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, 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:

In [1]:

from quantrocket.master import get_securities
securities = get_securities(vendors="usstock")
print(f"loaded {len(securities)} securities")
securities.head()

loaded 9 securities

Out[1]:

	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
FIBBG000BDTBL9	SPY	ARCX	US	USD	STK	True	America/New_York	SPDR S&P 500 ETF TRUST	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

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.

If the usstock-free-1d database is the first historical database you've collected, your securities DataFrame will contain 9 rows of sample tickers: AAPL, HD, JNJ, MSFT, SPY, MON, KKD, XOM, and AA. However, if you previously collected the learning bundle, then querying the securities master database may have returned many thousands of rows. If that is the case, you can execute the following cell to load only the 9 sample symbols, which will keep you aligned with this tutorial. Instead of querying everything in the securities master database, the following code first uses list_sids(...) to obtain the list of sids that is present in the usstock-free-1d history database that we collected in the previous tutorial, then uses get_securities(...) to retrieve only those securities from the securities master database.

In [2]:

from quantrocket.history import list_sids
free_sids = list_sids("usstock-free-1d")
securities = get_securities(sids=free_sids)
print(f"loaded {len(securities)} securities")
securities.head()

loaded 9 securities

Out[2]:

	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
FIBBG000BDTBL9	SPY	ARCX	US	USD	STK	True	America/New_York	SPDR S&P 500 ETF TRUST	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

Filter securities¶

Our strategy only targets stocks, so before creating our universe, we will filter the securities DataFrame to exclude ETFs (in our case, SPY is the only ETF in our sample data):

In [3]:

securities = securities[securities.Etf==False]
print(f"filtered to {len(securities)} securities")
securities.head()

filtered to 8 securities

Out[3]:

	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

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":

In [4]:

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

Out[4]:

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

Define a universe¶

Query securities¶

Filter securities¶

Create universe¶

Next Up¶