Agents will further entrench the incumbents in quantitative trading

Quantitative hedge funds make software designed to produce uncorrelated return streams.

Coding agents change the cost structure of that software, making it cheaper to build and maintain what you need, from signal construction, portfolio optimization and simulation, to execution and order management.

Likewise, research agents — who can design signals, write data pipelines, build and test statistical models — will lower the cost of trying new ideas. The whole research process will become more scalable. Time-to-market for new signals will go down.

So overall the effect will be lower R&D costs.

Does this provide for a good opening for startup managers? I believe not. Even if it’s now an order of magnitude cheaper and faster to write the software and to do the research, defensibility in this business ultimately comes from scale. Larger funds get:

• Cheaper and faster execution

• Lower cost of capital/more leverage

• More data, sometimes buying exclusivity

• Preferential market access and better connectivity

• Talent, and again paying for exclusivity in a way

• Brand and trust signal

Agents don’t change anything about those advantages, but there are a couple of counterpoints worth considering.

First, if the cost of R&D drops by an order of magnitude, some capacity-constrained strategies become worth trading that previously weren't worth the headcount. That makes for some kind of opening for smaller managers, for whom capacity constraints are less of an issue. But the drop in cost applies to incumbents as well, and if they’re good at combining strategies at scale, they’re in a better position to harvest the margin increase on a larger asset base.

A second counterpoint is institutional inertia: Incumbents may fail to adopt the new technology and their competitive edge in engineering and research will erode over time. For sure, there’s always some amount of drag from existing code, processes, integrations, etc., and in my experience this is more significant than one would expect in a tech-forward sector like quant trading. But what I’ve also observed is that it takes an extremely long time for large, ossified players to disappear. Even if their tech stacks are known to be outdated and it’s gotten hard to attract talent, many of those funds still manage billions years later thanks to the above mentioned scale advantages.

Big picture, the one thing that’s for sure is that agents will make markets more efficient. The time from new data or new idea to trading live in production will come down meaningfully over time, so any new effects will be arb’d away more quickly.

Quant devs and product engineers

Back when I was in quantitative trading I was a big proponent of the ‘quant developer’ role — a hybrid between quant researcher (strong math and stats background) and software engineer (often with an HPC background). Pure engineers tended to be ineffective since they weren’t able to spot problems with the data and didn’t have a good enough grasp of what great research tooling needed to look like, therefore building software that researchers found hard to use. Quant trading is the kind of problem that requires a mix of deep domain expertise and getting your hands dirty. If you’ve never tried to come up with a profitable signal, construct a portfolio or pick the right execution algo, you just don’t know about all the ways things can go wrong. The software you write will be liable to contain a large number of subtle defects. It looks mechanically correct but will be wrong statistically, in the sense that the resulting strategies won’t make any money — because of data bugs, causality violations, subtle issues with how the data is transformed, or code that’s too slow when it actually matters.

And the same was true in the other direction. Pure researchers also tended to be less effective since they were unable to write production quality code, therefore creating a large class of ‘translation bugs’, where signals were profitable in simulation on their local box but then failed to generate any alpha in production. Frequently this was due to overfitting and careless research, but often enough this was also because the spec wasn’t quite right or the data in production had subtle differences compared to what the researcher was working with. Closing the gap between research and production can eliminate those kinds of unforced errors.

Working in fintech the last couple of years, I’ve become a big proponent of the ‘product engineer’ role where the same considerations apply. Pure engineers without a strong grasp of the domain, deep understanding of the customer and the problem, and good product sensibilities tend to be ineffective. They’re unable to work in a self-directed manner and it takes a lot more cycles to get things right. Feedback loops with a PM and a QA team in the middle tend to be slow and costly.

Conversely, pure product managers without a strong engineering foundation also tend to be ineffective. They’re bad at estimation and often can’t tell which features are hard vs easy to build. They don’t have a good sense for what the right data model looks like and they will be less effective at communicating with engineers — again lengthening the feedback loops.

This raises the question whether everyone should be a product engineer or quant dev. I believe the answer is yes, by and large. At Kappa, we’ve been successful so far without hiring a PM, and that’s largely because our engineering team is made up of folks who deeply understand the problem and the customer, and who cherish getting their hands dirty. Likewise, in the hedge funds I worked at we did well by hiring researchers who were also strong developers as a pre-requisite, and vice versa developers who also had a strong grounding in math, stats and finance.

What does a great product engineer do? They use the product. They end-to-end test their own changes and do all the acceptance-testing themselves. They have an account in production and make use of feature flags to truly test the last mile. They sit in on customer interviews and do shifts on customer support. They’re goal-driven and care about customer experience above all else.

What does a great quant dev do? They obsess about the data. They visualize it, do outlier analysis and look at distributional properties. They read everything about markets, finance and statistics they can get their hands on. They understand arbitrage and market efficiency. They monitor signal performance in production. They’re goal-driven and care about portfolio performance and PnL above all else.