About US Equities

The US Equities dataset by AlgoSeek is survivorship bias-free daily coverage of every stock traded in the US Securities Information Processors (SIP) CTA/UTP feed since 1998. The dataset covers approximately 27,500 securities, starts in January 1998, and is delivered in any resolution from tick to daily. The Data is collected from the full SIP feed via our Equinix co-located servers, including all trades and quotes published to every exchange as well as FINRA. Over-the-Counter (OTC) trades are not included.

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.

About AlgoSeek

AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning. For more information about AlgoSeek, visit algoseek.com.

Add US Equities

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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 USEquityDataAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2018, 1, 1)
        self.set_end_date(2021, 6, 1)
        self.set_cash(100000)
        
        # Requesting single equity data, since we only trade AAPL
        self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
        
        # Historical data
        history = self.history(self.aapl, 60, Resolution.DAILY)
        self.debug(f"We got {len(history)} items from our history request")

    def on_data(self, slice: Slice) -> None:
        # Check if the current slice containing AAPL and if we hold any position
        # As we make use of the most updated price data to decide the order size
        if slice.contains_key(self.aapl) and slice[self.aapl] is not None and not self.portfolio.invested:
            self.set_holdings(self.aapl, 1)

Example Applications

The US Equities dataset enables you to accurately design Equity trading strategies. Examples include the following strategies:

Pricing

Provider offers 16 licensing options

Pricing