{"id":212157,"date":"2026-03-19T12:04:08","date_gmt":"2026-03-19T16:04:08","guid":{"rendered":"https:\/\/ibkrcampus.eu\/campus\/uncategorized\/more-of-the-disease-faster-what-happens-when-you-ask-an-llm-to-find-you-an-edge\/"},"modified":"2026-03-26T10:53:12","modified_gmt":"2026-03-26T10:53:12","slug":"more-of-the-disease-faster-what-happens-when-you-ask-an-llm-to-find-you-an-edge","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.eu\/campus\/ibkr-quant-news\/more-of-the-disease-faster-what-happens-when-you-ask-an-llm-to-find-you-an-edge\/","title":{"rendered":"More of the Disease, Faster (What Happens When You Ask an LLM to Find You an Edge)"},"content":{"rendered":"\n

The article “More of the Disease, Faster (What happens when you ask an LLM to find you an edge)” was originally published on Robot Wealth<\/a> blog.<\/em><\/p>\n\n\n\n

This week I discovered the \u201cvibe quant\u201d movement (or rather, it discovered me). People using LLMs to find trading strategies, validate them, and put them into production. The pitch is seductive: the LLM reads the literature, implements the ideas, backtests them, and you just supervise.<\/p>\n\n\n\n

I think this approach is going to cost people a lot of money. And worse, it\u2019s going to prevent them from ever actually learning to trade.<\/p>\n\n\n\n

Fair warning: this article contains some opinions that I suspect will be unpopular. I\u2019m going to argue that most public trading content is rubbish, that the academic literature isn\u2019t much better, and that LLMs are systematically incapable of teaching you the one thing that actually matters.<\/p>\n\n\n\n

I know those are big claims. But I\u2019ve been doing this long enough that I\u2019m fairly confident in these positions, and I think it\u2019s worth drawing a line in the sand.<\/p>\n\n\n\n

Before I get into the details, I want to put the punchline up front, because I think it\u2019s the most important idea in this article.<\/p>\n\n\n\n

When you do trading research properly (hypothesise, test, learn, iterate), each cycle teaches you something about market structure, about counterparties, about what kinds of constraints create opportunities.<\/p>\n\n\n\n

That understanding makes the next cycle better. It\u2019s a flywheel, and it compounds.<\/p>\n\n\n\n

The vibe quant approach skips the learning. The LLM does the thinking, so the trader\u2019s understanding is exactly as shallow on day 1,000 as on day 1. You end up permanently dependent on the machine, with no ability to evaluate whether the patterns it finds are real, persistent, or tradeable.<\/p>\n\n\n\n

It\u2019s a treadmill, literally going nowhere. The LLM just speeds up the belt.<\/p>\n\n\n\n

Most beginners find themselves on this treadmill. In Chapter 2 of my \u200bcase study\u200b<\/a>, I describe the \u201cbacktest cycle of doom\u201d: change a parameter, add a stop loss, optimise a filter, repeat until you\u2019ve designed your perfect equity curve. They think they\u2019re doing research. They aren\u2019t. They\u2019re doing something that feels <\/em>like research because it\u2019s technical and requires skill, but:<\/p>\n\n\n\n

Backtesting is an entirely technical problem, requiring little to no nuanced thinking or decision-making in the face of uncertainty. It doesn\u2019t require wrestling with difficult problems. In that sense, it\u2019s easy. And I was being seduced that I could \u201csolve trading\u201d by doing something easy.<\/strong><\/em><\/p>\n\n\n\n

The vibe quant movement is exactly this, just turbocharged. The LLM makes the easy part even easier, and even more seductive. But the hard part, the part that actually matters, doesn\u2019t get done at all.<\/p>\n\n\n\n

To see why, we need to talk about what the LLM actually can\u2019t do.<\/p>\n\n\n\n

When I look at any potential trade, the first question I ask is: who\u2019s losing money on the other side of this, and why will they keep doing it?<\/p>\n\n\n\n

That\u2019s the game. Edge comes from structural constraints, stuff other participants can\u2019t or won\u2019t do because of mandate restrictions, capacity limits, or operational awkwardness. If you can\u2019t answer that question, you don\u2019t have an edge.<\/p>\n\n\n\n

The problem is that this question is almost entirely absent from public trading content. The vast majority of what\u2019s out there is rubbish. Absolute rubbish. It\u2019s all patterns, signals, and backtests, passed off as if it actually means something useful.<\/p>\n\n\n\n

Even the academic literature is full of papers that exist because the author wanted to demonstrate they could speak the language of mathematics, not because they had anything useful to say about how markets actually work.<\/p>\n\n\n\n

\u201cWhy would anyone lose money on the other side of this trade?\u201d barely gets asked. And it\u2019s the most important question.<\/p>\n\n\n\n

LLMs are trained on this corpus.<\/p>\n\n\n\n

So when you ask one to help you find an edge, it does what it knows: it finds patterns, builds strategies, and runs backtests. It skips the most important part, because the most important part is barely represented in its training data.<\/p>\n\n\n\n

I recently wrote about the \u200bfour hats of the solo trader\u200b, and a reader who calls himself \u201cVibe Quant\u201d fed the article to an LLM. I want to be clear: this bloke is obviously smart, he\u2019s thinking carefully about his process, and he shared his experience generously. I\u2019m not having a go at him. But what happened next is a perfect illustration of the problem, and it\u2019s worth unpacking.<\/p>\n\n\n\n

The LLM\u2019s self-diagnosis was genuinely impressive:<\/p>\n\n\n\n

\u201cThe temptation isn\u2019t to blur Hats 1 and 2. It\u2019s to skip Hat 1 entirely (\u201cHat 1\u2033 represents the edge-first thinking I\u2019m banging on about). The LLM can read a paper on accrual anomalies at 6am, implement a multi-quarter earnings composite by 6:15, wire it into a screener by 6:30, and cite it in a morning briefing by 7. That\u2019s pure Hat 2 engineering at inhuman speed. But \u2018did we verify this edge exists in our universe, at our holding period, net of our constraints\u2019 never happened. We built the bridge without testing the soil.\u201d<\/p>\n\n\n\n

Spot on. The machine nailed the diagnosis.<\/p>\n\n\n\n

But then look at what happened next.<\/p>\n\n\n\n

Having identified that edge research was being skipped, the LLM \u201cset off to develop some new internal guidance systems for itself and some Python code to help validate.\u201d<\/p>\n\n\n\n

In short, it identified the disease, and then prescribed more of the disease. Faster.<\/p>\n\n\n\n

And that\u2019s not even a bug! It\u2019s the default behaviour!<\/p>\n\n\n\n

You tell the LLM to think about mechanism and edge, and it does what it knows: more implementation, more backtesting, more engineering. It gives you a plausible-sounding mechanism, sure. But it\u2019s synthesising from a corpus where genuine edge thinking is drowned out by noise and grift. It can\u2019t know that. It just sounds authoritative, because that\u2019s what it does.<\/p>\n\n\n\n

To have real confidence in any edge, you need two things: a plausible mechanism and evidence in the data.<\/p>\n\n\n\n

The mechanism answers: why does this exist? Who\u2019s the counterparty? What are their constraints, and why will they keep being there? Why can I compete for this edge?<\/p>\n\n\n\n

The evidence side is more straightforward to explain: does the data actually support this?<\/p>\n\n\n\n

You need both.<\/p>\n\n\n\n

Without a mechanism you\u2019re just curve-fitting. And a nice story about why something should work isn\u2019t enough either; you need to see it in the data.<\/p>\n\n\n\n

An LLM can help you with the evidence side (pull data, design experiments, build visualisations). But it can\u2019t teach you to think about mechanism, because the thinking it draws on doesn\u2019t ask that question.<\/p>\n\n\n\n

This matters enormously when things go wrong. When a strategy enters a drawdown (and it will), if you understand the mechanism, you can ask: has the counterparty adapted? Has the structural constraint been removed? You have a framework for deciding whether to sit tight, adapt, or walk away.<\/p>\n\n\n\n

If your entire basis for the trade is \u201cthe LLM found a pattern and wrote some validation code,\u201d you\u2019ve got nothing. You can\u2019t diagnose or adapt. Your only move is to go back to the LLM and ask it to find another pattern.<\/p>\n\n\n\n

You\u2019ll have constant anxiety that your edge isn\u2019t real (you don\u2019t know what it\u2019s based on, and deep down you know that making money trading is harder than asking an LLM to do it for you), and so you\u2019ll tinker and backtest and wonder and fret ad nauseam when you could be doing the productive work that gets you zeroing in on real edges.<\/p>\n\n\n\n

But it\u2019s even worse than that.<\/p>\n\n\n\n

Remember what I said up front? This is where it really hits home.<\/p>\n\n\n\n

A trader who works through the full hypothesise-test-learn-iterate loop, even slowly, even painfully (and honestly, it is painful sometimes), accumulates genuine understanding.<\/p>\n\n\n\n

Each edge you research teaches you something. That understanding compounds. The next piece of research is better because of everything you learned from the last one.<\/p>\n\n\n\n

The vibe quant approach has no compounding.<\/p>\n\n\n\n

There\u2019s no learning loop, because the LLM did the thinking. You\u2019re just as dependent on day 1,000 as on day 1.<\/p>\n\n\n\n

And that\u2019s the bit that I think people aren\u2019t seeing. The problem isn\u2019t just that the LLM might find bad patterns. It\u2019s that even if it finds good ones, you\u2019ll never develop the judgement to know the difference.<\/p>\n\n\n\n

You\u2019re running faster, but you\u2019re not going anywhere.<\/p>\n\n\n\n

Look, I\u2019m not anti-LLM. I use them heaps. They\u2019re brilliant for the grunt work: cleaning data, writing code, interpreting research, designing experiments. Hat 2, Hat 3, Hat 4 stuff. They make me faster at things I already know how to do.<\/p>\n\n\n\n

But they can\u2019t teach you the thing that actually matters, which is how to think about edge. That comes from working with people who do it for a living, who ask \u201cwho\u2019s on the other side?\u201d as a reflex. It comes from developing a good mental model of the market, its players and their constraints. It comes from the hypothesise-test-learn loop, done over and over.<\/p>\n\n\n\n

The LLM can hand you a fish. It can even build you a very impressive fishing rod. But it can\u2019t teach you where the fish are, because almost nobody who knows is writing about it on the internet.<\/p>\n","protected":false},"excerpt":{"rendered":"

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