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Mod note: Best of Eddie, this was originally posted on 2/11/13.

I suppose it was only a matter of time before regular guys with a head for algorithms found a place to try their luck on Wall Street. Internet start-up Quantopian exited beta last month and promises to democratize algorithmic trading for the masses.

Emerging from beta in January, Quantopian said it has built a browser-based algorithmic trading platform where anyone "with a mind for finance" can find the tools and infrastructure to learn, create, and test trading strategies, according to its site. Traders can post their quantitative results, measure their results, show their code and allow someone to clone their algo.

You can read more about the company here. I think this is a pretty cool tool, and it makes sense that something like this is finally available to the public. I can see it becoming a sort of GitHub where amateur quants can give the pros a run for their money.

In a Jan. 23 press release, the firm's founder and CEO John 'Fawce' Fawcett, said: "For way too long, Wall Street has kept quantitative finance to itself by hoarding information and providing little transparency or accountability. Fawcett went to say that his firm's goal is "to dispel that secrecy and grow the quant community by a thousand fold. We welcome talented people from around the world into our community by providing access to the data , infrastructure and mentorship necessary to participate."

No word on how you keep your secret sauce secret if you're collaborating on the algo with others in the community, but I'm betting the founders have given that plenty of thought.

So what do you guys think? Will this be the birthplace of a blue collar RenTech? Or is this just a place for CS guys to humble themselves in the markets? Any WSO quants gonna throw their hat in the ring and test out a homegrown algo? Can't do worse than Knight Capital, right?

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Comments (54)

  • Coffeemonster's picture

    The FAQ page has info on keeping your algos secret. Thank you for posting this.

  • BTbanker's picture

    Market efficiency will be a self fulfilling prophecy.

  • In reply to BTbanker
    awawgoian's picture

    BTbanker:
    Market efficiency will be a self fulfilling prophecy.

    My 'proof' against efficient market hypothesis: EMH requires common knowledge of some information X, game theoretically defined as an infinite descent of "everyone knows that everyone knows that everyone knows ... X". Each level in this hierarchy is a separate item of knowledge; there are therefore an infinite number of items of knowledge necessary for EMH. Each item of knowledge utilizes a discrete and nonzero number of neurons, of which we have a finite number; therefore no human can accumulate sufficient knowledge such that would imply the results of EMH. And this is even assuming that information is freely available to anyone who would wish to acquire it.

    The obvious route of counterargument comes from symbolic logic: while we can't possibly 'know' all that is required, symbolically we have arrived at the conclusions this would imply, so if each individual market participant consented, we could have a market that obeyed this model (notwithstanding the many issues in modeling a discrete reality with continuous models). But this isn't the case -- I myself could cause a violation of EMH if I wanted.

    Not to mention that I could barely understand the ramifications of even "everyone knows that everyone knows that everyone knows that everyone knows X" -- I don't think most humans can understand beyond 3-4 levels of indirection.

    Edit: more objections to EMH (as you can probably tell by now I have a problem with it)
    1) Assumes rational participants
    2) More particularly, assumes rational participants with identical goals
    3) Assumes knowledge of the future -- i.e. what impact their actions will have
    4) Assumes ubiquitous information reaching all participants at the same time

  • nontarget kid's picture

    I'm sure amateur quants might be able to write sophisticated algorithms just as the pros do, but I think the problem here is that the average joe will never be able to compete with the infrastructure the pros have. The big banks have entire teams of developers just working to improve the infrastructure, for example, the speed with which the algorithm sends and receives orders to and from the exchange. And we're not even talking about physical advantages. I'm sure the average joe can't rent office space in the same building as the exchange in order to get faster execution. Anyway, this sounds like a cool idea and it will be interesting to see what people come up with.

  • In reply to nontarget kid
    spoonfork's picture

    nontarget kid:
    I'm sure amateur quants might be able to write sophisticated algorithms just as the pros do, but I think the problem here is that the average joe will never be able to compete with the infrastructure the pros have. The big banks have entire teams of developers just working to improve the infrastructure, for example, the speed with which the algorithm sends and receives orders to and from the exchange. And we're not even talking about physical advantages. I'm sure the average joe can't rent office space in the same building as the exchange in order to get faster execution. Anyway, this sounds like a cool idea and it will be interesting to see what people come up with.

    This is assuming people are going to try to compete on the super short time frames. I doubt that what most will be trying to do. I know plenty of guys (including myself) that used to trade on short time frames that have abandoned that to the HFTers. Now they hold hours to weeks.

  • In reply to spoonfork
    nontarget kid's picture

    spoonfork:
    nontarget kid:
    I'm sure amateur quants might be able to write sophisticated algorithms just as the pros do, but I think the problem here is that the average joe will never be able to compete with the infrastructure the pros have. The big banks have entire teams of developers just working to improve the infrastructure, for example, the speed with which the algorithm sends and receives orders to and from the exchange. And we're not even talking about physical advantages. I'm sure the average joe can't rent office space in the same building as the exchange in order to get faster execution. Anyway, this sounds like a cool idea and it will be interesting to see what people come up with.

    This is assuming people are going to try to compete on the super short time frames. I doubt that what most will be trying to do. I know plenty of guys (including myself) that used to trade on short time frames that have abandoned that to the HFTers. Now they hold hours to weeks.

    Oh yeah, I didn't consider holding for longer periods of time. I think that's definitely a possibility then.

  • IlliniProgrammer's picture

    Eddie, the problem is that most of the people who can understand this stuff are either doing it somewhere if they're interested in it, or doing something other than finance if they're not interested in it.

    The people in the world who can both understand what an autocorrelation function is as well as how to run the primal and dual linear programs number probably about 100,000.

    50K of these people work in finance and love it; 50K of these people don't work in finance and love that.

  • In reply to IlliniProgrammer
    animalz's picture

    IlliniProgrammer:

    The people in the world who can both understand what an autocorrelation function is as well as how to run the primal and dual linear programs number probably about 100,000.

    50K of these people work in finance and love it; 50K of these people don't work in finance and love that.

    No, the number of people who CAN understand what an autocorrelation function (and other stuff) is numbers at least around 1000x the number you listed.

    However, probably only around 100,000 understand it at the moment because others do not care, are lazy, never heard about it etc.

  • IlliniProgrammer's picture

    No, the number of people who CAN understand what an autocorrelation function (and other stuff) is numbers at least around 1000x the number you listed.

    Only about 10% of the people in the country actually understand Calculus. This is based off of the fact that 1/3 of 18-22 year old Americans actually attend college and perhaps 1/3 of those take a calculus course. 10% of those will likely get nothing out of it leaving us with .9*1/9=10%.

    That is the base, base prereq to doing this stuff. Then you tack on calculus-based probability and linear algebra, as well as multivariable calculus. Now we're going to get into methods for generating empirical statistical relationships and evaluating time series', stochastic calculus, some moderately advanced economics, and of course all of the algorithms behind that.

    I think a hard working guy with a 125 IQ can understand 2/3 of this, but he won't be able to understand it implicitly and holistically on all of the levels he needs to when he's looking at the problem.

    So for an IQ distribution with mean 100, stdev 10, only about 0.6% of the population is capable of understanding everything required to generate trading models.

    I don't think this changes much. It maybe means a few physics and engineering guys who love research but are very very bored and moderately interested in the markets can do something with their spare time.

    I think there's a much better IRR on learning programming than there is on learning stats.

  • In reply to EURCHF parity
    IlliniProgrammer's picture

    EURCHF parity:
    This is more likely to impact quants' careers, long term: http://arxiv.org/pdf/1112.6209.pdf ("Building High-level Features Using Large Scale Unsupervised Learning", by Google Research).

    As well as all traders', for that matter. This machine learning stuff is pretty darned neat.

    In thirty years, the federal reserve and US economic policy will also be run by machine learning algorithms.

  • In reply to IlliniProgrammer
    awawgoian's picture

    IlliniProgrammer:
    No, the number of people who CAN understand what an autocorrelation function (and other stuff) is numbers at least around 1000x the number you listed.

    Only about 10% of the people in the country understand Calculus. This is based off of the fact that 1/3 of 18-22 year old Americans actually attend college and perhaps 1/3 of those take a calculus course. 10% of those will likely get nothing out of it leaving us with .9*1/9=10%.

    I think this is more a matter of what people choose to do, and where they come from, than what they're capable of. The main tools of calculus outside of proofs are integrals and derivatives, which are just a matter of pattern matching and memorization. Applied linear algebra is largely memorizing definitions and algorithms. I think almost anyone who wanted to could apply calculus / linear algebra procedurally (contrast to developing theory), but I agree that those capable of creating new theoretical knowledge are a much smaller set.

  • In reply to nontarget kid
    Unforseen's picture

    nontarget kid:
    I'm sure amateur quants might be able to write sophisticated algorithms just as the pros do, but I think the problem here is that the average joe will never be able to compete with the infrastructure the pros have. The big banks have entire teams of developers just working to improve the infrastructure, for example, the speed with which the algorithm sends and receives orders to and from the exchange. And we're not even talking about physical advantages. I'm sure the average joe can't rent office space in the same building as the exchange in order to get faster execution. Anyway, this sounds like a cool idea and it will be interesting to see what people come up with.

    I think websites like the pirate bay, 4chan, and others that operate with minimal hardware yet can handle enormous amounts of data is proof against this argument.

    I will be writing a post about TPB sometime this week.

  • IlliniProgrammer's picture

    I think this is more a matter of what people choose to do, and where they come from, than what they're capable of.

    But our choices and where we come from ultimately define what we're capable of.

    For instance, if you don't learn a language before ~20, you'll always speak it with a significant accent.

    I mean, calculus is a language in this case. And it's harder to learn and you don't understand it as well if you see it for the first time at 25, 30, or 35. The neural pathways simply aren't there, they take longer to develop, and when they do develop, they're weaker.

    A decent quant- one who can get hired on Wall Street- is ultimately a polyglot of mathematics, statistics, programming, and finance. Basically, he can speak ten different languages fluently and without an accent in the same conversation. (mind you that mathematics and stats have several sub-languages). On top of that, he brings insight and creativity and an intuitive understanding to the financial problem.

    Not everyone can do that. On the language front, I can speak English, German, and maybe two other languages fluently (with a mild Arianna Huffington-like accent) if I really set my mind to it. I can't speak ten. When I was 18, I could have learned maybe one or two more languages than that, but not ten other languages. I did extremely well on my Verbals for the SAT and GRE, but I'm not as verbally smart as a decent quant is quantitatively smart.

    So the bottom line is that:

    1.) This probably reduces the value of prestige. If you went to MIT, Harvard, Yale, Princeton, Stanford, or Berkeley, big deal. Can you beat the Math PhD from Clemson?
    2.) Prestige didn't matter that much in the first place on the street. If you were brilliant, you got hired as a quant. If you weren't brilliant, you didn't get hired. If you were somehow still brilliant and couldn't get hired by Wall Street but wanted to work in finance, Chicago would clearly take you.
    3.) So if you were capable of this stuff in the first place, and interested in finance, you were probably already in industry.

    Net-net, this gives the academic or researcher a few more options to satisfy his interest in the markets, but I don't think it changes much else. Who wants to spend a good 4000 hours studying math, stats, programming, finance, accounting, and econ when one can spend 2000 hours studying CS and earn just as much working for Google?

  • In reply to awawgoian
    BTbanker's picture

    awawgoian:
    BTbanker:
    Market efficiency will be a self fulfilling prophecy.

    My 'proof' against efficient market hypothesis: EMH requires common knowledge of some information X, game theoretically defined as an infinite descent of "everyone knows that everyone knows that everyone knows ... X". Each level in this hierarchy is a separate item of knowledge; there are therefore an infinite number of items of knowledge necessary for EMH. Each item of knowledge utilizes a discrete and nonzero number of neurons, of which we have a finite number; therefore no human can accumulate sufficient knowledge such that would imply the results of EMH. And this is even assuming that information is freely available to anyone who would wish to acquire it.

    The obvious route of counterargument comes from symbolic logic: while we can't possibly 'know' all that is required, symbolically we have arrived at the conclusions this would imply, so if each individual market participant consented, we could have a market that obeyed this model (notwithstanding the many issues in modeling a discrete reality with continuous models). But this isn't the case -- I myself could cause a violation of EMH if I wanted.

    Not to mention that I could barely understand the ramifications of even "everyone knows that everyone knows that everyone knows that everyone knows X" -- I don't think most humans can understand beyond 3-4 levels of indirection.

    Edit: more objections to EMH (as you can probably tell by now I have a problem with it)
    1) Assumes rational participants
    2) More particularly, assumes rational participants with identical goals
    3) Assumes knowledge of the future -- i.e. what impact their actions will have
    4) Assumes ubiquitous information reaching all participants at the same time


    Computers do not have a finite number of "neurons" therefore your 'proof' is false.
  • IlliniProgrammer's picture

    I'm sure amateur quants might be able to write sophisticated algorithms just as the pros do, but I think the problem here is that the average joe will never be able to compete with the infrastructure the pros have. The big banks have entire teams of developers just working to improve the infrastructure, for example, the speed with which the algorithm sends and receives orders to and from the exchange.

    Banks also have lots and lots of compliance and regulatory issues and waiting for a bulge bracket bank to make a small change to its technology is more painful than watching an aircraft carrier do a U-turn.

    I think the individual investor is at a disadvantage to a mid-sized trading firm- maybe like Sun Trading. No paperwork for changes, and money for stuff like Bloomberg Terminals and market data. And don't forget direct market access. But a really solid programmer can crush the technology of an average bank.

  • In reply to BTbanker
    IlliniProgrammer's picture

    BTbanker:

    Computers do not have a finite number of "neurons" therefore your 'proof' is false.

    There are 10^125 atoms in the universe. Even if the universe were turned into a computer, there would still be a finite amount of memory for a neural network.
  • In reply to IlliniProgrammer
    Edmundo Braverman's picture

    IlliniProgrammer:
    BTbanker:

    Computers do not have a finite number of "neurons" therefore your 'proof' is false.

    There are 10^125 atoms in the universe. Even if the universe were turned into a computer, there would still be a finite amount of memory for a neural network.

    HAHAHA. I was waiting for this argument. Well done.

    EDIT: That is, unless there really is something to the Hawking Paradox:

  • IlliniProgrammer's picture

    I think websites like the pirate bay, 4chan, and others that operate with minimal hardware yet can handle enormous amounts of data is proof against this argument.

    It's important to note that these sites may be data intensive but they're not computationally intensive.

    It's one thing to store and produce data over the internet. And these sites rely heavily on users' computing power.

    Trying to run a solid PPR on several billion samples of data?

    1.) Better be decent at distributed computing just to store everything and have some knowledge of Hadoop or JGroups. You're probably also going to want to compute everything on a distributed basis, too.
    2.) Better have multiple computers.
    3.) Better have a really, really efficient optimization algorithm.

    Yes, most optimizations are going to run in O(n) on your data, but how many passes do you have to do through all of that data to calculate your error each time? An error calculation on a billion samples?

    And how are you going to store everything? You'd better be using an array, not a hashmap, for memory efficiency.

    This problem is nontrivial in terms of actually implementing the distributed system in addition to the algorithms and efficient statistics behind it.

  • In reply to BTbanker
    awawgoian's picture

    BTbanker:
    awawgoian:
    BTbanker:
    Market efficiency will be a self fulfilling prophecy.

    My 'proof' against efficient market hypothesis: EMH requires common knowledge of some information X, game theoretically defined as an infinite descent of "everyone knows that everyone knows that everyone knows ... X". Each level in this hierarchy is a separate item of knowledge; there are therefore an infinite number of items of knowledge necessary for EMH. Each item of knowledge utilizes a discrete and nonzero number of neurons, of which we have a finite number; therefore no human can accumulate sufficient knowledge such that would imply the results of EMH. And this is even assuming that information is freely available to anyone who would wish to acquire it.

    The obvious route of counterargument comes from symbolic logic: while we can't possibly 'know' all that is required, symbolically we have arrived at the conclusions this would imply, so if each individual market participant consented, we could have a market that obeyed this model (notwithstanding the many issues in modeling a discrete reality with continuous models). But this isn't the case -- I myself could cause a violation of EMH if I wanted.

    Not to mention that I could barely understand the ramifications of even "everyone knows that everyone knows that everyone knows that everyone knows X" -- I don't think most humans can understand beyond 3-4 levels of indirection.

    Edit: more objections to EMH (as you can probably tell by now I have a problem with it)
    1) Assumes rational participants
    2) More particularly, assumes rational participants with identical goals
    3) Assumes knowledge of the future -- i.e. what impact their actions will have
    4) Assumes ubiquitous information reaching all participants at the same time


    Computers do not have a finite number of "neurons" therefore your 'proof' is false.

    If you are giving me that humans have a finite number of neurons, it holds as long as there are any human participants in the market with finite amount of neurons, and EMH fails. Computers have finite memory, mapping into finite "neurons". Furthermore, any induction step of knowledge takes nonzero computation time (applicable to human as well) -- even with infinite neurons, infinite descent requires infinite time.

    I think if you want to attack this argument, the best approach is to challenge the assumption that each inductive step constitutes a separate unit of knowledge.

  • In reply to Edmundo Braverman
    IlliniProgrammer's picture

    Edmundo Braverman:
    IlliniProgrammer:
    BTbanker:

    Computers do not have a finite number of "neurons" therefore your 'proof' is false.

    There are 10^125 atoms in the universe. Even if the universe were turned into a computer, there would still be a finite amount of memory for a neural network.

    HAHAHA. I was waiting for this argument. Well done.

    EDIT: That is, unless there really is something to the Hawking Paradox:


    I've seen researchers use this argument in Algorithms and Theory- that even though an algorithm doesn't take a mathematically infinite amount of resources to use, that it's effectively infinite/impossible. (They use some argument about the number of atoms in the universe, the time it takes for light to pass through a hydrogen atom, and some really long period of time- like the time since the universe was created.)
  • UFOinsider's picture

    Hey cool, I and a relative have been designing an algorithm and farming out the code to India, neither of us have time to program it all out. There's simply no way we can compete in the HTF arena, we just don't have the math skills and infrastructure resources, but we're willing to give it a shot in minutes/hours. Truthfully, we don't expect this to make us money, but why not try?

    Thank you for posting this, so far this just seemed like a hairbrained idea that my brother in law cooked up, so I'm definitely checking this out. My guess is that these guys are well ahead of us, so this little side project is probably headed for the scrap heap. Still cool though, even if it never goes live.

    Another thing: linking computers together via the internet as a multiplier is another thing that just jumped into my mind, and this has been used to lend processing power to solving very old math problems. Again, competing against a supercomputer in the HFT space is probably impossible, but not all algo trading is a mere contest of speed. I wonder wonder if anyone is working on this as well? Y/n/idk?

    Get busy living

  • IlliniProgrammer's picture

    Hey cool, I and a relative have been designing an algorithm and farming out the code to India, neither of us have time to program it all out. There's simply no way we can compete in the HTF arena, we just don't have the math skills and infrastructure resources, but we're willing to give it a shot in minutes/hours. Truthfully, we don't expect this to make us money, but why not try?

    UFO hit me up if you need algorithmic help on this.

    Another thing: linking computers together via the internet as a multiplier is another thing that just jumped into my mind, and this has been used to lend processing power to solving very old math problems. Again, competing against a supercomputer in the HFT space is probably impossible, but not all algo trading is a mere contest of speed. I wonder wonder if anyone is working on this as well? Y/n/idk?

    http://en.wikipedia.org/wiki/Apache_Hadoop
  • Macro Arbitrage's picture

    Most of the strategies on quantopian are very basic momentum strategies or rudimentary pairs trading ('statarb' if you want to sound smart). Nothing new here, really.

  • In reply to Edmundo Braverman
    BTbanker's picture

    Edmundo Braverman:
    IlliniProgrammer:
    BTbanker:

    Computers do not have a finite number of "neurons" therefore your 'proof' is false.

    There are 10^125 atoms in the universe. Even if the universe were turned into a computer, there would still be a finite amount of memory for a neural network.

    HAHAHA. I was waiting for this argument. Well done.

    EDIT: That is, unless there really is something to the Hawking Paradox:


    Yes, this is TOTALLY what I was alluding to the whole time.
  • In reply to IlliniProgrammer
    Macro Arbitrage's picture

    IlliniProgrammer:
    EURCHF parity:
    This is more likely to impact quants' careers, long term: http://arxiv.org/pdf/1112.6209.pdf ("Building High-level Features Using Large Scale Unsupervised Learning", by Google Research).

    As well as all traders', for that matter. This machine learning stuff is pretty darned neat.

    In thirty years, the federal reserve and US economic policy will also be run by machine learning algorithms.

    No doubt, was just thinking about that over breakfast this morning. I'd say make that a decade or two - so much of macroeconomic research is dedicated towards logical casual inference. If utilized properly, the tools of ML could identify such causal relationships that many overlooked since the beginning of time.

  • In reply to IlliniProgrammer
    UFOinsider's picture

    IlliniProgrammer:
    UFO hit me up if you need algorithmic help on this.

    Thanks dude, I'm a n00b xD

    Get busy living

  • LeveragedTiger's picture

    I have a feeling there are going to be a lot of quant models coming out of this that have no economic logic and/or are unprofitable strategies to trade on.

    "The power of accurate observation is commonly called cynicism by those who have not got it." - George Bernard Shaw

  • In reply to IlliniProgrammer
    animalz's picture

    IlliniProgrammer:
    No, the number of people who CAN understand what an autocorrelation function (and other stuff) is numbers at least around 1000x the number you listed.

    Only about 10% of the people in the country actually understand Calculus. This is based off of the fact that 1/3 of 18-22 year old Americans actually attend college and perhaps 1/3 of those take a calculus course. 10% of those will likely get nothing out of it leaving us with .9*1/9=10%.

    That is the base, base prereq to doing this stuff. Then you tack on calculus-based probability and linear algebra, as well as multivariable calculus. Now we're going to get into methods for generating empirical statistical relationships and evaluating time series', stochastic calculus, some moderately advanced economics, and of course all of the algorithms behind that.

    I think a hard working guy with a 125 IQ can understand 2/3 of this, but he won't be able to understand it implicitly and holistically on all of the levels he needs to when he's looking at the problem.

    So for an IQ distribution with mean 100, stdev 10, only about 0.6% of the population is capable of understanding everything required to generate trading models.

    I don't think this changes much. It maybe means a few physics and engineering guys who love research but are very very bored and moderately interested in the markets can do something with their spare time.

    I think there's a much better IRR on learning programming than there is on learning stats.

    nice reply

    remember, i was quoting you on word "can"

    and i truly believe that at least 1 in 7 humans (actually im pretty sure that its a much higher percentage) can comprehend this information

    hard working, good teachers, school system, environment etc.. are not a factor here because we are talking about ones potential to understand something

    to say that only 10% of the people in the country understand calculus
    and use this later in the argument implicates that only 10 % CAN understand calculus, which is ridiculous, and facts that you are basing it on are even funnier. its like saying that only 40% of human population can learn to use pc (basics), but like 40% actually never even talked on the phone or have used any "sophisticated" device because they live in underdeveloped regions, and probably don't even know about it

    how would your percentage work for Africa? you would calculate people there on your fingers?

    i am certain that ~95% of humans (don't quote me on this, i am not sure about % of people that are ill or about % of those with mental illnesses that are actually just "trapped inside" and are not "stupid") can finish any degree, and honestly its much, much more about hard work and organization than pure intelligence

    all healthy humans have incredible brains capable of doing amazing feats, majority of which yet to be discovered... they dwarf probably all of the concepts taught academically. they are not used in the right way because of how our society works today, but you cant say that they cant process it

    i understand that you have probably put a lot of time in your education, accomplished a lot, and are probably proud of it, but no need to put some magical "intelligence" aura around yourself because it is a lie. pretty much any person you know could do it under the right circumstances - they have all the potential needed

  • In reply to LeveragedTiger
    Xepa's picture

    jmayhem:
    I have a feeling there are going to be a lot of quant models coming out of this that have no economic logic and/or are unprofitable strategies to trade on.

    So the algo version of Seeking Alpha? :)

  • In reply to Xepa
    Edmundo Braverman's picture

    Xepa:
    jmayhem:
    I have a feeling there are going to be a lot of quant models coming out of this that have no economic logic and/or are unprofitable strategies to trade on.

    So the algo version of Seeking Alpha? :)

    LOL. That's not nice. Those kids work hard.

  • IlliniProgrammer's picture

    nice reply

    remember, i was quoting you on word "can"

    and i truly believe that at least 1 in 7 humans (actually im pretty sure that its a much higher percentage) can comprehend this information


    If you want to say 60% of Americans are capable of learning Calculus, I'll agree with you on that. It's not conceptually difficult, and sites like KhanAcademy.org make it even easier.

    Of course, you also need to be good at algebra to do it. Calculus is a bit bigger picture stuff, but you need to be decent at the tactical aspects of algebra for Calculus to be useful to you.

    My point is that once we start getting beyond this, you'll be able to get your typical 120 IQ American to either be able to understand what's going on at the high level or be able to understand mechanically what to do, but not necessarily understand both what to do and what they're doing.

    I think that subset of the population is more like 1-2% being capable of it, and 10% caring to learn the math rather than just becoming a system architect at Google or designing cars at GM. Then you have the people who could have learned this stuff if they had started when they were 20, but now their command of German isn't quite good enough to move on to Business German.

  • In reply to IlliniProgrammer
    awawgoian's picture

    IlliniProgrammer:
    I think this is more a matter of what people choose to do, and where they come from, than what they're capable of.

    ...

    1.) This probably reduces the value of prestige. If you went to MIT, Harvard, Yale, Princeton, Stanford, or Berkeley, big deal. Can you beat the Math PhD from Clemson?
    2.) Prestige didn't matter that much in the first place on the street. If you were brilliant, you got hired as a quant. If you weren't brilliant, you didn't get hired. If you were somehow still brilliant and couldn't get hired by Wall Street but wanted to work in finance, Chicago would clearly take you.
    3.) So if you were capable of this stuff in the first place, and interested in finance, you were probably already in industry.

    Net-net, this gives the academic or researcher a few more options to satisfy his interest in the markets, but I don't think it changes much else. Who wants to spend a good 4000 hours studying math, stats, programming, finance, accounting, and econ when one can spend 2000 hours studying CS and earn just as much working for Google?

    I pretty much agree with all of what you said re: capability and I liked the polyglot analogy; for the rest I think it's largely a matter of definition where we choose to draw the line between "could" and "could under X circumstances".

    Though I will say re: prestige, I think it still make sense for recruiters to focus on institutions where you'll get a higher hit rate, assuming there is no scarcity of quants -- this is why networking matters, I think?

    About Google -- is the salary actually comparable? If Glassdoor is to be trusted, senior developer @ Google makes around ~150k, which is less than e.g. VP tech (not to mention quant roles, which would probably pay more) would make at a bank.

  • IlliniProgrammer's picture

    About Google -- is the salary actually comparable? If Glassdoor is to be trusted, senior developer @ Google makes around ~150k, which is less than e.g. VP tech (not to mention quant roles, which would probably pay more) would make at a bank.

    VP is a rank for people with manager or manager-like responsibilities, at least in Sales and Trading and the organization supporting it. My understanding is that managers at Google make about as much as VPs (certainly back office and probably also front office) at banks, after adjusting for NYC cost of living.

    SVPs make more. So do MDs. But up through the first management rank, the pay is about the same, and the pay per hour is probably higher.

    And the question is- are you going to make SVP? VP is a fairly sure thing if you stick it out long enough, but SVP is very political and MD is even more political.

    This is one of those situations where the return on assets for the PhD needed to do this stuff has decreased significantly, but it's still generating...returns. But instead of perhaps a 15% ROA, you're now looking at closer to 4-5%. Meanwhile, the ROA for an engineering undergrad at a state school has increased from 20% to probably a whopping 30%.

    So for people considering getting PhDs and becoming quants, think long and hard about it. There is a little more upside if you're truly lucky and/or amazing (though half of all people in finance think they're in the top 5%), and for the workhorses out there, there's more hours per week and a little more pay, but otherwise the hustle and stress per dollar is about the same as that of a developer at Google.

    So do you want a California-style low-stress 37 hour week with surfing on Friday afternoons and $110K starting? Or do you want to earn 40% more for 60% more work and 30% higher cost of living (NYC)?

    I guess one other argument is that VP is inevitable at a bulge bracket if you stick it out long enough (10 years or so), but management at Google is not quite as inevitable- though you will probably get promoted into some expert role if you stick it out a very long time (like 20 years).

    If I had to do everything over again given the new facts on the ground, I'd pick Google. It's a really nice life. It pays the bills and leaves a lot left over for savings. But the facts were different in 2007. And Wall Street is nice too, especially if you're thrifty.

    But no, to an engineer, I wouldn't recommend Wall Street anymore. The job market has gotten tougher.

  • In reply to IlliniProgrammer
    UFOinsider's picture

    IlliniProgrammer:
    SVP is very political and MD is even more political.

    yup

    Get busy living

  • SirTradesaLot's picture

    I don't really get how this company plans to make money and I don't see an incentive for people to participate. Are people planning on selling their algos to others?

    adapt or die:
    What would P.T. Barnum say about you?

    MY BLOG

  • In reply to IlliniProgrammer
    Wall Street Jungle's picture

    IlliniProgrammer][quote:
    So for people considering getting PhDs and becoming quants, think long and hard about it. There is a little more upside if you're truly lucky and/or amazing (though half of all people in finance think they're in the top 5%), and for the workhorses out there, there's more hours per week and a little more pay, but otherwise the hustle and stress per dollar is about the same as that of a developer at Google.

    Right on target. I've definitely been thinking about this for a long time.

  • In reply to IlliniProgrammer
    Gomez Addams's picture

    Illini Programmer-

    Excellent post and lots of useful insights.

    But I am not sure about your comment "Chicago would clearly take you" I don't know the actual numbers, but once the hiring manager hears someone say that they were shot down all over NYC that is likely to be the end of the interview.

  • In reply to Gomez Addams
    IlliniProgrammer's picture

    Gomez Addams:
    Illini Programmer-

    Excellent post and lots of useful insights.

    But I am not sure about your comment "Chicago would clearly take you" I don't know the actual numbers, but once the hiring manager hears someone say that they were shot down all over NYC that is likely to be the end of the interview.


    When is someone going to say (volunteer) that?

    I mean, if they ask that question, you have to tell the truth, but you don't have to volunteer anything more than they ask for.

  • In reply to IlliniProgrammer
    Gomez Addams's picture

    IlliniProgrammer:
    Gomez Addams:
    Illini Programmer-

    Excellent post and lots of useful insights.

    But I am not sure about your comment "Chicago would clearly take you" I don't know the actual numbers, but once the hiring manager hears someone say that they were shot down all over NYC that is likely to be the end of the interview.


    When is someone going to say (volunteer) that?

    I mean, if they ask that question, you have to tell the truth, but you don't have to volunteer anything more than they ask for.

    True, but it is almost a certainty that the question will be asked, either directly or indirectly.

  • In reply to Gomez Addams
    IlliniProgrammer's picture

    Gomez Addams:

    True, but it is almost a certainty that the question will be asked, either directly or indirectly.


    So you tell the truth. You like Chicago's culture more (true- New York has a stupid culture if they don't like you).

    I always thought this was a BS question. Traders are in the business of evaluating risk and reward. If you see a solid candidate, you shouldn't care what everyone else thinks. The number of other offers may influence the price, but it shouldn't influence the hiring decision.

    The other offers don't become material until you start negotiating comp. At that point, when they ask if you have other offers, you have to say "nope". But until then, it takes a lot of finesse, but you can carefully avoid answering that question without actually looking like you avoided it. If you can't avoid answering that question, you need to work on your interview skills.

  • WreckEmFinance's picture

    as long as their are liars in industry, we will never truly reach the fulfillment of EMH. it like calculus a little bit. you can get infinitely closer to zero, but you can never get to zero. likewise, we will always have innovations that are good and that make the markets more efficient, but anyone who adheres to pure EMH is quite mistaken. there is a large body of academic evidence proving EMH is not a reality. anyways, back to liars. as long there are people who are willing to rig libor, et cetera, this will always be detriment to efficiency. and since we bankers are all greedy bastards by our nature, it's kind of a vicious cycle. dah well...just as long we stay on the freer side of free markets and free enterprise, i'm good to go

    "Everything comes to those who hustle while they wait."
    -Thomas Edison

  • IlliniProgrammer's picture

    Sounds like someone works for Bridgewater?

  • In reply to IlliniProgrammer
    Gomez Addams's picture

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