Meet the investors trying quantitative trading at home

ILLUSTRATION BY ALEXANDRA CITRIN-SAFADI/WSJ
ILLUSTRATION BY ALEXANDRA CITRIN-SAFADI/WSJ

Summary

Ordinary investors are experimenting with complex, computer-driven strategies.

Pietros Maneos trades stocks like many of Wall Street’s most sophisticated operations: running dozens of computer-driven strategies in parallel to chase market-beating returns. But he isn’t some tech-savvy math type. He is a published poet who doesn’t know how to code.

Maneos, 44 years old, uses online-trading platform Composer.trade to build, test and bet on quantitative trading algorithms that buy and sell stocks and exchange-traded funds out of his home office in Boca Raton, Fla. One algorithm, for example, holds a triple-leveraged exchange-traded fund tracking the Nasdaq-100 index if the S&P 500 index has recently trended higher—and Treasury bills otherwise.

He is currently running 72 such schemes he constructed with the application’s graphical interface, but can also type requests in plain English that Composer’s AI will translate into code.

“It’s like having my own personal black box," he said. “You could argue that I’m a hedge fund with 72 strategies."

Maneos is one of a growing group of regular investors adopting techniques similar to those that have exploded across Wall Street since the success of Jim Simons and his Renaissance Technologies hedge fund helped launch what many call the quant revolution.

So-called quant funds use algorithms coded into high-powered computers to spot market signals and automatically buy and sell stocks and other assets accordingly. Practitioners say quantitative investing utilizes far more information than can be crammed into any human head and can keep emotions from clouding trading decisions.

Some algorithms load up on riskier assets during calmer periods. Others seek to divine and follow price trends. But all tend to spring from a similar source: a cadre of coders with deep market knowledge—often Ph.D.s in math or physics—who design and build their models.

For years, ordinary people could only watch—or pay quant funds’ hefty management fees. Now, they are getting in the game. Home computers are more powerful than ever. Websites offer tutorials. Hobbyists share tips on social media. A host of books, YouTube videos and online-trading platforms such as Composer, Alpaca.markets and QuantConnect.com have sprung up to make it easier for amateurs to trade like the pros.

Some worry that is a power most people aren’t ready to wield. It isn’t just that a few wrong keystrokes in a complex approach could bring disaster. Many also warn that exploiting quickly shifting market patterns is a challenge even for the pros.

“Now retail investors can act just like their quant idols: build overly complicated models that look unbeatable until the day they go live," said hedge-fund veteran Andrew Beer, managing member of investment-management firm DBi.

Maneos, who said his strategies returned more than 33% last year, uses Composer because of its easy-to-use graphical and artificial-intelligence interfaces, which obviate the need to learn conventional computer languages and allow users to see and share their strategies in the form of simple flow charts.

Composer went live in 2021 and enables users to construct strategies from a list of more than 11,000 stocks and ETFs. Its user base nearly quintupled in 2023 and now numbers in the tens of thousands, said Benjamin Rollert, the company’s co-founder and chief executive officer.

Many users praise its simplicity. But several warned about the tax implications of wash sales and the absence of some common Wall Street risk-management tools, such as one that would automatically exit a strategy when a specified loss is reached. And some report losing money due to system glitches over the past two years that prevented trades from being executed as planned.

Rollert says his system’s stability is “pretty good" even relative to big brokers and points to pre-trade risk checks that his company has installed “to make sure you’re not doing anything crazy." Shorting stocks and trading options, penny stocks or with borrowed money are all prohibited, which “prevents most of the problems," he said.

Composer’s algorithms trade just once a day, and strategies are generally intended to run for months or longer. Many users are shooting for supersize returns with strategies that take positions in leveraged ETFs.

David Kaiser is an account manager for a closet-organizer company who lives in Holland, Mich. He has crafted strategies at his kitchen table that switch between different ETFs—such as some holding tech stocks and others containing Treasury bonds—based on signals designed to indicate whether the market is headed up, down or something in-between. His quant portfolio represents a small portion of his investments, he said, which makes him comfortable taking bigger swings with it.

“I’m 55, and I know I’m not where my peers are in terms of saving for retirement," said Kaiser. “I feel the need to be more aggressive."

Kaiser belongs to a community of more than 4,500 Composer users that has blossomed on the Discord instant-messaging platform over the past year-and-a-half. Enthusiasts there commiserate, exchange ideas and—in stark contrast with their professional counterparts—frequently share the details of their strategies. Members say it is a kinder, more mature and far more collaborative place than meme-stock cauldrons such as Reddit’s WallStreetBets.

But some have learned costly lessons. Walter Sanders, 41, a hospital-system software developer who lives in Macon, Ga., said he lost 15% in a week on a strategy he found on Discord but didn’t thoroughly vet.

He traced the source of his loss to a three-times leveraged ETF on Chinese shares that he believes was in the strategy primarily to make it look more lucrative in backtesting—a way of estimating historical profits and losses by running a trading scheme through past data.

Experienced quant traders say it is easy to construct strategies that appear as if they would have profited in the past, but which are unlikely to work in the future. They say warning signs of such contrivances include unrealistically high returns and underlying code that is complicated by dozens of lines of logic.

“I’m slowly learning that although the backtest may be what would have happened, that doesn’t mean there’s a lot of sense to it," said Sanders.

Daniel Hutchens, 40, is a former Microsoft cybersecurity engineer who codes his own trading strategies on platforms such as Interactive Brokers. But he recently improved the performance of one of them, he said, by replacing a Nasdaq-100 index-tracking ETF with others holding semiconductor stocks—an idea that developed on Discord after he shared his strategy there.

“I could be like, ‘Hey, here’s this harebrained, half-baked idea, play with it if you want,’" said Hutchens about posting on the site. “And then next thing you know, 45 iterations later, it’s actually something viable or something horrendous. You don’t know what you’re going to get. And it’s neat."

Write to Bob Henderson at bob.henderson@wsj.com

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