I know nothing about this world, but with things like "doctor rediscovers integration" I can't help but wonder if it's not deception but ignorance - that they think it really is where math complexity tops out at.
It is neither deception or ignorance.
It's the same reason some of the best physics students get PhD studentships where they are basically doing linear regression on some data.
Being very good at most disciplines is about having the fundamentals absolutely nailed.
In chess for example, you will probably need to get to a reasonably high level before you will be sure to see players not making obvious blunders.
Why do tech firms want developers who can write bubble sort backward in assembly when they'll never do anything that fundamental in their career? Because to get to that level you have to (usually) build solid mastery of the stuff you will use.
Trading is truly a complex endeavour - anybody who says it isn't has never tried to do it from scratch.
Id say the industry average for somebody moving to a new firm and trying to replicate what they did at their old firm is about 5%.
Im not sure what you'd call a problem where somebody has seen an existing solution, worked for years on it and in the general domain, and still would only have a 5% chance of reproducing that solution.
Because 95% of experienced candidates in trading were fired or are trying to scam their next employer.
“Oh, yeah, my <insert HFT pipeline or statarb model> can do sharpe <random int 1 to 10> for <random int 10 to 100> million pnl per year. Trust me bro”. Fucking annoying
Orders of magnitude more leave their jobs of their choosing than are fired.
These PMs are not the ones job hopping every year.
And 95% of interview candidates are not PMs.
> So the only scam is scamming yourself into a low salary position for a couple years till they fire you.
200k-300k USD salary is not low.
And 1 year garden leave / non compete? That’s literally 0.5M over 2 years for doing jack shit.
This is very appealing for tech SWEs or MBA product managers who are all talk and no walk.
But even with profit share / pnl cut, many firms pay you a salary, even before you turn a profit. It eventually gets deducted when you turn a profit.
> Orders of magnitude more leave their jobs of their choosing than are fired.
Hedge fund, maybe. Prop trading, no.
you mean backporting a high-level implementation to assembly? Or is writing code "backward" some crazy challenge interviewees have to do now?
> It is neither deception or ignorance.
How is it not ignorance of math?
> In chess for example, you will probably need to get to a reasonably high level before you will be sure to see players not making obvious blunders.
To extend the chess analogy, having the fundamentals absolutely nailed is critical at even a mid-level, because the payoff/effort ratio in avoiding blunders/mistakes is much higher than innovating or being creative.
The process of getting to a higher level involves rote learning of common tactics so you can instantly recognize opportunities, and then eventually learning deep into "opening theory" which is memorizing 10 starting moves + their replies because people much better than you have written lengthy books on the long-term ramifications of making certain moves. You're learning a vast repertoire of "existing solutions" so you can reproduce them on-demand, because those solutions are battle-tested to not have weaknesses.
Chess is a game where the amount you have to lose by being wrong is much higher than what you gain by being right. Fields where this is the case want to ensure to a greater extent that people focus on the fundamentals before they start coming up with new ideas.
The engineers are are incredibly smart people, and so the bots are "incredibly smart" but "finance" is criticised by "true academics" because finance is where brains go to die.
To use popular science "the three body problem" is much harder than "arb trade $10M profitably for a nice life in NYC", you just get paid less for solving the former.
It's like math v engineering - you can come up with some beautiful pde theory to describe this column in a building will bend under dynamic load and use it to figure out exactly the proportions.
But engineering is about figuring out "just make its ratio of width to height greater than x"
Because the goal is different - it's not about coming up with the most pleasing description or finding the most accurate model of something. It's about making stuff in the real world in a practical, reliable way.
The three body problem is also harder than running experiments in the LHC or analysing Hubble data or treating sick kids or building roads or running a business.
Anybody who says that finance is where brains go to die might do well to look in the mirror at their own brain. There are difficult challenges for smart people in basically every industry - anybody suggesting that people not working in academia are in some way stupider should probably reconsider the quality of their own brain.
There are many many reasons to dislike finance. That it is somehow pedestrian or for the less clever people is not true. Nobody who espouses the points you've made has ever put their money where there mouth is. Why not start a firm, making a billion dollars a year because you're so smart and fund fusion research with it? Because it's obviously way more difficult than they make out.
(Note that I personally have no opinion on this topic, as I'm not sufficiently informed to have one.)
The op is making some implication across numerous posts that it's all basically a big con and it's all very simple.
It is like claiming you don't need to be rocket scientist to go to the moon because they just use metal and screws.
The individual parts might be simple in isolation. But it is the complexity of conducting large scale, large scope research in an environment that gives you limited feedback and will adapt to your own behaviour changes that is where the smarts are needed.
OP seems to not understand the inherent difficult of doing any research.
Almost anybody could be taught to make a simple circuit and battery from some basic raw materials. The fact it is simple and easy now we know the answer does not mean it was simple or easy to discover. Some of the greatest minds dedicated their entire lives to discovering things that now most 10 years olds understand. That doesn't imply you only need to have the intellect of a 10 year old to make fundamental breakthroughs in science.
Working in quant trading is almost pure research - and so it requires a certain level of intellect - probably at least the intellect required to pursue a quantitative PhD successfully (not that they need the PhD but they need the capacity to be able to do one).
Not that it's particularly relevant to this discussion but the three body problem is easy. You can solve it numerically on a laptop with insane precision (much more precisely than would be useful for anything) or also write down an analytic solution (which is ugly and useless because it converge s extremely slowly, but still. See wikipedia.org/wiki/Three-body_problem).
> Unlike the two-body problem, the three-body problem has no general closed-form solution,[1] and it is impossible to write a standard equation that gives the exact movements of three bodies orbiting each other in space.
This seems like the opposite of your claim.
A similar claim is that roots of polynomials of degree 5 (and over) have no "general closed form solution" (with, as usual, the implicit qualification: "in terms of functions I'm currently comfortable with because I've seen them a lot"). That doesn't mean it's a difficult problem.
The two problems have in common that they are significantly harder than their smaller versions (two bodies, or degree 4). Historically, people spent a lot of time trying to find solutions for the larger problems in terms of the same functions that can be used to solve the smaller problems (conic sections, radicals). That turned out to not be possible. This is the historical origin of the meme "three body problem is unsolvable".
For polynomial roots, see wikipedia.org/wiki/Elliptic_function.
My interpretation of "finance is where brains go to die" is more along the lines of finance being less good for society at large compared to pure science. Like if someone invents something new and useful in a lab for their phd, then they go find a job in finance. The brain died because it was onto something and then abandoned it for being a cog in the machine.
e.g. LMAX Disruptor was a pretty impressive concurrency library a decade ago:
https://diabetesjournals.org/care/article/17/2/152/17985/A-M...