When people talk about algorithmic traders, it evokes images of rooms full of math PhDs creating complex models that place huge trades in milliseconds after economic or earnings data is released. But now, it’s retail traders who are turning in droves to automated trading, building the kind of programs in their basements more associated with Wall Street banks than the Reddit thread r/wallstreetbets.
Using these systems, though, has a downside: they can result in predictable, herd-like behavior, with data from Cboe Global Markets showing trades clustered at certain times of day. There’s a risk that more sophisticated market participants could exploit the predictability of small-volume traders at specific moments when retail demand for options spikes.
Speaking at the Options Industry Conference in Florida earlier this month, Henry Schwartz, vice president of market intelligence at Cboe Global Markets, showed a slide that surprised many of the traders, analysts and exchange executives in attendance.
On the slide was a chart showing how many zero-day-to-expiry options — the uber short-term contracts that have grown to more than half of the overall volume in the S&P 500 Index on some days — were traded every minute of the regular trading session so far in 2025. It showed that retail volume spikes at exactly 10 a.m. New York time, with smaller spikes at 10:15, 10:30 and 11 a.m., and a larger spike at 2 p.m.
Schwartz attributed the spikes in volume to systematic trading strategies by retail investors all programmed to run at the same moment. “Customers turn these systems on to sell condors and manage them and they pick their time intervals, and they tend to pick round minutes,” he said, referring to one of the more popular retail options strategies.
The pattern has been showing up in S&P 500 intraday data for at least a year, Schwartz confirmed in a follow-up interview with Bloomberg. The pattern was no longer visible when trades smaller than 10 lots were excluded, indicating that the trend is driven by the smallest participants in the market.
Kevin Darby, vice president of execution technologies at CQG, which builds algorithmic systems for investors, said it’s normal for market makers to change quotes based on patterns in order flow.
“If you’re going to make a bet, you have to think about what it would be like to be the bookie,” he said. “And so, in this particular scenario, if I’m the bookie, and I know that the 12:30 orders are about to come in, I’m going to move my odds a little bit.”
To be sure, the competition in a liquid contract like the S&P 500 may limit how much the influx of retail orders could shift the market.
Schwartz linked the tendency of retail trades to cluster at certain moments in the day to an “explosion” of software providers offering to help customers automate options trading strategies.
These software providers connect to retail brokers via Application Programming Interfaces (APIs), which allow different types of computer programs to communicate and share data. Brokers like Interactive Brokers Group, Inc., WeBull Corp. and Tradier Inc. all allow clients to plug in custom built trading software.
Traders can buy such tools off the shelf or build their own, sometimes by delegating the code writing process to artificial intelligence.
“In the last year we’ve had more requests from retail customers to build to our APIs than I’ve seen in the last 10 years combined,” Tony Zhang, chief strategist of options software and training provider OptionsPlay, said in a separate panel discussion at the conference.
While the clustering of trades raises the question of whether larger professional firms have noticed this and are lining up to pick off the retail crowd, advisors to the smaller traders defend the practice.
The popularity of particular times may be linked to common settings on backtesting tools, which allow a trader to run a scenario and see how much they could win or lose, based on historical data. Option Research & Technology Services and Options Omega are among the providers of such backtesting services.
Options Omega was co-founded by three retail traders based in Knoxville, Tennessee, who originally built backtesting tools for themselves before realizing there was broader demand for the product.
Troy McNeil, one of the firm’s founders, argues that the phenomenon of spikes in retail S&P 500 options volume at certain times of day is driven as much by small traders taking opposite positions. “I don’t know that everybody does Iron Condors at 10 a.m.,” he said. “People are doing a lot of different things — they’re selling, and they’re buying.”
Some strategies deployed by retail, such as automated covered call selling, are more akin to the kind of yield harvesting efforts associated with structured products, sophisticated financial instruments created by banks and sold to wealthy investors.
“They’re kind of in-housing their own structured product,” says Amy Wu Silverman, head of derivatives strategy at RBC Capital Markets. “Instead of buying the cookie, I’m going to buy the flour and the chocolate chips and then make the dough.”
Still, seeing the trading patterns illustrated so starkly was eye-opening to some in attendance at the conference. Matthew Amberson, founder of ORATS, said he was surprised that flows from retail bots were so visible in the S&P 500 market.
“It was shocking to see how many of those bots were in the market at the same time,” said Amberson. A group of traders all instructing bots to do the same thing at the same time is a “silly way” to trade, he told Bloomberg.
Written by: Bernard Goyder @Bloomberg
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