growth_estimates(self) -> _pd.DataFrame:
+        return self.get_growth_estimates()
 
     @property
     def sustainability(self) -> _pd.DataFrame:
@@ -261,22 +281,10 @@ def options(self) -> tuple:
     def news(self) -> list:
         return self.get_news()
 
-    @property
-    def trend_details(self) -> _pd.DataFrame:
-        return self.get_trend_details()
-
-    @property
-    def earnings_trend(self) -> _pd.DataFrame:
-        return self.get_earnings_trend()
-
     @property
     def earnings_dates(self) -> _pd.DataFrame:
         return self.get_earnings_dates()
 
-    @property
-    def earnings_forecasts(self) -> _pd.DataFrame:
-        return self.get_earnings_forecast()
-
     @property
     def history_metadata(self) -> dict:
         return self.get_history_metadata()

From bc719627d1dd516d9dd54ebcc390cd4e08fb85b8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Filip=20Kr=C3=A1sa?=
 <102549834+Fidasek009@users.noreply.github.com>
Date: Fri, 16 Aug 2024 17:40:40 +0200
Subject: [PATCH 10/16] Implement tests for Analysis

---
 tests/test_ticker.py | 118 ++++++++++++++++++++++++++++++++++++-------
 1 file changed, 101 insertions(+), 17 deletions(-)

diff --git a/tests/test_ticker.py b/tests/test_ticker.py
index 84ed9f731..6a04661fa 100644
--- a/tests/test_ticker.py
+++ b/tests/test_ticker.py
@@ -41,14 +41,17 @@
     ("balance_sheet", pd.DataFrame),
     ("quarterly_income_stmt", pd.DataFrame),
     ("income_stmt", pd.DataFrame),
-    ("analyst_price_target", pd.DataFrame),
-    ("revenue_forecasts", pd.DataFrame),
+    ("analyst_price_targets", dict),
+    ("earnings_estimate", pd.DataFrame),
+    ("revenue_estimate", pd.DataFrame),
+    ("earnings_history", pd.DataFrame),
+    ("eps_trend", pd.DataFrame),
+    ("eps_revisions", pd.DataFrame),
+    ("growth_estimates", pd.DataFrame),
     ("sustainability", pd.DataFrame),
     ("options", tuple),
     ("news", Any),
-    ("earnings_trend", pd.DataFrame),
     ("earnings_dates", pd.DataFrame),
-    ("earnings_forecasts", pd.DataFrame),
 )
 
 def assert_attribute_type(testClass: unittest.TestCase, instance, attribute_name, expected_type):
@@ -715,9 +718,11 @@ def tearDownClass(cls):
 
     def setUp(self):
         self.ticker = yf.Ticker("GOOGL", session=self.session)
+        self.ticker_no_analysts = yf.Ticker("^GSPC", session=self.session)
 
     def tearDown(self):
         self.ticker = None
+        self.ticker_no_analysts = None
 
     def test_recommendations(self):
         data = self.ticker.recommendations
@@ -748,23 +753,102 @@ def test_upgrades_downgrades(self):
         data_cached = self.ticker.upgrades_downgrades
         self.assertIs(data, data_cached, "data not cached")
 
-    # Below will fail because not ported to Yahoo API
+    def test_analyst_price_targets(self):
+        data = self.ticker.analyst_price_targets
+        self.assertIsInstance(data, dict, "data has wrong type")
 
-    # def test_analyst_price_target(self):
-    #     data = self.ticker.analyst_price_target
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
+        keys = {'current', 'low', 'high', 'mean', 'median'}
+        self.assertEqual(data.keys(), keys, "data has wrong keys")
 
-    #     data_cached = self.ticker.analyst_price_target
-    #     self.assertIs(data, data_cached, "data not cached")
+        data_cached = self.ticker.analyst_price_targets
+        self.assertIs(data, data_cached, "data not cached")
 
-    # def test_revenue_forecasts(self):
-    #     data = self.ticker.revenue_forecasts
-    #     self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
-    #     self.assertFalse(data.empty, "data is empty")
+    def test_earnings_estimate(self):
+        data = self.ticker.earnings_estimate
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
 
-    #     data_cached = self.ticker.revenue_forecasts
-    #     self.assertIs(data, data_cached, "data not cached")
+        columns = ['numberOfAnalysts', 'avg', 'low', 'high', 'yearAgoEps', 'growth']
+        self.assertEqual(data.columns.values.tolist(), columns, "data has wrong column names")
+
+        index = ['0q', '+1q', '0y', '+1y']
+        self.assertEqual(data.index.values.tolist(), index, "data has wrong row names")
+
+        data_cached = self.ticker.earnings_estimate
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_revenue_estimate(self):
+        data = self.ticker.revenue_estimate
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        columns = ['numberOfAnalysts', 'avg', 'low', 'high', 'yearAgoRevenue', 'growth']
+        self.assertEqual(data.columns.values.tolist(), columns, "data has wrong column names")
+
+        index = ['0q', '+1q', '0y', '+1y']
+        self.assertEqual(data.index.values.tolist(), index, "data has wrong row names")
+
+        data_cached = self.ticker.revenue_estimate
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_earnings_history(self):
+        data = self.ticker.earnings_history
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        columns = ['epsEstimate', 'epsActual', 'epsDifference', 'surprisePercent']
+        self.assertEqual(data.columns.values.tolist(), columns, "data has wrong column names")
+        self.assertIsInstance(data.index, pd.DatetimeIndex, "data has wrong index type")
+
+        data_cached = self.ticker.earnings_history
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_eps_trend(self):
+        data = self.ticker.eps_trend
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        columns = ['current', '7daysAgo', '30daysAgo', '60daysAgo', '90daysAgo']
+        self.assertEqual(data.columns.values.tolist(), columns, "data has wrong column names")
+
+        index = ['0q', '+1q', '0y', '+1y']
+        self.assertEqual(data.index.values.tolist(), index, "data has wrong row names")
+
+        data_cached = self.ticker.eps_trend
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_growth_estimates(self):
+        data = self.ticker.growth_estimates
+        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        columns = ['stock', 'industry', 'sector', 'index']
+        self.assertEqual(data.columns.values.tolist(), columns, "data has wrong column names")
+
+        index = ['0q', '+1q', '0y', '+1y', '+5y', '-5y']
+        self.assertEqual(data.index.values.tolist(), index, "data has wrong row names")
+
+        data_cached = self.ticker.growth_estimates
+        self.assertIs(data, data_cached, "data not cached")
+
+    def test_no_analysts(self):
+        attributes = [
+            'recommendations',
+            'upgrades_downgrades',
+            'earnings_estimate',
+            'revenue_estimate',
+            'earnings_history',
+            'eps_trend',
+            'growth_estimates',
+        ]
+
+        for attribute in attributes:
+            try:
+                data = getattr(self.ticker_no_analysts, attribute)
+                self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
+                self.assertTrue(data.empty, "data is not empty")
+            except Exception as e:
+                self.fail(f"Excpetion raised for attribute '{attribute}': {e}")
 
 
 

From 0c447d56d147037181bc284e911bb5706c962625 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Filip=20Kr=C3=A1sa?=
 <102549834+Fidasek009@users.noreply.github.com>
Date: Fri, 16 Aug 2024 18:37:31 +0200
Subject: [PATCH 11/16] Update `README.md` with analysis attributes

---
 README.md | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/README.md b/README.md
index edc668d13..d72a79fdf 100644
--- a/README.md
+++ b/README.md
@@ -126,6 +126,15 @@ msft.recommendations
 msft.recommendations_summary
 msft.upgrades_downgrades
 
+# show analysts data
+msft.analyst_price_targets
+msft.earnings_estimate
+msft.revenue_estimate
+msft.earnings_history
+msft.eps_trend
+msft.eps_revisions
+msft.growth_estimates
+
 # Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default.
 # Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.
 msft.earnings_dates

From 9cf62894e801b58d342568669eb42effe93224ff Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Filip=20Kr=C3=A1sa?=
 <102549834+Fidasek009@users.noreply.github.com>
Date: Fri, 16 Aug 2024 20:07:23 +0200
Subject: [PATCH 12/16] Fix ruff complaining

---
 yfinance/scrapers/analysis.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/yfinance/scrapers/analysis.py b/yfinance/scrapers/analysis.py
index ab74db4de..f2778f283 100644
--- a/yfinance/scrapers/analysis.py
+++ b/yfinance/scrapers/analysis.py
@@ -36,7 +36,7 @@ def analyst_price_targets(self) -> dict:
         try:
             data = self._fetch(['financialData'])
             data = data['quoteSummary']['result'][0]['financialData']
-        except:
+        except (TypeError, KeyError):
             self._analyst_price_targets = {}
             return self._analyst_price_targets
 
@@ -115,7 +115,7 @@ def earnings_history(self) -> pd.DataFrame:
         try:
             data = self._fetch(['earningsHistory'])
             data = data['quoteSummary']['result'][0]['earningsHistory']['history']
-        except:
+        except (TypeError, KeyError):
             self._earnings_history = pd.DataFrame()
             return self._earnings_history
 
@@ -201,7 +201,7 @@ def growth_estimates(self) -> pd.DataFrame:
         try:
             trends = self._fetch(['industryTrend', 'sectorTrend', 'indexTrend'])
             trends = trends['quoteSummary']['result'][0]
-        except:
+        except (TypeError, KeyError):
             self._growth_estimates = pd.DataFrame()
             return self._growth_estimates
 
@@ -260,5 +260,5 @@ def _fetch_earnings_trend(self) -> None:
         try:
             data = self._fetch(['earningsTrend'])
             self._earnings_trend = data['quoteSummary']['result'][0]['earningsTrend']['trend']
-        except:
+        except (TypeError, KeyError):
             self._earnings_trend = []

From 1baecc9d5bbb9bfb53335bdfc5a142f604676fed Mon Sep 17 00:00:00 2001
From: ValueRaider <ValueRaider@protonmail.com>
Date: Wed, 21 Aug 2024 21:11:52 +0100
Subject: [PATCH 13/16] Revert PR #2027, breaks with prepost=True

---
 yfinance/scrapers/history.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/yfinance/scrapers/history.py b/yfinance/scrapers/history.py
index 12cf88e77..9de625699 100644
--- a/yfinance/scrapers/history.py
+++ b/yfinance/scrapers/history.py
@@ -227,9 +227,7 @@ def history(self, period="1mo", interval="1d",
         # 2) fix weired bug with Yahoo! - returning 60m for 30m bars
         if interval.lower() == "30m":
             logger.debug(f'{self.ticker}: resampling 30m OHLC from 15m')
-            exchangeStartTime = pd.Timestamp(self._history_metadata["tradingPeriods"][0][0]["start"], unit='s')
-            offset = str(exchangeStartTime.minute % 30)+"min"
-            quotes2 = quotes.resample('30min', offset=offset)
+            quotes2 = quotes.resample('30min')
             quotes = pd.DataFrame(index=quotes2.last().index, data={
                 'Open': quotes2['Open'].first(),
                 'High': quotes2['High'].max(),

From 2f5203d677d28dad172d320c3731a964a4af5828 Mon Sep 17 00:00:00 2001
From: ValueRaider <ValueRaider@protonmail.com>
Date: Wed, 21 Aug 2024 21:27:58 +0100
Subject: [PATCH 14/16] New: repair bad dividends and div-adjusts. Plus other
 repair fixes.
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The new dividend repair logic looks for too big/small dividends/adjusts.
Because it analyses prices, it needs prices to be consistent - this means
for some exchanges, prices are converted to major currency e.g. pence -> £.
These exchanges are: .L, .TA, .JO
Also, multiday intervals are created by resampling adjusted 1d intervals.

Related change:
- _fix_prices_sudden_change() now fixes dividends with prices

Other repair fixes:
- add basic repair function to fix_Yahoo_returning_live_separate() to handle simple 100x price errors within same week/month
- fix _fix_prices_sudden_change() false positive on real massive price drop (clue = massive volume spike)
- price reconstruct fixes:
  - calibration: stop NaNs breaking, and detect currency unit mixup
  - fix a Pandas warning
---
 yfinance/scrapers/history.py | 907 +++++++++++++++++++++++++++++++----
 yfinance/utils.py            |  37 +-
 2 files changed, 858 insertions(+), 86 deletions(-)

diff --git a/yfinance/scrapers/history.py b/yfinance/scrapers/history.py
index 12cf88e77..f804dc51f 100644
--- a/yfinance/scrapers/history.py
+++ b/yfinance/scrapers/history.py
@@ -4,6 +4,7 @@
 import numpy as np
 import pandas as pd
 import time as _time
+import bisect
 
 from yfinance import shared, utils
 from yfinance.const import _BASE_URL_, _PRICE_COLNAMES_
@@ -74,6 +75,36 @@ def history(self, period="1mo", interval="1d",
         logger = utils.get_yf_logger()
         proxy = proxy or self.proxy
 
+        interval_user = interval
+        period_user = period
+        if repair and interval in ['5d', '1wk', '1mo', '3mo']:
+            # Yahoo's way of adjusting mutiday intervals is fundamentally broken.
+            # Have to fetch 1d, adjust, then resample.
+            if interval == '5d':
+                raise Exception("Yahoo's interval '5d' is nonsense, not supported with repair")
+            if start is None and end is None and period is not None:
+                tz = self.tz
+                if tz is None:
+                    # Every valid ticker has a timezone. A missing timezone is a problem.
+                    _exception = YFTzMissingError(self.ticker)
+                    err_msg = str(_exception)
+                    shared._DFS[self.ticker] = utils.empty_df()
+                    shared._ERRORS[self.ticker] = err_msg.split(': ', 1)[1]
+                    if raise_errors:
+                        raise _exception
+                    else:
+                        logger.error(err_msg)
+                    return utils.empty_df()
+                if period == 'ytd':
+                    start = _datetime.date(pd.Timestamp.utcnow().tz_convert(tz).year, 1, 1)
+                else:
+                    start = pd.Timestamp.utcnow().tz_convert(tz).date()
+                    start -= utils._interval_to_timedelta(period)
+                    start -= _datetime.timedelta(days=4)
+                period_user = period
+                period = None
+            interval = '1d'
+
         start_user = start
         end_user = end
         if start or period is None or period.lower() == "max":
@@ -248,11 +279,11 @@ def history(self, period="1mo", interval="1d",
         quote_type = self._history_metadata["instrumentType"]
         expect_capital_gains = quote_type in ('MUTUALFUND', 'ETF')
         tz_exchange = self._history_metadata["exchangeTimezoneName"]
+        currency = self._history_metadata["currency"]
 
         # Note: ordering is important. If you change order, run the tests!
         quotes = utils.set_df_tz(quotes, params["interval"], tz_exchange)
         quotes = utils.fix_Yahoo_dst_issue(quotes, params["interval"])
-        quotes = utils.fix_Yahoo_returning_live_separate(quotes, params["interval"], tz_exchange)
         intraday = params["interval"][-1] in ("m", 'h')
         if not prepost and intraday and "tradingPeriods" in self._history_metadata:
             tps = self._history_metadata["tradingPeriods"]
@@ -326,16 +357,74 @@ def history(self, period="1mo", interval="1d",
                 df["Capital Gains"] = 0.0
         logger.debug(f'{self.ticker}: OHLC after combining events: {quotes.index[0]} -> {quotes.index[-1]}')
 
+        df = utils.fix_Yahoo_returning_live_separate(df, params["interval"], tz_exchange, repair=repair, currency=currency)
+
         df = df[~df.index.duplicated(keep='first')]  # must do before repair
 
         if repair:
             # Do this before auto/back adjust
             logger.debug(f'{self.ticker}: checking OHLC for repairs ...')
+
+            df = df.sort_index()
+
+            # Must fix bad 'Adj Close' & dividends before 100x/split errors.
+            # First make currency consistent. On some exchanges, dividends often in different currency
+            # to prices, e.g. £ vs pence.
+            if currency in ["GBp", "ZAc", "ILA"]:
+                if currency == 'GBp':
+                    # UK £/pence
+                    currency = 'GBP'
+                    m = 0.01
+                elif currency == 'ZAc':
+                    # South Africa Rand/cents
+                    currency = 'ZAR'
+                    m = 0.01
+                elif currency == 'ILA':
+                    # Israel Shekels/Agora
+                    currency = 'ILS'
+                    m = 0.01
+
+                prices_in_subunits = True  # usually is true
+                if df.index[-1] > (pd.Timestamp.utcnow() - _datetime.timedelta(days=30)):
+                    try:
+                        ratio = self._history_metadata['regularMarketPrice'] / self._history_metadata['chartPreviousClose']
+                        if abs((ratio*m)-1) < 0.1:
+                            # within 10% of 100x
+                            prices_in_subunits = True
+                    except Exception:
+                        pass
+                if prices_in_subunits:
+                    for c in _PRICE_COLNAMES_:
+                        df[c] *= m
+                self._history_metadata["currency"] = currency
+
+                f_div = df['Dividends']!=0.0
+                if f_div.any():
+                    # But sometimes the dividend was in pence.
+                    # Heuristic is: if dividend yield is ridiculous high vs converted prices, then
+                    # assume dividend was also in pence and convert to GBP.
+                    # Threshold for "ridiculous" based on largest yield I've seen anywhere - 63.4%
+                    # If this simple heuritsic generates a false positive, then _fix_bad_div_adjust()
+                    # will detect and repair.
+                    divs = df[['Close','Dividends']].copy()
+                    divs['Close'] = divs['Close'].ffill().shift(1, fill_value=divs['Close'].iloc[0])
+                    divs = divs[f_div]
+                    div_pcts = (divs['Dividends'] / divs['Close']).to_numpy()
+                    if len(div_pcts) > 0 and np.average(div_pcts) > 1:
+                        df['Dividends'] *= m
+
+            df = self._fix_bad_div_adjust(df, interval, currency)
+
+            # Need the latest/last row to be repaired before 100x/split repair:
+            df_last = self._fix_zeroes(df.iloc[-1:], interval, tz_exchange, prepost)
+            if 'Repaired?' not in df.columns:
+                df['Repaired?'] = False
+            df = pd.concat([df.drop(df.index[-1]), df_last])
+
             df = self._fix_unit_mixups(df, interval, tz_exchange, prepost)
             df = self._fix_bad_stock_splits(df, interval, tz_exchange)
             # Must repair 100x and split errors before price reconstruction
             df = self._fix_zeroes(df, interval, tz_exchange, prepost)
-            df = self._fix_missing_div_adjust(df, interval, tz_exchange)
             df = df.sort_index()
 
         # Auto/back adjust
@@ -376,6 +465,9 @@ def history(self, period="1mo", interval="1d",
             mask_nan_or_zero = (df[data_colnames].isna() | (df[data_colnames] == 0)).all(axis=1)
             df = df.drop(mask_nan_or_zero.index[mask_nan_or_zero])
 
+        if interval != interval_user:
+            df = self._resample(df, interval, interval_user, period_user)
+
         logger.debug(f'{self.ticker}: yfinance returning OHLC: {df.index[0]} -> {df.index[-1]}')
 
         if self._reconstruct_start_interval is not None and self._reconstruct_start_interval == interval:
@@ -428,6 +520,41 @@ def get_actions(self, proxy=None) -> pd.Series:
             return actions[actions != 0].dropna(how='all').fillna(0)
         return pd.Series()
 
+    def _resample(self, df, df_interval, target_interval, period=None) -> pd.DataFrame:
+        # resample
+        if df_interval == target_interval:
+            return df
+        offset = None
+        if target_interval == '1wk':
+            resample_period = 'W-MON'
+        elif target_interval == '5d':
+            resample_period = '5D'
+        elif target_interval == '1mo':
+            resample_period = 'MS'
+        elif target_interval == '3mo':
+            resample_period = 'QS'
+            if period == 'ytd':
+                align_month = 'JAN'
+            else:
+                align_month = _datetime.datetime.now().strftime('%b').upper()
+            resample_period = f"QS-{align_month}"
+        else:
+            raise Exception(f"Not implemented resampling to interval '{target_interval}'")
+        resample_map = {
+            'Open': 'first', 'Low': 'min', 'High': 'max', 'Close': 'last',
+            'Volume': 'sum', 'Dividends': 'sum', 'Stock Splits': 'prod'
+            }
+        if 'Repaired?' in df.columns:
+            resample_map['Repaired?'] = 'any'
+        if 'Adj Close' in df.columns:
+            resample_map['Adj Close'] = resample_map['Close']
+        if 'Capital Gains' in df.columns:
+            resample_map['Capital Gains'] = 'sum'
+        df.loc[df['Stock Splits']==0.0, 'Stock Splits'] = 1.0
+        df2 = df.resample(resample_period, label='left', closed='left', offset=offset).agg(resample_map)
+        df2.loc[df2['Stock Splits']==1.0, 'Stock Splits'] = 0.0
+        return df2
+
     @utils.log_indent_decorator
     def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
         # Reconstruct values in df using finer-grained price data. Delimiter marks what to reconstruct
@@ -472,7 +599,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
         if self._reconstruct_start_interval is None:
             self._reconstruct_start_interval = interval
         if interval != self._reconstruct_start_interval and interval != nexts[self._reconstruct_start_interval]:
-            msg = "Hit max depth of 2 ('%s'->'%s'->'%s')", self._reconstruct_start_interval, nexts[self._reconstruct_start_interval], interval
+            msg = "Hit max depth of 2 ('{}'->'{}'->'{}')".format(self._reconstruct_start_interval, nexts[self._reconstruct_start_interval], interval)
             logger.info(msg, extra=log_extras)
             return df
 
@@ -709,6 +836,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
             df_new_calib = df_new[df_new.index.isin(common_index)][calib_cols].to_numpy()
             df_block_calib = df_block[df_block.index.isin(common_index)][calib_cols].to_numpy()
             calib_filter = (df_block_calib != tag)
+            calib_filter = calib_filter & (~np.isnan(df_new_calib))
             if not calib_filter.any():
                 # Can't calibrate so don't attempt repair
                 logger.info(f"Can't calibrate block starting {start_d} so aborting repair", extra=log_extras)
@@ -732,6 +860,13 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
                 ratio = 1.0
             else:
                 ratio = np.average(ratios, weights=weights)
+            if abs(ratio/0.0001 -1) < 0.01:
+                # ratio almost-equal 0.0001, so looks like Yahoo messed up currency unit.
+                # E.g. £ with pence. Can correct it.
+                df_block = df_block.copy()
+                for c in _PRICE_COLNAMES_:
+                    df_v2.loc[df_v2[c]!=tag, c] *= 100
+                ratio *= 100
             logger.debug(f"Price calibration ratio (raw) = {ratio:6f}", extra=log_extras)
             ratio_rcp = round(1.0 / ratio, 1)
             ratio = round(ratio, 1)
@@ -792,7 +927,7 @@ def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1):
                 elif "Adj Close" in bad_fields:
                     df_v2.loc[idx, "Adj Close"] = df_new_row["Adj Close"]
                 if "Volume" in bad_fields:
-                    df_v2.loc[idx, "Volume"] = df_new_row["Volume"]
+                    df_v2.loc[idx, "Volume"] = df_new_row["Volume"].round().astype('int')
                 df_v2.loc[idx, "Repaired?"] = True
                 n_fixed += 1
 
@@ -978,7 +1113,7 @@ def _fix_unit_switch(self, df, interval, tz_exchange):
             n = 1000
         else:
             n = 100
-        return self._fix_prices_sudden_change(df, interval, tz_exchange, n)
+        return self._fix_prices_sudden_change(df, interval, tz_exchange, n, correct_dividend=True)
 
     @utils.log_indent_decorator
     def _fix_zeroes(self, df, interval, tz_exchange, prepost):
@@ -992,11 +1127,6 @@ def _fix_zeroes(self, df, interval, tz_exchange, prepost):
         logger = utils.get_yf_logger()
         log_extras = {'yf_cat': 'price-repair-zeroes', 'yf_interval': interval, 'yf_symbol': self.ticker}
 
-        if df.shape[0] == 0:
-            if "Repaired?" not in df.columns:
-                df["Repaired?"] = False
-            return df
-
         intraday = interval[-1] in ("m", 'h')
 
         df = df.sort_index()  # important!
@@ -1020,6 +1150,8 @@ def _fix_zeroes(self, df, interval, tz_exchange, prepost):
             df2 = df2[~f_zero_or_nan_ignore]
             if df2.empty:
                 # No good data
+                if 'Repaired?' not in df.columns:
+                    df['Repaired?'] = False
                 return df
             df2 = df2.copy()
             f_prices_bad = (df2[price_cols] == 0.0) | df2[price_cols].isna()
@@ -1041,7 +1173,6 @@ def _fix_zeroes(self, df, interval, tz_exchange, prepost):
                 close_diff.iloc[0] = 0
                 close_chg_pct_abs = np.abs(close_diff / df2['Close'])
                 f_bad_price_chg = (close_chg_pct_abs > 0.05).to_numpy() & f_vol_zero
-                f_bad_price_chg = f_bad_price_chg & (~f_vol_bad)  # exclude where already know volume is bad
                 f_vol_bad = f_vol_bad | f_bad_price_chg
 
         # If stock split occurred, then trading must have happened.
@@ -1116,74 +1247,654 @@ def _fix_zeroes(self, df, interval, tz_exchange, prepost):
         return df2
 
     @utils.log_indent_decorator
-    def _fix_missing_div_adjust(self, df, interval, tz_exchange):
-        # Sometimes, if a dividend occurred today, then Yahoo has not adjusted historic data.
-        # Easy to detect and correct BUT ONLY IF the data 'df' includes today's dividend.
-        # E.g. if fetching historic prices before todays dividend, then cannot fix.
-
-        if df.empty:
-            return df
+    def _fix_bad_div_adjust(self, df, interval, currency):
+        # Look for dividend issues:
+        # - dividend ~100x the Close change (a currency unit mixup)
+        # - dividend missing from Adj Close
+        # - dividend is in Adj Close but adjustment is too much, or too small
 
-        logger = utils.get_yf_logger()
-        log_extras = {'yf_cat': 'div-adjust-repair', 'yf_interval': interval, 'yf_symbol': self.ticker}
+        # Experimental: also detect dividend in wrong currency e.g. $ not Israel Shekels.
+        # But only for big FX rates, otherwise false positives from price volatility.
 
         if df is None or df.empty:
             return df
-        interday = interval in ['1d', '1wk', '1mo', '3mo']
-        if not interday:
+        if interval != '1d':
             return df
 
-        df = df.sort_index()
+        logger = utils.get_yf_logger()
+        log_extras = {'yf_cat': 'div-adjust-repair-bad', 'yf_interval': interval, 'yf_symbol': self.ticker}
 
         f_div = (df["Dividends"] != 0.0).to_numpy()
         if not f_div.any():
             logger.debug('No dividends to check', extra=log_extras)
             return df
 
-        df2 = df.copy()
-        if df2.index.tz is None:
-            df2.index = df2.index.tz_localize(tz_exchange)
-        elif df2.index.tz != tz_exchange:
-            df2.index = df2.index.tz_convert(tz_exchange)
+        if self._history_metadata['currency'] == 'KWF':
+            # Kuwaiti Dinar divided into 1000 not 100
+            currency_divide = 1000
+        else:
+            currency_divide = 100
+        div_status_df = None
+        too_big_check_threshold = 0.035
 
-        div_indices = np.where(f_div)[0]
-        last_div_idx = div_indices[-1]
-        if last_div_idx == 0:
-            # Not enough data to recalculate the div-adjustment,
-            # because need close day before
-            logger.debug('Insufficient data to recalculate div-adjustment', extra=log_extras)
+        df = df.sort_index()
+        df2 = df.copy()
+        if 'Repaired?' not in df2.columns:
+            df2['Repaired?'] = False
+        df_modified = False
+
+        # 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()
+
+        f_div = (df2["Dividends"] != 0.0).to_numpy()
+        if not f_div.any():
+            logger.debug('No dividends to check', extra=log_extras)
             return df
+        div_indices = np.where(f_div)[0]
 
-        # To determine if Yahoo messed up, analyse price data between today's dividend and
-        # the previous dividend
-        if len(div_indices) == 1:
-            # No other divs in data
-            prev_idx = 0
-            prev_dt = None
-        else:
-            prev_idx = div_indices[-2]
-            prev_dt = df2.index[prev_idx]
-        f_no_adj = (df2['Close'] == df2['Adj Close']).to_numpy()[prev_idx:last_div_idx]
-        threshold_pct = 0.5
-        Yahoo_failed = (np.sum(f_no_adj) / len(f_no_adj)) > threshold_pct
-
-        # Fix Yahoo
-        if Yahoo_failed:
-            last_div_dt = df2.index[last_div_idx]
-            last_div_row = df2.loc[last_div_dt]
-            close_day_before = df2['Close'].iloc[last_div_idx - 1]
-            adj = 1.0 - df2['Dividends'].iloc[last_div_idx] / close_day_before
-            div = last_div_row['Dividends']
-            msg = f'Correcting missing div-adjustment preceding div = {div} @ {last_div_dt.date()}'
+        # Very rarely, the Close (not Adj Close) is already adjusted!
+        # Clue is it's often lower than Low. 
+        # E.g. ticker MPCC.OL - Oslo exchange data contradicts Yahoo.
+        # But sometimes the original data is bad, e.g. LSE sometimes close < low
+        # Can attempt to fix:
+        fixed_dates = []
+        for i in range(len(div_indices)-1, -1, -1):
+            div_idx = div_indices[i]
+            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]
+                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']:
+                    df2.loc[dt_before, 'Close'] = new_close
+                    adj_after = df2['Adj Close'].iloc[div_idx] / df2['Close'].iloc[div_idx]
+                    adj = adj_after * (1.0 - div/df2['Close'].iloc[div_idx-1])
+                    df2.loc[dt_before, 'Adj Close'] = df2['Close'].iloc[div_idx-1] * adj
+                    df2.loc[dt_before, 'Repaired?'] = True
+                    df_modified = True
+                    fixed_dates.append(df2.index[div_idx].date())
+        if len(fixed_dates) > 0:
+            msg = f"Repaired double-adjustment on div days {[str(d) for d in fixed_dates]}"
             logger.info(msg, extra=log_extras)
-            logger.debug(f'prev_dt={prev_dt}', extra=log_extras)
 
-            if interval == '1d':
-                # exclusive
-                df2.loc[:last_div_dt - _datetime.timedelta(seconds=1), 'Adj Close'] *= adj
+        # Check dividends if too big/small for the price action
+        for i in range(len(div_indices)-1, -1, -1):
+            div_idx = div_indices[i]
+            dt = df2.index[div_idx]
+            div = df2['Dividends'].iloc[div_idx]
+
+            if div_idx == 0:
+                continue
+
+            div_pct = div / df2['Close'].iloc[div_idx-1]
+
+            # Check if dividend is 100x market movement.
+            div_too_big = False
+            div_too_small = False
+            div_too_small_improvement_threshold = 1
+            # div_too_big_improvement_threshold = 1
+            div_too_big_improvement_threshold = 2
+
+            drop_c2l = df2['Close'].iloc[div_idx-1] - df2['Low'].iloc[div_idx]
+            # drop_c2c = df2['Close'].iloc[div_idx-1] - df2['Close'].iloc[div_idx]
+            # drop = drop_c2c
+            drop = drop_c2l
+            if div_idx < len(df2)-1:
+                # # In low-volume scenarios, the price drop is day after not today.
+                # if df2['Close'].iloc[div_idx-1] == df2['Close'].iloc[div_idx] or \
+                #    df2['Low'].iloc[div_idx] == df2['High'].iloc[div_idx]:
+                #     drop = np.max(df2['Close'].iloc[div_idx-1:div_idx+1].to_numpy() - df2['Low'].iloc[div_idx:div_idx+2].to_numpy())
+                # elif df2['Volume'].iloc[div_idx]==0:
+                #     if drop == 0.0:
+                #         drop = np.max(df2['Close'].iloc[div_idx-1:div_idx+1].to_numpy() - df2['Low'].iloc[div_idx:div_idx+2].to_numpy())
+                # Hmm, can I always look ahead 1 day? Catch: increases FP rate of div-too-small for tiny divs.
+                drops = df2['Close'].iloc[div_idx-1:div_idx+1].to_numpy() - df2['Low'].iloc[div_idx:div_idx+2].to_numpy()
+                drop_2Dmax = np.max(drops)
             else:
-                # inclusive
-                df2.loc[:last_div_dt, 'Adj Close'] *= adj
+                drop_2Dmax = drop
+
+            if (len(df2)-div_idx) < 4:
+                end = min(len(df2), div_idx+4)
+                start = max(0, end-8)
+            else:
+                start = max(0, div_idx-4)
+                end = min(len(df2), start+8)
+            if end-start < 4:
+                # Not enough data to estimate volatility
+                typical_volatility = np.nan
+            else:
+                diffs = df2['Close'].iloc[start:end-1].to_numpy() - df2['Low'].iloc[start+1:end].to_numpy()
+                typical_volatility = np.median(np.abs(diffs))
+
+            if drop == 0.0 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):
+                # Check for div-too-big
+                if div_pct > too_big_check_threshold:
+                    if drop_2Dmax <= 0.0:
+                        div_too_big = True
+                    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
+
+                # 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 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
+
+            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
+            row = pd.DataFrame([div_status]).set_index('date')
+            if div_status_df is None:
+                div_status_df = row
+            else:
+                div_status_df = pd.concat([div_status_df, row])
+
+        if div_status_df is None and not df_modified:
+            return df
+        div_status_df = div_status_df.sort_index()
+
+        def cluster_dividends(df, column='div', threshold=10):
+            n = len(df)
+            sorted_df = df.sort_values(column)
+            clusters = []
+            current_dts = [sorted_df.index[0]]
+            currents_vals = [sorted_df[column].iloc[0]]
+            for i in range(1, n):
+                dt = sorted_df.index[i]
+                div = sorted_df[column].iloc[i]
+                if (div / np.mean(currents_vals)) < threshold:
+                    # Add
+                    current_dts.append(dt)
+                    currents_vals.append(div)
+                else:
+                    # New cluster
+                    clusters.append(current_dts)
+                    current_dts = [dt]
+                    currents_vals = [div]
+            clusters.append(current_dts)
+
+            cluster_labels = np.array([-1]*n)
+            ctr = 0
+            for i, cluster in enumerate(clusters):
+                nc = len(cluster)
+                cluster_labels[ctr:ctr+nc] = i
+                ctr += nc
+            return cluster_labels
+
+        # Check if the present div-adjustment is too big/small, or missing
+        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]
+
+            # First, check if Yahoo failed to apply dividend to Adj Close
+            pre_adj = df2['Adj Close'].iloc[div_idx-1] / df2['Close'].iloc[div_idx-1]
+            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 > 0.2:
+                    adjClose_chg_pct = np.min(adjClose_chgs_pct)
+                    close_chg_pct = np.min(close_chgs_pct)
+                    if adjClose_chg_pct < 0.0 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
+            div_adj_is_too_small = implied_div_yield < (0.1*div_pct)
+
+            # ... 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
+
+            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['adj_too_small'] = 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)):
+                        div_status_df[k] = False
+                    elif isinstance(v, int):
+                        div_status_df[k] = 0
+                    elif isinstance(v, float):
+                        div_status_df[k] = 0.0
+                    else:
+                        raise Exception(k,v,type(v))
+                div_status_df.loc[dt, k] = v
+
+        checks = ['adj_missing', 'adj_too_small', 'adj_exceeds_div', 'adj_exceeds_prices', 'div_too_big', 'div_too_small', 'fx_mixup']
+        checks = [c for c in checks if c in div_status_df.columns]
+
+        div_status_df['phantom'] = False
+        phantom_proximity_threshold = _datetime.timedelta(days=7)
+        f = div_status_df['div_too_big'] | div_status_df['adj_too_small']
+        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]
+            dts_to_check = div_status_df.index[f]
+            for i in indices:
+                div = div_status_df.iloc[i]
+                div_dt = div.name
+                phantom_dt = None
+                if i > 0:
+                    prev_div = div_status_df.iloc[i-1]
+                    ratio1 = (div['div']/currency_divide) / prev_div['div']
+                    ratio2 = div['div'] / prev_div['div']
+                    divergence = min(abs(ratio1-1.0), abs(ratio2-1.0))
+                    if abs(div_dt-prev_div.name) <= phantom_proximity_threshold and not prev_div['phantom'] and divergence < 0.01:
+                        if prev_div.name in dts_to_check:
+                            # Both this and previous are anomolous, so mark smallest drop as phantom
+                            drop = div['drop']
+                            drop_prev = prev_div['drop']
+                            if drop > 1.5*drop_prev:
+                                phantom_dt = prev_div.name
+                            else:
+                                phantom_dt = div_dt
+                        else:
+                            phantom_dt = div_dt
+                elif i < len(div_status_df)-1:
+                    next_div = div_status_df.iloc[i+1]
+                    ratio1 = (div['div']/currency_divide) / next_div['div']
+                    ratio2 = div['div'] / next_div['div']
+                    divergence = min(abs(ratio1-1.0), abs(ratio2-1.0))
+                    if abs(div_dt-next_div.name) <= phantom_proximity_threshold and divergence < 0.01:
+                        if next_div.name in dts_to_check:
+                            # Both this and previous are anomolous, so mark smallest drop as phantom
+                            drop = div['drop']
+                            drop_next = next_div['drop']
+                            if drop > 1.5*drop_next:
+                                phantom_dt = next_div.name
+                            else:
+                                phantom_dt = div_dt
+                        else:
+                            phantom_dt = div_dt
+                if phantom_dt:
+                    div_status_df.loc[phantom_dt, 'phantom'] = True
+                    for c in checks:
+                        if c in div_status_df.columns:
+                            div_status_df.loc[phantom_dt, c] = False
+
+        # There might be other phantom dividends - in close proximity and almost-equal to another div.
+        # But harder to decide which is the phantom and which is real.
+        # Assume phantom has much smaller price drop, otherwise assume is newer.
+        # ratio_threshold = 0.01
+        ratio_threshold =  0.08  # increased for KAP.IL 2022-July
+        div_status_df = div_status_df.sort_index()
+        for i in range(1, len(div_status_df)):
+            div = div_status_df.iloc[i]
+            div_dt = div.name
+            this_is_phantom = False
+            last_is_phantom = False
+            drop = div['drop']
+            last_div = div_status_df.iloc[i-1]
+            ratio = div['div'] / last_div['div']
+            if abs(div_dt-last_div.name) <= phantom_proximity_threshold and not last_div['phantom'] and not div['phantom'] and abs(ratio-1.0) < ratio_threshold:
+                last_drop = div_status_df['drop'].iloc[i-1]
+                if drop > 1.5*last_drop:
+                    last_is_phantom = True
+                else:
+                    this_is_phantom = True
+            if last_is_phantom or this_is_phantom:
+                phantom_div_dt = div_dt if this_is_phantom else div_status_df.index[i-1]
+                div_status_df.loc[phantom_div_dt, 'phantom'] = True
+                for c in checks:
+                    if c in div_status_df.columns:
+                        div_status_df.loc[phantom_div_dt, c] = False
+        checks.append('phantom')
+
+        if not div_status_df[checks].any().any():
+            # Maybe failed to detect a too-small div. If div is ~0.01x of previous and next, then
+            # treat as a 0.01x error
+            if len(div_status_df) > 1:
+                for i in range(0, len(div_status_df)):
+                    if div_status_df['phantom'].iloc[i]:
+                        continue
+                    r_pre, r_post = None, None
+                    if i > 0:
+                        r_pre = div_status_df['%'].iloc[i-1] / div_status_df['%'].iloc[i]
+                    if i < (len(div_status_df)-1):
+                        r_post = div_status_df['%'].iloc[i+1] / div_status_df['%'].iloc[i]
+                    r_pre = r_pre or r_post
+                    r_post = r_post or r_pre
+                    if abs(r_pre-currency_divide)<20 and abs(r_post-currency_divide)<20:
+                        div_dt = div_status_df.index[i]
+                        div_status_df.loc[div_dt, 'div_too_small'] = True
+
+        if not div_status_df[checks].any().any():
+            # Perfect
+            return df
+        for c in checks:
+            if not div_status_df[c].any():
+                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
+
+        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)
+                time_diffs.loc[time_diffs.index[0]] = time_diffs.iloc[1]
+                div_pcts['period'] = time_diffs
+                div_pcts['avg yr yield'] = div_pcts['%'] / div_pcts['period']
+
+            for c in checks:
+                f_fail = cluster[c].to_numpy()
+                n_fail = np.sum(f_fail)
+                if n_fail in [0, n]:
+                    continue
+                pct_fail = np.sum(f_fail) / n
+                if c == 'div_too_big':
+                    true_threshold = 1.0
+                    fals_threshold = 0.2
+                    if 'adj_too_small' not in cluster.columns:
+                        # More likely that 'div_too_big' are false positives: NOT too big
+                         fals_threshold = 2/3
+                    elif cluster['adj_too_small'].all():
+                        # The present adjustment also contradicts the dividend yields, 
+                        # more likely the dividends are too big.
+                        if (cluster['vol'][fc][f_fail]==0).all():
+                            # No trading volume to cross-check, so higher thresholds
+                            fals_threshold = 2/3
+                        else:
+                            true_threshold = 1/2
+                    if pct_fail >= true_threshold:
+                        div_status_df.loc[fc, c] = True
+                        continue
+                    elif pct_fail <= fals_threshold:
+                        div_status_df.loc[fc, c] = False
+                        continue
+
+                if c == 'div_too_small':
+                    true_threshold = 1.0
+                    fals_threshold = 0.1
+                    if 'adj_exceeds_div' not in cluster.columns and 'adj_exceeds_prices' not in cluster.columns:
+                        # More likely that 'div_too_small' are false positives: NOT too small
+                        fals_threshold = 2/3
+                    elif ('adj_exceeds_div' in cluster.columns and cluster['adj_exceeds_div'].all()) \
+                         ('adj_exceeds_prices' in cluster.columns and cluster['adjadj_exceeds_prices_exceeds_div'].all()):
+                        # More likely that falses are false negatives
+                        true_threshold = 0.5
+                    if pct_fail >= true_threshold:
+                        div_status_df.loc[fc, c] = True
+                        continue
+                    elif pct_fail <= fals_threshold:
+                        div_status_df.loc[fc, c] = False
+                        continue
+
+                if c == 'adj_missing':
+                    if cluster[c].iloc[-1] and n_fail == 1:
+                        # Only the latest/last row is missing, genuine error
+                        continue
+                if c == 'adj_too_small':
+                    continue
+
+                if c == 'phantom' and self.ticker in ['KAP.IL', 'SAND']:
+                    # Manually approve, but these are probably safe to assume ok
+                    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
+
+        # These arrays track changes for constructing compact log messages
+        div_repairs = {}
+        for cid in 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)):
+                row = cluster.iloc[i]
+                dt = row.name
+
+                adj_missing = 'adj_missing' in row and row['adj_missing']
+                adj_too_small = 'adj_too_small' in row and row['adj_too_small']
+                adj_exceeds_div = 'adj_exceeds_div' in row and row['adj_exceeds_div']
+                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']
+
+                adj_too_big = adj_exceeds_div or adj_exceeds_prices
+
+                n_failed_checks = np.sum([row[c] for c in checks if c in row])
+                if n_failed_checks == 1:
+                    if adj_too_small or adj_too_big:
+                        k = 'too-small div-adjust' if adj_too_small 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
+                        df2_nan.loc[:enddt, 'Repaired?'] = True
+                        cluster.loc[dt, 'Fixed?'] = True
+
+                    elif div_too_small:
+                        # Fix both dividend and adjustment
+                        # - div_too_small looks fine, the adj also needs repair because compared against div
+                        k = 'too-small div'
+                        correction = currency_divide
+                        correct_div = row['div'] * correction
+                        df2.loc[dt, 'Dividends'] = correct_div
+                        # adj is correct *compared to the present div*, so needs rescaling
+                        # to match corrected dividend
+                        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
+                        df2_nan.loc[:enddt, 'Repaired?'] = True
+                        cluster.loc[dt, 'Fixed?'] = True
+                        div_repairs.setdefault(k, []).append(dt)
+
+                    elif div_too_big:
+                        k = 'too-big div'
+                        correction = 1.0/currency_divide
+
+                        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_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)
+                        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 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
+                        df2_nan.loc[:enddt, 'Adj Close'] *= adj_correction
+                        df2_nan.loc[:enddt, 'Repaired?'] = True
+                        cluster.loc[dt, 'Fixed?'] = True
+
+                elif n_failed_checks == 2:
+                    if div_too_big and adj_missing:
+                        # A currency unit mixup AND adjustment missing
+                        k = 'too-big div and missing div-adjust'
+                        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
+                        df2_nan.loc[:enddt, 'Repaired?'] = True
+                        cluster.loc[dt, 'Fixed?'] = True
+
+                    elif div_too_big and adj_too_small:
+                        # Adj Close is correct, just need to fix Dividend.
+                        # Probably just a currency unit mixup.
+                        df2.loc[dt, 'Dividends'] /= currency_divide
+                        k = 'too-big div'
+                        if 'FX was repaired' in row and row['FX was repaired']:
+                            # Complication: not just a currency unit mixup, but
+                            # mixed up the local currency with $. So need to 
+                            # recalculate adjustment.
+                            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
+                            df2_nan.loc[:enddt, 'Repaired?'] = True
+                            # Currently not logging this FX-fix event, since I refactored fixing.
+                            k += " and FX mixup"
+                        div_repairs.setdefault(k, []).append(dt)
+                        cluster.loc[dt, 'Fixed?'] = True
+
+                    elif adj_too_big and div_too_big:
+                        # Assume div 100x error, and that Yahoo used this wrong dividend.
+                        # 'adj_too_big=True' is probably redundant information, knowing div too big
+                        # is enough to require also fixing adjustment
+                        k = 'too-big div & div-adjust'
+                        div_repairs.setdefault(k, []).append(dt)
+                        target_div_pct = row['%']/currency_divide
+                        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
+
+        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)
+
+        if 'Adj' in df2.columns:
+            raise Exception('"Adj" has snuck in df2')
+
+        if not df2_nan.empty:
+            df2 = pd.concat([df2, df2_nan]).sort_index()
 
         return df2
 
@@ -1234,10 +1945,9 @@ def _fix_bad_stock_splits(self, df, interval, tz_exchange):
                 split_idx += 5
             cutoff_idx = min(df.shape[0], split_idx)  # add one row after to detect big change
             df_pre_split = df.iloc[0:cutoff_idx+1]
-            logger.debug(f'split_idx={split_idx} split_dt={split_dt}', extra=log_extras)
+            logger.debug(f'split_idx={split_idx} split_dt={split_dt.date()} split={split:.4f}', extra=log_extras)
             logger.debug(f'df dt range: {df_pre_split.index[0].date()} -> {df_pre_split.index[-1].date()}', extra=log_extras)
-
-            df_pre_split_repaired = self._fix_prices_sudden_change(df_pre_split, interval, tz_exchange, split, correct_volume=True)
+            df_pre_split_repaired = self._fix_prices_sudden_change(df_pre_split, interval, tz_exchange, split, correct_volume=True, correct_dividend=True)
             # Merge back in:
             if cutoff_idx == df.shape[0]-1:
                 df = df_pre_split_repaired
@@ -1250,7 +1960,7 @@ def _fix_bad_stock_splits(self, df, interval, tz_exchange):
         return df
 
     @utils.log_indent_decorator
-    def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_volume=False):
+    def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_volume=False, correct_dividend=False):
         if df.empty:
             return df
 
@@ -1260,6 +1970,7 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
         split = change
         split_rcp = 1.0 / split
         interday = interval in ['1d', '1wk', '1mo', '3mo']
+        multiday = interval in ['1wk', '1mo', '3mo']
 
         if change in [100.0, 0.01]:
             fix_type = '100x error'
@@ -1271,7 +1982,7 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
             # start_min = 1 year before oldest split
             f = df['Stock Splits'].to_numpy() != 0.0
             start_min = (df.index[f].min() - _dateutil.relativedelta.relativedelta(years=1)).date()
-        logger.debug(f'start_min={start_min} change={change}', extra=log_extras)
+        logger.debug(f'start_min={start_min} change={change:.4f} (rcp={1.0/change:.4f})', extra=log_extras)
 
         OHLC = ['Open', 'High', 'Low', 'Close']
 
@@ -1291,11 +2002,16 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
         # If stock is currently suspended and not in USA, then usually Yahoo introduces
         # 100x errors into suspended intervals. Clue is no price change and 0 volume.
         # Better to use last active trading interval as baseline.
-        f_no_activity = (df2['Low'] == df2['High']) & (df2['Volume']==0)
+        # f_no_activity = (df2['Low'] == df2['High']) & (df2['Volume']==0)
+        # Update: intra-interval 100x/0.01x errors can trick Low==High
+        f_no_activity = df2['Volume']==0
         f_no_activity = f_no_activity | df2[OHLC].isna().all(axis=1)
         appears_suspended = f_no_activity.any() and np.where(f_no_activity)[0][0]==0
         f_active = ~f_no_activity
         idx_latest_active = np.where(f_active & np.roll(f_active, 1))[0]
+        if len(idx_latest_active) == 0:
+            # In rare cases, not enough trading activity for 2+ consecutive days e.g. CLC.L
+            idx_latest_active = np.where(f_active)[0]
         if len(idx_latest_active) == 0:
             idx_latest_active = None
         else:
@@ -1307,8 +2023,8 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
 
         if logger.isEnabledFor(logging.DEBUG):
             df_debug = df2.copy()
-            df_debug = df_debug.drop(['Adj Close', 'Volume', 'Dividends', 'Repaired?'], axis=1, errors='ignore')
-            debug_cols = ['Low', 'High']
+            df_debug = df_debug.drop(['Adj Close', 'Volume', 'Dividends', 'Stock Splits', 'Repaired?'], axis=1, errors='ignore')
+            debug_cols = ['Close']
             df_debug = df_debug.drop([c for c in OHLC if c not in debug_cols], axis=1, errors='ignore')
         else:
             debug_cols = []
@@ -1336,6 +2052,9 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
             price_data = price_data.astype('float')
         for j in range(price_data.shape[1]):
             price_data[:,j] *= adj
+            if logger.isEnabledFor(logging.DEBUG):
+                if OHLC[j] in df_debug.columns:
+                    df_debug[OHLC[j]] *= adj
         if df_dtype == np.int64:
             price_data = price_data.astype('int')
 
@@ -1358,8 +2077,8 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
             # Possible if data was too old for reconstruction.
             _1d_change_minx[f_na] = 1.0
         if logger.isEnabledFor(logging.DEBUG):
-            df_debug['1D change X'] = _1d_change_minx
-            df_debug['1D change X'] = df_debug['1D change X'].round(2).astype('str')
+            df_debug['1D %'] = _1d_change_minx
+            df_debug['1D %'] = df_debug['1D %'].round(2).astype('str')
 
         # If all 1D changes are closer to 1.0 than split, exit
         split_max = max(split, split_rcp)
@@ -1420,22 +2139,38 @@ 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
         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)
+        # In rare cases e.g. real disasters, the price actually drops massively on huge volume
+        if f_up_shifts.any():
+            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 vol_change_pct > 5:
+                    # big volume change +500% = false positive
+                    if f_up_ndims == 1:
+                        f_up[i] = False
+                    else:
+                        f_up[i,:] = False
         f = f_down | f_up
         if logger.isEnabledFor(logging.DEBUG):
             if not correct_columns_individually:
                 df_debug['r'] = r
-                df_debug['f_down'] = f_down
-                df_debug['f_up'] = f_up
+                df_debug['down'] = f_down
+                df_debug['up'] = f_up
                 df_debug['r'] = df_debug['r'].round(2).astype('str')
             else:
                 for j in range(len(OHLC)):
                     c = OHLC[j]
                     if c in debug_cols:
                         df_debug[c + '_r'] = r[:, j]
-                        df_debug[c + '_f_down'] = f_down[:, j]
-                        df_debug[c + '_f_up'] = f_up[:, j]
                         df_debug[c + '_r'] = df_debug[c + '_r'].round(2).astype('str')
-
+                        df_debug[c + '_down'] = f_down[:, j]
+                        df_debug[c + '_up'] = f_up[:, j]
 
         if not f.any():
             logger.debug(f'No {fix_type}s detected', extra=log_extras)
@@ -1465,15 +2200,15 @@ def _fix_prices_sudden_change(self, df, interval, tz_exchange, change, correct_v
                     return df
 
         # 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+'_f_down' for c in debug_cols]].any(axis=1) | df_debug[[c+'_f_up' for c in debug_cols]].any(axis=1)
-        #     else:
-        #         f_change = df_debug['f_down'] | df_debug['f_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(f"price-repair-split: my workings:" + '\n' + str(df_debug[f_change]))
+            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 = 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]))
 
         def map_signals_to_ranges(f, f_up, f_down):
             # Ensure 0th element is False, because True is nonsense
@@ -1680,6 +2415,8 @@ def map_signals_to_ranges(f, f_up, f_down):
                 logger.debug(f"range={r} m={m}", extra=log_extras)
                 for c in ['Open', 'High', 'Low', 'Close', 'Adj Close']:
                     df2.iloc[r[0]:r[1], df2.columns.get_loc(c)] *= m
+                if correct_dividend:
+                    df2.iloc[r[0]:r[1], df2.columns.get_loc('Dividends')] *= m
                 if correct_volume:
                     col_loc = df2.columns.get_loc("Volume")
                     df2.iloc[r[0]:r[1], col_loc] = (df2.iloc[r[0]:r[1], col_loc] * m_rcp).round().astype('int')
diff --git a/yfinance/utils.py b/yfinance/utils.py
index 2ede6810b..0968f9d15 100644
--- a/yfinance/utils.py
+++ b/yfinance/utils.py
@@ -431,8 +431,16 @@ def _interval_to_timedelta(interval):
         return relativedelta(months=1)
     elif interval == "3mo":
         return relativedelta(months=3)
+    elif interval == "6mo":
+        return relativedelta(months=6)
     elif interval == "1y":
         return relativedelta(years=1)
+    elif interval == "2y":
+        return relativedelta(years=2)
+    elif interval == "5y":
+        return relativedelta(years=5)
+    elif interval == "10y":
+        return relativedelta(years=10)
     elif interval == "1wk":
         return _pd.Timedelta(days=7)
     else:
@@ -586,7 +594,7 @@ def fix_Yahoo_returning_prepost_unrequested(quotes, interval, tradingPeriods):
     return quotes
 
 
-def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
+def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange, repair=False, currency=None):
     # Yahoo bug fix. If market is open today then Yahoo normally returns
     # todays data as a separate row from rest-of week/month interval in above row.
     # Seems to depend on what exchange e.g. crypto OK.
@@ -624,6 +632,30 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
                     # Yahoo returning last interval duplicated, which means
                     # Yahoo is not returning live data (phew!)
                     return quotes
+
+                ss = quotes['Stock Splits'].iloc[-2:].replace(0,1).prod()
+                if repair:
+                    # First, check if one row is ~100x the other. A £/pence mixup on LSE.
+                    # Avoid if a stock split near 100
+                    if currency == 'KWF':
+                        # Kuwaiti Dinar divided into 1000 not 100
+                        currency_divide = 1000
+                    else:
+                        currency_divide = 100
+                    # if ss < 75 or ss > 125:
+                    if abs(ss/currency_divide-1) > 0.25:
+                        ratio = quotes.loc[idx1, const._PRICE_COLNAMES_] / quotes.loc[idx2, const._PRICE_COLNAMES_]
+                        if ((ratio/currency_divide-1).abs() < 0.05).all():
+                            # newer prices are 100x
+                            for c in const._PRICE_COLNAMES_:
+                                quotes.loc[idx2, c] *= 100
+                        elif((ratio*currency_divide-1).abs() < 0.05).all():
+                            # newer prices are 0.01x
+                            for c in const._PRICE_COLNAMES_:
+                                quotes.loc[idx2, c] *= 0.01
+
+                # quotes.loc[idx2, 'Stock Splits'] = 2  # wtf? why doing this?
+
                 if _np.isnan(quotes.loc[idx2, "Open"]):
                     quotes.loc[idx2, "Open"] = quotes["Open"].iloc[n - 1]
                 # Note: nanmax() & nanmin() ignores NaNs, but still need to check not all are NaN to avoid warnings
@@ -641,6 +673,9 @@ def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
                 if "Adj Close" in quotes.columns:
                     quotes.loc[idx2, "Adj Close"] = quotes["Adj Close"].iloc[n - 1]
                 quotes.loc[idx2, "Volume"] += quotes["Volume"].iloc[n - 1]
+                quotes.loc[idx2, "Dividends"] += quotes["Dividends"].iloc[n - 1]
+                if ss != 1.0:
+                    quotes.loc[idx2, "Stock Splits"] = ss
                 quotes = quotes.drop(quotes.index[n - 1])
 
     return quotes

From a74ad474d98e6d5b7a681253aae5d6b55f9e43cd Mon Sep 17 00:00:00 2001
From: ValueRaider <ValueRaider@protonmail.com>
Date: Thu, 22 Aug 2024 15:44:39 +0100
Subject: [PATCH 15/16] Repair dividends: fine-tune logic + unit tests

Fix detecting div_adj_exceeds_prices.
Fine-tune fixing div_too_big.
Unit tests.
---
 tests/data/1398-HK-1d-bad-div-fixed.csv | 649 +++++++++++++++++++++
 tests/data/1398-HK-1d-bad-div.csv       | 649 +++++++++++++++++++++
 tests/data/3988-HK-1d-bad-div-fixed.csv | 649 +++++++++++++++++++++
 tests/data/3988-HK-1d-bad-div.csv       | 649 +++++++++++++++++++++
 tests/data/ABDP-L-1d-bad-div-fixed.csv  | 666 +++++++++++++++++++++
 tests/data/ABDP-L-1d-bad-div.csv        | 666 +++++++++++++++++++++
 tests/data/ADIG-L-1d-bad-div-fixed.csv  | 666 +++++++++++++++++++++
 tests/data/ADIG-L-1d-bad-div.csv        | 666 +++++++++++++++++++++
 tests/data/CALM-1d-no-bad-divs.csv      | 663 +++++++++++++++++++++
 tests/data/CLC-L-1d-bad-div-fixed.csv   | 666 +++++++++++++++++++++
 tests/data/CLC-L-1d-bad-div.csv         | 666 +++++++++++++++++++++
 tests/data/ELCO-L-1d-bad-div-fixed.csv  | 666 +++++++++++++++++++++
 tests/data/ELCO-L-1d-bad-div.csv        | 666 +++++++++++++++++++++
 tests/data/EWG-1d-no-bad-divs.csv       | 663 +++++++++++++++++++++
 tests/data/HSBK-IL-1d-no-bad-divs.csv   | 666 +++++++++++++++++++++
 tests/data/IBE-MC-1d-no-bad-divs.csv    | 678 ++++++++++++++++++++++
 tests/data/KAP-IL-1d-bad-div-fixed.csv  | 666 +++++++++++++++++++++
 tests/data/KAP-IL-1d-bad-div.csv        | 666 +++++++++++++++++++++
 tests/data/KEN-TA-1d-bad-div-fixed.csv  | 652 +++++++++++++++++++++
 tests/data/KEN-TA-1d-bad-div.csv        | 652 +++++++++++++++++++++
 tests/data/KME-MI-1d-bad-div-fixed.csv  | 675 ++++++++++++++++++++++
 tests/data/KME-MI-1d-bad-div.csv        | 675 ++++++++++++++++++++++
 tests/data/KMR-L-1d-no-bad-divs.csv     | 666 +++++++++++++++++++++
 tests/data/KWS-L-1d-bad-div-fixed.csv   | 666 +++++++++++++++++++++
 tests/data/KWS-L-1d-bad-div.csv         | 666 +++++++++++++++++++++
 tests/data/LSC-L-1d-bad-div-fixed.csv   | 665 +++++++++++++++++++++
 tests/data/LSC-L-1d-bad-div.csv         | 665 +++++++++++++++++++++
 tests/data/NPK-1d-no-bad-divs.csv       | 663 +++++++++++++++++++++
 tests/data/NVT-L-1d-bad-div-fixed.csv   | 666 +++++++++++++++++++++
 tests/data/NVT-L-1d-bad-div.csv         | 666 +++++++++++++++++++++
 tests/data/PSH-L-1d-bad-div-fixed.csv   | 666 +++++++++++++++++++++
 tests/data/PSH-L-1d-bad-div.csv         | 666 +++++++++++++++++++++
 tests/data/REL-L-1d-bad-div-fixed.csv   | 666 +++++++++++++++++++++
 tests/data/REL-L-1d-bad-div.csv         | 666 +++++++++++++++++++++
 tests/data/RGL-L-1d-bad-div-fixed.csv   | 666 +++++++++++++++++++++
 tests/data/RGL-L-1d-bad-div.csv         | 666 +++++++++++++++++++++
 tests/data/SAND-1d-bad-div-fixed.csv    | 663 +++++++++++++++++++++
 tests/data/SAND-1d-bad-div.csv          | 663 +++++++++++++++++++++
 tests/data/SCR-TO-1d-bad-div-fixed.csv  | 663 +++++++++++++++++++++
 tests/data/SCR-TO-1d-bad-div.csv        | 663 +++++++++++++++++++++
 tests/data/SERE-L-1d-bad-div-fixed.csv  | 666 +++++++++++++++++++++
 tests/data/SERE-L-1d-bad-div.csv        | 666 +++++++++++++++++++++
 tests/data/SOLB-BR-1d-bad-div-fixed.csv | 678 ++++++++++++++++++++++
 tests/data/SOLB-BR-1d-bad-div.csv       | 678 ++++++++++++++++++++++
 tests/data/TEM-L-1d-bad-div-fixed.csv   | 666 +++++++++++++++++++++
 tests/data/TEM-L-1d-bad-div.csv         | 666 +++++++++++++++++++++
 tests/data/TEP-PA-1d-bad-div-fixed.csv  | 678 ++++++++++++++++++++++
 tests/data/TEP-PA-1d-bad-div.csv        | 678 ++++++++++++++++++++++
 tests/data/TISG-MI-1d-no-bad-divs.csv   | 584 +++++++++++++++++++
 tests/test_price_repair.py              | 730 ++++++++++++++++++++++++
 tests/test_prices.py                    | 492 ----------------
 yfinance/scrapers/history.py            |  62 +-
 52 files changed, 33276 insertions(+), 519 deletions(-)
 create mode 100644 tests/data/1398-HK-1d-bad-div-fixed.csv
 create mode 100644 tests/data/1398-HK-1d-bad-div.csv
 create mode 100644 tests/data/3988-HK-1d-bad-div-fixed.csv
 create mode 100644 tests/data/3988-HK-1d-bad-div.csv
 create mode 100644 tests/data/ABDP-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/ABDP-L-1d-bad-div.csv
 create mode 100644 tests/data/ADIG-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/ADIG-L-1d-bad-div.csv
 create mode 100644 tests/data/CALM-1d-no-bad-divs.csv
 create mode 100644 tests/data/CLC-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/CLC-L-1d-bad-div.csv
 create mode 100644 tests/data/ELCO-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/ELCO-L-1d-bad-div.csv
 create mode 100644 tests/data/EWG-1d-no-bad-divs.csv
 create mode 100644 tests/data/HSBK-IL-1d-no-bad-divs.csv
 create mode 100644 tests/data/IBE-MC-1d-no-bad-divs.csv
 create mode 100644 tests/data/KAP-IL-1d-bad-div-fixed.csv
 create mode 100644 tests/data/KAP-IL-1d-bad-div.csv
 create mode 100644 tests/data/KEN-TA-1d-bad-div-fixed.csv
 create mode 100644 tests/data/KEN-TA-1d-bad-div.csv
 create mode 100644 tests/data/KME-MI-1d-bad-div-fixed.csv
 create mode 100644 tests/data/KME-MI-1d-bad-div.csv
 create mode 100644 tests/data/KMR-L-1d-no-bad-divs.csv
 create mode 100644 tests/data/KWS-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/KWS-L-1d-bad-div.csv
 create mode 100644 tests/data/LSC-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/LSC-L-1d-bad-div.csv
 create mode 100644 tests/data/NPK-1d-no-bad-divs.csv
 create mode 100644 tests/data/NVT-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/NVT-L-1d-bad-div.csv
 create mode 100644 tests/data/PSH-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/PSH-L-1d-bad-div.csv
 create mode 100644 tests/data/REL-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/REL-L-1d-bad-div.csv
 create mode 100644 tests/data/RGL-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/RGL-L-1d-bad-div.csv
 create mode 100644 tests/data/SAND-1d-bad-div-fixed.csv
 create mode 100644 tests/data/SAND-1d-bad-div.csv
 create mode 100644 tests/data/SCR-TO-1d-bad-div-fixed.csv
 create mode 100644 tests/data/SCR-TO-1d-bad-div.csv
 create mode 100644 tests/data/SERE-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/SERE-L-1d-bad-div.csv
 create mode 100644 tests/data/SOLB-BR-1d-bad-div-fixed.csv
 create mode 100644 tests/data/SOLB-BR-1d-bad-div.csv
 create mode 100644 tests/data/TEM-L-1d-bad-div-fixed.csv
 create mode 100644 tests/data/TEM-L-1d-bad-div.csv
 create mode 100644 tests/data/TEP-PA-1d-bad-div-fixed.csv
 create mode 100644 tests/data/TEP-PA-1d-bad-div.csv
 create mode 100644 tests/data/TISG-MI-1d-no-bad-divs.csv
 create mode 100644 tests/test_price_repair.py

diff --git a/tests/data/1398-HK-1d-bad-div-fixed.csv b/tests/data/1398-HK-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..d786f71f2
--- /dev/null
+++ b/tests/data/1398-HK-1d-bad-div-fixed.csv
@@ -0,0 +1,649 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
+2022-01-03 00:00:00+08:00,4.449999809265137,4.449999809265137,4.400000095367432,4.420000076293945,3.504674898559325,42688622.0,0.0,0.0,True
+2022-01-04 00:00:00+08:00,4.429999828338623,4.449999809265137,4.389999866485596,4.440000057220459,3.520532966294506,146990360.0,0.0,0.0,True
+2022-01-05 00:00:00+08:00,4.449999809265137,4.550000190734863,4.440000057220459,4.519999980926514,3.5839656809292446,312079015.0,0.0,0.0,True
+2022-01-06 00:00:00+08:00,4.539999961853027,4.539999961853027,4.440000057220459,4.5,3.5681076131940634,144072250.0,0.0,0.0,True
+2022-01-07 00:00:00+08:00,4.46999979019165,4.579999923706055,4.449999809265137,4.570000171661377,3.6236119595022314,242535732.0,0.0,0.0,True
+2022-01-10 00:00:00+08:00,4.599999904632568,4.650000095367432,4.579999923706055,4.639999866485596,3.679115418422372,270492205.0,0.0,0.0,True
+2022-01-11 00:00:00+08:00,4.599999904632568,4.659999847412109,4.579999923706055,4.630000114440918,3.671186384554782,186392481.0,0.0,0.0,True
+2022-01-12 00:00:00+08:00,4.630000114440918,4.650000095367432,4.590000152587891,4.610000133514404,3.6553283168196007,230956599.0,0.0,0.0,True
+2022-01-13 00:00:00+08:00,4.610000133514404,4.699999809265137,4.599999904632568,4.699999809265137,3.7266898434749227,213448950.0,0.0,0.0,True
+2022-01-14 00:00:00+08:00,4.710000038146973,4.739999771118164,4.670000076293945,4.710000038146973,3.73461909918952,226355010.0,0.0,0.0,True
+2022-01-17 00:00:00+08:00,4.690000057220459,4.71999979019165,4.670000076293945,4.699999809265137,3.7266898434749227,137932161.0,0.0,0.0,True
+2022-01-18 00:00:00+08:00,4.679999828338623,4.71999979019165,4.630000114440918,4.670000076293945,3.7029029637191577,138734371.0,0.0,0.0,True
+2022-01-19 00:00:00+08:00,4.679999828338623,4.710000038146973,4.639999866485596,4.670000076293945,3.7029029637191577,129644509.0,0.0,0.0,True
+2022-01-20 00:00:00+08:00,4.710000038146973,4.710000038146973,4.619999885559082,4.690000057220459,3.718761031454339,246026991.0,0.0,0.0,True
+2022-01-21 00:00:00+08:00,4.690000057220459,4.699999809265137,4.630000114440918,4.699999809265137,3.7266898434749227,173314946.0,0.0,0.0,True
+2022-01-24 00:00:00+08:00,4.699999809265137,4.75,4.670000076293945,4.71999979019165,3.7425483549041174,181865349.0,0.0,0.0,True
+2022-01-25 00:00:00+08:00,4.650000095367432,4.739999771118164,4.650000095367432,4.71999979019165,3.7425483549041174,198777082.0,0.0,0.0,True
+2022-01-26 00:00:00+08:00,4.739999771118164,4.75,4.710000038146973,4.730000019073486,3.7504776106187148,160528048.0,0.0,0.0,True
+2022-01-27 00:00:00+08:00,4.679999828338623,4.71999979019165,4.670000076293945,4.71999979019165,3.7425483549041174,128188395.0,0.0,0.0,True
+2022-01-28 00:00:00+08:00,4.739999771118164,4.75,4.670000076293945,4.679999828338623,3.7108317757397415,106436134.0,0.0,0.0,True
+2022-01-31 00:00:00+08:00,4.71999979019165,4.71999979019165,4.71999979019165,4.71999979019165,3.7425483549041174,0.0,0.0,0.0,True
+2022-02-04 00:00:00+08:00,4.710000038146973,4.789999961853027,4.710000038146973,4.78000020980835,3.790123445497688,158406204.0,0.0,0.0,True
+2022-02-07 00:00:00+08:00,4.760000228881836,4.849999904632568,4.739999771118164,4.840000152587891,3.8376980923972455,272642318.0,0.0,0.0,True
+2022-02-08 00:00:00+08:00,4.860000133514404,4.900000095367432,4.829999923706055,4.860000133514404,3.8535561601324266,214256288.0,0.0,0.0,True
+2022-02-09 00:00:00+08:00,4.889999866485596,4.900000095367432,4.849999904632568,4.889999866485596,3.877343483582205,223439580.0,0.0,0.0,True
+2022-02-10 00:00:00+08:00,4.889999866485596,4.889999866485596,4.840000152587891,4.889999866485596,3.877343483582205,207400910.0,0.0,0.0,True
+2022-02-11 00:00:00+08:00,4.889999866485596,4.929999828338623,4.849999904632568,4.929999828338623,3.909060062746581,278373812.0,0.0,0.0,True
+2022-02-14 00:00:00+08:00,4.929999828338623,4.940000057220459,4.860000133514404,4.880000114440918,3.8694146715616213,200959806.0,0.0,0.0,True
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+2022-02-17 00:00:00+08:00,4.789999961853027,4.800000190734863,4.730000019073486,4.789999961853027,3.798052257518272,181702145.0,0.0,0.0,True
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+2022-02-21 00:00:00+08:00,4.789999961853027,4.840000152587891,4.730000019073486,4.829999923706055,3.829768836682648,179237601.0,0.0,0.0,True
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+2022-03-15 00:00:00+08:00,4.599999904632568,4.599999904632568,4.349999904632568,4.429999828338623,3.5126037105799086,517738144.0,0.0,0.0,True
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+2022-03-18 00:00:00+08:00,4.559999942779541,4.670000076293945,4.53000020980835,4.630000114440918,3.671186384554782,466329103.0,0.0,0.0,True
+2022-03-21 00:00:00+08:00,4.619999885559082,4.650000095367432,4.550000190734863,4.590000152587891,3.6394700272374125,144232518.0,0.0,0.0,True
+2022-03-22 00:00:00+08:00,4.630000114440918,4.650000095367432,4.579999923706055,4.630000114440918,3.671186384554782,155120637.0,0.0,0.0,True
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+2022-03-28 00:00:00+08:00,4.690000057220459,4.699999809265137,4.650000095367432,4.690000057220459,3.718761031454339,118402829.0,0.0,0.0,True
+2022-03-29 00:00:00+08:00,4.690000057220459,4.71999979019165,4.659999847412109,4.71999979019165,3.7425483549041174,168733498.0,0.0,0.0,True
+2022-03-30 00:00:00+08:00,4.75,4.789999961853027,4.730000019073486,4.760000228881836,3.774265377762507,237372211.0,0.0,0.0,True
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diff --git a/tests/data/1398-HK-1d-bad-div.csv b/tests/data/1398-HK-1d-bad-div.csv
new file mode 100644
index 000000000..e98d4d095
--- /dev/null
+++ b/tests/data/1398-HK-1d-bad-div.csv
@@ -0,0 +1,649 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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diff --git a/tests/data/3988-HK-1d-bad-div-fixed.csv b/tests/data/3988-HK-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..9a0f58dc9
--- /dev/null
+++ b/tests/data/3988-HK-1d-bad-div-fixed.csv
@@ -0,0 +1,649 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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diff --git a/tests/data/3988-HK-1d-bad-div.csv b/tests/data/3988-HK-1d-bad-div.csv
new file mode 100644
index 000000000..b09f40b89
--- /dev/null
+++ b/tests/data/3988-HK-1d-bad-div.csv
@@ -0,0 +1,649 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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diff --git a/tests/data/ABDP-L-1d-bad-div-fixed.csv b/tests/data/ABDP-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..33305a84a
--- /dev/null
+++ b/tests/data/ABDP-L-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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diff --git a/tests/data/ABDP-L-1d-bad-div.csv b/tests/data/ABDP-L-1d-bad-div.csv
new file mode 100644
index 000000000..c78dabf61
--- /dev/null
+++ b/tests/data/ABDP-L-1d-bad-div.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/ADIG-L-1d-bad-div-fixed.csv b/tests/data/ADIG-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..97301fbe7
--- /dev/null
+++ b/tests/data/ADIG-L-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/ADIG-L-1d-bad-div.csv b/tests/data/ADIG-L-1d-bad-div.csv
new file mode 100644
index 000000000..7d270ff18
--- /dev/null
+++ b/tests/data/ADIG-L-1d-bad-div.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/CALM-1d-no-bad-divs.csv b/tests/data/CALM-1d-no-bad-divs.csv
new file mode 100644
index 000000000..138cf8856
--- /dev/null
+++ b/tests/data/CALM-1d-no-bad-divs.csv
@@ -0,0 +1,663 @@
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diff --git a/tests/data/CLC-L-1d-bad-div-fixed.csv b/tests/data/CLC-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..cad98569f
--- /dev/null
+++ b/tests/data/CLC-L-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/CLC-L-1d-bad-div.csv b/tests/data/CLC-L-1d-bad-div.csv
new file mode 100644
index 000000000..7a8d425b9
--- /dev/null
+++ b/tests/data/CLC-L-1d-bad-div.csv
@@ -0,0 +1,666 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gains
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diff --git a/tests/data/ELCO-L-1d-bad-div-fixed.csv b/tests/data/ELCO-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..5a22d2823
--- /dev/null
+++ b/tests/data/ELCO-L-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/ELCO-L-1d-bad-div.csv b/tests/data/ELCO-L-1d-bad-div.csv
new file mode 100644
index 000000000..797a0e075
--- /dev/null
+++ b/tests/data/ELCO-L-1d-bad-div.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/EWG-1d-no-bad-divs.csv b/tests/data/EWG-1d-no-bad-divs.csv
new file mode 100644
index 000000000..b0b5f5fdf
--- /dev/null
+++ b/tests/data/EWG-1d-no-bad-divs.csv
@@ -0,0 +1,663 @@
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diff --git a/tests/data/HSBK-IL-1d-no-bad-divs.csv b/tests/data/HSBK-IL-1d-no-bad-divs.csv
new file mode 100644
index 000000000..7e7308da8
--- /dev/null
+++ b/tests/data/HSBK-IL-1d-no-bad-divs.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/IBE-MC-1d-no-bad-divs.csv b/tests/data/IBE-MC-1d-no-bad-divs.csv
new file mode 100644
index 000000000..b9584ba2d
--- /dev/null
+++ b/tests/data/IBE-MC-1d-no-bad-divs.csv
@@ -0,0 +1,678 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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diff --git a/tests/data/KAP-IL-1d-bad-div-fixed.csv b/tests/data/KAP-IL-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..fa0700b2b
--- /dev/null
+++ b/tests/data/KAP-IL-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/KAP-IL-1d-bad-div.csv b/tests/data/KAP-IL-1d-bad-div.csv
new file mode 100644
index 000000000..fce70bd52
--- /dev/null
+++ b/tests/data/KAP-IL-1d-bad-div.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/KEN-TA-1d-bad-div-fixed.csv b/tests/data/KEN-TA-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..3924afb47
--- /dev/null
+++ b/tests/data/KEN-TA-1d-bad-div-fixed.csv
@@ -0,0 +1,652 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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diff --git a/tests/data/KEN-TA-1d-bad-div.csv b/tests/data/KEN-TA-1d-bad-div.csv
new file mode 100644
index 000000000..e56b6508f
--- /dev/null
+++ b/tests/data/KEN-TA-1d-bad-div.csv
@@ -0,0 +1,652 @@
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diff --git a/tests/data/KME-MI-1d-bad-div-fixed.csv b/tests/data/KME-MI-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..4b9f14478
--- /dev/null
+++ b/tests/data/KME-MI-1d-bad-div-fixed.csv
@@ -0,0 +1,675 @@
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diff --git a/tests/data/KME-MI-1d-bad-div.csv b/tests/data/KME-MI-1d-bad-div.csv
new file mode 100644
index 000000000..87d873b8a
--- /dev/null
+++ b/tests/data/KME-MI-1d-bad-div.csv
@@ -0,0 +1,675 @@
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diff --git a/tests/data/KMR-L-1d-no-bad-divs.csv b/tests/data/KMR-L-1d-no-bad-divs.csv
new file mode 100644
index 000000000..9c956befc
--- /dev/null
+++ b/tests/data/KMR-L-1d-no-bad-divs.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/KWS-L-1d-bad-div-fixed.csv b/tests/data/KWS-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..d3830ccd0
--- /dev/null
+++ b/tests/data/KWS-L-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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diff --git a/tests/data/KWS-L-1d-bad-div.csv b/tests/data/KWS-L-1d-bad-div.csv
new file mode 100644
index 000000000..85d22c395
--- /dev/null
+++ b/tests/data/KWS-L-1d-bad-div.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/LSC-L-1d-bad-div-fixed.csv b/tests/data/LSC-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..a8e3d1ac6
--- /dev/null
+++ b/tests/data/LSC-L-1d-bad-div-fixed.csv
@@ -0,0 +1,665 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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diff --git a/tests/data/LSC-L-1d-bad-div.csv b/tests/data/LSC-L-1d-bad-div.csv
new file mode 100644
index 000000000..dc5f5bcc1
--- /dev/null
+++ b/tests/data/LSC-L-1d-bad-div.csv
@@ -0,0 +1,665 @@
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diff --git a/tests/data/NPK-1d-no-bad-divs.csv b/tests/data/NPK-1d-no-bad-divs.csv
new file mode 100644
index 000000000..294cdd0d8
--- /dev/null
+++ b/tests/data/NPK-1d-no-bad-divs.csv
@@ -0,0 +1,663 @@
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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
new file mode 100644
index 000000000..c86b08d88
--- /dev/null
+++ b/tests/data/NVT-L-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/NVT-L-1d-bad-div.csv b/tests/data/NVT-L-1d-bad-div.csv
new file mode 100644
index 000000000..701299180
--- /dev/null
+++ b/tests/data/NVT-L-1d-bad-div.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/PSH-L-1d-bad-div-fixed.csv b/tests/data/PSH-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..83db6483c
--- /dev/null
+++ b/tests/data/PSH-L-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/PSH-L-1d-bad-div.csv b/tests/data/PSH-L-1d-bad-div.csv
new file mode 100644
index 000000000..9675acd90
--- /dev/null
+++ b/tests/data/PSH-L-1d-bad-div.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/REL-L-1d-bad-div-fixed.csv b/tests/data/REL-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..71ca32d15
--- /dev/null
+++ b/tests/data/REL-L-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/REL-L-1d-bad-div.csv b/tests/data/REL-L-1d-bad-div.csv
new file mode 100644
index 000000000..e0d60c5a2
--- /dev/null
+++ b/tests/data/REL-L-1d-bad-div.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/RGL-L-1d-bad-div-fixed.csv b/tests/data/RGL-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..a8f6d3746
--- /dev/null
+++ b/tests/data/RGL-L-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/RGL-L-1d-bad-div.csv b/tests/data/RGL-L-1d-bad-div.csv
new file mode 100644
index 000000000..4dff37ebc
--- /dev/null
+++ b/tests/data/RGL-L-1d-bad-div.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/SAND-1d-bad-div-fixed.csv b/tests/data/SAND-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..a4b2763d3
--- /dev/null
+++ b/tests/data/SAND-1d-bad-div-fixed.csv
@@ -0,0 +1,663 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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diff --git a/tests/data/SAND-1d-bad-div.csv b/tests/data/SAND-1d-bad-div.csv
new file mode 100644
index 000000000..defb2f892
--- /dev/null
+++ b/tests/data/SAND-1d-bad-div.csv
@@ -0,0 +1,663 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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diff --git a/tests/data/SCR-TO-1d-bad-div-fixed.csv b/tests/data/SCR-TO-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..210b01d25
--- /dev/null
+++ b/tests/data/SCR-TO-1d-bad-div-fixed.csv
@@ -0,0 +1,663 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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diff --git a/tests/data/SCR-TO-1d-bad-div.csv b/tests/data/SCR-TO-1d-bad-div.csv
new file mode 100644
index 000000000..0daff1558
--- /dev/null
+++ b/tests/data/SCR-TO-1d-bad-div.csv
@@ -0,0 +1,663 @@
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diff --git a/tests/data/SERE-L-1d-bad-div-fixed.csv b/tests/data/SERE-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..84edc6f2c
--- /dev/null
+++ b/tests/data/SERE-L-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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diff --git a/tests/data/SERE-L-1d-bad-div.csv b/tests/data/SERE-L-1d-bad-div.csv
new file mode 100644
index 000000000..4fe7e419c
--- /dev/null
+++ b/tests/data/SERE-L-1d-bad-div.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/SOLB-BR-1d-bad-div-fixed.csv b/tests/data/SOLB-BR-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..70b51c24a
--- /dev/null
+++ b/tests/data/SOLB-BR-1d-bad-div-fixed.csv
@@ -0,0 +1,678 @@
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diff --git a/tests/data/SOLB-BR-1d-bad-div.csv b/tests/data/SOLB-BR-1d-bad-div.csv
new file mode 100644
index 000000000..4c1447eea
--- /dev/null
+++ b/tests/data/SOLB-BR-1d-bad-div.csv
@@ -0,0 +1,678 @@
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diff --git a/tests/data/TEM-L-1d-bad-div-fixed.csv b/tests/data/TEM-L-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..84c10e387
--- /dev/null
+++ b/tests/data/TEM-L-1d-bad-div-fixed.csv
@@ -0,0 +1,666 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Repaired?
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diff --git a/tests/data/TEM-L-1d-bad-div.csv b/tests/data/TEM-L-1d-bad-div.csv
new file mode 100644
index 000000000..a213a871e
--- /dev/null
+++ b/tests/data/TEM-L-1d-bad-div.csv
@@ -0,0 +1,666 @@
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diff --git a/tests/data/TEP-PA-1d-bad-div-fixed.csv b/tests/data/TEP-PA-1d-bad-div-fixed.csv
new file mode 100644
index 000000000..85169e868
--- /dev/null
+++ b/tests/data/TEP-PA-1d-bad-div-fixed.csv
@@ -0,0 +1,678 @@
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diff --git a/tests/data/TEP-PA-1d-bad-div.csv b/tests/data/TEP-PA-1d-bad-div.csv
new file mode 100644
index 000000000..bc028575b
--- /dev/null
+++ b/tests/data/TEP-PA-1d-bad-div.csv
@@ -0,0 +1,678 @@
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diff --git a/tests/data/TISG-MI-1d-no-bad-divs.csv b/tests/data/TISG-MI-1d-no-bad-divs.csv
new file mode 100644
index 000000000..2ba05ac90
--- /dev/null
+++ b/tests/data/TISG-MI-1d-no-bad-divs.csv
@@ -0,0 +1,584 @@
+Datetime,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
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diff --git a/tests/test_price_repair.py b/tests/test_price_repair.py
new file mode 100644
index 000000000..514230a1a
--- /dev/null
+++ b/tests/test_price_repair.py
@@ -0,0 +1,730 @@
+from .context import yfinance as yf
+from .context import session_gbl
+
+import unittest
+
+import os
+import datetime as _dt
+import numpy as _np
+import pandas as _pd
+
+
+class TestPriceRepairAssumptions(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+        cls.dp = os.path.dirname(__file__)
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def test_resampling(self):
+        for tkr in ['GOOGL', 'GLEN.L', '2330.TW']:
+            dat = yf.Ticker(tkr, session=self.session)
+
+            intervals = ['1d', '1wk', '1mo', '3mo']
+            periods = ['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd']#, 'max']
+            # Yahoo handles period=max weird. For tkr=INTC, interval=1d starts 5 years before interval=1mo
+            for i in range(len(intervals)):
+                interval = intervals[i]
+                if interval == '1d':
+                    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)
+                    # dfr = dat._lazy_load_price_history()._resample(df_1d, '1d', interval, period)
+                    dfr = dat.history(interval=interval, period=period, repair=True)
+
+                    debug = False
+                    if len(dfr) != len(df_truth):
+                        if dfr.index[1] == df_truth.index[0]:
+                            # print("  - resampled has extra row at start")
+                            pass
+                        elif dfr.index[0] == df_truth.index[1]:
+                            print("  - resampled missing a row at start")
+                            debug = True
+                        else:
+                            print("  - resampled index different length")
+                            debug = True
+                    elif (dfr.index != df_truth.index).all():
+                        print("  - resampled index mismatch:")
+                        print(dfr.index == df_truth.index)
+                        debug = True
+                    else:
+                        # vol_match = dfr['Volume'] == df_truth['Volume']
+                        vol_diff_pct0 = (dfr['Volume'].iloc[0] - df_truth['Volume'].iloc[0])/df_truth['Volume'].iloc[0]
+                        vol_diff_pct1 = (dfr['Volume'].iloc[-1] - df_truth['Volume'].iloc[-1])/df_truth['Volume'].iloc[-1]
+                        vol_diff_pct = _np.array([vol_diff_pct0, vol_diff_pct1])
+                        vol_match = vol_diff_pct > -0.23
+                        vol_match_nmatch = _np.sum(vol_match)
+                        vol_match_ndiff = len(vol_match) - vol_match_nmatch
+                        if vol_match.all():
+                            # print("  - volume match 100%")
+                            pass
+                        elif vol_match_ndiff == 1 and not vol_match[0]:
+                            # print("  - volume almost-perfect match, only first different")
+                            # debug = True
+                            pass
+                        else:
+                            # print(f"  - volume match {vol_match_nmatch}/{len(vol_match)} {vol_match.to_numpy()}")
+                            print(f"  - volume significantly different in first row: vol_diff_pct={vol_diff_pct*100}%")
+                            debug = True
+
+                    if debug:
+                        print("- investigate:")
+                        print("- df_truth:")
+                        print(df_truth)#[['Open', 'Close', 'Volume']])
+                        df_1d = dat.history(interval='1d', period=period)
+                        print("- df_1d:")
+                        print(df_1d)#[['Open', 'Close', 'Volume']])
+                        print("- dfr:")
+                        print(dfr)#[['Open', 'Close', 'Volume']])
+                        return
+
+
+
+class TestPriceRepair(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = session_gbl
+        cls.dp = os.path.dirname(__file__)
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def test_types(self):
+        tkr = 'INTC'
+        dat = yf.Ticker(tkr, session=self.session)
+
+        data = dat.history(period="3mo", interval="1d", prepost=True, repair=True)
+        self.assertIsInstance(data, _pd.DataFrame, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")
+
+        reconstructed = dat._lazy_load_price_history()._reconstruct_intervals_batch(data, "1wk", True)
+        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.
+        # Need to test that '_reconstruct_intervals_batch()' can handle this.
+
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+
+        dt_now = _pd.Timestamp.utcnow()
+        td_60d = _dt.timedelta(days=60)
+
+        # Round time for 'requests_cache' reuse
+        dt_now = dt_now.ceil("1h")
+
+        for tkr in tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+            end_dt = dt_now
+            start_dt = end_dt - td_60d
+            dat.history(start=start_dt, end=end_dt, interval="2m", repair=True)
+
+    # pass
+    def test_repair_100x_random_weekly(self):
+        # Setup:
+        tkr = "PNL.L"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+        hist = dat._lazy_load_price_history()
+
+        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
+        df = _pd.DataFrame(data={"Open":      [470.5,   473.5, 474.5, 470],
+                                 "High":      [476,     476.5, 477,   480],
+                                 "Low":       [470.5,   470,   465.5, 468.26],
+                                 "Close":     [475,     473.5, 472,   473.5],
+                                 "Adj Close": [474.865, 468.6, 467.1, 468.6],
+                                 "Volume": [2295613, 2245604, 3000287, 2635611]},
+                           index=_pd.to_datetime([_dt.date(2022, 10, 24),
+                                                  _dt.date(2022, 10, 17),
+                                                  _dt.date(2022, 10, 10),
+                                                  _dt.date(2022, 10, 3)]))
+        df = df.sort_index()
+        df.index.name = "Date"
+        df_bad = df.copy()
+        df_bad.loc["2022-10-24", "Close"] *= 100
+        df_bad.loc["2022-10-17", "Low"] *= 100
+        df_bad.loc["2022-10-03", "Open"] *= 100
+        df.index = df.index.tz_localize(tz_exchange)
+        df_bad.index = df_bad.index.tz_localize(tz_exchange)
+
+        # Run test
+
+        df_repaired = hist._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
+
+        # First test - no errors left
+        for c in data_cols:
+            try:
+                self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
+            except AssertionError:
+                print(df[c])
+                print(df_repaired[c])
+                raise
+
+        # Second test - all differences should be either ~1x or ~100x
+        ratio = df_bad[data_cols].values / df[data_cols].values
+        ratio = ratio.round(2)
+        # - round near-100 ratio to 100:
+        f = ratio > 90
+        ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
+        # - now test
+        f_100 = ratio == 100
+        f_1 = ratio == 1
+        self.assertTrue((f_100 | f_1).all())
+
+        self.assertTrue("Repaired?" in df_repaired.columns)
+        self.assertFalse(df_repaired["Repaired?"].isna().any())
+
+    # 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.
+
+        tkr = "PNL.L"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+        hist = dat._lazy_load_price_history()
+
+        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
+        df = _pd.DataFrame(data={"Open":      [400,    398,    392.5,  417],
+                                 "High":      [421,    425,    419,    420.5],
+                                 "Low":       [400,    380.5,  376.5,  396],
+                                 "Close":     [410,    409.5,  402,    399],
+                                 "Adj Close": [409.75, 393.43, 386.22, 383.34],
+                                 "Volume": [3232600, 3773900, 10835000, 4257900]},
+                           index=_pd.to_datetime([_dt.date(2020, 3, 30),
+                                                  _dt.date(2020, 3, 23),
+                                                  _dt.date(2020, 3, 16),
+                                                  _dt.date(2020, 3, 9)]))
+        df = df.sort_index()
+        # Simulate data missing split-adjustment:
+        df[data_cols] *= 100.0
+        df["Volume"] *= 0.01
+        #
+        df.index.name = "Date"
+        # Create 100x errors:
+        df_bad = df.copy()
+        df_bad.loc["2020-03-30", "Close"] *= 100
+        df_bad.loc["2020-03-23", "Low"] *= 100
+        df_bad.loc["2020-03-09", "Open"] *= 100
+        df.index = df.index.tz_localize(tz_exchange)
+        df_bad.index = df_bad.index.tz_localize(tz_exchange)
+
+        df_repaired = hist._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
+
+        # First test - no errors left
+        for c in data_cols:
+            try:
+                self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
+            except AssertionError:
+                print("Mismatch in column", c)
+                print("- df_repaired:")
+                print(df_repaired[c])
+                print("- answer:")
+                print(df[c])
+                raise
+
+        # Second test - all differences should be either ~1x or ~100x
+        ratio = df_bad[data_cols].values / df[data_cols].values
+        ratio = ratio.round(2)
+        # - round near-100 ratio to 100:
+        f = ratio > 90
+        ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
+        # - now test
+        f_100 = ratio == 100
+        f_1 = ratio == 1
+        self.assertTrue((f_100 | f_1).all())
+
+        self.assertTrue("Repaired?" in df_repaired.columns)
+        self.assertFalse(df_repaired["Repaired?"].isna().any())
+
+    # pass
+    def test_repair_100x_random_daily(self):
+        tkr = "PNL.L"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+        hist = dat._lazy_load_price_history()
+
+        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
+        df = _pd.DataFrame(data={"Open":      [478,    476,   476,   472],
+                                 "High":      [478,    477.5, 477,   475],
+                                 "Low":       [474.02, 474,   473,   470.75],
+                                 "Close":     [475.5,  475.5, 474.5, 475],
+                                 "Adj Close": [475.5,  475.5, 474.5, 475],
+                                 "Volume": [436414, 485947, 358067, 287620]},
+                           index=_pd.to_datetime([_dt.date(2022, 11, 1),
+                                                  _dt.date(2022, 10, 31),
+                                                  _dt.date(2022, 10, 28),
+                                                  _dt.date(2022, 10, 27)]))
+        for c in data_cols:
+            df[c] = df[c].astype('float')
+        df = df.sort_index()
+        df.index.name = "Date"
+        df_bad = df.copy()
+        df_bad.loc["2022-11-01", "Close"] *= 100
+        df_bad.loc["2022-10-31", "Low"] *= 100
+        df_bad.loc["2022-10-27", "Open"] *= 100
+        df.index = df.index.tz_localize(tz_exchange)
+        df_bad.index = df_bad.index.tz_localize(tz_exchange)
+
+        df_repaired = hist._fix_unit_random_mixups(df_bad, "1d", tz_exchange, prepost=False)
+
+        # First test - no errors left
+        for c in data_cols:
+            self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
+
+        # Second test - all differences should be either ~1x or ~100x
+        ratio = df_bad[data_cols].values / df[data_cols].values
+        ratio = ratio.round(2)
+        # - round near-100 ratio to 100:
+        f = ratio > 90
+        ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
+        # - now test
+        f_100 = ratio == 100
+        f_1 = ratio == 1
+        self.assertTrue((f_100 | f_1).all())
+
+        self.assertTrue("Repaired?" in df_repaired.columns)
+        self.assertFalse(df_repaired["Repaired?"].isna().any())
+
+    # pass
+    def test_repair_100x_block_daily(self):
+        # Some 100x errors are not sporadic.
+        # Sometimes Yahoo suddenly shifts from cents->$ from some recent date.
+
+        tkrs = ['AET.L', 'SSW.JO']
+        intervals = ['1d', '1wk']
+        # tkrs = ['AET.L']
+        # intervals = ['1wk']
+        # import yfinance as yf ; yf.enable_debug_mode()
+
+        for tkr in tkrs:
+            for interval in intervals:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+                hist = dat._lazy_load_price_history()
+
+                data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
+                fp = os.path.join(self.dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error.csv")
+                if not os.path.isfile(fp):
+                    continue
+                df_bad = _pd.read_csv(fp, index_col="Date")
+                df_bad.index = _pd.to_datetime(df_bad.index, utc=True).tz_convert(tz_exchange)
+                df_bad = df_bad.sort_index()
+
+                df = df_bad.copy()
+                fp = os.path.join(self.dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error-fixed.csv")
+                df = _pd.read_csv(fp, index_col="Date")
+                df.index = _pd.to_datetime(df.index, utc=True).tz_convert(tz_exchange)
+                df = df.sort_index()
+
+                df_repaired = hist._fix_unit_switch(df_bad, interval, tz_exchange)
+                df_repaired = df_repaired.sort_index()
+
+                # First test - no errors left
+                for c in data_cols:
+                    try:
+                        self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
+                    except Exception:
+                        print("- repaired:")
+                        print(df_repaired[c])
+                        print("- correct:")
+                        print(df[c])
+                        print(f"TEST FAIL on column '{c}' (tkr={tkr} interval={interval})")
+                        raise
+
+                # Second test - all differences should be either ~1x or ~100x
+                ratio = df_bad[data_cols].values / df[data_cols].values
+                ratio = ratio.round(2)
+                # - round near-100 ratio to 100:
+                f = ratio > 90
+                ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
+                # - now test
+                f_100 = (ratio == 100) | (ratio == 0.01)
+                f_1 = ratio == 1
+                self.assertTrue((f_100 | f_1).all())
+
+                self.assertTrue("Repaired?" in df_repaired.columns)
+                self.assertFalse(df_repaired["Repaired?"].isna().any())
+
+    # pass
+    def test_repair_zeroes_daily(self):
+        tkr = "BBIL.L"
+        dat = yf.Ticker(tkr, session=self.session)
+        hist = dat._lazy_load_price_history()
+        tz_exchange = dat.fast_info["timezone"]
+
+        df_bad = _pd.DataFrame(data={"Open":      [0,      102.04, 102.04],
+                                     "High":      [0,      102.1,  102.11],
+                                     "Low":       [0,      102.04, 102.04],
+                                     "Close":     [103.03, 102.05, 102.08],
+                                     "Adj Close": [102.03, 102.05, 102.08],
+                                     "Volume": [560, 137, 117]},
+                               index=_pd.to_datetime([_dt.datetime(2022, 11, 1),
+                                                      _dt.datetime(2022, 10, 31),
+                                                      _dt.datetime(2022, 10, 30)]))
+        df_bad = df_bad.sort_index()
+        df_bad.index.name = "Date"
+        df_bad.index = df_bad.index.tz_localize(tz_exchange)
+
+        repaired_df = hist._fix_zeroes(df_bad, "1d", tz_exchange, prepost=False)
+
+        correct_df = df_bad.copy()
+        correct_df.loc["2022-11-01", "Open"] = 102.080002
+        correct_df.loc["2022-11-01", "Low"] = 102.032501
+        correct_df.loc["2022-11-01", "High"] = 102.080002
+        for c in ["Open", "Low", "High", "Close"]:
+            self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-8).all())
+
+        self.assertTrue("Repaired?" in repaired_df.columns)
+        self.assertFalse(repaired_df["Repaired?"].isna().any())
+
+    # pass
+    def test_repair_zeroes_daily_adjClose(self):
+        # Test that 'Adj Close' is reconstructed correctly,
+        # particularly when a dividend occurred within 1 day.
+
+        tkr = "INTC"
+        df = _pd.DataFrame(data={"Open":      [28.95, 28.65, 29.55, 29.62, 29.25],
+                                 "High":      [29.12, 29.27, 29.65, 31.17, 30.30],
+                                 "Low":       [28.21, 28.43, 28.61, 29.53, 28.80],
+                                 "Close":     [28.24, 29.05, 28.69, 30.32, 30.19],
+                                 "Adj Close": [28.12, 28.93, 28.57, 29.83, 29.70],
+                                 "Volume":    [36e6, 51e6, 49e6, 58e6, 62e6],
+                                 "Dividends": [0, 0, 0.365, 0, 0]},
+                           index=_pd.to_datetime([_dt.datetime(2023, 2, 8),
+                                                  _dt.datetime(2023, 2, 7),
+                                                  _dt.datetime(2023, 2, 6),
+                                                  _dt.datetime(2023, 2, 3),
+                                                  _dt.datetime(2023, 2, 2)]))
+        df = df.sort_index()
+        df.index.name = "Date"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+        df.index = df.index.tz_localize(tz_exchange)
+        hist = dat._lazy_load_price_history()
+
+        rtol = 5e-3
+        for i in [0, 1, 2]:
+            df_slice = df.iloc[i:i+3]
+            for j in range(3):
+                df_slice_bad = df_slice.copy()
+                df_slice_bad.loc[df_slice_bad.index[j], "Adj Close"] = 0.0
+
+                df_slice_bad_repaired = hist._fix_zeroes(df_slice_bad, "1d", tz_exchange, prepost=False)
+                for c in ["Close", "Adj Close"]:
+                    self.assertTrue(_np.isclose(df_slice_bad_repaired[c], df_slice[c], rtol=rtol).all())
+                self.assertTrue("Repaired?" in df_slice_bad_repaired.columns)
+                self.assertFalse(df_slice_bad_repaired["Repaired?"].isna().any())
+
+    # pass
+    def test_repair_zeroes_hourly(self):
+        tkr = "INTC"
+        dat = yf.Ticker(tkr, session=self.session)
+        tz_exchange = dat.fast_info["timezone"]
+        hist = dat._lazy_load_price_history()
+
+        correct_df = hist.history(period="5d", interval="1h", auto_adjust=False, repair=True)
+
+        df_bad = correct_df.copy()
+        bad_idx = correct_df.index[10]
+        df_bad.loc[bad_idx, "Open"] = _np.nan
+        df_bad.loc[bad_idx, "High"] = _np.nan
+        df_bad.loc[bad_idx, "Low"] = _np.nan
+        df_bad.loc[bad_idx, "Close"] = _np.nan
+        df_bad.loc[bad_idx, "Adj Close"] = _np.nan
+        df_bad.loc[bad_idx, "Volume"] = 0
+
+        repaired_df = hist._fix_zeroes(df_bad, "1h", tz_exchange, prepost=False)
+
+        for c in ["Open", "Low", "High", "Close"]:
+            try:
+                self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-7).all())
+            except AssertionError:
+                print("COLUMN", c)
+                print("- repaired_df")
+                print(repaired_df)
+                print("- correct_df[c]:")
+                print(correct_df[c])
+                print("- diff:")
+                print(repaired_df[c] - correct_df[c])
+                raise
+
+        self.assertTrue("Repaired?" in repaired_df.columns)
+        self.assertFalse(repaired_df["Repaired?"].isna().any())
+
+    # pass
+    def test_repair_bad_stock_splits(self):
+        # Stocks that split in 2022 but no problems in Yahoo data,
+        # so repair should change nothing
+        good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
+        good_tkrs += ['AEI', 'GHI', 'IRON', 'LXU', 'NUZE', 'RSLS', 'TISI']
+        good_tkrs += ['BOL.ST', 'TUI1.DE']
+        intervals = ['1d', '1wk', '1mo', '3mo']
+        for tkr in good_tkrs:
+            for interval in intervals:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+                hist = dat._lazy_load_price_history()
+
+                df_good = dat.history(start='2020-01-01', end=_dt.date.today(), interval=interval, auto_adjust=False)
+
+                repaired_df = hist._fix_bad_stock_splits(df_good, interval, tz_exchange)
+
+                # Expect no change from repair
+                df_good = df_good.sort_index()
+                repaired_df = repaired_df.sort_index()
+                for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                    try:
+                        self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
+                    except Exception:
+                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
+                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
+                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
+                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
+                        raise
+
+        bad_tkrs = ['4063.T', 'ALPHA.PA', 'AV.L', 'CNE.L', 'MOB.ST', 'SPM.MI']
+        bad_tkrs.append('LA.V')  # special case - stock split error is 3 years ago! why not fixed?
+        for tkr in bad_tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+            tz_exchange = dat.fast_info["timezone"]
+            hist = dat._lazy_load_price_history()
+
+            interval = '1d'
+            fp = os.path.join(self.dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
+            if not os.path.isfile(fp):
+                interval = '1wk'
+                fp = os.path.join(self.dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
+            df_bad = _pd.read_csv(fp, index_col="Date")
+            df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
+
+            repaired_df = hist._fix_bad_stock_splits(df_bad, "1d", tz_exchange)
+
+            fp = os.path.join(self.dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split-fixed.csv")
+            correct_df = _pd.read_csv(fp, index_col="Date")
+            correct_df.index = _pd.to_datetime(correct_df.index, utc=True)
+
+            repaired_df = repaired_df.sort_index()
+            correct_df = correct_df.sort_index()
+            for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                try:
+                    self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
+                except AssertionError:
+                    print(f"tkr={tkr} COLUMN={c}")
+                    # print("- repaired_df")
+                    # print(repaired_df)
+                    # print("- correct_df[c]:")
+                    # print(correct_df[c])
+                    # print("- diff:")
+                    # print(repaired_df[c] - correct_df[c])
+                    raise
+
+        # Had very high price volatility in Jan-2021 around split date that could
+        # be mistaken for missing stock split adjustment. And old logic did think
+        # column 'High' required fixing - wrong!
+        sketchy_tkrs = ['FIZZ']
+        intervals = ['1wk']
+        for tkr in sketchy_tkrs:
+            for interval in intervals:
+                dat = yf.Ticker(tkr, session=self.session)
+                tz_exchange = dat.fast_info["timezone"]
+                hist = dat._lazy_load_price_history()
+
+                df_good = hist.history(start='2020-11-30', end='2021-04-01', interval=interval, auto_adjust=False)
+
+                repaired_df = hist._fix_bad_stock_splits(df_good, interval, tz_exchange)
+
+                # Expect no change from repair
+                df_good = df_good.sort_index()
+                repaired_df = repaired_df.sort_index()
+                for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
+                    try:
+                        self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
+                    except AssertionError:
+                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
+                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
+                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
+                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
+                        raise
+
+    # 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 = []
+        false_positives = []
+
+        # Tickers are not random. Either their errors were really bad, or
+        # they discovered bugs/gaps in repair logic.
+
+        # bad_tkrs += ['MPCC.OL']  # has yahoo fixed?
+
+        # 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
+
+        # Adj too small
+        bad_tkrs += ['ADIG.L']
+        bad_tkrs += ['CLC.L']
+        bad_tkrs += ['RGL.L']
+        bad_tkrs += ['SERE.L']
+
+        # Div 100x
+        bad_tkrs += ['ABDP.L']
+        bad_tkrs += ['ELCO.L']
+        bad_tkrs += ['KWS.L']
+        bad_tkrs += ['PSH.L']
+        bad_tkrs += ['SCR.TO']
+
+        # Missing div adjusts:
+        bad_tkrs += ['1398.HK']
+        bad_tkrs += ['3988.HK']
+        bad_tkrs += ['KEN.TA']
+
+        # Phantom divs
+        bad_tkrs += ['KAP.IL']  # 1x 1d phantom div, and false positives 0.01x in 1wk
+        bad_tkrs += ['SAND']
+        bad_tkrs += ['SOLB.BR']  # 1x phantom div, but some false-positive 100x. and had to improve phantom detection
+        bad_tkrs += ['TEM.L']
+        bad_tkrs += ['TEP.PA']
+
+        # Maybe test tickers with mix of adj-too-small and 100x
+
+        false_positives += ['CALM']  # tiny div on 2023-10-31
+        false_positives += ['EWG']  # tiny div 2022-12-13
+        false_positives += ['HSBK.IL']  # normal divs but 1wk volatility uncovered logic bug
+        false_positives += ['IBE.MC']  # 2x 0.01x divs only detected when compared to others. pass
+        false_positives += ['KMR.L']
+        false_positives += ['TISG.MI']
+
+        for tkr in false_positives:
+            # Nothing should change
+            dat = yf.Ticker(tkr, session=self.session)
+            hist = dat._lazy_load_price_history()
+            hist.history(period='1mo')  # init metadata for currency
+            currency = hist._history_metadata['currency']
+
+            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)
+
+            repaired_df = hist._fix_bad_div_adjust(df, interval, currency)
+
+            c = 'Dividends'
+            self.assertTrue(_np.isclose(repaired_df[c].to_numpy(), df[c].to_numpy(), rtol=1e-12, equal_nan=True).all())
+            c = 'Adj Close'
+            try:
+                f_close = _np.isclose(repaired_df[c].to_numpy(), df[c].to_numpy(), rtol=1e-12, equal_nan=True)
+                self.assertTrue(f_close.all())
+            except Exception:
+                f_diff = ~f_close
+                print(f"tkr={tkr} interval={interval}")
+                print("- repaired_df:")
+                print(repaired_df[c][f_diff])
+                print("- df:")
+                print(df[c][f_diff])
+                print("- diff:")
+                print(repaired_df[c][f_diff] - df[c][f_diff])
+                raise
+
+        for tkr in bad_tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+            hist = dat._lazy_load_price_history()
+            hist.history(period='1mo')  # init metadata for currency
+            currency = hist._history_metadata['currency']
+
+            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)
+            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)
+
+            repaired_df = hist._fix_bad_div_adjust(df_bad, interval, currency)
+
+            c = 'Dividends'
+            f_close = _np.isclose(repaired_df[c].to_numpy(), correct_df[c].to_numpy(), rtol=1e-12, equal_nan=True)
+            try:
+                self.assertTrue(f_close.all())
+            except Exception:
+                f_diff = ~f_close
+                print(f"tkr={tkr} interval={interval}")
+                print("- repaired_df:")
+                print(repaired_df[c][f_diff])
+                print("- correct_df:")
+                print(correct_df[c][f_diff])
+                print("- diff:")
+                print(repaired_df[c][f_diff] - correct_df[c][f_diff])
+                raise
+
+            c = 'Adj Close'
+            try:
+                f_close = _np.isclose(repaired_df[c].to_numpy(), correct_df[c].to_numpy(), rtol=5e-7, equal_nan=True)
+                self.assertTrue(f_close.all())
+            except Exception:
+                f_diff = ~f_close
+                print(f"tkr={tkr} interval={interval}")
+                print("- repaired_df:")
+                print(repaired_df[c][f_diff])
+                print("- correct_df:")
+                print(correct_df[c][f_diff])
+                print("- diff:")
+                print(repaired_df[c][f_diff] - correct_df[c][f_diff])
+                raise
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/test_prices.py b/tests/test_prices.py
index 0e9ba68c9..9791a0b06 100644
--- a/tests/test_prices.py
+++ b/tests/test_prices.py
@@ -3,7 +3,6 @@
 
 import unittest
 
-import os
 import datetime as _dt
 import pytz as _tz
 import numpy as _np
@@ -451,496 +450,5 @@ def test_aggregate_capital_gains(self):
         dat.history(start=start, end=end, interval=interval)
 
 
-class TestPriceRepair(unittest.TestCase):
-    session = None
-
-    @classmethod
-    def setUpClass(cls):
-        cls.session = session_gbl
-
-    @classmethod
-    def tearDownClass(cls):
-        if cls.session is not None:
-            cls.session.close()
-
-    def test_types(self):
-        tkr = 'INTC'
-        dat = yf.Ticker(tkr, session=self.session)
-
-        data = dat.history(period="3mo", interval="1d", prepost=True, repair=True)
-        self.assertIsInstance(data, _pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-        reconstructed = dat._lazy_load_price_history()._reconstruct_intervals_batch(data, "1wk", True)
-        self.assertIsInstance(reconstructed, _pd.DataFrame, "data has wrong type")
-        self.assertFalse(data.empty, "data is empty")
-
-    def test_reconstruct_2m(self):
-        # 2m repair requires 1m data.
-        # Yahoo restricts 1m fetches to 7 days max within last 30 days.
-        # Need to test that '_reconstruct_intervals_batch()' can handle this.
-
-        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
-
-        dt_now = _pd.Timestamp.utcnow()
-        td_60d = _dt.timedelta(days=60)
-
-        # Round time for 'requests_cache' reuse
-        dt_now = dt_now.ceil("1h")
-
-        for tkr in tkrs:
-            dat = yf.Ticker(tkr, session=self.session)
-            end_dt = dt_now
-            start_dt = end_dt - td_60d
-            dat.history(start=start_dt, end=end_dt, interval="2m", repair=True)
-
-    def test_repair_100x_random_weekly(self):
-        # Setup:
-        tkr = "PNL.L"
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
-        df = _pd.DataFrame(data={"Open":      [470.5, 473.5, 474.5, 470],
-                                 "High":      [476,   476.5, 477,   480],
-                                 "Low":       [470.5, 470,   465.5, 468.26],
-                                 "Close":     [475,   473.5, 472,   473.5],
-                                 "Adj Close": [470.1, 468.6, 467.1, 468.6],
-                                 "Volume": [2295613, 2245604, 3000287, 2635611]},
-                           index=_pd.to_datetime([_dt.date(2022, 10, 24),
-                                                  _dt.date(2022, 10, 17),
-                                                  _dt.date(2022, 10, 10),
-                                                  _dt.date(2022, 10, 3)]))
-        df = df.sort_index()
-        df.index.name = "Date"
-        df_bad = df.copy()
-        df_bad.loc["2022-10-24", "Close"] *= 100
-        df_bad.loc["2022-10-17", "Low"] *= 100
-        df_bad.loc["2022-10-03", "Open"] *= 100
-        df.index = df.index.tz_localize(tz_exchange)
-        df_bad.index = df_bad.index.tz_localize(tz_exchange)
-
-        # Run test
-
-        df_repaired = hist._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
-
-        # First test - no errors left
-        for c in data_cols:
-            try:
-                self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-            except AssertionError:
-                print(df[c])
-                print(df_repaired[c])
-                raise
-
-        # Second test - all differences should be either ~1x or ~100x
-        ratio = df_bad[data_cols].values / df[data_cols].values
-        ratio = ratio.round(2)
-        # - round near-100 ratio to 100:
-        f = ratio > 90
-        ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
-        # - now test
-        f_100 = ratio == 100
-        f_1 = ratio == 1
-        self.assertTrue((f_100 | f_1).all())
-
-        self.assertTrue("Repaired?" in df_repaired.columns)
-        self.assertFalse(df_repaired["Repaired?"].isna().any())
-
-    def test_repair_100x_random_weekly_preSplit(self):
-        # PNL.L has a stock-split in 2022. Sometimes requesting data before 2022 is not split-adjusted.
-
-        tkr = "PNL.L"
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
-        df = _pd.DataFrame(data={"Open":      [400,    398,    392.5,  417],
-                                 "High":      [421,    425,    419,    420.5],
-                                 "Low":       [400,    380.5,  376.5,  396],
-                                 "Close":     [410,    409.5,  402,    399],
-                                 "Adj Close": [393.91, 393.43, 386.22, 383.34],
-                                 "Volume": [3232600, 3773900, 10835000, 4257900]},
-                           index=_pd.to_datetime([_dt.date(2020, 3, 30),
-                                                  _dt.date(2020, 3, 23),
-                                                  _dt.date(2020, 3, 16),
-                                                  _dt.date(2020, 3, 9)]))
-        df = df.sort_index()
-        # Simulate data missing split-adjustment:
-        df[data_cols] *= 100.0
-        df["Volume"] *= 0.01
-        #
-        df.index.name = "Date"
-        # Create 100x errors:
-        df_bad = df.copy()
-        df_bad.loc["2020-03-30", "Close"] *= 100
-        df_bad.loc["2020-03-23", "Low"] *= 100
-        df_bad.loc["2020-03-09", "Open"] *= 100
-        df.index = df.index.tz_localize(tz_exchange)
-        df_bad.index = df_bad.index.tz_localize(tz_exchange)
-
-        df_repaired = hist._fix_unit_random_mixups(df_bad, "1wk", tz_exchange, prepost=False)
-
-        # First test - no errors left
-        for c in data_cols:
-            try:
-                self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-            except AssertionError:
-                print("Mismatch in column", c)
-                print("- df_repaired:")
-                print(df_repaired[c])
-                print("- answer:")
-                print(df[c])
-                raise
-
-        # Second test - all differences should be either ~1x or ~100x
-        ratio = df_bad[data_cols].values / df[data_cols].values
-        ratio = ratio.round(2)
-        # - round near-100 ratio to 100:
-        f = ratio > 90
-        ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
-        # - now test
-        f_100 = ratio == 100
-        f_1 = ratio == 1
-        self.assertTrue((f_100 | f_1).all())
-
-        self.assertTrue("Repaired?" in df_repaired.columns)
-        self.assertFalse(df_repaired["Repaired?"].isna().any())
-
-    def test_repair_100x_random_daily(self):
-        tkr = "PNL.L"
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
-        df = _pd.DataFrame(data={"Open":      [478,    476,   476,   472],
-                                 "High":      [478,    477.5, 477,   475],
-                                 "Low":       [474.02, 474,   473,   470.75],
-                                 "Close":     [475.5,  475.5, 474.5, 475],
-                                 "Adj Close": [475.5,  475.5, 474.5, 475],
-                                 "Volume": [436414, 485947, 358067, 287620]},
-                           index=_pd.to_datetime([_dt.date(2022, 11, 1),
-                                                  _dt.date(2022, 10, 31),
-                                                  _dt.date(2022, 10, 28),
-                                                  _dt.date(2022, 10, 27)]))
-        df = df.sort_index()
-        df.index.name = "Date"
-        df_bad = df.copy()
-        df_bad.loc["2022-11-01", "Close"] *= 100
-        df_bad.loc["2022-10-31", "Low"] *= 100
-        df_bad.loc["2022-10-27", "Open"] *= 100
-        df.index = df.index.tz_localize(tz_exchange)
-        df_bad.index = df_bad.index.tz_localize(tz_exchange)
-
-        df_repaired = hist._fix_unit_random_mixups(df_bad, "1d", tz_exchange, prepost=False)
-
-        # First test - no errors left
-        for c in data_cols:
-            self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-
-        # Second test - all differences should be either ~1x or ~100x
-        ratio = df_bad[data_cols].values / df[data_cols].values
-        ratio = ratio.round(2)
-        # - round near-100 ratio to 100:
-        f = ratio > 90
-        ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
-        # - now test
-        f_100 = ratio == 100
-        f_1 = ratio == 1
-        self.assertTrue((f_100 | f_1).all())
-
-        self.assertTrue("Repaired?" in df_repaired.columns)
-        self.assertFalse(df_repaired["Repaired?"].isna().any())
-
-    def test_repair_100x_block_daily(self):
-        # Some 100x errors are not sporadic.
-        # Sometimes Yahoo suddenly shifts from cents->$ from some recent date.
-
-        tkrs = ['AET.L', 'SSW.JO']
-        for tkr in tkrs:
-            for interval in ['1d', '1wk']:
-                dat = yf.Ticker(tkr, session=self.session)
-                tz_exchange = dat.fast_info["timezone"]
-                hist = dat._lazy_load_price_history()
-
-                data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
-                _dp = os.path.dirname(__file__)
-                fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error.csv")
-                if not os.path.isfile(fp):
-                    continue
-                df_bad = _pd.read_csv(fp, index_col="Date")
-                df_bad.index = _pd.to_datetime(df_bad.index, utc=True).tz_convert(tz_exchange)
-                df_bad = df_bad.sort_index()
-
-                df = df_bad.copy()
-                fp = os.path.join(_dp, "data", tkr.replace('.','-') + '-' + interval + "-100x-error-fixed.csv")
-                df = _pd.read_csv(fp, index_col="Date")
-                df.index = _pd.to_datetime(df.index, utc=True).tz_convert(tz_exchange)
-                df = df.sort_index()
-
-                df_repaired = hist._fix_unit_switch(df_bad, interval, tz_exchange)
-                df_repaired = df_repaired.sort_index()
-
-                # First test - no errors left
-                for c in data_cols:
-                    try:
-                        self.assertTrue(_np.isclose(df_repaired[c], df[c], rtol=1e-2).all())
-                    except:
-                        print("- repaired:")
-                        print(df_repaired[c])
-                        print("- correct:")
-                        print(df[c])
-                        print(f"TEST FAIL on column '{c}' (tkr={tkr} interval={interval})")
-                        raise
-
-                # Second test - all differences should be either ~1x or ~100x
-                ratio = df_bad[data_cols].values / df[data_cols].values
-                ratio = ratio.round(2)
-                # - round near-100 ratio to 100:
-                f = ratio > 90
-                ratio[f] = (ratio[f] / 10).round().astype(int) * 10  # round ratio to nearest 10
-                # - now test
-                f_100 = (ratio == 100) | (ratio == 0.01)
-                f_1 = ratio == 1
-                self.assertTrue((f_100 | f_1).all())
-
-                self.assertTrue("Repaired?" in df_repaired.columns)
-                self.assertFalse(df_repaired["Repaired?"].isna().any())
-
-    def test_repair_zeroes_daily(self):
-        tkr = "BBIL.L"
-        dat = yf.Ticker(tkr, session=self.session)
-        hist = dat._lazy_load_price_history()
-        tz_exchange = dat.fast_info["timezone"]
-
-        df_bad = _pd.DataFrame(data={"Open":      [0,      102.04, 102.04],
-                                     "High":      [0,      102.1,  102.11],
-                                     "Low":       [0,      102.04, 102.04],
-                                     "Close":     [103.03, 102.05, 102.08],
-                                     "Adj Close": [102.03, 102.05, 102.08],
-                                     "Volume": [560, 137, 117]},
-                               index=_pd.to_datetime([_dt.datetime(2022, 11, 1),
-                                                      _dt.datetime(2022, 10, 31),
-                                                      _dt.datetime(2022, 10, 30)]))
-        df_bad = df_bad.sort_index()
-        df_bad.index.name = "Date"
-        df_bad.index = df_bad.index.tz_localize(tz_exchange)
-
-        repaired_df = hist._fix_zeroes(df_bad, "1d", tz_exchange, prepost=False)
-
-        correct_df = df_bad.copy()
-        correct_df.loc["2022-11-01", "Open"] = 102.080002
-        correct_df.loc["2022-11-01", "Low"] = 102.032501
-        correct_df.loc["2022-11-01", "High"] = 102.080002
-        for c in ["Open", "Low", "High", "Close"]:
-            self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-8).all())
-
-        self.assertTrue("Repaired?" in repaired_df.columns)
-        self.assertFalse(repaired_df["Repaired?"].isna().any())
-
-    def test_repair_zeroes_daily_adjClose(self):
-        # Test that 'Adj Close' is reconstructed correctly,
-        # particularly when a dividend occurred within 1 day.
-
-        tkr = "INTC"
-        df = _pd.DataFrame(data={"Open":      [28.95, 28.65, 29.55, 29.62, 29.25],
-                                 "High":      [29.12, 29.27, 29.65, 31.17, 30.30],
-                                 "Low":       [28.21, 28.43, 28.61, 29.53, 28.80],
-                                 "Close":     [28.24, 29.05, 28.69, 30.32, 30.19],
-                                 "Adj Close": [28.12, 28.93, 28.57, 29.83, 29.70],
-                                 "Volume":    [36e6, 51e6, 49e6, 58e6, 62e6],
-                                 "Dividends": [0, 0, 0.365, 0, 0]},
-                           index=_pd.to_datetime([_dt.datetime(2023, 2, 8),
-                                                  _dt.datetime(2023, 2, 7),
-                                                  _dt.datetime(2023, 2, 6),
-                                                  _dt.datetime(2023, 2, 3),
-                                                  _dt.datetime(2023, 2, 2)]))
-        df = df.sort_index()
-        df.index.name = "Date"
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        df.index = df.index.tz_localize(tz_exchange)
-        hist = dat._lazy_load_price_history()
-
-        rtol = 5e-3
-        for i in [0, 1, 2]:
-            df_slice = df.iloc[i:i+3]
-            for j in range(3):
-                df_slice_bad = df_slice.copy()
-                df_slice_bad.loc[df_slice_bad.index[j], "Adj Close"] = 0.0
-
-                df_slice_bad_repaired = hist._fix_zeroes(df_slice_bad, "1d", tz_exchange, prepost=False)
-                for c in ["Close", "Adj Close"]:
-                    self.assertTrue(_np.isclose(df_slice_bad_repaired[c], df_slice[c], rtol=rtol).all())
-                self.assertTrue("Repaired?" in df_slice_bad_repaired.columns)
-                self.assertFalse(df_slice_bad_repaired["Repaired?"].isna().any())
-
-    def test_repair_zeroes_hourly(self):
-        tkr = "INTC"
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        correct_df = hist.history(period="5d", interval="1h", auto_adjust=False, repair=True)
-
-        df_bad = correct_df.copy()
-        bad_idx = correct_df.index[10]
-        df_bad.loc[bad_idx, "Open"] = _np.nan
-        df_bad.loc[bad_idx, "High"] = _np.nan
-        df_bad.loc[bad_idx, "Low"] = _np.nan
-        df_bad.loc[bad_idx, "Close"] = _np.nan
-        df_bad.loc[bad_idx, "Adj Close"] = _np.nan
-        df_bad.loc[bad_idx, "Volume"] = 0
-
-        repaired_df = hist._fix_zeroes(df_bad, "1h", tz_exchange, prepost=False)
-
-        for c in ["Open", "Low", "High", "Close"]:
-            try:
-                self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-7).all())
-            except AssertionError:
-                print("COLUMN", c)
-                print("- repaired_df")
-                print(repaired_df)
-                print("- correct_df[c]:")
-                print(correct_df[c])
-                print("- diff:")
-                print(repaired_df[c] - correct_df[c])
-                raise
-
-        self.assertTrue("Repaired?" in repaired_df.columns)
-        self.assertFalse(repaired_df["Repaired?"].isna().any())
-
-    def test_repair_bad_stock_splits(self):
-        # Stocks that split in 2022 but no problems in Yahoo data,
-        # so repair should change nothing
-        good_tkrs = ['AMZN', 'DXCM', 'FTNT', 'GOOG', 'GME', 'PANW', 'SHOP', 'TSLA']
-        good_tkrs += ['AEI', 'GHI', 'IRON', 'LXU', 'NUZE', 'RSLS', 'TISI']
-        good_tkrs += ['BOL.ST', 'TUI1.DE']
-        intervals = ['1d', '1wk', '1mo', '3mo']
-        for tkr in good_tkrs:
-            for interval in intervals:
-                dat = yf.Ticker(tkr, session=self.session)
-                tz_exchange = dat.fast_info["timezone"]
-                hist = dat._lazy_load_price_history()
-
-                _dp = os.path.dirname(__file__)
-                df_good = dat.history(start='2020-01-01', end=_dt.date.today(), interval=interval, auto_adjust=False)
-
-                repaired_df = hist._fix_bad_stock_splits(df_good, interval, tz_exchange)
-
-                # Expect no change from repair
-                df_good = df_good.sort_index()
-                repaired_df = repaired_df.sort_index()
-                for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
-                    try:
-                        self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
-                    except:
-                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
-                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
-                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
-                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
-                        raise
-
-        bad_tkrs = ['4063.T', 'ALPHA.PA', 'AV.L', 'CNE.L', 'MOB.ST', 'SPM.MI']
-        bad_tkrs.append('LA.V')  # special case - stock split error is 3 years ago! why not fixed?
-        for tkr in bad_tkrs:
-            dat = yf.Ticker(tkr, session=self.session)
-            tz_exchange = dat.fast_info["timezone"]
-            hist = dat._lazy_load_price_history()
-
-            _dp = os.path.dirname(__file__)
-            interval = '1d'
-            fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
-            if not os.path.isfile(fp):
-                interval = '1wk'
-                fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split.csv")
-            df_bad = _pd.read_csv(fp, index_col="Date")
-            df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
-
-            repaired_df = hist._fix_bad_stock_splits(df_bad, "1d", tz_exchange)
-
-            fp = os.path.join(_dp, "data", tkr.replace('.','-')+'-'+interval+"-bad-stock-split-fixed.csv")
-            correct_df = _pd.read_csv(fp, index_col="Date")
-            correct_df.index = _pd.to_datetime(correct_df.index, utc=True)
-
-            repaired_df = repaired_df.sort_index()
-            correct_df = correct_df.sort_index()
-            for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
-                try:
-                    self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
-                except AssertionError:
-                    print(f"tkr={tkr} COLUMN={c}")
-                    # print("- repaired_df")
-                    # print(repaired_df)
-                    # print("- correct_df[c]:")
-                    # print(correct_df[c])
-                    # print("- diff:")
-                    # print(repaired_df[c] - correct_df[c])
-                    raise
-
-        # Had very high price volatility in Jan-2021 around split date that could
-        # be mistaken for missing stock split adjustment. And old logic did think
-        # column 'High' required fixing - wrong!
-        sketchy_tkrs = ['FIZZ']
-        intervals = ['1wk']
-        for tkr in sketchy_tkrs:
-            for interval in intervals:
-                dat = yf.Ticker(tkr, session=self.session)
-                tz_exchange = dat.fast_info["timezone"]
-                hist = dat._lazy_load_price_history()
-
-                _dp = os.path.dirname(__file__)
-                df_good = hist.history(start='2020-11-30', end='2021-04-01', interval=interval, auto_adjust=False)
-
-                repaired_df = hist._fix_bad_stock_splits(df_good, interval, tz_exchange)
-
-                # Expect no change from repair
-                df_good = df_good.sort_index()
-                repaired_df = repaired_df.sort_index()
-                for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
-                    try:
-                        self.assertTrue((repaired_df[c].to_numpy() == df_good[c].to_numpy()).all())
-                    except AssertionError:
-                        print(f"tkr={tkr} interval={interval} COLUMN={c}")
-                        df_dbg = df_good[[c]].join(repaired_df[[c]], lsuffix='.good', rsuffix='.repaired')
-                        f_diff = repaired_df[c].to_numpy() != df_good[c].to_numpy()
-                        print(df_dbg[f_diff | _np.roll(f_diff, 1) | _np.roll(f_diff, -1)])
-                        raise
-
-    def test_repair_missing_div_adjust(self):
-        tkr = '8TRA.DE'
-
-        dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.fast_info["timezone"]
-        hist = dat._lazy_load_price_history()
-
-        _dp = os.path.dirname(__file__)
-        df_bad = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust.csv"), index_col="Date")
-        df_bad.index = _pd.to_datetime(df_bad.index)
-
-        repaired_df = hist._fix_missing_div_adjust(df_bad, "1d", tz_exchange)
-
-        correct_df = _pd.read_csv(os.path.join(_dp, "data", tkr.replace('.','-')+"-1d-missing-div-adjust-fixed.csv"), index_col="Date")
-        correct_df.index = _pd.to_datetime(correct_df.index)
-
-        repaired_df = repaired_df.sort_index()
-        correct_df = correct_df.sort_index()
-        for c in ["Open", "Low", "High", "Close", "Adj Close", "Volume"]:
-            try:
-                self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=5e-6).all())
-            except:
-                print(f"tkr={tkr} COLUMN={c}")
-                print("- repaired_df")
-                print(repaired_df)
-                print("- correct_df[c]:")
-                print(correct_df[c])
-                print("- diff:")
-                print(repaired_df[c] - correct_df[c])
-                raise
-
-
 if __name__ == '__main__':
     unittest.main()
diff --git a/yfinance/scrapers/history.py b/yfinance/scrapers/history.py
index f804dc51f..e9dd8fc49 100644
--- a/yfinance/scrapers/history.py
+++ b/yfinance/scrapers/history.py
@@ -1483,10 +1483,10 @@ def cluster_dividends(df, column='div', threshold=10):
                 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 > 0.2:
-                    adjClose_chg_pct = np.min(adjClose_chgs_pct)
-                    close_chg_pct = np.min(close_chgs_pct)
-                    if adjClose_chg_pct < 0.0 and adjClose_chg_pct < 1.0001*close_chg_pct:
+                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
 
@@ -1503,11 +1503,15 @@ def cluster_dividends(df, column='div', threshold=10):
             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['adj_too_small'] = div_adj_is_too_small
+            div_status['div_exceeds_adj'] = div_adj_is_too_small
 
             for k,v in div_status.items():
                 if k not in div_status_df:
@@ -1521,12 +1525,12 @@ def cluster_dividends(df, column='div', threshold=10):
                         raise Exception(k,v,type(v))
                 div_status_df.loc[dt, k] = v
 
-        checks = ['adj_missing', 'adj_too_small', 'adj_exceeds_div', 'adj_exceeds_prices', 'div_too_big', 'div_too_small', 'fx_mixup']
+        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]
 
         div_status_df['phantom'] = False
         phantom_proximity_threshold = _datetime.timedelta(days=7)
-        f = div_status_df['div_too_big'] | div_status_df['adj_too_small']
+        f = div_status_df['div_too_big'] | div_status_df['div_exceeds_adj']
         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]
@@ -1668,17 +1672,26 @@ def cluster_dividends(df, column='div', threshold=10):
                 if c == 'div_too_big':
                     true_threshold = 1.0
                     fals_threshold = 0.2
-                    if 'adj_too_small' not in cluster.columns:
-                        # More likely that 'div_too_big' are false positives: NOT too big
-                         fals_threshold = 2/3
-                    elif cluster['adj_too_small'].all():
-                        # The present adjustment also contradicts the dividend yields, 
+
+                    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.
                         if (cluster['vol'][fc][f_fail]==0).all():
                             # No trading volume to cross-check, so higher thresholds
                             fals_threshold = 2/3
                         else:
-                            true_threshold = 1/2
+                            # Relax thresholds
+                            true_threshold = 2/5
+
+                    elif 'adj_exceeds_prices' in cluster.columns and 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
+                        fals_threshold = 1/2
+
                     if pct_fail >= true_threshold:
                         div_status_df.loc[fc, c] = True
                         continue
@@ -1689,13 +1702,9 @@ def cluster_dividends(df, column='div', threshold=10):
                 if c == 'div_too_small':
                     true_threshold = 1.0
                     fals_threshold = 0.1
-                    if 'adj_exceeds_div' not in cluster.columns and 'adj_exceeds_prices' not in cluster.columns:
-                        # More likely that 'div_too_small' are false positives: NOT too small
+                    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
-                    elif ('adj_exceeds_div' in cluster.columns and cluster['adj_exceeds_div'].all()) \
-                         ('adj_exceeds_prices' in cluster.columns and cluster['adjadj_exceeds_prices_exceeds_div'].all()):
-                        # More likely that falses are false negatives
-                        true_threshold = 0.5
                     if pct_fail >= true_threshold:
                         div_status_df.loc[fc, c] = True
                         continue
@@ -1707,7 +1716,7 @@ def cluster_dividends(df, column='div', threshold=10):
                     if cluster[c].iloc[-1] and n_fail == 1:
                         # Only the latest/last row is missing, genuine error
                         continue
-                if c == 'adj_too_small':
+                if c == 'div_exceeds_adj':
                     continue
 
                 if c == 'phantom' and self.ticker in ['KAP.IL', 'SAND']:
@@ -1759,18 +1768,17 @@ def cluster_dividends(df, column='div', threshold=10):
                 dt = row.name
 
                 adj_missing = 'adj_missing' in row and row['adj_missing']
-                adj_too_small = 'adj_too_small' in row and row['adj_too_small']
+                div_exceeds_adj = 'div_exceeds_adj' in row and row['div_exceeds_adj']
                 adj_exceeds_div = 'adj_exceeds_div' in row and row['adj_exceeds_div']
                 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']
 
-                adj_too_big = adj_exceeds_div or adj_exceeds_prices
-
                 n_failed_checks = np.sum([row[c] for c in checks if c in row])
                 if n_failed_checks == 1:
-                    if adj_too_small or adj_too_big:
-                        k = 'too-small div-adjust' if adj_too_small else 'too-big div-adjust'
+                    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)
@@ -1847,7 +1855,7 @@ def cluster_dividends(df, column='div', threshold=10):
                         df2_nan.loc[:enddt, 'Repaired?'] = True
                         cluster.loc[dt, 'Fixed?'] = True
 
-                    elif div_too_big and adj_too_small:
+                    elif div_too_big and div_exceeds_adj:
                         # Adj Close is correct, just need to fix Dividend.
                         # Probably just a currency unit mixup.
                         df2.loc[dt, 'Dividends'] /= currency_divide
@@ -1869,7 +1877,7 @@ def cluster_dividends(df, column='div', threshold=10):
                         div_repairs.setdefault(k, []).append(dt)
                         cluster.loc[dt, 'Fixed?'] = True
 
-                    elif adj_too_big and div_too_big:
+                    elif div_too_big and adj_exceeds_prices:
                         # Assume div 100x error, and that Yahoo used this wrong dividend.
                         # 'adj_too_big=True' is probably redundant information, knowing div too big
                         # is enough to require also fixing adjustment

From a2b5d6bea938f998a98fe1ecf177e3c36726a034 Mon Sep 17 00:00:00 2001
From: ValueRaider <ValueRaider@protonmail.com>
Date: Thu, 22 Aug 2024 16:29:07 +0100
Subject: [PATCH 16/16] Fix 2x old tests

---
 tests/test_prices.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/tests/test_prices.py b/tests/test_prices.py
index d1f435d99..c0f0c01e1 100644
--- a/tests/test_prices.py
+++ b/tests/test_prices.py
@@ -178,9 +178,8 @@ def test_intraDayWithEvents(self):
             if df_daily_divs.shape[0] == 0:
                 continue
 
-            last_div_date = df_daily_divs.index[-1]
-            start_d = last_div_date.date()
-            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            start_d = df_daily_divs.index[0].date()
+            end_d = df_daily_divs.index[-1].date() + _dt.timedelta(days=1)
             df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
             self.assertTrue((df_intraday["Dividends"] != 0.0).any())
 
@@ -206,9 +205,8 @@ def test_intraDayWithEvents_tase(self):
             if df_daily_divs.shape[0] == 0:
                 continue
 
-            last_div_date = df_daily_divs.index[-1]
-            start_d = last_div_date.date()
-            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            start_d = df_daily_divs.index[0].date()
+            end_d = df_daily_divs.index[-1].date() + _dt.timedelta(days=1)
             df_intraday = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
             self.assertTrue((df_intraday["Dividends"] != 0.0).any())