Alignment of AI is hard, and aligned to whom? I just finished the safety chapter in Stripe Press's "An Oral History of AI", and there's a good quote in it: "It's an interesting question, how to tell the difference between a hallucination and deception." (I'll let you figure out who said it, you know their name).
Discord's not going away, but I looked at setting up a self hosted matrix server last night and had it up with an element client in 20 minutes with docker compose. I don't think I'll do a discord<->matrix bridge (since it requires extra stuff on the discord side and I think you lose encryption, which isn't that important), but if you have buddies who you want to keep in contact with, it's not a bad solution
I explored but am avoiding big, public channels because there honestly isn't that much chatting going on in the ones that I found
Almost all clients support file sharing. The Movim client <https://mov.im/> supports screen sharing. It should be noted that both Jitsi & Zoom use XMPP for the roster/chat/handshaking as well; so if you set up a Jitsi server for calls & such, you would already have a working XMPP server on your server that could be co-opted.
I never managed to get file sharing working on my self hosted discord instance. Needing to setup DNS entries and subdomains for every little feature got tiresome and I gave up.
Also the default of "save all shared files on the server" didn't sit well with me.
But xmpp is so damn complicated I am 100% sure I am misunderstanding everything and doing something wrong.
I wonder how fun being a modern quant really is. It seems like one of those things that sounds more fun in your head, but the reality is different. Kinda like "studying physics", going pro in a sport, or becoming a rockstar. People see the end result and don't see how much work it takes to get there or what the day to day is really like
Im reasonably familiar with the exotics quant space.
It’s essentially IT/data work - the days of sophisticated maths are mostly gone. There always was a lot of code, but these days for most people there’s little to no new maths.
From what I’ve seen, post-2008 the job changed significantly, with more IT, less maths, more standardization - basically the job moved from bespoke everything to super industrialized. You’ll be able to have your model work for one underlying and one product, but what’s really useful is for lots of underlyings and many products - and that’s very hard.
That being said, and that’s important, you must understand the maths behind, otherwise you won’t be able to do anything useful.
I started my career in derivatives. Mostly vanilla, but I did have a look in the exotics.
Intellectually, it's interesting when you start. There's all these weird payoffs that you are introduced to, and it feels like a game.
The thing is, there's a limit to how exotic things can get. People have already figured out how to price most of the things you can imagine, including all the things that customers normally ask for. Most of the day goes on looking after your hedges, basically implementing the model.
It's like a zoo. When you arrive there's a bunch of different, interesting animals. After a while, you've met them all. There's no new animals, just variations of existing ones.
However the thing that is really an issue is how the business works. Over time I came to the conclusion that the quants in the derivs space are really secondary to the salespeople. How important is the quant who can get the price right to within 1%, when the sales guy can talk the customer into overpaying by 5%? Sometimes it feels like the customer is not even shopping the structure around at all, he just feels comfortable with his sales guy and is willing to hand over a few million bucks of customer money with barely any thought.
This matches my experiences as a non-quant, but havin done support work for quite a few of them. You can feel the novelty slough off of them as they get burned down into realizing they're just fitting curves.
Spotting fish who you can overcharge is not really a sustainable business model. You can do it once but your colleagues will find out, move to a competitor, and next time they'll rip them off a bit less than you did and you'll have to rip them off less than that.
There is, eventually, a shortage of dumb money. The sustainable way of making money involves competition, and this involves knowing the "right" price within a tight tolerance.
No, the feedback loop isn't closed. The fish customer just keeps handing his spread to his favourite sales guy.
I've witnesses this several times, some trader always uses the same relationship, irrespective of cost. I've seen this both in terms of friends from a long time ago helping each other, family, or backhanders.
And so the real winner is that sales guy. I've known people climb to the very top of well known institutions on the back of relationships with just one hedge fund.
What you're describing is a sort of ideal market from an economics textbook.
The situation you are describing definitely happens, people have their mates they like working with and a single relationship can make a whole career.
Normally though, such relationships do not involve the sales person charging significantly above market rates. The client usually has very strong incentives to reduce costs. While a single salesperson might build a career on a chummy relationship this isn't a sustainable approach for an entire firm to take because it is too unusual. The majority of the revenue is coming from client/sales relationships where the client is at least somewhat price sensitive and sufficiently savvy to get more than one quote.
> but these days for most people there’s little to no new maths.
You are right. For most people there's little to no new maths.
But not for all. There's still plenty of good quality math to be done in the exotics space. However, there's a bit of Catch 22 that prevents people from doing new math: all the big shops have had exotics libraries since before 2008, and because of the exotics hiatus between about 2008 and maybe 2013, the research momentum was lost. After that, most quants in the space were happy to find ways to use the old stuff, and apply small tweaks at the margins. Most small shops use vendor models (Numerix, Murex) or open source (QuantLib), and people who use vendor solutions or open source are not looking for cutting edge stuff.
I assume exotic derivatives (binary, asian, barrier options...) and structured notes that predominantly use above said derivatives (autocallables, barrier reverse convertibles, accumulators etc.)
For a structured products introduction you may take a look at this one: https://sspa.ch/en/book/
It's a very simple book, very high level, but explains the most popular structured products in a very simple manner. If you can read a payoff diagram, then this is the simplest intro.
Looking at their website though, they seem to have some nice online material there also. For example this explains the 5 most popular products, and perhaps that's good enough for an introduction (really these 5 products cover 90% of the market anyway, though there's no limit to how exotic some bespoke structures can get): https://sspa.ch/en/lab/?underlying=CH0012221716&final_fixing...
In case you're interested in getting to get to learn about them on a deeper level I would recommend https://www.amazon.com/Exotic-Options-Hybrids-Structuring-Pr.... This book explains not only the products, but also the pricing dynamics and hedging too.
Despite its appalling Amazon reviews I consider this book to be a real gem when it comes to the introduction to vol trading (basically dynamic hedging of equity derivatives)
Patrick Boyle, early in his YouTube career, made some videos on exotic options. This is a lot more engaging and possibly more informative than reading Hull.
There's definitely room for new math but , at least for banks, the process of getting your fancy model validated by internal model validation teams and regulators is so time and energy consuming that most people don't want to bother with using all the fancy math they could use and instead rely on simplifications and simple extensions.
What happened? Is this a case of the actual job changing, or just title inflation? I’d expect the quants to be the ones doing the math and implementing the kernels…
Several things : immediately after 2008 less demand for exotics because clients were afraid of them, cutting costs rather than increasing revenue ( that’s industrialization with IT and standardization ) and more importantly, industry reaching some kind of maturity, with large quant libraries which are pretty stable these days.
I always wanted to be a quant, until I actually was a quant (internship). The division of labour in modern banks / market markets is so high that the scope of an individual’s work becomes much less than you would expect.
i don't know what the point of this book is - there's nothing rockstar about being a quant. not only do not all quants "generate alpha", even the ones that do are just overworked data scientists. ask anyone that actually works in the industry - fancy math is no longer a thing ("exotic option pricing"). so would "how i became an accountant" be just as interesting? how about (more accurately) would "how i became a data scientist"?
In finance, if you have a better understanding of how to price a certain asset then you can make better trades than your competitors and thus make money.
Some assets are very easy to price, like bonds. Others are esoteric, like Exotics which are options or other securities with uncommon pricing structures. This is where the "fancy math" kicks in. But as time goes on, more and more firms figure out how to better price all the various assets that they trade, which erodes the competitive edge between market participants.
The assertion is that today, most firms have most things mostly figured out as far as how to value them. There's little to no competitive advantage to be mined from esoteric stochastic calculus. In contrast, it's rumoured that one of the most successful firms of all time, Renaissance, owes a large part of their success to their absolutely pristine data which comes from a massive data ingestion and cleaning pipeline, allowing them to get a clearer statistical picture of what the current market forces at play are, and how they're going to manifest.
The “fancy math” associated with quant usually refers to pricing derivatives like options.
The thing is that there isn’t really strong institutional demand for exotic derivatives, people are happy using existing methods and just applying those to current markets.
The other type of fancy math has to do with deriving alpha, which is also not that complex, from a statistics perspective you’re mostly using linear regression or other basic forms of regression.
The hard part of quant is implementation, making sure your data is right, hunting through poorly understood markets, and managing risks carefully and understanding them.
There’s also ML but that’s equally complex in quant as it is anywhere else.
> The hard part of quant is implementation, making sure your data is right, hunting through poorly understood markets, and managing risks carefully and understanding them.
In my experience I have seen far more division of labor than you describe. Real quants don’t do work like making sure your data is right or even much of implementation; they delegate that to software engineers. But a cheap quant shop might be too cheap to hire SWEs so quants end up doing this work instead. The real quant work is just hunting through poorly understood markets.
The days of doing some calculus, having a moment of brilliant insight, and writing down a pricing formula then getting paid millions probably only ever existed in people's imagination.
Fancy math definitely is part of derivatives pricing. However financial world has become too complicated for simple models. Adding things like the risk of counterparty default to your pricing equation quickly leads you into the world of equations without closed form solutions. The common approach these days is some kind of huge multi factor Monte Carlo model. These are still solving pricing equations but the challenge is more about numerical methods than brilliant algebraic gymnastics.
Good instinct. A lot of the day to day is debugging nitty things, reconciling small differences in results, trying not to make dumb mistakes. Almost all attempts to do very smart theoretical novel work fail, often because of extremely mundane engineering and data issues.
There’s a great quote from Nick Patterson of RenTech who says that the most sophisticated technique they generally used was linear regression, and the main thing was avoiding stupid mistakes:
“I joined a hedged fund, Renaissance Technologies, I'll make a comment about that. It's funny that I think the most important thing to do on data analysis is to do the simple things right. So, here's a kind of non-secret about what we did at renaissance: in my opinion, our most important statistical tool was simple regression with one target and one independent variable. It's the simplest statistical model you can imagine. Any reasonably smart high school student could do it. Now we have some of the smartest people around, working in our hedge fund, we have string theorists we recruited from Harvard, and they're doing simple regression. Is this stupid and pointless? Should we be hiring stupider people and paying them less? And the answer is no. And the reason is nobody tells you what the variables you should be regressing [are]. What's the target. Should you do a nonlinear transform before you regress? What's the source? Should you clean your data? Do you notice when your results are obviously rubbish? And so on. And the smarter you are the less likely you are to make a stupid mistake. And that's why I think you often need smart people who appear to be doing something technically very easy, but actually usually not so easy.”
Imo the fun quant stuff these days is about predicting returns and not pricing derivatives. As others have said here, the pricing part is mostly commoditised and more about managing software.
I don't think quants actually trade, though? Like the division of labor usually separates research, code, and trading. Unless you start your own sole prop quant trading shop, which is fun but unless you're the next Jim Simons it can be pretty hard to do it profitably.
if you are doing equity statarb, its all low touch strategies, so yes the quants 'trade' in that they can write the strategies. The traders in that environment are more like support, usually.
and I wonder how much of your success / failure is luck vs advanced-anything. You could wager 7-figure bankrolls in vegas on blackjack or baccarat and _possibly_ double or triple it. Doesn't mean your equation-solving had anything to do with it (in fact it expressly doesn't in that case).
> and I wonder how much of your success / failure is luck vs advanced-anything
Well take someone who YOLO on a 0 DTE option and makes 80x (not unheard of) vs someone who wins the same amount over, say, four years and 8 000 trades... Well it's not impossible that the YOLOer is more skilled and has an edge (while also being a degen but that's not the point): it is just very unlikely.
Thousands, tens of thousands, hundreds of thousands of trades is not the same as "gambling one night in Vegas".
Thank you, I'll try to grab a table when it resets :) ! I've been getting into poker (always wanted to) since I found a lecture series from John Hopkins, and severely disappointed by my options to play online in NY (real or fake money). I just want to get reps in
Somehow I've totally missed pokemon stadium minigames even though I did play a lot of pokemon back in the day. Or maybe my memory is just not what it used to be. Either way, thanks for mentioning them and hope you have a great gaming session later on!
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