People didn't stop working on this in 1989 - they realised they can make lots of money doing it and do it privately.
Not having made it big myself I obviously don’t know the meta these days, but last I had any inside baseball, the non-stationarity and friction just kill you on trying to get fancy as opposed to just nailing it on the fundamentals.
Extreme execution quality is a game, people make money in both traditional liquidity provision and agency execution by being fast as hell and managing risk well.
Individual signals that are individually somewhat mundane but composed well via straightforward linear-ish regressions is a game: people get (ever decaying) alpha out of bright ideas (and rotate new signals in).
And I’m sure that LLMs have started playing a role, there’s a legitimate capability increase in spite of the dubious production-worthiness.
But as a blind wager, I bet prop trading is about what it was 5 years ago on better gear: elite execution (no pun intended) on known-good ways to generate alpha.
1. Your automated system should be as fast as possible.
2. Stick with known, basic fundamental strategies.
3. Try new ideas around how to give those same strategies more predictive power (signal).
#1 is straight technical execution.
#3 is constantly evolving.
Is how I understood this.
And as sort of an afterthought I guess the better you are at #1 the less good you need to be at #3 and the worse you are at #1 the better you need to be at #3?
Mind elaborating?
Anyone who has figured out something relatively profitable isn't telling anyone how they did it.
Corollary: someone who is selling you tools or strategies on how to make tons and tons of money, is probably not making tons and tons of money employing said tools and strategies, but instead making their money by having you buy their advice.
The fact that you can't reveal how means you can't prove you're not Ponzi. If you reveal how, they don't need you.
This is why I am wary of all those +10 minute YT vids telling you how you can't make significant amounts of money quickly or reliably in a short amount of time with very limited capital.
If you were running this yourself with $1M input capital, that'd be $20k/year per 1M of input - so $20K is a nice number to try and beat selling a product that promulgates a strategy.
But you're going to run into the question from people using the product: "Yeah - but HOW DOES IT WORK??!!!" and once you tell them does your ability to get paid disappear? Do they simply re-package your strategy as their own and cease to pay you (and worse start charging for your work)? Is your strategy so complicated that the value of the tool itself doing the heavy lifting makes it sticky?
Getting people to put their money into some Black Box kind of strategy would probably be challenging - but Ive never tried it - it may be easier than giving away free beer for all I know. Sounds like a fun MVP effort really. Give it a try - who knows what might happen.
Maybe it's just what I know, but I can't help but think the "strategies" are a lot like security exploits--some cleverness, some technical facility, but mainly the result of staring at the system for a really long time and stumbling on things.
Because then your competition knows which strategies don't work, and also what types of strategies you work on.
Don't leak information.
And then everything regresses to the Dark Forest game theory.
I am assuming, he/she minds a lot.
Quant trading is about "going fast" or "being super right", so either you'd need to be sitting on some huge llama.cpp/transformer improvement (possible but unlikely) or its more likely just some boring math applied faster than others.
Even if they are using a "LLM", they wont tell you or even hint at it - "efficient market" n all that.
Remember all quants need to be "the smartest in the world" or their whole industry falls apart, wait till you find out its all "high school math" based on algo's largely derived 30/40 years ago (okay not as true for "quants" but most "trading" isn't as complex as they'd like you/us to believe).
Saying it's all high school math is a bit of a loaded phrase. "High school math" incorporates basically all practical computer science and machine learning and statistics.
If I suspect you could probably build a particle accelerator without using more math than a bit of calculus - that doesn't make it easy or simple to build one.
Very few people I've worked with have ever said they are doing cutting edge math - it's more like scientific research . The space of ideas is huge, and the ways to ruin yourself innumerable. It's more about people who have a scientific mindset who can make progress in a very high noise and adaptive environment.
It's probably more about avoiding blunders than it is having some genius paradigm shifting idea.
Moreover, the collaborative environment at a prop firm can't be understated. Ideas and strategies are continuously debated, tested, and refined. This collective brainpower often leads to more robust strategies than what you might come up with on your own.
That said, there are successful solo traders, but they often specialize in niche markets where they can leverage unique insights or strategies that aren't as capital intensive. It's definitely not for everyone and comes with its own set of challenges and risks.
A car designer still needs a car factory of some sort, and there's a negotiation there about how the winnings are divided.
In the trading world there are a variety of strategies. Something very infra dependent is not going to be easy to move to a new shop. But there are shops that will do a deal with you depending on what knowledge you are bringing, what infra they have, what your funding needs are, what data you need, and so on.
I too believe this is key towards successful trading. Put in other words, even with an exceptionally successful algorithm, you still need a really good system for managing capital.
In this line of business, your capital is the raw material. You cannot operate without money. A highly leveraged setup can get completely wiped out during massive swings - triggering margin calls and automatic liquidation of positions at the worst possible price (maximizing your loss). Just ask ex-billionaire investor/trader Bill Hwang[1].
1. https://www.bloomberg.com/news/features/2021-04-08/how-bill-...
Im responding to the comment "do use llama3" not "breakdown your start"
> Very few people I've worked with have ever said they are doing cutting edge math - it's more like scientific research . The space of ideas is huge, and the ways to ruin yourself innumerable. It's more about people who have a scientific mindset who can make progress in a very high noise and adaptive environment.
This statement is largely true of any "edge research", as I watch the loss totals flow by on my 3rd monitor I can think of 30 different avenues of exploration (of which none are related to finance).
Trading is largely high school Math, on top of very complex code, infrastructure, and optimizations.
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.
> 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.
you mean backporting a high-level implementation to assembly? Or is writing code "backward" some crazy challenge interviewees have to do now?
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.
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.
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.
(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).
e.g. LMAX Disruptor was a pretty impressive concurrency library a decade ago:
https://diabetesjournals.org/care/article/17/2/152/17985/A-M...
What algos are you referring to derived 30 or 40 years ago? Do you understand the decay for a typical strategy? None of this makes any sense.
To be "super right" you just have to make money over a timeline, you set, according to your own models. If I choose a 5 year timeline for a portfolio, I just have to show my portfolio outperforming "your preferred index here" over that timeline - simple (kind of, I ignore other metrics than "make me money" here).
Depending on what your trading will depend on which algo's you will use, the way to calculate the price of an Option/Derivative hasn't changed in my understanding for 20/30 years - how fast you can calculate, forecast, and trade on that information has.
My statement wont hold true in a conversation with an "investing legend", but to the audiance who asks "do you use llama3" its clearly an appropriate response.
Aside from the "theoretical" developments the other comment mentioned, your implication that there is some fixed truth is not reflected in my career.
Anybody who has even a passing familiarity with doing quant research would understand that black scholes and it's descendants are very basic results about basic assumptions. It says if the price is certain types of random walk and also crucially a martingale and Markov - then there is a closed form answer.
First and foremost black scholes is inconsistent with the market it tries to describe (vol smiles anyone??), so anybody claiming it's how you should price options has never been anywhere near trading options in a way that doesn't shit money away.
In reality the assumptions don't hold - log returns aren't gaussian, the process is almost certainly neither Markov or martingale.
The guys doing the very best option pricing are building empirical (so not theoretical) models that adjust for all sorts stuff like temporary correlations that appear between assets, dynamics of how different instruments move together, autocorrelation in market behaviour spikes and patterns of irregular events and hundreds of other things .
I don't know of any firm anywhere that is trading profitably at scale and is using 20 year old or even purely theoretical models.
The entire industry moved away from the theory driven approach about 20 years ago for the simple reason that is inferior in every way to the data driven approach that now dominates
That’s not true. It is true that the black scholes model was found in the 70s but since then you have
- stochastic vol models
- jump diffusion
-local vol or Dupire models
- levy process
- binomial pricing models
all came well After the initial model was derived.
Also a lot of work in how to calculate vols or prices far faster has happened.
The industry has definitely changed a lot in the past 20 years.
Since the GFC it’s not about crazy new products (on derivatives desks), but it’s about getting discounting/funding rates precisely right (depending on counterparty, collateral and netting agreements, onshore/offshore, etc), and about compliance and reporting.
Not true. Most of the magic happens in estimating the volatility surface, BSM's magic variable. But I've also seen interesting work in expanding the rates components. All this before we get into the drift functions.
In vanilla equity options, sure. But that’s like saying we solved rockets in WWII. The foundational models were derived by then; everything that followed was refinement, extension and application.
How you can calculate fast, forecast, and trade on that information has
There. Fixed it for you. ;)
The old joke of two economists ignoring a possible $100 bill on the sidewalk is an ironic adage. There are hundreds of bills on the sidewalk, the real problem is prioritizing which bills to pick up before the 50mph steamroller blindsides those courageous enough to dare play.
It's a lot like quantum mechanics or whatever it is that makes the observation of a photon changes. Except with the caveat that the first to recognize the trend can direct it's change (for profit).
Going fast means scalping?