Version 1.1.0

Merge pull request #2682 from ranaroussi/dev
sync dev -> main
2026-01-24 16:01:54 +00:00 · 2026-01-24 15:57:45 +00:00 · 2026-01-24 12:08:09 +00:00 · 2026-01-23 14:09:03 +01:00 · 2026-01-23 08:44:47 +01:00 · 2026-01-22 19:36:37 +00:00
28 changed files with 5020 additions and 211 deletions
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -1,7 +1,13 @@
 version: 2
 updates:
+
 - package-ecosystem: pip
  directory: "/"
  schedule:
    interval: daily
  open-pull-requests-limit: 10
+
+- package-ecosystem: "github-actions"
+  directory: "/"
+  schedule:
+    interval: "weekly"
--- a/.github/workflows/auto_close_default_issues.yml
+++ b/.github/workflows/auto_close_default_issues.yml
@@ -8,7 +8,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Check if issue uses custom template
-        uses: actions/github-script@v7
+        uses: actions/github-script@v8
        with:
          script: |
            const issue = context.payload.issue;
--- a/.github/workflows/deploy_doc.yml
+++ b/.github/workflows/deploy_doc.yml
@@ -13,10 +13,10 @@ jobs:

    steps:
    - name: Check out the repository
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6

    - name: Set up Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
      with:
        python-version: '3.x'

--- a/.github/workflows/pyright.yml
+++ b/.github/workflows/pyright.yml
@@ -13,10 +13,10 @@ jobs:

    steps:
    - name: Checkout code
-      uses: actions/checkout@v2
+      uses: actions/checkout@v6

    - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v6

    - name: Install dependencies
      run: |
--- a/.github/workflows/python-publish.yml
+++ b/.github/workflows/python-publish.yml
@@ -13,9 +13,9 @@ jobs:
    runs-on: ubuntu-latest

    steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v6
    - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v6
      with:
        python-version: '3.x'
    - name: Install dependencies
--- a/.github/workflows/ruff.yml
+++ b/.github/workflows/ruff.yml
@@ -9,7 +9,7 @@ jobs:
  ruff:
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
      - uses: astral-sh/ruff-action@v3
        with:
          args: check --ignore E702 . --exclude yfinance/pricing_pb2.py
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,19 @@
 Change Log
 ===========

+1.1.0
+-----
+Features:
+- New price repair: capital gains double-counting #2667
+Fixes:
+- Fix Saudi Arabia MIC Code #2665 @salsugair
+- Add config to __all__ #2673 @kbluck
+- Fix Python 3.9 compatibility regression in calendars.py #2674 @orionnelson
+- Improve stock-split repair false-positives #2668
+- Docs for capital-gains repair; Fix warnings in price-repair 27cb8aa
+Already in main:
+- Upgrade to Pandas 3.0 (Fixes #2679) #2683
+
 1.0
 ---
 yfinance been stable a long time now, time to grow up
--- a/README.md
+++ b/README.md
@@ -53,7 +53,7 @@ Install `yfinance` from PYPI using `pip`:
 $ pip install yfinance
 ```

-### [yfinance relies on the community to investigate bugs and contribute code. Here's how you can help.](CONTRIBUTING.md)
+### [yfinance relies on the community to investigate bugs and contribute code. Here's how you can help.](https://github.com/ranaroussi/yfinance/blob/main/CONTRIBUTING.md)

 ---

@@ -64,7 +64,7 @@ $ pip install yfinance
 ### Legal Stuff

 **yfinance** is distributed under the **Apache Software License**. See
-the [LICENSE.txt](./LICENSE.txt) file in the release for details.
+the [LICENSE.txt](https://github.com/ranaroussi/yfinance/blob/main/LICENSE.txt) file in the release for details.

 AGAIN - yfinance is **not** affiliated, endorsed, or vetted by Yahoo, Inc. It's
 an open-source tool that uses Yahoo's publicly available APIs, and is
--- a/doc/source/advanced/price_repair.rst
+++ b/doc/source/advanced/price_repair.rst
@@ -111,6 +111,7 @@ Fix errors in dividends:
 2. duplicate dividend (within 7 days)
 3. dividend 100x too big/small for the ex-dividend price drop
 4. ex-div date wrong (price drop is few days/weeks after)
+5. **NEW: capital gains double-counted**

 Most errors I've seen are on London stock exchange (£/pence mixup), but no exchange is safe.

@@ -275,3 +276,27 @@ TETY.ST
   2022-06-21 00:00:00+02:00  71.599998  60.007881        0.0
   2022-06-20 00:00:00+02:00  71.800003  60.175503        0.0
   2022-06-17 00:00:00+02:00  71.000000  59.505021        0.0
+
+
+Capital-gains double-counted
+----------------------------
+
+Clue: price drop matches dividend better than dividend+capital gains.
+
+DODFX
+
+.. code-block:: text
+
+   # ORIGINAL:
+                                  Close  Adj Close  Dividends  Capital Gains
+   Date                                                                     
+   2025-12-18 00:00:00-05:00  16.219999  16.219999      0.837          0.417
+   2025-12-17 00:00:00-05:00  16.920000  15.665999      0.000          0.000
+
+.. code-block:: text
+
+   # REPAIRED:
+                                  Close  Adj Close  Dividends  Capital Gains
+   Date                                                                     
+   2025-12-18 00:00:00-05:00  16.219999  16.219999       0.42          0.417
+   2025-12-17 00:00:00-05:00  16.920000  16.083000       0.00          0.000
--- a/meta.yaml
+++ b/meta.yaml
@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "1.0" %}
+{% set version = "1.1.0" %}

 package:
  name: "{{ name|lower }}"
--- a/tests/data/AET-L-1wk-100x-error-fixed.csv
+++ b/tests/data/AET-L-1wk-100x-error-fixed.csv
@@ -1,37 +0,0 @@
-Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
-2022-05-30 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-05-23 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-05-16 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,532454,0,0
-2022-05-09 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-05-02 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-04-25 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-04-18 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-04-11 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-04-04 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-03-28 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-03-21 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-03-14 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-03-07 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-02-28 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-02-21 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-02-14 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-02-07 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-01-31 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-01-24 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-01-17 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-01-10 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2022-01-03 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-12-27 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-12-20 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-12-13 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-12-06 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-11-29 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-11-22 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-11-15 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-11-08 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-11-01 00:00:00+00:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-10-25 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-10-18 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-10-11 00:00:00+01:00,14.5500,14.5500,14.5500,14.5500,14.5500,0,0,0
-2021-10-04 00:00:00+01:00,14.8000,15.3400,14.4000,14.5500,14.5500,2171373,0,0
-2021-09-27 00:00:00+01:00,15.6000,16.0000,14.9000,15.0500,15.0500,3860549,0,0
--- a/tests/data/AET-L-1wk-100x-error.csv
+++ b/tests/data/AET-L-1wk-100x-error.csv
@@ -1,37 +0,0 @@
-Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits
-2022-05-30 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-05-23 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-05-16 00:00:00+01:00,14.550000190734863,14.550000190734863,0.14550000429153442,0.14550000429153442,0.14550000429153442,532454,0.0,0.0
-2022-05-09 00:00:00+01:00,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,14.550000190734863,0,0.0,0.0
-2022-05-02 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-04-25 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-04-18 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-04-11 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-04-04 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-03-28 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-03-21 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-03-14 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-03-07 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-02-28 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2022-02-21 00:00:00+00:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
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-2021-10-11 00:00:00+01:00,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0.14550000429153442,0,0.0,0.0
-2021-10-04 00:00:00+01:00,14.800000190734863,15.34000015258789,0.14399999380111694,0.14550000429153442,0.14550000429153442,2171373,0.0,0.0
-2021-09-27 00:00:00+01:00,15.600000381469727,16.0,14.899999618530273,15.050000190734863,15.050000190734863,3860549,0.0,0.0
--- a/tests/data/DODFX-1d-cg-double-count-fixed.csv
+++ b/tests/data/DODFX-1d-cg-double-count-fixed.csv
@@ -0,0 +1,503 @@
+Date,Open,High,Low,Close,Adj Close,Volume,Dividends,Stock Splits,Capital Gains
+2024-01-10 00:00:00-05:00,12.147500038147,12.147500038147,12.147500038147,12.147500038147,11.2976727169889,0,0,0,0
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+2024-01-12 00:00:00-05:00,12.1400003433228,12.1400003433228,12.1400003433228,12.1400003433228,11.2906976935409,0,0,0,0
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+2024-01-17 00:00:00-05:00,11.7924995422363,11.7924995422363,11.7924995422363,11.7924995422363,10.9675077114674,0,0,0,0
+2024-01-18 00:00:00-05:00,11.8400001525879,11.8400001525879,11.8400001525879,11.8400001525879,11.0116852251883,0,0,0,0
+2024-01-19 00:00:00-05:00,11.9025001525879,11.9025001525879,11.9025001525879,11.9025001525879,11.0698127858052,0,0,0,0
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+2024-01-30 00:00:00-05:00,12.0299997329712,12.0299997329712,12.0299997329712,12.0299997329712,11.1883926192031,0,0,0,0
+2024-01-31 00:00:00-05:00,11.9324998855591,11.9324998855591,11.9324998855591,11.9324998855591,11.0977137665536,0,0,0,0
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--- a/tests/data/DODFX-1d-cg-double-count.csv
+++ b/tests/data/DODFX-1d-cg-double-count.csv
@@ -0,0 +1,503 @@
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--- a/tests/data/JENYX-1d-cg-double-count-fixed.csv
+++ b/tests/data/JENYX-1d-cg-double-count-fixed.csv
--- a/tests/data/JENYX-1d-cg-double-count.csv
+++ b/tests/data/JENYX-1d-cg-double-count.csv
--- a/tests/data/VWILX-1d-cg-double-count-fixed.csv
+++ b/tests/data/VWILX-1d-cg-double-count-fixed.csv
@@ -0,0 +1,503 @@
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--- a/tests/data/VWILX-1d-cg-double-count.csv
+++ b/tests/data/VWILX-1d-cg-double-count.csv
@@ -0,0 +1,503 @@
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+2026-01-06 00:00:00-05:00,118.98999786376953,118.98999786376953,118.98999786376953,118.98999786376953,118.98999786376953,0,0.0,0.0,0.0
+2026-01-07 00:00:00-05:00,118.0199966430664,118.0199966430664,118.0199966430664,118.0199966430664,118.0199966430664,0,0.0,0.0,0.0
+2026-01-08 00:00:00-05:00,117.5,117.5,117.5,117.5,117.5,0,0.0,0.0,0.0
+2026-01-09 00:00:00-05:00,118.62000274658203,118.62000274658203,118.62000274658203,118.62000274658203,118.62000274658203,0,0.0,0.0,0.0
--- a/tests/test_price_repair.py
+++ b/tests/test_price_repair.py
@@ -123,7 +123,7 @@ class TestPriceRepair(unittest.TestCase):

        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]

-        dt_now = _pd.Timestamp.utcnow()
+        dt_now = _pd.Timestamp.now('UTC')
        td_60d = _dt.timedelta(days=60)

        # Round time for 'requests_cache' reuse
@@ -303,10 +303,9 @@ class TestPriceRepair(unittest.TestCase):
        # 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()
+        # intervals = ['1d', '1wk']
+        # Give up repairing 1wk intervals directly. Instead will resample from 1d
+        intervals = ['1d']

        for tkr in tkrs:
            for interval in intervals:
@@ -530,33 +529,36 @@ class TestPriceRepair(unittest.TestCase):
                    # 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()
+        false_positives = {}
+        # FIZZ had very high price volatility in Jan-2021 around split date:
+        false_positives['FIZZ'] = {'interval': '1d', 'start': '2020-11-30', 'end': '2021-04-01'}
+        # GME has crazy price action in Jan 2021, mistaken for missing 2007 split
+        false_positives['GME'] = {'interval': '1d', 'start': '2007-01-01', 'end': '2023-01-01'}
+        # NVDA has a ~33% price drop on 2004-08-06, confused with earlier 3:2 split
+        false_positives['NVDA'] = {'interval': '1d', 'start': '2001-07-01', 'end': '2007-09-15'}
+        # yf.config.debug.logging = True
+        for tkr, args in false_positives.items():
+            interval = args['interval']
+            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)
+            df_good = hist.history(auto_adjust=False, **args)

-                repaired_df = hist._fix_bad_stock_splits(df_good, interval, tz_exchange)
+            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
+            # 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_bad_div_adjusts(self):
        interval = '1d'
@@ -601,7 +603,6 @@ class TestPriceRepair(unittest.TestCase):

        # Phantom divs
        bad_tkrs += ['KAP.IL']  # 1x 1d phantom div, and false positives 0.01x in 1wk
-        bad_tkrs += ['SAND']
        bad_tkrs += ['TEM.L']
        bad_tkrs += ['TEP.PA']

@@ -695,6 +696,44 @@ class TestPriceRepair(unittest.TestCase):
                print(repaired_df[c][f_diff] - correct_df[c][f_diff])
                raise

+    def test_repair_capital_gains_double_count(self):
+        bad_tkrs = ['DODFX', 'VWILX', 'JENYX']
+        for tkr in bad_tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+            hist = dat._lazy_load_price_history()
+
+            interval = '1d'
+            fp = os.path.join(self.dp, "data", tkr.replace('.','-')+'-'+interval+"-cg-double-count.csv")
+
+            df_bad = _pd.read_csv(fp, index_col="Date")
+            df_bad.index = _pd.to_datetime(df_bad.index, utc=True)
+
+            repaired_df = hist._repair_capital_gains(df_bad)
+
+            fp = os.path.join(self.dp, "data", tkr.replace('.','-')+'-'+interval+"-cg-double-count-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:
+                    f = (correct_df['Capital Gains']!=0).to_numpy()
+                    f2 = f|_np.roll(f,1)|_np.roll(f,2)|_np.roll(f,-1)|_np.roll(f,-2)
+                    print(f"tkr={tkr} COLUMN={c}")
+                    print("- repaired_df")
+                    print(repaired_df[f2].drop(['Open', 'High', 'Low', 'Volume', 'Capital Gains'], axis=1))
+                    print("- repaired_df[c]")
+                    print(repaired_df[f2][c])
+                    print("- correct_df[c]:")
+                    print(correct_df[f2][c])
+                    print("- diff:")
+                    print(repaired_df[f2][c] - correct_df[f2][c])
+                    raise
+
+

 if __name__ == '__main__':
    unittest.main()
--- a/tests/test_prices.py
+++ b/tests/test_prices.py
@@ -69,7 +69,7 @@ class TestPriceHistory(unittest.TestCase):
            dat = yf.Ticker(tkr, session=self.session)
            tz = dat._get_ticker_tz(timeout=None)

-            dt_utc = _pd.Timestamp.utcnow()
+            dt_utc = _pd.Timestamp.now('UTC')
            dt = dt_utc.astimezone(_tz.timezone(tz))
            start_d = dt.date() - _dt.timedelta(days=7)
            df = dat.history(start=start_d, interval="1h")
@@ -89,7 +89,7 @@ class TestPriceHistory(unittest.TestCase):
            dat = yf.Ticker(tkr, session=self.session)
            tz = dat._get_ticker_tz(timeout=None)

-            dt_utc = _pd.Timestamp.utcnow()
+            dt_utc = _pd.Timestamp.now('UTC')
            dt = dt_utc.astimezone(_tz.timezone(tz))
            if dt.time() < _dt.time(17, 0):
                continue
--- a/yfinance/init.py
+++ b/yfinance/init.py
@@ -56,4 +56,4 @@ def set_config(proxy=_NOTSET, retries=_NOTSET):
    if retries is not _NOTSET:
        warnings.warn("Set retries via new config control: yf.config.network.retries = retries", DeprecationWarning)
        config.network.retries = retries
-__all__ += ["set_config"]
+__all__ += ['config', 'set_config']
--- a/yfinance/base.py
+++ b/yfinance/base.py
@@ -480,7 +480,7 @@ class TickerBase:

        # Process dates
        tz = self._get_ticker_tz(timeout=10)
-        dt_now = pd.Timestamp.utcnow().tz_convert(tz)
+        dt_now = pd.Timestamp.now('UTC').tz_convert(tz)
        if start is not None:
            start = utils._parse_user_dt(start, tz)
        if end is not None:
--- a/yfinance/calendars.py
+++ b/yfinance/calendars.py
@@ -1,5 +1,6 @@
+from __future__ import annotations # Just in case
 import json
-from typing import Any, Optional
+from typing import Any, Optional, List, Union, Dict
 import warnings
 import numpy as np
 from requests import Session, Response, exceptions
@@ -30,7 +31,7 @@ class CalendarQuery:
    ```
    """

-    def __init__(self, operator: str, operand: list[Any] | list["CalendarQuery"]):
+    def __init__(self, operator: str, operand: Union[List[Any], List["CalendarQuery"]]):
        """
        :param operator: Operator string, e.g., 'eq', 'gte', 'and', 'or'.
        :param operand: List of operands: can be values (str, int), or other Operands instances (nested).
@@ -179,8 +180,8 @@ class Calendars:

    def __init__(
        self,
-        start: Optional[str | datetime | date] = None,
-        end: Optional[str | datetime | date] = None,
+        start: Optional[Union[str, datetime, date]] = None,
+        end: Optional[Union[str, datetime, date]] = None,
        session: Optional[Session] = None,
    ):
        """
@@ -208,9 +209,9 @@ class Calendars:
        self._most_active_qy: CalendarQuery = CalendarQuery("or", [])

        self._cache_request_body = {}
-        self.calendars: dict[str, pd.DataFrame] = {}
+        self.calendars: Dict[str, pd.DataFrame] = {}

-    def _parse_date_param(self, _date: Optional[str | datetime | date | int]) -> str:
+    def _parse_date_param(self, _date: Optional[Union[str, datetime, date, int]]) -> str:
        if not _date:
            return ""
        else:
--- a/yfinance/const.py
+++ b/yfinance/const.py
@@ -366,7 +366,7 @@ _MIC_TO_YAHOO_SUFFIX = {
    'XJSE': 'JO',  # South Africa
    'XKRX': 'KS', 'KQKS': 'KQ',  # South Korea
    'BMEX': 'MC',  # Spain
-    'XTAD': 'SAU',  # Saudi Arabia
+    'XSAU': 'SR',  # Saudi Arabia
    'XSTO': 'ST',  # Sweden
    'XSWX': 'SW',  # Switzerland
    'ROCO': 'TWO', 'XTAI': 'TW',  # Taiwan
--- a/yfinance/scrapers/fundamentals.py
+++ b/yfinance/scrapers/fundamentals.py
@@ -117,7 +117,7 @@ class Financials:
        url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
        # Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
        start_dt = datetime.datetime(2016, 12, 31)
-        end = pd.Timestamp.utcnow().ceil("D")
+        end = pd.Timestamp.now('UTC').ceil("D")
        url += f"&period1={int(start_dt.timestamp())}&period2={int(end.timestamp())}"

        # Step 3: fetch and reshape data
--- a/yfinance/scrapers/history.py
+++ b/yfinance/scrapers/history.py
@@ -101,9 +101,9 @@ class PriceHistory:
                        logger.error(err_msg)
                    return utils.empty_df()
                if period == 'ytd':
-                    start = _datetime.date(pd.Timestamp.utcnow().tz_convert(tz).year, 1, 1)
+                    start = _datetime.date(pd.Timestamp.now('UTC').tz_convert(tz).year, 1, 1)
                else:
-                    start = pd.Timestamp.utcnow().tz_convert(tz).date()
+                    start = pd.Timestamp.now('UTC').tz_convert(tz).date()
                    start -= utils._interval_to_timedelta(period)
                    start -= _datetime.timedelta(days=4)
                period_user = period
@@ -141,7 +141,7 @@ class PriceHistory:
                start_dt = end_dt - utils._interval_to_timedelta('1mo')
                start = int(start_dt.timestamp())
            elif not end:
-                end_dt = pd.Timestamp.utcnow().tz_convert(tz)
+                end_dt = pd.Timestamp.now('UTC').tz_convert(tz)
                end = int(end_dt.timestamp())
        else:
            if period.lower() == "max":
@@ -198,7 +198,7 @@ class PriceHistory:
        get_fn = self._data.get
        if end is not None:
            end_dt = pd.Timestamp(end, unit='s').tz_localize("UTC")
-            dt_now = pd.Timestamp.utcnow()
+            dt_now = pd.Timestamp.now('UTC')
            data_delay = _datetime.timedelta(minutes=30)
            if end_dt + data_delay <= dt_now:
                # Date range in past so safe to fetch through cache:
@@ -457,6 +457,10 @@ class PriceHistory:
            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)
+
+            # New:
+            df = self._repair_capital_gains(df)
+
            df = df.sort_index()

        # Auto/back adjust
@@ -673,7 +677,7 @@ class PriceHistory:
            min_dt = None
        else:
            m -= _datetime.timedelta(days=1)  # allow space for 1-day padding
-            min_dt = pd.Timestamp.utcnow() - m
+            min_dt = pd.Timestamp.now('UTC') - 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}", extra=log_extras)
        if min_dt is not None:
@@ -1020,7 +1024,7 @@ class PriceHistory:
            return df, currency
        last_row = df.iloc[np.where(f_volume)[0][-1]]
        prices_in_subunits = True  # usually is true
-        if last_row.name > (pd.Timestamp.utcnow() - _datetime.timedelta(days=30)):
+        if last_row.name > (pd.Timestamp.now('UTC') - _datetime.timedelta(days=30)):
            try:
                ratio = self._history_metadata['regularMarketPrice'] / last_row['Close']
                if abs((ratio*m)-1) < 0.1:
@@ -1403,6 +1407,120 @@ class PriceHistory:

        return df2

+    @utils.log_indent_decorator
+    def _repair_capital_gains(self, df):
+        # Yahoo has started double-counting capital gains in Adj Close,
+        # by pre-adding it to dividends column.
+
+        if 'Capital Gains' not in df.columns:
+            return df
+        if (df['Capital Gains'] == 0).all():
+            return df
+
+        debug = False
+        # debug = True
+
+        logger = utils.get_yf_logger()
+        log_extras = {'yf_cat': 'repair-capital-gains', 'yf_symbol': self.ticker}
+
+        df = df.copy()
+        df = df.sort_index()
+
+        # Consider price drop to decide if Yahoo double-counted - 
+        #   drop should = true dividend + capital gains
+        # But need to account for normal price volatility:
+        df['Price_Change%'] = df['Close'].pct_change(fill_method=None).abs()
+        no_distributions = (df['Dividends'] == 0) & (df['Capital Gains'] == 0)
+        price_drop_pct_mean = df.loc[no_distributions, 'Price_Change%'].mean()
+        df = df.drop('Price_Change%', axis=1)
+
+        # Add columns if not present
+        if 'Repaired?' not in df.columns:
+            df['Repaired?'] = False
+        df['Adj'] = df['Adj Close'] / df['Close']
+
+        if debug:
+            df['ScaleFactor'] = np.nan
+            df['correction'] = np.nan
+            df['AdjYahoo'] = (df['Adj Close']/df['Close']).round(4)
+
+            print(f"# price_drop_pct_mean = {price_drop_pct_mean:.4f}")
+
+        dts = df[df['Capital Gains'] > 0].index
+        c = df['Close'].to_numpy()
+        ac = df['Adj Close'].to_numpy()
+        dcs = {}
+        for dt in dts:
+            idx = df.index.get_loc(dt)
+
+            if idx > 0:
+                # Need a row before for price drop
+
+                dividend = df['Dividends'].iloc[idx]
+                capital_gains = df['Capital Gains'].iloc[idx]
+                if dividend < capital_gains:
+                    # Not possible for 'dividend' to be including capital gains
+                    continue
+
+                div_pct = dividend / c[idx-1]
+                cg_pct = capital_gains / c[idx-1]
+
+                # Check whether adjusted price drop is closer to dividend vs dividend+capital_gains
+                price_drop_pct = (c[idx-1] - c[idx]) / c[idx-1]
+                price_drop_pct_excl_vol = price_drop_pct - price_drop_pct_mean
+                diff_div = abs(price_drop_pct_excl_vol - div_pct)
+                diff_total = abs(price_drop_pct_excl_vol - (div_pct + cg_pct))
+                cg_is_double_counted = diff_div < diff_total
+
+                dcs[idx] = cg_is_double_counted
+
+                if debug:
+                    print(f"# {dt.date()}: div = {div_pct*100:.1f}%, cg = {cg_pct*100:.1f}%")
+                    print(f"- price_drop_pct = {price_drop_pct*100:.1f}%")
+                    print(f"- price_drop_pct_excl_vol = {price_drop_pct_excl_vol*100:.1f}%")
+                    print(f"- diff_div = {diff_div:.4f}")
+                    print(f"- diff_total = {diff_total:.4f}")
+                    print(f"- cg_is_double_counted = {cg_is_double_counted}")
+
+        pct_double_counted = sum(dcs.values()) / len(dcs)
+        if debug:
+            print(f"- pct_double_counted = {pct_double_counted*100:.1f}%")
+
+        if pct_double_counted >= 0.666:
+            for idx in dcs.keys():
+                dt = df.index[idx]
+
+                dividend = df['Dividends'].iloc[idx]
+                capital_gains = df['Capital Gains'].iloc[idx]
+
+                # Instead of calculating new adjustment from scratch, 
+                # reverse the double-count from existing adjustment.
+                # In case don't have all events after last date.
+                dividend_true = dividend - capital_gains
+
+                df.loc[dt, 'Dividends'] = dividend_true
+
+                # Correct adjustment for dates before and including this distribution date
+                adj_before = (ac[idx-1]/c[idx-1]) / (ac[idx]/c[idx])
+                adj_correct = 1.0 - (dividend_true + capital_gains) / c[idx-1]
+                correction = adj_correct / adj_before
+                df.loc[:dt-_datetime.timedelta(1), 'Adj'] *= correction
+                df.loc[:dt, 'Repaired?'] = True
+                msg = f"Repaired capital-gains double-count at {dt.date()}. Adj correction = {correction:.4f}"
+                logger.info(msg, extra=log_extras)
+
+                if debug:
+                    df.loc[dt, 'correction'] = correction
+
+        df['Adj Close'] = df['Close'] * df['Adj']
+
+        if debug:
+            df['Adj'] = df['Adj'].round(4)
+        else:
+            df = df.drop('Adj', axis=1)
+
+        return df
+
    @utils.log_indent_decorator
    def _fix_bad_div_adjust(self, df, interval, currency):
        # Look for dividend issues:
@@ -1418,6 +1536,12 @@ class PriceHistory:
        if interval in ['1wk', '1mo', '3mo', '1y']:
            return df

+        if 'Capital Gains' in df.columns and (df['Capital Gains']>0).any():
+            # So there are capital gains. This function only considers dividends. 
+            # I don't want to deal with capital gains being wrong as well!
+            # But if you find capital gains that need repair e.g. 100x error, then report to our Github.
+            return df
+
        logger = utils.get_yf_logger()
        log_extras = {'yf_cat': 'div-adjust-repair-bad', 'yf_interval': interval, 'yf_symbol': self.ticker}

@@ -2510,6 +2634,14 @@ class PriceHistory:

        OHLC = ['Open', 'High', 'Low', 'Close']

+        if interday and interval != '1d':
+            # Yahoo creates multi-day intervals using potentiall corrupt data, e.g.
+            # the Close could be 100x Open. This means have to correct each OHLC column
+            # individually
+            correct_columns_individually = True
+        else:
+            correct_columns_individually = False
+
        # Do not attempt repair of the split is small,
        # could be mistaken for normal price variance
        if 0.8 < split < 1.25:
@@ -2545,25 +2677,33 @@ class PriceHistory:
            log_msg += f' ({df2.index[idx_latest_active].date()})'
        logger.debug(log_msg, extra=log_extras)

-        if logger.isEnabledFor(logging.DEBUG):
-            df_debug = df2.copy()
-            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')
+        df_workings = df2.copy()
+        df_workings = df_workings.drop(['Adj Close', 'Dividends', 'Stock Splits', 'Repaired?'], axis=1, errors='ignore')
+        df_workings = df_workings.rename(columns={'Volume': 'Vol'})
+        fna = df_workings['Vol'].isna()
+        if fna.any():
+            df_workings['VolStr'] = ''
+            df_workings.loc[fna, 'VolStr'] = 'NaN'
+            df_workings.loc[~fna, 'VolStr'] = (df_workings['Vol'][~fna]/1e6).astype('int').astype('str') + 'm'
+            df_workings['Vol'] = df_workings['VolStr'] ; df_workings.drop('VolStr', axis=1)
        else:
-            debug_cols = []
+            df_workings['Vol'] = (df_workings['Vol']/1e6).astype('int').astype('str') + 'm'
+        debug_cols = ['Close']
+        df_workings = df_workings.drop([c for c in OHLC if c not in debug_cols], axis=1, errors='ignore')

        # Calculate daily price % change. To reduce effect of price volatility,
        # calculate change for each OHLC column.
        if interday and interval != '1d' and split not in [100.0, 100, 0.001]:
            # Avoid using 'Low' and 'High'. For multiday intervals, these can be
-            # very volatile so reduce ability to detect genuine stock split errors
+            # very volatile which reduces ability to detect genuine stock split errors
            _1d_change_x = np.full((n, 2), 1.0)
-            price_data = df2[['Open','Close']].to_numpy()
+            price_data_cols = ['Open','Close']
+            price_data = df2[price_data_cols].to_numpy()
            f_zero = price_data == 0.0
        else:
            _1d_change_x = np.full((n, 4), 1.0)
-            price_data = df2[OHLC].to_numpy()
+            price_data_cols = OHLC
+            price_data = df2[price_data_cols].to_numpy()
            f_zero = price_data == 0.0
        if f_zero.any():
            price_data[f_zero] = 1.0
@@ -2581,9 +2721,8 @@ class PriceHistory:
            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 OHLC[j] in df_workings.columns:
+                df_workings[price_data_cols[j]] *= adj
        if df_dtype == np.int64:
            price_data = price_data.astype('int')

@@ -2593,37 +2732,34 @@ class PriceHistory:
            _1d_change_x[f_zero_num_denom] = 1.0
        if interday and interval != '1d':
            # average change
-            _1d_change_minx = np.average(_1d_change_x, axis=1)
+            _1d_change_denoised = np.average(_1d_change_x, axis=1)
        else:
            # # change nearest to 1.0
            # diff = np.abs(_1d_change_x - 1.0)
            # j_indices = np.argmin(diff, axis=1)
-            # _1d_change_minx = _1d_change_x[np.arange(n), j_indices]
+            # _1d_change_denoised = _1d_change_x[np.arange(n), j_indices]
            # Still sensitive to extreme-low low. Try median:
-            _1d_change_minx = np.median(_1d_change_x, axis=1)
-        f_na = np.isnan(_1d_change_minx)
+            _1d_change_denoised = np.median(_1d_change_x, axis=1)
+        f_na = np.isnan(_1d_change_denoised)
        if f_na.any():
            # Possible if data was too old for reconstruction.
-            _1d_change_minx[f_na] = 1.0
-        if logger.isEnabledFor(logging.DEBUG):
-            df_debug['1D %'] = _1d_change_minx
-            df_debug['1D %'] = df_debug['1D %'].round(2).astype('str')
+            _1d_change_denoised[f_na] = 1.0

        # 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):
+        if np.max(_1d_change_denoised) < (split_max - 1) * 0.5 + 1 and np.min(_1d_change_denoised) > 1.0 / ((split_max - 1) * 0.5 + 1):
            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.
        # Key = ignore 1D changes outside of interquartile range
-        q1, q3 = np.percentile(_1d_change_minx, [25, 75])
+        q1, q3 = np.percentile(_1d_change_denoised, [25, 75])
        iqr = q3 - q1
        lower_bound = q1 - 1.5 * iqr
        upper_bound = q3 + 1.5 * iqr
-        f = (_1d_change_minx >= lower_bound) & (_1d_change_minx <= upper_bound)
-        avg = np.mean(_1d_change_minx[f])
-        sd = np.std(_1d_change_minx[f])
+        f = (_1d_change_denoised >= lower_bound) & (_1d_change_denoised <= upper_bound)
+        avg = np.mean(_1d_change_denoised[f])
+        sd = np.std(_1d_change_denoised[f])
        # Now can calculate SD as % of mean
        sd_pct = sd / avg
        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)
@@ -2641,29 +2777,34 @@ class PriceHistory:

        # 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
+        r = _1d_change_denoised / split_rcp
        split_max = max(split, split_rcp)
        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"threshold={threshold:.3f}", extra=log_extras)
+        logger.debug(f"threshold={threshold:.3f}, threshold_rcp={1.0/threshold:.3f}", extra=log_extras)

        if 'Repaired?' not in df2.columns:
            df2['Repaired?'] = False

-        if interday and interval != '1d':
-            # Yahoo creates multi-day intervals using potentiall corrupt data, e.g.
-            # the Close could be 100x Open. This means have to correct each OHLC column
-            # individually
-            correct_columns_individually = True
-        else:
-            correct_columns_individually = False
-
        if correct_columns_individually:
            _1d_change_x = np.full((n, 4), 1.0)
            price_data = df2[OHLC].replace(0.0, 1.0).to_numpy()
+            price_data_cols = OHLC
+            # _1d_change_x = np.full((n, len(price_data_cols)), 1.0)
+            # price_data = df2[price_data_cols].replace(0.0, 1.0).to_numpy()
            _1d_change_x[1:] = price_data[1:, ] / price_data[:-1, ]
        else:
-            _1d_change_x = _1d_change_minx
+            _1d_change_x = _1d_change_denoised
+
+        if correct_columns_individually:
+            for j in range(len(price_data_cols)):
+                c = price_data_cols[j]
+                df_workings[c+' 1D %'] = _1d_change_x[:, j]
+                df_workings[c+' 1D %'] = df_workings[c+' 1D %'].round(3)
+        else:
+            df_workings['1D %'] = _1d_change_denoised
+            # df_workings['1D %'] = df_workings['1D %'].round(2).astype('str')
+            df_workings['1D %'] = df_workings['1D %'].round(3)

        r = _1d_change_x / split_rcp
        f_down = _1d_change_x < 1.0 / threshold
@@ -2680,46 +2821,178 @@ class PriceHistory:
        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]:
+            nf_up_shifts = ~f_up_shifts
+            flat_indices = np.where(nf_up_shifts)[0]
+            f_down_ndims = len(f_down.shape)
+            down_dts = df2.index[f_down if f_down_ndims==1 else f_down.any(axis=1)]
+            for idx in np.where(f_up_shifts)[0]:
+                i = idx-1  # this is when price actually dropped
+                dt = df2.index[i]
                v = df2['Volume'].iloc[i]
+
                vol_change_pct = 0 if v == 0 else df2['Volume'].iloc[i-1] / v
+                logger.debug(f"- vol_change_pct = {vol_change_pct:.4f}")
                if multiday and (i+1 < len(df2)):
                    next_v = df2['Volume'].iloc[i+1]
                    if next_v > 0:
                        vol_change_pct = max(vol_change_pct, df2['Volume'].iloc[i] / next_v)
-                if vol_change_pct > 5:
-                    # big volume change +500%
-                    # Could be false-positive, but need some more checks
-                    lookback = max(0, i-10)
-                    lookahead = min(len(df2), i+10)
-                    if (df2['Stock Splits'].iloc[lookback:lookahead]!=0.0).any():
-                        # There's a stock split near the volume spike, so 
-                        # assume false positive
-                        continue
-                    avg_vol_after = df2['Volume'].iloc[lookback:i-1].mean()
-                    if not np.isnan(avg_vol_after) and avg_vol_after > 0 and v/avg_vol_after < 2.0:
-                        # volume spike is actually a step-change, so 
-                        # probably missing stock split
-                        continue
-                    if f_up_ndims == 1:
-                        f_up[i] = False
+
+                # if vol_change_pct > 5:
+                #     # big volume change +500%
+                #     # Could be false-positive, but need some more checks
+                #     lookback = max(0, i-10)
+                #     lookahead = min(len(df2), i+10)
+                #     if (df2['Stock Splits'].iloc[lookback:lookahead]!=0.0).any():
+                #         # There's a stock split near the volume spike, so 
+                #         # assume false positive
+                #         continue
+                #     avg_vol_after = df2['Volume'].iloc[lookback:i-1].mean()
+                #     if not np.isnan(avg_vol_after) and avg_vol_after > 0 and v/avg_vol_after < 2.0:
+                #         # volume spike is actually a step-change, so 
+                #         # probably missing stock split
+                #         continue
+                #     if f_up_ndims == 1:
+                #         f_up[idx] = False
+                #     else:
+                #         f_up[idx,:] = False
+
+                # New method: look for a volume spike
+
+                # Select 20 rows after i (earlier in time)
+                # are not triggers (big price moves).
+                i_pos_in_flat_indices = nf_up_shifts[:i].sum()
+                start = max(0, i_pos_in_flat_indices - 15)
+                end = min(len(flat_indices), start+30+1)
+                block = df2.iloc[flat_indices[start:end]]
+                block = block.sort_index()
+                # block_before = block.loc[:dt-_datetime.timedelta(1)]
+                down_dts_from = down_dts[down_dts>=dt]
+                if len(down_dts_from) > 0:
+                    next_down_dt = min(down_dts_from)
+                    if next_down_dt == dt:
+                        # Only this row has price drop, so will look like a volume spike but
+                        # is definitely a data error to repair.
+                        block_after = None
                    else:
-                        f_up[i,:] = False
+                        block_after = block.loc[dt+_datetime.timedelta(1):next_down_dt-_datetime.timedelta(1)]
+                else:
+                    block_after = block.loc[dt+_datetime.timedelta(1):]
+                if block_after is not None and block_after.empty:
+                    block_after = None
+
+                def _calc_volume_zscore_weighted(volume, dt, block):
+                    distances = np.abs((block.index - dt).total_seconds())
+                    distances /= distances.max()
+                    weights = np.exp(-distances)
+                    weights = np.array(weights) / np.sum(weights)
+                    values = block['Volume'].to_numpy()
+                    weighted_mean = np.sum(values * weights)
+                    weighted_variance = np.sum(weights * (values - weighted_mean) ** 2)
+                    weighted_std = np.sqrt(weighted_variance)
+                    # print(f"# weighted_variance = {weighted_variance:.4f}")
+                    # print(f"# weighted_std = {weighted_std:.4f}")
+                    z_score = (volume - weighted_mean) / weighted_std
+                    # print(f"z_score = {z_score:.4f}")
+                    return z_score
+
+                def _calc_volume_zscore(volume, block):
+                    # print(f"_calc_volume_zscore(volume={volume})")
+                    values = block['Volume'].to_numpy()
+                    std = np.std(values, ddof=1)
+                    if std == 0.0:
+                        return 0
+                    mean = np.mean(values)
+                    z_score = (volume - mean) / std
+                    return z_score
+
+                # z_score_before = _calc_volume_zscore(v, block_before)
+                # print(f"z_score_before = {z_score_before:.4f}")
+                if block_after is not None:
+                    z_score_after  = _calc_volume_zscore(v, block_after)
+                    # print(f"z_score_after  = {z_score_after:.4f}")
+                    z_score_after_d1 = _calc_volume_zscore(block_after['Volume'].iloc[0], block_after)
+                    # print(f"z_score_after_d1 = {z_score_after_d1:.4f}")
+                    # z_score_after_d2 = _calc_volume_zscore(block_after['Volume'].iloc[1], block_after)
+                    # print(f"z_score_after_d2 = {z_score_after_d2:.4f}")
+
+                    if max(z_score_after, z_score_after_d1) > 2:
+                        # There was a volume spike around this date, so
+                        # probably something happened NOT a missing stock split.
+                        logger.debug(f"Detected false-positive split error on {dt.date()}, ignoring price drop")
+                        if f_up_ndims == 1:
+                            f_up[idx] = False
+                        else:
+                            f_up[idx,:] = False
        f = f_down | f_up
-        if logger.isEnabledFor(logging.DEBUG):
-            if not correct_columns_individually:
-                df_debug['r'] = r
-                df_debug['down'] = f_down
-                df_debug['up'] = f_up
-                df_debug['r'] = df_debug['r'].round(2).astype('str')
+        if not correct_columns_individually:
+            df_workings['r'] = r
+            df_workings['down'] = f_down
+            df_workings['up'] = f_up
+            df_workings['r'] = df_workings['r'].round(2).astype('str')
+            df_workings['f'] = f
+        else:
+            for j in range(len(price_data_cols)):
+                c = price_data_cols[j]
+                df_workings[c+'_r'] = r[:, j]
+                df_workings[c+'_r'] = df_workings[c+'_r'].round(2).astype('str')
+                df_workings[c+'_down'] = f_down[:, j]
+                df_workings[c+'_up'] = f_up[:, j]
+                df_workings[c+'_f'] = f[:, j]
+
+        # Possible that extreme events caused the price spikes/dumps.
+        # So for each signal, calculate local stdev for a custom threshold.
+        for idx in np.where(f)[0]:
+            dt = df2.index[idx]
+            idx_end = min(len(df2)-1, idx+2)
+            if interval.endswith('d'):
+                lookback = 10
+            elif interval.endswith('m'):
+                lookback = 100
            else:
-                for j in range(len(OHLC)):
-                    c = OHLC[j]
-                    if c in debug_cols:
-                        df_debug[c + '_r'] = r[:, 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]
+                lookback = 3
+            idx_start = max(0, idx-lookback)
+            changes_local = df_workings.iloc[idx_start:idx_end]
+            if correct_columns_individually:
+                cols = price_data_cols
+            else:
+                cols = ['n/a']
+            for c in cols:
+                if c == 'n/a':
+                    clean_changes = changes_local['1D %'][~changes_local['f']].to_numpy()
+                else:
+                    clean_changes = changes_local[c+' 1D %'][~changes_local[c+'_f']].to_numpy()
+                avg = np.mean(clean_changes)
+                sd = np.std(clean_changes)
+                sd_pct = sd / avg
+
+                largest_change_pct = 5 * sd_pct
+                if interday and interval != '1d':
+                    largest_change_pct *= 3
+                    if interval in ['1mo', '3mo']:
+                        largest_change_pct *= 2
+                threshold = (split_max + 1.0 + largest_change_pct) * 0.5
+                if correct_columns_individually:
+                    big_change = df_workings[c+' 1D %'].iloc[idx]
+                else:
+                    big_change = df_workings['1D %'].iloc[idx]
+                    if big_change < threshold and big_change > 1.0/threshold:
+                        # This price change is actually similar to local price volatily. False positive
+                        if correct_columns_individually:
+                            logger.debug(f"Unusual '{c}' price action @ {dt.date()} is actually similar to local price volatility, so ignoring (StdDev % mean = {sd_pct*100:.1f}%)")
+                            df_workings.loc[dt, c+'_f'] = False
+                        else:
+                            logger.debug(f"Unusual price action @ {dt.date()} is actually similar to local price volatility, so ignoring (StdDev % mean = {sd_pct*100:.1f}%)")
+                            df_workings.loc[dt, 'f'] = False
+        if not correct_columns_individually:
+            f_down = f_down & df_workings['f'].to_numpy()
+            f_up = f_up & df_workings['f'].to_numpy()
+        else:
+            for j in range(len(price_data_cols)):
+                c = price_data_cols[j]
+                if c in debug_cols:
+                    f_down[:, j] = f_down[:, j] & df_workings[c+'_f']
+                    f_up[:, j] = f_up[:, j] & df_workings[c+'_f']
+        f = f_down | f_up

        if not f.any():
            logger.debug(f'No {fix_type}s detected', extra=log_extras)
@@ -2755,15 +3028,15 @@ class PriceHistory:
                    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']
+            df_workings['i'] = list(range(0, df_workings.shape[0]))
+            df_workings['i_rev'] = df_workings.shape[0]-1 - df_workings['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)
+                f_change = df_workings[[c+'_down' for c in debug_cols]].any(axis=1) | df_workings[[c+'_up' for c in debug_cols]].any(axis=1)
            else:
-                f_change = df_debug['down'] | df_debug['up']
+                f_change = df_workings['down'] | df_workings['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]))
+                logger.debug("price-repair-split: my workings:" + '\n' + str(df_workings[f_change]))

        def map_signals_to_ranges(f, f_up, f_down):
            # Ensure 0th element is False, because True is nonsense
--- a/yfinance/scrapers/quote.py
+++ b/yfinance/scrapers/quote.py
@@ -143,7 +143,7 @@ class FastInfo:
        if self._prices_1y.empty:
            return self._prices_1y

-        dnow = pd.Timestamp.utcnow().tz_convert(self.timezone).date()
+        dnow = pd.Timestamp.now('UTC').tz_convert(self.timezone).date()
        d1 = dnow
        d0 = (d1 + datetime.timedelta(days=1)) - utils._interval_to_timedelta("1y")
        if fullDaysOnly and self._exchange_open_now():
@@ -170,7 +170,7 @@ class FastInfo:
        return self._md

    def _exchange_open_now(self):
-        t = pd.Timestamp.utcnow()
+        t = pd.Timestamp.now('UTC')
        self._get_exchange_metadata()

        # if self._today_open is None and self._today_close is None:
@@ -231,7 +231,7 @@ class FastInfo:
        if self._shares is not None:
            return self._shares

-        shares = self._tkr.get_shares_full(start=pd.Timestamp.utcnow().date()-pd.Timedelta(days=548))
+        shares = self._tkr.get_shares_full(start=pd.Timestamp.now('UTC').date()-pd.Timedelta(days=548))
        # if shares is None:
        #     # Requesting 18 months failed, so fallback to shares which should include last year
        #     shares = self._tkr.get_shares()
@@ -699,9 +699,9 @@ class Quote:
            for k in keys:
                url += "&type=" + k
            # Request 6 months of data
-            start = pd.Timestamp.utcnow().floor("D") - datetime.timedelta(days=365 // 2)
+            start = pd.Timestamp.now('UTC').floor("D") - datetime.timedelta(days=365 // 2)
            start = int(start.timestamp())
-            end = pd.Timestamp.utcnow().ceil("D")
+            end = pd.Timestamp.now('UTC').ceil("D")
            end = int(end.timestamp())
            url += f"&period1={start}&period2={end}"

--- a/yfinance/version.py
+++ b/yfinance/version.py
@@ -1 +1 @@
-version = "1.0"
+version = "1.1.0"
Author	SHA1	Message	Date
ValueRaider	fa755363dc	Version 1.1.0	2026-01-24 16:01:54 +00:00
ValueRaider	7874a45e3b	Merge pull request #2682 from ranaroussi/dev sync dev -> main	2026-01-24 15:57:45 +00:00
ValueRaider	9e6d00c674	Merge pull request #2683 from ctasada/ctasada/issue-2679 Upgrade to Pandas 3.0 (Fixes #2679)	2026-01-24 12:08:09 +00:00
Carlos-Tasada	04cd07eb6a	Timestamp.now(tz) already exists in Pandas since 9 years ago, so there's no need to upgrade Pandas right now. Keeping the change removes the deprecation warning, achieving the goal of this PR	2026-01-23 14:09:03 +01:00
Carlos-Tasada	25bf12cb92	Upgrade to Pandas 3.0 (Fixes #2679 ) Pandas 3.0 introduced many changes. One of them is a noisy deprecation warning `Pandas4Warning: Timestamp.utcnow is deprecated and will be removed in a future version. Use Timestamp.now('UTC') instead.` This PR upgrades YFinance to use the latest Pandas 3.0.0 version, removing the warning	2026-01-23 08:44:47 +01:00
ValueRaider	27cb8aa2a2	Docs for capital-gains repair; Fix warnings in price-repair	2026-01-22 19:36:37 +00:00
ValueRaider	947cd4420c	Merge pull request #2671 from sd416/main Add GitHub Actions to Dependabot configuration	2026-01-20 19:18:43 +00:00
sd416	fd3965ad92	Add GitHub Actions to Dependabot configuration and updating actions Add GitHub Actions to Dependabot configuration Add GitHub Actions to Dependabot configuration Bump actions/checkout from 2 to 6 Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 6. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v2...v6) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Bump actions/setup-python from 4 to 6 Bumps [actions/setup-python](https://github.com/actions/setup-python) from 4 to 6. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v4...v6) --- updated-dependencies: - dependency-name: actions/setup-python dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Bump actions/github-script from 7 to 8 Bumps [actions/github-script](https://github.com/actions/github-script) from 7 to 8. - [Release notes](https://github.com/actions/github-script/releases) - [Commits](https://github.com/actions/github-script/compare/v7...v8) --- updated-dependencies: - dependency-name: actions/github-script dependency-version: '8' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com>	2026-01-20 05:04:51 +00:00
ValueRaider	bf94ae937b	Merge pull request #2668 from ranaroussi/fix/price-split-repair-false-positives Improve stock-split repair false-positives	2026-01-19 23:11:02 +00:00
ValueRaider	496d238bfa	Merge pull request #2667 from ranaroussi/feature/price-repair-capital-gains-double-count New price repair: capital gains double-counting	2026-01-19 23:10:49 +00:00
ValueRaider	ce4c07be1f	New price repair: capital gains double-counting	2026-01-19 23:10:05 +00:00
ValueRaider	f984876b35	Improve stock-split repair false-positives	2026-01-19 23:09:16 +00:00
ValueRaider	8d6188c926	Merge pull request #2674 from orionnelson/hotfix/py39-compat-regression-2672 Fix Python 3.9 compatibility regression in calendars.py	2026-01-19 23:06:40 +00:00
Orion Nelson	12e1951f80	Fix Python 3.9 compatibility regression in calendars.py Fixes #2673 syntax issue	2026-01-18 16:05:43 -05:00
ValueRaider	47a8f64ff7	Merge pull request #2673 from kbluck/fix/config-init-all Add config to __all__	2026-01-17 22:32:39 +00:00
Kevin Bluck	0f1d091ac8	Add config to __all__	2026-01-17 13:08:24 -08:00
ValueRaider	42411d44df	Merge pull request #2669 from ranaroussi/main sync main -> dev	2026-01-11 12:58:34 +00:00
ValueRaider	b0e855e7ec	Merge pull request #2661 from peter199711/fix-pypi-link Fix broken contributing link in PyPI documentation	2026-01-10 12:20:11 +00:00
ValueRaider	ea455c16a5	Merge pull request #2664 from Timour-Ilyas/fix-license-link Fix broken LICENSE.txt link in PyPI documentation	2026-01-10 12:19:47 +00:00
ValueRaider	3e33a75230	Merge pull request #2665 from salsugair/fix-Saudi-Arabia-MIC-Code Fix Saudi Arabia MIC Code	2026-01-10 12:19:21 +00:00
salsugair	0df671b566	Fix Saudi Arabia MIC Code fix Saudi Arabia mic code XSAU and yahoo finance suffix SR	2026-01-08 19:53:01 +03:00
Ilyas Timour	03ec05d286	Fix broken LICENSE.txt link in PyPI documentation The relative link works on GitHub but is broken on PyPI. Replaced it with an absolute GitHub URL.	2026-01-07 14:42:03 +01:00
Peter	5c7b41d510	Fix broken contributing link in PyPI documentation	2026-01-05 11:01:15 +08:00