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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 "

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

quanttnauq 9mo ago
[dead]
mathisfun123 9mo ago
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"?
- 609venezia 9mo ago
  > ask anyone that actually works in the industry - fancy math is no longer a thing Huh? What happened? This is a very interesting claim I would love to hear elaborated
  - sharifhsn 9mo ago
    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.
    - kccqzy 9mo ago
      > 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.
  - stackghost 9mo ago
    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.
  - fancyfredbot 9mo ago
    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.
7777777phil 9mo ago
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.
highfrequency 9mo ago
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.
- Rzor 9mo ago
  >Almost all attempts to do very smart theoretical novel work fail, Unless you happen to be in a place like RenTech, perhaps?
  - highfrequency 9mo ago
    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.” http://www.thetalkingmachines.com/episodes/ai-safety-and-legacy-bletchley-park http://www.thetalkingmachines.com/episodes/ai-safety-and-leg...
Agingcoder 9mo ago
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.
- mikert89 9mo ago
  theres math, its mostly about pulling in obscure data sources, rank and file in alot of hedge funds dont even get to see what actually makes money
- credit_guy 9mo ago
  > 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. But there's still good math left out there.
  - bmitc 9mo ago
    What is being meant by exotics in this discussion?
    - e-master 9mo ago
      I assume exotic derivatives (binary, asian, barrier options...) and structured notes that predominantly use above said derivatives (autocallables, barrier reverse convertibles, accumulators etc.)
conformist 9mo ago
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.
- mhh__ 9mo ago
  Yes although if you are predicting the returns swaps and options and so on then you need to be able to do both
mhh__ 9mo ago
What kind of quant? Trading huge equity portfolios and getting paid a lot? Pretty fun Pricing structured products all day in a bank that charges you for lunch? Not great
- altmanaltman 9mo ago
  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.
  - frankc 9mo ago
    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.
gosub100 9mo ago
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).
- TacticalCoder 9mo ago
  > 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".
- KellyCriterion 9mo ago
  Strictly wrong: - a casino is a random game - stock market is a game of incomplete informatoin The one cant related to the other by whatever equation.
  - gosub100 9mo ago
    The series of a shuffle of a 6-deck shoe is not "incomplete information"?
    - mikrl 9mo ago
      Taleb calls it the Ludic Fallacy (game fallacy) The statistics of games are understandable, defined and easy to work with. The statistics of markets, as Soros spent his career investigating, are filled with feedback loops, and as Taleb investigated, fat tails.

quanttnauq 9mo ago

[dead]

mathisfun123 9mo ago

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"?

609venezia 9mo ago
> ask anyone that actually works in the industry - fancy math is no longer a thing Huh? What happened? This is a very interesting claim I would love to hear elaborated
- sharifhsn 9mo ago
  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.
  - kccqzy 9mo ago
    > 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.
- stackghost 9mo ago
  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.
- fancyfredbot 9mo ago
  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.

7777777phil 9mo ago

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.

highfrequency 9mo ago

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.

Rzor 9mo ago
>Almost all attempts to do very smart theoretical novel work fail, Unless you happen to be in a place like RenTech, perhaps?
- highfrequency 9mo ago
  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.” http://www.thetalkingmachines.com/episodes/ai-safety-and-legacy-bletchley-park http://www.thetalkingmachines.com/episodes/ai-safety-and-leg...

Agingcoder 9mo ago

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.

mikert89 9mo ago
theres math, its mostly about pulling in obscure data sources, rank and file in alot of hedge funds dont even get to see what actually makes money
credit_guy 9mo ago
> 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. But there's still good math left out there.
- bmitc 9mo ago
  What is being meant by exotics in this discussion?
  - e-master 9mo ago
    I assume exotic derivatives (binary, asian, barrier options...) and structured notes that predominantly use above said derivatives (autocallables, barrier reverse convertibles, accumulators etc.)

conformist 9mo ago

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.

mhh__ 9mo ago
Yes although if you are predicting the returns swaps and options and so on then you need to be able to do both

mhh__ 9mo ago

What kind of quant? Trading huge equity portfolios and getting paid a lot? Pretty fun Pricing structured products all day in a bank that charges you for lunch? Not great

altmanaltman 9mo ago
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.
- frankc 9mo ago
  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.

gosub100 9mo ago

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).

TacticalCoder 9mo ago
> 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".
KellyCriterion 9mo ago
Strictly wrong: - a casino is a random game - stock market is a game of incomplete informatoin The one cant related to the other by whatever equation.
- gosub100 9mo ago
  The series of a shuffle of a 6-deck shoe is not "incomplete information"?
  - mikrl 9mo ago
    Taleb calls it the Ludic Fallacy (game fallacy) The statistics of games are understandable, defined and easy to work with. The statistics of markets, as Soros spent his career investigating, are filled with feedback loops, and as Taleb investigated, fat tails.