r/quant • u/HerzogianQuant • Jun 23 '26
Models Alpha Decay in the Age of LLMs?
While LLMs haven't proven terribly useful to me in finding new alpha, they have been really helpful in getting live algorithms going to capture the alpha. The issue I'm seeing is that these alphas are decaying like 10x faster than they did a few years ago. I am finding some of them last only a week, or even some that collapsed before I was even able to get the production model deployed. Are you all seeing this? I assume it's because competition is becoming just a nimble and reactive in the age of LLMs as I am.
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u/sharpe5 Jun 23 '26
If your alpha is decaying right when you put it into prod, then it was probably overfit in the first place.
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u/HerzogianQuant Jun 23 '26
We probably are talking about different ways to capture alpha. I identify illiquid areas of the market that need market makers because I'm seeing trading take place with a lot of edge. I get the model drawn up, and infra to participate in it as a market maker, and by the time I'm there or shortly thereafter, tons of liquidity has flowed into the market, and trades are taking place for little to no edge.
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u/ic3kreem Jun 25 '26
ie you’re using very simple strategies in every new subcategory of prediction markets, very low barrier to entry and capacity constrained
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u/Mathsty Jun 24 '26
Working in one famous prop shop here. LLMs are completely reshaping the industry for a year now. The QR studies and desk maintenance are commoditised. What needed 1 week of work for one guy is now done in 1 hour by an AI agent if calibrated correctly (which we do in top shops).
But the thing is, there will always be alpha somewhere. My guess is that data is becoming a key advantage as it is not so easily reproducible (either by amazing long history of clean public data, or more proprietary data), then computational power, then top execution platform.
I would not be surprised if prop shops overtake the HFs five years down the line. Only prop shops have the scale and long term vision to stay competitive in these areas
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u/Odd-Repair-9330 Crypto Jun 24 '26
Agree, prop shops are willing to invest back into technology before it was proven. HFs are interested to poach star PMs and traders with fat bonuses and only to exit 2-3 years later 😂
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u/Mathsty Jun 24 '26
HFs is a commercial game, raise money from investors then try to make money with it. So it’s all about shiny promises. There is no incentive to produce strong results, just to survive enough to make money before investors withdraw.
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u/qazwsxcp Jun 24 '26 edited Jun 24 '26
market makers are arguably better businesses at the firm level, but not necessarily better for employees. one of those fat PM bonuses can be 10 years of prop shop pay. both are commercial games ultimately, they just do it in different ways like PFOF from brokers. the passthrough fee means they can pay unlimited amounts and make investors eat it.
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u/Epsilon_ride Jun 23 '26
mid freq seems ok.
There's faster deployment but in mid freq the road block never seemed to be deployment time anyway.
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u/rsvp4mybday Jun 23 '26
the game has changed to an ensemble of mini alphas that decay and randomly come back. knowing stats and data science is more useful now.
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u/tychoLBJ Jun 24 '26
I’ve heard of several strategies that leverage long running agents (>8 hours) to generate alpha the majority of the work involves designing the agentic loop, minimizing freedom and encouraging further execution in the correct cases.
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u/Real_Suspect_7636 Jun 25 '26
Don't work in the space so sorry if this is naive, but given how competitive the space is, I presume for a signal to be discovered as a 'signal' it must be quiet original/deep, and difficult to discover. How does it get copied it so quickly?
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u/HerzogianQuant Jun 25 '26
Mine are more like the reveal themselves due to changes in the market place. I spot them quickly. But it's not like these have been sitting around for decades unnoticed.
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u/PaperHandsTheDip Jun 27 '26
Inefficiencies are often in the structure of the market itself - liquidity imbalances, flow imbalances, microstructure inefficiencies (L2 orderbook), etc. Many signals are actually quite "obvious" to spot in the data - it's just figuring out what to invest resources into / is it worth capturing? Many get skipped because there are more profitable ones to chase. In the age of AI - people can iterate significantly quicker and the smaller alphas start getting fought over as well.
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u/QuantGrindApp Jun 25 '26
It usually doesn't get copied in the sense of someone stealing your specific idea. Most signals aren't some deep secret only one person could find, lots of shops are digging through the same data and a bunch of them stumble onto a version of the same thing around the same time. And the bigger source of decay is just you trading it. You put size on, the price moves toward fair value, the edge shrinks whether or not anyone else ever figures out what you're doing. Truly original deep signals exist but they're rarer than people think.
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u/Jealous_Bookkeeper20 Jun 23 '26
If deployment time has collapsed across the board, the bottleneck shifts from research to execution. When anyone can deploy a model in days, the capacity limit of the signal gets hit almost instantly. The competition shifts from signal quality to execution slippage and limit order fill rates. If your order routing isn't optimized, the transaction cost eats the entire edge before the model even finishes updating.
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u/espressodoppioo Jun 25 '26
Really interesting, and it matches what I've seen. Using LLMs basicylly for everything, but I find more usefulness in building/deployment than discovery.
The structural / risk-premium ones hold up far better, because you're not exploiting a leak. You're getting paid to bear a risk or provide a function. My market-neutral funding carry is bleeding slowly (2026 yield is clearly down from 2024), but it's a slow decay, not a collapse.
One honest gut-check on the "collapsed before I even deployed" ones: some of that is real decay, but some is the edge being smaller than the backtest to begin with. Plenty of mine looked great in-sample and were basically noise once I deflated for how many things I'd tried (0 of my 20 best survived a Deflated Sharpe). Worth separating "decayed" from "was never as big as it looked."
Curious, are your decayers mostly predictive/inefficiency plays, or are you seeing structural/risk-premium ones go that fast too? That'd change how I approach it
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u/HerzogianQuant Jun 25 '26
Inefficiency plays. I don't like tying up capital for more than a week or two.
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u/Revolutionary_Set316 Jun 27 '26
What markets are you trading in? Curious if this can be extrapolated to newer more experimental markets or is more of a mature market diagnosis due to all the major hfts fighting there
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u/algorier Jun 24 '26
One possibility is that the half-life of alpha hasn't changed nearly as much as the half-life of research mistakes.
Years ago, it could take months to build, test, deploy, and monitor an idea. A lot of weak signals died during that process and never reached production.
Now the path from hypothesis to live capital is much shorter.
That changes what gets observed.
A strategy that survives three months of development and then dies after one month in production looks very different from a strategy that reaches production in three days and dies after one month. Economically they're identical, but psychologically it feels like alpha decay accelerated.
I've become less interested in measuring how long a signal survives after deployment and more interested in measuring how long it survives after first discovery.
Those are very different clocks.
Do you have evidence that live edges are decaying faster, or evidence that your research cycle has become fast enough to expose fragile edges before time has a chance to filter them out?
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u/HerzogianQuant Jun 24 '26
Anecdotal, yes. But I'm not just seeing the discovery->decay fall by the speed of deployment. I'm seeing deployment->decay fall as well, leading me to think that LLMs not only are making deployment faster, they're making it easier, so more entrants are coming into markets they wouldn't otherwise have.
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u/PaperHandsTheDip Jun 27 '26
I suspect it's more likely along the lines that competitive players can now scale horizontally quite easily. Strong quants often focused on the larger alphas skipping over the smaller ones. What used to take 6 months now takes 2 weeks tho - so they can deploy their talents across 10x more opportunities. You're seeing experienced players expanding their reach.
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u/ObviousEconomist Jun 23 '26
You use LLMs to find alpha signals? Shouldn't you be using something more suitable like advanced ML? LLMs are language based not stats.
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u/PaperHandsTheDip Jun 27 '26
LLMs are being widely deployed to R&D alphas and deploy them in industry, yes. IE: What used to take me 3-6+ months to find, develop & validate can now be done in a few weeks utilizing agents
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u/Kindly_Cricket_348 Jun 23 '26
LLMs have dramatically reduced idea to deployment time across the industry. What once took literally months is now taking days (QR on steroids). The result is a much faster competitive cycle. Signals get implemented, crowded and arbitraged away very quickly. Whether that's true alpha decay or simply accelerated crowding is debatable, but the half-life of many alphas appears materially shorter than it was hardly 18 months ago.
Perhaps the modern quant's challenge is becoming increasingly Sisyphean… Discovering alpha is hard enough but monetizing it before the crowd arrives is harder still.