4 ms·

OneChronos, a proposed trading venue, wants to use AI to match orders

30 points by chollida1 9y ago

en4bz 9y ago
Don't really see how this could possibly comply with reg nms.
- sjbase 9y ago
  OneChronos cofounder here. The NMS order protection rule certainly doesn't make things easier, but periodic auctions have a unique compliance and fairness advantage there. The question most continuous limit order books face is "What is NBBO right now?" That question can't be answered thanks to the speed of light being finite. But because of the way our auctions allow time for orders and market data to arrive at the matching engine, we can more thoughtfully construct a consistent view of "What was NBBO 1 millisecond ago, at the moment we struck our auction?" This is a much easier question to answer, and also has the effect of smoothing out network jitter, data gaps, etc. Also, the auctions are combinatorial but are computed across linear components (stock symbols). NBBO applies to symbols individually, so it can be taken into account as a standard constraint on each symbol.
sgt101 9y ago
The thing about combinatorial auctions is that they are... I last looked about 2006 and I remember one problem with 11 participants on both sides requiring 2^38 steps to find a solution (make the set of bundles for the buyers that clear the auction at the best available price) within a not very strong bound from optimal. The details are vague as having sat down and done the sums I thought - well that's just not a goer is it - and dropped the issue like a hot spikey thing. So - many challenges.
- alephnan 9y ago
  > requiring 2^38 steps to find a solution Isn't the premise of the company to use "AI" to find a solution? I'm going to go out on a limb and assume AI here means deep learning. I took both an AI and ML course during college where the FAQ was "what's the difference?". At least from the way the courses were presented, the AI class focused on traversing these exponential search spaces. ML on the other hand searched for local maxima / approximations. Obviously, you seem more familiar with combinatorial auctions. I'd love to understand where an ML approach fits in here.
  - sgt101 9y ago
    The thing is that if your deepnet makes a mistake and classifies a penguin as a panda it's just one of those things. If it clears an auction outside of the guarantees that you have given then everyone sues you. The reasoners that people build for auctions and poker and so on are constructed to have well understood properties so that they can be used in applications like stocks and shares, organ transplants and other critical systems.
    - sebastos 9y ago
      Based on the responses I'm seeing from user lpage elsewhere in the thread, it sounds like you can establish lower bounds external to the AI aspect of the settlement layer, making it "at least as good". So perhaps the optimization is, in some sense, all upside?
lpage 9y ago
Hi, Kelly and Steve here, founders of OneChronos. Happy to answer any questions. In short, we're applying combinatorial auctions to financial markets. Combinatorial auctions are generally speaking computationally intractable. Our art pertains to structuring the problem in a way that's a good fit for finance, while remaining tractable. The classical approach to solving combinatorial auctions for indivisible goods on the "hard" cases (ones without a totally unimodular constraint matrix) generally involves encoding the problem as an integer program, at which point you're faced with solving an NP-complete combinatorial optimization problem. Needless to say, that doesn't lend itself well to financial market speeds or scales. As such, we've taken a different approach based around guided search, similar to AlphaGo. The Bloomberg article doesn't go into this, but the other half of the model involves inverting the traditional market event loop. As it stands, market participants receive market data and fills from geographically discrepant trading venues, action it via their models, and update their resting orders accordingly. We invert that model by putting points-of-presence equipped with highly precise network timestamping infrastructure at all of the major financial data centers. We use said infrastructure to timestamp snippets of code (our bidding language) that market participants send to us, as well as market data from other exchanges. At discrete, random intervals on the order of every ~100ms we resequence data in accordance with its origin timestamp (buffering by max time-of-flight) and feed it into our client's code for them to act upon and enter orders into our combinatorial auction. The net effect is an environment that's agnostic to geography, immune to latency arbitrage, and free of the traditional exchange race conditions. That's a huge win for everyone currently stuck in the prisoner's dilemma outcome that results from continuously reinvesting in what's now commoditized low latency trading technology. Combinatorial auctions aren't a new concept, nor are Lamport clocks/distributed systems primitives based on timestamps, nor is the notion of using code as a proxy bidder (ad exchanges). Our art is around making combinatorial auctions practical for finance, both from an API standpoint, and from a computational tractability one. There's a huge impedance mismatch between what market participants want to do from an economic standpoint (trade on a portfolio level/express contingencies/force atomicity across actions) and what they can currently do with traditional limit order book/auction based venues (simulating the aforementioned by cancel/replacing as quickly as possible). Fixing this will be a big win, and broaden market participation.
- 9y ago

30 points by chollida1 9y ago

en4bz 9y ago

Don't really see how this could possibly comply with reg nms.

sjbase 9y ago
OneChronos cofounder here. The NMS order protection rule certainly doesn't make things easier, but periodic auctions have a unique compliance and fairness advantage there. The question most continuous limit order books face is "What is NBBO right now?" That question can't be answered thanks to the speed of light being finite. But because of the way our auctions allow time for orders and market data to arrive at the matching engine, we can more thoughtfully construct a consistent view of "What was NBBO 1 millisecond ago, at the moment we struck our auction?" This is a much easier question to answer, and also has the effect of smoothing out network jitter, data gaps, etc. Also, the auctions are combinatorial but are computed across linear components (stock symbols). NBBO applies to symbols individually, so it can be taken into account as a standard constraint on each symbol.

sgt101 9y ago

The thing about combinatorial auctions is that they are... I last looked about 2006 and I remember one problem with 11 participants on both sides requiring 2^38 steps to find a solution (make the set of bundles for the buyers that clear the auction at the best available price) within a not very strong bound from optimal. The details are vague as having sat down and done the sums I thought - well that's just not a goer is it - and dropped the issue like a hot spikey thing. So - many challenges.

alephnan 9y ago
> requiring 2^38 steps to find a solution Isn't the premise of the company to use "AI" to find a solution? I'm going to go out on a limb and assume AI here means deep learning. I took both an AI and ML course during college where the FAQ was "what's the difference?". At least from the way the courses were presented, the AI class focused on traversing these exponential search spaces. ML on the other hand searched for local maxima / approximations. Obviously, you seem more familiar with combinatorial auctions. I'd love to understand where an ML approach fits in here.
- sgt101 9y ago
  The thing is that if your deepnet makes a mistake and classifies a penguin as a panda it's just one of those things. If it clears an auction outside of the guarantees that you have given then everyone sues you. The reasoners that people build for auctions and poker and so on are constructed to have well understood properties so that they can be used in applications like stocks and shares, organ transplants and other critical systems.
  - sebastos 9y ago
    Based on the responses I'm seeing from user lpage elsewhere in the thread, it sounds like you can establish lower bounds external to the AI aspect of the settlement layer, making it "at least as good". So perhaps the optimization is, in some sense, all upside?

lpage 9y ago

Hi, Kelly and Steve here, founders of OneChronos. Happy to answer any questions. In short, we're applying combinatorial auctions to financial markets. Combinatorial auctions are generally speaking computationally intractable. Our art pertains to structuring the problem in a way that's a good fit for finance, while remaining tractable. The classical approach to solving combinatorial auctions for indivisible goods on the "hard" cases (ones without a totally unimodular constraint matrix) generally involves encoding the problem as an integer program, at which point you're faced with solving an NP-complete combinatorial optimization problem. Needless to say, that doesn't lend itself well to financial market speeds or scales. As such, we've taken a different approach based around guided search, similar to AlphaGo. The Bloomberg article doesn't go into this, but the other half of the model involves inverting the traditional market event loop. As it stands, market participants receive market data and fills from geographically discrepant trading venues, action it via their models, and update their resting orders accordingly. We invert that model by putting points-of-presence equipped with highly precise network timestamping infrastructure at all of the major financial data centers. We use said infrastructure to timestamp snippets of code (our bidding language) that market participants send to us, as well as market data from other exchanges. At discrete, random intervals on the order of every ~100ms we resequence data in accordance with its origin timestamp (buffering by max time-of-flight) and feed it into our client's code for them to act upon and enter orders into our combinatorial auction. The net effect is an environment that's agnostic to geography, immune to latency arbitrage, and free of the traditional exchange race conditions. That's a huge win for everyone currently stuck in the prisoner's dilemma outcome that results from continuously reinvesting in what's now commoditized low latency trading technology. Combinatorial auctions aren't a new concept, nor are Lamport clocks/distributed systems primitives based on timestamps, nor is the notion of using code as a proxy bidder (ad exchanges). Our art is around making combinatorial auctions practical for finance, both from an API standpoint, and from a computational tractability one. There's a huge impedance mismatch between what market participants want to do from an economic standpoint (trade on a portfolio level/express contingencies/force atomicity across actions) and what they can currently do with traditional limit order book/auction based venues (simulating the aforementioned by cancel/replacing as quickly as possible). Fixing this will be a big win, and broaden market participation.

9y ago