Basics

Importing the SDK

Now that you have the SDK installed, the first step is to import the OpenBB SDK in your preferred code editor. Nearly everything required to interact with any function from the OpenBB Terminal gets imported in one line. Begin a Python script or Notebook file with:

from openbb_terminal.sdk import openbb

Example snippets used in the remainder of this guide will assume the code block above is used.

Navigation

In the same way as operating the OpenBB Terminal, functions are divided into menus which are scrollable after importing into a jupyter notebook. Entering a period, ., after openbb, will display the Sub-menus available.

An alternate way to view the contents of a menu is to use Python's built-in help.

help(openbb.stocks.dd)

Docstrings

In addition to Python's built-in help, docstrings are also displayed in the Contextual Help window, within a Jupyter environment or by running help(openbb.economy.events) in your code editor.

help(openbb.economy.events)

Help on Operation in module openbb_terminal.core.library.operation:

<openbb_terminal.core.library.operation.Operation object>
    Get economic calendar for countries between specified dates

    Parameters
    ----------
    countries : [List[str],str]
        List of countries to include in calendar.  Empty returns all
    start_date : str
        Start date for calendar
    end_date : str
        End date for calendar

    Returns
    -------
    pd.DataFrame
        Economic calendar

How to use the SDK

Your First Function

To get started using the OpenBB SDK you can try the below code for your first function.

openbb.economy.events()

	Time (GMT)	Country	Event	actual	consensus	previous	Date
0	01:30	France	French Unemployment Rate	7.3%	7.3%	7.4%	2022-11-15
1	02:00	United Kingdom	Average Earnings ex Bonus	5.7%	5.5%	5.5%	2022-11-15
2	02:00	United Kingdom	Average Earnings Index +Bonus	6.0%	5.9%	6.1%	2022-11-15
3	02:00	United Kingdom	Claimant Count Change	3.3K	17.3K	3.9K	2022-11-15
4	02:00	United Kingdom	Employment Change 3M/3M	-52K	-25K	-109K	2022-11-15

- The results from data functions are not stored to memory unless explicitly instructed to. Most functions returning data are presented as a Pandas DataFrame.

Modify the syntax slightly to deliver the output into a DataFrame:

economic_calendar = openbb.economy.events()

Defined as a variable, interacting with the results becomes a matter of manipulating tables. For example, the code block below will filter the results of the events function to display only events scheduled at a specific time.

economic_calendar = openbb.economy.events()
economic_calendar.set_index(keys = ['Time (GMT)'], append = True, inplace = True)
events = economic_calendar.filter(like = '9:00', axis = 0)

events

	Country	Event	actual	consensus	previous	Date
09:00	United States	Fed Governor Cook Speaks	-	-	-	2022-11-15
09:00	Germany	German Buba Balz Speaks	-	-	-	2022-11-15
09:00	Germany	German Buba Vice President Buch Speaks	-	-	-	2022-11-15

Passing Results to Another Function

Let's take a look at another example where the input to a function is a list. It may be desirable to derive that list from a different function. This can be useful for screening tickers, or analyzing particular industries or sectors. The Comparison Analysis sub-module, within Stocks, is one set of functions that can benefit from this kind of workflow. Instead of something like:

openbb.stocks.ca.screener(similar = ['AAPL', 'NFLX', 'META', 'AMZN', 'MSFT', 'GOOGL', 'DIS', 'TSLA'], data_type = 'valuation')

Try, openbb.etf.holdings, to populate a list dynamically:

symbols = openbb.etf.holdings('DIA')
dia_symbols = list(symbols.index.drop(['N/A']))
dia_valuation = openbb.stocks.ca.screener(similar = dia_symbols, data_type = 'valuation')
dia_valuation = dia_valuation.sort_values(by = ['Price'], ascending = False).convert_dtypes()

dia_valuation.head(5)

	Ticker	Market Cap	P/E	Fwd P/E	PEG	P/S	P/B	P/C	P/FCF	EPS this Y	EPS next Y	EPS past 5Y	EPS next 5Y	Sales past 5Y	Price	Change	Volume
25	UNH	5.0033e+11	25.17	20.61	1.77	1.59	6.43	12.88	19.61	0.128	0.1315	0.201	0.1422	0.092	503.01	-0.0209	5007787
10	GS	1.282e+11	10.18	10.17	N/A	2.08	1.24	0.45	2.98	1.403	0.0976	0.296	-0.0912	0.113	382.88	0.0014	3184768
11	HD	3.1097e+11	18.86	17.86	1.2	2	1334.43	247	89.72	0.301	0.0361	0.192	0.157	0.098	311.93	0.0163	9239159
1	AMGN	1.5543e+11	22.86	15.4	3.38	5.9	41.77	13.54	32.04	-0.165	0.0486	0.001	0.0677	0.025	283.6	-0.006	2761083
18	MCD	2.0272e+11	34.3	26.01	5.14	8.71	N/A	71.67	118.65	0.591	0.0522	0.13	0.0667	-0.012	267.84	-0.0163	5421817

Displaying Charts

The OpenBB SDK has built-in charting libraries for Matplotlib, for any chart available from the Terminal. User style sheets can be added to the folder (more on this in Importing and Exporting Data), ~/OpenBBUserData/styles/user. Styles are shared properties between the OpenBB Terminal and the SDK.

Displaying charts in Jupyter Notebooks requires an additional line of code. You can either render a static image with %matplotlib inline or add in pan/zoom functionality with %matplotlib widget.

Functions, such as candle, exist to display charts. Others, like those within the Technical Analysis module, have the option to return either, a chart or raw data. The next examples will outline a few different scenarios. First, let's get some data:

spy_daily = openbb.stocks.load(
        symbol = 'SPY',
        start_date = '1993-11-01',
        monthly = True)

Data from the previous example, spy_daily, can be used in the openbb.stocks.candle function, for example:

openbb.stocks.candle(
    data = spy_daily,
    asset_type = 'SPY - Monthly Chart from November, 1993',
    symbol = ''
)

The function will also respond to individual tickers without saving the data first as done with load:

openbb.stocks.candle('SPY')

Where functions in the Terminal display either a chart or raw data, the command will have an additional _chart component. For example, Donchian Channels:

openbb.ta.donchian(openbb.stocks.load('SPY', interval = 15))

date	DCL_20_20	DCM_20_20	DCU_20_20
2022-11-15 14:45:00	394.49	398.33	402.17
2022-11-15 15:00:00	394.49	398.195	401.9
2022-11-15 15:15:00	394.49	398.195	401.9
2022-11-15 15:30:00	394.49	398.105	401.72
2022-11-15 15:45:00	394.49	398.027	401.565

openbb.ta.donchian_chart(
    data = openbb.stocks.load('SPY', interval = 15),
    symbol = 'SPY 15 Minute Data'
)

Futures curves are another example where this syntax is applied:

openbb.futures.curve_chart('GE')

The intros section for each module explore further functionality and provide sample code snippets. For example, an introduction to Stocks can be found here.