Bump version to 0.2.14

Add failback for decryption error in info interface

.github/ISSUE_TEMPLATE/bug_report.md

@@ -7,7 +7,9 @@ assignees: ''
 
 ---
 
-# READ BEFORE POSTING
+# IMPORTANT
+
+If you want help, you got to read this first, follow the instructions.
 
 ### Are you up-to-date?
 
@@ -23,20 +25,19 @@ and comparing against [PIP](https://pypi.org/project/yfinance/#history).
 
 ### Does Yahoo actually have the data?
 
-Are spelling ticker *exactly* same as Yahoo?
+Are you spelling ticker *exactly* same as Yahoo?
 
-Visit `finance.yahoo.com` and confim they have your data. Maybe your ticker was delisted.
+Then visit `finance.yahoo.com` and confirm they have the data you want. Maybe your ticker was delisted, or your expectations of `yfinance` are wrong.
 
 ### Are you spamming Yahoo?
 
-Yahoo Finance free service has limit on query rate dependent on request - roughly 500/minute for prices, 10/minute for info. Them delaying or blocking your spam is not a bug.
+Yahoo Finance free service has rate-limiting depending on request type - roughly 60/minute for prices, 10/minute for info. Once limit hit, Yahoo can delay, block, or return bad data. Not a `yfinance` bug.
 
 ### Still think it's a bug?
 
-Delete this default message and submit your bug report here, providing the following as best you can:
+Delete this default message (all of it) and submit your bug report here, providing the following as best you can:
 
-- Simple code that reproduces your problem
-- Error message, with traceback if shown
-- Info about your system:
-  - yfinance version
-  - operating system
+- Simple code that reproduces your problem, that we can copy-paste-run
+- Exception message with full traceback, or proof `yfinance` returning bad data
+- `yfinance` version and Python version
+- Operating system type

CHANGELOG.rst

@@ -1,6 +1,39 @@
 Change Log
 ===========
 
+0.2.14
+------
+Fix Ticker.info dict by fetching from API #1461
+
+0.2.13
+------
+Price bug fixes:
+- fetch big-interval with Capital Gains #1455
+- merging dividends & splits with prices #1452
+
+0.2.12
+------
+Disable annoying 'backup decrypt' msg
+
+0.2.11
+------
+Fix history_metadata accesses for unusual symbols #1411
+
+0.2.10
+------
+General
+- allow using sqlite3 < 3.8.2 #1380
+- add another backup decrypt option #1379
+Prices
+- restore original download() timezone handling #1385
+- fix & improve price repair #1289 2a2928b 86d6acc
+- drop intraday intervals if in post-market but prepost=False #1311
+Info
+- fast_info improvements:
+  - add camelCase keys, add dict functions values() & items() #1368
+  - fix fast_info["previousClose"] #1383
+- catch TypeError Exception #1397
+
 0.2.9
 -----
 - Fix fast_info bugs #1362

README.md

@@ -42,12 +42,10 @@ Yahoo! finance API is intended for personal use only.**
 
 ---
 
-## What's new in version 0.2
+## News [2023-01-27]
+Since December 2022 Yahoo has been encrypting the web data that `yfinance` scrapes for non-market data. Fortunately the decryption keys are available, although Yahoo moved/changed them several times hence `yfinance` breaking several times. `yfinance` is now better prepared for any future changes by Yahoo.
 
-- Optimised web scraping
-- All 3 financials tables now match website so expect keys to change. If you really want old tables, use [`Ticker.get_[income_stmt|balance_sheet|cashflow](legacy=True, ...)`](https://github.com/ranaroussi/yfinance/blob/85783da515761a145411d742c2a8a3c1517264b0/yfinance/base.py#L968)
-- price data improvements: fix bug NaN rows with dividend; new repair feature for missing or 100x prices `download(repair=True)`; new attribute `Ticker.history_metadata`
-[See release notes for full list of changes](https://github.com/ranaroussi/yfinance/releases/tag/0.2.1)
+Why is Yahoo doing this? We don't know. Is it to stop scrapers? Maybe, so we've implemented changes to reduce load on Yahoo. In December we rolled out version 0.2 with optimised scraping. Then in 0.2.6 introduced `Ticker.fast_info`, providing much faster access to some `info` elements wherever possible e.g. price stats and forcing users to switch (sorry but we think necessary). `info` will continue to exist for as long as there are elements without a fast alternative.
 
 ## Quick Start
 
@@ -60,33 +58,28 @@ import yfinance as yf
 
 msft = yf.Ticker("MSFT")
 
-# fast access to subset of stock info
-msft.basic_info
-# slow access to all stock info
+# get all stock info (slow)
 msft.info
+# fast access to subset of stock info (opportunistic)
+msft.fast_info
 
 # get historical market data
-hist = msft.history(period="max")
+hist = msft.history(period="1mo")
 
 # show meta information about the history (requires history() to be called first)
 msft.history_metadata
 
 # show actions (dividends, splits, capital gains)
 msft.actions
-
-# show dividends
 msft.dividends
-
-# show splits
 msft.splits
-
-
-# show capital gains (for mutual funds & etfs)
-msft.capital_gains
+msft.capital_gains  # only for mutual funds & etfs
 
 # show share count
+# - yearly summary:
 msft.shares
-msft.get_shares_full()
+# - accurate time-series count:
+msft.get_shares_full(start="2022-01-01", end=None)
 
 # show financials:
 # - income statement
@@ -100,13 +93,9 @@ msft.cashflow
 msft.quarterly_cashflow
 # see `Ticker.get_income_stmt()` for more options
 
-# show major holders
+# show holders
 msft.major_holders
-
-# show institutional holders
 msft.institutional_holders
-
-# show mutualfund holders
 msft.mutualfund_holders
 
 # show earnings
@@ -165,19 +154,6 @@ msft.option_chain(..., proxy="PROXY_SERVER")
 ...
 ```
 
-To use a custom `requests` session (for example to cache calls to the
-API or customize the `User-agent` header), pass a `session=` argument to
-the Ticker constructor.
-
-```python
-import requests_cache
-session = requests_cache.CachedSession('yfinance.cache')
-session.headers['User-agent'] = 'my-program/1.0'
-ticker = yf.Ticker('msft', session=session)
-# The scraped response will be stored in the cache
-ticker.actions
-```
-
 To initialize multiple `Ticker` objects, use
 
 ```python
@@ -198,62 +174,47 @@ import yfinance as yf
 data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
 ```
 
-I've also added some options to make life easier :)
+`yf.download()` and `Ticker.history()` have many options for configuring fetching and processing, e.g.:
 
 ```python
-data = yf.download(  # or pdr.get_data_yahoo(...
-        # tickers list or string as well
-        tickers = "SPY AAPL MSFT",
-
-        # use "period" instead of start/end
-        # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
-        # (optional, default is '1mo')
-        period = "ytd",
-
-        # fetch data by interval (including intraday if period < 60 days)
-        # valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
-        # (optional, default is '1d')
-        interval = "5d",
-
-        # Whether to ignore timezone when aligning ticker data from 
-        # different timezones. Default is False.
-        ignore_tz = False,
-
-        # group by ticker (to access via data['SPY'])
-        # (optional, default is 'column')
-        group_by = 'ticker',
-
-        # adjust all OHLC automatically
-        # (optional, default is False)
-        auto_adjust = True,
-
-        # attempt repair of missing data or currency mixups e.g. $/cents
-        repair = False,
-
-        # download pre/post regular market hours data
-        # (optional, default is False)
-        prepost = True,
-
-        # use threads for mass downloading? (True/False/Integer)
-        # (optional, default is True)
-        threads = True,
-
-        # proxy URL scheme use use when downloading?
-        # (optional, default is None)
-        proxy = None
-    )
+yf.download(tickers = "SPY AAPL",  # list of tickers
+            period = "1y",         # time period
+            interval = "1d",       # trading interval
+            ignore_tz = True,      # ignore timezone when aligning data from different exchanges?
+            prepost = False)       # download pre/post market hours data?
 ```
 
-### Timezone cache store
+Review the [Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
+
+### Smarter scraping
+
+To use a custom `requests` session (for example to cache calls to the
+API or customize the `User-agent` header), pass a `session=` argument to
+the Ticker constructor.
 
-When fetching price data, all dates are localized to stock exchange timezone. 
-But timezone retrieval is relatively slow, so yfinance attemps to cache them 
-in your users cache folder. 
-You can direct cache to use a different location with `set_tz_cache_location()`:
 ```python
-import yfinance as yf
-yf.set_tz_cache_location("custom/cache/location")
-...
+import requests_cache
+session = requests_cache.CachedSession('yfinance.cache')
+session.headers['User-agent'] = 'my-program/1.0'
+ticker = yf.Ticker('msft', session=session)
+# The scraped response will be stored in the cache
+ticker.actions
+```
+
+Combine a `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-limiter/blocker that can corrupt data.
+```python
+from requests import Session
+from requests_cache import CacheMixin, SQLiteCache
+from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
+from pyrate_limiter import Duration, RequestRate, Limiter
+class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
+    pass
+
+session = CachedLimiterSession(
+    limiter=Limiter(RequestRate(2, Duration.SECOND*5),  # max 2 requests per 5 seconds
+    bucket_class=MemoryQueueBucket,
+    backend=SQLiteCache("yfinance.cache"),
+)
 ```
 
 ### Managing Multi-Level Columns
@@ -271,6 +232,18 @@ yfinance?](https://stackoverflow.com/questions/63107801)
         -   How to download single or multiple tickers into a single
             dataframe with single level column names and a ticker column
 
+### Timezone cache store
+
+When fetching price data, all dates are localized to stock exchange timezone. 
+But timezone retrieval is relatively slow, so yfinance attemps to cache them 
+in your users cache folder. 
+You can direct cache to use a different location with `set_tz_cache_location()`:
+```python
+import yfinance as yf
+yf.set_tz_cache_location("custom/cache/location")
+...
+```
+
 ---
 
 ## `pandas_datareader` override

meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.2.9" %}
+{% set version = "0.2.14" %}
 
 package:
   name: "{{ name|lower }}"

tests/prices.py

@@ -24,9 +24,7 @@ class TestPriceHistory(unittest.TestCase):
 
     def test_daily_index(self):
         tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
-
         intervals = ["1d", "1wk", "1mo"]
-
         for tkr in tkrs:
             dat = yf.Ticker(tkr, session=self.session)
 
@@ -44,8 +42,8 @@ class TestPriceHistory(unittest.TestCase):
 
             dt_utc = _tz.timezone("UTC").localize(_dt.datetime.utcnow())
             dt = dt_utc.astimezone(_tz.timezone(tz))
-
-            df = dat.history(start=dt.date() - _dt.timedelta(days=1), interval="1h")
+            start_d = dt.date() - _dt.timedelta(days=7)
+            df = dat.history(start=start_d, interval="1h")
 
             dt0 = df.index[-2]
             dt1 = df.index[-1]
@@ -55,7 +53,6 @@ class TestPriceHistory(unittest.TestCase):
                 print("Ticker = ", tkr)
                 raise
 
-
     def test_duplicatingDaily(self):
         tkrs = ["IMP.JO", "BHG.JO", "SSW.JO", "BP.L", "INTC"]
         test_run = False
@@ -110,22 +107,27 @@ class TestPriceHistory(unittest.TestCase):
     def test_intraDayWithEvents(self):
         # TASE dividend release pre-market, doesn't merge nicely with intra-day data so check still present
 
-        tkr = "ICL.TA"
-        # tkr = "ESLT.TA"
-        # tkr = "ONE.TA"
-        # tkr = "MGDL.TA"
-        start_d = _dt.date.today() - _dt.timedelta(days=60)
-        end_d = None
-        df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
-        df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
-        if df_daily_divs.shape[0] == 0:
-            self.skipTest("Skipping test_intraDayWithEvents() because 'ICL.TA' has no dividend in last 60 days")
+        tase_tkrs = ["ICL.TA", "ESLT.TA", "ONE.TA", "MGDL.TA"]
+        test_run = False
+        for tkr in tase_tkrs:
+            start_d = _dt.date.today() - _dt.timedelta(days=59)
+            end_d = None
+            df_daily = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="1d", actions=True)
+            df_daily_divs = df_daily["Dividends"][df_daily["Dividends"] != 0]
+            if df_daily_divs.shape[0] == 0:
+                # self.skipTest("Skipping test_intraDayWithEvents() because 'ICL.TA' has no dividend in last 60 days")
+                continue
 
-        last_div_date = df_daily_divs.index[-1]
-        start_d = last_div_date.date()
-        end_d = last_div_date.date() + _dt.timedelta(days=1)
-        df = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
-        self.assertTrue((df["Dividends"] != 0.0).any())
+            last_div_date = df_daily_divs.index[-1]
+            start_d = last_div_date.date()
+            end_d = last_div_date.date() + _dt.timedelta(days=1)
+            df = yf.Ticker(tkr, session=self.session).history(start=start_d, end=end_d, interval="15m", actions=True)
+            self.assertTrue((df["Dividends"] != 0.0).any())
+            test_run = True
+            break
+
+        if not test_run:
+            self.skipTest("Skipping test_intraDayWithEvents() because no tickers had a dividend in last 60 days")
 
     def test_dailyWithEvents(self):
         # Reproduce issue #521
@@ -228,9 +230,13 @@ class TestPriceHistory(unittest.TestCase):
                 print("{}-without-events missing these dates: {}".format(tkr, missing_from_df2))
                 raise
 
+    def test_monthlyWithEvents2(self):
+        # Simply check no exception from internal merge
+        tkr = "ABBV"
+        yf.Ticker("ABBV").history(period="max", interval="1mo")
+
     def test_tz_dst_ambiguous(self):
         # Reproduce issue #1100
-
         try:
             yf.Ticker("ESLT.TA", session=self.session).history(start="2002-10-06", end="2002-10-09", interval="1d")
         except _tz.exceptions.AmbiguousTimeError:
@@ -261,6 +267,116 @@ class TestPriceHistory(unittest.TestCase):
             print("Weekly data not aligned to Monday")
             raise
 
+    def test_prune_post_intraday_us(self):
+        # Half-day before USA Thanksgiving. Yahoo normally 
+        # returns an interval starting when regular trading closes, 
+        # even if prepost=False.
+
+        # Setup
+        tkr = "AMZN"
+        interval = "1h"
+        interval_td = _dt.timedelta(hours=1)
+        time_open = _dt.time(9, 30)
+        time_close = _dt.time(16)
+        special_day = _dt.date(2022, 11, 25)
+        time_early_close = _dt.time(13)
+        dat = yf.Ticker(tkr, session=self.session)
+
+        # Run
+        start_d = special_day - _dt.timedelta(days=7)
+        end_d = special_day + _dt.timedelta(days=7)
+        df = dat.history(start=start_d, end=end_d, interval=interval, prepost=False, keepna=True)
+        tg_last_dt = df.loc[str(special_day)].index[-1]
+        self.assertTrue(tg_last_dt.time() < time_early_close)
+
+        # Test no other afternoons (or mornings) were pruned
+        start_d = _dt.date(special_day.year, 1, 1)
+        end_d = _dt.date(special_day.year+1, 1, 1)
+        df = dat.history(start=start_d, end=end_d, interval="1h", prepost=False, keepna=True)
+        last_dts = _pd.Series(df.index).groupby(df.index.date).last()
+        f_early_close = (last_dts+interval_td).dt.time < time_close
+        early_close_dates = last_dts.index[f_early_close].values
+        self.assertEqual(len(early_close_dates), 1)
+        self.assertEqual(early_close_dates[0], special_day)
+
+        first_dts = _pd.Series(df.index).groupby(df.index.date).first()
+        f_late_open = first_dts.dt.time > time_open
+        late_open_dates = first_dts.index[f_late_open]
+        self.assertEqual(len(late_open_dates), 0)
+
+    def test_prune_post_intraday_omx(self):
+        # Half-day before Sweden Christmas. Yahoo normally 
+        # returns an interval starting when regular trading closes, 
+        # even if prepost=False.
+        # If prepost=False, test that yfinance is removing prepost intervals.
+
+        # Setup
+        tkr = "AEC.ST"
+        interval = "1h"
+        interval_td = _dt.timedelta(hours=1)
+        time_open = _dt.time(9)
+        time_close = _dt.time(17,30)
+        special_day = _dt.date(2022, 12, 23)
+        time_early_close = _dt.time(13, 2)
+        dat = yf.Ticker(tkr, session=self.session)
+
+        # Half trading day Jan 5, Apr 14, May 25, Jun 23, Nov 4, Dec 23, Dec 30
+        half_days = [_dt.date(special_day.year, x[0], x[1]) for x in [(1,5), (4,14), (5,25), (6,23), (11,4), (12,23), (12,30)]]
+
+        # Yahoo has incorrectly classified afternoon of 2022-04-13 as post-market.
+        # Nothing yfinance can do because Yahoo doesn't return data with prepost=False.
+        # But need to handle in this test.
+        expected_incorrect_half_days = [_dt.date(2022,4,13)]
+        half_days = sorted(half_days+expected_incorrect_half_days)
+
+        # Run
+        start_d = special_day - _dt.timedelta(days=7)
+        end_d = special_day + _dt.timedelta(days=7)
+        df = dat.history(start=start_d, end=end_d, interval=interval, prepost=False, keepna=True)
+        tg_last_dt = df.loc[str(special_day)].index[-1]
+        self.assertTrue(tg_last_dt.time() < time_early_close)
+
+        # Test no other afternoons (or mornings) were pruned
+        start_d = _dt.date(special_day.year, 1, 1)
+        end_d = _dt.date(special_day.year+1, 1, 1)
+        df = dat.history(start=start_d, end=end_d, interval="1h", prepost=False, keepna=True)
+        last_dts = _pd.Series(df.index).groupby(df.index.date).last()
+        f_early_close = (last_dts+interval_td).dt.time < time_close
+        early_close_dates = last_dts.index[f_early_close].values
+        unexpected_early_close_dates = [d for d in early_close_dates if not d in half_days]
+        self.assertEqual(len(unexpected_early_close_dates), 0)
+        self.assertEqual(len(early_close_dates), len(half_days))
+        self.assertTrue(_np.equal(early_close_dates, half_days).all())
+
+        first_dts = _pd.Series(df.index).groupby(df.index.date).first()
+        f_late_open = first_dts.dt.time > time_open
+        late_open_dates = first_dts.index[f_late_open]
+        self.assertEqual(len(late_open_dates), 0)
+
+    def test_prune_post_intraday_asx(self):
+        # Setup
+        tkr = "BHP.AX"
+        interval = "1h"
+        interval_td = _dt.timedelta(hours=1)
+        time_open = _dt.time(10)
+        time_close = _dt.time(16,12)
+        # No early closes in 2022
+        dat = yf.Ticker(tkr, session=self.session)
+
+        # Test no afternoons (or mornings) were pruned
+        start_d = _dt.date(2022, 1, 1)
+        end_d = _dt.date(2022+1, 1, 1)
+        df = dat.history(start=start_d, end=end_d, interval="1h", prepost=False, keepna=True)
+        last_dts = _pd.Series(df.index).groupby(df.index.date).last()
+        f_early_close = (last_dts+interval_td).dt.time < time_close
+        early_close_dates = last_dts.index[f_early_close].values
+        self.assertEqual(len(early_close_dates), 0)
+
+        first_dts = _pd.Series(df.index).groupby(df.index.date).first()
+        f_late_open = first_dts.dt.time > time_open
+        late_open_dates = first_dts.index[f_late_open]
+        self.assertEqual(len(late_open_dates), 0)
+
     def test_weekly_2rows_fix(self):
         tkr = "AMZN"
         start = _dt.date.today() - _dt.timedelta(days=14)
@@ -270,11 +386,53 @@ class TestPriceHistory(unittest.TestCase):
         df = dat.history(start=start, interval="1wk")
         self.assertTrue((df.index.weekday == 0).all())
 
+    def test_aggregate_capital_gains(self):
+        # Setup
+        tkr = "FXAIX"
+        dat = yf.Ticker(tkr, session=self.session)
+        start = "2017-12-31"
+        end = "2019-12-31"
+        interval = "3mo"
+
+        df = dat.history(start=start, end=end, interval=interval)
+
+class TestPriceRepair(unittest.TestCase):
+    session = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.session = requests_cache.CachedSession(backend='memory')
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.session is not None:
+            cls.session.close()
+
+    def test_reconstruct_2m(self):
+        # 2m repair requires 1m data.
+        # Yahoo restricts 1m fetches to 7 days max within last 30 days.
+        # Need to test that '_reconstruct_intervals_batch()' can handle this.
+
+        tkrs = ["BHP.AX", "IMP.JO", "BP.L", "PNL.L", "INTC"]
+
+        dt_now = _pd.Timestamp.utcnow()
+        td_7d = _dt.timedelta(days=7)
+        td_60d = _dt.timedelta(days=60)
+
+        # Round time for 'requests_cache' reuse
+        dt_now = dt_now.ceil("1h")
+
+        for tkr in tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
+            end_dt = dt_now
+            start_dt = end_dt - td_60d
+            df = dat.history(start=start_dt, end=end_dt, interval="2m", repair=True)
+
     def test_repair_100x_weekly(self):
         # Setup:
         tkr = "PNL.L"
         dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
         data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
         df = _pd.DataFrame(data={"Open":      [470.5, 473.5, 474.5, 470],
@@ -283,22 +441,22 @@ class TestPriceHistory(unittest.TestCase):
                                  "Close":     [475,   473.5, 472,   473.5],
                                  "Adj Close": [475,   473.5, 472,   473.5],
                                  "Volume": [2295613, 2245604, 3000287, 2635611]},
-                                index=_pd.to_datetime([_dt.date(2022, 10, 23),
-                                                       _dt.date(2022, 10, 16),
-                                                       _dt.date(2022, 10, 9),
-                                                       _dt.date(2022, 10, 2)]))
+                                index=_pd.to_datetime([_dt.date(2022, 10, 24),
+                                                       _dt.date(2022, 10, 17),
+                                                       _dt.date(2022, 10, 10),
+                                                       _dt.date(2022, 10, 3)]))
         df = df.sort_index()
         df.index.name = "Date"
         df_bad = df.copy()
-        df_bad.loc["2022-10-23", "Close"] *= 100
-        df_bad.loc["2022-10-16", "Low"] *= 100
-        df_bad.loc["2022-10-2", "Open"] *= 100
+        df_bad.loc["2022-10-24", "Close"] *= 100
+        df_bad.loc["2022-10-17", "Low"] *= 100
+        df_bad.loc["2022-10-03", "Open"] *= 100
         df.index = df.index.tz_localize(tz_exchange)
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
 
         # Run test
 
-        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange)
+        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
@@ -326,7 +484,7 @@ class TestPriceHistory(unittest.TestCase):
 
         tkr = "PNL.L"
         dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
         data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
         df = _pd.DataFrame(data={"Open":      [400,   398,    392.5,   417],
@@ -353,7 +511,7 @@ class TestPriceHistory(unittest.TestCase):
         df.index = df.index.tz_localize(tz_exchange)
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
 
-        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange)
+        df_repaired = dat._fix_unit_mixups(df_bad, "1wk", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
@@ -381,7 +539,7 @@ class TestPriceHistory(unittest.TestCase):
     def test_repair_100x_daily(self):
         tkr = "PNL.L"
         dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
         data_cols = ["Low", "High", "Open", "Close", "Adj Close"]
         df = _pd.DataFrame(data={"Open":      [478,    476,   476,   472],
@@ -403,7 +561,7 @@ class TestPriceHistory(unittest.TestCase):
         df.index = df.index.tz_localize(tz_exchange)
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
 
-        df_repaired = dat._fix_unit_mixups(df_bad, "1d", tz_exchange)
+        df_repaired = dat._fix_unit_mixups(df_bad, "1d", tz_exchange, prepost=False)
 
         # First test - no errors left
         for c in data_cols:
@@ -423,7 +581,7 @@ class TestPriceHistory(unittest.TestCase):
     def test_repair_zeroes_daily(self):
         tkr = "BBIL.L"
         dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
         df_bad = _pd.DataFrame(data={"Open":      [0,      102.04, 102.04],
                                      "High":      [0,      102.1,  102.11],
@@ -438,7 +596,7 @@ class TestPriceHistory(unittest.TestCase):
         df_bad.index.name = "Date"
         df_bad.index = df_bad.index.tz_localize(tz_exchange)
 
-        repaired_df = dat._fix_zeroes(df_bad, "1d", tz_exchange)
+        repaired_df = dat._fix_zeroes(df_bad, "1d", tz_exchange, prepost=False)
 
         correct_df = df_bad.copy()
         correct_df.loc["2022-11-01", "Open"] = 102.080002
@@ -450,40 +608,31 @@ class TestPriceHistory(unittest.TestCase):
     def test_repair_zeroes_hourly(self):
         tkr = "INTC"
         dat = yf.Ticker(tkr, session=self.session)
-        tz_exchange = dat.info["exchangeTimezoneName"]
+        tz_exchange = dat.fast_info["timezone"]
 
-        df_bad = _pd.DataFrame(data={"Open":      [29.68, 29.49, 29.545, _np.nan, 29.485],
-                                     "High":      [29.68, 29.625, 29.58, _np.nan, 29.49],
-                                     "Low":       [29.46, 29.4, 29.45, _np.nan, 29.31],
-                                     "Close":     [29.485, 29.545, 29.485, _np.nan, 29.325],
-                                     "Adj Close": [29.485, 29.545, 29.485, _np.nan, 29.325],
-                                     "Volume": [3258528, 2140195, 1621010, 0, 0]},
-                                index=_pd.to_datetime([_dt.datetime(2022,11,25, 9,30),
-                                                       _dt.datetime(2022,11,25, 10,30),
-                                                       _dt.datetime(2022,11,25, 11,30),
-                                                       _dt.datetime(2022,11,25, 12,30),
-                                                       _dt.datetime(2022,11,25, 13,00)]))
-        df_bad = df_bad.sort_index()
-        df_bad.index.name = "Date"
-        df_bad.index = df_bad.index.tz_localize(tz_exchange)
+        correct_df = dat.history(period="1wk", interval="1h", auto_adjust=False, repair=True)
 
-        repaired_df = dat._fix_zeroes(df_bad, "1h", tz_exchange)
+        df_bad = correct_df.copy()
+        bad_idx = correct_df.index[10]
+        df_bad.loc[bad_idx, "Open"] = _np.nan
+        df_bad.loc[bad_idx, "High"] = _np.nan
+        df_bad.loc[bad_idx, "Low"] = _np.nan
+        df_bad.loc[bad_idx, "Close"] = _np.nan
+        df_bad.loc[bad_idx, "Adj Close"] = _np.nan
+        df_bad.loc[bad_idx, "Volume"] = 0
+
+        repaired_df = dat._fix_zeroes(df_bad, "1h", tz_exchange, prepost=False)
 
-        correct_df = df_bad.copy()
-        idx = _pd.Timestamp(2022,11,25, 12,30).tz_localize(tz_exchange)
-        correct_df.loc[idx, "Open"] = 29.485001
-        correct_df.loc[idx, "High"] = 29.49
-        correct_df.loc[idx, "Low"] = 29.43
-        correct_df.loc[idx, "Close"] = 29.455
-        correct_df.loc[idx, "Adj Close"] = 29.455
-        correct_df.loc[idx, "Volume"] = 609164
         for c in ["Open", "Low", "High", "Close"]:
             try:
                 self.assertTrue(_np.isclose(repaired_df[c], correct_df[c], rtol=1e-7).all())
             except:
                 print("COLUMN", c)
+                print("- repaired_df")
                 print(repaired_df)
+                print("- correct_df[c]:")
                 print(correct_df[c])
+                print("- diff:")
                 print(repaired_df[c] - correct_df[c])
                 raise
 

tests/ticker.py

@@ -52,12 +52,16 @@ class TestTicker(unittest.TestCase):
     def test_badTicker(self):
         # Check yfinance doesn't die when ticker delisted
 
-        tkr = "AM2Z.TA"
+        tkr = "DJI"  # typo of "^DJI"
         dat = yf.Ticker(tkr, session=self.session)
         dat.history(period="1wk")
         dat.history(start="2022-01-01")
         dat.history(start="2022-01-01", end="2022-03-01")
         yf.download([tkr], period="1wk")
+
+        for k in dat.fast_info:
+            dat.fast_info[k]
+
         dat.isin
         dat.major_holders
         dat.institutional_holders
@@ -91,43 +95,48 @@ class TestTicker(unittest.TestCase):
     def test_goodTicker(self):
         # that yfinance works when full api is called on same instance of ticker
 
-        tkr = "IBM"
-        dat = yf.Ticker(tkr, session=self.session)
+        tkrs = ["IBM"]
+        tkrs.append("QCSTIX")  # weird ticker, no price history but has previous close
+        for tkr in tkrs:
+            dat = yf.Ticker(tkr, session=self.session)
 
-        dat.isin
-        dat.major_holders
-        dat.institutional_holders
-        dat.mutualfund_holders
-        dat.dividends
-        dat.splits
-        dat.actions
-        dat.shares
-        dat.get_shares_full()
-        dat.info
-        dat.calendar
-        dat.recommendations
-        dat.earnings
-        dat.quarterly_earnings
-        dat.income_stmt
-        dat.quarterly_income_stmt
-        dat.balance_sheet
-        dat.quarterly_balance_sheet
-        dat.cashflow
-        dat.quarterly_cashflow
-        dat.recommendations_summary
-        dat.analyst_price_target
-        dat.revenue_forecasts
-        dat.sustainability
-        dat.options
-        dat.news
-        dat.earnings_trend
-        dat.earnings_dates
-        dat.earnings_forecasts
+            dat.history(period="1wk")
+            dat.history(start="2022-01-01")
+            dat.history(start="2022-01-01", end="2022-03-01")
+            yf.download([tkr], period="1wk")
 
-        dat.history(period="1wk")
-        dat.history(start="2022-01-01")
-        dat.history(start="2022-01-01", end="2022-03-01")
-        yf.download([tkr], period="1wk")
+            for k in dat.fast_info:
+                dat.fast_info[k]
+
+            dat.isin
+            dat.major_holders
+            dat.institutional_holders
+            dat.mutualfund_holders
+            dat.dividends
+            dat.splits
+            dat.actions
+            dat.shares
+            dat.get_shares_full()
+            dat.info
+            dat.calendar
+            dat.recommendations
+            dat.earnings
+            dat.quarterly_earnings
+            dat.income_stmt
+            dat.quarterly_income_stmt
+            dat.balance_sheet
+            dat.quarterly_balance_sheet
+            dat.cashflow
+            dat.quarterly_cashflow
+            dat.recommendations_summary
+            dat.analyst_price_target
+            dat.revenue_forecasts
+            dat.sustainability
+            dat.options
+            dat.news
+            dat.earnings_trend
+            dat.earnings_dates
+            dat.earnings_forecasts
 
 
 class TestTickerHistory(unittest.TestCase):
@@ -682,6 +691,7 @@ class TestTickerInfo(unittest.TestCase):
         self.symbols += ["ESLT.TA", "BP.L", "GOOGL"]
         self.symbols.append("QCSTIX")  # good for testing, doesn't trade
         self.symbols += ["BTC-USD", "IWO", "VFINX", "^GSPC"]
+        self.symbols += ["SOKE.IS", "ADS.DE"]  # detected bugs
         self.tickers = [yf.Ticker(s, session=self.session) for s in self.symbols]
 
     def tearDown(self):
@@ -702,6 +712,10 @@ class TestTickerInfo(unittest.TestCase):
         fast_info_keys = sorted(list(fast_info_keys))
 
         key_rename_map = {}
+        key_rename_map["currency"] = "currency"
+        key_rename_map["quote_type"] = "quoteType"
+        key_rename_map["timezone"] = "exchangeTimezoneName"
+
         key_rename_map["last_price"] = ["currentPrice", "regularMarketPrice"]
         key_rename_map["open"] = ["open", "regularMarketOpen"]
         key_rename_map["day_high"] = ["dayHigh", "regularMarketDayHigh"]
@@ -709,11 +723,9 @@ class TestTickerInfo(unittest.TestCase):
         key_rename_map["previous_close"] = ["previousClose"]
         key_rename_map["regular_market_previous_close"] = ["regularMarketPreviousClose"]
 
-        # preMarketPrice
-
         key_rename_map["fifty_day_average"] = "fiftyDayAverage"
         key_rename_map["two_hundred_day_average"] = "twoHundredDayAverage"
-        key_rename_map["year_change"] = "52WeekChange"
+        key_rename_map["year_change"] = ["52WeekChange", "fiftyTwoWeekChange"]
         key_rename_map["year_high"] = "fiftyTwoWeekHigh"
         key_rename_map["year_low"] = "fiftyTwoWeekLow"
 
@@ -722,23 +734,29 @@ class TestTickerInfo(unittest.TestCase):
         key_rename_map["three_month_average_volume"] = "averageVolume"
 
         key_rename_map["market_cap"] = "marketCap"
-        key_rename_map["shares"] = "floatShares"
-        key_rename_map["timezone"] = "exchangeTimezoneName"
+        key_rename_map["shares"] = "sharesOutstanding"
 
-        approximate_keys = {"fifty_day_average", "ten_day_average_volume"}
-        approximate_keys.update({"market_cap"})
+        for k in list(key_rename_map.keys()):
+            if '_' in k:
+                key_rename_map[yf.utils.snake_case_2_camelCase(k)] = key_rename_map[k]
 
-        # bad_keys = []
+        # Note: share count items in info[] are bad. Sometimes the float > outstanding!
+        # So often fast_info["shares"] does not match. 
+        # Why isn't fast_info["shares"] wrong? Because using it to calculate market cap always correct.
         bad_keys = {"shares"}
 
         # Loose tolerance for averages, no idea why don't match info[]. Is info wrong?
         custom_tolerances = {}
+        custom_tolerances["year_change"] = 1.0
         # custom_tolerances["ten_day_average_volume"] = 1e-3
         custom_tolerances["ten_day_average_volume"] = 1e-1
         # custom_tolerances["three_month_average_volume"] = 1e-2
         custom_tolerances["three_month_average_volume"] = 5e-1
         custom_tolerances["fifty_day_average"] = 1e-2
         custom_tolerances["two_hundred_day_average"] = 1e-2
+        for k in list(custom_tolerances.keys()):
+            if '_' in k:
+                custom_tolerances[yf.utils.snake_case_2_camelCase(k)] = custom_tolerances[k]
 
         for k in fast_info_keys:
             if k in key_rename_map:
@@ -756,7 +774,6 @@ class TestTickerInfo(unittest.TestCase):
                         continue
 
                     if k in bad_keys:
-                        # Doesn't match, investigate why
                         continue
 
                     if k in custom_tolerances:
@@ -768,15 +785,22 @@ class TestTickerInfo(unittest.TestCase):
                     correct = ticker.info[m]
                     test = ticker.fast_info[k]
                     # print(f"Testing: symbol={ticker.ticker} m={m} k={k}: test={test} vs correct={correct}")
-                    if k == "market_cap" and ticker.fast_info["currency"] in ["GBp", "ILA"]:
+                    if k in ["market_cap","marketCap"] and ticker.fast_info["currency"] in ["GBp", "ILA"]:
                         # Adjust for currency to match Yahoo:
                         test *= 0.01
-                    if correct is None:
-                        self.assertTrue(test is None or (not np.isnan(test)), f"{k}: {test} must be None or real value because correct={correct}")
-                    elif isinstance(test, float) or isinstance(correct, int):
-                        self.assertTrue(np.isclose(test, correct, rtol=rtol), f"{k}: {test} != {correct}")
-                    else:
-                        self.assertEqual(test, correct, f"{k}: {test} != {correct}")
+                    try:
+                        if correct is None:
+                            self.assertTrue(test is None or (not np.isnan(test)), f"{k}: {test} must be None or real value because correct={correct}")
+                        elif isinstance(test, float) or isinstance(correct, int):
+                            self.assertTrue(np.isclose(test, correct, rtol=rtol), f"{ticker.ticker} {k}: {test} != {correct}")
+                        else:
+                            self.assertEqual(test, correct, f"{k}: {test} != {correct}")
+                    except:
+                        if k in ["regularMarketPreviousClose"] and ticker.ticker in ["ADS.DE"]:
+                            # Yahoo is wrong, is returning post-market close not regular
+                            continue
+                        else:
+                            raise
 
 
 

yfinance/base.py

@@ -23,6 +23,7 @@ from __future__ import print_function
 
 import time as _time
 import datetime as _datetime
+import dateutil as _dateutil
 from typing import Optional
 
 import pandas as _pd
@@ -54,9 +55,12 @@ class FastInfo:
         self._tkr = tickerBaseObject
 
         self._prices_1y = None
+        self._prices_1wk_1h_prepost = None
+        self._prices_1wk_1h_reg = None
         self._md = None
 
         self._currency = None
+        self._quote_type = None
         self._exchange = None
         self._timezone = None
 
@@ -82,31 +86,49 @@ class FastInfo:
         self._10d_avg_vol = None
         self._3mo_avg_vol = None
 
+        # attrs = utils.attributes(self)
+        # self.keys = attrs.keys()
+        # utils.attributes is calling each method, bad! Have to hardcode
+        _properties = ["currency", "quote_type", "exchange", "timezone"]
+        _properties += ["shares", "market_cap"]
+        _properties += ["last_price", "previous_close", "open", "day_high", "day_low"]
+        _properties += ["regular_market_previous_close"]
+        _properties += ["last_volume"]
+        _properties += ["fifty_day_average", "two_hundred_day_average", "ten_day_average_volume", "three_month_average_volume"]
+        _properties += ["year_high", "year_low", "year_change"]
+
+        # Because released before fixing key case, need to officially support 
+        # camel-case but also secretly support snake-case
+        base_keys = [k for k in _properties if not '_' in k]
+
+        sc_keys = [k for k in _properties if '_' in k]
+
+        self._sc_to_cc_key = {k:utils.snake_case_2_camelCase(k) for k in sc_keys}
+        self._cc_to_sc_key = {v:k for k,v in self._sc_to_cc_key.items()}
+ 
+        self._public_keys = sorted(base_keys + list(self._sc_to_cc_key.values()))
+        self._keys = sorted(self._public_keys + sc_keys)
+
     # dict imitation:
     def keys(self):
-        # attrs = utils.attributes(self)
-        # return attrs.keys()
-        # utils.attributes is calling each method, bad!
-        # Have to hardcode
-        keys = ["currency", "exchange", "timezone"]
-        keys += ["shares", "market_cap"]
-        keys += ["last_price", "previous_close", "open", "day_high", "day_low"]
-        keys += ["regular_market_previous_close"]
-        keys += ["last_volume"]
-        keys += ["fifty_day_average", "two_hundred_day_average", "ten_day_average_volume", "three_month_average_volume"]
-        keys += ["year_high", "year_low", "year_change"]
-        return keys
+        return self._public_keys
     def items(self):
-        return [(k,self[k]) for k in self.keys()]
+        return [(k,self[k]) for k in self._public_keys]
+    def values(self):
+        return [self[k] for k in self._public_keys]
     def get(self, key, default=None):
         if key in self.keys():
+            if key in self._cc_to_sc_key:
+                key = self._cc_to_sc_key[key]
             return self[key]
         return default
     def __getitem__(self, k):
         if not isinstance(k, str):
             raise KeyError(f"key must be a string")
-        if not k in self.keys():
+        if not k in self._keys:
             raise KeyError(f"'{k}' not valid key. Examine 'FastInfo.keys()'")
+        if k in self._cc_to_sc_key:
+            k = self._cc_to_sc_key[k]
         return getattr(self, k)
     def __contains__(self, k):
         return k in self.keys()
@@ -124,7 +146,7 @@ class FastInfo:
 
     def _get_1y_prices(self, fullDaysOnly=False):
         if self._prices_1y is None:
-            self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, debug=False)
+            self._prices_1y = self._tkr.history(period="380d", auto_adjust=False, debug=False, keepna=True)
             self._md = self._tkr.get_history_metadata()
             try:
                 ctp = self._md["currentTradingPeriod"]
@@ -140,12 +162,23 @@ class FastInfo:
         if self._prices_1y.empty:
             return self._prices_1y
 
-        dt1 = self._prices_1y.index[-1]
+        dnow = pd.Timestamp.utcnow().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():
             # Exclude today
-            dt1 -= utils._interval_to_timedelta("1h")
-        dt0 = dt1 - utils._interval_to_timedelta("1y") + utils._interval_to_timedelta("1d")
-        return self._prices_1y.loc[dt0:dt1]
+            d1 -= utils._interval_to_timedelta("1d")
+        return self._prices_1y.loc[str(d0):str(d1)]
+
+    def _get_1wk_1h_prepost_prices(self):
+        if self._prices_1wk_1h_prepost is None:
+            self._prices_1wk_1h_prepost = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=True, debug=False)
+        return self._prices_1wk_1h_prepost
+
+    def _get_1wk_1h_reg_prices(self):
+        if self._prices_1wk_1h_reg is None:
+            self._prices_1wk_1h_reg = self._tkr.history(period="1wk", interval="1h", auto_adjust=False, prepost=False, debug=False)
+        return self._prices_1wk_1h_reg
 
     def _get_exchange_metadata(self):
         if self._md is not None:
@@ -189,6 +222,17 @@ class FastInfo:
         self._currency = md["currency"]
         return self._currency
 
+    @property
+    def quote_type(self):
+        if self._quote_type is not None:
+            return self._quote_type
+
+        if self._tkr._history_metadata is None:
+            self._get_1y_prices()
+        md = self._tkr.get_history_metadata()
+        self._quote_type = md["instrumentType"]
+        return self._quote_type
+
     @property
     def exchange(self):
         if self._exchange is not None:
@@ -226,25 +270,39 @@ class FastInfo:
             return self._last_price
         prices = self._get_1y_prices()
         if prices.empty:
-            self._last_price = self._get_exchange_metadata()["regularMarketPrice"]
+            md = self._get_exchange_metadata()
+            if "regularMarketPrice" in md:
+                self._last_price = md["regularMarketPrice"]
         else:
             self._last_price = float(prices["Close"].iloc[-1])
+            if _np.isnan(self._last_price):
+                md = self._get_exchange_metadata()
+                if "regularMarketPrice" in md:
+                    self._last_price = md["regularMarketPrice"]
         return self._last_price
 
     @property
     def previous_close(self):
         if self._prev_close is not None:
             return self._prev_close
-        prices = self._get_1y_prices()
+        prices = self._get_1wk_1h_prepost_prices()
+        fail = False
         if prices.empty:
-            # Very few symbols have previousClose despite no 
-            # no trading data. E.g. 'QCSTIX'.
-            # So fallback to original info[] if available.
-            self._tkr.info  # trigger fetch
-            if "previousClose" in self._tkr._quote._retired_info:
-                self._prev_close = self._tkr._quote._retired_info["previousClose"]
+            fail = True
         else:
-            self._prev_close = float(prices["Close"].iloc[-2])
+            prices = prices[["Close"]].groupby(prices.index.date).last()
+            if prices.shape[0] < 2:
+                # Very few symbols have previousClose despite no 
+                # no trading data e.g. 'QCSTIX'.
+                fail = True
+            else:
+                self._prev_close = float(prices["Close"].iloc[-2])
+        if fail:
+            # Fallback to original info[] if available.
+            self._tkr.info  # trigger fetch
+            k = "previousClose"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._prev_close = self._tkr._quote._retired_info[k]
         return self._prev_close
 
     @property
@@ -252,13 +310,19 @@ class FastInfo:
         if self._reg_prev_close is not None:
             return self._reg_prev_close
         prices = self._get_1y_prices()
-        if prices.empty:
+        if prices.shape[0] == 1:
+            # Tiny % of tickers don't return daily history before last trading day, 
+            # so backup option is hourly history:
+            prices = self._get_1wk_1h_reg_prices()
+            prices = prices[["Close"]].groupby(prices.index.date).last()
+        if prices.shape[0] < 2:
             # Very few symbols have regularMarketPreviousClose despite no 
             # no trading data. E.g. 'QCSTIX'.
             # So fallback to original info[] if available.
             self._tkr.info  # trigger fetch
-            if "regularMarketPreviousClose" in self._tkr._quote._retired_info:
-                self._reg_prev_close = self._tkr._quote._retired_info["regularMarketPreviousClose"]
+            k = "regularMarketPreviousClose"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._reg_prev_close = self._tkr._quote._retired_info[k]
         else:
             self._reg_prev_close = float(prices["Close"].iloc[-2])
         return self._reg_prev_close
@@ -268,7 +332,12 @@ class FastInfo:
         if self._open is not None:
             return self._open
         prices = self._get_1y_prices()
-        self._open = None if prices.empty else float(prices["Open"].iloc[-1])
+        if prices.empty:
+            self._open = None
+        else:
+            self._open = float(prices["Open"].iloc[-1])
+            if _np.isnan(self._open):
+                self._open = None
         return self._open
 
     @property
@@ -276,7 +345,12 @@ class FastInfo:
         if self._day_high is not None:
             return self._day_high
         prices = self._get_1y_prices()
-        self._day_high = None if prices.empty else float(prices["High"].iloc[-1])
+        if prices.empty:
+            self._day_high = None
+        else:
+            self._day_high = float(prices["High"].iloc[-1])
+            if _np.isnan(self._day_high):
+                self._day_high = None
         return self._day_high
 
     @property
@@ -284,7 +358,12 @@ class FastInfo:
         if self._day_low is not None:
             return self._day_low
         prices = self._get_1y_prices()
-        self._day_low = None if prices.empty else float(prices["Low"].iloc[-1])
+        if prices.empty:
+            self._day_low = None
+        else:
+            self._day_low = float(prices["Low"].iloc[-1])
+            if _np.isnan(self._day_low):
+                self._day_low = None
         return self._day_low
 
     @property
@@ -371,6 +450,8 @@ class FastInfo:
             return self._year_high
 
         prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            prices = self._get_1y_prices(fullDaysOnly=False)
         self._year_high = float(prices["High"].max())
         return self._year_high
 
@@ -380,6 +461,8 @@ class FastInfo:
             return self._year_low
 
         prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            prices = self._get_1y_prices(fullDaysOnly=False)
         self._year_low = float(prices["Low"].min())
         return self._year_low
 
@@ -389,8 +472,9 @@ class FastInfo:
             return self._year_change
 
         prices = self._get_1y_prices(fullDaysOnly=True)
-        self._year_change = (prices["Close"].iloc[-1] - prices["Close"].iloc[0]) / prices["Close"].iloc[0]
-        self._year_change = float(self._year_change)
+        if prices.shape[0] >= 2:
+            self._year_change = (prices["Close"].iloc[-1] - prices["Close"].iloc[0]) / prices["Close"].iloc[0]
+            self._year_change = float(self._year_change)
         return self._year_change
 
     @property
@@ -411,8 +495,9 @@ class FastInfo:
             # E.g. 'BTC-USD'
             # So fallback to original info[] if available.
             self._tkr.info
-            if "marketCap" in self._tkr._quote._retired_info:
-                self._mcap = self._tkr._quote._retired_info["marketCap"]
+            k = "marketCap"
+            if self._tkr._quote._retired_info is not None and k in self._tkr._quote._retired_info:
+                self._mcap = self._tkr._quote._retired_info[k]
         else:
             self._mcap = float(shares * self.last_price)
         return self._mcap
@@ -471,11 +556,13 @@ class TickerBase:
                 Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
                 Intraday data cannot extend last 60 days
             start: str
-                Download start date string (YYYY-MM-DD) or _datetime.
+                Download start date string (YYYY-MM-DD) or _datetime, inclusive.
                 Default is 1900-01-01
+                E.g. for start="2020-01-01", the first data point will be on "2020-01-01"
             end: str
-                Download end date string (YYYY-MM-DD) or _datetime.
+                Download end date string (YYYY-MM-DD) or _datetime, exclusive.
                 Default is now
+                E.g. for end="2023-01-01", the last data point will be on "2022-12-31"
             prepost : bool
                 Include Pre and Post market data in results?
                 Default is False
@@ -483,8 +570,9 @@ class TickerBase:
                 Adjust all OHLC automatically? Default is True
             back_adjust: bool
                 Back-adjusted data to mimic true historical prices
-            repair: bool
-                Detect currency unit 100x mixups and attempt repair
+            repair: bool or "silent"
+                Detect currency unit 100x mixups and attempt repair.
+                If True, fix & print summary. If "silent", just fix.
                 Default is False
             keepna: bool
                 Keep NaN rows returned by Yahoo?
@@ -587,6 +675,7 @@ class TickerBase:
             self._history_metadata = data["chart"]["result"][0]["meta"]
         except Exception:
             self._history_metadata = {}
+        self._history_metadata = utils.format_history_metadata(self._history_metadata)
 
         err_msg = "No data found for this date range, symbol may be delisted"
         fail = False
@@ -663,36 +752,37 @@ class TickerBase:
         quotes = utils.set_df_tz(quotes, params["interval"], tz_exchange)
         quotes = utils.fix_Yahoo_dst_issue(quotes, params["interval"])
         quotes = utils.fix_Yahoo_returning_live_separate(quotes, params["interval"], tz_exchange)
+        intraday = params["interval"][-1] in ("m", 'h')
+        if not prepost and intraday and "tradingPeriods" in self._history_metadata:
+            quotes = utils.fix_Yahoo_returning_prepost_unrequested(quotes, params["interval"], self._history_metadata)
 
         # actions
         dividends, splits, capital_gains = utils.parse_actions(data["chart"]["result"][0])
         if not expect_capital_gains:
             capital_gains = None
 
-        if start is not None:
-            # Note: use pandas Timestamp as datetime.utcfromtimestamp has bugs on windows
-            # https://github.com/python/cpython/issues/81708
-            startDt = _pd.Timestamp(start, unit='s')
-            if dividends is not None:
-                dividends = dividends[dividends.index>=startDt]
-            if capital_gains is not None:
-                capital_gains = capital_gains[capital_gains.index>=startDt]
-            if splits is not None:
-                splits = splits[splits.index >= startDt]
-        if end is not None:
-            endDt = _pd.Timestamp(end, unit='s')
-            if dividends is not None:
-                dividends = dividends[dividends.index<endDt]
-            if capital_gains is not None:
-                capital_gains = capital_gains[capital_gains.index<endDt]
-            if splits is not None:
-                splits = splits[splits.index < endDt]
         if splits is not None:
             splits = utils.set_df_tz(splits, interval, tz_exchange)
         if dividends is not None:
             dividends = utils.set_df_tz(dividends, interval, tz_exchange)
         if capital_gains is not None:
             capital_gains = utils.set_df_tz(capital_gains, interval, tz_exchange)
+        if start is not None:
+            startDt = quotes.index[0].floor('D')
+            if dividends is not None:
+                dividends = dividends.loc[startDt:]
+            if capital_gains is not None:
+                capital_gains = capital_gains.loc[startDt:]
+            if splits is not None:
+                splits = splits.loc[startDt:]
+        if end is not None:
+            endDt = _pd.Timestamp(end, unit='s').tz_localize(tz)
+            if dividends is not None:
+                dividends = dividends[dividends.index < endDt]
+            if capital_gains is not None:
+                capital_gains = capital_gains[capital_gains.index < endDt]
+            if splits is not None:
+                splits = splits[splits.index < endDt]
 
         # Prepare for combine
         intraday = params["interval"][-1] in ("m", 'h')
@@ -727,10 +817,10 @@ class TickerBase:
             else:
                 df["Capital Gains"] = 0.0
 
-        if repair:
+        if repair==True or repair=="silent":
             # Do this before auto/back adjust
-            df = self._fix_zeroes(df, interval, tz_exchange)
-            df = self._fix_unit_mixups(df, interval, tz_exchange)
+            df = self._fix_zeroes(df, interval, tz_exchange, prepost, silent=(repair=="silent"))
+            df = self._fix_unit_mixups(df, interval, tz_exchange, prepost, silent=(repair=="silent"))
 
         # Auto/back adjust
         try:
@@ -774,31 +864,40 @@ class TickerBase:
 
     # ------------------------
 
-    def _reconstruct_intervals_batch(self, df, interval, tag=-1):
+    def _reconstruct_intervals_batch(self, df, interval, prepost, tag=-1, silent=False):
         if not isinstance(df, _pd.DataFrame):
             raise Exception("'df' must be a Pandas DataFrame not", type(df))
+        if interval == "1m":
+            # Can't go smaller than 1m so can't reconstruct
+            return df
 
         # Reconstruct values in df using finer-grained price data. Delimiter marks what to reconstruct
 
+        debug = False
+        # debug = True
+
+        if interval[1:] in ['d', 'wk', 'mo']:
+            # Interday data always includes pre & post
+            prepost = True
+            intraday = False
+        else:
+            intraday = True
+
         price_cols = [c for c in ["Open", "High", "Low", "Close", "Adj Close"] if c in df]
         data_cols = price_cols + ["Volume"]
 
         # If interval is weekly then can construct with daily. But if smaller intervals then
         # restricted to recent times:
-        # - daily = hourly restricted to last 730 days
-        sub_interval = None
-        td_range = None
-        if interval == "1wk":
-            # Correct by fetching week of daily data
-            sub_interval = "1d"
-            td_range = _datetime.timedelta(days=7)
-        elif interval == "1d":
-            # Correct by fetching day of hourly data
-            sub_interval = "1h"
-            td_range = _datetime.timedelta(days=1)
-        elif interval == "1h":
-            sub_interval = "30m"
-            td_range = _datetime.timedelta(hours=1)
+        intervals = ["1wk", "1d", "1h", "30m", "15m", "5m", "2m", "1m"]
+        itds = {i:utils._interval_to_timedelta(interval) for i in intervals}
+        nexts = {intervals[i]:intervals[i+1] for i in range(len(intervals)-1)}
+        min_lookbacks = {"1wk":None, "1d":None, "1h":_datetime.timedelta(days=730)}
+        for i in ["30m", "15m", "5m", "2m"]:
+            min_lookbacks[i] = _datetime.timedelta(days=60)
+        min_lookbacks["1m"] = _datetime.timedelta(days=30)
+        if interval in nexts:
+            sub_interval = nexts[interval]
+            td_range = itds[interval]
         else:
             print("WARNING: Have not implemented repair for '{}' interval. Contact developers".format(interval))
             raise Exception("why here")
@@ -810,76 +909,107 @@ class TickerBase:
         f_repair_rows = f_repair.any(axis=1)
 
         # Ignore old intervals for which Yahoo won't return finer data:
-        if sub_interval == "1h":
-            f_recent = _datetime.date.today() - df.index.date < _datetime.timedelta(days=730)
+        m = min_lookbacks[sub_interval]
+        if m is None:
+            min_dt = None
+        else:
+            m -= _datetime.timedelta(days=1)  # allow space for 1-day padding
+            min_dt = _pd.Timestamp.utcnow() - m
+            min_dt = min_dt.tz_convert(df.index.tz).ceil("D")
+        if debug:
+            print(f"- min_dt={min_dt} interval={interval} sub_interval={sub_interval}")
+        if min_dt is not None:
+            f_recent = df.index >= min_dt
             f_repair_rows = f_repair_rows & f_recent
-        elif sub_interval in ["30m", "15m"]:
-            f_recent = _datetime.date.today() - df.index.date < _datetime.timedelta(days=60)
-            f_repair_rows = f_repair_rows & f_recent
-        if not f_repair_rows.any():
-            print("data too old to fix")
-            return df
+            if not f_repair_rows.any():
+                if debug:
+                    print("data too old to repair")
+                return df
 
         dts_to_repair = df.index[f_repair_rows]
         indices_to_repair = _np.where(f_repair_rows)[0]
 
         if len(dts_to_repair) == 0:
+            if debug:
+                print("dts_to_repair[] is empty")
             return df
 
         df_v2 = df.copy()
-        df_noNa = df[~df[price_cols].isna().any(axis=1)]
+        f_good = ~(df[price_cols].isna().any(axis=1))
+        f_good = f_good & (df[price_cols].to_numpy()!=tag).all(axis=1)
+        df_good = df[f_good]
 
         # Group nearby NaN-intervals together to reduce number of Yahoo fetches
         dts_groups = [[dts_to_repair[0]]]
         last_dt = dts_to_repair[0]
         last_ind = indices_to_repair[0]
         td = utils._interval_to_timedelta(interval)
-        if interval == "1mo":
-            grp_td_threshold = _datetime.timedelta(days=28)
-        elif interval == "1wk":
-            grp_td_threshold = _datetime.timedelta(days=28)
-        elif interval == "1d":
-            grp_td_threshold = _datetime.timedelta(days=14)
-        elif interval == "1h":
-            grp_td_threshold = _datetime.timedelta(days=7)
+        # Note on setting max size: have to allow space for adding good data
+        if sub_interval == "1mo":
+            grp_max_size = _dateutil.relativedelta.relativedelta(years=2)
+        elif sub_interval == "1wk":
+            grp_max_size = _dateutil.relativedelta.relativedelta(years=2)
+        elif sub_interval == "1d":
+            grp_max_size = _dateutil.relativedelta.relativedelta(years=2)
+        elif sub_interval == "1h":
+            grp_max_size = _dateutil.relativedelta.relativedelta(years=1)
+        elif sub_interval == "1m":
+            grp_max_size = _datetime.timedelta(days=5)  # allow 2 days for buffer below
         else:
-            grp_td_threshold = _datetime.timedelta(days=2)
-            # grp_td_threshold = _datetime.timedelta(days=7)
+            grp_max_size = _datetime.timedelta(days=30)
+        if debug:
+            print("- grp_max_size =", grp_max_size)
         for i in range(1, len(dts_to_repair)):
             ind = indices_to_repair[i]
             dt = dts_to_repair[i]
-            if (dt-dts_groups[-1][-1]) < grp_td_threshold:
-                dts_groups[-1].append(dt)
-            elif ind - last_ind <= 3:
+            if dt.date() < dts_groups[-1][0].date()+grp_max_size:
                 dts_groups[-1].append(dt)
             else:
                 dts_groups.append([dt])
             last_dt = dt
             last_ind = ind
 
+        if debug:
+            print("Repair groups:")
+            for g in dts_groups:
+                print(f"- {g[0]} -> {g[-1]}")
+
         # Add some good data to each group, so can calibrate later:
         for i in range(len(dts_groups)):
             g = dts_groups[i]
             g0 = g[0]
-            i0 = df_noNa.index.get_loc(g0)
+            i0 = df_good.index.get_indexer([g0], method="nearest")[0]
             if i0 > 0:
-                dts_groups[i].insert(0, df_noNa.index[i0-1])
+                if (min_dt is None or df_good.index[i0-1] >= min_dt) and \
+                    ((not intraday) or df_good.index[i0-1].date()==g0.date()):
+                    i0 -= 1
             gl = g[-1]
-            il = df_noNa.index.get_loc(gl)
-            if il < len(df_noNa)-1:
-                dts_groups[i].append(df_noNa.index[il+1])
+            il = df_good.index.get_indexer([gl], method="nearest")[0]
+            if il < len(df_good)-1:
+                if (not intraday) or df_good.index[il+1].date()==gl.date():
+                    il += 1
+            good_dts = df_good.index[i0:il+1]
+            dts_groups[i] += good_dts.to_list()
+            dts_groups[i].sort()
 
         n_fixed = 0
         for g in dts_groups:
             df_block = df[df.index.isin(g)]
+            if debug:
+                print("- df_block:")
+                print(df_block)
 
             start_dt = g[0]
             start_d = start_dt.date()
             if sub_interval == "1h" and (_datetime.date.today() - start_d) > _datetime.timedelta(days=729):
                 # Don't bother requesting more price data, Yahoo will reject
+                if debug:
+                    print(f"- Don't bother requesting {sub_interval} price data, Yahoo will reject")
                 continue
             elif sub_interval in ["30m", "15m"] and (_datetime.date.today() - start_d) > _datetime.timedelta(days=59):
                 # Don't bother requesting more price data, Yahoo will reject
+                if debug:
+                    print(f"- Don't bother requesting {sub_interval} price data, Yahoo will reject")
                 continue
 
             td_1d = _datetime.timedelta(days=1)
@@ -893,15 +1023,25 @@ class TickerBase:
                 fetch_start = g[0]
                 fetch_end = g[-1] + td_range
 
-            prepost = interval == "1d"
-            df_fine = self.history(start=fetch_start, end=fetch_end, interval=sub_interval, auto_adjust=False, prepost=prepost, repair=False, keepna=True)
+            # The first and last day returned by Yahoo can be slightly wrong, so add buffer:
+            fetch_start -= td_1d
+            fetch_end += td_1d
+            if intraday:
+                fetch_start = fetch_start.date()
+                fetch_end = fetch_end.date()+td_1d
+            if debug:
+                print(f"- fetching {sub_interval} prepost={prepost} {fetch_start}->{fetch_end}")
+            r = "silent" if silent else True
+            df_fine = self.history(start=fetch_start, end=fetch_end, interval=sub_interval, auto_adjust=False, actions=False, prepost=prepost, repair=r, keepna=True)
             if df_fine is None or df_fine.empty:
-                print("YF: WARNING: Cannot reconstruct because Yahoo not returning data in interval")
+                if not silent:
+                    print("YF: WARNING: Cannot reconstruct because Yahoo not returning data in interval")
                 continue
+            # Discard the buffer
+            df_fine = df_fine.loc[g[0] : g[-1]+itds[sub_interval]-_datetime.timedelta(milliseconds=1)]
 
             df_fine["ctr"] = 0
             if interval == "1wk":
-                # df_fine["Week Start"] = df_fine.index.tz_localize(None).to_period("W-SUN").start_time
                 weekdays = ["MON", "TUE", "WED", "THU", "FRI", "SAT", "SUN"]
                 week_end_day = weekdays[(df_block.index[0].weekday()+7-1)%7]
                 df_fine["Week Start"] = df_fine.index.tz_localize(None).to_period("W-"+week_end_day).start_time
@@ -916,7 +1056,8 @@ class TickerBase:
                 grp_col = "intervalID"
             df_fine = df_fine[~df_fine[price_cols].isna().all(axis=1)]
 
-            df_new = df_fine.groupby(grp_col).agg(
+            df_fine_grp = df_fine.groupby(grp_col)
+            df_new = df_fine_grp.agg(
                 Open=("Open", "first"),
                 Close=("Close", "last"),
                 AdjClose=("Adj Close", "last"),
@@ -930,31 +1071,42 @@ class TickerBase:
                 new_index = _np.append([df_fine.index[0]], df_fine.index[df_fine["intervalID"].diff()>0])
                 df_new.index = new_index
 
+            if debug:
+                print("- df_new:")
+                print(df_new)
+
             # Calibrate! Check whether 'df_fine' has different split-adjustment.
             # If different, then adjust to match 'df'
-            df_block_calib = df_block[price_cols]
-            common_index = df_block_calib.index[df_block_calib.index.isin(df_new.index)]
+            common_index = _np.intersect1d(df_block.index, df_new.index)
             if len(common_index) == 0:
                 # Can't calibrate so don't attempt repair
+                if debug:
+                    print("Can't calibrate so don't attempt repair")
                 continue
-            df_new_calib = df_new[df_new.index.isin(common_index)][price_cols]
-            df_block_calib = df_block_calib[df_block_calib.index.isin(common_index)]
-            calib_filter = (df_block_calib != tag).to_numpy()
+            df_new_calib = df_new[df_new.index.isin(common_index)][price_cols].to_numpy()
+            df_block_calib = df_block[df_block.index.isin(common_index)][price_cols].to_numpy()
+            calib_filter = (df_block_calib != tag)
             if not calib_filter.any():
                 # Can't calibrate so don't attempt repair
+                if debug:
+                    print("Can't calibrate so don't attempt repair")
                 continue
-            # Avoid divide-by-zero warnings printing:
-            df_new_calib = df_new_calib.to_numpy()
-            df_block_calib = df_block_calib.to_numpy()
+            # Avoid divide-by-zero warnings:
             for j in range(len(price_cols)):
-                c = price_cols[j]
                 f = ~calib_filter[:,j]
                 if f.any():
                     df_block_calib[f,j] = 1
                     df_new_calib[f,j] = 1
-            ratios = (df_block_calib / df_new_calib)[calib_filter]
-            ratio = _np.mean(ratios)
-            #
+            ratios = df_block_calib[calib_filter] / df_new_calib[calib_filter]
+            weights = df_fine_grp.size()
+            weights.index = df_new.index
+            weights = weights[weights.index.isin(common_index)].to_numpy().astype(float)
+            weights = weights[:,None]  # transpose
+            weights = _np.tile(weights, len(price_cols))  # 1D -> 2D
+            weights = weights[calib_filter]  # flatten
+            ratio = _np.average(ratios, weights=weights)
+            if debug:
+                print(f"- price calibration ratio (raw) = {ratio}")
             ratio_rcp = round(1.0 / ratio, 1)
             ratio = round(ratio, 1)
             if ratio == 1 and ratio_rcp == 1:
@@ -973,13 +1125,22 @@ class TickerBase:
                     df_new["Volume"] *= ratio_rcp
 
             # Repair!
-            bad_dts = df_block.index[(df_block[price_cols]==tag).any(axis=1)]
+            bad_dts = df_block.index[(df_block[price_cols+["Volume"]]==tag).any(axis=1)]
 
+            if debug:
+                no_fine_data_dts = []
+                for idx in bad_dts:
+                    if not idx in df_new.index:
+                        # Yahoo didn't return finer-grain data for this interval, 
+                        # so probably no trading happened.
+                        no_fine_data_dts.append(idx)
+                if len(no_fine_data_dts) > 0:
+                    print(f"Yahoo didn't return finer-grain data for these intervals:")
+                    print(no_fine_data_dts)
             for idx in bad_dts:
                 if not idx in df_new.index:
                     # Yahoo didn't return finer-grain data for this interval, 
                     # so probably no trading happened.
-                    # print("no fine data")
                     continue
                 df_new_row = df_new.loc[idx]
 
@@ -1008,9 +1169,12 @@ class TickerBase:
                     df_v2.loc[idx, "Volume"] = df_new_row["Volume"]
                 n_fixed += 1
 
+        if debug:
+            print("df_v2:") ; print(df_v2)
+
         return df_v2
 
-    def _fix_unit_mixups(self, df, interval, tz_exchange):
+    def _fix_unit_mixups(self, df, interval, tz_exchange, prepost, silent=False):
         # Sometimes Yahoo returns few prices in cents/pence instead of $/£
         # I.e. 100x bigger
         # Easy to detect and fix, just look for outliers = ~100x local median
@@ -1032,7 +1196,7 @@ class TickerBase:
         # adding it to dependencies.
         from scipy import ndimage as _ndimage
 
-        data_cols = ["High", "Open", "Low", "Close"]  # Order important, separate High from Low
+        data_cols = ["High", "Open", "Low", "Close", "Adj Close"]  # Order important, separate High from Low
         data_cols = [c for c in data_cols if c in df2.columns]
         f_zeroes = (df2[data_cols]==0).any(axis=1)
         if f_zeroes.any():
@@ -1057,7 +1221,7 @@ class TickerBase:
             df2.loc[fi, c] = tag
 
         n_before = (df2[data_cols].to_numpy()==tag).sum()
-        df2 = self._reconstruct_intervals_batch(df2, interval, tag=tag)
+        df2 = self._reconstruct_intervals_batch(df2, interval, prepost, tag, silent)
         n_after = (df2[data_cols].to_numpy()==tag).sum()
 
         if n_after > 0:
@@ -1080,6 +1244,11 @@ class TickerBase:
                 if fi[j]:
                     df2.loc[idx, c] = df.loc[idx, c] * 0.01
                 #
+                c = "Adj Close"
+                j = data_cols.index(c)
+                if fi[j]:
+                    df2.loc[idx, c] = df.loc[idx, c] * 0.01
+                #
                 c = "High"
                 j = data_cols.index(c)
                 if fi[j]:
@@ -1094,7 +1263,7 @@ class TickerBase:
 
         n_fixed = n_before - n_after_crude
         n_fixed_crudely = n_after - n_after_crude
-        if n_fixed > 0:
+        if not silent and n_fixed > 0:
             report_msg = f"{self.ticker}: fixed {n_fixed}/{n_before} currency unit mixups "
             if n_fixed_crudely > 0:
                 report_msg += f"({n_fixed_crudely} crudely) "
@@ -1114,7 +1283,7 @@ class TickerBase:
 
         return df2
 
-    def _fix_zeroes(self, df, interval, tz_exchange):
+    def _fix_zeroes(self, df, interval, tz_exchange, prepost, silent=False):
         # Sometimes Yahoo returns prices=0 or NaN when trades occurred.
         # But most times when prices=0 or NaN returned is because no trades.
         # Impossible to distinguish, so only attempt repair if few or rare.
@@ -1122,6 +1291,12 @@ class TickerBase:
         if df.shape[0] == 0:
             return df
 
+        debug = False
+        # debug = True
+
+        intraday = interval[-1] in ("m", 'h')
+
+        df = df.sort_index()  # important!
         df2 = df.copy()
 
         if df2.index.tz is None:
@@ -1130,16 +1305,34 @@ class TickerBase:
             df2.index = df2.index.tz_convert(tz_exchange)
 
         price_cols = [c for c in ["Open", "High", "Low", "Close", "Adj Close"] if c in df2.columns]
-        f_zero_or_nan = (df2[price_cols] == 0.0).values | df2[price_cols].isna().values
+        f_prices_bad = (df2[price_cols] == 0.0) | df2[price_cols].isna()
+        df2_reserve = None
+        if intraday:
+            # Ignore days with >50% intervals containing NaNs
+            df_nans = pd.DataFrame(f_prices_bad.any(axis=1), columns=["nan"])
+            df_nans["_date"] = df_nans.index.date
+            grp = df_nans.groupby("_date")
+            nan_pct = grp.sum() / grp.count()
+            dts = nan_pct.index[nan_pct["nan"]>0.5]
+            f_zero_or_nan_ignore = _np.isin(f_prices_bad.index.date, dts)
+            df2_reserve = df2[f_zero_or_nan_ignore]
+            df2 = df2[~f_zero_or_nan_ignore]
+            f_prices_bad = (df2[price_cols] == 0.0) | df2[price_cols].isna()
+
+        f_high_low_good = (~df2["High"].isna()) & (~df2["Low"].isna())
+        f_vol_bad = (df2["Volume"]==0).to_numpy() & f_high_low_good & (df2["High"]!=df2["Low"]).to_numpy()
+
         # Check whether worth attempting repair
-        if f_zero_or_nan.any(axis=1).sum() == 0:
+        f_prices_bad = f_prices_bad.to_numpy()
+        f_bad_rows = f_prices_bad.any(axis=1) | f_vol_bad
+        if not f_bad_rows.any():
+            if debug:
+                print("no bad data to repair")
             return df
-        if f_zero_or_nan.sum() == len(price_cols)*len(df2):
+        if f_prices_bad.sum() == len(price_cols)*len(df2):
             # Need some good data to calibrate
-            return df
-        # - avoid repair if many zeroes/NaNs
-        pct_zero_or_nan = f_zero_or_nan.sum() / (len(price_cols)*len(df2))
-        if f_zero_or_nan.any(axis=1).sum()>2 and pct_zero_or_nan > 0.05:
+            if debug:
+                print("no good data to calibrate")
             return df
 
         data_cols = price_cols + ["Volume"]
@@ -1148,17 +1341,31 @@ class TickerBase:
         tag = -1.0
         for i in range(len(price_cols)):
             c = price_cols[i]
-            df2.loc[f_zero_or_nan[:,i], c] = tag
+            df2.loc[f_prices_bad[:,i], c] = tag
+        df2.loc[f_vol_bad, "Volume"] = tag
         # If volume=0 or NaN for bad prices, then tag volume for repair
-        df2.loc[f_zero_or_nan.any(axis=1) & (df2["Volume"]==0), "Volume"] = tag
-        df2.loc[f_zero_or_nan.any(axis=1) & (df2["Volume"].isna()), "Volume"] = tag
+        f_vol_zero_or_nan = (df2["Volume"].to_numpy()==0) | (df2["Volume"].isna().to_numpy())
+        df2.loc[f_prices_bad.any(axis=1) & f_vol_zero_or_nan, "Volume"] = tag
+        # If volume=0 or NaN but price moved in interval, then tag volume for repair
+        f_change = df2["High"].to_numpy() != df2["Low"].to_numpy()
+        df2.loc[f_change & f_vol_zero_or_nan, "Volume"] = tag
 
         n_before = (df2[data_cols].to_numpy()==tag).sum()
-        df2 = self._reconstruct_intervals_batch(df2, interval, tag=tag)
+        dts_tagged = df2.index[(df2[data_cols].to_numpy()==tag).any(axis=1)]
+        df2 = self._reconstruct_intervals_batch(df2, interval, prepost, tag, silent)
         n_after = (df2[data_cols].to_numpy()==tag).sum()
+        dts_not_repaired = df2.index[(df2[data_cols].to_numpy()==tag).any(axis=1)]
         n_fixed = n_before - n_after
-        if n_fixed > 0:
-            print("{}: fixed {} price=0.0 errors in {} price data".format(self.ticker, n_fixed, interval))
+        if not silent and n_fixed > 0:
+            msg = f"{self.ticker}: fixed {n_fixed}/{n_before} value=0 errors in {interval} price data"
+            if n_fixed < 4:
+                dts_repaired = sorted(list(set(dts_tagged).difference(dts_not_repaired)))
+                msg += f": {dts_repaired}"
+            print(msg)
+
+        if df2_reserve is not None:
+            df2 = _pd.concat([df2, df2_reserve])
+        df2 = df2.sort_index()
 
         # Restore original values where repair failed (i.e. remove tag values)
         f = df2[data_cols].values==tag
@@ -1700,6 +1907,6 @@ class TickerBase:
 
     def get_history_metadata(self) -> dict:
         if self._history_metadata is None:
-            raise RuntimeError("Metadata was never retrieved so far, "
-                               "call history() to retrieve it")
+            # Request intraday data, because then Yahoo returns exchange schedule.
+            self.history(period="1wk", interval="1h", prepost=True)
         return self._history_metadata

yfinance/data.py

@@ -15,6 +15,8 @@ else:
 import requests as requests
 import re
 from bs4 import BeautifulSoup
+import random
+import time
 
 from frozendict import frozendict
 
@@ -60,7 +62,7 @@ def _extract_extra_keys_from_stores(data):
             new_keys_uniq.append(k)
             new_keys_uniq_values.add(v)
 
-    return new_keys_uniq
+    return [data[k] for k in new_keys_uniq]
 
 
 def decrypt_cryptojs_aes_stores(data, keys=None):
@@ -202,6 +204,11 @@ class TickerData:
             proxy = {"https": proxy}
         return proxy
 
+    def get_raw_json(self, url, user_agent_headers=None, params=None, proxy=None, timeout=30):
+        response = self.get(url, user_agent_headers=user_agent_headers, params=params, proxy=proxy, timeout=timeout)
+        response.raise_for_status()
+        return response.json()
+
     def _get_decryption_keys_from_yahoo_js(self, soup):
         result = None
 
@@ -250,15 +257,16 @@ class TickerData:
                 response_js.close()
             if len(re_keys) == key_count:
                 break
-        re_obj = {}
-        missing_val = False
-        for k in re_keys:
-            if not re_data.get(k):
-                missing_val = True
-                break
-            re_obj.update({k: re_data.get(k)})
-        if not missing_val:
-            return [''.join(re_obj.values())]
+        if len(re_keys) > 0:
+            re_obj = {}
+            missing_val = False
+            for k in re_keys:
+                if not re_data.get(k):
+                    missing_val = True
+                    break
+                re_obj.update({k: re_data.get(k)})
+            if not missing_val:
+                return [''.join(re_obj.values())]
 
         return []
 
@@ -289,19 +297,22 @@ class TickerData:
         # Gather decryption keys:
         soup = BeautifulSoup(response.content, "html.parser")
         keys = self._get_decryption_keys_from_yahoo_js(soup)
+        # if len(keys) == 0:
+        #     msg = "No decryption keys could be extracted from JS file."
+        #     if "requests_cache" in str(type(response)):
+        #         msg += " Try flushing your 'requests_cache', probably parsing old JS."
+        #     print("WARNING: " + msg + " Falling back to backup decrypt methods.")
         if len(keys) == 0:
-            msg = "No decryption keys could be extracted from JS file."
-            if "requests_cache" in str(type(response)):
-                msg += " Try flushing your 'requests_cache', probably parsing old JS."
-            print("WARNING: " + msg + " Falling back to backup decrypt methods.")
-        if len(keys) == 0:
+            keys = []
+            try:
+                extra_keys = _extract_extra_keys_from_stores(data)
+                keys = [''.join(extra_keys[-4:])]
+            except:
+                pass
+            #
             keys_url = "https://github.com/ranaroussi/yfinance/raw/main/yfinance/scrapers/yahoo-keys.txt"
             response_gh = self.cache_get(keys_url)
-            keys = response_gh.text.splitlines()
-            extra_keys = _extract_extra_keys_from_stores(data)
-            if len(extra_keys) < 10:
-                # Only brute-force with these extra keys if few
-                keys += extra_keys
+            keys += response_gh.text.splitlines()
 
         # Decrypt!
         stores = decrypt_cryptojs_aes_stores(data, keys)

yfinance/multi.py

@@ -29,7 +29,7 @@ from . import Ticker, utils
 from . import shared
 
 
-def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=False,
+def download(tickers, start=None, end=None, actions=False, threads=True, ignore_tz=None,
              group_by='column', auto_adjust=False, back_adjust=False, repair=False, keepna=False,
              progress=True, period="max", show_errors=True, interval="1d", prepost=False,
              proxy=None, rounding=False, timeout=10):
@@ -44,11 +44,13 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
             Intraday data cannot extend last 60 days
         start: str
-            Download start date string (YYYY-MM-DD) or _datetime.
+            Download start date string (YYYY-MM-DD) or _datetime, inclusive.
             Default is 1900-01-01
+            E.g. for start="2020-01-01", the first data point will be on "2020-01-01"
         end: str
-            Download end date string (YYYY-MM-DD) or _datetime.
+            Download end date string (YYYY-MM-DD) or _datetime, exclusive.
             Default is now
+            E.g. for end="2023-01-01", the last data point will be on "2022-12-31"
         group_by : str
             Group by 'ticker' or 'column' (default)
         prepost : bool
@@ -68,7 +70,7 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             How many threads to use for mass downloading. Default is True
         ignore_tz: bool
             When combining from different timezones, ignore that part of datetime.
-            Default is False
+            Default depends on interval. Intraday = False. Day+ = True.
         proxy: str
             Optional. Proxy server URL scheme. Default is None
         rounding: bool
@@ -80,6 +82,14 @@ def download(tickers, start=None, end=None, actions=False, threads=True, ignore_
             seconds. (Can also be a fraction of a second e.g. 0.01)
     """
 
+    if ignore_tz is None:
+        # Set default value depending on interval
+        if interval[1:] in ['m', 'h']:
+            # Intraday
+            ignore_tz = False
+        else:
+            ignore_tz = True
+
     # create ticker list
     tickers = tickers if isinstance(
         tickers, (list, set, tuple)) else tickers.replace(',', ' ').split()

yfinance/scrapers/quote.py

@@ -7,11 +7,11 @@ from yfinance import utils
 from yfinance.data import TickerData
 
 
-info_retired_keys_price = {"currentPrice", "dayHigh", "dayLow", "open", "previousClose", "volume"}
+info_retired_keys_price = {"currentPrice", "dayHigh", "dayLow", "open", "previousClose", "volume", "volume24Hr"}
 info_retired_keys_price.update({"regularMarket"+s for s in ["DayHigh", "DayLow", "Open", "PreviousClose", "Price", "Volume"]})
-info_retired_keys_price.update({"fiftyTwoWeekLow", "fiftyTwoWeekHigh", "fiftyTwoWeekChange", "fiftyDayAverage", "twoHundredDayAverage"})
+info_retired_keys_price.update({"fiftyTwoWeekLow", "fiftyTwoWeekHigh", "fiftyTwoWeekChange", "52WeekChange", "fiftyDayAverage", "twoHundredDayAverage"})
 info_retired_keys_price.update({"averageDailyVolume10Day", "averageVolume10days", "averageVolume"})
-info_retired_keys_exchange = {"currency", "exchange", "exchangeTimezoneName", "exchangeTimezoneShortName"}
+info_retired_keys_exchange = {"currency", "exchange", "exchangeTimezoneName", "exchangeTimezoneShortName", "quoteType"}
 info_retired_keys_marketCap = {"marketCap"}
 info_retired_keys_symbol = {"symbol"}
 info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_retired_keys_marketCap | info_retired_keys_symbol
@@ -19,6 +19,7 @@ info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_
 
 PRUNE_INFO = True
 # PRUNE_INFO = False
+_BASIC_URL_ = "https://query1.finance.yahoo.com/v7/finance/quote"
 
 
 from collections.abc import MutableMapping
@@ -87,13 +88,16 @@ class Quote:
         self._calendar = None
 
         self._already_scraped = False
-        self._already_scraped_complementary = False
+        self._already_fetched = False
+        self._already_fetched_complementary = False
 
     @property
     def info(self) -> dict:
         if self._info is None:
-            self._scrape(self.proxy)
-            self._scrape_complementary(self.proxy)
+            # self._scrape(self.proxy)  # decrypt broken
+            self._fetch(self.proxy)
+
+            self._fetch_complementary(self.proxy)
 
         return self._info
 
@@ -236,12 +240,34 @@ class Quote:
         except Exception:
             pass
 
-    def _scrape_complementary(self, proxy):
-        if self._already_scraped_complementary:
+    def _fetch(self, proxy):
+        if self._already_fetched:
             return
-        self._already_scraped_complementary = True
+        self._already_fetched = True
 
-        self._scrape(proxy)
+        result = self._data.get_raw_json(
+            _BASIC_URL_, params={"formatted": "true", "lang": "en-US", "symbols": self._data.ticker}, proxy=proxy
+        )
+        query1_info = next(
+            (info for info in result.get("quoteResponse", {}).get("result", []) if info["symbol"] == self._data.ticker),
+            None,
+        )
+        for k, v in query1_info.items():
+            if isinstance(v, dict) and "raw" in v and "fmt" in v:
+                query1_info[k] = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
+            elif isinstance(v, str):
+                query1_info[k] = v.replace("\xa0", " ")
+            elif isinstance(v, (int, bool)):
+                query1_info[k] = v
+        self._info = query1_info
+
+    def _fetch_complementary(self, proxy):
+        if self._already_fetched_complementary:
+            return
+        self._already_fetched_complementary = True
+
+        # self._scrape(proxy)  # decrypt broken
+        self._fetch(proxy)
         if self._info is None:
             return
 
@@ -283,11 +309,14 @@ class Quote:
 
             json_str = self._data.cache_get(url=url, proxy=proxy).text
             json_data = json.loads(json_str)
-            key_stats = json_data["timeseries"]["result"][0]
-            if k not in key_stats:
-                # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+            try:
+                key_stats = json_data["timeseries"]["result"][0]
+                if k not in key_stats:
+                    # Yahoo website prints N/A, indicates Yahoo lacks necessary data to calculate
+                    v = None
+                else:
+                    # Select most recent (last) raw value in list:
+                    v = key_stats[k][-1]["reportedValue"]["raw"]
+            except Exception:
                 v = None
-            else:
-                # Select most recent (last) raw value in list:
-                v = key_stats[k][-1]["reportedValue"]["raw"]
             self._info[k] = v

yfinance/scrapers/yahoo-keys.txt

@@ -3,3 +3,6 @@ ad4d90b3c9f2e1d156ef98eadfa0ff93e4042f6960e54aa2a13f06f528e6b50ba4265a26a1fd5b9c
 e9a8ab8e5620b712ebc2fb4f33d5c8b9c80c0d07e8c371911c785cf674789f1747d76a909510158a7b7419e86857f2d7abbd777813ff64840e4cbc514d12bcae
 6ae2523aeafa283dad746556540145bf603f44edbf37ad404d3766a8420bb5eb1d3738f52a227b88283cca9cae44060d5f0bba84b6a495082589f5fe7acbdc9e
 3365117c2a368ffa5df7313a4a84988f73926a86358e8eea9497c5ff799ce27d104b68e5f2fbffa6f8f92c1fef41765a7066fa6bcf050810a9c4c7872fd3ebf0
+15d8f57919857d5a5358d2082c7ef0f1129cfacd2a6480333dcfb954b7bb67d820abefebfdb0eaa6ef18a1c57f617b67d7e7b0ec040403b889630ae5db5a4dbb
+db9630d707a7d0953ac795cd8db1ca9ca6c9d8239197cdfda24b4e0ec9c37eaec4db82dab68b8f606ab7b5b4af3e65dab50606f8cf508269ec927e6ee605fb78
+3c895fb5ddcc37d20d3073ed74ee3efad59bcb147c8e80fd279f83701b74b092d503dcd399604c6d8be8f3013429d3c2c76ed5b31b80c9df92d5eab6d3339fce

yfinance/ticker.py

@@ -133,10 +133,6 @@ class Ticker(TickerBase):
     def shares(self) -> _pd.DataFrame :
         return self.get_shares()
 
-    @property
-    def market_cap(self) -> float:
-        return self.calc_market_cap()
-
     @property
     def info(self) -> dict:
         return self.get_info()

yfinance/utils.py

@@ -300,6 +300,11 @@ def camel2title(strings: List[str], sep: str = ' ', acronyms: Optional[List[str]
     return strings
 
 
+def snake_case_2_camelCase(s):
+    sc = s.split('_')[0] + ''.join(x.title() for x in s.split('_')[1:])
+    return sc
+
+
 def _parse_user_dt(dt, exchange_tz):
     if isinstance(dt, int):
         # Should already be epoch, test with conversion:
@@ -443,6 +448,35 @@ def set_df_tz(df, interval, tz):
     return df
 
 
+def fix_Yahoo_returning_prepost_unrequested(quotes, interval, metadata):
+    # Sometimes Yahoo returns post-market data despite not requesting it.
+    # Normally happens on half-day early closes.
+    #
+    # And sometimes returns pre-market data despite not requesting it.
+    # E.g. some London tickers.
+    tps_df = metadata["tradingPeriods"]
+    tps_df["_date"] = tps_df.index.date
+    quotes["_date"] = quotes.index.date
+    idx = quotes.index.copy()
+    quotes = quotes.merge(tps_df, how="left", validate="many_to_one")
+    quotes.index = idx
+    # "end" = end of regular trading hours (including any auction)
+    f_drop = quotes.index >= quotes["end"]
+    f_drop = f_drop | (quotes.index < quotes["start"])
+    if f_drop.any():
+        # When printing report, ignore rows that were already NaNs:
+        f_na = quotes[["Open","Close"]].isna().all(axis=1)
+        n_nna = quotes.shape[0] - _np.sum(f_na)
+        n_drop_nna = _np.sum(f_drop & ~f_na)
+        quotes_dropped = quotes[f_drop]
+        # if debug and n_drop_nna > 0:
+        #     print(f"Dropping {n_drop_nna}/{n_nna} intervals for falling outside regular trading hours")
+        quotes = quotes[~f_drop]
+    metadata["tradingPeriods"] = tps_df.drop(["_date"], axis=1)
+    quotes = quotes.drop(["_date", "start", "end"], axis=1)
+    return quotes
+
+
 def fix_Yahoo_returning_live_separate(quotes, interval, tz_exchange):
     # Yahoo bug fix. If market is open today then Yahoo normally returns 
     # todays data as a separate row from rest-of week/month interval in above row. 
@@ -518,7 +552,7 @@ def safe_merge_dfs(df_main, df_sub, interval):
 
         df["_NewIndex"] = new_index
         # Duplicates present within periods but can aggregate
-        if data_col_name == "Dividends":
+        if data_col_name in ["Dividends", "Capital Gains"]:
             # Add
             df = df.groupby("_NewIndex").sum()
             df.index.name = None
@@ -656,6 +690,71 @@ def is_valid_timezone(tz: str) -> bool:
     return True
 
 
+def format_history_metadata(md):
+    if not isinstance(md, dict):
+        return md
+    if len(md) == 0:
+        return md
+
+    tz = md["exchangeTimezoneName"]
+
+    for k in ["firstTradeDate", "regularMarketTime"]:
+        if k in md and md[k] is not None:
+            md[k] = _pd.to_datetime(md[k], unit='s', utc=True).tz_convert(tz)
+
+    if "currentTradingPeriod" in md:
+        for m in ["regular", "pre", "post"]:
+            if m in md["currentTradingPeriod"]:
+                for t in ["start", "end"]:
+                    md["currentTradingPeriod"][m][t] = \
+                        _pd.to_datetime(md["currentTradingPeriod"][m][t], unit='s', utc=True).tz_convert(tz)
+                del md["currentTradingPeriod"][m]["gmtoffset"]
+                del md["currentTradingPeriod"][m]["timezone"]
+
+    if "tradingPeriods" in md:
+        if md["tradingPeriods"] == {"pre":[], "post":[]}:
+            del md["tradingPeriods"]
+
+    if "tradingPeriods" in md:
+        tps = md["tradingPeriods"]
+        if isinstance(tps, list):
+            # Only regular times
+            regs_dict = [tps[i][0] for i in range(len(tps))]
+            pres_dict = None
+            posts_dict = None
+        elif isinstance(tps, dict):
+            # Includes pre- and post-market
+            pres_dict = [tps["pre"][i][0] for i in range(len(tps["pre"]))]
+            posts_dict = [tps["post"][i][0] for i in range(len(tps["post"]))]
+            regs_dict = [tps["regular"][i][0] for i in range(len(tps["regular"]))]
+        else:
+            raise Exception()
+
+        def _dict_to_table(d):
+            df = _pd.DataFrame.from_dict(d).drop(["timezone", "gmtoffset"], axis=1)
+            df["end"] = _pd.to_datetime(df["end"], unit='s', utc=True).dt.tz_convert(tz)
+            df["start"] = _pd.to_datetime(df["start"], unit='s', utc=True).dt.tz_convert(tz)
+            df.index = _pd.to_datetime(df["start"].dt.date)
+            df.index = df.index.tz_localize(tz)
+            return df
+
+        df = _dict_to_table(regs_dict)
+        df_cols = ["start", "end"]
+        if pres_dict is not None:
+            pre_df = _dict_to_table(pres_dict)
+            df = df.merge(pre_df.rename(columns={"start":"pre_start", "end":"pre_end"}), left_index=True, right_index=True)
+            df_cols = ["pre_start", "pre_end"]+df_cols
+        if posts_dict is not None:
+            post_df = _dict_to_table(posts_dict)
+            df = df.merge(post_df.rename(columns={"start":"post_start", "end":"post_end"}), left_index=True, right_index=True)
+            df_cols = df_cols+["post_start", "post_end"]
+        df = df[df_cols]
+        df.index.name = "Date"
+
+        md["tradingPeriods"] = df
+
+    return md
+
 class ProgressBar:
     def __init__(self, iterations, text='completed'):
         self.text = text
@@ -718,7 +817,14 @@ class _KVStore:
         with self._cache_mutex:
             self.conn = _sqlite3.connect(filename, timeout=10, check_same_thread=False)
             self.conn.execute('pragma journal_mode=wal')
-            self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT) without rowid')
+            try:
+                self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT) without rowid')
+            except Exception as e:
+                if 'near "without": syntax error' in str(e):
+                    # "without rowid" requires sqlite 3.8.2. Older versions will raise exception
+                    self.conn.execute('create table if not exists "kv" (key TEXT primary key, value TEXT)')
+                else:
+                    raise
             self.conn.commit()
         _atexit.register(self.close)
 

yfinance/version.py

@@ -1 +1 @@
-version = "0.2.9"
+version = "0.2.14"