Bump version to 0.2.15

Merge pull request #1480 from kennykos/get_full_info
'info' fetch now gets same data as scrape
2023-04-09 21:07:16 +01:00 · 2023-04-09 21:01:34 +01:00 · 2023-04-07 12:21:01 +01:00 · 2023-04-06 12:21:57 +01:00 · 2023-04-05 18:23:36 +01:00 · 2023-03-25 11:39:21 +00:00
16 changed files with 1873 additions and 369 deletions
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.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?

-Visit `finance.yahoo.com` and confim they have your data. Maybe your ticker was delisted.
+Are you spelling ticker *exactly* same as Yahoo?

-Then check that you are spelling ticker *exactly* same as Yahoo.
+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 (roughly 100/s). 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:

- Info about your system:
-  - yfinance version
-  - operating system
- Simple code that reproduces your problem
- The error message
+- 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
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,81 @@
 Change Log
 ===========

+0.2.15
+------
+Restore missing Ticker.info keys #1480
+
+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
+
+0.2.7
+-----
+- Fix Yahoo decryption, smarter this time #1353
+- Rename basic_info -> fast_info #1354
+
+0.2.6
+-----
+- Fix Ticker.basic_info lazy-loading #1342
+
+0.2.5
+-----
+- Fix Yahoo data decryption again #1336
+- New: Ticker.basic_info - faster Ticker.info #1317
+
+0.2.4
+-----
+- Fix Yahoo data decryption #1297
+- New feature: 'Ticker.get_shares_full()' #1301
+- Improve caching of financials data #1284
+- Restore download() original alignment behaviour #1283
+- Fix the database lock error in multithread download #1276
+
+0.2.3
+-----
+- Make financials API '_' use consistent
+
+0.2.2
+-----
+- Restore 'financials' attribute (map to 'income_stmt')
+
+0.2.1
+-----
+Release!
+
 0.2.0rc5
 --------
 - Improve financials error handling #1243
--- a/README.md
+++ b/README.md
@@ -42,6 +42,11 @@ Yahoo! finance API is intended for personal use only.**

 ---

+## 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.
+
+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.~ `info` now fixed and much faster than before.
+
 ## Quick Start

 ### The Ticker module
@@ -53,30 +58,26 @@ import yfinance as yf

 msft = yf.Ticker("MSFT")

-# get stock info
+# get all stock info
 msft.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
+# - accurate time-series count:
+msft.get_shares_full(start="2022-01-01", end=None)

 # show financials:
 # - income statement
@@ -90,13 +91,9 @@ 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
@@ -155,18 +152,7 @@ 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
-```
+### Multiple tickers

 To initialize multiple `Ticker` objects, use

@@ -181,70 +167,54 @@ tickers.tickers['AAPL'].history(period="1mo")
 tickers.tickers['GOOG'].actions
 ```

-### Fetching data for multiple tickers
+To download price history into one table:

 ```python
 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 True. False may be useful for 
-        # minute/hourly data.
-        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,
-
-        # identify and attempt repair of currency unit 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
+            prepost = False,       # download pre/post market hours data?
+            repair = True)         # repair obvious price errors e.g. 100x?
 ```

-### 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
@@ -262,9 +232,7 @@ 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

---
-
-## `pandas_datareader` override
+### `pandas_datareader` override

 If your code uses `pandas_datareader` and you want to download data
 faster, you can "hijack" `pandas_datareader.data.get_data_yahoo()`
@@ -281,6 +249,18 @@ yf.pdr_override() # <== that's all it takes :-)
 data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")
 ```

+### 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")
+...
+```
+
 ---

 ## Installation
@@ -308,11 +288,15 @@ To install `yfinance` using `conda`, see
 -   [html5lib](https://pypi.org/project/html5lib) \>= 1.1
 -   [cryptography](https://pypi.org/project/cryptography) \>= 3.3.2

-### Optional (if you want to use `pandas_datareader`)
+#### Optional (if you want to use `pandas_datareader`)

 -   [pandas\_datareader](https://github.com/pydata/pandas-datareader)
    \>= 0.4.0

+## Developers: want to contribute?
+
+`yfinance` relies on community to investigate bugs and contribute code. Developer guide: https://github.com/ranaroussi/yfinance/discussions/1084
+
 ---

 ### Legal Stuff
--- a/meta.yaml
+++ b/meta.yaml
@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.2.0" %}
+{% set version = "0.2.15" %}

 package:
  name: "{{ name|lower }}"
--- a/tests/prices.py
+++ b/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

--- a/tests/ticker.py
+++ b/tests/ticker.py
@@ -9,6 +9,7 @@ Specific test class:

 """
 import pandas as pd
+import numpy as np

 from .context import yfinance as yf

@@ -51,12 +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
@@ -65,6 +70,7 @@ class TestTicker(unittest.TestCase):
        dat.splits
        dat.actions
        dat.shares
+        dat.get_shares_full()
        dat.info
        dat.calendar
        dat.recommendations
@@ -89,42 +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.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):
@@ -528,6 +540,65 @@ class TestTickerMiscFinancials(unittest.TestCase):
        data_cached = self.ticker_old_fmt.get_cashflow(legacy=True, freq="quarterly")
        self.assertIs(data, data_cached, "data not cached")

+    def test_income_alt_names(self):
+        i1 = self.ticker.income_stmt
+        i2 = self.ticker.incomestmt
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.financials
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.get_income_stmt()
+        i2 = self.ticker.get_incomestmt()
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.get_financials()
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.quarterly_income_stmt
+        i2 = self.ticker.quarterly_incomestmt
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.quarterly_financials
+        self.assertTrue(i1.equals(i3))
+
+        i1 = self.ticker.get_income_stmt(freq="quarterly")
+        i2 = self.ticker.get_incomestmt(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+        i3 = self.ticker.get_financials(freq="quarterly")
+        self.assertTrue(i1.equals(i3))
+
+    def test_balance_sheet_alt_names(self):
+        i1 = self.ticker.balance_sheet
+        i2 = self.ticker.balancesheet
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_balance_sheet()
+        i2 = self.ticker.get_balancesheet()
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.quarterly_balance_sheet
+        i2 = self.ticker.quarterly_balancesheet
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_balance_sheet(freq="quarterly")
+        i2 = self.ticker.get_balancesheet(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+
+    def test_cash_flow_alt_names(self):
+        i1 = self.ticker.cash_flow
+        i2 = self.ticker.cashflow
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_cash_flow()
+        i2 = self.ticker.get_cashflow()
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.quarterly_cash_flow
+        i2 = self.ticker.quarterly_cashflow
+        self.assertTrue(i1.equals(i2))
+
+        i1 = self.ticker.get_cash_flow(freq="quarterly")
+        i2 = self.ticker.get_cashflow(freq="quarterly")
+        self.assertTrue(i1.equals(i2))
+
    def test_sustainability(self):
        data = self.ticker.sustainability
        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
@@ -594,16 +665,145 @@ class TestTickerMiscFinancials(unittest.TestCase):
        self.assertIsInstance(data, pd.DataFrame, "data has wrong type")
        self.assertFalse(data.empty, "data is empty")

-    def test_info(self):
-        data = self.ticker.info
-        self.assertIsInstance(data, dict, "data has wrong type")
-        self.assertIn("symbol", data.keys(), "Did not find expected key in info dict")
-        self.assertEqual("GOOGL", data["symbol"], "Wrong symbol value in info dict")
+    def test_shares_full(self):
+        data = self.ticker.get_shares_full()
+        self.assertIsInstance(data, pd.Series, "data has wrong type")
+        self.assertFalse(data.empty, "data is empty")

    def test_bad_freq_value_raises_exception(self):
        self.assertRaises(ValueError, lambda: self.ticker.get_cashflow(freq="badarg"))


+class TestTickerInfo(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 setUp(self):
+        self.symbols = []
+        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):
+        self.ticker = None
+
+    def test_info(self):
+        data = self.tickers[0].info
+        self.assertIsInstance(data, dict, "data has wrong type")
+        self.assertIn("symbol", data.keys(), "Did not find expected key in info dict")
+        self.assertEqual(self.symbols[0], data["symbol"], "Wrong symbol value in info dict")
+
+    def test_fast_info(self):
+        yf.scrapers.quote.PRUNE_INFO = False
+
+        fast_info_keys = set()
+        for ticker in self.tickers:
+            fast_info_keys.update(set(ticker.fast_info.keys()))
+        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"]
+        key_rename_map["day_low"] = ["dayLow", "regularMarketDayLow"]
+        key_rename_map["previous_close"] = ["previousClose"]
+        key_rename_map["regular_market_previous_close"] = ["regularMarketPreviousClose"]
+
+        key_rename_map["fifty_day_average"] = "fiftyDayAverage"
+        key_rename_map["two_hundred_day_average"] = "twoHundredDayAverage"
+        key_rename_map["year_change"] = ["52WeekChange", "fiftyTwoWeekChange"]
+        key_rename_map["year_high"] = "fiftyTwoWeekHigh"
+        key_rename_map["year_low"] = "fiftyTwoWeekLow"
+
+        key_rename_map["last_volume"] = ["volume", "regularMarketVolume"]
+        key_rename_map["ten_day_average_volume"] = ["averageVolume10days", "averageDailyVolume10Day"]
+        key_rename_map["three_month_average_volume"] = "averageVolume"
+
+        key_rename_map["market_cap"] = "marketCap"
+        key_rename_map["shares"] = "sharesOutstanding"
+
+        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]
+
+        # 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:
+                k2 = key_rename_map[k]
+            else:
+                k2 = k
+
+            if not isinstance(k2, list):
+                k2 = [k2]
+
+            for m in k2:
+                for ticker in self.tickers:
+                    if not m in ticker.info:
+                        # print(f"symbol={ticker.ticker}: fast_info key '{k}' mapped to info key '{m}' but not present in info")
+                        continue
+
+                    if k in bad_keys:
+                        continue
+
+                    if k in custom_tolerances:
+                        rtol = custom_tolerances[k]
+                    else:
+                        rtol = 5e-3
+                        # rtol = 1e-4
+
+                    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 in ["market_cap","marketCap"] and ticker.fast_info["currency"] in ["GBp", "ILA"]:
+                        # Adjust for currency to match Yahoo:
+                        test *= 0.01
+                    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
+
+
+
 def suite():
    suite = unittest.TestSuite()
    suite.addTest(TestTicker('Test ticker'))
@@ -611,6 +811,7 @@ def suite():
    suite.addTest(TestTickerHolders('Test holders'))
    suite.addTest(TestTickerHistory('Test Ticker history'))
    suite.addTest(TestTickerMiscFinancials('Test misc financials'))
+    suite.addTest(TestTickerInfo('Test info & fast_info'))
    return suite


--- a/yfinance/base.py
+++ b/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
@@ -39,7 +40,8 @@ from . import shared
 from .scrapers.analysis import Analysis
 from .scrapers.fundamentals import Fundamentals
 from .scrapers.holders import Holders
-from .scrapers.quote import Quote
+from .scrapers.quote import Quote, FastInfo
+import json as _json

 _BASE_URL_ = 'https://query2.finance.yahoo.com'
 _SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
@@ -76,6 +78,8 @@ class TickerBase:
        self._quote = Quote(self._data)
        self._fundamentals = Fundamentals(self._data)

+        self._fast_info = FastInfo(self)
+
    def stats(self, proxy=None):
        ticker_url = "{}/{}".format(self._scrape_url, self.ticker)

@@ -97,11 +101,13 @@ 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
@@ -109,8 +115,9 @@ 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?
@@ -213,6 +220,7 @@ 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
@@ -289,36 +297,37 @@ 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')
@@ -353,10 +362,10 @@ 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:
@@ -400,31 +409,40 @@ 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")
@@ -436,76 +454,107 @@ 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)
@@ -519,15 +568,25 @@ 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
@@ -542,7 +601,8 @@ 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"),
@@ -556,31 +616,42 @@ 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:
@@ -599,13 +670,22 @@ 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]

@@ -634,9 +714,12 @@ 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
@@ -658,7 +741,7 @@ 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():
@@ -683,7 +766,7 @@ 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:
@@ -706,6 +789,11 @@ 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]:
@@ -720,7 +808,7 @@ 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) "
@@ -740,7 +828,7 @@ 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.
@@ -748,6 +836,12 @@ 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:
@@ -756,16 +850,34 @@ 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"]
@@ -774,17 +886,31 @@ 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
@@ -820,7 +946,7 @@ class TickerBase:
        return tz

    def _fetch_ticker_tz(self, debug_mode, proxy, timeout):
-        # Query Yahoo for basic price data just to get returned timezone
+        # Query Yahoo for fast price data just to get returned timezone

        params = {"range": "1d", "interval": "1d"}

@@ -894,6 +1020,15 @@ class TickerBase:
        data = self._quote.info
        return data

+    @property
+    def fast_info(self):
+        return self._fast_info
+
+    @property
+    def basic_info(self):
+        print("WARNING: 'Ticker.basic_info' is renamed to 'Ticker.fast_info', hopefully purpose is clearer")
+        return self.fast_info
+
    def get_sustainability(self, proxy=None, as_dict=False):
        self._quote.proxy = proxy
        data = self._quote.sustainability
@@ -998,6 +1133,12 @@ class TickerBase:
            return data.to_dict()
        return data

+    def get_incomestmt(self, proxy=None, as_dict=False, pretty=False, freq="yearly", legacy=False):
+        return self.get_income_stmt(proxy, as_dict, pretty, freq, legacy)
+
+    def get_financials(self, proxy=None, as_dict=False, pretty=False, freq="yearly", legacy=False):
+        return self.get_income_stmt(proxy, as_dict, pretty, freq, legacy)
+
    def get_balance_sheet(self, proxy=None, as_dict=False, pretty=False, freq="yearly", legacy=False):
        """
        :Parameters:
@@ -1031,7 +1172,10 @@ class TickerBase:
            return data.to_dict()
        return data

-    def get_cashflow(self, proxy=None, as_dict=False, pretty=False, freq="yearly", legacy=False):
+    def get_balancesheet(self, proxy=None, as_dict=False, pretty=False, freq="yearly", legacy=False):
+        return self.get_balance_sheet(proxy, as_dict, pretty, freq, legacy)
+
+    def get_cash_flow(self, proxy=None, as_dict=False, pretty=False, freq="yearly", legacy=False):
        """
        :Parameters:
            as_dict: bool
@@ -1064,6 +1208,9 @@ class TickerBase:
            return data.to_dict()
        return data

+    def get_cashflow(self, proxy=None, as_dict=False, pretty=False, freq="yearly", legacy=False):
+        return self.get_cash_flow(proxy, as_dict, pretty, freq, legacy)
+
    def get_dividends(self, proxy=None):
        if self._history is None:
            self.history(period="max", proxy=proxy)
@@ -1106,6 +1253,58 @@ class TickerBase:
            return data.to_dict()
        return data

+    def get_shares_full(self, start=None, end=None, proxy=None):
+        # Process dates
+        tz = self._get_ticker_tz(debug_mode=False, proxy=None, timeout=10)
+        dt_now = _pd.Timestamp.utcnow().tz_convert(tz)
+        if start is not None:
+            start_ts = utils._parse_user_dt(start, tz)
+            start = _pd.Timestamp.fromtimestamp(start_ts).tz_localize("UTC").tz_convert(tz)
+            start_d = start.date()
+        if end is not None:
+            end_ts = utils._parse_user_dt(end, tz)
+            end = _pd.Timestamp.fromtimestamp(end_ts).tz_localize("UTC").tz_convert(tz)
+            end_d = end.date()
+        if end is None:
+            end = dt_now
+        if start is None:
+            start = end - _pd.Timedelta(days=548)  # 18 months
+        if start >= end:
+            print("ERROR: start date must be before end")
+            return None
+        start = start.floor("D")
+        end = end.ceil("D")
+        
+        # Fetch
+        ts_url_base = "https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{0}?symbol={0}".format(self.ticker)
+        shares_url = ts_url_base + "&period1={}&period2={}".format(int(start.timestamp()), int(end.timestamp()))
+        try:
+            json_str = self._data.cache_get(shares_url).text
+            json_data = _json.loads(json_str)
+        except:
+            print(f"{self.ticker}: Yahoo web request for share count failed")
+            return None
+        try:
+            fail = json_data["finance"]["error"]["code"] == "Bad Request"
+        except:
+            fail = False
+        if fail:
+            print(f"{self.ticker}: Yahoo web request for share count failed")
+            return None
+
+        shares_data = json_data["timeseries"]["result"]
+        if not "shares_out" in shares_data[0]:
+            return None
+        try:
+            df = _pd.Series(shares_data[0]["shares_out"], index=_pd.to_datetime(shares_data[0]["timestamp"], unit="s"))
+        except Exception as e:
+            print(f"{self.ticker}: Failed to parse shares count data: "+str(e))
+            return None
+
+        df.index = df.index.tz_localize(tz)
+        df = df.sort_index()
+        return df
+
    def get_isin(self, proxy=None) -> Optional[str]:
        # *** experimental ***
        if self._isin is not None:
@@ -1242,8 +1441,8 @@ class TickerBase:
        dates[cn] = _pd.to_datetime(dates[cn], format="%b %d, %Y, %I %p")
        # - instead of attempting decoding of ambiguous timezone abbreviation, just use 'info':
        self._quote.proxy = proxy
-        dates[cn] = dates[cn].dt.tz_localize(
-            tz=self._quote.info["exchangeTimezoneName"])
+        tz = self._get_ticker_tz(debug_mode=False, proxy=proxy, timeout=30)
+        dates[cn] = dates[cn].dt.tz_localize(tz)

        dates = dates.set_index("Earnings Date")

@@ -1253,6 +1452,6 @@ 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
--- a/yfinance/data.py
+++ b/yfinance/data.py
@@ -14,6 +14,9 @@ else:

 import requests as requests
 import re
+from bs4 import BeautifulSoup
+import random
+import time

 from frozendict import frozendict

@@ -46,20 +49,45 @@ def lru_cache_freezeargs(func):
    return wrapped


-def decrypt_cryptojs_aes(data):
-    encrypted_stores = data['context']['dispatcher']['stores']
-    _cs = data["_cs"]
-    _cr = data["_cr"]
+def _extract_extra_keys_from_stores(data):
+    new_keys = [k for k in data.keys() if k not in ["context", "plugins"]]
+    new_keys_values = set([data[k] for k in new_keys])

-    _cr = b"".join(int.to_bytes(i, length=4, byteorder="big", signed=True) for i in json.loads(_cr)["words"])
-    password = hashlib.pbkdf2_hmac("sha1", _cs.encode("utf8"), _cr, 1, dklen=32).hex()
+    # Maybe multiple keys have same value - keep one of each
+    new_keys_uniq = []
+    new_keys_uniq_values = set()
+    for k in new_keys:
+        v = data[k]
+        if not v in new_keys_uniq_values:
+            new_keys_uniq.append(k)
+            new_keys_uniq_values.add(v)
+
+    return [data[k] for k in new_keys_uniq]
+
+
+def decrypt_cryptojs_aes_stores(data, keys=None):
+    encrypted_stores = data['context']['dispatcher']['stores']
+
+    password = None
+    if keys is not None:
+        if not isinstance(keys, list):
+            raise TypeError("'keys' must be list")
+        candidate_passwords = keys
+    else:
+        candidate_passwords = []
+
+    if "_cs" in data and "_cr" in data:
+        _cs = data["_cs"]
+        _cr = data["_cr"]
+        _cr = b"".join(int.to_bytes(i, length=4, byteorder="big", signed=True) for i in json.loads(_cr)["words"])
+        password = hashlib.pbkdf2_hmac("sha1", _cs.encode("utf8"), _cr, 1, dklen=32).hex()

    encrypted_stores = b64decode(encrypted_stores)
    assert encrypted_stores[0:8] == b"Salted__"
    salt = encrypted_stores[8:16]
    encrypted_stores = encrypted_stores[16:]

-    def EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5") -> tuple:
+    def _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5") -> tuple:
        """OpenSSL EVP Key Derivation Function
        Args:
            password (Union[str, bytes, bytearray]): Password to generate key from.
@@ -98,19 +126,42 @@ def decrypt_cryptojs_aes(data):
        key, iv = key_iv[:keySize], key_iv[keySize:final_length]
        return key, iv

-    key, iv = EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
+    def _decrypt(encrypted_stores, password, key, iv):
+        if usePycryptodome:
+            cipher = AES.new(key, AES.MODE_CBC, iv=iv)
+            plaintext = cipher.decrypt(encrypted_stores)
+            plaintext = unpad(plaintext, 16, style="pkcs7")
+        else:
+            cipher = Cipher(algorithms.AES(key), modes.CBC(iv))
+            decryptor = cipher.decryptor()
+            plaintext = decryptor.update(encrypted_stores) + decryptor.finalize()
+            unpadder = padding.PKCS7(128).unpadder()
+            plaintext = unpadder.update(plaintext) + unpadder.finalize()
+            plaintext = plaintext.decode("utf-8")
+        return plaintext

-    if usePycryptodome:
-        cipher = AES.new(key, AES.MODE_CBC, iv=iv)
-        plaintext = cipher.decrypt(encrypted_stores)
-        plaintext = unpad(plaintext, 16, style="pkcs7")
+    if not password is None:
+        try:
+            key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
+        except:
+            raise Exception("yfinance failed to decrypt Yahoo data response")
+        plaintext = _decrypt(encrypted_stores, password, key, iv)
    else:
-        cipher = Cipher(algorithms.AES(key), modes.CBC(iv))
-        decryptor = cipher.decryptor()
-        plaintext = decryptor.update(encrypted_stores) + decryptor.finalize()
-        unpadder = padding.PKCS7(128).unpadder()
-        plaintext = unpadder.update(plaintext) + unpadder.finalize()
-        plaintext = plaintext.decode("utf-8")
+        success = False
+        for i in range(len(candidate_passwords)):
+            # print(f"Trying candiate pw {i+1}/{len(candidate_passwords)}")
+            password = candidate_passwords[i]
+            try:
+                key, iv = _EVPKDF(password, salt, keySize=32, ivSize=16, iterations=1, hashAlgorithm="md5")
+
+                plaintext = _decrypt(encrypted_stores, password, key, iv)
+
+                success = True
+                break
+            except:
+                pass
+        if not success:
+            raise Exception("yfinance failed to decrypt Yahoo data response")

    decoded_stores = json.loads(plaintext)
    return decoded_stores
@@ -153,6 +204,72 @@ 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
+
+        key_count = 4
+        re_script = soup.find("script", string=re.compile("root.App.main")).text
+        re_data = json.loads(re.search("root.App.main\s+=\s+(\{.*\})", re_script).group(1))
+        re_data.pop("context", None)
+        key_list = list(re_data.keys())
+        if re_data.get("plugins"):  # 1) attempt to get last 4 keys after plugins
+            ind = key_list.index("plugins")
+            if len(key_list) > ind+1:
+                sub_keys = key_list[ind+1:]
+                if len(sub_keys) == key_count:
+                    re_obj = {}
+                    missing_val = False
+                    for k in sub_keys:
+                        if not re_data.get(k):
+                            missing_val = True
+                            break
+                        re_obj.update({k: re_data.get(k)})
+                    if not missing_val:
+                        result = re_obj
+
+        if not result is None:
+            return [''.join(result.values())]
+
+        re_keys = []    # 2) attempt scan main.js file approach to get keys
+        prefix = "https://s.yimg.com/uc/finance/dd-site/js/main."
+        tags = [tag['src'] for tag in soup.find_all('script') if prefix in tag.get('src', '')]
+        for t in tags:
+            response_js = self.cache_get(t)
+            #
+            if response_js.status_code != 200:
+                time.sleep(random.randrange(10, 20))
+                response_js.close()
+            else:
+                r_data = response_js.content.decode("utf8")
+                re_list = [
+                    x.group() for x in re.finditer(r"context.dispatcher.stores=JSON.parse((?:.*?\r?\n?)*)toString", r_data)
+                ]
+                for rl in re_list:
+                    re_sublist = [x.group() for x in re.finditer(r"t\[\"((?:.*?\r?\n?)*)\"\]", rl)]
+                    if len(re_sublist) == key_count:
+                        re_keys = [sl.replace('t["', '').replace('"]', '') for sl in re_sublist]
+                        break
+                response_js.close()
+            if len(re_keys) == key_count:
+                break
+        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 []
+
    @lru_cache_freezeargs
    @lru_cache(maxsize=cache_maxsize)
    def get_json_data_stores(self, sub_page: str = None, proxy=None) -> dict:
@@ -164,7 +281,8 @@ class TickerData:
        else:
            ticker_url = "{}/{}".format(_SCRAPE_URL_, self.ticker)

-        html = self.get(url=ticker_url, proxy=proxy).text
+        response = self.get(url=ticker_url, proxy=proxy)
+        html = response.text

        # The actual json-data for stores is in a javascript assignment in the webpage
        try:
@@ -176,15 +294,37 @@ class TickerData:

        data = json.loads(json_str)

-        if "_cs" in data and "_cr" in data:
-            data = decrypt_cryptojs_aes(data)
+        # 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:
+            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()

-        if "context" in data and "dispatcher" in data["context"]:
-            # Keep old code, just in case
-            data = data['context']['dispatcher']['stores']
+        # Decrypt!
+        stores = decrypt_cryptojs_aes_stores(data, keys)
+        if stores is None:
+            # Maybe Yahoo returned old format, not encrypted
+            if "context" in data and "dispatcher" in data["context"]:
+                stores = data['context']['dispatcher']['stores']
+        if stores is None:
+            raise Exception(f"{self.ticker}: Failed to extract data stores from web request")

        # return data
-        new_data = json.dumps(data).replace('{}', 'null')
+        new_data = json.dumps(stores).replace('{}', 'null')
        new_data = re.sub(
            r'{[\'|\"]raw[\'|\"]:(.*?),(.*?)}', r'\1', new_data)

--- a/yfinance/multi.py
+++ b/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=True,
+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 True
+            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()
--- a/yfinance/scrapers/fundamentals.py
+++ b/yfinance/scrapers/fundamentals.py
@@ -195,7 +195,7 @@ class Financials:
        url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
        # Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
        start_dt = datetime.datetime(2016, 12, 31)
-        end = (datetime.datetime.now() + datetime.timedelta(days=366))
+        end = pd.Timestamp.utcnow().ceil("D")
        url += "&period1={}&period2={}".format(int(start_dt.timestamp()), int(end.timestamp()))

        # Step 3: fetch and reshape data
--- a/yfinance/scrapers/quote.py
+++ b/yfinance/scrapers/quote.py
@@ -7,6 +7,530 @@ from yfinance import utils
 from yfinance.data import TickerData


+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", "52WeekChange", "fiftyDayAverage", "twoHundredDayAverage"})
+info_retired_keys_price.update({"averageDailyVolume10Day", "averageVolume10days", "averageVolume"})
+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
+
+
+PRUNE_INFO = True
+# PRUNE_INFO = False
+_BASIC_URL_ = "https://query2.finance.yahoo.com/v10/finance/quoteSummary"
+
+
+from collections.abc import MutableMapping
+class InfoDictWrapper(MutableMapping):
+    """ Simple wrapper around info dict, intercepting 'gets' to 
+    print how-to-migrate messages for specific keys. Requires
+    override dict API"""
+
+    def __init__(self, info):
+        self.info = info
+
+    def keys(self):
+        return self.info.keys()
+
+    def __str__(self):
+        return self.info.__str__()
+
+    def __repr__(self):
+        return self.info.__repr__()
+
+    def __contains__(self, k):
+        return k in self.info.keys()
+
+    def __getitem__(self, k):
+        if k in info_retired_keys_price:
+            print(f"Price data removed from info (key='{k}'). Use Ticker.fast_info or history() instead")
+            return None
+        elif k in info_retired_keys_exchange:
+            print(f"Exchange data removed from info (key='{k}'). Use Ticker.fast_info or Ticker.get_history_metadata() instead")
+            return None
+        elif k in info_retired_keys_marketCap:
+            print(f"Market cap removed from info (key='{k}'). Use Ticker.fast_info instead")
+            return None
+        elif k in info_retired_keys_symbol:
+            print(f"Symbol removed from info (key='{k}'). You know this already")
+            return None
+        return self.info[self._keytransform(k)]
+
+    def __setitem__(self, k, value):
+        self.info[self._keytransform(k)] = value
+
+    def __delitem__(self, k):
+        del self.info[self._keytransform(k)]
+
+    def __iter__(self):
+        return iter(self.info)
+    
+    def __len__(self):
+        return len(self.info)
+
+    def _keytransform(self, k):
+        return k
+
+
+class FastInfo:
+    # Contain small subset of info[] items that can be fetched faster elsewhere.
+    # Imitates a dict.
+    def __init__(self, tickerBaseObject):
+        utils.print_once("Note: 'info' dict is now fixed & improved, 'fast_info' no longer faster")
+
+        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
+
+        self._shares = None
+        self._mcap = None
+
+        self._open = None
+        self._day_high = None
+        self._day_low = None
+        self._last_price = None
+        self._last_volume = None
+
+        self._prev_close = None
+
+        self._reg_prev_close = None
+
+        self._50d_day_average = None
+        self._200d_day_average = None
+        self._year_high = None
+        self._year_low = None
+        self._year_change = None
+
+        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):
+        return self._public_keys
+    def items(self):
+        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:
+            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()
+    def __iter__(self):
+        return iter(self.keys())
+
+    def __str__(self):
+        return "lazy-loading dict with keys = " + str(self.keys())
+    def __repr__(self):
+        return self.__str__()
+
+    def toJSON(self, indent=4):
+        d = {k:self[k] for k in self.keys()}
+        return _json.dumps({k:self[k] for k in self.keys()}, indent=indent)
+
+    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, keepna=True)
+            self._md = self._tkr.get_history_metadata()
+            try:
+                ctp = self._md["currentTradingPeriod"]
+                self._today_open = pd.to_datetime(ctp["regular"]["start"], unit='s', utc=True).tz_convert(self.timezone)
+                self._today_close = pd.to_datetime(ctp["regular"]["end"], unit='s', utc=True).tz_convert(self.timezone)
+                self._today_midnight = self._today_close.ceil("D")
+            except:
+                self._today_open = None
+                self._today_close = None
+                self._today_midnight = None
+                raise
+
+        if self._prices_1y.empty:
+            return self._prices_1y
+
+        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
+            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:
+            return self._md
+
+        self._get_1y_prices()
+        self._md = self._tkr.get_history_metadata()
+        return self._md
+
+    def _exchange_open_now(self):
+        t = pd.Timestamp.utcnow()
+        self._get_exchange_metadata()
+
+        # if self._today_open is None and self._today_close is None:
+        #     r = False
+        # else:
+        #     r = self._today_open <= t and t < self._today_close
+
+        # if self._today_midnight is None:
+        #     r = False
+        # elif self._today_midnight.date() > t.tz_convert(self.timezone).date():
+        #     r = False
+        # else:
+        #     r = t < self._today_midnight
+
+        last_day_cutoff = self._get_1y_prices().index[-1] + datetime.timedelta(days=1)
+        last_day_cutoff += datetime.timedelta(minutes=20)
+        r = t < last_day_cutoff
+
+        # print("_exchange_open_now() returning", r)
+        return r
+
+    @property
+    def currency(self):
+        if self._currency is not None:
+            return self._currency
+
+        if self._tkr._history_metadata is None:
+            self._get_1y_prices()
+        md = self._tkr.get_history_metadata()
+        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:
+            return self._exchange
+
+        self._exchange = self._get_exchange_metadata()["exchangeName"]
+        return self._exchange
+
+    @property
+    def timezone(self):
+        if self._timezone is not None:
+            return self._timezone
+
+        self._timezone = self._get_exchange_metadata()["exchangeTimezoneName"]
+        return self._timezone
+
+    @property
+    def shares(self):
+        if self._shares is not None:
+            return self._shares
+
+        shares = self._tkr.get_shares_full(start=pd.Timestamp.utcnow().date()-pd.Timedelta(days=548))
+        if shares is None:
+            # Requesting 18 months failed, so fallback to shares which should include last year
+            shares = self._tkr.get_shares()
+        if shares is not None:
+            if isinstance(shares, pd.DataFrame):
+                shares = shares[shares.columns[0]]
+            self._shares = int(shares.iloc[-1])
+        return self._shares
+
+    @property
+    def last_price(self):
+        if self._last_price is not None:
+            return self._last_price
+        prices = self._get_1y_prices()
+        if prices.empty:
+            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_1wk_1h_prepost_prices()
+        fail = False
+        if prices.empty:
+            fail = True
+        else:
+            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
+    def regular_market_previous_close(self):
+        if self._reg_prev_close is not None:
+            return self._reg_prev_close
+        prices = self._get_1y_prices()
+        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
+            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
+
+    @property
+    def open(self):
+        if self._open is not None:
+            return self._open
+        prices = self._get_1y_prices()
+        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
+    def day_high(self):
+        if self._day_high is not None:
+            return self._day_high
+        prices = self._get_1y_prices()
+        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
+    def day_low(self):
+        if self._day_low is not None:
+            return self._day_low
+        prices = self._get_1y_prices()
+        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
+    def last_volume(self):
+        if self._last_volume is not None:
+            return self._last_volume
+        prices = self._get_1y_prices()
+        self._last_volume = None if prices.empty else int(prices["Volume"].iloc[-1])
+        return self._last_volume
+
+    @property
+    def fifty_day_average(self):
+        if self._50d_day_average is not None:
+            return self._50d_day_average
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._50d_day_average = None
+        else:
+            n = prices.shape[0]
+            a = n-50
+            b = n
+            if a < 0:
+                a = 0
+            self._50d_day_average = float(prices["Close"].iloc[a:b].mean())
+
+        return self._50d_day_average
+
+    @property
+    def two_hundred_day_average(self):
+        if self._200d_day_average is not None:
+            return self._200d_day_average
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._200d_day_average = None
+        else:
+            n = prices.shape[0]
+            a = n-200
+            b = n
+            if a < 0:
+                a = 0
+
+            self._200d_day_average = float(prices["Close"].iloc[a:b].mean())
+
+        return self._200d_day_average
+
+    @property
+    def ten_day_average_volume(self):
+        if self._10d_avg_vol is not None:
+            return self._10d_avg_vol
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._10d_avg_vol = None
+        else:
+            n = prices.shape[0]
+            a = n-10
+            b = n
+            if a < 0:
+                a = 0
+            self._10d_avg_vol = int(prices["Volume"].iloc[a:b].mean())
+
+        return self._10d_avg_vol
+
+    @property
+    def three_month_average_volume(self):
+        if self._3mo_avg_vol is not None:
+            return self._3mo_avg_vol
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        if prices.empty:
+            self._3mo_avg_vol = None
+        else:
+            dt1 = prices.index[-1]
+            dt0 = dt1 - utils._interval_to_timedelta("3mo") + utils._interval_to_timedelta("1d")
+            self._3mo_avg_vol = int(prices.loc[dt0:dt1, "Volume"].mean())
+
+        return self._3mo_avg_vol
+
+    @property
+    def year_high(self):
+        if self._year_high is not None:
+            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
+
+    @property
+    def year_low(self):
+        if self._year_low is not None:
+            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
+
+    @property
+    def year_change(self):
+        if self._year_change is not None:
+            return self._year_change
+
+        prices = self._get_1y_prices(fullDaysOnly=True)
+        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
+    def market_cap(self):
+        if self._mcap is not None:
+            return self._mcap
+
+        try:
+            shares = self.shares
+        except Exception as e:
+            if "Cannot retrieve share count" in str(e):
+                shares = None
+            else:
+                raise
+
+        if shares is None:
+            # Very few symbols have marketCap despite no share count.
+            # E.g. 'BTC-USD'
+            # So fallback to original info[] if available.
+            self._tkr.info
+            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
+
+
 class Quote:

    def __init__(self, data: TickerData, proxy=None):
@@ -14,18 +538,22 @@ class Quote:
        self.proxy = proxy

        self._info = None
+        self._retired_info = None
        self._sustainability = None
        self._recommendations = None
        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

@@ -130,6 +658,19 @@ class Quote:
        except Exception:
            pass

+        # Delete redundant info[] keys, because values can be accessed faster
+        # elsewhere - e.g. price keys. Hope is reduces Yahoo spam effect.
+        # But record the dropped keys, because in rare cases they are needed.
+        self._retired_info = {}
+        for k in info_retired_keys:
+            if k in self._info:
+                self._retired_info[k] = self._info[k]
+                if PRUNE_INFO:
+                    del self._info[k]
+        if PRUNE_INFO:
+            # InfoDictWrapper will explain how to access above data elsewhere
+            self._info = InfoDictWrapper(self._info)
+
        # events
        try:
            cal = pd.DataFrame(quote_summary_store['calendarEvents']['earnings'])
@@ -155,12 +696,56 @@ 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
+        modules = ['summaryProfile', 'financialData', 'quoteType',
+                     'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
+        result = self._data.get_raw_json(
+            _BASIC_URL_ + f"/{self._data.ticker}", params={"modules": ",".join(modules), "ssl": "true"}, proxy=proxy
+        )
+        result["quoteSummary"]["result"][0]["symbol"] = self._data.ticker
+        query1_info = next(
+            (info for info in result.get("quoteSummary", {}).get("result", []) if info["symbol"] == self._data.ticker),
+            None,
+        )
+        # Most keys that appear in multiple dicts have same value. Except 'maxAge' because
+        # Yahoo not consistent with days vs seconds. Fix it here:
+        for k in query1_info:
+            if "maxAge" in query1_info[k] and query1_info[k]["maxAge"] == 1:
+                query1_info[k]["maxAge"] = 86400
+        query1_info = {
+            k1: v1 
+            for k, v in query1_info.items() 
+            if isinstance(v, dict) 
+            for k1, v1 in v.items() 
+            if v1
+        }
+        # recursively format but only because of 'companyOfficers'
+        def _format(k, v):
+            if isinstance(v, dict) and "raw" in v and "fmt" in v:
+                v2 = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
+            elif isinstance(v, list):
+                v2 = [_format(None, x) for x in v]
+            elif isinstance(v, dict):
+                v2 = {k:_format(k, x) for k, x in v.items()}
+            elif isinstance(v, str):
+                v2 = v.replace("\xa0", " ")
+            else:
+                v2 = v
+            return v2
+        for k, v in query1_info.items():
+            query1_info[k] = _format(k, v)
+        self._info = query1_info

-        self._scrape(proxy)
+    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

@@ -194,17 +779,22 @@ class Quote:
            for k in keys:
                url += "&type=" + k
            # Request 6 months of data
-            url += "&period1={}".format(
-                int((datetime.datetime.now() - datetime.timedelta(days=365 // 2)).timestamp()))
-            url += "&period2={}".format(int((datetime.datetime.now() + datetime.timedelta(days=1)).timestamp()))
+            start = pd.Timestamp.utcnow().floor("D") - datetime.timedelta(days=365 // 2)
+            start = int(start.timestamp())
+            end = pd.Timestamp.utcnow().ceil("D")
+            end = int(end.timestamp())
+            url += f"&period1={start}&period2={end}"

            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
--- a/yfinance/scrapers/yahoo-keys.txt
+++ b/yfinance/scrapers/yahoo-keys.txt
@@ -0,0 +1,8 @@
+daf93e37cbf219cd4c1f3f74ec4551265ec5565b99e8c9322dccd6872941cf13c818cbb88cba6f530e643b4e2329b17ec7161f4502ce6a02bb0dbbe5fc0d0474
+ad4d90b3c9f2e1d156ef98eadfa0ff93e4042f6960e54aa2a13f06f528e6b50ba4265a26a1fd5b9cd3db0d268a9c34e1d080592424309429a58bce4adc893c87
+e9a8ab8e5620b712ebc2fb4f33d5c8b9c80c0d07e8c371911c785cf674789f1747d76a909510158a7b7419e86857f2d7abbd777813ff64840e4cbc514d12bcae
+6ae2523aeafa283dad746556540145bf603f44edbf37ad404d3766a8420bb5eb1d3738f52a227b88283cca9cae44060d5f0bba84b6a495082589f5fe7acbdc9e
+3365117c2a368ffa5df7313a4a84988f73926a86358e8eea9497c5ff799ce27d104b68e5f2fbffa6f8f92c1fef41765a7066fa6bcf050810a9c4c7872fd3ebf0
+15d8f57919857d5a5358d2082c7ef0f1129cfacd2a6480333dcfb954b7bb67d820abefebfdb0eaa6ef18a1c57f617b67d7e7b0ec040403b889630ae5db5a4dbb
+db9630d707a7d0953ac795cd8db1ca9ca6c9d8239197cdfda24b4e0ec9c37eaec4db82dab68b8f606ab7b5b4af3e65dab50606f8cf508269ec927e6ee605fb78
+3c895fb5ddcc37d20d3073ed74ee3efad59bcb147c8e80fd279f83701b74b092d503dcd399604c6d8be8f3013429d3c2c76ed5b31b80c9df92d5eab6d3339fce
--- a/yfinance/ticker.py
+++ b/yfinance/ticker.py
@@ -161,6 +161,22 @@ class Ticker(TickerBase):
    def quarterly_income_stmt(self) -> _pd.DataFrame:
        return self.get_income_stmt(pretty=True, freq='quarterly')

+    @property
+    def incomestmt(self) -> _pd.DataFrame:
+        return self.income_stmt
+
+    @property
+    def quarterly_incomestmt(self) -> _pd.DataFrame:
+        return self.quarterly_income_stmt
+
+    @property
+    def financials(self) -> _pd.DataFrame:
+        return self.income_stmt
+
+    @property
+    def quarterly_financials(self) -> _pd.DataFrame:
+        return self.quarterly_income_stmt
+
    @property
    def balance_sheet(self) -> _pd.DataFrame:
        return self.get_balance_sheet(pretty=True)
@@ -177,13 +193,21 @@ class Ticker(TickerBase):
    def quarterly_balancesheet(self) -> _pd.DataFrame:
        return self.quarterly_balance_sheet

+    @property
+    def cash_flow(self) -> _pd.DataFrame:
+        return self.get_cash_flow(pretty=True, freq="yearly")
+
+    @property
+    def quarterly_cash_flow(self) -> _pd.DataFrame:
+        return self.get_cash_flow(pretty=True, freq='quarterly')
+
    @property
    def cashflow(self) -> _pd.DataFrame:
-        return self.get_cashflow(pretty=True, freq="yearly")
+        return self.cash_flow

    @property
    def quarterly_cashflow(self) -> _pd.DataFrame:
-        return self.get_cashflow(pretty=True, freq='quarterly')
+        return self.quarterly_cash_flow

    @property
    def recommendations_summary(self):
--- a/yfinance/tickers.py
+++ b/yfinance/tickers.py
@@ -34,12 +34,8 @@ class Tickers:
        tickers = tickers if isinstance(
            tickers, list) else tickers.replace(',', ' ').split()
        self.symbols = [ticker.upper() for ticker in tickers]
-        ticker_objects = {}
+        self.tickers = {ticker:Ticker(ticker, session=session) for ticker in self.symbols}

-        for ticker in self.symbols:
-            ticker_objects[ticker] = Ticker(ticker, session=session)
-
-        self.tickers = ticker_objects
        # self.tickers = _namedtuple(
        #     "Tickers", ticker_objects.keys(), rename=True
        # )(*ticker_objects.values())
--- a/yfinance/utils.py
+++ b/yfinance/utils.py
@@ -35,6 +35,7 @@ import os as _os
 import appdirs as _ad
 import sqlite3 as _sqlite3
 import atexit as _atexit
+from functools import lru_cache

 from threading import Lock

@@ -49,6 +50,25 @@ user_agent_headers = {
    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}


+# From https://stackoverflow.com/a/59128615
+from types import FunctionType
+from inspect import getmembers
+def attributes(obj):
+    disallowed_names = {
+      name for name, value in getmembers(type(obj)) 
+        if isinstance(value, FunctionType)}
+    return {
+      name: getattr(obj, name) for name in dir(obj) 
+        if name[0] != '_' and name not in disallowed_names and hasattr(obj, name)}
+
+
+@lru_cache(maxsize=20)
+def print_once(msg):
+    # 'warnings' module suppression of repeat messages does not work. 
+    # This function replicates correct behaviour
+    print(msg)
+
+
 def is_isin(string):
    return bool(_re.match("^([A-Z]{2})([A-Z0-9]{9})([0-9]{1})$", string))

@@ -288,6 +308,11 @@ 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:
@@ -307,7 +332,11 @@ def _parse_user_dt(dt, exchange_tz):

 def _interval_to_timedelta(interval):
    if interval == "1mo":
-        return _dateutil.relativedelta(months=1)
+        return _dateutil.relativedelta.relativedelta(months=1)
+    elif interval == "3mo":
+        return _dateutil.relativedelta.relativedelta(months=3)
+    elif interval == "1y":
+        return _dateutil.relativedelta.relativedelta(years=1)
    elif interval == "1wk":
        return _pd.Timedelta(days=7, unit='d')
    else: 
@@ -427,6 +456,35 @@ 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. 
@@ -502,7 +560,7 @@ 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
@@ -607,7 +665,7 @@ def safe_merge_dfs(df_main, df_sub, interval):
        if interval.endswith('m') or interval.endswith('h') or interval == "1d":
            # Update: is possible with daily data when dividend very recent
            f_missing = ~df_sub.index.isin(df.index)
-            df_sub_missing = df_sub[f_missing]
+            df_sub_missing = df_sub[f_missing].copy()
            keys = {"Adj Open", "Open", "Adj High", "High", "Adj Low", "Low", "Adj Close",
                    "Close"}.intersection(df.columns)
            df_sub_missing[list(keys)] = _np.nan
@@ -640,6 +698,71 @@ 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
@@ -702,7 +825,14 @@ 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)

@@ -743,8 +873,10 @@ class _TzCache:
    """Simple sqlite file cache of ticker->timezone"""

    def __init__(self):
-        self._tz_db = None
        self._setup_cache_folder()
+        # Must init db here, where is thread-safe
+        self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
+        self._migrate_cache_tkr_tz()

    def _setup_cache_folder(self):
        if not _os.path.isdir(self._db_dir):
@@ -776,11 +908,6 @@ class _TzCache:

    @property
    def tz_db(self):
-        # lazy init
-        if self._tz_db is None:
-            self._tz_db = _KVStore(_os.path.join(self._db_dir, "tkr-tz.db"))
-            self._migrate_cache_tkr_tz()
-
        return self._tz_db

    def _migrate_cache_tkr_tz(self):
--- a/yfinance/version.py
+++ b/yfinance/version.py
@@ -1 +1 @@
-version = "0.2.0rc5"
+version = "0.2.15"
Author	SHA1	Message	Date
ValueRaider	4152f7c897	Bump version to 0.2.15	2023-04-09 21:07:16 +01:00
ValueRaider	fc4350e463	Merge pull request #1480 from kennykos/get_full_info 'info' fetch now gets same data as scrape	2023-04-09 21:01:34 +01:00
ValueRaider	13556afd90	README.md: reorganise & link to 'How to contribute'	2023-04-07 12:21:01 +01:00
Value Raider	6a63ce9e15	Demote 'fast_info' Demote 'fast_info': - inform user can revert to 'info' - remove from README - relocate class from base.py -> quote.py	2023-04-06 12:21:57 +01:00
kennykos	63699a6aad	'info' fetch now gets same data as scrape * Changed base url to "https://query2.finance.yahoo.com/v10/finance/quoteSummary" * instead of just getting the quote, we now get * ``` items = ['summaryProfile', 'financialData', 'quoteType', 'defaultKeyStatistics', 'assetProfile', 'summaryDetail'] ``` which is the same as in the scrape function	2023-04-05 18:23:36 +01:00
Value Raider	5367f62bd7	Bump version to 0.2.14	2023-03-25 11:39:21 +00:00
ValueRaider	27cb90c596	Merge pull request #1461 from qianyun210603/main Add failback for decryption error in info interface	2023-03-25 11:33:27 +00:00
BookSword	6c2682654a	Fetch 'info' dict via API	2023-03-24 18:04:07 +00:00
Value Raider	ef1205388c	Bump version to 0.2.13	2023-03-21 18:56:32 +00:00
Value Raider	bb477989d4	Fix price-events merge when occurred pre-market	2023-03-21 18:52:35 +00:00
ValueRaider	478dc0a350	Merge pull request #1452 from ranaroussi/hotfix/prices-merge-events Fix filtering events older than prices for merging	2023-03-21 18:16:29 +00:00
ValueRaider	195a7aa304	Merge pull request #1455 from mppics/fix/aggregate_capital_gains Adding fix and test for aggregating Capital Gains	2023-03-18 17:24:53 +00:00
Matt Piccoli	a58d7456fe	Adding fix and test for aggregating Capital Gains	2023-03-18 12:57:26 -04:00
ValueRaider	1edeaf07dc	Merge pull request #1448 from ivan23kor/feature/clarify-end-argument Clarify that interval is [start; end) in docstrings	2023-03-09 22:04:58 +00:00
Ivan Korostelev	7f04a9dcb6	Clarify that interval is [start; end) in docstrings	2023-03-09 14:27:21 -07:00
ValueRaider	7b95f554bd	README: fix rate-limiting example	2023-02-21 12:24:35 +00:00
Value Raider	ca8c1c8cb4	Bump version to 0.2.12	2023-02-16 12:01:25 +00:00
ValueRaider	6b8b0d5c86	Merge pull request #1422 from ranaroussi/hotfix/disable-decrypt-fail-msg Disable annoying 'backup decrypt' msg	2023-02-16 12:00:16 +00:00
Value Raider	952a04338f	Disable annoying 'backup decrypt' msg	2023-02-15 16:46:55 +00:00
ValueRaider	62a442bd15	Update yahoo-keys.txt	2023-02-14 00:06:06 +00:00
ValueRaider	e96f4f3cc0	Update yahoo-keys.txt	2023-02-12 09:57:25 +00:00
ValueRaider	cd5d0dfc3b	Bump version to 0.2.11	2023-02-10 16:59:20 +00:00
ValueRaider	ece41cdb06	Merge pull request #1411 from sdeibel/main Fix format_history_metadata for some symbols	2023-02-10 16:30:03 +00:00
ValueRaider	c362d54b1a	Fix other metadata accesses + tests	2023-02-09 19:41:50 +00:00
Stephan Deibel	543e4fe582	Fix format_history_metadata for some symbols Fix format_history_metadata when firstTradeDate is None, as is the case for QCSTIX and probably others.	2023-02-09 13:46:52 -05:00
ValueRaider	53fca7016e	Bump version to 0.2.10	2023-02-07 22:05:17 +00:00
ValueRaider	4b6529c3a5	Merge pull request #1406 from ranaroussi/dev dev -> main	2023-02-07 22:03:20 +00:00
ValueRaider	8957147926	Merge branch 'main' into dev	2023-02-07 22:02:46 +00:00
ValueRaider	4c7392ed17	Merge pull request #1403 from ranaroussi/fix/decrypt-keys Fix decrypt keys	2023-02-07 21:55:33 +00:00
ValueRaider	0efda4f5af	Fix filtering events older than prices for merging	2023-02-07 21:45:35 +00:00
ValueRaider	508de4aefb	Dev version 0.2.10b3	2023-02-07 14:09:08 +00:00
ValueRaider	3d39992280	Add resilience to price repair When calibrating price repair, use weighted average to estimate stock split ratio, is more resilient	2023-02-07 14:07:08 +00:00
ValueRaider	b462836540	Merge pull request #1385 from ranaroussi/fix/download-tz-behaviour Restore original download() timezone handling	2023-02-07 13:16:03 +00:00
ValueRaider	2795660c28	Add a 5th backup key	2023-02-07 13:10:03 +00:00
ValueRaider	3dc87753ea	Fix _get_decryption_keys_from_yahoo_js() returning ''	2023-02-07 13:09:49 +00:00
ValueRaider	645cc19037	Merge pull request #1379 from ranaroussi/feature/improve-decrypt Add another backup decrypt option	2023-02-06 22:24:22 +00:00
ValueRaider	86d6acccf7	Fix dumb bugs in price repair - 1 more	2023-02-05 18:17:47 +00:00
ValueRaider	4fa32a98ed	Merge pull request #1397 from Matt-Seath/dev Catch TypeError Exception	2023-02-05 13:49:48 +00:00
Matt Seath	35f4071c0b	Catch TypeError Exception Addresses recent issue where calling Ticker.info would occasionally result in a TypeError Exception at line 287.	2023-02-05 11:49:40 +10:00
ValueRaider	86b00091a9	Fix dumb bugs in price repair	2023-02-02 21:57:55 +00:00
ValueRaider	2a2928b4a0	Fix 'tradingPeriods' parsing when empty - 0.2.10b2	2023-02-01 13:31:54 +00:00
ValueRaider	d47133e5bf	Dev version 0.2.10b1	2023-01-31 22:12:11 +00:00
ValueRaider	8f0c58dafa	Dev version 0.2.10b0	2023-01-31 22:02:41 +00:00
ValueRaider	27a721c7dd	Merge pull request #1380 from ranaroussi/fix/old-sqlite-error Allow using sqlite3 < 3.8.2	2023-01-31 19:52:22 +00:00
ValueRaider	3e964d5319	Merge pull request #1383 from ranaroussi/fix/fast-info-prepost Fix fast_info["previousClose"]	2023-01-31 19:51:46 +00:00
ValueRaider	84a31ae0b4	Merge pull request #1311 from ranaroussi/feature/prices-metadata-prune-prepost Drop intraday intervals if in post-market but prepost=False	2023-01-31 19:50:00 +00:00
ValueRaider	891b533ec2	Drop intraday intervals if in prepost but prepost=False	2023-01-31 19:48:47 +00:00
ValueRaider	b9fb3e4979	Restore original download() tz handling: day/week/etc = ignore	2023-01-31 00:00:45 +00:00
ValueRaider	09342982a4	Add 'quoteType'. Improve handling tickers without trading	2023-01-30 23:53:06 +00:00
ValueRaider	da8c49011e	fast_info: Fix previousClose & yearChange	2023-01-30 16:06:55 +00:00
ValueRaider	b805f0a010	Add another backup decrypt option	2023-01-29 23:09:45 +00:00
ValueRaider	5b0feb3d20	Fix tests	2023-01-29 16:53:26 +00:00
ValueRaider	ecbfc2957d	bug_report: tighten language (again)	2023-01-29 13:58:02 +00:00
ValueRaider	e96248dec7	README: fix narrative ordering	2023-01-29 13:52:13 +00:00
ValueRaider	7d0045f03c	README: simplify API overview with link to Wiki	2023-01-29 13:49:01 +00:00
ValueRaider	c3d7449844	Merge pull request #1289 from ranaroussi/fix/price-repair Fix & improve price repair	2023-01-29 13:02:48 +00:00
ValueRaider	a4f11b0243	Fix price repair tests, remove unrelated changes	2023-01-29 13:01:54 +00:00
ValueRaider	1702fd0797	bug_report: tighten language	2023-01-29 00:54:27 +00:00
ValueRaider	464b3333d7	Allow using sqlite3 < 3.8.2	2023-01-29 00:34:46 +00:00
ValueRaider	685f2ec351	Merge branch 'dev' into fix/price-repair	2023-01-28 23:26:56 +00:00
ValueRaider	aad46baf28	price repair: Fix 'min_dt', add 'silent' mode	2023-01-28 23:14:28 +00:00
ValueRaider	a97db0aac6	README: add how-to for requests rate-limiting	2023-01-28 23:10:38 +00:00
ValueRaider	af5f96f97e	Merge pull request #1368 from ranaroussi/fix/fast-info-camel-case `fast_info` usability improvements	2023-01-28 22:28:42 +00:00
ValueRaider	a4bdaea888	fast_info: add camelCase, items() & values()	2023-01-28 22:27:51 +00:00
ValueRaider	ac5a9d2793	Merge pull request #1367 from ranaroussi/main main -> dev	2023-01-27 22:09:59 +00:00
ValueRaider	b17ad32a47	Merge pull request #1366 from ranaroussi/doc/readme-explain-instability README: comment on instability, tidy Ticker 'Quick start'	2023-01-27 18:31:32 +00:00
ValueRaider	af39855e28	README: comment on instability, tidy Ticker 'Quick start'	2023-01-27 17:36:25 +00:00
ValueRaider	ac6e047f0d	Bump version to 0.2.9	2023-01-26 22:21:46 +00:00
ValueRaider	1e24337f29	Bump version to 0.2.8	2023-01-26 22:20:11 +00:00
ValueRaider	2cc82ae12f	Merge pull request #1362 from ranaroussi/hotfix/fast-info-bugs Ticker.fast_info: fix teething bugs	2023-01-26 22:03:06 +00:00
ValueRaider	d11f385049	Make fast_info JSON-serializable via toJSON()	2023-01-26 21:45:53 +00:00
ValueRaider	7377611e1f	Add 'get(key, default)' to fast_info	2023-01-26 21:23:31 +00:00
ValueRaider	f3b5fb85c9	Remove exception raise from 'get_shares_full()'	2023-01-26 21:14:48 +00:00
ValueRaider	a4faef83ac	'fast_info' fixes: unusual symbols ; improve migration message ; 'regular_market_previous_close'	2023-01-26 21:02:18 +00:00
ValueRaider	e1184f745b	Update yahoo-keys.txt	2023-01-26 17:06:03 +00:00
ValueRaider	fe630008e9	Bump version to 0.2.7	2023-01-26 17:03:00 +00:00
ValueRaider	b43072cf0a	Merge pull request #1354 from ranaroussi/hotfix/rename-basic-info Rename 'basic_info' -> 'fast_info'	2023-01-26 17:00:54 +00:00
ValueRaider	ad3f4cabc9	Improve 'get_shares_full()' error handling	2023-01-26 16:58:26 +00:00
ValueRaider	f70567872c	Merge pull request #1353 from ranaroussi/hotfix/smart-decryption Add decrypt key extraction from JS + GitHub backup	2023-01-26 16:44:23 +00:00
ValueRaider	a8ade72113	Rename 'basic_info' -> 'fast_info' ; Fix info tests	2023-01-26 16:36:25 +00:00
ValueRaider	1dcc8c9c8b	Remove dead debug code	2023-01-26 14:57:15 +00:00
ValueRaider	dd5462b307	Add decrypt key extraction from JS + GitHub backup	2023-01-26 14:52:18 +00:00
ValueRaider	e39c03e8e3	Hardcode decrypt keys in GitHub for fix w/o PIP `yfinance` will query this file via web request as a last resort. Avoids having to release a new PIP version just for a key update.	2023-01-26 14:20:03 +00:00
ValueRaider	9297504b84	Merge pull request #1346 from ranaroussi/main main -> dev sync	2023-01-25 22:16:22 +00:00
ValueRaider	3971115ab9	Bump version to 0.2.6	2023-01-25 19:10:31 +00:00
ValueRaider	b5badbbc61	Merge pull request #1342 from ranaroussi/hotfix/basic_info Fix 'Ticker.basic_info' lazy-loading	2023-01-25 19:09:37 +00:00
ValueRaider	ba8621f5be	Fix Ticker.basic_info.keys() calling each method	2023-01-25 18:35:54 +00:00
ValueRaider	8e5c94a4eb	Bump version to 0.2.5	2023-01-25 16:45:30 +00:00
ValueRaider	66a1c1a174	Merge pull request #1337 from ranaroussi/dev dev -> main	2023-01-25 16:40:56 +00:00
ValueRaider	ab6214df79	Merge pull request #1336 from ranaroussi/hotfix/decryption Hardcode decryption keys	2023-01-25 16:40:38 +00:00
ValueRaider	dc5d42c8e2	Add another key	2023-01-25 15:46:07 +00:00
ValueRaider	ab75495cd3	Hardcode decryption keys	2023-01-25 14:45:04 +00:00
ValueRaider	39c1ecc7a2	Improve price repair - reduce spam, improve data reliability Extend 'reconstruct groups' to reduce Yahoo spam ; Extend fetch range to avoid first/last day irregularities ; Improve handling of 'max fetch days' Yahoo limit	2023-01-25 14:37:43 +00:00
ValueRaider	af7720668c	Merge pull request #1328 from CollieIsCute/main use dict comprehension to improve speed	2023-01-25 13:42:44 +00:00
Collie Tsai	9051fba601	use dict comprehension to improve speed	2023-01-25 21:15:54 +08:00
ValueRaider	03ea6acec0	Merge pull request #1317 from ranaroussi/feature/prune-info `Ticker.basic_info` - fast but minimal alternative to `info[]`	2023-01-25 11:28:22 +00:00
ValueRaider	ddc93033d7	Reorder contents of bug_report.md	2023-01-23 11:53:00 +00:00
ValueRaider	eb6d830e2a	Fix repair volume=0 ; Tidy code	2023-01-21 23:00:30 +00:00
ValueRaider	2b0ae5a6c1	Remove 'repair_intervals'	2023-01-21 16:58:45 +00:00
ValueRaider	1636839b67	Handle request to reconstruct 1m	2023-01-20 00:13:28 +00:00
ValueRaider	65b97d024b	Improve reporting	2023-01-20 00:13:02 +00:00
ValueRaider	fb77d35863	Update README	2023-01-19 22:33:54 +00:00
ValueRaider	197d2968e3	Add 'repair_intervals', rename 'repair'->'repair_prices'	2023-01-19 22:19:16 +00:00
ValueRaider	7460dbea17	If reconstructing 1d interval with 1h, always request prepost	2023-01-19 22:18:46 +00:00
ValueRaider	b49fd797fc	Fix & improve price repair Fix repair calibration & volume=0 repair ; Extend repair to sub-hour ; Avoid attempting repair of mostly-NaN days	2023-01-19 22:18:46 +00:00
ValueRaider	6bd8fb2290	Improve test ; Add more keys to basic_info	2023-01-19 14:57:34 +00:00
ValueRaider	cd1e16ad9e	Add test ; Fix 1y price stats	2023-01-19 00:37:17 +00:00
ValueRaider	3fd9ea2204	Remove more info[] keys - #2	2023-01-18 16:55:31 +00:00
ValueRaider	d5a1266cbe	Remove more info[] keys	2023-01-17 20:13:32 +00:00
ValueRaider	89bbe8ad4c	Override Ticker.basic_info __str__()	2023-01-17 19:49:42 +00:00
ValueRaider	e44c6f8b0e	Add 'Ticker.basic_info'	2023-01-17 14:10:28 +00:00
ValueRaider	0ba810fda5	Improve 'history_metadata' formatting	2023-01-16 18:30:28 +00:00
ValueRaider	677bbfed8b	Add Ticker.market_cap helper ; Tidy info[] blacklist	2023-01-16 11:23:35 +00:00
ValueRaider	97671b78dd	Move info migrate msgs from 'is in' to '[]'	2023-01-14 23:11:02 +00:00
ValueRaider	2865c0df9f	Prune info[] with migration instructions Remove redundant keys from info[] that are better found elsewhere ; Print instructions if old keys accessed via InfoDictWrapper	2023-01-14 23:07:04 +00:00
ValueRaider	0c037ddd12	Bump version to 0.2.4	2023-01-14 22:58:53 +00:00
ValueRaider	3ee4674098	Merge pull request #1302 from ranaroussi/dev dev -> main	2023-01-14 22:58:33 +00:00
ValueRaider	5d9a91da4a	Improve 'get_shares_full()' error handling ; Minor fixes	2023-01-14 22:44:54 +00:00
ValueRaider	47c579ff22	Merge pull request #1297 from alexa-infra/fix-stores-decryption Fix stores decrypt	2023-01-14 20:06:52 +00:00
ValueRaider	caf5cba801	Merge pull request #1301 from ranaroussi/feature/share-count Feature/share count	2023-01-14 19:53:45 +00:00
ValueRaider	486c7894ce	get_shares_full(): convert to pd.Series, add test	2023-01-14 17:32:54 +00:00
ValueRaider	db8a00edae	get_shares_full(): remove caching, tidy API	2023-01-14 17:11:57 +00:00
ValueRaider	805523b924	Fix 'get_shares_full()' post-rebase	2023-01-14 16:58:58 +00:00
ValueRaider	32ab2e648d	get_shares_full() set default range 1yr	2023-01-14 16:35:54 +00:00
ValueRaider	4d91ae740a	Add date args to 'shares_full()' and caching	2023-01-14 16:35:54 +00:00
ValueRaider	05ec4b4312	Add full share count history via 'shares_full'	2023-01-14 16:35:51 +00:00
ValueRaider	cd2c1ada14	Improve decrypt key deduction	2023-01-14 15:41:33 +00:00
ValueRaider	4ca9642403	Ensure 'requests_cache' responses processed ; Improve naming	2023-01-14 14:20:40 +00:00
Alexey Vasilyev	b438f29a71	Fix decryption	2023-01-14 08:06:35 +01:00
ValueRaider	4db178b8d6	Merge pull request #1284 from ranaroussi/fix/financials-caching Improve caching of financials data	2023-01-12 11:47:04 +00:00
ValueRaider	38637a9821	Merge pull request #1283 from DE0CH/ignore-tz-false Change default value to ignore_tz to False	2023-01-08 12:45:00 +00:00
Deyao Chen	de8c0bdcdd	Change default value to ignore_tz to False Bring the behavior of download() to be the same as 0.1.77.	2023-01-08 11:47:13 +08:00
ValueRaider	fd35975cf9	Improve caching of financials data	2023-01-07 18:02:16 +00:00
ValueRaider	1495834a09	Merge pull request #1276 from gogog22510/main Fix the database lock error in multithread download	2023-01-04 23:10:22 +00:00
ValueRaider	2a7588dead	Tidy DB lock fix	2023-01-04 21:32:54 +00:00
gogog22510	051de748b9	Fix the database lock error in multithread download	2023-01-04 12:37:59 -05:00
ValueRaider	97adb30d41	Merge pull request #1262 from ranaroussi/main Sync `main` -> `dev`	2022-12-20 20:42:10 +00:00
ValueRaider	eacfbc45c0	Bump version to 0.2.3	2022-12-20 11:57:04 +00:00
ValueRaider	8deddd7ee9	Make financials API '_' use consistent	2022-12-20 11:56:57 +00:00
ValueRaider	beb494b67e	README: add small section on version 0.2	2022-12-20 11:37:16 +00:00
ValueRaider	e2948a8b48	Bump version to 0.2.2	2022-12-20 11:33:04 +00:00
ValueRaider	ff3d3f2f78	Restore 'financials' attribute (map to 'income_stmt')	2022-12-20 11:32:19 +00:00
ValueRaider	85783da515	README: update 'repair' doc	2022-12-19 23:30:29 +00:00
ValueRaider	9dbfad4294	Bump version to 0.2.1	2022-12-19 23:19:42 +00:00