invest 0.0.2

Python access to structure stock market information

invest

Python access to structure stock market information

To install: pip install invest

• invest
• Quick Start
  • ticker_symbols argument
  • Notes
• Configuring Ticker objects
  • Configure a Ticker instance
  • Example
  • Configure a Tickers instance
• Getting (only) specific information about tickers
  • Example: Historical data
  • Example: Specific 'info' fields
• Notes

Table of contents generated with markdown-toc

Quick Start

from invest import Tickers

Get a default list of tickers

tickers = Tickers()

tickers is a dict-like container of tickers. So you can do dict-like things with it, like...

• ask for it's length

len(tickers)

4039

• list the keys

list(tickers)[:5]

['EGLE', 'KMPH', 'LONG', 'CYBR', 'PTC']

• check for containment of a key

'GOOG' in tickers

True

The values of this dict-like object are Ticker instances.

ticker = tickers['GOOG']
ticker

Ticker('GOOG')

This ticker object is also dict-like. Let's see how many keys there are:

len(ticker)

40

What are these keys?

list(ticker)

['balancesheet',
 'dividends',
 'get_sustainability',
 'get_info',
 'get_institutional_holders',
 'sustainability',
 'quarterly_balance_sheet',
 'get_balance_sheet',
 'info',
 'quarterly_earnings',
 'isin',
 'earnings',
 'history',
 'get_balancesheet',
 'get_financials',
 'balance_sheet',
 'get_earnings',
 'options',
 'splits',
 'get_recommendations',
 'get_major_holders',
 'get_dividends',
 'actions',
 'recommendations',
 'cashflow',
 'get_cashflow',
 'get_splits',
 'major_holders',
 'institutional_holders',
 'option_chain',
 'get_actions',
 'quarterly_financials',
 'get_calendar',
 'quarterly_cashflow',
 'calendar',
 'financials',
 'quarterly_balancesheet',
 'get_mutualfund_holders',
 'get_isin',
 'mutualfund_holders']

Let's look at one of these, 'info', which contains a dict with a bunch of information about the ticker...

info = ticker['info']
print(*info, sep=', ')

zip, sector, fullTimeEmployees, longBusinessSummary, city, phone, state, country, companyOfficers, website, maxAge, address1, industry, previousClose, regularMarketOpen, twoHundredDayAverage, trailingAnnualDividendYield, payoutRatio, volume24Hr, regularMarketDayHigh, navPrice, averageDailyVolume10Day, totalAssets, regularMarketPreviousClose, fiftyDayAverage, trailingAnnualDividendRate, open, toCurrency, averageVolume10days, expireDate, yield, algorithm, dividendRate, exDividendDate, beta, circulatingSupply, startDate, regularMarketDayLow, priceHint, currency, trailingPE, regularMarketVolume, lastMarket, maxSupply, openInterest, marketCap, volumeAllCurrencies, strikePrice, averageVolume, priceToSalesTrailing12Months, dayLow, ask, ytdReturn, askSize, volume, fiftyTwoWeekHigh, forwardPE, fromCurrency, fiveYearAvgDividendYield, fiftyTwoWeekLow, bid, tradeable, dividendYield, bidSize, dayHigh, exchange, shortName, longName, exchangeTimezoneName, exchangeTimezoneShortName, isEsgPopulated, gmtOffSetMilliseconds, quoteType, symbol, messageBoardId, market, annualHoldingsTurnover, enterpriseToRevenue, beta3Year, profitMargins, enterpriseToEbitda, 52WeekChange, morningStarRiskRating, forwardEps, revenueQuarterlyGrowth, sharesOutstanding, fundInceptionDate, annualReportExpenseRatio, bookValue, sharesShort, sharesPercentSharesOut, fundFamily, lastFiscalYearEnd, heldPercentInstitutions, netIncomeToCommon, trailingEps, lastDividendValue, SandP52WeekChange, priceToBook, heldPercentInsiders, nextFiscalYearEnd, mostRecentQuarter, shortRatio, sharesShortPreviousMonthDate, floatShares, enterpriseValue, threeYearAverageReturn, lastSplitDate, lastSplitFactor, legalType, lastDividendDate, morningStarOverallRating, earningsQuarterlyGrowth, dateShortInterest, pegRatio, lastCapGain, shortPercentOfFloat, sharesShortPriorMonth, category, fiveYearAverageReturn, regularMarketPrice, logo_url

info['shortName']

'Alphabet Inc.'

info['sector']

'Communication Services'

df = ticker['history']
df

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	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2020-10-28	1559.739990	1561.349976	1514.619995	1516.619995	1834000	0	0
2020-10-29	1522.359985	1593.709961	1522.239990	1567.239990	2003100	0	0
2020-10-30	1672.109985	1687.000000	1604.459961	1621.010010	4329100	0	0
2020-11-02	1628.160034	1660.770020	1616.030029	1626.030029	2535400	0	0
2020-11-03	1631.780029	1661.699951	1616.619995	1650.209961	1661700	0	0
2020-11-04	1710.280029	1771.364990	1706.030029	1749.130005	3570900	0	0
2020-11-05	1781.000000	1793.640015	1750.510010	1763.369995	2065800	0	0
2020-11-06	1753.949951	1772.430054	1740.349976	1761.750000	1660900	0	0
2020-11-09	1790.900024	1818.060059	1760.020020	1763.000000	2268300	0	0
2020-11-10	1731.089966	1763.000000	1717.300049	1740.390015	2636100	0	0
2020-11-11	1750.000000	1764.219971	1747.364990	1752.709961	1264000	0	0
2020-11-12	1747.630005	1768.270020	1745.599976	1749.839966	1247500	0	0
2020-11-13	1757.630005	1781.040039	1744.550049	1777.020020	1499900	0	0
2020-11-16	1771.699951	1799.069946	1767.689941	1781.380005	1246800	0	0
2020-11-17	1776.939941	1785.000000	1767.000000	1770.150024	1147100	0	0
2020-11-18	1765.229980	1773.469971	1746.140015	1746.780029	1173500	0	0
2020-11-19	1738.380005	1769.589966	1737.005005	1763.920044	1249900	0	0
2020-11-20	1765.209961	1774.000000	1741.859985	1742.189941	2313500	0	0
2020-11-23	1749.599976	1753.900024	1717.719971	1734.859985	2161600	0	0
2020-11-24	1730.500000	1771.599976	1727.689941	1768.880005	1578000	0	0
2020-11-25	1772.890015	1778.540039	1756.540039	1771.430054	1045800	0	0
2020-11-27	1773.089966	1804.000000	1772.439941	1793.189941	884900	0	0

from mplfinance import plot as candlestick_plot  # pip install mplfinance if you don't have it already

candlestick_plot(df)

But these are daily metrics and only for the recent (yes, I'm doing this on a Thanksgiving week-end!) past.

How do I get something different? Like a longer history, and/or at a finer time-granularity?

See the next Configuring Ticker objects section on how to do that.

ticker_symbols argument

The first argument of Tickers is the ticker_symbols argument.

One can specify a collection (list, set, tuple, etc.) of ticker symbol strings, or a path to a file containing a pickle of such a collection.

The default is the string 'local_list' which has the effect of using a default list (currently of about 4000 tickers), but it's contents can change in the future.

Note that this ticker_symbols will have an effect on such affairs as list(tickers), len(tickers), or s in tickers, when it's relevant to use these.

But any Tickers object will allow access to any ticker symbol, regardless if it's in the ticker_symbols collection or not.

tickers = Tickers(ticker_symbols=('GOOG', 'AAPL', 'AMZN'))
assert list(tickers) == ['GOOG', 'AAPL', 'AMZN']
assert len(tickers) == 3
assert 'AAPL' in tickers
assert 'NFLX' not in tickers
# and yet we have access to NFLX info
assert tickers['NFLX']['info']['shortName'] == 'Netflix, Inc.'

Notes

• Both Tickers and Ticker instances have tab-triggered auto-suggestion enabled when you get an item. Example: tickers['AA<now press the TAB button...>.
• The specification of

Configuring Ticker objects

Configure a Ticker instance

You can instantiate a Ticker instance directly, from any valid ticker symbol. The Tickers class is just a way to make a collection of tickers to work with.

from invest import Tickers, Ticker

ticker = Ticker('GOOG')
ticker

Ticker('GOOG')

But you'll notice that Ticker (and Tickers) have more than one argument.

from inspect import signature
print(signature(Tickers))
print(signature(Ticker))

(ticker_symbols='local_list', **kwargs_for_method_keys)
(ticker_symbol: str, **kwargs_for_method_keys)

What's this kwargs_for_method_keys?

Well, at the time of writing this, Ticker object is just a convenient dict-like interface to the attributes of the Ticker of the yfinance package which is itself a convenient python interface to the yahoo finance API.

When you do list(ticker), you're just getting a list of attributes of yfinance.Ticker: Both properties and methods that don't require any arguments. Though these methods don't require any arguments -- meaning all their arguments have defaults -- you can still specify if you want to use different defaults.

That's where kwargs_for_method_keys comes in. It specifies what arg=val pairs that should be used for particular methods of yfinance.Ticker.

If you want to know more about what you can do with the Ticker object, you might want to check out yfinance's and yahoo finance API's documentation.

For the basics though, invest provides the help_me_with function (as a standalone function or as a method in Tickers and Ticker) for quick access to essentials.

Ticker.help_me_with('history')

history
wraps <function TickerBase.history at 0x11a064940>, whose signature is:
(self, period='1mo', interval='1d', start=None, end=None, prepost=False, actions=True, auto_adjust=True, back_adjust=False, proxy=None, rounding=False, tz=None, **kwargs)

        :Parameters:
            period : str
                Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
                Either Use period parameter or use start and end
            interval : str
                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.
                Default is 1900-01-01
            end: str
                Download end date string (YYYY-MM-DD) or _datetime.
                Default is now
            prepost : bool
                Include Pre and Post market data in results?
                Default is False
            auto_adjust: bool
                Adjust all OHLC automatically? Default is True
            back_adjust: bool
                Back-adjusted data to mimic true historical prices
            proxy: str
                Optional. Proxy server URL scheme. Default is None
            rounding: bool
                Round values to 2 decimal places?
                Optional. Default is False = precision suggested by Yahoo!
            tz: str
                Optional timezone locale for dates.
                (default data is returned as non-localized dates)
            **kwargs: dict
                debug: bool
                    Optional. If passed as False, will suppress
                    error message printing to console.

Example

Here's you can get history to give you something different.

Say, get data for the last day, with a granularity of 15 minutes.

ticker = Ticker('GOOG', history=dict(period='1d', interval='15m'))
ticker

Ticker('GOOG', history={'period': '1d', 'interval': '15m'})

Your ticker is almost identical to the previous one we made, or the one we got from Tickers, except for the fact that asking for ticker['history'] is going to give you something different.

df = ticker['history']
df

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	Open	High	Low	Close	Volume	Dividends	Stock Splits
Datetime
2020-11-27 09:30:00-05:00	1773.089966	1789.890015	1772.439941	1785.000000	119289	0	0
2020-11-27 09:45:00-05:00	1785.380005	1786.979980	1780.229980	1785.089966	50660	0	0
2020-11-27 10:00:00-05:00	1785.489990	1786.989990	1780.959961	1785.800049	50797	0	0
2020-11-27 10:15:00-05:00	1785.319946	1795.925049	1785.319946	1791.589966	72146	0	0
2020-11-27 10:30:00-05:00	1792.060059	1798.999878	1792.060059	1796.699951	48097	0	0
2020-11-27 10:45:00-05:00	1796.800049	1800.199951	1795.060059	1799.959961	56292	0	0
2020-11-27 11:00:00-05:00	1800.359985	1800.449951	1797.130005	1797.660034	41882	0	0
2020-11-27 11:15:00-05:00	1797.819946	1802.599976	1796.949951	1802.579956	60333	0	0
2020-11-27 11:30:00-05:00	1802.579956	1804.000000	1797.550049	1798.185059	45667	0	0
2020-11-27 11:45:00-05:00	1798.099976	1798.603027	1788.000000	1788.739990	47900	0	0
2020-11-27 12:00:00-05:00	1789.000000	1791.599976	1787.329956	1787.500000	36459	0	0
2020-11-27 12:15:00-05:00	1787.347534	1788.530029	1782.574951	1787.952759	46400	0	0
2020-11-27 12:30:00-05:00	1787.260010	1788.920044	1785.640015	1785.640015	45660	0	0
2020-11-27 12:45:00-05:00	1785.829956	1793.420044	1785.219971	1792.520020	97273	0	0
2020-11-27 13:00:00-05:00	1793.189941	1793.189941	1793.189941	1793.189941	46982	0	0

from mplfinance import plot as candlestick_plot  # pip install mplfinance if you don't have it already

candlestick_plt(df)

Configure a Tickers instance

Let's say we wanted all ticker instances that Tickers gives us to have their history be over a specific interval of time in the past (say, during the 2020 pandemic), at 5 day intervals...

tickers = Tickers(ticker_symbols={'NFLX', 'AMZN', 'DAL'},  # demoing the fact that we can specify an explicit collection of ticker symbols
                  history=dict(start='2020-03-01', end='2020-10-31', interval='5d'))
list(tickers)

['DAL', 'AMZN', 'NFLX']

See that indeed, all tickers given by tickers are configured according to our wishes.

tickers['NFLX']

Ticker('NFLX', history={'start': '2020-03-01', 'end': '2020-10-31', 'interval': '5d'})

from mplfinance import plot as candlestick_plot  # pip install mplfinance if you don't have it already

candlestick_plot(tickers['NFLX']['history'])

candlestick_plot(tickers['AMZN']['history'])

So Netflix and Amazon did well.

Delta, less so:

candlestick_plot(tickers['DAL']['history'])

Getting (only) specific information about tickers

Tickers and Ticker are convenient if you want to analyze several aspects of a ticker since you can poke around the various keys (e.g. info, history, etc.).

But if a particular analysis only needs one of these, it's more convenient to use TickersWithSpecificInfo, which gives you the same interface as Tickers (in fact, it's a subclass if Tickers), but fixes the key.

Example: Historical data

For example, if you're only interested in the historical data (a.k.a. the 'history' key), you might do this:

from invest import TickersWithSpecificInfo

tickers = TickersWithSpecificInfo(specific_key='history', start='2008-01-01', end='2009-01-01', interval='1mo')  # 2008 historical data, month granularity
tickers

TickersWithSpecificInfo(ticker_symbols=<local_list>, specific_key=history, start=2008-01-01, end=2009-01-01, interval=1mo)

candlestick_plot(tickers['GOOG'])

candlestick_plot(tickers['NFLX'])

candlestick_plot(tickers['AMZN'])

candlestick_plot(tickers['AAPL'])

Example: Specific 'info' fields

from invest import TickersWithSpecificInfo

the_info_that_i_want = ['shortName', 'sector', 'earningsQuarterlyGrowth', 'sharesShortPriorMonth']
tickers = TickersWithSpecificInfo(specific_key='info', val_trans=lambda d: {k: d[k] for k in the_info_that_i_want}) 
tickers

TickersWithSpecificInfo(ticker_symbols=<local_list>, specific_key=info, val_trans=<function <lambda> at 0x11c2374c0>)

Now, you won't get the overwhelming amount of information you usually get with info:

tickers['AAPL']

{'shortName': 'Apple Inc.',
 'sector': 'Technology',
 'earningsQuarterlyGrowth': -0.074,
 'sharesShortPriorMonth': 83252522}

faang_tickers = ('FB', 'AMZN', 'AAPL', 'NFLX', 'GOOG')
the_info_that_i_want = ['shortName', 'sector', 'earningsQuarterlyGrowth', 'sharesShortPriorMonth']
tickers = TickersWithSpecificInfo(faang_tickers, specific_key='info', val_trans=lambda d: {k: d[k] for k in the_info_that_i_want}) 
tickers

TickersWithSpecificInfo(ticker_symbols=('FB', 'AMZN', 'AAPL', 'NFLX', 'GOOG'), specific_key=info, val_trans=<function <lambda> at 0x11c237a60>)

info_df = pd.DataFrame(list(tickers.values()))
info_df

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	shortName	sector	earningsQuarterlyGrowth	sharesShortPriorMonth
0	Facebook, Inc.	Communication Services	0.288	21187652
1	Amazon.com, Inc.	Consumer Cyclical	1.967	2509939
2	Apple Inc.	Technology	-0.074	83252522
3	Netflix, Inc.	Communication Services	0.187	9416477
4	Alphabet Inc.	Communication Services	0.591	2381334

Notes

• Though Tickers allows you to deal with a collection of tickers, it does so (for time being) by calling yahoo's API for each individual ticker. The API does, on the other hand, contain some bulk tickers routes which we intend to integrate.