invest 0.1.2
Python access to structure stock market information
License: mit
Keywords: finance, stock market, trading, markets, python, quant, data

# invest

Python access to structure stock market information


To install:	```pip install invest```

- [invest](#invest)
- [Quick Start](#quick-start)
  * [ticker_symbols argument](#ticker-symbols-argument)
  * [Notes](#notes)
- [Configuring Ticker objects](#configuring-ticker-objects)
  * [Configure a Ticker instance](#configure-a-ticker-instance)
  * [Example](#example)
  * [Configure a Tickers instance](#configure-a-tickers-instance)
- [Getting (only) specific information about tickers](#getting--only--specific-information-about-tickers)
  * [Example: Historical data](#example--historical-data)
  * [Example: Specific `'info'` fields](#example--specific---info---fields)
- [BulkHistory](#bulkhistory)
  * [Notes](#notes-1)
- [All information](#all-information)


# Quick Start


```python
from invest import Tickers
```

Get a default list of tickers


```python
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


```python
len(tickers)
```


    4039



- list the keys


```python
list(tickers)[:5]
```




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



- check for containment of a key


```python
'GOOG' in tickers
```




    True



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


```python
ticker = tickers['GOOG']
ticker
```




    Ticker('GOOG')



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


```python
len(ticker)
```




    40



What are these keys?


```python
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...


```python
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



```python
info['shortName']
```




    'Alphabet Inc.'




```python
info['sector']
```




    'Communication Services'




```python
df = ticker['history']
df
```




<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Open</th>
      <th>High</th>
      <th>Low</th>
      <th>Close</th>
      <th>Volume</th>
      <th>Dividends</th>
      <th>Stock Splits</th>
    </tr>
    <tr>
      <th>Date</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2020-10-28</th>
      <td>1559.739990</td>
      <td>1561.349976</td>
      <td>1514.619995</td>
      <td>1516.619995</td>
      <td>1834000</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-10-29</th>
      <td>1522.359985</td>
      <td>1593.709961</td>
      <td>1522.239990</td>
      <td>1567.239990</td>
      <td>2003100</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-10-30</th>
      <td>1672.109985</td>
      <td>1687.000000</td>
      <td>1604.459961</td>
      <td>1621.010010</td>
      <td>4329100</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-02</th>
      <td>1628.160034</td>
      <td>1660.770020</td>
      <td>1616.030029</td>
      <td>1626.030029</td>
      <td>2535400</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-03</th>
      <td>1631.780029</td>
      <td>1661.699951</td>
      <td>1616.619995</td>
      <td>1650.209961</td>
      <td>1661700</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-04</th>
      <td>1710.280029</td>
      <td>1771.364990</td>
      <td>1706.030029</td>
      <td>1749.130005</td>
      <td>3570900</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-05</th>
      <td>1781.000000</td>
      <td>1793.640015</td>
      <td>1750.510010</td>
      <td>1763.369995</td>
      <td>2065800</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-06</th>
      <td>1753.949951</td>
      <td>1772.430054</td>
      <td>1740.349976</td>
      <td>1761.750000</td>
      <td>1660900</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-09</th>
      <td>1790.900024</td>
      <td>1818.060059</td>
      <td>1760.020020</td>
      <td>1763.000000</td>
      <td>2268300</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-10</th>
      <td>1731.089966</td>
      <td>1763.000000</td>
      <td>1717.300049</td>
      <td>1740.390015</td>
      <td>2636100</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-11</th>
      <td>1750.000000</td>
      <td>1764.219971</td>
      <td>1747.364990</td>
      <td>1752.709961</td>
      <td>1264000</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-12</th>
      <td>1747.630005</td>
      <td>1768.270020</td>
      <td>1745.599976</td>
      <td>1749.839966</td>
      <td>1247500</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-13</th>
      <td>1757.630005</td>
      <td>1781.040039</td>
      <td>1744.550049</td>
      <td>1777.020020</td>
      <td>1499900</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-16</th>
      <td>1771.699951</td>
      <td>1799.069946</td>
      <td>1767.689941</td>
      <td>1781.380005</td>
      <td>1246800</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-17</th>
      <td>1776.939941</td>
      <td>1785.000000</td>
      <td>1767.000000</td>
      <td>1770.150024</td>
      <td>1147100</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-18</th>
      <td>1765.229980</td>
      <td>1773.469971</td>
      <td>1746.140015</td>
      <td>1746.780029</td>
      <td>1173500</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-19</th>
      <td>1738.380005</td>
      <td>1769.589966</td>
      <td>1737.005005</td>
      <td>1763.920044</td>
      <td>1249900</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-20</th>
      <td>1765.209961</td>
      <td>1774.000000</td>
      <td>1741.859985</td>
      <td>1742.189941</td>
      <td>2313500</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-23</th>
      <td>1749.599976</td>
      <td>1753.900024</td>
      <td>1717.719971</td>
      <td>1734.859985</td>
      <td>2161600</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-24</th>
      <td>1730.500000</td>
      <td>1771.599976</td>
      <td>1727.689941</td>
      <td>1768.880005</td>
      <td>1578000</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-25</th>
      <td>1772.890015</td>
      <td>1778.540039</td>
      <td>1756.540039</td>
      <td>1771.430054</td>
      <td>1045800</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27</th>
      <td>1773.089966</td>
      <td>1804.000000</td>
      <td>1772.439941</td>
      <td>1793.189941</td>
      <td>884900</td>
      <td>0</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
</div>




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

candlestick_plot(df)
```


    
![png](https://raw.githubusercontent.com/thorwhalen/invest/master/img/output_23_0.png)
    


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.


```python
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.'
```


```python

```

## 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 


```python

```

# 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. 


```python
from invest import Tickers, Ticker

ticker = Ticker('GOOG')
ticker
```




    Ticker('GOOG')



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


```python
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.


```python
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.


```python
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.


```python
df = ticker['history']
df
```




<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Open</th>
      <th>High</th>
      <th>Low</th>
      <th>Close</th>
      <th>Volume</th>
      <th>Dividends</th>
      <th>Stock Splits</th>
    </tr>
    <tr>
      <th>Datetime</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2020-11-27 09:30:00-05:00</th>
      <td>1773.089966</td>
      <td>1789.890015</td>
      <td>1772.439941</td>
      <td>1785.000000</td>
      <td>119289</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 09:45:00-05:00</th>
      <td>1785.380005</td>
      <td>1786.979980</td>
      <td>1780.229980</td>
      <td>1785.089966</td>
      <td>50660</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 10:00:00-05:00</th>
      <td>1785.489990</td>
      <td>1786.989990</td>
      <td>1780.959961</td>
      <td>1785.800049</td>
      <td>50797</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 10:15:00-05:00</th>
      <td>1785.319946</td>
      <td>1795.925049</td>
      <td>1785.319946</td>
      <td>1791.589966</td>
      <td>72146</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 10:30:00-05:00</th>
      <td>1792.060059</td>
      <td>1798.999878</td>
      <td>1792.060059</td>
      <td>1796.699951</td>
      <td>48097</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 10:45:00-05:00</th>
      <td>1796.800049</td>
      <td>1800.199951</td>
      <td>1795.060059</td>
      <td>1799.959961</td>
      <td>56292</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 11:00:00-05:00</th>
      <td>1800.359985</td>
      <td>1800.449951</td>
      <td>1797.130005</td>
      <td>1797.660034</td>
      <td>41882</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 11:15:00-05:00</th>
      <td>1797.819946</td>
      <td>1802.599976</td>
      <td>1796.949951</td>
      <td>1802.579956</td>
      <td>60333</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 11:30:00-05:00</th>
      <td>1802.579956</td>
      <td>1804.000000</td>
      <td>1797.550049</td>
      <td>1798.185059</td>
      <td>45667</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 11:45:00-05:00</th>
      <td>1798.099976</td>
      <td>1798.603027</td>
      <td>1788.000000</td>
      <td>1788.739990</td>
      <td>47900</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 12:00:00-05:00</th>
      <td>1789.000000</td>
      <td>1791.599976</td>
      <td>1787.329956</td>
      <td>1787.500000</td>
      <td>36459</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 12:15:00-05:00</th>
      <td>1787.347534</td>
      <td>1788.530029</td>
      <td>1782.574951</td>
      <td>1787.952759</td>
      <td>46400</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 12:30:00-05:00</th>
      <td>1787.260010</td>
      <td>1788.920044</td>
      <td>1785.640015</td>
      <td>1785.640015</td>
      <td>45660</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 12:45:00-05:00</th>
      <td>1785.829956</td>
      <td>1793.420044</td>
      <td>1785.219971</td>
      <td>1792.520020</td>
      <td>97273</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2020-11-27 13:00:00-05:00</th>
      <td>1793.189941</td>
      <td>1793.189941</td>
      <td>1793.189941</td>
      <td>1793.189941</td>
      <td>46982</td>
      <td>0</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
</div>




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

candlestick_plt(df)
```


    
![png](https://raw.githubusercontent.com/thorwhalen/invest/master/img/output_45_0.png)
    


## 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...


```python
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.


```python
tickers['NFLX']
```




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




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

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


    
![png](https://raw.githubusercontent.com/thorwhalen/invest/master/img/output_51_0.png)
    



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


    
![png](https://raw.githubusercontent.com/thorwhalen/invest/master/img/output_52_0.png)
    


So Netflix and Amazon did well. 

Delta, less so:


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


    
![png](https://raw.githubusercontent.com/thorwhalen/invest/master/img/output_54_0.png)
    


# 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:


```python
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)




```python
candlestick_plot(tickers['GOOG'])
```


    
![png](https://raw.githubusercontent.com/thorwhalen/invest/master/img/output_60_0.png)
    



```python
candlestick_plot(tickers['NFLX'])
```


    
![png](https://raw.githubusercontent.com/thorwhalen/invest/master/img/output_61_0.png)
    



```python
candlestick_plot(tickers['AMZN'])
```


    
![png](https://raw.githubusercontent.com/thorwhalen/invest/master/img/output_62_0.png)
    



```python
candlestick_plot(tickers['AAPL'])
```


    
![png](https://raw.githubusercontent.com/thorwhalen/invest/master/img/output_63_0.png)
    


## Example: Specific `'info'` fields


```python
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`:


```python
tickers['AAPL']
```




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




```python
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>)




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




<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>shortName</th>
      <th>sector</th>
      <th>earningsQuarterlyGrowth</th>
      <th>sharesShortPriorMonth</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Facebook, Inc.</td>
      <td>Communication Services</td>
      <td>0.288</td>
      <td>21187652</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Amazon.com, Inc.</td>
      <td>Consumer Cyclical</td>
      <td>1.967</td>
      <td>2509939</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Apple Inc.</td>
      <td>Technology</td>
      <td>-0.074</td>
      <td>83252522</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Netflix, Inc.</td>
      <td>Communication Services</td>
      <td>0.187</td>
      <td>9416477</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Alphabet Inc.</td>
      <td>Communication Services</td>
      <td>0.591</td>
      <td>2381334</td>
    </tr>
  </tbody>
</table>
</div>


# BulkHistory


```python
from invest import BulkHistory

tickers = BulkHistory(start='2019-01-01', end='2020-01-01', interval='1mo')  # 2019 historical data, month granularity
tickers
```




    BulkHistory(ticker_symbols=['FB', 'AMZN', 'AAPL', 'NFLX', 'GOOG'], history={'start': '2019-01-01', 'end': '2020-01-01', 'interval': '1mo'})




```python
candlestick_plot(tickers['FB'])
```

    [*********************100%***********************]  5 of 5 completed



    
![png](https://raw.githubusercontent.com/thorwhalen/invest/master/img/output_72_1.png)
    


Notice that the data doesn't download again when we ask for `GOOG` data. That's because the first download bulk downloaded the data for our whole list of ticker symbols.


```python
candlestick_plot(tickers['GOOG'])
```


    
![png](https://raw.githubusercontent.com/thorwhalen/invest/master/img/output_74_0.png)
    


## 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. 
For historical data (`history`), we have `BulkHistory` that uses the bulk API (through `yfinance.Tickers`), 
but for other information (such at the `info` key), we don't (yet).


# All information

Some utils to get all data on a single ticker.


```python
from invest import Ticker
from invest import all_info, all_info_printable_string

ticker = Ticker('GOOG')
d = dict(all_info(ticker))  # all_info is a generator of (key, val) pairs (only for non-empty values), so can use dict to accumulate
list(d)  # list of keys (data names) we have data (values) for, for the given ticker
```

    ['financials',
     'quarterly_balance_sheet',
     'institutional_holders',
     'major_holders',
     'history',
     'quarterly_earnings',
     'info',
     'mutualfund_holders',
     'calendar',
     'option_chain',
     'quarterly_cashflow',
     'recommendations',
     'cashflow',
     'options',
     'balance_sheet',
     'quarterly_financials',
     'isin',
     'earnings']




```python
print(f"The following data is for ticker: {ticker.ticker_symbol}\n\n")
print(all_info_printable_string(ticker))
```


    The following data is for ticker: GOOG
    
    
    
    ----------financials-------------
                                             2019-12-31   2018-12-31   2017-12-31  \
    Research Development                     2.6018e+10   2.1419e+10   1.6625e+10   
    Effect Of Accounting Charges                   None         None         None   
    Income Before Tax                        3.9625e+10   3.4913e+10   2.7193e+10   
    Minority Interest                              None         None         None   
    Net Income                               3.4343e+10   3.0736e+10   1.2662e+10   
    Selling General Administrative           2.7461e+10   2.3256e+10   1.9733e+10   
    Gross Profit                             8.9961e+10    7.727e+10   6.5272e+10   
    Ebit                                     3.6482e+10   3.2595e+10   2.8914e+10   
    Operating Income                         3.6482e+10   3.2595e+10   2.8914e+10   
    Other Operating Expenses                       None         None         None   
    Interest Expense                             -1e+08    -1.14e+08    -1.09e+08   
    Extraordinary Items                            None         None         None   
    Non Recurring                                  None         None         None   
    Other Items                                    None         None         None   
    Income Tax Expense                        5.282e+09    4.177e+09   1.4531e+10   
    Total Revenue                           1.61857e+11  1.36819e+11  1.10855e+11   
    Total Operating Expenses                1.25375e+11  1.04224e+11   8.1941e+10   
    Cost Of Revenue                          7.1896e+10   5.9549e+10   4.5583e+10   
    Total Other Income Expense Net            3.143e+09    2.318e+09   -1.721e+09   
    Discontinued Operations                        None         None         None   
    Net Income From Continuing Ops           3.4343e+10   3.0736e+10   1.2662e+10   
    Net Income Applicable To Common Shares   3.4343e+10   3.0736e+10   1.2662e+10   
    
                                            2016-12-31  
    Research Development                    1.3948e+10  
    Effect Of Accounting Charges                  None  
    Income Before Tax                        2.415e+10  
    Minority Interest                             None  
    Net Income                              1.9478e+10  
    Selling General Administrative           1.747e+10  
    Gross Profit                            5.5134e+10  
    Ebit                                    2.3716e+10  
    Operating Income                        2.3716e+10  
    Other Operating Expenses                      None  
    Interest Expense                         -1.24e+08  
    Extraordinary Items                           None  
    Non Recurring                                 None  
    Other Items                                   None  
    Income Tax Expense                       4.672e+09  
    Total Revenue                           9.0272e+10  
    Total Operating Expenses                6.6556e+10  
    Cost Of Revenue                         3.5138e+10  
    Total Other Income Expense Net            4.34e+08  
    Discontinued Operations                       None  
    Net Income From Continuing Ops          1.9478e+10  
    Net Income Applicable To Common Shares  1.9478e+10  
    
    ----------quarterly_balance_sheet-------------
                                        2020-09-30    2020-06-30    2020-03-31  \
    Intangible Assets                 1.520000e+09  1.697000e+09  1.840000e+09   
    Total Liab                        8.632300e+10  7.117000e+10  6.974400e+10   
    Total Stockholder Equity          2.129200e+11  2.073220e+11  2.036590e+11   
    Other Current Liab                2.406800e+10  2.193400e+10  2.187200e+10   
    Total Assets                      2.992430e+11  2.784920e+11  2.734030e+11   
    Common Stock                      5.730700e+10  5.593700e+10  5.368800e+10   
    Other Current Assets              5.425000e+09  5.579000e+09  5.165000e+09   
    Retained Earnings                 1.555670e+11  1.516810e+11  1.510680e+11   
    Other Liab                        1.323700e+10  1.278500e+10  1.406300e+10   
    Good Will                         2.087000e+10  2.082400e+10  2.073400e+10   
    Treasury Stock                    4.600000e+07 -2.960000e+08 -1.097000e+09   
    Other Assets                      3.799000e+09  3.626000e+09  3.478000e+09   
    Cash                              2.012900e+10  1.774200e+10  1.964400e+10   
    Total Current Liabilities         4.820000e+10  4.365800e+10  4.018900e+10   
    Deferred Long Term Asset Charges  9.720000e+08  8.950000e+08  7.300000e+08   
    Short Long Term Debt              9.990000e+08  9.990000e+08           NaN   
    Other Stockholder Equity          4.600000e+07 -2.960000e+08 -1.097000e+09   
    Property Plant Equipment          9.358200e+10  9.031500e+10  8.796600e+10   
    Total Current Assets              1.643690e+11  1.490690e+11  1.470180e+11   
    Long Term Investments             1.510300e+10  1.296100e+10  1.236700e+10   
    Net Tangible Assets               1.905300e+11  1.848010e+11  1.810850e+11   
    Short Term Investments            1.124670e+11  1.033380e+11  9.758500e+10   
    Net Receivables                   2.551300e+10  2.159500e+10  2.373500e+10   
    Long Term Debt                    1.282800e+10  2.963000e+09  3.960000e+09   
    Inventory                         8.350000e+08  8.150000e+08  8.890000e+08   
    Accounts Payable                  4.391000e+09  4.064000e+09  4.099000e+09   
    
                                        2019-12-31  
    Intangible Assets                 1.979000e+09  
    Total Liab                        7.446700e+10  
    Total Stockholder Equity          2.014420e+11  
    Other Current Liab                2.215900e+10  
    Total Assets                      2.759090e+11  
    Common Stock                      5.055200e+10  
    Other Current Assets              4.412000e+09  
    Retained Earnings                 1.521220e+11  
    Other Liab                        1.447800e+10  
    Good Will                         2.062400e+10  
    Treasury Stock                   -1.232000e+09  
    Other Assets                      3.063000e+09  
    Cash                              1.849800e+10  
    Total Current Liabilities         4.522100e+10  
    Deferred Long Term Asset Charges  7.210000e+08  
    Short Long Term Debt                       NaN  
    Other Stockholder Equity         -1.232000e+09  
    Property Plant Equipment          8.458700e+10  
    Total Current Assets              1.525780e+11  
    Long Term Investments             1.307800e+10  
    Net Tangible Assets               1.788390e+11  
    Short Term Investments            1.011770e+11  
    Net Receivables                   2.749200e+10  
    Long Term Debt                    3.958000e+09  
    Inventory                         9.990000e+08  
    Accounts Payable                  5.561000e+09  
    
    ----------institutional_holders-------------
                                    Holder    Shares Date Reported   % Out  \
    0           Vanguard Group, Inc. (The)  22204175    2020-09-29  0.0673   
    1                       Blackrock Inc.  20032538    2020-09-29  0.0607   
    2        Price (T.Rowe) Associates Inc  13396372    2020-09-29  0.0406   
    3             State Street Corporation  11589194    2020-09-29  0.0351   
    4                             FMR, LLC   7687258    2020-09-29  0.0233   
    5        Geode Capital Management, LLC   4431554    2020-09-29  0.0134   
    6      Capital International Investors   4071062    2020-09-29  0.0123   
    7           Northern Trust Corporation   3981710    2020-09-29  0.0121   
    8              AllianceBernstein, L.P.   3889575    2020-09-29  0.0118   
    9  Bank Of New York Mellon Corporation   3519043    2020-09-29  0.0107   
    
             Value  
    0  32631255580  
    1  29439817844  
    2  19687308291  
    3  17031479502  
    4  11297194356  
    5   6512611758  
    6   5982832715  
    7   5851521016  
    8   5716119420  
    9   5171585592  
    
    ----------major_holders-------------
            0                                      1
    0   5.84%        % of Shares Held by All Insider
    1  68.32%       % of Shares Held by Institutions
    2  72.56%        % of Float Held by Institutions
    3    3396  Number of Institutions Holding Shares
    
    ----------history-------------
                       Open         High          Low        Close   Volume  \
    Date                                                                      
    2020-10-30  1672.109985  1687.000000  1604.459961  1621.010010  4329100   
    2020-11-02  1628.160034  1660.770020  1616.030029  1626.030029  2535400   
    2020-11-03  1631.780029  1661.699951  1616.619995  1650.209961  1661700   
    2020-11-04  1710.280029  1771.364990  1706.030029  1749.130005  3570900   
    2020-11-05  1781.000000  1793.640015  1750.510010  1763.369995  2065800   
    2020-11-06  1753.949951  1772.430054  1740.349976  1761.750000  1660900   
    2020-11-09  1790.900024  1818.060059  1760.020020  1763.000000  2268300   
    2020-11-10  1731.089966  1763.000000  1717.300049  1740.390015  2636100   
    2020-11-11  1750.000000  1764.219971  1747.364990  1752.709961  1264000   
    2020-11-12  1747.630005  1768.270020  1745.599976  1749.839966  1247500   
    2020-11-13  1757.630005  1781.040039  1744.550049  1777.020020  1499900   
    2020-11-16  1771.699951  1799.069946  1767.689941  1781.380005  1246800   
    2020-11-17  1776.939941  1785.000000  1767.000000  1770.150024  1147100   
    2020-11-18  1765.229980  1773.469971  1746.140015  1746.780029  1173500   
    2020-11-19  1738.380005  1769.589966  1737.005005  1763.920044  1249900   
    2020-11-20  1765.209961  1774.000000  1741.859985  1742.189941  2313500   
    2020-11-23  1749.599976  1753.900024  1717.719971  1734.859985  2161600   
    2020-11-24  1730.500000  1771.599976  1727.689941  1768.880005  1578000   
    2020-11-25  1772.890015  1778.540039  1756.540039  1771.430054  1045800   
    2020-11-27  1773.089966  1804.000000  1772.439941  1793.189941   884900   
    2020-11-30  1781.180054  1788.064941  1755.010010  1765.175049   871053   
    
                Dividends  Stock Splits  
    Date                                 
    2020-10-30          0             0  
    2020-11-02          0             0  
    2020-11-03          0             0  
    2020-11-04          0             0  
    2020-11-05          0             0  
    2020-11-06          0             0  
    2020-11-09          0             0  
    2020-11-10          0             0  
    2020-11-11          0             0  
    2020-11-12          0             0  
    2020-11-13          0             0  
    2020-11-16          0             0  
    2020-11-17          0             0  
    2020-11-18          0             0  
    2020-11-19          0             0  
    2020-11-20          0             0  
    2020-11-23          0             0  
    2020-11-24          0             0  
    2020-11-25          0             0  
    2020-11-27          0             0  
    2020-11-30          0             0  
    
    ----------quarterly_earnings-------------
                 Revenue     Earnings
    Quarter                          
    4Q2019   46075000000  10671000000
    1Q2020   41159000000   6836000000
    2Q2020   38297000000   6959000000
    3Q2020   46173000000  11247000000
    
    ----------info-------------
    {'zip': '94043', 'sector': 'Communication Services', 'fullTimeEmployees': 132121, 'longBusinessSummary': 'Alphabet Inc. provides online advertising services in the United States, Europe, the Middle East, Africa, the Asia-Pacific, Canada, and Latin America. It offers performance and brand advertising services. The company operates through Google and Other Bets segments. The Google segment offers products, such as Ads, Android, Chrome, Google Cloud, Google Maps, Google Play, Hardware, Search, and YouTube, as well as technical infrastructure. It also offers digital content, cloud services, hardware devices, and other miscellaneous products and services. The Other Bets segment includes businesses, including Access, Calico, CapitalG, GV, Verily, Waymo, and X, as well as Internet and television services. The company has an agreement with Sabre Corporation to develop an artificial intelligence-driven technology platform for travel. Alphabet Inc. was founded in 1998 and is headquartered in Mountain View, California.', 'city': 'Mountain View', 'phone': '650-253-0000', 'state': 'CA', 'country': 'United States', 'companyOfficers': [], 'website': 'http://www.abc.xyz', 'maxAge': 1