151: Rick Lane – Perspective From a Trading Technologist—Attacking Unique Problems

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Boys and girls, welcome. What's up? So I trust by now you've had a chance to hear the two previous episodes with Aaron Brown. In my opinion, he was a real standout guest, someone well worth listening to. If you haven't yet heard those two episodes, please add that to your to-do list. I think that's very important.

Now on this particular episode, I have another brilliant guest for you, and that is the CEO of Trading Technologies, Rick Lane. You may recall Trading Technologies have sponsored the podcast and my events in the past. But bringing Rick on the podcast was one hundred percent my idea. This is not like a sponsored interview or anything funny like that, just to be clear. The real reason why I did ask Rick to come on the podcast was because of this. You know, if we're being real with each other.

Not everyone is cut out to be a trader, okay? And that's totally fine. Trust me, it's not a bad thing. And saying this, that also doesn't mean you cannot play an important role within trading. And for some of you, that means leveraging skills which you've already attained. And this is something I want to bring attention to.

Rick has never been a trader and prior to teaming up with his cousin, a interest rate trader, he hadn't even heard of a bid and offer. And if he had, he certainly didn't know the difference or the meaning of the two. He used to model combat scenarios for the military. He was also a product manager at Google at one point. And like I mentioned, nowadays he's the CEO of Trading Technologies, which is one of the leading futures trading platforms widely used by banks, hedge funds, prop traders, etc.

So I won't say too much more on this because this is what we discuss in the first part of our conversation. It's really interesting. And then later on we discuss some of the unique challenges to companies developing trading software. Are the latency wars driving to a close? Uh plus Rick brings up something which will probably blow your mind. It certainly blew uh my mind. And then later on, Rick also shares

some advice for anyone who may wish to play the role of a technologist in the world of trading. All right, so let's get into it. This is episode 151. Here is my guest, Rick Lane.

How was the FIA expo the other week? Yeah, it was good. Uh busy as always. Um, you know, we had a lot to talk about. Norensic, uh our our acquisition of Norensic was um recent news and so that that drew a lot of

attention, a lot of excitement. Um, there's there seems to be a uh a real interest in that space in general, but also the application of machine learning to to it, but also to capital markets more broadly. So There are a lot of people stopping by the booth and and uh wanting to dig into the Noranzik platform.

Okay. And were you on any panels or give a talk or anything like that? You know, I uh I managed to escape any speaking duties this year. So it was a it was a relatively laid back conference for me. Uh nice, nice. Cool man. Well tell us a little bit about Uranzik because we were talking about that last time before the power cutout.

Last time we were speaking, I think you'd only just acquired them. It was like a a week or so ago. So what is Nurensic? Tell us a little bit about that and what got you interested in it? Sure. So Norensic is a uh was a uh company founded about two and a half years ago in in twenty fifteen here in Chicago. Uh their their product and and their innovation was uh um applying machine learning, uh a a subset of artificial intelligence to

the trade surveillance problem. Um, and I call it a problem because there's trade surveillance software has existed for many, many years. And by that I mean uh software that that aims to identify when a trader or a trading firm is participating in manipulative trading practices or, you know, bad behavior in general. And, you know, as defined by whatever governing body is defining that that asset class or that

that trading uh area. And so it's been a problem uh historically because it's as volumes of of trading data continue to grow with the advent of of automation and now that just about every trader these days has some level of automation.

Finding bad behavior can be very difficult to do as somewhat of a needle in a haystack problem. But the real problem has always been false positives. And so If you're a compliance officer at a trading firm or at an FCM or at a bank and your job is to monitor your firm or your customer's trading. Um generally

historically your options have been rule based systems. And by that I mean uh you encode uh you know, if then checks that basically say if if you see this this behavior, this action, followed by this action, followed immediately by this action. Then flag this as let's call it spoofing or something, you know, name your name your bad behavior. And the problem is that. uh n it's very difficult to to identify accurately traitor intent.

And so applying these relatively rigid rules really results in lots of false positives that Um y y the the aim is to not let anything slip through slip through the cracks and and fly under the radar. And so you they you end up designing these rules to basically catch everything. And so it becomes the job of the compliance officer or or, you know, the poor soul whose job it is to to to sift through these reams and reams of data of these of these alerts to p to basically determine

Um, you know, were any of these actually bad behavior? Uh, the vast majority of them likely weren't. And so long story short, it it it becomes a very difficult problem to solve well.

and and you what you end up having is with all this noise and all these false positives, um, it becomes hard for the compliance office or or whomever to really do at the end of the day what what they're trying to do and that's identify bad behaviour. So With the advent of machine learning and the the application of it to this problem, the the folks over at Norensic really caught on to something that.

uh allowed them to uh apply machine learning to really identify with far less, far fewer uh uh false positives essentially trader intent. And so they're they're able to train this model uh on known um data sets that regulators have given them, that clients have given them.

uh uh obviously anonymized data sets but but known beh known instances of bad behavior and they're able to feed this through this this machine learning uh platform To essentially get smarter and be able to better identify uh similar patterns with with varying levels of confidence.

um on on real time data. And so uh it has resulted in in what what's called Norensic Score, which does just that. It's a a platform whose whose reason for being is making it incredibly easy to take, you know, hundreds of millions of of of records of trade data within a trading firm and very quickly identify, uh, you know, sift sift the the noise from the meaningful bits of of insight and allow a firm or a c compliance officer or or even an individual trader

um to to understand uh very easily their trading behavior and to make sure that they're staying above board. Where we find this really compelling is it's still to this day that the Norensic score product um is uh a an installed on-site application and and kind of uh there there is a a an integration process and an installation process when a a firm wants to say, hey, you know, I'd love, I'd love to have my trading activity monitored by this.

Where we see this as being a really compelling bolt-on to what we're doing at TT is in making this really software as a service, the same way our TT platform is. So if you're a a an FCM, a large bank. uh trading firm or even an individual trader and and you want to uh have the protection of something like score.

Um it's literally just a a click away uh and and all of your trade activity without doing any integration, without doing any installation, um will will run through this process and you'll get your reports, it daily reports, hope soon to be real-time reports. So really bolting what's still kind of a a relatively high installation and integration cost platform like Norensic that does these really i innovative

really game changing things in the surveillance space and making it as easy as as ticking a box um i i for the trader is something that we think is is really gonna bring this type of technology to the masses. Um and I think it it'll be good for everyone.

Okay, so by the sounds of things it was certainly up until this point it's been widely used by people in compliance roles. Is that gonna continue to be the case moving forward now that you guys have acquired it? Like do you feel as though it's gonna be used or be of benefit to like maybe even individual traders themselves who are using the T T platform, for example?

S so we do. So uh what I would say is actually the Norensic Score product um a has uh penetration in in trading firms as well. And that's that's trading firms who uh may be doing that uh uh on the request of their of their FCM, of their broker. Uh uh but it may just be a trading firm um who uh just simply wants to to make sure that they're taking every step they can. You know, trading firms can get very large and and being able to police

all of the traders when a within a firm can can be difficult. So Um we're we're actually seeing adoption uh uh kind of across the board, but to answer your question We absolutely do think that even the individual trader uh would would benefit from something like this. And and now that it's as accessible and we haven't really figured out individual trader level pricing yet.

Um, but it would uh suffice it to say, it would the commercial impact to an individual trader would be so small that it would be a no brainer for them to turn this on. And and by that I mean not just monitoring a as an individual trader, my own trading activity. But imagine now with with MiFID, uh th this thing we all love that's coming down the the pike here in a couple of months. MIFID has certain algo uh testing and stress testing requirements uh for automated traders.

Imagine now not only uh running your algo against known data sets to make sure that, you know, from a MIFID compliance perspective. your algo uh doesn't go haywire or doesn't violate risk limits. But also now imagine being able to see your compliance score for that algo. Because you might be building something that you're worried you think it's above board and you think it's not, you know, participating in any of these known manipulative trading practices.

But, you know, it's it uh i programming is difficult and and and and building automated trading strategies is is very difficult as as you know and as and I'm sure a lot lots of your listeners know. So making sure that maybe some emergent behaviors that become that come out of that algo aren't doing something that could that could cause a red flag. So it's almost like preemptive uh compliance analysis before I even turn this algo on.

I just wanna I wanna rest assured that it's not doing something um that's you know that that could be frowned upon. So we see this as as really having application even down to the individual trader.

and where we want to take this uh beyond compliance. And that's where I think long term this is really exciting to us because machine learning is, you know, a couple of years ago, it was that that interesting thing that very few people knew about.

uh just that it was doing some cool stuff at places like Google and and Facebook and Amazon. Now it's really becoming a household name and I think i in just a few more years you're gonna see machine learning uh and and sort of deep learning uh uh uh that that that science of artificial intelligence really start to have applications throughout trading.

uh much more than than just compliance. And and I think this actually gives us a shot in the arm at T T to take uh this type of technology which still even though it's it's starting to be a household name, it still takes a fairly um unique skill set and and experience set and and and this gives us a shot in the arm to really take this type of platform and break it out of just the compliance space and and start to give

traders who have access to all their data on the T T platform, really valuable insights that would be very difficult to to obtain. the old the in the traditional ways of of analyzing trade data and market data. So we're we're excited, uh, both for the short term but also where we think machine learning can can be applied down the road. Just briefly, can you explain to us what is was it Mithid or Mythod? What's that about?

So MIFID, and I'm not I'm not gonna try to explain what that acronym stands for. But it's the new European regulatory MiFID two actually, it's the it's the sequel. It's New European uh regulations for for trading and a big component of that is uh algo, uh automated trading, uh re the registering and approving of of algos um uh in the futures markets, European futures markets, and part of that requirement is not only at a trading firm having a an approval workflow. So if you're an algo developer

um and you've built a new version of an algo, you can't just go turn that on under MiFID um and in production. You need to have someone kind of green green stamp it, say th that th this algo uh is approved to be run in production. And part of that is uh uh requirements around algo stress testing. So making sure that uh that your algo when run against uh uh essentially back testing, when run against known uh date uh date ranges and and for the for the given contracts that it's trading.

that the algo doesn't violate certain constraints, that it doesn't exceed risk limits, that it doesn't um, you know, over message. Uh so that stress testing piece of this is uh is is the biggest l uh amount of work for companies like T T, or at least for T T it was. Um it's actually a good thing though because back testing is something that we've always we've never really developed a a back testing solution at TT that is commensurate with the rest of our of our product.

Um and so this is kind of forcing our hand. So we're actually really excited. It's a lot of work, but we're really excited about it. And this requirement actually goes act uh becomes uh live January second. So a lot of firms are scrambling. Um a lot of our client uh customers uh as well. to make sure that they're MIFID two compliant. But this algo back testing is is a big component of that.

So who does this apply to? Like surely this doesn't apply to independent traders who are just trading their own account from home. I believe it does. I I I believe any any automated trading, regardless of how how large or or small the firm is, even down to the individual trader, is subject to the algo constraints and the algo r regulations around stress testing and what have you. Right. So this is only in futures and only in your European markets?

That that's correct. The Markets and Financial Instruments Directive is what it stands for. Uh that's correct. Um it is only European and it is only uh derivatives, I believe. Right. Well if anyone's interested we'll dig up a link and post it in the show notes for more information.

I suspect your listeners who are active uh automated traders trading European markets are are well aware of it. But um if they're not ti the the clock is ticking um and January second. Uh for all i if uh all signs are pointing to that date holding, so But the clock is ticking. Okay. Okay. Right.

Well let's hear a little bit about your backstory, Rick. You have a very interesting background. You actually prior to trading, you were working with the military. Can you tell us a little bit about some of the things you were doing there? Sure. Um I was actually working for a uh US consulting uh company whose primary uh client was the US Department of Defense. So the various branches of the military, the uh the Defense Department, the

uh uh the CIA, the NSA, the FBI, so on and so forth. Um I worked in a um in a modeling and simulation uh group there. So uh w we essentially built war games Um everything from campaign level uh w war game simulations. So, you know, if if we went to

war with country XYZ, what would happen? And if the if it was in the year twenty twenty five, uh and and we had these capabilities and they had these capabilities, sort of what would happen? And so we you know, we would we'd build a computer simulation that would

obviously not spit out the right answer because, you know, these were all very wild uh theories and and and guesses. It was more about facilitating the exercise itself. And so Um, like most uh militaries, I suspect uh the US uh US military partakes in war games just to keep keep their minds sharp, just to think through some of these thought exercises.

And so w uh our our platform and the technology we built um uh helped facilitate that. So everything from that type of war game all the way down to very granular analysis uh around uh uh combat modeling around how uh terrorist networks um grow and mini and are manip manipulated, um, things like that. And so, um uh i in a lot of ways um uh that those those skills that I that I learned and and the types of uh clients that I had, and I did that for about three years.

in many ways similar to my experience coming into trading. I originally came uh in into the trading world um because my cousin, who is actually one of the largest uh interest rate traders at at the time. Um this was around two thousand five. he saw an impending change in the interest rate markets and in the futures markets in general. And that was uh he saw the the inflection point where automated trading

um was really gonna change the dynamics of of of trading in general. And so he was a a floor trader. So he he basically convinced me to leave the this world of of war gaming to come out and help him kind of evolve uh into an automated trading shop. uh and c sort of evolve with with the industry. And so um I did that. I I took him up on this offer uh and and came out and learned the industry for the first, you know, six to twelve months, which

was one of the more humbling experiences of my life. But then started to build uh started to build our own internal trading system that let us um uh try to stay ahead of the curve with respect to automated trading. And and you know, he was right, obviously. I remember when I came out to visit him before I accepted the offer in the fall of two thousand five.

the floor was still a madhouse. It was it was just still craziness. He brought me down. It was one of the most W one of the most vivid memories of of of my life. And when I came back in started uh in April of two thousand six, so about five or six months later, you could hear a pin drop down there. So it it it it changed incredibly rapidly a as everyone knows and and you know, the rest is history, but

Um th the the reason I say it it's similar in a lot of ways to what I was doing in the in the defense world, in the world war gaming world, was because in the war gaming world I was building uh really complex technology to to to do really complex things. So applying graph theory, for instance, to terrorist network modeling. And, you know, there's lots of really interesting science and math around

graph theory around how to look at a network and identify key players or identify key channels of communication, that sort of thing. Well, this technology is being used by two, three, four star generals in in the various armed forces.

and they're not technology experts. They're not graph theory experts. And so we the challenge was really taking very complex technology and presenting it in a way That people who didn't necessarily understand the underlying technology could still gain insight from the analysis that it was doing for them.

And and it's funny because in the back in the trading world now, fast forward to, you know, a year or two years on the job, I was the only developer uh there. I was it was me and a bunch of other uh traders, guys that were still down on the floor. And it became a sort of an untenable situation for me because

I was the only one writing code. Everyone else was down on the floor and I was up in an office and we had this intercom system. So anytime a you know uh anytime some market imbalance occurred, a big order came in or someone saw an opportunity, they'd yell up through this through this intercom system.

And as quickly as I could, I would uh write up a a program that that did exactly what they needed it to do. They'd say, you know, we wanna lean we wanna work this leg. Uh when we get filled, turn around and do this, this, and this. If we don't get filled, then do this.

And I would frantically try to string this together as quickly as I could, uh, hating every minute of it because I they wanted to turn this thing on in the production markets, you know, i i immediately and every bone in my body said, Well, you know, software always has bugs and we need to test it and And so there's always this kind of uh tet conflict and tension between me, the main person producing the technology, and the traders.

And so I basically said, Screw it. I'm gonna build a a platform that lets these traders who know nothing about programming Um, essentially uh uh uh control their own destiny, allow them to build their own trading algos on the floor. They all had these kind of handheld Windows computers. So I built a visual interface that let them essentially put these blocks together and these blocks all did something that these guys understood really well.

We had a block that would make markets in a certain order. We had a block that would uh you know pull prices in from a certain instrument. And they could string these things together in interesting ways that let them essentially build their own algos on the fly without me having to be in the loop.

Um and do it very quickly and also do it very safely. And and so Um, in a lot of ways, this the same kind of lesson learned that I had in in the in the wargaming space was was was paying dividends in this space too, because I was having these these traders who would profess that they knew nothing about programming, essentially programming, although they didn't know it. And so it it was just a really interesting thing. Anyway, uh

just to catch you up to sort of where we are today, we realized we had something with this visual with this visual algo development. Um something unique and let me let me jump in here. So I have a few questions just before we get too far ahead of ourselves. Um, around kind of around the time where you came onto the floor um or started working with your cousin, I should say. But just one question prior to that.

You know, these types of things you were doing with the working for the military or contracting to the military, you know, this this sounds like very complex, advanced type of modeling and and work. How were you able to test the accuracy of your models? Like is that something that you had to do? In some cases we did. So uh uh in the um in s in in things that were testable, uh we did. Um

And uh I probably need to be a little careful about what I talk about in terms of what we're actually doing. But there were put it put it this way: some of the modeling we were doing. um we knew what the right answers were or should be and and we knew the constraints and we knew based on a certain set of inputs what the output should look and so we were able to test that that type of model very well. And we had enough data and enough know how and subject matter expertise

in house to test that. Some of the broader things. So the the more uh the more uh uh high level models where we were really testing You know, if this capability is even possible thirty years from now. Um, what w what will it mean for our for our naval forces? Well, first of all, we don't even know if that technology will exist thirty years from now, so we certainly don't know if what the computer tells us is going to happen is is is realistic or or is correct. So

there was really this this spectrum and the sliding scale between these types of of modeling exercises that we had to do. And obviously it was depending on the client, uh and and depending on the problem they were trying to solve. Um but in some cases we could, in some cases uh t testing that was was just completely impossible. Sure. Okay. I'd be interested to hear a little bit more about your cousin's motivations for uh wanting you to come and work with him.

Uh, so how come he had the foresight to uh want to automate some parts of his trading operation. Like, why did he feel as though it was necessary to do so in order to stay relevant? Like what if he didn't do that? What did he think would happen? What if he continued to just keep doing what he'd been doing? Great question. I mean, uh uh what gave him that foresight, I I don't know. I think he probably saw enough. Who knows? I i if it was around uh you know, you probably saw enough instances where

Um, clearly something that could move faster than a human was getting an advantage over him. Um, he probably saw, you know, the inflection point of technology in that space, just in general becoming more mature. You know, these these were these were guys and girls that were down in the pit trading all day, every day. But nevertheless, technology was was becoming more and more prevalent, you know, whether it was the the handheld computers they were using in the pit.

uh whether it was uh advancements and and clear investments that that like the CME and the Board of Trade were making in their electronic trading platforms. I think he just really uh at the end of the day realized that technology had had kind of disrupted just about every industry, uh, save uh you know, the the capital markets and trading and and and in it had already done so to some extent in the equities market.

Um, and I think he just he just w was, you know, uh thoughtful enough to to realize that it would happen to him as well and he and he didn't want to become a dodo bird essentially. So Um he you know, he d he didn't want to become irrelevant. And so it started out slow for us. It wasn't, you know, fully automating his trade, uh, because you know, he he was a really, really good trader and and in a way that

even today, even with today's incredible machine learning technology, um, I think it would be really tough to replicate uh with with a with a computer. Um and so this was really more about augmenting his trading initially. Um just giving him that extra little bit of edge and in the firm, that extra little bit of edge, even not necessarily in in in terms of trading, but even looking at post trade analysis. So uh really uh uh overhauling his

his uh the tools that he had to look at his trading from yesterday and figure out what went wrong or what w well Um, so it w it wasn't really he he he didn't realize he had to fully automate on day one, but he knew he knew that's where things were going and we took baby steps uh to get there. Right. And when you came to work with him at the time, did you have any interest in markets or did you just kind of see it as Something that might be a cool technology project to work on.

That's that's a really great question. So I I I I went to Georgia Tech undergrad uh uh for for college and there was zero exposure to capital markets or to trading or really to finance in general. there. It's an engineering school through and through. That's changed, actually, I'm I'm I'm happy to say. But back fifteen years ago I came out of school with zero experience. I didn't know what a bid and an offer was, uh believe it or not. And

Um, so no, I came in about as green as someone can. T it was really the latter. It was it was me seeing that uh there is a really challenging industry, that technology hadn't quite taken a foothold. Um and and I thought, you know, it would be an interesting challenge for me personally. So um and s I was obviously right, but uh no, I I I came in I came in knowing about as little as you can know and so the first six months or so

was essentially boot camp for me. Just getting getting up to speed and and knowing the mechanics of the markets themselves so that I could I could even hope to start to contribute in some way on the on the on the computer side. Are you ready to get serious about trading? Then join Tasty Trade, Investopedia's best platform for options trading in 2026.

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Now, before you started telling us a little bit about some of the things you began to uh build and develop once you started working with your cousin and, you know, the other um people who were part of his operation, the other traders. Your goal from the sounds of things was really to try and Make things simple and easy to use, right? Yes.

Okay. Do you think there's a downside? This might be a bit of a strange question, but do you think there's a downside to making things too easy for users? Like Are they maybe doing something which they don't really understand?

I don't know if that was a good question. Yeah, no, no, it it's a great question. It's one that we've asked ourselves. And and let me let me just say the reason I asked this question is because You know, sometimes I get people emailing me who are like interested in getting into automated trading or they're like, I've got the strategy, I want to be able to back test it.

And I know there are like some software platforms around which like make it very easy for you to be able to do that, like especially like more simple strategies. Um but I think There can be that can also be dangerous as well. You know, if someone doesn't actually take the time to understand some of the pitfalls of back testing and um how the results can be misleading, etcetera. So by making software really easy for the end user, is there also a downside to that? Does that make sense?

It makes total sense. Um uh and and the answer is uh i i in certain cases, absolutely. And and and I think what what really matters if someone if you're if you're gonna give someone uh something that really makes what used to be very difficult, very easy. Um, particularly in a space where there's so much to lose if if what they do is broken or wrong or unintended uh you know, has unintended consequences.

i in order for that to work, I think you need to also offer more than just whatever that tool is. And in other words, one of the things that that we realized early on, because trust me the the uh the in the initial users of of the the what we call the Algo Design Lab or ADL, uh, of this were people who had no business

And my cousin will laugh if he ever if he if he hears this, uh who had no business building ultra low latency trading strategies, uh, you know, without without applying any any testing or any uh quality uh assurance processes to. Um, but the reality is we we knew that going into this.

And we made sure that uh all of the surrounding uh aspects of the the of the technology, so whether it's a scripting language or whether it's a visual uh tool like ours was Um we we put in you know really in the user's face uh what is going to happen uh when they run this algo, um coupled with uh uh s r f really sufficient and granular risk controls. around making sure that um they can control their automated trading um in in a way that even if

they do something dumb. Even if they're, you know, there's a a flaw in their logic or or, you know, they they back test something and and think that the back testing is is indicative of what's gonna happen when they run it going forward.

that they can still mitigate problems when they arise. And and so it's a great question, it really is. And and it's something that we've always taken seriously because we are giving, you know, i uh this is this is a really bad analogy, but but it's the first thing that pops into my head.

Um, you know, you're giving someone a loaded weapon and it's a very powerful weapon and you want to make sure that they that they use it well and that they they follow all the safety procedures and that that it's it's incredibly hard to do something dumb with.

Um and so we made sure and and really have have continued over the last now eight or nine years as this product has been on the market to make sure that we are guiding users through it so that, you know, for instance, you can build a a bit of logic that we will visually call out to the user and say, hey.

We need to cat get your attention. We think that what you just did here could lead to uh, you know, endlessly lifting the offer or hitting the bid and or we think that you um could get into a a a loop of placing orders and and um you know violate your risk limits or so we're we're actually able to highlight these common pitfalls and and frankly we do this because these are things that I did.

um when I was writing code when I first came out to to Chicago to to to build these trading strategies, these are all the problems that I had uh and and mistakes that I made. And so um w we we encoded these common pitfalls so that the trader or the user or the the developer who's using the platform

can can really rest assured that when when this thing says it's ready to go, um, it's gonna do what they intended it to do. But nevertheless, you know, it's it's still obviously possible to make mistakes. But um

It's a great question and and I think I think we you just can't be cavalier about it. Whether you're the user or whether you're the technology provider, you need to understand that you're giving something that that that is very powerful to to the end user and you need to make sure that they are confident and feel confident. Um that they can control the experience. Yeah. No, I did like your analogy of um so it's like handing someone a loaded weapon. I think that's great.

Now, initially you developed the software for internal purposes. At some point you decided to start offering the software out to other people as well. And I I find that kinda interesting because obviously, like I said, you developed it for internal purposes and it was working well for um your guys. Why did you want to share it with others?

Great question. So we this was probably middle of 2008. So things were the world was was changing and we realized that with the Algo Design Lab um we had something that while it was an advantage to us, it it allowed our traders to control more of of what they used to depend on me or or or others for. Um, we also knew that in the hands of others, we didn't view it that as cannibalizing our existing business and our ex our existing trading.

We also knew frankly that trading technologies, the you know, the eight hundred pound gorilla in the room at the time. didn't have something like this. And so we thought, well, TT is a really successful company. Um they are in the business of of building tools for traders and you know lots of professional traders have it. Why can't we supplement our our existing trading operation by selling this this software commercially?

And and unlike TT at the time, you know, this is built by a trading firm, you know, built by traders that's in active use today. And in fact, the the when we launched, we went to FIA Expo, the the big annual um trade show that that actually just wrapped up. Um, we went in and at our booth in our demo, we had live trading uh on on my cousin's account. We had uh we had algors running all throughout the day.

We were so proud of that that we brought these giant speakers and these subwoofers that every time we got filled, it would like shake the it would shake the room that we were in. And and it worked. People came over and they couldn't believe that we were letting these things run as we were, you know, engaging in conversation and and were like, Oh yeah, by the way, the the people who built these algos, you know, they've never written a line of code in their lives and and so

It it was we we we fit we thought we had something unique compared to T T, so we said why l let's give it a shot. Let's try to sell this thing commercially. um because we we didn't think it would jeopardize our existing trading. Um and and so that's that's why we initially uh uh set set out to to build the commercial uh trading arm of that. Yeah. That's very cool. And it actually reminds me of something that uh are you do you know of Minoj Narang?

Um the name sounds vaguely familiar, but no, I don't I don't believe so. Yeah, he started uh Tradeworks, um he's since left that started mana partners now uh Well known in the high frequency space. I interviewed him, must have been earlier in the year. And that's actually something he said. He said that. sometimes a lot of people don't realize the actual technology um and the software that's developed like inside some of these trading firms actually has more value

Than just the P and L that it can extract from the markets. You know what I mean? Um, yeah, so that's a really interesting point. Now, obviously Uh, this software was acquired by T T and you've come on at T T now. So I'd just like to ask a few questions, I guess more around fintech and general kind of things about the technology space. One of those being, or I think this might be a good question to kick things off.

Having your background, um, you know, you come from a developer's background, does that have significant benefits for someone who's in the role of CEO at a technology company? Like Are there great benefits to the company to have a CEO who comes from a development background? Well I'm biased. Uhhuh. So so I I I think it does. Um

I think it does and and and look I I I think it also brings its own set of challenges as well. You know, I I've always long thought uh very analytic analytically about um every you know, most of the problems that I faced in my career. Um, and in a lot of cases in this role you have to kind of take a step back.

But I I really I think it does. Uh and I think it does for a couple of reasons. Number one, I mean our our job uh our our reason for being is is delivering and and building and deploying technology. And so I think h understanding the constraints in which we're able to do that well. And and and by that I mean the you know, the the rate and the pace at which we can innovate.

um helps me forecast, it helps me um uh keep uh priorities in line where they should be, you know, when when when you're you're constantly trying to to balance a global sales force who is hearing feedback from from the trading community and and wanting us to build, build, build and build new features and build new access to new markets and build new access to new asset classes.

um with the realities of how long it takes to actually build these things and and and do this work. And so I think having that and and you know, I I I'm still I actually still contribute code to this day. Um it it it both helps me balance uh out my, you know, my sanity to some extent, but it also I think keeps my finger on the pulse of how the machine that we have that that that builds

um th you know and and that continues to innovate how that's how that machine is running. And so I I think having that that insight um is is really valuable for us and and I've obviously surrounded myself uh with with uh a a a room full of really great leaders and and and great people who can help

uh help me with with things outside of technology as well. Um but but uh no I I I think it helps a lot. Um and and I think it um it just keeps me closer to the to the to the people and the the the processes that we have. um and and uh h helps me uh keep track and and and really at the end of the day plan better and I think that's that's half the job is is planning and prioritizing and and trying to figure out uh the best way to apply the limited and scarce resources that we have.

Sure. Yeah. And I I realised as soon as I asked that question that um of course you might be a little bit biased there, but no, awesome answer. Why do you choose to stay involved in fintech rather than technology in another industry? Um like is there anything which excites you specifically about fintech?

Absolutely. I mean and it's why I came back. So we glossed over a little bit. So I I as you mentioned D T acquired um the the the company that we co founded back in two thousand ten. And I spent about two years integrating the algo design lab into T T's platform. But then I left. I I actually went to to Google outside of this industry, about as outside of this industry as you can get.

Um and and pretty much immediately uh I I felt the draw to come back. Um i it it turns out that um less than a year later the former CEO um called me up and said, Hey, I w I want you to come back in in the CTO role. and really I want you to rebuild this technology from the ground up. And at at the time, um uh it was it was a no brainer for me to come back and and the reason for that is because

at least the trading world and fintech is an incredibly broad term, you know, everything from banking to payments to to trading and what what we're doing in our little niche uh corner of the world. But at least in our space There are problems that are being solved that are are truly not being solved anywhere else. And and in in ways

that uh that aren't being applied anywhere else. And you know, working at a place like Google, you're working with a lot of really smart people who are solving a l a lot of really challenging scale problems and data problems uh and latency problems. But those pale in comparison to to a lot of the scale and and latency problems uh that that we have to solve in in our world. And so I think just coming into work every day, knowing that that you're doing something that

um that a lot of people have failed at doing, um and and trying to solve problems that a lot of people have tr have failed at solving i is exciting in and of its own self. But Um, I think our space in general and in in particular I should say uh really poses some problems that are unique in the technology world and and that's just that's just fun to do.

Are you able to maybe give us a couple of examples of some of those problems and just to bounce another question off that, like You know, and you don't even have to be specific to T T here, but what are some of the greatest challenges for companies who are developing trading software in current times?

I'll start with the first part of that question. I I mean one i i i in its it's you've probably had you've probably had uh two dozen people talk about this uh on your show, but but it's it it bears repeating again. uh the the you know we we we aren't um

Uh our technology in in in in lots of cases likely isn't as fast as you know the fastest uh uh uh elite prop trading firms in the world. Nevertheless, uh we measure we measure uh success in terms of latency i by anything less than thirty microseconds uh from tick to trade, which is just a ridiculously small number and and trying to shave even five microseconds from that.

um i is is is such an incredibly challenging problem that that not only um requires uh a a an incredible uh uh uh mastery of of software development and and you know really eking every last bit out of out of uh you know, traditional cpu and computer um but but hardware expertise and networking expertise and and and that race to always be um incredibly fast so that our clients and our and our end users Um have that great advantage. Um, but do so at global scale.

No one else in the world is solving these problems. You know, Google obviously has global scale, but you know, when you when you do a Google search, they're happy if they deliver that r answer to you in 30 milliseconds.

Well thirty milliseconds i is a lifetime. You may as well you may as well be s down on the floor trading, um if if you're gonna be th if it's gonna take you thirty milliseconds to make a decision. And so It's those types of problems that really, you know, generally in the technology world, you can either have really, really fast. or lots of throughput at global scale. Trying to do both is is a problem that, you know,

still we invest a lot of money and time in in solving um and you know keeps keeps a lot of people up at at night um because it it it is an incredibly uh difficult uh problem to solve and to stay continue to innovate in. But then the second part of your question, um uh you know, w what are what are challenges generally for for people entering this market or even in this space?

Um everything I just said, you know, i if if performance is and and and there there are lots of platforms out there that that don't have to shave microseconds off, that do really great things. And so that that's not always a problem for everyone in this space. Um, but nevertheless, uh it's it's an industry that changes, I suspect, rapidly than most.

um and a and in as unforgiving as m th than most in terms of Um, you know, for us uh and and for I think for anyone who's who's trading multiple markets or building technology that that rights to m that that connects the you know, their users and their clients to multiple markets. Um you've got different regulatory uh uh regimes. You've got uh different exchanges who who trade at different hours of the day, who who use different technology that lets you connect to them.

um who have different rules around what, you know, how orders need to be formatted and and they're always changing. And exchanges have have, you know, changed their platforms, upgrade their platforms.

have uh you know unplanned outages, um, you know, have different holiday schedules because one's in in Asia Pack and the other one's in the US. Um and and th that that problem in general, it would if I were if I were trying to be an upstart uh technology company in this space, that stuff would make me would keep me up at night because that the that's I think why there is such a barrier to

to to to new entrants in this space. Um it's just it th the this this industry and this world in general is growing and changing at such a rapid clip and and it's not slowing down and and it's only becoming more complex. And layering on um, you know, uh making sure that that

your technology is there when you when your clients need it most because this is an industry that forget building technology. If you're if you're a participant, if you're a trader, you need to know that that um you know that that the market is accessible and at your fingertips when you need it.

it these are all just really challenging problems to solve and even if you're not trying to shave microseconds off in some capacity you have to solve them and so I I think those are that that that really presents the toughest kind of set of

challenges for for other providers. Yeah. And just as you bring it up there, what is your take on the whole latency race? Like obviously it's been a big thing over recent years, but you know, are we almost at a point where it's literally And I don't like to really use this word, so I'm gonna use it loosely, but like impossible to get much quicker. Like is there are we almost at the end of that? Trend.

So so I what I'd say is some some people are and in some uh in in some instances. In other words, um you take uh you take the you know, just pick the the the most sophisticated uh uh prop trading firm or hedge fund that you can think of and and and whoever that is probably has Um uh in fact I know they have some really, really smart people doing some really really interesting things in in terms of performance uh for very specific trades.

Um, you know, they can they can embed that that that that algo on silicon, they can put it in in an FPGA, uh basically a programmable hardware device. They can even embed algos in in network switches. Um And but those uh those are few and far between in terms of the firms that have the resources and the capability to do that, but also few and far between in terms of the the the applications where that level of that type of trading uh can be applied.

Um, then you take it a step back and and look at the a a a company like us Uh so so so to answer your question, I I do think we're we're uh a select few are are about as close to zero as they're going to get. Obviously they're measure they measure their latency in nanoseconds and you can get smaller than nanoseconds, but

Um they're they're really butting up against the speed of light constraints um uh more than anything else. And until we can go faster than speed of light, um w we'll we'll have a problem getting faster. But but but what we're also seeing is technologies that were five years ago only available to a select few, um, become commoditized. And so where T T stands is we're not we're not embedding algos on network switches.

But we are uh leveraging uh some commodity, but even some what what we would probably not classify as commodity because it's slightly higher end and slightly more specific, but still lets us and our client base um remain incredibly flexible. In other words, if you've built an algo and you embed it on a network switch and then you realize, oh crap, I I did this one thing wrong and I need to now go change it.

Well, you know, good luck uh getting getting that that fix embedded in there. Obviously it's possible, but it's gonna take forever. uh versus you know you're using uh a platform like the algorith design lab, you know, you can within a minute make a change, test something, see it doesn't work. make another tweak and deploy to production and do so, you know, where your algo is quoting in twenty to forty microseconds. That's really, really compelling and that's that's possible because

a lot of the technologies that we now leverage, which were incredibly expensive and probably out of our and most people's reach five years ago, now are. And so I think what you'll see is you're there's almost two curves. There's the one that the the you know, the Uber you know, top point zero one percent of participants are are able to do in terms of microwave towers and and all the the types of things I mentioned earlier.

Um and then there's kind of the the commodity curve that and and they will eventually converge. You know, t ten years from now T T will certainly have microwave towers and we'll we'll we will have uh algos embedded on silicon um for certain applications and for certain clients. Um but but I but I think I I think that's the way this is going to continue to play out. it's still something that we have to invest in and focus on and and make sure we're

you know, uh a hundred percent acutely aware of our performance and of our clients' performance because it is still very important. Um, but that race to zero, um, I I I think i is is pretty close to over for some. Okay.

Can you just explain to us what it means to embed an algo on a network switch? Like sounds sophisticated. What what actually is that? Yeah, so it it's um uh and and believe me, I I know uh only enough to be dangerous here. So in general, building hardware based solutions for automated trading is is ideal because uh software is by all uh in in relative terms incredibly slow when it comes to hardware.

Um so the moment you know the moment a message comes from a a an exchange on a network device and that network device passes that message uh via an Ethernet cable. To your server. And that server receives that message and now gives it that now the server passes that message up to your trading application.

that that process passing it up to the trading application and having your trading application react to that price update by saying, Oh, you know what? Now I want to go submit a new order, that's incredibly, incredibly slow. Software is is is relatively incredibly slow.

And so what a lot of firms do in terms of embedding algos on chips is never never reaching that last point. In other words, that if you've got a switch that's on your network, a price update comes in, the switch itself can can decode that price update.

can uh encode a new wire to go, whether you're you know quoting options or whether you're doing something like spreading two two contracts against each other, that switch itself uh at at hardware speed, so at line rate speed, meaning very, very fast. can can can w most switches, all their job is to do is to take a packet on the wire and send it somewhere else. These switches can take a packet on the wire.

uh actually gain some meaning from it, whether it's a price update or whether it's an order fill or something. and actually uh have some embedded code that says, ah, based on that price update, I'm gonna change my price of this order to this price and and it can do that without uh without ever Not only ever having to go to software, but we never even s need to send that packet to a server to process. So the switch is able to.

um uh as fast as anything else in a network can do, it can actually respond and participate in in automated trading. And so that that's that's where that comes into play. Again, it's it's where we've seen it being applied. It's only for very specific um types of strategies that that are even possible to embed on a chip like that.

Um, some people, you know, have trading strategies that that simply you can't you couldn't do that for. But for those that are are are very specialized and and highly tuned, um, that type of performance uh can can is is is pretty tough to beat. That's incredible. It really is. Do these sorts of things have like a computer screen where they can be monitored or that's a great question. Uh y so so that that's a that's a great uh a great observation. And the answer is

generally not, or at least it's not the type of computer screen that you're used to. And so the th it's a great observation because as you start to go further and further down this rabbit hole. of of incredibly uh precise, incredibly uh sp uh uh tailor made solutions for for you know gaining as much late uh re reducing as much latency as possible. all the other things we take for granted, uh, in terms of software uh become harder and harder.

So having having a monitor that that you can see everything that's happening, where you can have a you know, a stop button, um uh that that all becomes much, much more difficult. You know, making changes as I alluded to earlier becomes much more difficult.

So so the the people that are doing this, they've got systems in place that that they can shut things down if they need to, and and certainly they've got monitoring software that can um look at something like the equivalent of a drop copy or that can that can keep track of orders that have been placed.

But it's not a screen like you and I are both used to looking at, um, because it is s something sitting on a on a network device essentially. That's incredible. Yeah, yeah, it really is. Man. Wow. More's blown. Yeah.

Um, okay. So are there any other trends which you anticipate may evolve in the near future? Like are there any new features you're thinking about adding or like any up and coming things that we should be um mindful of? Uh you know, I mentioned it at the start of this call, uh th I I do think that um everything that we just talked about actually in light of that, uh what how can how can a trader

uh differentiate him or herself um in in in the face of such rapid technological innovation. And and I think it really it's about trading smarter. And so we and we've talked a lot about this internally at TT. over the last few years, you know, as much as we're investing in in speed, um, we also need to and want to invest in in giving tools that allow the trading community to trade smarter and and trade quicker and quicker to quicker to market. If you're not gonna be the fastest

one in a trade, if you can be the first one doing it or the safest one doing it, there's real value in that. And so I think because of of that trend, I think with something like machine learning where it's it's one of its primary benefits uh to our space is uh taking a problem that that most traders are facing today, which is being inundated inundated with with data, both their own trade data, market data

trying to glean insight from that so that they can so that they can trade smarter, so that they can analyze their trading activity, potentially glean insights from from that data. We think that technologies like machine learning, as they become more accessible and more approachable, um will start to empower even, you know, an individual trader to to uh and and that really we think is the new frontier in trading is is g enabling that trader to make more sense of their data.

um and m turn it really from something which is a burden today uh into something that that could be a gold mine um for for new insights. And so I think we'll see that trend continue. Um it's exciting for us. I mean we're we're now just getting our feet wet.

with with machine learning or w you know, uh by virtue of the Norensic acquisition, but um I think you'll start to see a lot of data related technologies that are more about scale and less about performance um continue to to emerge and evolve. Right, okay.

Awesome, man. Well, Rick, let me ask you one last question. One of the reasons why I wanted to bring you onto the podcast is just to is to speak with you because you're not a trader, right? But you're very involved in the industry. Obviously you're CEO of trading technologies and obviously not everyone is cut out for trading, okay? But That doesn't mean they can't be involved in the industry in some capacity.

For anyone who m like kinda likes h the path that you've taken or would like to get involved in a technology company that works in the trading space. Is there any advice? I know it's a very kind of broad question, but is there anything you might suggest for someone to, you know, give them a leg up and help them get into this kind of space?

We have conversations like this. You know, we have a we have a a a big intern program, internship program at T T and obviously that's somewhat of a predetermined selected pool of people that that have an interest in our space. But they ask those types of questions all the time and and you know it's it's

This is an industry that um is is changing so much from a technology perspective that even the definition of trader has fundamentally changed. I I I not not I think, I know. And and and I think when we look at our user base. Um more and more um the the trader, even the e what what you would probably classify as the traditional trader.

uh m in in more more times than not, they're coming into this industry with a highly techno technical background, computer science, engineering, um, mathematics, uh and and I think that's Um really just a uh a sign of the times, but also um uh indicative of

the types of skill sets and and and talents that someone needs to to really just to make sense of this world. Um, you know, it's it's you can't it it it uh unless you're a very select few even just being a quote unquote, you know, old school trader still requires you to be able to

manage and analyze and process an incredible amount of data and do so quickly and and um and I and I think that's that's more and more becoming uh you know the the skill set of of the the typical type of of you know you know new college grad who's coming into the space.

But if someone's a technologist and and wants to have a leg up I mean I I I think i in in a lot of cases, at least from T T's perspective um uh uh having an interest and and and a a track record of wanting to and and doing and solving um interesting technology problems. um w will certainly give you a leg up. I mean I would say that roughly fifty percent of the people we interview for

uh for uh technical roles at T T have some experience in trading. Um I think it's it's an industry that if you don't e if you don't have some passion uh for Uh, for the industry itself and not just the technology, it's gonna be hard

to be willing to face the ch the incredible challenges that you face every day. And so I think um the the the the folks who come in to to interview for a technical role who have a have a clear passion for wanting to solve some of these really uh interesting problems and sometimes it's even market structure problems. uh not necessarily you know p pure technology problems. I think inherently they have a leg up.

Um, because we we know how gruelling and brutal this this industry can and unforgiving this industry can be. And so that we we I think think that people that come to the table with with a clear predisposition to be interested in the space are probably the ones who are gonna thrive the most. Um so th that that that's what I'd say. It's it's not for the fan of heart, uh as as I'm sure most

But your listeners know. Um and so we we know we want people to come in who who um who who think the space i uh in general is is an exciting one for them. Very good. Now that's awesome, Rick. I I think that's that's really helpful. So if someone wants to find out more about uh trading technologies, obviously they can go to trading technologies dot com. Um and what about you, Rick? I know you're on Twitter. Um, what's your Twitter handle?

It's the worst for things like this. Uh so the company, uh our our T T account is at trading underscore tech T-E-C-H. Mine is uh at Rick underscore lane, except it's uh R1 CK, not R-I-C-K, it's R1 C K underscore L4N3. Uh d don't ask, but that that's how I spell Rick underscore Lane for Twitter. Um but but you can you can uh

Uh the the company is always tweeting my hand aloud as well. So at trading underscore tech is a good good one to follow as well. Great. And as listeners know, I'll be sure to include all those links in the show notes. Rick, I really appreciate you doing this. Thank you very much. Thank you, Aaron. This was great. You've reached the end of this episode of Chat with Traders, but rest assured there are more episodes. soon. with traders.