About US Equity Coarse Universe
The US Equity Coarse Universe dataset by QuantConnect is a daily universe of all trading stocks in the US for a given day with the end of day price and volume. The data covers 30,000 US Equities in total, with approximately 8,000 Equities per day. The data starts in January 1998 and is delivered each trading day. This dataset is created by taking the closing auction price tick from the daily L1 trade and quote exchange dumps.
This dataset depends on the US Equity Security Master dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
QuantConnect/LEAN combines the data of this dataset with MorningStar Fundamental data in runtime. You can use this dataset in local development without the Morningstar counterpart.
About QuantConnect
QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
About QuantConnect
QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
Algorithm Example
from AlgorithmImports import *
from QuantConnect.DataSource import *
class USEquityCoarseUniverseConstituentsDataAlgorithm(QCAlgorithm):
_number_of_symbols = 3
_changes = None
def initialize(self) -> None:
self.set_start_date(2021, 1, 1)
self.set_end_date(2021, 7, 1)
self.set_cash(100000)
# Set Asynchronous to True to improve speed performance
self.universe_settings.asynchronous = True
# Requesting data
self.add_universe(self.fundamental_selection_function)
def fundamental_selection_function(self, fundamental: List[Fundamental]) -> List[Symbol]:
sortedByDollarVolume = sorted(fundamental, key=lambda x: x.dollar_volume, reverse=True)
return [ x.symbol for x in sortedByDollarVolume[:self._number_of_symbols] ]
def on_data(self, slice: Slice) -> None:
# if we have no changes, do nothing
if self._changes is None: return
# liquidate removed securities
for security in self._changes.removed_securities:
if security.invested:
self.liquidate(security.symbol)
# we want 1/N allocation in each security in our universe
for security in self._changes.added_securities:
self.set_holdings(security.symbol, 1 / self._number_of_symbols)
self._changes = None
def on_securities_changed(self, changes: SecurityChanges) -> None:
self._changes = changes
for security in changes.added_securities:
# Historical data
history = self.history(security.symbol, 7, Resolution.DAILY)
self.debug(f"We got {len(history)} from our history request for {security.symbol}")
Example Applications
The US Equity Coarse Universe dataset enables you to accurately design a universe of US Equities. Examples include the following strategies:
- Selecting securities with the largest dollar volume
- Selecting securities within a specific price range
- Selecting securities that have fundamental data available (together with MorningStar Fundamental data in Fundamental objects)
Pricing
Cloud Access
Free access for universe selection strategies on the QuantConnect Cloud. Create custom filters using price and volume for the whole US stock universe.
On Premise Download
On premise download of US Equity Coarse Universe constituent data files, including closing price and volume for the day for local backtesting.
Bulk Updates
On premise download of all US Equity Coarse Universe constituent data files, including closing price and volume for the day for local backtesting.
Bulk Download
On premise download of all US Equity Coarse Universe constituent data files, including closing price and volume for the day for local backtesting.
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