071: Eric Hunsader – Rigged Markets, Stealing and Corrupt Regulators w/ Founder of Nanex

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What's up traders? Welcome to episode number seventy-one. Thank you very much for tuning in. A big guest on the podcast this week, a man who for many won't need any introduction, and that is Eric Scott Hunsaider. Eric started out as an algorithmic trader in the early eighties, soon after became a self taught programmer, and since then he's written many software applications for financial data.

But today, Eric is the founder of Nanex, a whole market streaming data feed which transmits 20 billion data points every day. Well known for speaking out against the many issues that surround high frequency trading, Eric will tell you straight. HFT firms are stealing money, exchanges have rigged the market, and the regulators that allow this type of activity to continue are corrupt individuals. So as you can imagine, this makes for some really interesting conversation.

Additionally, we talk about quote stuffing, many flash crashes that are occurring on a daily basis, and why Eric recently received a$750,000 whistleblower award from the SEC. Now, this interview is a real eye opener, and I'm excited to share it with you. I'm Aaron Freifeld, and here is Eric Huntsader. Eric, it's incredible to be speaking with you. How's it going, man?

Stoin free, mate. I like it. Well, thank you very much for being here. Let's get right into it because I certainly have a lot of questions for you, mostly around market microstructure and of course HFT.

But before we get too deep into that, I'm keen to know how you got to where you are now. So let's start from where you began programming because you've been labelled as one of the most gifted programmers in the United States by The Guardian. Where did you learn from and how did you get into programming? Well, I started in eighty four, um and uh they weren't really teaching uh programming languages in college. It wasn't mainstream at all.

So I uh I taught myself. Um got a book called uh C Primer Plus written by Stephen Wade. who by the way later became a customer of mine about fifteen years later, but that's beside the point. Anyway, I bought his book and it was qu really went through the entire C language and I devoured it. I mean I was I read through the whole thing in just a couple of days. and um really enjoyed writing code.

And actually the reason I wanted to learn computer programming languages because I had my own algorithm in a Lotus one two three spreadsheet uh calculating uh thirty minute oscillators for the uh S P five hundred features. which I would then call in to down on the floor when the signals went off. And that wasn't uh fast enough to run optimization, so I turned to see how I could do it faster and that led me to I need to program and C seemed to be the best language.

And I started that in eighty four and I have not stopped. That's very cool. Now do you still use C for the most part to this day? Yes. Yeah. Okay, excellent. Now So you mentioned that you had some interest in in trading, um, as you'd already created an algorithm before eighty four. Where did that interest in trading and and finance come from? The Wall Street Journal. Um just fascinated by uh that whole world. Um how you could you could be independent and cr you know create a living uh with not

with not having to hire a lot of people. It was a it was a way to kinda get to do something on your own that was It was exciting. Yeah, yeah, no doubt, no doubt. And I think that's what, you know, obviously attracts a lot of people to trading and, you know, it really sort of draws on that entrepreneurial streak that many of us have.

So what was your experience like, you know, in those first few years trading? Um I from what I understand you're not an active trader still to this day. So how long did that last for?

It lasted about two and a half years and it was during that time that uh I actu it was actually quite profitable and uh you know, enough to make a living off of and uh enough to buy more computers and it was also during this time that I really developed uh a love, if you will, for writing software and not so much of a love for following the system.

because I would always second guess it. You know, writing software software will always do what you tell it to do day in and day out. Whereas trading there's a lot of unknowns um that you can't account for and uh I just decided that I was going to uh learn how to write s or get better at writing code. And I would come back to trading one day.

And I never did. And so it was at the same time I was developing the system. I was ac I was also archiving the data that I was using in the system, which was, you know, the futures data from the C M E. Which by the way I was able to um archive on a three hundred and sixty K floppy disk. The entire exchange is trading for the day.

And uh started selling that data to other people who wanted to develop algos and you know, created a bulletin board system and and sold the data online. You know, this was quite a bit before the internet where people had to dial up. And uh you know, that business was actually taking off and bringing in income. And so it was it was a pretty easy decision to to say, Hey, you know what? I'll come back to trading

later in life. Sure, sure. So is that how Nanax came to be today or did you have any other roles in the finance world or even more than Oh no, Nanax Nanax didn't come along until the year two thousand. So this you know, so around the eighty nine I I one of m one of my customers who was buying data from me was uh man by the name of Tom Joseph who runs Trading Techniques in Ohio. And and him and I really got along and he wanted me to to wr write software for his company.

And I thought that was a um it was a great opportunity and so I took it and I went to work for him and we did great things. But we we had um I I remember we were riding around in his uh Cadillac with a an old compact computer hooked up to a cellphone modem thing. It was a b it was a big brick. But we were we had real time streaming charts, um, mobile. You watch the S P five hundred features. And so I you know, I wrote a lot of a lot of software back in in those days.

That um you know, a lot of it Well we were doing you know, we were doing Windows kind of stuff before Windows even came along. So that when the you know the next iteration of software came down the pike it was you know, we were always iterative and learning on top of the knowledge base that we had built up be f before. So then it was a few years doing that, um, a company called CQG, which is uh still a company around today.

they bought Tom's software and uh wanted me to come along with it. So I d I did because um Windows had just come out and they wanted to port their software over to C G for Windows. And I thought that was uh interesting and so I went to do that. And uh and then about three years into that that's when the internet came along. And and you know, people who had been writing software for a while have been

we just knew the internet was gonna be huge. I mean with within within a few days of really seeing how things were connected, it was so obvious that this was going to be uh one of those revolutionary things. And uh I went to the CEO of CQG, Tim Mather, and I said, Hey Tim, I wanna do this. This is what we need to do. And if you guys don't wanna do it, I want you to yeah, I w I wanna go and do it on my own.

And uh he was cool with that and so I left and started my own company and in a very short period of time had written uh real time streaming job applet. that uh uh you know somebody could type in a symbol and they would get a streaming chart and time and sales and that was called uh we called it live charts and then we had a desktop application that went with it called QChart. And uh quote dot com came along and saw it and they we kind of merged together. They gave me a bunch of stock.

uh in return for the software. And so we partnered together and in the space of eighteen months or two years, um, we went from zero to ten thousand paying customers. And so which was a novel thing back uh then because everybody was giving ev things away for free. And and really the only sites making any money were were the dating sites and us. And uh it was just it was f fascinating watching, you know. Interacting with people who for the first time ever saw

that stock prices actually changed during the day. I mean so many people had this notion that, you know, the the numbers they see see in the Wall Street Journal, those closing prints is the only price for that stock for the day. And so that brought on a whole group of people, you know, day traders who wanted to um to participate and this was the you know, big internet bull market of course, which made it all the more wild and crazy. And so

Winding up, um Lycos came along and uh wanted to buy quote dot com out and actually what's what what really goes on? Um that whole IPL business.

you know, the venture capitalists where they want to cash out. I mean that was what they were always looking at and it was it was the term is a liquidity event where we can turn our worthless stock paper into money and I was, you know I was fine with that and so we uh cashed out and I I got a chunk of Lyco stock and I knowing how the markets were and how crazy that bubble was, I had it all sold in about two weeks.

Which, you know, at the time, you know, a lot of people were telling me I was being an idiot, but you know, that was in December of ninety nine and I uh I guess that was one of my better trades. And that's when I s that's when I started founded Nanax, the company

Okay, sure. So tell us a little bit about Nanax and what you're actually doing today. And and by the way, that was really cool to get your your backstory. So yeah, give us a little insight to what Nanex does today and you know what you're doing on a day-to-day basis. So, you know, one of the th one of the things that I've always been developing over the years, in fact m the code name for the w what I call NCOR now, the data feed is They called it generation size.

Well, because it was gonna it was the fifth time that I was gonna start with a blank sheet of paper and design the ideal ticker plant. uh that uh so that you could build things on top of it and not have to worry about all of the the difficulties that go into putting together a ticker plan. Yeah, with you know, invol which involves maintenance and and databasing and normalization and um duplication and and and then have a nice API on top of it so that writing software for it would be a joy.

And so that's what I set out to do in in two thousand and um I logged the hours and it was about twenty five thousand hours later. I had it in a stage where I was comfortable releasing it. Um to my first customer and that was in two thousand and four. And uh remembering the the crazy times of going from zero to ten thousand paying subscribers, I wanted to go this I wanted to do this one a little bit slower and more manageable. So I never

advertised um it was available. I partnered with a company by the way, uh called Data uh at the time it was called Data Transmission Network. now I think they're owned by Schneider. But they're still my partner and they're essentially they do all of the non software related things. Um They manage the networks, they uh deal with the exchanges, which is a big part of the equation, th the uh the contracts with customers, the billing

everything that's not really related to processing um the real time stream. So by not advertising or wanting to push it and you know, I wasn't out to conquer the world and and, you know, be the the king of real time. I just wanted kinda like build a solid niche that um that was proud of that I could write software on top of. And so um by never advertising it

we got a a very wide variety of of customers using it'cause they weren't targeted. Right. Okay. So who are the you know, you said you've got a wide variety of customers using uh Encore Who are those like who are those types of customers? Like are they big institutional funds who utilize your data or are they, you know, independent guys? Yes.

Yeah. Name me a group and I'll say yes. We got one we got we have representations from all groups, including high frequency trading firms who want to, you know, do uh back testing on it. You know, we have s we have a very unique data set. Um

we pride ourselves in um really, you know, drilling down to the issues. In fact, we help we helped the exchanges actually debug some of their issues that they had with the diff the f the various um trade conditions for example and whether it sets the higher or lower or you know crazy rules that they have.

Whenever something didn't match up, we would track it all the way down to the source to find out exactly why that's not Why this condition is d is doing X when you have a document of doing it Y. And and so it's that attention to detail and and also just the joy of working with uh the API. It really is out of the box. for somebody's somebody to work with a data feed, usually there's a a long process in understanding all the different ways to parse that data.

But with Encore it's it's a really unique experience where in where an experienced programmer can come along who's written for other data feeds and within one day will totally get it and we'll be we'll be actually getting you know getting down to writing what he wants to instead of working with Uh, the with the API, with the actual nuts and bolts of the feed. Okay. Okay. So just so we understand, why is the data that you provide so unique from other data providers that exist to this day?

Well one we don't we don't exclude anything. A lot a lot of other data providers will squeeze out the information that they don't feel is necessary. They'll they'll normalize it way before it's supposed to be normalized. And um what I don't I don't believe in that at all. The other thing is I've developed uh real time compression that d drops the size of the information uh by to a twentieth of its original size.

One of the things that I've I've specialized over the years in working in on s in software is uh data compression, data transmission, and uh graphical interface. So the compression part I sat down and tried to figure out a way where I could I could not sacrifice speed. To get the tightest compression possible. And I had been sta stabbing at it for many, many years in the past. And I finally got a a breakthrough that allowed the compression to drop fast lower and lower

than I had a I had expected in the beginning and as an added bonus was w was very fast. And it was so fast that it actually ended up being back in the day faster to compress it and transmit it than not to compress it at all because of the serialized serial latency cost on networks. So the data is tiny, but it's rich with all the information within. It's very easy it's hard to describe, but it's very it has things in it.

that when programmers see it and use it for the first time realize the huge advantage that they get. For example, one of the one of the more expensive tasks in dealing with the ticker is Taking that symbol and associating it with a memory pointer or where where you're actually gonna start doing some work with it. That association there is usually a pretty expensive process. With our feed, that we've cut that cost down to zero. It's literally no cost to have that instant

um you you're working with that data immediately and we save you these important steps along the way. It just makes it easier. You know, unless you're a software guy and you've used it, you won't It's h it's impossible for me to explain, but the guys who have used it There's that light bulb moment when they get it.

And, you know, usually just like, Oh, that's brilliant. That's just awesome and that's the last we hear of'em because they don't need our help anymore. I mean, they're off and and going. That's awesome. Yeah, that's that's really interesting. Now Your your flagship product here, um, Encore, I understand it collects around about six billion data points each and every day, which is huge. Well, we're up to twenty

twenty billion ish. Wow, okay. Starting at the beginning of the year. Uh you know, they keep adding options exchanges and And they keep developing new options products and I mean there's a million different option contracts out there and there's fifteen exchanges and you know, fifteen times a million times one quote per second gets you to fifteen million a second.

Wow, well. Okay. So twenty billion data points per day. Yep. Can you give us some idea about the infrastructure and service space required to support this data? Well it's about a petabyte, uh or are now. But again, a petabyte, so um you have gigabyte, and then a thousand gigabytes is a terabyte, and then a thousand terabytes is a petabyte. Okay. Okay. Right. And is all is all that stored locally or do you use like um sort of a cloud system?

We we are we do have we do have um some of it in the cloud, some of the more recent data in the cloud, but we have it in triplicate in different locations. It's um It's it's an it's an onslaught of data that's coming at us every day. You know, it's it's it could be a terabyte a day that has to go through the system and uh We've you know, it's all it's automated to to check it, to test it, to um to nor to to w you know, look for errors and and correct those errors.

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So let's just change topics here. Let's dive into the subject of high frequency trading. So one of the questions I want to start out with is asking you, when did you first begin to notice HFT activity in the data you were collecting? Well, I remember looking at uh you know, just checking on how the compression was doing, um And seeing quotes from you know certain ex I think it was Arca was the first one I I really noticed it on where it was a obvious pattern where the quote would

would step up a penny and then down to and then up to, you know, a pattern like that over and over again over a short period of time. And I I thought that was interesting, but it wasn't enough for me to like like really sound the alarm or or it was it just looked like Miller somebody's testing some system and and it's it's not a it's not something that will h happen all the time. And then and then it wasn't until the flash crash came along and you know just

overwhelmed the the data systems throughout Wall Street. Uh the SIP just got crushed that afternoon. And uh And we s you know, my my colleague Jeff and I, we saw the SC wasn't getting anywhere with their with with assembl even assembling the data to start processing and seeing what happened. And you know, the beauty of the Encore is you can go back to any day um and replay that day just like it happened in real time. I mean

where where you can see what the options were doing, w what the futures were doing, what the ETFs were doing and the underlings, all of that, as if you were there on that day. So you can go through scenarios and you can see what the relationships are. And so I remarked to um Jeff Donovan, my um my programmer out in California, I said, You know what? Why don't we spend a week just seeing if we can find anything unusual in the data set? And uh Oh God, do I regret that.

Um yeah, so so you know, it was very short period of time we discovered, oh my gosh, look NYC was was thirty seconds late updating the you know, the SIP quotes and uh We published uh the uh the first thing that we've ever written and it got picked up on Zero Hedge and boom overnight, uh

you know, people were calling us. Um the Andre Krylenko from the C F T C for example saw it and he was like, Oh, you gotta come out to Washington, we wanna talk to you and and uh and that's kinda where it all Where the rabbit hole got deeper. Okay, okay. So um yeah and you you do a really great job of explaining um that that 30 second delay that occurred during the flash crash uh in the documentary, Money and Speed. So I'll make sure to link to that documentary um in the show notes.

Uh just wanna ask you to explain the SIP, if you could please. Well the ship is a consolidated feat, and so what's what's really unique about the US stock market that I don't think is replicated anywhere else and God I hope nobody does replicates that model. But they sell two data feeds. And uh one of'em's faster than the other, but by law they're not it's not supposed to be. And and

And so the CIP was supposed to be the only source of real time information. I mean written and referenced everywhere in the uh in Reagan MS, which is the regulation that that brought really ushered in high frequency trading in the US stock market. And uh so the SIP was being used by a lot of firms for the the real time data, I mean including Goldman Sachs's Sigma X Dark Pool, many Dark Pools were using it, many exchanges

were using it. In fact the NYC still uses it to this day, um for f knowing where the prices are at the other exchanges. And so the SIP is a consolidated fee, which should be the only fee, but the exchanges also sell direct fees independently. Okay, got it, got it.

So just before I fire off some more questions about HFT, I think it might be a good idea to to ask you what's your interpretation of HFT? Because I know many people have, you know, slightly different um understandings of you know, the f the phrase HFT and high frequency trading so When you speak about HFT, what are you referring to? Okay, I'm gonna make this really simple. HFT is all about electronic trading. I I like to use this phrase. Electronic trading brought down costs.

High frequency trading brought down ethics. High frequency trading is a form of trading that takes zero risk. It results in perfect trading days. It's it's where you have the sell side already executing at the same time as the buy side is executing. It's risk free arbitrage and it can only work if you have collusion with the exchanges. The exchanges are fully aware of what's going on and because they sell these high priced data feeds is just what's driving all of their income.

And so it's this this this symbiotic relationship between uh b between the uh high frequency firms and the exchanges. High frequency trading requires Um special access, it requires um

special uh wink wink nudge nudge nud uh nudge nudge deals with the regulator, you know, so so they don't get fined into oblivion. It requires a lobbyist. It Y it doesn't it's not so much the code, it's it's so much um the infrastructure that you have to build around it and then hope to God you don't, you know, suffer a regulatory breakdown. So I'm all for electronic trading. I I would prefer no rules at all as long as it was equal for everyone. I believe that that the smartest guy should win.

And the smartest guy shouldn't have to worry about hiring a lobbyist to win. I I have a big problem when the rules are not transparent, when the rules clearly say X is illegal but X is going on all the time. I it's hard for smart people to operate in that environment. It's it's hard for me as a businessman to decide I'm gonna support that kind of product even though I know that is illegal and it should not be, you know, one day if a

somebody with ethics ever got into a a position of power to regulate it would shut it down. I'm not gonna do that. I'm not gonna when the when the biggest risk is regulatory risk and not market risk and um or the traditional types of risk in this industry, uh smart people are gonna say no.

Okay. Okay. So so what just so we we really understand this issue, um, you know, I know it's a very complex issue, um and maybe to get a a complete understanding of it might be slightly beyond the scope of this podcast, but What is the big issue? Like what's the problem with HFT if you had to like kind of put your finger on it? Well, okay, so I I wanna I wanna say H of T for the most part is really is something that exists within the US stock market more than anywhere else.

In fact, compared to a uh a central limit order book that you'll find in many other places, including like the CME is a central limit order book, ninety-nine percent of the issues don't exist. The high frequency trading in the US stock market exists.

because the laws of physics and the distances between exchanges and the lack of superv uh regulatory oversight and the exchanges allowing um or giving these guys faster data feeds Essentially Reagan MS was supposed to tie together ten ex that's to say ten stock exchanges so that the liquidity would pool together. So before, if you had one exchange trading one stock, all the liquidity would be at that exchange, the central limit order book.

They wanted competition, so instead of having one exchange, we'll have multiple exchanges, let's say ten. And the idea was if we tie them all together with the CIP, with consolidated feed, and Reg and MS which says um

has this trade to rule which if one of those other exchanges has a better price, you can't trade outside of that other exchange. You have to go access that liquidity by th those pieces in Wagon MS The thought was we'll have competition and we'll still have the pooling of all that liquidity together. What HFT does in the US stock market is it guarantees that that liquidity is not all pooled together. And once they see

executions going off at one of those exchanges. They have faster connections and the ability to race ahead. let that executing market order and and cancel their orders at the other exchanges and even go in the same direction ahead of those orders um depending on what their statistical models say. So th they're in effect um destroying that liquidity concentration.

So that when traders see, oh, there's two twenty thousand shares of forward offered at the market, even though the twenty thousand is five thousand at NYC and six thousand at NASBEC and four thousand over at BAT. They're never gonna get twenty thousand. Okay, so one of the counter arguments that you often hear from those who are in favour of HFT, they often say that HFT provides participants with tighter spreads.

Is that a true. Spreads change within so the first question I always ask and I never get an answer by the way, is how are you measuring your spreads? Are you measuring the widest in a second, the narrowest in a second? Are you aggregating over longer periods of time? Because I can show you thousands and thousands of examples where stock will move one percent within a second, yet maintain a one cent spread during that second.

So so from one point of view it's a one percent spread, and another point of w way to measure it, it's a one cent spread. And I I can guarantee you uh who's gonna actually uh participate in that one in the benefit of the one cent spread and who's gonna get reamed on that one percent spread. Uh it's not gonna be the retail investor. Okay, so are there any other oh that's probably the wrong phrasing. Are there any benefits that come from HFTs being active in the marketplace?

Yeah, uh they push the envelope for um transmitting information from point A to point B. In other words, they're you know, they're they're providing an incentive for for the millimeter

um microwave dishes to improve, to drop latency, for FPGA cards to be more efficient. I mean Wall Street has always played a strong role in um being on the cutting edge of technology and kinda supported the you know the early engineering efforts at Intel and Dell and and and Cisco who you know might not have um developed really high tech bleeding edge kind of thing.

And and so that that's one one benefit they provide. They certainly uh do not provide any benefit to somebody um trading on the other side of them.

So a term that you often hear associated with HFT is quote stuffing. Um I know you've mentioned this in the past also and I think you might have been almost hinting to it a little bit earlier on. Um, could you explain what is quote stuffing and has this um not always existed on some level? Well, actually that's a term that I coined. When when when we saw well, one of the reasons that the SIP got so delayed back in the day was because of the excess traffic on it.

uh you know, g noisy quotes, quote spam. And I remember when I was Jess and I would try to come up with a name for it, I wanted something that was gonna be easily Googlable to know that, hey, we created that. And so, um, I don't remember whether it's him or I came up with it, but coat stuffing is the term we used and um And now it's being used everywhere. Uh code stuffing was is simply was is a tool to allow you to slow down uh another data feed. On demand.

Like if you want back to back in the day, if you wanted the NYSC's direct feed to be a little slower, it was a matter of putting X amount of quotes in this inactive symbol which also is processing the symbols that you were interested in. It was uh it was just more network traffic. It was of effectively tiny D D D you know, denial of service kind of thing going on.

And when when you are an internalizer or a wholesaler who is who is buying and selling on direct feeds and you're giving the retail customer prices based on the sip, well, whenever those two are out of sync, it's free money. You buy yourself the drug seed, you pick out of whatever number you think you can get away with giving the retailers a price for their order. And if those two are out of sync, it's it's instant money.

Because you know, the regulatory requirement was that you had to be within what the SIP price was showing, you know the and regardless if the SIP was slower or not. I I remember having a discussion with somebody at a big bank w one of these firms and telling them when I first discovered, hey, you know what, you can actually I can tell you exactly how you can slow down one any one of these fees and their reply came back

Yeah, we know we've got we've got these latency tables, dude. Oh, it's just like, Well, yeah, of course you you know, I then I just sheepishly gr you know, grinned to myself, Yeah. You would. Makes total sense. But so y all the things that we discovered and published, these they all knew this was going on. And uh

Whatever they called it to slow systems down. I don't know. But um that's what closed stuffing is all about. And it only really works if you got something that has that has two data feeds pricing the same thing. So it doesn't.

Well actually I you know it you can actually affect the the cent central limit order books as well, like the CMEs because you gotta remember one machine is It's gonna process more than just one symbol, except for S B Y p I mean the E Mini probably's only processed by one, but there probably are systems there that process like the ten year no and the f in the five year

And so if you like put a load on the the ten or the five, it's also going to put a load on the other on the other contracts in there and you know it could cause a d arbitrage situation with the cash market. Ciao. It's just putting noise in the system f uh in in order to slow it down. And so one of the things you have to

to do all the time. Um, because of that, you know, you have to combat that too, because other others are gonna do it. You've gotta maintain your latency tables. You gotta know what those limits are. So if the exchange upgrades a network or a machine you know, all of a sudden that number is different. You gotta know when that changes. And so you're always pinging and testing to see what that limit is. And there's you know, there's no cost for sending bogus quotes to the system, so why not?

And so a lot of the noise that we see is that.

Interesting. Okay. Okay. That's a really great answer there, Eric. Thank you very much for for you know digging into that. Um In various documentaries you've featured in, you've actually described HFT activity as stealing and rigging the market. Do you actually believe that is the case, you know, in a literal sense? Oh, I have no doubt. No doubt. Uh I you know, anybody who who disbelieves, I welcome you to come to my office. I guarantee you after two or three hours, you will leave change.

In fact, Nate who h works here, um, in the office next to me always says, you know, everytime somebody comes here When they leave their their mouth is like on the floor and they're just like like you can tell it was information overlap.

'Cause I can show you, I can show you exactly what's happening. I can show you documentation of of the proposals that exchanges have made. I can show you, you know, finds that have come out years later at you know Years after they were denying this stuff ever existed. Oh it's it's totally wrecked. Okay, okay. Well I mean I'd love to come by your office sometime if um if I'm ever in the States there, so I'll keep that in mind. Um now

Let's let's get to the bottom of this. So who is the real victim here of HFT? Is it retail traders? Is it longer-term investors? Who's really affected by this? Everyone. And it's not you know, the thing is there's no buslet of children going over a cliff. It's just a a a billion paper cuts suffered by everyone. It's you know, it's something in the system that doesn't need to be in there. One of the real damaging effects it has is it kills a diversity of participants.

Where if if everything is based on speed and even a little bit of intelligence that takes too long it's a work out or you don't have that regulatory advantage, you kinda get sidelined and so you end up with a market that you know, it's just filled with lemming machines that that don't have a diversity of opinion and might not uh at the moment do something

you know, when the market is crashing, do something what might look stupid and jump in there and buy, they're not gonna do that because they're faster than the other ones and and you end up getting these these these m microstructure um uh you know, gapes in the in reality when all of a sudden, you know, b a large cap stock will just suddenly evaporate five percent and then suddenly recover.

And, you know, lots of times you look on a chart and you'll see a little spike down, you know, think it was a bad price and then when you actually dig into it you see the well there was a thousand trades. And a penny all the way down, got executed a penny all the way up. in in a half a second and then you see that this happens dozens of times a day and then you see that it's in really big cap stock.

And then you see the SEC s you know saying, Well, people shouldn't use stop orders anymore And then you think, Well, wait a minute, you know, that's never been a problem in the past. And so it changes the whole way the market is set up. to be not really cons not really assistant assisting the retail trader. For example, what kind of orders do you have available to you as a retail trader? uh buy, sell, market, but I'm not supposed to use those anymore. Limit.

and uh and stop. And I'm not supposed to see stuff anymore either. There's where's the order that says, I wanna buy whenever the market drops, you know, suddenly this much percent at any one of these changes or I wanna I wanna trade at nine forty five in the morning. Oh look, I've got surgery scheduled in the morning, I'm not gonna be watching the market and I sure don't wanna get eaten alive at the open. But I wanna buy at nine forty five.

you know, AM, you know, fifteen minutes after it opens. And where's that order? Well I mean nobody's catering to you know, the group that's actually getting eaten alive. Hmm, okay. So if this really is such a big issue in the market structure, why have regulators not Stepped in and put a complete stop to it. I know there's been fines handed out, you know, here and there, but there's not a there's not been a complete stop put in place. Why is this the case?

And I don't say that lightly. I say that after years of banging my head on it. and talking with them and reading documents and calling them out on it and and being right. Everything I've published that used to be called a conspiracy nut for has been proven true. I mean the SEC just gave me uh seven hundred and fifty K whistleblower fine for for f you know, the discovery I made at the very b top of the rabbit hole.

It these guys are they're they're looking for their next job and they get wined and dined by, you know, these firms. The whole sh the whole thing with the IEX for example the them coming in and and um really promising in the future employment. You just they're no why would they stick their necks out when they don't have to? Th th there's no there's no a bonus check in it for them for doing the right thing. So so in that case, on the on the I mean, on the other hand

To what extent are the exchanges also uh responsible for this type of activity that goes on? They're totally I mean, they're totally responsible because they're selling these data feeds and they're ensuring that they're faster. It's not So the law is really clear that you you can't provide data fast, core data faster on your direct feed than you provide it to the set. And that is what the NYC has fined five million dollars for.

And that is what the NYSE swore to me, uh the CT the CTO of NYSE on stage. I was next to him on stage, we were both invited to speak at the T D um uh annual event in Toronto. And was Larry Liebowitz, who's actually the brother of of John Stewart, and he has the gift of talking like his brother does. And he was there on stage painting me as a conspiracy person because I was saying that their feed was slow. And what was s really funny about that interaction was I had done the study

in July of uh July twenty first or yeah, of two thousand and ten. This is after the flash crash. And Larry was saying that, Oh, there were some system issues and but we got them fixed and everything is fine now. And I w and I was asking him, I said, were they w well how long did it take you to fix them? And well we had to fix right away. We saw it and fixed it right away. And I said to him, I said, So it would have been fixed in June. And yeah, of course, you know, look he looks at me like um

I don't know what I'm talking about. He said, Yes. And then I said, Well, this study was done in July and it shows the same thing happening. And um this is a kind of this is that's part for the course. I had a long discussion with Bill O'B the now disgraced Bill O'Brien, you know, the guy who had a a f a little fight s or spat with um Brad Katsyama on C N B C who you know he argued things with me that he spent the whole afternoon, you know, arguing his point.

But it wasn't based on on needing concrete or reproducible. It was just based on what he was saying. I mean they're very smooth talkers so that talking to a reporter I it's so easy to pay a boozle them because it's a very technical thing and they're, you know, in a position of authority. And and I think how this all got this way is in the United States we we give the exchanges, um, they're self regulating organizations, SROs, which means that they have legal immunity.

Which, you know over the years if you have legal immunity, which means you can't be sued for even if you egregiously do something wrong. Well I I know human nature a little bit and you mix that in with capitalism and having shareholders. im legal immunity is just a a downward path to the place that we're at today. And that is why.

Yeah, and you make some really great points in there, Eric, and I you know, I imagine you'd be a very difficult man to argue with because you have the data and I mean at the end of the day, the data does not lie. Um so I've never lost a debate w when it comes to data. And I never talk about anything I don't have data on. I mean if I've if it's not a subject I'm comfortable with or I don't have a data s to support it, I'm not gonna say anything about it.

And so yeah, so that so at the end of the day I never but you know the thing is, that doesn't work for sound bites. It the it it's not a news story and And I can't cut reporters of access off where the NYC can or th or Nasdaq can, and that's a big deal. Mm-hmm, yeah Yeah,'cause I mean, there's such a big difference between fact and opinion. I mean, they're two very different things, so

I mean, just moving on, is H F T here to stay? Like should we just accept that it's not going anywhere and this is the new market or not so fast? Well, so I ask you this. Um the is it should we accept that a person of integrity and um will never get into a position of power at the CFT T C or the SEC?

You know, somebody who's very knowledgeable and has integrity, if you're telling me that that person will never get in a position of power, then I will tell you that H of T is here to stay. But the day somebody like me gets in there The their days are numbered'cause you don't have to create any new laws. You don't even have to, you know, try to set precedent. You just go and say, Well, we find that person that much for doing X. You did the same thing for a longer period of time.

Um, you get the same punishment. Like for example, Sarah, who's you know, facing a lifetime in prison, which by the way I think is is just a travesty. He's not doing anything different than these guys are doing. And why is he being singled out? I mean, it's just it's wrong. And so if somebody ever gets in a position of power like that, they're they're in a a heap of trouble. So you tell me. If is that how is that how society is gonna be going forward?

Is that what the new s the the new is that the new new thing? Yeah, well you do raise an interesting point. I mean is that something that you would like to step up to if the position was ever available and I've offered do I've offered doing it for free. Yep. As as long as I've got authority and I'm not gonna cr I like I said, I'm not gonna create any new rules. I'm not gonna try to set any new precedents.

I'm just gonna go through the existing laws and what's already been done and apply'em equally and transparently. Okay. And what sort of response did you get when you offered to um to do it for free? I was dead silent. Dead hile. Right, right. Okay, Eric. So I just have a couple questions left here, and these questions are mainly based around um

mini flash crashes. So I think for the most part um listeners are probably familiar with the flash crash that occurred in 2010. Um I wanna ask you about these mini flash crashes that seem to occur more frequently. So Uh I guess first of all, what is a mini flash crash and how frequently are you seeing these occur? Well, we define it as something that happens within a second that involves more than 500 trades, that involves a price move of several percent.

And and it it always reverses. So there's the crash and then there's the recovery and the crash involves a frenzy of trading activity and the recoverer reinvolved a lot of activity, though not as quite as much as on the way down. They they also happen in reverse equally. So you'll get a frenzy to the upside. And then the recovery and this will all happen easily within a second and often involves a thousand trades and will often involve all ten exchanges and many dark pools to boot.

So this is a phenomenon that did not exist before two thousand and seven. After two thousand and seven they started appearing more and more frequently and we ha we still have them to this day in big cap stocks like just today, H. O. T. Uh um large cap stock for hotel I I'm not sure what the name of it is, but the symbol is H. O. T. It's a hotel and properties. Um

company just out of the blue, uh ripped higher and and then right back down again. And what's going on is somebody's trying to access the liquidity gets discovered by one of these high frequency firms that that m maybe be a little bit more predatory, sees it and races ahead to to trade in the same direction, knowing that that algorithm that they're using is gonna go keep going.

and buying more. And they're they know that they're the fastest. And so the and other firms who may not be the fastest but they know that there are plenty of people slower than them we're gonna jump on. And so on a until you've got, you know, the guys at the very end of it who are y the you know, the ultimate suckers who are gonna end up getting dumped on when the whole thing reverses and comes back the other way.

And so what the what these kinds of events have caused, how it hurts everybody all the participants, is you really can't have a a flat stop order in the market anymore because you're gonna get taken out by one of these And you can't really participate in these because they don't always execute at all exchanges. So in other words, you know if you were to w wanted a place in order to get executed whatever a stock dropped below X amount, it they don't always go off

at all the ten exchanges. Sometimes they're confined to a few, especially at that last part of it. And so you're never gonna get filled. You can't really participate in them in a way that you can benefit from them, which would in turn prevent them from happening or be as severe. And you're going to end up getting stopped out by them, which means, you know, people are less likely to use stops.

So the way that the the SC came around to fixing this was creating this thing called limit up, limit down which is a very complicated um essentially l moving average based on all the trades in the last five minutes and um and updates in certain ways. It doesn't update all the time. rules, this that with the other rules about them, but they're basically about five percent away from

the the last price. And so now that those have been rolled out and put in place, we see a lot of these little flash crashes will go within a penny of them and reverse. That's the point that they reverse at in price. So they've they've ended up becoming magnets, so to speak. Whereas before the you know, the stock might, you know, drop two percent on one of these. Now they're pretty much guaranteed to go the full five percent and back.

Mm. Okay. Okay, that's that's really fascinating. It sounds very complex. Um, it's almost like they've just kind of put a band aid on the issue and not really solved it. That's exactly what they did, which you know, kinda came back and bit them on on August twenty fourth when You know, the problem with this is it it it it contains one stock well well, if you call a you know, Walmart dropping five percent and back in inside of a a second if you call that contained.

But what ha when you have multiple stocks doing it and they're members of ETFs or indexes and one of them is halted and the other one's not and you know or five of them are halted and they come out of the halt at various times. It makes it very complicated for the people who are trying to arm at the ETF. And you know, whenever there's complication or confusion that r instantly results in more volatility and um and less participation.

Yeah, well so you end up you end up with these with liquidity evaporating out of v very quickly. So you know, I guess I c I could ask you, where's the where's where are these tight spreads that um are being claimed by these high frequency firms if we have these many flash crashes all the time. I mean well I'm ta I mean dozens and dozens every day occur.

So if there were truly were you know providing tighter spreads, you wouldn't have this. We certainly didn't have it before two thousand and seven. Yeah. Now that's wild.

Now, from what you see going on, you know, on a day to day basis, what's the likelihood that we could experience something much more significant and damaging than the two thousand ten flash crash? Well, I guess twenty fourth's pretty bad. Um the Treasury incident back in uh October of two thousand fourteen was pretty bad. The dollar five percent crash uh one year ago today was pretty bad. So that's the solutions. We're just not gonna talk about them.

Last question I've got for you, Eric. Um now I know you've been a great supporter of the development of IEX and then potentially becoming a a recognised exchange. Uh what's significant about what IEX is doing? They haven't they're people of integrity. Remember I said what will happen when you get somebody of integrity and power? So if the I becomes an exchange, they're now SRO, which means they have legal immunity.

Which you know, also means that they've got they can see what's going on and somebody with integrity and power to say, Hey, this firm, what you're doing is wrong and calling them out on it, not having, you know, the that um worry, oh, I can't do that, I can't talk to them and tell'em not to do that because they're buying my data feeds. That's all of our income and these guys generate all of our income. We can't we can't tell them to stop.

So you get a person of you know, you get people of integrity with power and these things will go away. And that's what IEX represents. Okay, sure. No, good answer. And um I'm hoping to have one of the guys from IX on the podcast um sometime soon, so that should be a a good discussion too.

Well Eric, let's wrap this up. It's been a eye-opening conversation, that's for sure. I mean I've really appreciated having you here and thank you very much for setting aside the time to do this. Um, I know you're a busy man, you've got a lot going on. So um yeah, it was it was really good to to speak with you. Where can listeners go to find out more about you?

our website, Nanix dot net, and it's hard to miss us. Just uh Google or nanix dot net. Yeah, I'm I frequently post on Twitter, um, under Nanix L L C. Awesome. Well I strongly encourage you guys listening to go and follow Eric on Twitter at Nanax L L C. Um he posts a lot of really great content, very outspoken and um

Some awesome visualizations, um, which I get a real kick out of looking at. So um Eric, once again, thank you very much for doing this, man. Um we'll speak again soon. Thanks for having me. You've reached the end of this episode of Chat with Traders, but rest assured there are more episodes. And zero high. Chat with traders.