About US Equity Options

The US Equity Options data by AlgoSeek provides Option data, including prices, strikes, expires, and open interest. The data covers 4,000 Symbols, starts in January 2012, and is delivered on a minute frequency. This dataset is created by monitoring Options Price Reporting Authority (OPRA) data feed, which consolidates last sale and quotation information originating from the national securities exchanges that have been approved by the Securities and Exchange Commission.

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 of the underlying security. The US Equity Options dataset also depends on the US Equity Option Universe dataset because the US Equity Options Universe dataset contains information on the available contracts and their daily Greeks and implied volatility values.

About AlgoSeek

AlgoSeek was in 2014 with the goal of providing the highest quality, most accurate, ready-to-use data in the financial data industry. AlgoSeek provides access to Equities, ETFs, ETNs, Equity Indices, Equity Options, Futures, and Future Options for quantitative firms and traders.

Add US Equity Options

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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 USEquityOptionsDataAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2020, 6, 1)
        self.set_end_date(2020, 8, 1)
        self.set_cash(100000)
        self.universe_settings.asynchronous = True
        # Requesting data
        self.underlying = self.add_equity("GOOG").symbol
        option = self.add_option("GOOG")
        self.option_symbol = option.symbol
        # To speculate trade the underlying with a low cost, filter for the ATM calls that expiring in the current week
        # -2/+2 strike buffer is given for small price change
        option.set_filter(lambda u: u.include_weeklys().calls_only().strikes(-2, +2).expiration(0, 6))
        
        self.contract = None

    def on_data(self, slice: Slice) -> None:
        # Close the underlying position if the option contract is exercised
        if self.portfolio[self.underlying].invested:
            self.liquidate(self.underlying)

        # Select with the lastest option chain data only
        chain = slice.option_chains.get(self.option_symbol)
        if self.contract and not self.portfolio[self.contract.symbol].invested and chain:
            # Select the call contracts with the furthest expiration (week end)
            furthest_expiry = sorted(calls, key = lambda x: x.expiry, reverse=True)[0].expiry
            furthest_expiry_calls = [contract for contract in calls if contract.expiry == furthest_expiry]
            
            # Get the ATM call for speculate trade with low cost and limited loss
            self.contract = sorted(furthest_expiry_calls, key = lambda x: abs(chain.underlying.price - x.strike))[0]
            self.market_order(self.contract.symbol, 1)
                
                
    def on_securities_changed(self, changes: SecurityChanges) -> None:
        
        for security in changes.added_securities:
            # Historical data
            history = self.history(security.symbol, 10, Resolution.MINUTE)
            self.debug(f"We got {len(history)} from our history request for {security.symbol}")

Example Applications

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

Pricing

Provider offers 10 licensing options

Pricing