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> XTX -- the name refers to a mathematical formula used in its trading algorithms Wow so they do linear regressions?? Incredible. But in all seriousness, for

> XTX -- the name refers to a mathematical formula used in its trading algorithms Wow so they do linear regressions?? Incredible. But in all seriousness, for how long are we going to dress up statistics as some new cool and exciting methods? Are VC's and bosses really this easily fooled?

deepnotderp 7y ago
Some (not all!) of the techniques at places like RenTec are fairly sophisticated. Hence their returns ;)
- BubRoss 7y ago
  Like what?
  - larnmar 7y ago
    Nobody really knows, except for a few people who aren’t telling. There’s some clues in the mathematical backgrounds of the kind of people they hire though. Fifteen years ago they were snapping up experts in stochastic calculus. Nowadays, I’m not sure whom they’re hiring.
    - throwawaymath 7y ago
      They're not exactly forthcoming, but it's not the hyperbolic mystery the mythos would have you believe. You can piece together what matters to them - in the abstract - by searching MathSciNet for papers published by their researchers prior to joining. For example, it's pretty easy to see they hired an information theorist away from Princeton in 2017. But obviously that's not enough to reproduce everything they do, or else they wouldn't still be legendary. As it happens a lot of what makes them successful is not the sophistication of their trading algorithms and research, but also the sophistication of the execution and reliability. As an aside, from friends there Renaissance hires researchers in three primary ways: 1. They have a small network of professors who they solicit for promising new PhDs willing to leave academia each year. 2. They watch professors and postdocs in specific specializations, and reach out to those whose research meaningfully interacts with a thesis they're interested in internally. 3. They send small groups to conferences to poach people working elsewhere in industry (particularly tech) whose work is applicable to their own. They also do hire people who directly apply of course, but most hires are reactive. They especially like to hire people whose work or research looks like it might begin to encroach on their own, or is just notable and impressive. The math is certainly important to them, but that's just one dimension of it.
      - deepnotderp 7y ago
        Would you say that their edge in algorithms comes mostly from signal processing or from ML? I'm guessing it's mostly signal processing and signal extraction?
    - chatmasta 7y ago
      I would be surprised if rentech's profits are based purely on algorithmic advantages. More likely, IMO, is they've curated some really good sources of alternative data and combined it with otherwise standard mathematical and technical trading techniques. They’ve probably also got a well-engineered pipeline for identifying that data, systematically evaluating it, and bringing it to production. This is of course pure speculation. But I doubt all that alpha would come from mathematical differences alone. The market isn’t magic... it’s just a question of having access to the right information and the ability to capitalize on it.
      - deepnotderp 7y ago
        Two Sigma has an entire team devoted to alternative data collection, with much worse results. Alternative data isn't as useful as you think- short term truth is only one variable in market prices.
        chatmasta 7y ago
        I’m not saying they have access to a single magical data source. Rather, they’re very good at identifying when a source of data is valuable to them, and milking it until it runs dry.
        deepnotderp 7y ago
        That doesn't match what I've heard nor what their hiring patterns show.
- hogFeast 7y ago
  Nick Patterson has said they never used anything more complicated than linear regression whilst he was there. The trick was how to use it, not the tool itself...I think people who read a lot of textbooks get obsessed with finding the next complex "secret silver bullet" technique. Good analytical work with basic techniques is often more worthwhile.
  - superpermutat0r 7y ago
    Linear regression can be the reduction form of many sophisticated methods. For example, HMM or CRF like inference can be easily done with linear regression. Hierarchical models with several layers of symbolic inference can be done with linear regression. Reinforcement learning too. You can reduce almost anything to a binary classifier and implement it in practice to work well. The reduction is tricky but performance is not.
    - hogFeast 7y ago
      As I explained, he said it was none of this.
      - superpermutat0r 7y ago
        I do not see where you explained it. Doing cost sensitive linear regression (which is pretty trivial to implement) allows you to approximate Markov models, any hierarchical model, and all sorts of other stuff (like minimizing different cost functions, quantile regression etc.) All achievable with the same linear regression algorithm and additional data modifications. Maybe sometimes you need to change the weight update rule, but that's it.
        hogFeast 7y ago
        It is funny to watch you struggle with this (maybe this is why you don't see). He specifically said that wasn't what they were doing (and said it was often simple linear regression with one or two variables), they used no complicated techniques (he even pointed out that people assume that must be true...but it isn't), and that what they did was a combination of good analytical work/hiring. Btw, this is also quite obvious from reading Zuckerman's book. I understand why almost no-one gets it. And that is why firms like RenTech are able to print money. I actually do work like this in a similar field, and everyone assumes you have some kind of secret algo that is the product of some unpublished, very complex work (these days, usually deep/reinforcement learning). But the reality is doing the simple stuff well and using that to build a deep understanding of the data. I suppose that is less fun for researchers who want to publish flashy stuff about deep learning and write lots of equations on whiteboards...but I prefer the money.
- msla 7y ago
  > Some (not all!) of the techniques at places like RenTec are fairly sophisticated. Hence their returns ;) Non sequitur. A chimp throwing darts can beat professional brokers. https://www.forbes.com/sites/rickferri/2012/12/20/any-monkey-can-beat-the-market/#50780309630a https://www.forbes.com/sites/rickferri/2012/12/20/any-monkey... Maybe their sophistication is a really good PRNG? Or just one that smells of bananas? Or maybe they've lucked into a model which happens to be doing well now, but won't after the market shifts a bit. Like how the random pickers above did well because random picks include a lot of small companies, and small companies did well over the period being considered in the study.
  - deepnotderp 7y ago
    Go calculate the p-value of making 66% before fees for 30+ years.
    - roenxi 7y ago
      Which still leaves a non-sequiter in saying the sophisticated methods led to the high returns. It is more likely to be some form of 'cheating' where they are actually making the money off something that isn't normal trading. Or even corruption in the worst case. Beating the market by very large margins is a suspicious activity. If raw mathematical insight is enough to get a 66% return then there are a lot of mathematicians out thereto figure out what is happening. If that is the secret sauce it would be extraordinary in a history littered with best performers who were just flat out frauds.
      - deleted 7y ago
        [deleted]
zelly 7y ago
What they actually do: colocated servers next to the exchange collecting $0.0000001 per trade with nanosecond latency. Create mystique of doing spooky AI to get more capital which they use to hedge even more trades/second, making more money, which they then attribute to AI which gets them even more capital.
- Beefin 7y ago
  They’re creating liquidity !! /s
- anonu 7y ago
  So much negativity in this thread.
- throwawaymath 7y ago
  That's part of it, but HFT is also a pretty saturated field. It's typically not enough to be fast and physically near the exchange these days; that's a necessary but insufficient condition. A lot of nontrivial math and CS is still poured into HFT at places like Two Sigma, Jump and Hudson River Trading.
- skunkworker 7y ago
  Time to make your own microwave network
- mjfl 7y ago
  I like that it's become a common understanding that AI research is most effective in the marketing department.
- vecter 7y ago
  If you knew anything about HFT, you'd know that speed got arbitraged out more than a decade ago. Everyone is fast now. In order to make money, you need true alpha and predictive ability. It's sad to see you disparage their impressive achievements when what they've done is incredibly difficult to accomplish, especially as such a latecomer. I know because I tried it myself ten years ago when it was even less competitive and couldn't make it work.
abbadadda 7y ago
Love this comment! Still, XTX is a cool name, even if the author of the article didn't really get it: https://math.stackexchange.com/questions/2624986/the-meaning-behind-xtx-1 https://math.stackexchange.com/questions/2624986/the-meaning... . The nice thing is no VCs are really necessary to fund trading firms like this. Usually the owners have 100% of the capital, split between one or more owners. Not positive of XTX's capital structure, but it is unusual to have VCs involved. GETCO was an exception, and they were pressed to eventually go public (as KCG): https://finance.yahoo.com/quote/KCG/ https://finance.yahoo.com/quote/KCG/

deepnotderp 7y ago

Some (not all!) of the techniques at places like RenTec are fairly sophisticated. Hence their returns ;)

BubRoss 7y ago
Like what?
- larnmar 7y ago
  Nobody really knows, except for a few people who aren’t telling. There’s some clues in the mathematical backgrounds of the kind of people they hire though. Fifteen years ago they were snapping up experts in stochastic calculus. Nowadays, I’m not sure whom they’re hiring.
  - throwawaymath 7y ago
    They're not exactly forthcoming, but it's not the hyperbolic mystery the mythos would have you believe. You can piece together what matters to them - in the abstract - by searching MathSciNet for papers published by their researchers prior to joining. For example, it's pretty easy to see they hired an information theorist away from Princeton in 2017. But obviously that's not enough to reproduce everything they do, or else they wouldn't still be legendary. As it happens a lot of what makes them successful is not the sophistication of their trading algorithms and research, but also the sophistication of the execution and reliability. As an aside, from friends there Renaissance hires researchers in three primary ways: 1. They have a small network of professors who they solicit for promising new PhDs willing to leave academia each year. 2. They watch professors and postdocs in specific specializations, and reach out to those whose research meaningfully interacts with a thesis they're interested in internally. 3. They send small groups to conferences to poach people working elsewhere in industry (particularly tech) whose work is applicable to their own. They also do hire people who directly apply of course, but most hires are reactive. They especially like to hire people whose work or research looks like it might begin to encroach on their own, or is just notable and impressive. The math is certainly important to them, but that's just one dimension of it.
    - deepnotderp 7y ago
      Would you say that their edge in algorithms comes mostly from signal processing or from ML? I'm guessing it's mostly signal processing and signal extraction?
  - chatmasta 7y ago
    I would be surprised if rentech's profits are based purely on algorithmic advantages. More likely, IMO, is they've curated some really good sources of alternative data and combined it with otherwise standard mathematical and technical trading techniques. They’ve probably also got a well-engineered pipeline for identifying that data, systematically evaluating it, and bringing it to production. This is of course pure speculation. But I doubt all that alpha would come from mathematical differences alone. The market isn’t magic... it’s just a question of having access to the right information and the ability to capitalize on it.
    - deepnotderp 7y ago
      Two Sigma has an entire team devoted to alternative data collection, with much worse results. Alternative data isn't as useful as you think- short term truth is only one variable in market prices.
      - chatmasta 7y ago
        I’m not saying they have access to a single magical data source. Rather, they’re very good at identifying when a source of data is valuable to them, and milking it until it runs dry.
        deepnotderp 7y ago
        That doesn't match what I've heard nor what their hiring patterns show.
hogFeast 7y ago
Nick Patterson has said they never used anything more complicated than linear regression whilst he was there. The trick was how to use it, not the tool itself...I think people who read a lot of textbooks get obsessed with finding the next complex "secret silver bullet" technique. Good analytical work with basic techniques is often more worthwhile.
- superpermutat0r 7y ago
  Linear regression can be the reduction form of many sophisticated methods. For example, HMM or CRF like inference can be easily done with linear regression. Hierarchical models with several layers of symbolic inference can be done with linear regression. Reinforcement learning too. You can reduce almost anything to a binary classifier and implement it in practice to work well. The reduction is tricky but performance is not.
  - hogFeast 7y ago
    As I explained, he said it was none of this.
    - superpermutat0r 7y ago
      I do not see where you explained it. Doing cost sensitive linear regression (which is pretty trivial to implement) allows you to approximate Markov models, any hierarchical model, and all sorts of other stuff (like minimizing different cost functions, quantile regression etc.) All achievable with the same linear regression algorithm and additional data modifications. Maybe sometimes you need to change the weight update rule, but that's it.
      - hogFeast 7y ago
        It is funny to watch you struggle with this (maybe this is why you don't see). He specifically said that wasn't what they were doing (and said it was often simple linear regression with one or two variables), they used no complicated techniques (he even pointed out that people assume that must be true...but it isn't), and that what they did was a combination of good analytical work/hiring. Btw, this is also quite obvious from reading Zuckerman's book. I understand why almost no-one gets it. And that is why firms like RenTech are able to print money. I actually do work like this in a similar field, and everyone assumes you have some kind of secret algo that is the product of some unpublished, very complex work (these days, usually deep/reinforcement learning). But the reality is doing the simple stuff well and using that to build a deep understanding of the data. I suppose that is less fun for researchers who want to publish flashy stuff about deep learning and write lots of equations on whiteboards...but I prefer the money.
msla 7y ago
> Some (not all!) of the techniques at places like RenTec are fairly sophisticated. Hence their returns ;) Non sequitur. A chimp throwing darts can beat professional brokers. https://www.forbes.com/sites/rickferri/2012/12/20/any-monkey-can-beat-the-market/#50780309630a https://www.forbes.com/sites/rickferri/2012/12/20/any-monkey... Maybe their sophistication is a really good PRNG? Or just one that smells of bananas? Or maybe they've lucked into a model which happens to be doing well now, but won't after the market shifts a bit. Like how the random pickers above did well because random picks include a lot of small companies, and small companies did well over the period being considered in the study.
- deepnotderp 7y ago
  Go calculate the p-value of making 66% before fees for 30+ years.
  - roenxi 7y ago
    Which still leaves a non-sequiter in saying the sophisticated methods led to the high returns. It is more likely to be some form of 'cheating' where they are actually making the money off something that isn't normal trading. Or even corruption in the worst case. Beating the market by very large margins is a suspicious activity. If raw mathematical insight is enough to get a 66% return then there are a lot of mathematicians out thereto figure out what is happening. If that is the secret sauce it would be extraordinary in a history littered with best performers who were just flat out frauds.
    - deleted 7y ago
      [deleted]

zelly 7y ago

What they actually do: colocated servers next to the exchange collecting $0.0000001 per trade with nanosecond latency. Create mystique of doing spooky AI to get more capital which they use to hedge even more trades/second, making more money, which they then attribute to AI which gets them even more capital.

Beefin 7y ago
They’re creating liquidity !! /s
anonu 7y ago
So much negativity in this thread.
throwawaymath 7y ago
That's part of it, but HFT is also a pretty saturated field. It's typically not enough to be fast and physically near the exchange these days; that's a necessary but insufficient condition. A lot of nontrivial math and CS is still poured into HFT at places like Two Sigma, Jump and Hudson River Trading.
skunkworker 7y ago
Time to make your own microwave network
mjfl 7y ago
I like that it's become a common understanding that AI research is most effective in the marketing department.
vecter 7y ago
If you knew anything about HFT, you'd know that speed got arbitraged out more than a decade ago. Everyone is fast now. In order to make money, you need true alpha and predictive ability. It's sad to see you disparage their impressive achievements when what they've done is incredibly difficult to accomplish, especially as such a latecomer. I know because I tried it myself ten years ago when it was even less competitive and couldn't make it work.

abbadadda 7y ago

Love this comment! Still, XTX is a cool name, even if the author of the article didn't really get it: https://math.stackexchange.com/questions/2624986/the-meaning-behind-xtx-1 https://math.stackexchange.com/questions/2624986/the-meaning... . The nice thing is no VCs are really necessary to fund trading firms like this. Usually the owners have 100% of the capital, split between one or more owners. Not positive of XTX's capital structure, but it is unusual to have VCs involved. GETCO was an exception, and they were pressed to eventually go public (as KCG): https://finance.yahoo.com/quote/KCG/ https://finance.yahoo.com/quote/KCG/