Bump version to 0.2.16

'info' fetch now gets same data as scrape

CHANGELOG.rst

@@ -1,6 +1,14 @@
 Change Log
 ===========
 
+0.2.16
+------
+Fix 'fast_info deprecated' msg appearing at Ticker() init
+
+0.2.15
+------
+Restore missing Ticker.info keys #1480
+
 0.2.14
 ------
 Fix Ticker.info dict by fetching from API #1461

README.md

@@ -45,7 +45,7 @@ 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.
+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
 
@@ -58,10 +58,8 @@ import yfinance as yf
 
 msft = yf.Ticker("MSFT")
 
-# get all stock info (slow)
+# get all stock info
 msft.info
-# fast access to subset of stock info (opportunistic)
-msft.fast_info
 
 # get historical market data
 hist = msft.history(period="1mo")
@@ -154,6 +152,8 @@ msft.option_chain(..., proxy="PROXY_SERVER")
 ...
 ```
 
+### Multiple tickers
+
 To initialize multiple `Ticker` objects, use
 
 ```python
@@ -167,7 +167,7 @@ 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
@@ -180,8 +180,8 @@ data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
 yf.download(tickers = "SPY AAPL",  # list of tickers
             period = "1y",         # time period
             interval = "1d",       # trading interval
-            ignore_tz = True,      # ignore timezone when aligning data from different exchanges?
-            prepost = False)       # download pre/post market hours data?
+            prepost = False,       # download pre/post market hours data?
+            repair = True)         # repair obvious price errors e.g. 100x?
 ```
 
 Review the [Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
@@ -232,21 +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
 
-### Timezone cache store
-
-When fetching price data, all dates are localized to stock exchange timezone. 
-But timezone retrieval is relatively slow, so yfinance attemps to cache them 
-in your users cache folder. 
-You can direct cache to use a different location with `set_tz_cache_location()`:
-```python
-import yfinance as yf
-yf.set_tz_cache_location("custom/cache/location")
-...
-```
-
----
-
-## `pandas_datareader` override
+### `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()`
@@ -263,6 +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
@@ -290,11 +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

meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "yfinance" %}
-{% set version = "0.2.14" %}
+{% set version = "0.2.16" %}
 
 package:
   name: "{{ name|lower }}"

yfinance/base.py

@@ -40,7 +40,7 @@ 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'
@@ -48,461 +48,6 @@ _SCRAPE_URL_ = 'https://finance.yahoo.com/quote'
 _ROOT_URL_ = 'https://finance.yahoo.com'
 
 
-class FastInfo:
-    # Contain small subset of info[] items that can be fetched faster elsewhere.
-    # Imitates a dict.
-    def __init__(self, tickerBaseObject):
-        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 TickerBase:
     def __init__(self, ticker, session=None):
         self.ticker = ticker.upper()
@@ -533,7 +78,7 @@ class TickerBase:
         self._quote = Quote(self._data)
         self._fundamentals = Fundamentals(self._data)
 
-        self._fast_info = FastInfo(self)
+        self._fast_info = None
 
     def stats(self, proxy=None):
         ticker_url = "{}/{}".format(self._scrape_url, self.ticker)
@@ -1477,6 +1022,8 @@ class TickerBase:
 
     @property
     def fast_info(self):
+        if self._fast_info is None:
+            self._fast_info = FastInfo(self)
         return self._fast_info
 
     @property

yfinance/scrapers/quote.py

@@ -19,7 +19,7 @@ info_retired_keys = info_retired_keys_price | info_retired_keys_exchange | info_
 
 PRUNE_INFO = True
 # PRUNE_INFO = False
-_BASIC_URL_ = "https://query1.finance.yahoo.com/v7/finance/quote"
+_BASIC_URL_ = "https://query2.finance.yahoo.com/v10/finance/quoteSummary"
 
 
 from collections.abc import MutableMapping
@@ -74,6 +74,462 @@ class InfoDictWrapper(MutableMapping):
         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:
 
@@ -244,21 +700,43 @@ class Quote:
         if self._already_fetched:
             return
         self._already_fetched = True
-
+        modules = ['summaryProfile', 'financialData', 'quoteType',
+                     'defaultKeyStatistics', 'assetProfile', 'summaryDetail']
         result = self._data.get_raw_json(
-            _BASIC_URL_, params={"formatted": "true", "lang": "en-US", "symbols": self._data.ticker}, proxy=proxy
+            _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("quoteResponse", {}).get("result", []) if info["symbol"] == self._data.ticker),
+            (info for info in result.get("quoteSummary", {}).get("result", []) if info["symbol"] == self._data.ticker),
             None,
         )
-        for k, v in query1_info.items():
+        # 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:
-                query1_info[k] = v["fmt"] if k in {"regularMarketTime", "postMarketTime"} else v["raw"]
+                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):
-                query1_info[k] = v.replace("\xa0", " ")
-            elif isinstance(v, (int, bool)):
-                query1_info[k] = v
+                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
 
     def _fetch_complementary(self, proxy):

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
 
@@ -61,6 +62,13 @@ def attributes(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))
 

yfinance/version.py

@@ -1 +1 @@
-version = "0.2.14"
+version = "0.2.16"