ETF

The ETF module provides programmatic access to the commands from within the OpenBB Terminal. Import the OpenBB SDK module, and then access the functions similarly to how the Terminal menus are navigated. The code completion will be activated upon entering ., after, openbb.etf ​

How to Use

​The examples provided below will assume that the following import block is included at the beginning of the Python script or Notebook file: ​

from openbb_terminal.sdk import openbb
import pandas as pd

​Below is a brief description of each function within the ETF module:

Path	Type	Description
openbb.etf.candle	Function	Chart OHLC + Volume + Moving Averages
openbb.etf.disc	Sub-Module	Best/Worst/Highest Volume ETFs Today
openbb.etf.etf_by_category	Function	Lookup by Category
openbb.etf.etf_by_name	Function	Lookup by Name
openbb.etf.holdings	Function	Holdings and Weights
openbb.etf.ld	Function	Lookup by Description
openbb.etf.load	Function	Get Historical Price Data
openbb.etf.ln	Function	Lookup by Name (More Details Than by_name)
openbb.etf.news	Function	News Headlines for a Ticker
openbb.etf.overview	Function	Basic Statistics for an ETF
openbb.etf.scr	Sub-Module	ETF Screener
openbb.etf.summary	Function	Text Description and Summary of an ETF
openbb.etf.symbols	Dictionary	Dictionary of {Ticker:Name}
openbb.etf.weights	Function	Table or Pie Graph of Sector Weightings

Alternatively you can print the contents of the ETF SDK with: ​

help(openbb.etf)

Examples

etf_by_category

​ ETFs are categorized into different buckets. Use the code block below as a way to generate a list of all categories: ​

etf_list = pd.DataFrame.from_dict(openbb.etf.etf_by_category(''))
categories = list(etf_list['category'].drop_duplicates())
categories = pd.DataFrame(categories[1::], columns = ['Type'])

categories.head(6)

Type
Financials
Emerging Markets
Industrials
Factors
Utilities
Bonds

​Replacing the empty category in the syntax above will return the ETFs within that category: ​

etf_category = pd.DataFrame.from_dict(openbb.etf.etf_by_category('Emerging Markets'))
etf_category.head(2)

symbol	name	currency	summary	category_group	category	family	exchange	market
AAXJ	iShares MSCI All Country Asia ex Japan ETF	USD	The investment seeks to track the investment results of the MSCI AC Asia ex Japan Index.	Equities	Emerging Markets	BlackRock Asset Management	NMS	us_market
			The fund will invest at least 90% of its assets in the component securities of the index and in investments that have economic characteristics that are substantially identical to the component securities of the index. The index is a free float-adjusted market capitalization index designed to measure equity market performance of securities from the following 11 developed and emerging market countries or regions: China, Hong Kong, India, Indonesia, Malaysia, Pakistan, the Philippines, Singapore, South Korea, Taiwan and Thailand.
JHEM	John Hancock Multifactor Emerging Markets ETF	USD	The investment seeks to provide investment results that closely correspond, before fees and expenses, to the performance of the John Hancock Dimensional Emerging Markets Index (the index).	Equities	Emerging Markets	John Hancock	PCX	us_market
			The fund normally invests at least 80% of its net assets (plus any borrowings for investment purposes) in securities included in the index, in depositary receipts representing securities included in the index, and in underlying stocks in respect of depositary receipts included in the index. The index is designed to comprise a subset of securities associated with emerging markets, which may include frontier markets (emerging markets in an earlier stage of development).

​

ETF Tickers

A list of all tickers in the specific category can be generated from the index of the above DataFrame, etf_category:

symbols = etf_category.index.to_list()

Performance Metrics

This list of tickers can then be used for comparison analysis, or portfolio optimization. For example, comparing the performance metrics of the Emerging Market category:

performance = openbb.stocks.ca.screener(similar = symbols, data_type = 'performance')
performance = performance.sort_values(by=['3M'], ascending=False)
performance.head(5)

Ticker	1W	1M	3M	6M	1Y	YTD	1W Volatility	1M Volatility	Recom	Avg Volume	Rel Volume	Price	Change	Volume
CHB	0.0301	-0.015	0.1123	0.0086	-0.132	-0.1277	0.0105	0.0048		1130	0.04	8.22	0.0123	21
INCO	0.0193	0.0716	0.1092	0.1543	0.2706	0.3171	0.0066	0.0049		17810	1.62	59.77	0.0073	14060
GLIN	0.0251	0.0733	0.1083	0.221	0.2704	0.3319	0.0105	0.0079		20360	0.89	43.5	0.0133	8833
ILF	0.0497	0.0631	0.1032	0.062	0.3082	0.2768	0.0141	0.0135		1.18e+06	2.16	29.23	0.0215	1237337
SMIN	0.0174	0.0573	0.0914	0.2128	0.2848	0.3447	0.0078	0.0074		99980	5.61	69.6	0.0053	272774

Holdings

To peer into the holdings of a specific ETF:

holdings = openbb.etf.holdings('DIA').reset_index()
holdings.head(5)

	Symbol	Name	% Of Etf	Shares
0	UNH	UnitedHealth Group Incorporated	10.09%	5985297
1	GS	The Goldman Sachs Group, Inc.	7.51%	5985297
2	HD	The Home Depot, Inc.	6.03%	5985297
3	AMGN	Amgen Inc.	5.61%	5985297
4	MCD	McDonald's Corporation	5.35%	5985297

Disc

The current top gainers, losers, and volume for ETFs is returned with:

openbb.etf.disc.mover(sort_type = 'decliners')

openbb.etf.disc.mover(sort_type = 'gainers')

openbb.etf.disc.mover(sort_type = 'active')

With no sort_type chosen, it will default to gainers:

movers = openbb.etf.disc.mover()
movers.head(5)

		Price	Chg	%Chg	Vol
0	Direxion Daily Semiconductor Bear 3X Shares	35.3101	3.3101	10.34	24.5M
1	ProShares UltraShort Bloomberg Natural Gas	18.09	1.19	7.04	4.4M
2	MicroSectors FANG & Innovation -3x Inverse Leveraged ETN	28.98	1.83	6.74	160.1K
3	Direxion Daily Dow Jones Internet Bear 3X Shares	32.14	1.91	6.32	554.2K
4	Direxion Daily S&P 500 High Beta Bear 3X Shares	6.4652	0.3752	6.16	1.2M