Disclaimer

Define a universe

QuantRocket relies heavily on the concept of universes, which are user-defined groupings of securities. Universes provide a convenient way to refer to and manipulate groups of securities when collecting historical data, running a trading strategy, etc. You can create universes based on exchanges, security types, sectors, liquidity, or any criteria you like. A universe could consist of one or two securities or thousands of securities.

Query securities

To create our first universe, we will query securities from the securities master database, optionally pare them down to a subset of securities, then upload the pared down securities to create our universe. Learn more about universes in the usage guide.

Security listings for our sample stocks were automatically collected during historical data collection. We query the stock listings from the securities master database using the get_securities function, which loads them into a pandas DataFrame:

from quantrocket.master import get_securities
# by specifying sec_types='STK', we exclude ETFs, which are present in the sample data 
securities = get_securities(vendors="usstock", sec_types="STK")
securities.head()

	Symbol	Exchange	Country	Currency	SecType	Etf	Timezone	Name	PriceMagnifier	Multiplier	Delisted	DateDelisted	LastTradeDate	RolloverDate
Sid
FIBBG000B9XRY4	AAPL	XNAS	US	USD	STK	False	America/New_York	APPLE INC	1	1	False	NaT	NaT	NaT
FIBBG000BFWKC0	MON	XNYS	US	USD	STK	False	America/New_York	MONSANTO CO	1	1	True	2018-06-06	NaT	NaT
FIBBG000BKZB36	HD	XNYS	US	USD	STK	False	America/New_York	HOME DEPOT INC	1	1	False	NaT	NaT	NaT
FIBBG000BMHYD1	JNJ	XNYS	US	USD	STK	False	America/New_York	JOHNSON & JOHNSON	1	1	False	NaT	NaT	NaT
FIBBG000BPH459	MSFT	XNAS	US	USD	STK	False	America/New_York	MICROSOFT CORP	1	1	False	NaT	NaT	NaT

Note the Sid index in the DataFrame: Sid is short for "security ID" and is the unique identifier for a particular security or contract. Sids are used throughout QuantRocket to refer to securities.

Create universe

To create a universe consisting of these securities, we simply upload the list of sids to the create_universe function. We'll name the universe "usstock-free":

from quantrocket.master import create_universe
create_universe("usstock-free", sids=securities.index.tolist())

{'code': 'usstock-free', 'provided': 8, 'inserted': 8, 'total_after_insert': 8}

The function output confirms the name and size of our new universe.

Filter securities

As a demonstration, we could also pare down the securities to create a universe from a subset of securities. One way to do this is using qgrid, a tool that provides Excel-like filtering and sorting of DataFrames inside Jupyter notebooks. We limit the number of columns to make the grid more readable:

import qgrid
widget = qgrid.show_grid(securities[["Symbol","Exchange","Name","Delisted"]])
widget

(this is an image of a grid, execute the above cell to see the actual grid)

Use the grid to filter the DataFrame by one or more columns. For example, you could exclude delisted stocks. After filtering, use get_changed_df() to access the filtered DataFrame:

filtered_securities = widget.get_changed_df()
filtered_securities.head()

	Symbol	Exchange	Name	Delisted
Sid
FIBBG000B9XRY4	AAPL	XNAS	APPLE INC	False
FIBBG000BKZB36	HD	XNYS	HOME DEPOT INC	False
FIBBG000BMHYD1	JNJ	XNYS	JOHNSON & JOHNSON	False
FIBBG000BPH459	MSFT	XNAS	MICROSOFT CORP	False
FIBBG000GZQ728	XOM	XNYS	EXXON MOBIL CORP	False

Then we can create a universe from the filtered securities.

create_universe("usstock-free-active", sids=filtered_securities.index.tolist())

{'code': 'usstock-free-active',
 'provided': 6,
 'inserted': 6,
 'total_after_insert': 6}

---

Next Up

Part 3: Momentum Factor Research