About US Equity Option Universe
The US Equity Option Universe dataset by QuantConnect lists the available US Equity Options contracts and the current Implied Volatility and Greeks. The data covers 4,000 Symbols, starts in January 2012, and is delivered on a daily update frequency. To create this dataset, we use our implementation of the forward tree pricing model, which accounts for the interest rate, dividend payments, and daily closing prices. The values in this dataset are the same values you can get from daily indicators with mirror Options.
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.
This dataset does not contain market data. For market data, see US Equity Options by AlgoSeek.
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 *
class USEquityOptionsUniverseAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2021, 1, 1)
self.set_end_date(2021, 2, 1)
self.set_cash(10000000)
# Asynchronous can use computational resources efficiently
self.universe_settings.asynchronous = True
# Subscribe to the underlying for the underlying position
# Set the data normalization mode to raw for strike price comparability
self.equity = self.add_equity("GOOG", data_normalization_mode=DataNormalizationMode.RAW).symbol
# Requesting option data and filter for the hedge candidates
option = self.add_option(self.equity)
option.set_filter(self.option_filter)
self.option_symbol = option.symbol
# Set scheduled event to buy a hedge option contract at market open to eliminate the intra-day movement
self.schedule.on(
self.date_rules.every_day(self.equity),
self.time_rules.after_market_open(self.equity, 1),
self.buy_hedge_contract
)
# Set a scheduled event to sell the hedge contract before market close, since we want to earn from inter-day movement
# Leave 2 minutes contingency to fill
self.schedule.on(
self.date_rules.every_day(self.equity),
self.time_rules.before_market_close(self.equity, 2),
self.sell_hedge_contract
)
self.hedge = None
def option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
# Select the contracts with delta very close to -1 and high open interest
# This can effectively hedge most of the price change of the underlying and ensure the liquidity
# Make sure the contract is expiring close for its tradbility
return universe.include_weeklys().puts_only().expiration(2, 7).delta(-1, -0.95).open_interest(10, 1000)
def buy_hedge_contract(self) -> None:
chain = self.current_slice.option_chains.get(self.option_symbol)
if chain:
# Order the underlying if not hold, the order size should match the option contract
# Order only if option chain data ready for hedging
if not self.portfolio[self.equity].invested:
self.market_order(self.equity, self.securities[self.option_symbol].symbol_properties.contract_multiplier)
# Get the contract with delta closest to -1 (lowest possible delta)
contract = sorted(chain, key=lambda x: x.greeks.delta)[0]
self.hedge = contract.symbol
# Buy 1 deep ITM put with delta close to -1 to eliminate the intraday movement
self.market_order(self.hedge, 1)
def sell_hedge_contract(self) -> None:
# Check if any hedge contract position, if so, liquidate before market close to expose to underlying overnight movement
if self.hedge:
self.liquidate(self.hedge)
self.hedge = None
Example Applications
The US Equity Options Universe dataset enables you to accurately design Option strategies. Examples include the following strategies:
- Buying put Options to hedge against downward price movement in positive Equity positions
- Exploiting arbitrage opportunities that arise when the price of Option contracts deviate from their theoretical value
Pricing
Cloud Access
Free access for US Equity Option universe selection on the QuantConnect Cloud. Create custom filters using implied volatility, Greeks, and open interest for the US Equity Options.
On Premise Download
On premise download of US Equity Options universe data files, including price, implied volatility, Greeks, and open interest for local backtesting.
Bulk Updates
Bulk download of the entire US Equity Option universe dataset
Bulk Download
Bulk download of the entire US Equity Option universe dataset
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