Dear Community,

QuantConnect now provides free, consolidated OPRA equity and index options data for cloud-hosted live algorithms, with complete US market coverage and live bars down to one-second resolution. This new data feed enables your QuantConnect live algorithms to subscribe to equity and index option chains, automating popular option strategies such as the wheel, spreads, straddles, and iron condors.

The new data feed is baked into all live-trading servers, along with our US Equities, Futures, Future Options, FX, and Crypto live feeds. The data is delivered over low-latency fiber-optic connections and internal local networks, providing exceptional uptime and resilience. As with all our data feeds, we have fully redundant networks serving the live strategies, providing redundancy in the event of fiber, switch, server, or feed outages. 

Options data requires enormous bandwidth and compute, making it particularly challenging to do well. Over the last 6 months, we’ve invested heavily in upgrading our ticker plant technology to handle the new loads, requiring both hardware and software updates. We set up a test environment for an AI loop in which Claude Fable could modify the ticker plant source code, deploy it, and measure the results. The resulting speed and resource improvements reduced market open tick latency from over a minute to having 90% of bars delivered within 250ms. With this baseline, we can deploy bars as low as 1-second resolution for community strategies. Behind the scenes, the ticker plants continuously track approximately 2.1 million option contracts and process their updates in real time.

Streamed OPRA latency recordings over trading day.

Streaming thousands of contracts over the internet is challenging for most data vendors, resulting in high latency and delays. By serving it over our local cloud networks, you can build strategies with access to thousands of contracts, second by second. We made several optimizations and fixes to LEAN, focused on improving the “tick to event handler” latency experienced by a strategy. 

As with all our data sets and live feeds, the new release includes a powerful historical data service that can deliver the data as recently as from a few seconds earlier. This allows you to warm up your strategy to have your signals ready for trading, no matter when you deploy your algorithm. In addition to trade and quote data feeds, the deployment also includes a new open-interest data feed, serving open-interest as it is broadcast by the exchanges each morning. 

To use this in your strategy, add the option underlying and contract filter you’d like to use:

class BasicOptionAlgorithm(QCAlgorithm):

    def initialize(self):
        self.universe_settings.asynchronous = True
        option = self.add_option("SPY")
        option.set_filter(self._filter)
        self._symbol = option.symbol
        
    def _filter(self, universe):
        return universe.expiration(0, 7).delta(0.35, 0.75)
        
    def on_data(self, data):
        chain = data.option_chains.get(self._symbol)
        if chain:
            contract = sorted(chain, key=lambda x: (x.expiry, x.greeks.delta))[0]

The feed includes index-option pricing like SPX and NDX; however, to calculate the Greeks, you need to source the underlying index price from a third-party vendor. You can use index options like so:

class BasicIndexOptionAlgorithm(QCAlgorithm):
    def initialize(self):
        self.universe_settings.asynchronous = True
        option = self.add_index_option("SPX","SPXW")
        option.set_filter(self._filter)
        self._symbol = option.symbol

    def _filter(self, universe):
        return universe.expiration(0, 7).delta(0.35, 0.75)

    def on_data(self, data):
        chain = data.option_chains.get(self._symbol)
        if chain:
            contract = sorted(chain, key=lambda x: (x.expiry, x.greeks.delta))[0]

If you’re interested in using option strategies, we strongly recommend reviewing our built-in Option Strategies trading helper. These helpers simplify the construction and execution of common multi-leg option strategies. Each strategy includes a full tutorial for how to use it and the expected payoff from a trade. We have implemented 41 such strategies and tutorials for your review. 

QuantConnect’s LEAN algorithmic trading engine simulates these option strategies in a minute-by-minute, point-in-time backtest, giving you realistic margin usage for your portfolio. Making the most of your available capital is important for achieving the best return, and with LEAN’s modeling, your research will reflect the actual brokerage leverage available in live trading. To get started with QuantConnect Equities and Index Options, copy one of the examples to a new project and use Mia to customize the strategy to your goals. Once ready, you can deploy the strategy to any of our brokerages that support option trading: Interactive, TradeStation, Tasty Trade, Alpaca, Schwab, Tradier, WeBull, or Public. The feed is included free with any QuantConnect live trading server. 

Happy Trading!

QuantConnect Team