Version 0.2.45

sync dev -> main

.github/workflows/pyright.yml

@@ -0,0 +1,27 @@
+name: Pyright
+
+on:
+  pull_request:
+    branches:
+      - master
+      - main
+      - dev
+
+jobs:
+  pyright:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pyright
+
+    - name: Run Pyright
+      run: pyright . --level error

CHANGELOG.rst

@@ -1,6 +1,19 @@
 Change Log
 ===========
 
+0.2.45
+------
+Features:
+- Screener #2066 @ericpien
+Fixes
+- Tickers keyerror #2068 @antoniouaa
+- IndexError in some history() debug messages #2087
+- improve dividend repair #2090
+Maintenance
+- fix unit tests contextual imports #2067
+- fix typos #2072 @algonell
+- add Pyright type checking #2059 @marco-carvalho
+
 0.2.44
 ------
 Features:
@@ -359,7 +372,7 @@ Jumping to 0.2 for this big update. 0.1.* will continue to receive bug-fixes
 - Fix timezone handling
 - Fix handling of missing data
 - Clean&format earnings_dates table
-- Add ``.get_earnings_dates()`` to retreive earnings calendar
+- Add ``.get_earnings_dates()`` to retrieve earnings calendar
 - Added ``.get_earnings_history()`` to fetch earnings data
 
 0.1.70
@@ -671,7 +684,7 @@ Jumping to 0.2 for this big update. 0.1.* will continue to receive bug-fixes
 - Removed 5 second wait for every failed fetch
 - Reduced TTL for Yahoo!'s cookie
 - Keeps track of failed downloads and tries to re-download all failed downloads one more time before giving up
-- Added progress bar (can be turned off useing ``progress=False``)
+- Added progress bar (can be turned off using ``progress=False``)
 
 0.0.7
 -------

README.md

@@ -28,7 +28,6 @@ Yahoo! finance API is intended for personal use only.**
 <a target="new" href="https://pypi.python.org/pypi/yfinance"><img border=0 src="https://img.shields.io/pypi/v/yfinance.svg?maxAge=60%" alt="PyPi version"></a>
 <a target="new" href="https://pypi.python.org/pypi/yfinance"><img border=0 src="https://img.shields.io/pypi/status/yfinance.svg?maxAge=60" alt="PyPi status"></a>
 <a target="new" href="https://pypi.python.org/pypi/yfinance"><img border=0 src="https://img.shields.io/pypi/dm/yfinance.svg?maxAge=2592000&label=installs&color=%2327B1FF" alt="PyPi downloads"></a>
-<a target="new" href="https://travis-ci.com/github/ranaroussi/yfinance"><img border=0 src="https://img.shields.io/travis/ranaroussi/yfinance/main.svg?maxAge=1" alt="Travis-CI build status"></a>
 <a target="new" href="https://www.codefactor.io/repository/github/ranaroussi/yfinance"><img border=0 src="https://www.codefactor.io/repository/github/ranaroussi/yfinance/badge" alt="CodeFactor"></a>
 <a target="new" href="https://github.com/ranaroussi/yfinance"><img border=0 src="https://img.shields.io/github/stars/ranaroussi/yfinance.svg?style=social&label=Star&maxAge=60" alt="Star this repo"></a>
 <a target="new" href="https://twitter.com/aroussi"><img border=0 src="https://img.shields.io/twitter/follow/aroussi.svg?style=social&label=Follow&maxAge=60" alt="Follow me on twitter"></a>
@@ -272,6 +271,17 @@ software_ticker = software.ticker
 software_ticker.history()
 ```
 
+### Market Screener
+The `Screener` module allows you to screen the market based on specified queries.
+
+#### Query Construction
+To create a query, you can use the `EquityQuery` class to construct your filters step by step. The queries support operators: `GT` (greater than), `LT` (less than), `BTWN` (between), `EQ` (equals), and logical operators `AND` and `OR` for combining multiple conditions.
+
+#### Screener
+The `Screener` class is used to execute the queries and return the filtered results. You can set a custom body for the screener or use predefined configurations.
+
+<!-- TODO: link to Github Pages for more including list of predefined bodies, supported fields, operands, and sample code -->
+
 ### Logging
 
 `yfinance` now uses the `logging` module to handle messages, default behaviour is only print errors. If debugging, use `yf.enable_debug_mode()` to switch logging to debug with custom formatting.

meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.2.44" %}
+{% set version = "0.2.45" %}
 
 package:
   name: "{{ name|lower }}"

pyrightconfig.json

@@ -0,0 +1,15 @@
+{
+    "typeCheckingMode": "basic",
+    "reportGeneralTypeIssues": "warning",
+    "reportArgumentType": "warning",
+    "reportOptionalMemberAccess": "warning",
+    "reportOperatorIssue": "warning",
+    "reportAttributeAccessIssue": "warning",
+    "reportMissingImports": "warning",
+    "reportReturnType": "warning",
+    "reportAssignmentType": "warning",
+    "reportOptionalSubscript": "warning",
+    "reportOptionalIterable": "warning",
+    "reportCallIssue": "warning",
+    "reportUnhashable": "warning"
+  }

tests/test_price_repair.py

@@ -1,5 +1,5 @@
-from .context import yfinance as yf
-from .context import session_gbl
+from tests.context import yfinance as yf
+from tests.context import session_gbl
 
 import unittest
 

tests/test_prices.py

@@ -1,5 +1,5 @@
-from .context import yfinance as yf
-from .context import session_gbl
+from tests.context import yfinance as yf
+from tests.context import session_gbl
 
 import unittest
 

tests/test_screener.py

@@ -0,0 +1,133 @@
+import unittest
+from unittest.mock import patch, MagicMock
+from yfinance.const import PREDEFINED_SCREENER_BODY_MAP
+from yfinance.screener.screener import Screener
+from yfinance.screener.screener_query import EquityQuery
+
+
+class TestScreener(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(self):
+        self.screener = Screener()
+        self.query = EquityQuery('gt',['eodprice',3])
+
+    def test_set_default_body(self):
+        self.screener.set_default_body(self.query)
+
+        self.assertEqual(self.screener.body['offset'], 0)
+        self.assertEqual(self.screener.body['size'], 100)
+        self.assertEqual(self.screener.body['sortField'], 'ticker')
+        self.assertEqual(self.screener.body['sortType'], 'desc')
+        self.assertEqual(self.screener.body['quoteType'], 'equity')
+        self.assertEqual(self.screener.body['query'], self.query.to_dict())
+        self.assertEqual(self.screener.body['userId'], '')
+        self.assertEqual(self.screener.body['userIdType'], 'guid')
+
+    def test_set_predefined_body(self):
+        k = 'most_actives'
+        self.screener.set_predefined_body(k)
+        self.assertEqual(self.screener.body, PREDEFINED_SCREENER_BODY_MAP[k])
+
+    def test_set_predefined_body_invalid_key(self):
+        with self.assertRaises(ValueError):
+            self.screener.set_predefined_body('invalid_key')
+
+    def test_set_body(self):
+        body = {
+            "offset": 0,
+            "size": 100,
+            "sortField": "ticker",
+            "sortType": "desc",
+            "quoteType": "equity",
+            "query": self.query.to_dict(),
+            "userId": "",
+            "userIdType": "guid"
+        }
+        self.screener.set_body(body)
+
+        self.assertEqual(self.screener.body, body)
+
+    def test_set_body_missing_keys(self):
+        body = {
+            "offset": 0,
+            "size": 100,
+            "sortField": "ticker",
+            "sortType": "desc",
+            "quoteType": "equity"
+        }
+        with self.assertRaises(ValueError):
+            self.screener.set_body(body)
+
+    def test_set_body_extra_keys(self):
+        body = {
+            "offset": 0,
+            "size": 100,
+            "sortField": "ticker",
+            "sortType": "desc",
+            "quoteType": "equity",
+            "query": self.query.to_dict(),
+            "userId": "",
+            "userIdType": "guid",
+            "extraKey": "extraValue"
+        }
+        with self.assertRaises(ValueError):
+            self.screener.set_body(body)
+
+    def test_patch_body(self):
+        initial_body = {
+            "offset": 0,
+            "size": 100,
+            "sortField": "ticker",
+            "sortType": "desc",
+            "quoteType": "equity",
+            "query": self.query.to_dict(),
+            "userId": "",
+            "userIdType": "guid"
+        }
+        self.screener.set_body(initial_body)
+        patch_values = {"size": 50}
+        self.screener.patch_body(patch_values)
+
+        self.assertEqual(self.screener.body['size'], 50)
+        self.assertEqual(self.screener.body['query'], self.query.to_dict())
+
+    def test_patch_body_extra_keys(self):
+        initial_body = {
+            "offset": 0,
+            "size": 100,
+            "sortField": "ticker",
+            "sortType": "desc",
+            "quoteType": "equity",
+            "query": self.query.to_dict(),
+            "userId": "",
+            "userIdType": "guid"
+        }
+        self.screener.set_body(initial_body)
+        patch_values = {"extraKey": "extraValue"}
+        with self.assertRaises(ValueError):
+            self.screener.patch_body(patch_values)
+
+    @patch('yfinance.screener.screener.YfData.post')
+    def test_fetch(self, mock_post):
+        mock_response = MagicMock()
+        mock_response.json.return_value = {'finance': {'result': [{}]}}
+        mock_post.return_value = mock_response
+
+        self.screener.set_default_body(self.query)
+        response = self.screener._fetch()
+
+        self.assertEqual(response, {'finance': {'result': [{}]}})
+
+    @patch('yfinance.screener.screener.YfData.post')
+    def test_fetch_and_parse(self, mock_post):
+        mock_response = MagicMock()
+        mock_response.json.return_value = {'finance': {'result': [{'key': 'value'}]}}
+        mock_post.return_value = mock_response
+
+        self.screener.set_default_body(self.query)
+        self.screener._fetch_and_parse()
+        self.assertEqual(self.screener.response, {'key': 'value'})
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_ticker.py

@@ -10,8 +10,8 @@ Specific test class:
 """
 import pandas as pd
 
-from .context import yfinance as yf
-from .context import session_gbl
+from tests.context import yfinance as yf
+from tests.context import session_gbl
 from yfinance.exceptions import YFPricesMissingError, YFInvalidPeriodError, YFNotImplementedError, YFTickerMissingError, YFTzMissingError, YFDataException
 
 
@@ -848,7 +848,7 @@ class TestTickerAnalysts(unittest.TestCase):
                 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}")
+                self.fail(f"Exception raised for attribute '{attribute}': {e}")
 
 
 

tests/test_utils.py

@@ -14,7 +14,7 @@ from unittest import TestSuite
 import pandas as pd
 # import numpy as np
 
-from .context import yfinance as yf
+from tests.context import yfinance as yf
 
 import unittest
 # import requests_cache

yfinance/__init__.py

@@ -27,6 +27,8 @@ from .utils import enable_debug_mode
 from .cache import set_tz_cache_location
 from .domain.sector import Sector
 from .domain.industry import Industry
+from .screener.screener import Screener
+from .screener.screener_query import EquityQuery
 
 __version__ = version.version
 __author__ = "Ran Aroussi"
@@ -34,4 +36,5 @@ __author__ = "Ran Aroussi"
 import warnings
 warnings.filterwarnings('default', category=DeprecationWarning, module='^yfinance')
 
-__all__ = ['download', 'Ticker', 'Tickers', 'enable_debug_mode', 'set_tz_cache_location', 'Sector', 'Industry']
+__all__ = ['download', 'Ticker', 'Tickers', 'enable_debug_mode', 'set_tz_cache_location', 'Sector', 'Industry',
+           'EquityQuery','Screener']

yfinance/const.py

@@ -304,4 +304,258 @@ SECTOR_INDUSTY_MAPPING = {
                     'utilities-regulated-gas',
                     'utilities-independent-power-producers',
                     'utilities-regulated-water'}
+}
+EQUITY_SCREENER_EQ_MAP = {
+    "region": {
+        "za", "ve", "vn", "us", "tw", "th", "tr", "sr", "sg", "sa", "se", "ru", "ro", "qa", "pt", "pk", "pl",
+        "ph", "nz", "nl", "mx", "pe", "no", "my", "lv", "lt", "kw", "jp", "is", "il", "lk", "kr", "it", "in",
+        "ie", "hu", "id", "hk", "gb", "fi", "eg", "dk", "gr", "fr", "es", "ee", "de", "cz", "cl", "ca", "be",
+        "at", "cn", "br", "au", "ar", "ch"
+    },
+    "sector": {
+        "Basic Materials", "Industrials", "Communication Services", "Healthcare",
+        "Real Estate", "Technology", "Energy", "Utilities", "Financial Services",
+        "Consumer Defensive", "Consumer Cyclical"
+    },
+    "exchanges": {
+        "NMS", "NAS", "YHD", "NYQ", "NGM", "NCM", "BSE"
+    },
+    "peer_group": {
+        "US Fund Equity Energy",
+        "US CE Convertibles",
+        "EAA CE UK Large-Cap Equity",
+        "EAA CE Other",
+        "US Fund Financial",
+        "India CE Multi-Cap",
+        "US Fund Foreign Large Blend",
+        "US Fund Consumer Cyclical",
+        "EAA Fund Global Equity Income",
+        "China Fund Sector Equity Financial and Real Estate",
+        "US Fund Equity Precious Metals",
+        "EAA Fund RMB Bond - Onshore",
+        "China Fund QDII Greater China Equity",
+        "US Fund Large Growth",
+        "EAA Fund Germany Equity",
+        "EAA Fund Hong Kong Equity",
+        "EAA CE UK Small-Cap Equity",
+        "US Fund Natural Resources",
+        "US CE Preferred Stock",
+        "India Fund Sector - Financial Services",
+        "US Fund Diversified Emerging Mkts",
+        "EAA Fund South Africa & Namibia Equity",
+        "China Fund QDII Sector Equity",
+        "EAA CE Sector Equity Biotechnology",
+        "EAA Fund Switzerland Equity",
+        "US Fund Large Value",
+        "EAA Fund Asia ex-Japan Equity",
+        "US Fund Health",
+        "US Fund China Region",
+        "EAA Fund Emerging Europe ex-Russia Equity",
+        "EAA Fund Sector Equity Industrial Materials",
+        "EAA Fund Japan Large-Cap Equity",
+        "EAA Fund EUR Corporate Bond",
+        "US Fund Technology",
+        "EAA CE Global Large-Cap Blend Equity",
+        "Mexico Fund Mexico Equity",
+        "US Fund Trading--Leveraged Equity",
+        "EAA Fund Sector Equity Consumer Goods & Services",
+        "US Fund Large Blend",
+        "EAA Fund Global Flex-Cap Equity",
+        "EAA Fund EUR Aggressive Allocation - Global",
+        "EAA Fund China Equity",
+        "EAA Fund Global Large-Cap Growth Equity",
+        "US CE Options-based",
+        "EAA Fund Sector Equity Financial Services",
+        "EAA Fund Europe Large-Cap Blend Equity",
+        "EAA Fund China Equity - A Shares",
+        "EAA Fund USD Corporate Bond",
+        "EAA Fund Eurozone Large-Cap Equity",
+        "China Fund Aggressive Allocation Fund",
+        "EAA Fund Sector Equity Technology",
+        "EAA Fund Global Emerging Markets Equity",
+        "EAA Fund EUR Moderate Allocation - Global",
+        "EAA Fund Other Bond",
+        "EAA Fund Denmark Equity",
+        "EAA Fund US Large-Cap Blend Equity",
+        "India Fund Large-Cap",
+        "Paper & Forestry",
+        "Containers & Packaging",
+        "US Fund Miscellaneous Region",
+        "Energy Services",
+        "EAA Fund Other Equity",
+        "Homebuilders",
+        "Construction Materials",
+        "China Fund Equity Funds",
+        "Steel",
+        "Consumer Durables",
+        "EAA Fund Global Large-Cap Blend Equity",
+        "Transportation Infrastructure",
+        "Precious Metals",
+        "Building Products",
+        "Traders & Distributors",
+        "Electrical Equipment",
+        "Auto Components",
+        "Construction & Engineering",
+        "Aerospace & Defense",
+        "Refiners & Pipelines",
+        "Diversified Metals",
+        "Textiles & Apparel",
+        "Industrial Conglomerates",
+        "Household Products",
+        "Commercial Services",
+        "Food Retailers",
+        "Semiconductors",
+        "Media",
+        "Automobiles",
+        "Consumer Services",
+        "Technology Hardware",
+        "Transportation",
+        "Telecommunication Services",
+        "Oil & Gas Producers",
+        "Machinery",
+        "Retailing",
+        "Healthcare",
+        "Chemicals",
+        "Food Products",
+        "Diversified Financials",
+        "Real Estate",
+        "Insurance",
+        "Utilities",
+        "Pharmaceuticals",
+        "Software & Services",
+        "Banks"
+    }
+}
+EQUITY_SCREENER_FIELDS = {
+    # EQ Fields
+    "region",
+    "sector",
+    "peer_group",
+    "exchanges",
+
+    # price
+    "eodprice",
+    "intradaypricechange",
+    "lastclosemarketcap.lasttwelvemonths",
+    "percentchange",
+    "lastclose52weekhigh.lasttwelvemonths",
+    "fiftytwowkpercentchange",
+    "intradayprice",
+    "lastclose52weeklow.lasttwelvemonths",
+    "intradaymarketcap",
+
+    # trading
+    "beta",
+    "avgdailyvol3m",
+    "pctheldinsider",
+    "pctheldinst",
+    "dayvolume",
+    "eodvolume",
+
+    # short interest
+    "short_percentage_of_shares_outstanding.value",
+    "short_interest.value",
+    "short_percentage_of_float.value",
+    "days_to_cover_short.value",
+    "short_interest_percentage_change.value",
+
+    # valuation
+    "bookvalueshare.lasttwelvemonths",
+    "lastclosemarketcaptotalrevenue.lasttwelvemonths",
+    "lastclosetevtotalrevenue.lasttwelvemonths",
+    "pricebookratio.quarterly",
+    "peratio.lasttwelvemonths",
+    "lastclosepricetangiblebookvalue.lasttwelvemonths",
+    "lastclosepriceearnings.lasttwelvemonths",
+    "pegratio_5y",
+
+    # profitability
+    "consecutive_years_of_dividend_growth_count",
+    "returnonassets.lasttwelvemonths",
+    "returnonequity.lasttwelvemonths",
+    "forward_dividend_per_share",
+    "forward_dividend_yield",
+    "returnontotalcapital.lasttwelvemonths",
+
+    # leverage
+    "lastclosetevebit.lasttwelvemonths",
+    "netdebtebitda.lasttwelvemonths",
+    "totaldebtequity.lasttwelvemonths",
+    "ltdebtequity.lasttwelvemonths",
+    "ebitinterestexpense.lasttwelvemonths",
+    "ebitdainterestexpense.lasttwelvemonths",
+    "lastclosetevebitda.lasttwelvemonths",
+    "totaldebtebitda.lasttwelvemonths",
+
+    # liquidity
+    "quickratio.lasttwelvemonths",
+    "altmanzscoreusingtheaveragestockinformationforaperiod.lasttwelvemonths",
+    "currentratio.lasttwelvemonths",
+    "operatingcashflowtocurrentliabilities.lasttwelvemonths",
+
+    # income statement
+    "totalrevenues.lasttwelvemonths",
+    "netincomemargin.lasttwelvemonths",
+    "grossprofit.lasttwelvemonths",
+    "ebitda1yrgrowth.lasttwelvemonths",
+    "dilutedepscontinuingoperations.lasttwelvemonths",
+    "quarterlyrevenuegrowth.quarterly",
+    "epsgrowth.lasttwelvemonths",
+    "netincomeis.lasttwelvemonths",
+    "ebitda.lasttwelvemonths",
+    "dilutedeps1yrgrowth.lasttwelvemonths",
+    "totalrevenues1yrgrowth.lasttwelvemonths",
+    "operatingincome.lasttwelvemonths",
+    "netincome1yrgrowth.lasttwelvemonths",
+    "grossprofitmargin.lasttwelvemonths",
+    "ebitdamargin.lasttwelvemonths",
+    "ebit.lasttwelvemonths",
+    "basicepscontinuingoperations.lasttwelvemonths",
+    "netepsbasic.lasttwelvemonths"
+    "netepsdiluted.lasttwelvemonths",
+
+    # balance sheet
+    "totalassets.lasttwelvemonths",
+    "totalcommonsharesoutstanding.lasttwelvemonths",
+    "totaldebt.lasttwelvemonths",
+    "totalequity.lasttwelvemonths",
+    "totalcurrentassets.lasttwelvemonths",
+    "totalcashandshortterminvestments.lasttwelvemonths",
+    "totalcommonequity.lasttwelvemonths",
+    "totalcurrentliabilities.lasttwelvemonths",
+    "totalsharesoutstanding",
+
+    # cash flow
+    "forward_dividend_yield",
+    "leveredfreecashflow.lasttwelvemonths",
+    "capitalexpenditure.lasttwelvemonths",
+    "cashfromoperations.lasttwelvemonths",
+    "leveredfreecashflow1yrgrowth.lasttwelvemonths",
+    "unleveredfreecashflow.lasttwelvemonths",
+    "cashfromoperations1yrgrowth.lasttwelvemonths",
+
+    # ESG
+    "esg_score",
+    "environmental_score",
+    "governance_score",
+    "social_score",
+    "highest_controversy"
+}
+
+PREDEFINED_SCREENER_BODY_MAP = {
+    'aggressive_small_caps': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]},{"operator":"or","operands":[{"operator":"LT","operands":["epsgrowth.lasttwelvemonths",15]}]}]},"userId":"","userIdType":"guid"},
+    'day_gainers': {"offset":0,"size":25,"sortField":"percentchange","sortType":"DESC","quoteType":"EQUITY","query":{"operator":"AND","operands":[{"operator":"gt","operands":["percentchange",3]},{"operator":"eq","operands":["region","us"]},{"operator":"or","operands":[{"operator":"BTWN","operands":["intradaymarketcap",2000000000,10000000000]},{"operator":"BTWN","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"GT","operands":["intradaymarketcap",100000000000]}]},{"operator":"gte","operands":["intradayprice",5]},{"operator":"gt","operands":["dayvolume",15000]}]},"userId":"","userIdType":"guid"},
+    'day_losers': {"offset":0,"size":25,"sortField":"percentchange","sortType":"ASC","quoteType":"EQUITY","query":{"operator":"AND","operands":[{"operator":"lt","operands":["percentchange",-2.5]},{"operator":"eq","operands":["region","us"]},{"operator":"or","operands":[{"operator":"BTWN","operands":["intradaymarketcap",2000000000,10000000000]},{"operator":"BTWN","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"GT","operands":["intradaymarketcap",100000000000]}]},{"operator":"gte","operands":["intradayprice",5]},{"operator":"gt","operands":["dayvolume",20000]}]},"userId":"","userIdType":"guid"},
+    'growth_technology_stocks': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"BTWN","operands":["quarterlyrevenuegrowth.quarterly",50,100]},{"operator":"GT","operands":["quarterlyrevenuegrowth.quarterly",100]},{"operator":"BTWN","operands":["quarterlyrevenuegrowth.quarterly",25,50]}]},{"operator":"or","operands":[{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",25,50]},{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",50,100]},{"operator":"GT","operands":["epsgrowth.lasttwelvemonths",100]}]},{"operator":"eq","operands":["sector","Technology"]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
+    'most_actives': {"offset":0,"size":25,"sortField":"dayvolume","sortType":"DESC","quoteType":"EQUITY","query":{"operator":"AND","operands":[{"operator":"eq","operands":["region","us"]},{"operator":"or","operands":[{"operator":"BTWN","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"GT","operands":["intradaymarketcap",100000000000]},{"operator":"BTWN","operands":["intradaymarketcap",2000000000,10000000000]}]},{"operator":"gt","operands":["dayvolume",5000000]}]},"userId":"","userIdType":"guid"},
+    'most_shorted_stocks': {"size":25,"offset":0,"sortField":"short_percentage_of_shares_outstanding.value","sortType":"DESC","quoteType":"EQUITY","topOperator":"AND","query":{"operator":"AND","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["region","us"]}]},{"operator":"gt","operands":["intradayprice",1]},{"operator":"gt","operands":["avgdailyvol3m",200000]}]},"userId":"","userIdType":"guid"},
+    'small_cap_gainers': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"lt","operands":["intradaymarketcap",2000000000]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
+    'undervalued_growth_stocks': {"offset":0,"size":25,"sortType":"DESC","sortField":"eodvolume","quoteType":"EQUITY","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"BTWN","operands":["peratio.lasttwelvemonths",0,20]}]},{"operator":"or","operands":[{"operator":"LT","operands":["pegratio_5y",1]}]},{"operator":"or","operands":[{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",25,50]},{"operator":"BTWN","operands":["epsgrowth.lasttwelvemonths",50,100]},{"operator":"GT","operands":["epsgrowth.lasttwelvemonths",100]}]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
+    'undervalued_large_caps': {"offset":0,"size":25,"sortField":"eodvolume","sortType":"desc","quoteType":"equity","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"BTWN","operands":["peratio.lasttwelvemonths",0,20]}]},{"operator":"lt","operands":["pegratio_5y",1]},{"operator":"btwn","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NMS"]},{"operator":"eq","operands":["exchange","NYQ"]}]}]},"userId":"","userIdType":"guid"},
+    'conservative_foreign_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Foreign Large Value"]},{"operator":"EQ","operands":["categoryname","Foreign Large Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Large Growth"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Growth"]},{"operator":"EQ","operands":["categoryname","Foreign Large Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Blend"]},{"operator":"EQ","operands":["categoryname","Foreign Small/Mid Value"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",4]},{"operator":"EQ","operands":["performanceratingoverall",5]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"EQ","operands":["riskratingoverall",1]},{"operator":"EQ","operands":["riskratingoverall",3]},{"operator":"EQ","operands":["riskratingoverall",2]}]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
+    'high_yield_bond': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",4]},{"operator":"EQ","operands":["performanceratingoverall",5]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"EQ","operands":["riskratingoverall",1]},{"operator":"EQ","operands":["riskratingoverall",3]},{"operator":"EQ","operands":["riskratingoverall",2]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","High Yield Bond"]}]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
+    'portfolio_anchors': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Large Blend"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",4]},{"operator":"EQ","operands":["performanceratingoverall",5]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
+    'solid_large_growth_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Large Growth"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",5]},{"operator":"EQ","operands":["performanceratingoverall",4]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
+    'solid_midcap_growth_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"fundnetassets","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"or","operands":[{"operator":"EQ","operands":["categoryname","Mid-Cap Growth"]}]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",5]},{"operator":"EQ","operands":["performanceratingoverall",4]}]},{"operator":"lt","operands":["initialinvestment",100001]},{"operator":"lt","operands":["annualreturnnavy1categoryrank",50]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"},
+    'top_mutual_funds': {"offset":0,"size":25,"sortType":"DESC","sortField":"percentchange","quoteType":"MUTUALFUND","query":{"operator":"and","operands":[{"operator":"gt","operands":["intradayprice",15]},{"operator":"or","operands":[{"operator":"EQ","operands":["performanceratingoverall",5]},{"operator":"EQ","operands":["performanceratingoverall",4]}]},{"operator":"gt","operands":["initialinvestment",1000]},{"operator":"or","operands":[{"operator":"eq","operands":["exchange","NAS"]}]}]},"userId":"","userIdType":"guid"}
 }

yfinance/data.py

@@ -330,6 +330,14 @@ class YfData(metaclass=SingletonMeta):
 
     @utils.log_indent_decorator
     def get(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        return self._make_request(url, request_method = self._session.get, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
+    
+    @utils.log_indent_decorator
+    def post(self, url, body, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        return self._make_request(url, request_method = self._session.post, user_agent_headers=user_agent_headers, body=body, params=params, proxy=proxy, timeout=timeout)
+    
+    @utils.log_indent_decorator
+    def _make_request(self, url, request_method, user_agent_headers=None, body=None, params=None, proxy=None, timeout=30):
         # Important: treat input arguments as immutable.
 
         if len(url) > 200:
@@ -363,7 +371,11 @@ class YfData(metaclass=SingletonMeta):
             'timeout': timeout,
             'headers': user_agent_headers or self.user_agent_headers
         }
-        response = self._session.get(**request_args)
+
+        if body:
+            request_args['json'] = body
+            
+        response = request_method(**request_args)
         utils.get_yf_logger().debug(f'response code={response.status_code}')
         if response.status_code >= 400:
             # Retry with other cookie strategy
@@ -375,7 +387,7 @@ class YfData(metaclass=SingletonMeta):
             request_args['params']['crumb'] = crumb
             if strategy == 'basic':
                 request_args['cookies'] = {cookie.name: cookie.value}
-            response = self._session.get(**request_args)
+            response = request_method(**request_args)
             utils.get_yf_logger().debug(f'response code={response.status_code}')
 
         return response
@@ -397,4 +409,4 @@ class YfData(metaclass=SingletonMeta):
         utils.get_yf_logger().debug(f'get_raw_json(): {url}')
         response = self.get(url, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
         response.raise_for_status()
-        return response.json()
+        return response.json()

yfinance/multi.py

@@ -200,10 +200,6 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             if (shared._DFS[tkr] is not None) and (shared._DFS[tkr].shape[0] > 0):
                 shared._DFS[tkr].index = shared._DFS[tkr].index.tz_localize(None)
 
-    if len(tickers) == 1:
-        ticker = tickers[0]
-        return shared._DFS[ticker]
-
     try:
         data = _pd.concat(shared._DFS.values(), axis=1, sort=True,
                           keys=shared._DFS.keys(), names=['Ticker', 'Price'])

yfinance/scrapers/funds.py

@@ -14,7 +14,7 @@ Supports ETF and Mutual Funds Data
 Queried Modules: quoteType, summaryProfile, fundProfile, topHoldings
 
 Notes: 
-- fundPerformance module is not implemented as better data is queriable using history
+- fundPerformance module is not implemented as better data is queryable using history
 '''
 class FundsData:
     def __init__(self, data: YfData, symbol: str, proxy=None):

yfinance/scrapers/history.py

@@ -3,6 +3,7 @@ import dateutil as _dateutil
 import logging
 import numpy as np
 import pandas as pd
+from math import isclose
 import time as _time
 import bisect
 
@@ -145,7 +146,7 @@ class PriceHistory:
         params["interval"] = interval.lower()
         params["includePrePost"] = prepost
 
-        # 1) fix weired bug with Yahoo! - returning 60m for 30m bars
+        # 1) fix weird bug with Yahoo! - returning 60m for 30m bars
         if params["interval"] == "30m":
             params["interval"] = "15m"
 
@@ -253,9 +254,15 @@ class PriceHistory:
             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]}')
+        if quotes.empty:
+            msg = f'{self.ticker}: yfinance received OHLC data: EMPTY'
+        elif len(quotes) == 1:
+            msg = f'{self.ticker}: yfinance received OHLC data: {quotes.index[0]} only'
+        else:
+            msg = f'{self.ticker}: yfinance received OHLC data: {quotes.index[0]} -> {quotes.index[-1]}'
+        logger.debug(msg)
 
-        # 2) fix weired bug with Yahoo! - returning 60m for 30m bars
+        # 2) fix weird 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')
@@ -290,7 +297,13 @@ class PriceHistory:
                 self._history_metadata_formatted = True
                 tps = self._history_metadata["tradingPeriods"]
             quotes = utils.fix_Yahoo_returning_prepost_unrequested(quotes, params["interval"], tps)
-        logger.debug(f'{self.ticker}: OHLC after cleaning: {quotes.index[0]} -> {quotes.index[-1]}')
+        if quotes.empty:
+            msg = f'{self.ticker}: OHLC after cleaning: EMPTY'
+        elif len(quotes) == 1:
+            msg = f'{self.ticker}: OHLC after cleaning: {quotes.index[0]} only'
+        else:
+            msg = f'{self.ticker}: OHLC after cleaning: {quotes.index[0]} -> {quotes.index[-1]}'
+        logger.debug(msg)
 
         # actions
         dividends, splits, capital_gains = utils.parse_actions(data["chart"]["result"][0])
@@ -353,7 +366,13 @@ class PriceHistory:
                 df.loc[df["Capital Gains"].isna(), "Capital Gains"] = 0
             else:
                 df["Capital Gains"] = 0.0
-        logger.debug(f'{self.ticker}: OHLC after combining events: {quotes.index[0]} -> {quotes.index[-1]}')
+        if df.empty:
+            msg = f'{self.ticker}: OHLC after combining events: EMPTY'
+        elif len(df) == 1:
+            msg = f'{self.ticker}: OHLC after combining events: {df.index[0]} only'
+        else:
+            msg = f'{self.ticker}: OHLC after combining events: {df.index[0]} -> {df.index[-1]}'
+        logger.debug(msg)
 
         df = utils.fix_Yahoo_returning_live_separate(df, params["interval"], tz_exchange, repair=repair, currency=currency)
 
@@ -425,7 +444,13 @@ class PriceHistory:
         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 df.empty:
+            msg = f'{self.ticker}: yfinance returning OHLC: EMPTY'
+        elif len(df) == 1:
+            msg = f'{self.ticker}: yfinance returning OHLC: {df.index[0]} only'
+        else:
+            msg = f'{self.ticker}: yfinance returning OHLC: {df.index[0]} -> {df.index[-1]}'
+        logger.debug(msg)
 
         if self._reconstruct_start_interval is not None and self._reconstruct_start_interval == interval:
             self._reconstruct_start_interval = None
@@ -682,7 +707,7 @@ class PriceHistory:
             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}", extra=log_extras)
-            # Temp disable erors printing
+            # Temp disable errors printing
             logger = utils.get_yf_logger()
             if hasattr(logger, 'level'):
                 # YF's custom indented logger doesn't expose level
@@ -1352,9 +1377,26 @@ class PriceHistory:
             # 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
+            if isclose(df2['Low'].iloc[div_idx], df2['Close'].iloc[div_idx-1]*100, rel_tol = 0.025):
+                # Price has jumped ~100x on ex-div day, need to fix immediately.
+                drop_c2l = df2['Close'].iloc[div_idx-1]*100 - df2['Low'].iloc[div_idx]
+                div_pct = div / (df2['Close'].iloc[div_idx-1]*100)
+                true_adjust = 1.0 - div / (df2['Close'].iloc[div_idx-1]*100)
+                present_adj = df2['Adj Close'].iloc[div_idx-1] / df2['Close'].iloc[div_idx-1]
+                if not isclose(present_adj, true_adjust, rel_tol = 0.025):
+                    df2.loc[:dt-_datetime.timedelta(seconds=1), 'Adj Close'] = true_adjust * df2['Close'].loc[:dt-_datetime.timedelta(seconds=1)]
+                    df2.loc[:dt-_datetime.timedelta(seconds=1), 'Repaired?'] = True
+            elif isclose(df2['Low'].iloc[div_idx], df2['Close'].iloc[div_idx-1]*0.01, rel_tol = 0.025):
+                # Price has dropped ~100x on ex-div day, need to fix immediately.
+                drop_c2l = df2['Close'].iloc[div_idx-1]*0.01 - df2['Low'].iloc[div_idx]
+                div_pct = div / (df2['Close'].iloc[div_idx-1]*0.01)
+                true_adjust = 1.0 - div / (df2['Close'].iloc[div_idx-1]*100)
+                present_adj = df2['Adj Close'].iloc[div_idx-1] / df2['Close'].iloc[div_idx-1]
+                if not isclose(present_adj, true_adjust, rel_tol = 0.025):
+                    df2.loc[:dt-_datetime.timedelta(seconds=1), 'Adj Close'] = true_adjust * df2['Close'].loc[:dt-_datetime.timedelta(seconds=1)]
+                    df2.loc[:dt-_datetime.timedelta(seconds=1), 'Repaired?'] = True
+            else:
+                drop_c2l = df2['Close'].iloc[div_idx-1] - df2['Low'].iloc[div_idx]
             drop = drop_c2l
             if div_idx < len(df2)-1:
                 # # In low-volume scenarios, the price drop is day after not today.
@@ -1364,8 +1406,10 @@ class PriceHistory:
                 # 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()
+                # drops = df2['Close'].iloc[div_idx-1:div_idx+1].to_numpy() - df2['Low'].iloc[div_idx:div_idx+2].to_numpy()
+                drops = np.array([drop, df2['Close'].iloc[div_idx] - df2['Low'].iloc[div_idx+1]])
                 drop_2Dmax = np.max(drops)
             else:
                 drops = np.array([drop])
@@ -1578,7 +1622,7 @@ class PriceHistory:
                     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
+                            # Both this and previous are anomalous, so mark smallest drop as phantom
                             drop = div['drop']
                             drop_prev = prev_div['drop']
                             if drop > 1.5*drop_prev:
@@ -1594,7 +1638,7 @@ class PriceHistory:
                     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
+                            # Both this and previous are anomalous, so mark smallest drop as phantom
                             drop = div['drop']
                             drop_next = next_div['drop']
                             if drop > 1.5*drop_next:
@@ -1704,21 +1748,27 @@ class PriceHistory:
                 adjDeltas = x['Adj Low'].iloc[1:].to_numpy() - x['Adj Close'].iloc[:-1].to_numpy()
                 adjDeltas = np.append([0.0], adjDeltas)
                 x['adjDelta'] = adjDeltas
+                for i in np.where(x['Dividends']>0)[0]:
+                    x.loc[x.index[i], 'adjDelta'] += x['Dividends'].iloc[i]*x['Adj'].iloc[i]
                 deltas = x[['delta', 'adjDelta']]
-                if div_pct > 0.15 and div_pct < 1.0:  # avoid analysing impossibly-big dividends here
+                if div_pct > 0.05 and div_pct < 1.0:
                     adjDiv = div * x['Adj'].iloc[0]
                     f = deltas['adjDelta'] > (adjDiv*0.6)
                     if f.any():
-                        for idx in np.where(f)[0]:
-                            adjDelta_max_drop_idx = idx
-                            adjDelta_max_drop = deltas['adjDelta'].iloc[idx]
-                            if adjDelta_max_drop > 1.001*deltas['delta'].iloc[adjDelta_max_drop_idx]:
+                        indices = np.where(f)[0]
+                        for idx in indices:
+                            adjDelta_drop = deltas['adjDelta'].iloc[idx]
+                            if adjDelta_drop > 1.001*deltas['delta'].iloc[idx]:
                                 # Adjusted price has risen by more than unadjusted, should not happen.
                                 # See if Adjusted price later falls by a similar amount. This would mean
                                 # dividend has been applied too early.
-                                ratios = (-1*deltas['adjDelta'])/adjDelta_max_drop
+                                ratios = (-1*deltas['adjDelta'])/adjDelta_drop
                                 f_near1_or_above = ratios>=0.8
-                                if f_near1_or_above.any():
+                                # Update: only check for wrong date if no coincident split.
+                                # Because if a split, more likely the div is missing split
+                                split = df2['Stock Splits'].loc[dt]
+                                pre_split = div_status_df['div_pre_split'].loc[dt]
+                                if (split==0.0 or (not pre_split)) and f_near1_or_above.any():
                                     near_indices = np.where(f_near1_or_above)[0]
                                     if len(near_indices) > 1:
                                         penalties = np.zeros(len(near_indices))
@@ -1736,7 +1786,7 @@ class PriceHistory:
                                     div_date_wrong = True
                                     div_true_date = ratios.index[reversal_idx]
                                     break
-                                elif adjDelta_max_drop > 0.39*adjDiv:
+                                elif adjDelta_drop > 0.39*adjDiv:
                                     # Still true that applied adjustment exceeds price action, 
                                     # just not clear what solution is (if any).
                                     div_adj_exceeds_prices = True
@@ -1753,6 +1803,27 @@ class PriceHistory:
             div_status['div_date_wrong'] = div_date_wrong
             div_status['div_true_date'] = div_true_date
 
+            if div_adj_exceeds_prices:
+                split = df2['Stock Splits'].loc[dt]
+                if split != 0.0:
+                    # Check again if div missing split. Use looser tolerance
+                    # as we know the adjustment seems wrong.
+                    div_postSplit = div / split
+                    if div_postSplit > div:
+                        # Use volatility-adjusted drop
+                        typical_volatility = div_status_df['vol'].loc[dt]
+                        drop = div_status_df['drop'].loc[dt]
+                        _drop = drop - typical_volatility
+                    else:
+                        drop_2Dmax = div_status_df['drop_2Dmax'].loc[dt]
+                        _drop = drop_2Dmax
+                    if _drop > 0:
+                        diff = abs(div-_drop)
+                        diff_postSplit = abs(div_postSplit-_drop)
+                        if diff_postSplit <= (diff*1.1):
+                            # possibilities.append({'state':'div-pre-split', 'diff':diff_postSplit})
+                            div_status_df.loc[dt, 'div_pre_split'] = True
+
             for k,v in div_status.items():
                 if k not in div_status_df:
                     if isinstance(v, (bool, np.bool_)):
@@ -1828,7 +1899,7 @@ class PriceHistory:
                     if 'div_date_wrong' in cluster.columns and (cluster[c] == cluster['div_date_wrong']).all():
                         continue
 
-                    if 'adj_exceeds_prices' in cluster.columns and (cluster[c] == cluster['adj_exceeds_prices']).all():
+                    if 'adj_exceeds_prices' in cluster.columns and (cluster[c] == (cluster[c] & cluster['adj_exceeds_prices'])).all():
                         # More likely that true-positive. Maybe the div never happened
                         continue
 
@@ -1852,6 +1923,11 @@ class PriceHistory:
 
                     if pct_fail >= true_threshold:
                         div_status_df.loc[fc, c] = True
+                        if 'div_date_wrong' in div_status_df.columns:
+                            # reset this as well
+                            div_status_df.loc[fc, 'div_date_wrong'] = False
+                            div_status_df.loc[fc, 'div_true_date'] = pd.NaT
+                            cluster = div_status_df[fc].sort_index()
                         continue
                     elif pct_fail <= fals_threshold:
                         div_status_df.loc[fc, c] = False
@@ -1937,6 +2013,11 @@ class PriceHistory:
                         div_too_big = False
                         cluster.loc[dt, 'div_too_big'] = False
                         n_failed_checks -= 1
+                    if div_exceeds_adj:
+                        # false-positive
+                        div_exceeds_adj = False
+                        cluster.loc[dt, 'div_exceeds_adj'] = False
+                        n_failed_checks -= 1
 
                 if div_pre_split:
                     if adj_exceeds_prices:

yfinance/screener/__init__.py

@@ -0,0 +1,4 @@
+from .screener import Screener
+from .screener_query import EquityQuery
+
+__all__ = ['EquityQuery', 'Screener']

yfinance/screener/screener.py

@@ -0,0 +1,105 @@
+from typing import Dict
+
+from yfinance import utils
+from yfinance.data import YfData
+from yfinance.const import _BASE_URL_, PREDEFINED_SCREENER_BODY_MAP
+from .screener_query import Query
+
+_SCREENER_URL_ = f"{_BASE_URL_}/v1/finance/screener"
+
+class Screener:
+    def __init__(self, session=None, proxy=None):
+        self.proxy = proxy
+        self.session = session
+
+        self._data: YfData = YfData(session=session)
+        self._body: Dict = {}
+        self._response: Dict = {}
+        self._body_updated = False
+        self._accepted_body_keys = {"offset","size","sortField","sortType","quoteType","query","userId","userIdType"}
+        self._predefined_bodies = PREDEFINED_SCREENER_BODY_MAP.keys()
+
+    @property
+    def body(self) -> Dict:
+        return self._body
+    
+    @property
+    def response(self) -> Dict:
+        if self._body_updated or self._response is None:
+            self._fetch_and_parse()
+        
+        self._body_updated = False
+        return self._response
+    
+    @property
+    def predefined_bodies(self) -> Dict:
+        return self._predefined_bodies
+
+    def set_default_body(self, query: Query, offset: int = 0, size: int = 100, sortField: str = "ticker", sortType: str = "desc", quoteType: str = "equity", userId: str = "", userIdType: str = "guid") -> None:
+        self._body_updated = True
+
+        self._body = {
+            "offset": offset,
+            "size": size,
+            "sortField": sortField,
+            "sortType": sortType,
+            "quoteType": quoteType,
+            "query": query.to_dict(),
+            "userId": userId,
+            "userIdType": userIdType
+        }
+
+    def set_predefined_body(self, k: str) -> None:
+        body = PREDEFINED_SCREENER_BODY_MAP.get(k, None)
+        if not body:
+            raise ValueError(f'Invalid key {k} provided for predefined screener')
+        
+        self._body_updated = True
+        self._body = body
+
+    def set_body(self, body: Dict) -> None:
+        missing_keys = [key for key in self._accepted_body_keys if key not in body]
+        if missing_keys:
+            raise ValueError(f"Missing required keys in body: {missing_keys}")
+
+        extra_keys = [key for key in body if key not in self._accepted_body_keys]
+        if extra_keys:
+            raise ValueError(f"Body contains extra keys: {extra_keys}")
+
+        self._body_updated = True
+        self._body = body
+
+
+    def patch_body(self, values: Dict) -> None:
+        extra_keys = [key for key in values if key not in self._accepted_body_keys]
+        if extra_keys:
+            raise ValueError(f"Body contains extra keys: {extra_keys}")
+        
+        self._body_updated = True
+        for k in values:
+            self._body[k] = values[k]
+
+    def _validate_body(self) -> None:
+        if not all(k in self._body for k in self._accepted_body_keys):
+            raise ValueError("Missing required keys in body")
+
+    def _fetch(self) -> Dict:
+        params_dict = {"corsDomain": "finance.yahoo.com", "formatted": "false", "lang": "en-US", "region": "US"}
+        response = self._data.post(_SCREENER_URL_, body=self.body, user_agent_headers=self._data.user_agent_headers, params=params_dict, proxy=self.proxy)
+        response.raise_for_status()
+        return response.json()
+    
+    def _fetch_and_parse(self) -> None:
+        response = None
+        self._validate_body()
+        
+        try:
+            response = self._fetch()
+            self._response = response['finance']['result'][0]
+        except Exception as e:
+            logger = utils.get_yf_logger()
+            logger.error(f"Failed to get screener data for '{self._body.get('query', "query not set")}' reason: {e}")
+            logger.debug("Got response: ")
+            logger.debug("-------------")
+            logger.debug(f" {response}")
+            logger.debug("-------------")

yfinance/screener/screener_query.py

@@ -0,0 +1,86 @@
+import numbers
+from typing import List, Union, Dict, Set
+
+from yfinance.const import EQUITY_SCREENER_EQ_MAP, EQUITY_SCREENER_FIELDS
+from yfinance.exceptions import YFNotImplementedError
+
+class Query:
+    def __init__(self, operator: str, operand: Union[numbers.Real, str, List['Query']]):
+        self.operator = operator
+        self.operands = operand
+    
+    def to_dict(self) -> Dict:
+        raise YFNotImplementedError('to_dict() needs to be implemented by children classes')
+
+class EquityQuery(Query):
+    def __init__(self, operator: str, operand: Union[numbers.Real, str, List['EquityQuery']]):
+        operator = operator.upper()
+
+        if not isinstance(operand, list):
+            raise TypeError('Invalid operand type')
+        if len(operand) <= 0:
+            raise ValueError('Invalid field for Screener')
+            
+        if operator in {'OR','AND'}: 
+            self._validate_or_and_operand(operand)
+        elif operator == 'EQ': 
+            self._validate_eq_operand(operand)
+        elif operator == 'BTWN': 
+            self._validate_btwn_operand(operand)
+        elif operator in {'GT','LT'}: 
+            self._validate_gt_lt(operand)
+        else: 
+            raise ValueError('Invalid Operator Value')
+
+        self.operator = operator
+        self.operands = operand
+        self._valid_eq_map = EQUITY_SCREENER_EQ_MAP
+        self._valid_fields = EQUITY_SCREENER_FIELDS
+        
+    @property
+    def valid_eq_map(self) -> Dict:
+        return self._valid_eq_map
+
+    @property
+    def valid_fields(self) -> Set:
+        return self._valid_fields
+    
+    def _validate_or_and_operand(self, operand: List['EquityQuery']) -> None:
+        if len(operand) <= 1: 
+            raise ValueError('Operand must be length longer than 1')
+        if all(isinstance(e, EquityQuery) for e in operand) is False: 
+            raise TypeError('Operand must be type EquityQuery for OR/AND')
+
+    def _validate_eq_operand(self, operand: List[Union[str, numbers.Real]]) -> None:
+        if len(operand) != 2:
+            raise ValueError('Operand must be length 2 for EQ')
+        if operand[0] not in EQUITY_SCREENER_FIELDS:
+            raise ValueError('Invalid field for Screener')
+        if operand[0] not in EQUITY_SCREENER_EQ_MAP:
+            raise ValueError('Invalid EQ key')
+        if operand[1] not in EQUITY_SCREENER_EQ_MAP[operand[0]]:
+            raise ValueError('Invalid EQ value')
+    
+    def _validate_btwn_operand(self, operand: List[Union[str, numbers.Real]]) -> None:
+        if len(operand) != 3: 
+            raise ValueError('Operand must be length 3 for BTWN')
+        if operand[0] not in EQUITY_SCREENER_FIELDS:
+            raise ValueError('Invalid field for Screener')
+        if isinstance(operand[1], numbers.Real) is False:
+            raise TypeError('Invalid comparison type for BTWN')
+        if isinstance(operand[2], numbers.Real) is False:
+            raise TypeError('Invalid comparison type for BTWN')
+
+    def _validate_gt_lt(self, operand: List[Union[str, numbers.Real]]) -> None:
+        if len(operand) != 2:
+            raise ValueError('Operand must be length 2 for GT/LT')
+        if operand[0] not in EQUITY_SCREENER_FIELDS:
+            raise ValueError('Invalid field for Screener')
+        if isinstance(operand[1], numbers.Real) is False:
+            raise TypeError('Invalid comparison type for GT/LT')
+
+    def to_dict(self) -> Dict:
+        return {
+            "operator": self.operator,
+            "operands": [operand.to_dict() if isinstance(operand, EquityQuery) else operand for operand in self.operands]
+        }

yfinance/version.py

@@ -1 +1 @@
-version = "0.2.44"
+version = "0.2.45"