202: Wayne Himelsein – Betting Against Stability

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What's up folks? Welcome to Chat With Traders Podcast number two hundred and two. Featuring on this episode, I have with me Wayne Hemelseen. Wayne is a clever, mathematical mind who views markets through a quantitative lens. He started trading on a proprietary trading desk in the mid-90s doing statistical arbitrage. Come 99, Wayne launched a fund and begun managing money for clients and institutions.

Then in 2011, Wayne founded Logica Capital Advisors, where he currently takes the role of Chief Investment Officer. Logica is a six person team using various model driven strategies which for the most part generate alpha from trading volatility. So that's essentially what this episode is all about. You know, after some brief discussion about Wayne's early prop days, we then move on to talk about trading long volatility, modeling methods.

And Wayne imparts some wisdom from his twenty five years in markets. Just in FYI, this interview was recorded ninth of june twenty twenty. And if you do like this episode, Wayne's also done a really good interview with RealVision TV that you might want to check out. Uh I'll pop a link in the show notes. Anyway, let's get to it now. Here is Wayne Himelsin for episode 202.

One of the places I'd like to begin with and we'll we won't spend too much time on this, but it might just be good for a little bit of context. You began trading mid nineties, I believe. Uh where did you start out and who were you trading for? Sure, yeah. Uh so the firm you're right, it was mid nineties, n uh specifically nineteen ninety five, so

uh can't get more mid than the middle. Uh the firm was called Carlin Financial, and I say was uh because they uh actually acquired around the year 2000. They were acquired by Royal Bank of Canada. So it became RBC's prop desk. But Carlin had a it was a really cool firm, um and uh some history to that is

Uh of course, yeah, I'm sure you've heard of Millennium Capital. So Millennium was started by uh two individuals, uh Izzy Englender, who's of course now famous for his involvement with Millennium and

uh built it up and then his partner when it was first started was a gentleman named Ron Shear. So they had started together and I guess over the first few years uh returns weren't so hot or it wasn't working out as hoped. And so Ron um and and Izzy had a uh some disagreement about the future and split up and Ron said, I'm out of here, and he left to start a proprietary trading firm of his own.

uh which was Carlin uh financial. And so uh it was Izzy's ex partner. And of course the funny future story is that Millennium and Izzy went on to become one of the biggest, best hedge funds in the world and Carlin uh of course was a good prop trading shop, but certainly not the the the magnitude of growth of millennium. So but that was its history and Ron was an amazing uh trader.

uh and uh had a uh I think what was most beautiful about the environment was the diversity of trading styles on a floor.

And so I was at their midtown Manhattan office. I think they had about, let's say, fifty or so offices around the country. It was a decent sized prop firm. Uh I think one of the larger ones at the time. So the but the mid-Manhattan office had some of the better, I guess, traders who who had been at Generally bigger shops before that, and now had come to trade a mix of either their own capital, the firm's capital, a bunch of smaller hedge funds.

Uh but the uh the the the nice part was that everyone uh for the most part had really good experience at some meaningful place. And I I remembered of course so well the guy who sat immediately to my left. had been on the floor of the it was either the SIBO or the Amaz I forget exactly what but he had been on a trading floor for he told me thirteen, fourteen years. And so, you know, all he was he everything that would come out of his mouth would be how you

see the order flow on the floor. And so that's like on my left ear and then my right ear, uh, was a a Lehman Brothers fixed income uh arbitrager and and you know, and hit nine eight, nine years at Lehman. And so he he had it just a completely different view of the world and and one's on my right, one's on my left and so I I you know, and this went on as you went on to the different desks or around the the floor I should say. Th there was that much diversity in

views and approaches and um so that was just a beautiful experience to come out of college. I I came out of U C Berkeley, I had a great education, but then to be immersed in this world of of uh just knowledgeable traders doing so many different things and and all very interesting and me just like a I was I was just uh uh uh so immersed and absorbed in in how much I could learn and and and grow in that environment and I did.

Being surrounded by such a diverse mix of traders, how were you trading in those early days? Like what type of strategies did you gravitate towards? Yeah, that's a great question. So it's uh of course we always gravitate right to what is more naturally us, I guess, is the uh generally would be the path. And so for me

Uh I had a really strong math background. I was always a quant at nature. Uh and when I say at nature, when you're like it's I just looked at the world numerically or geometrically. I this is the when I

when I you know, I was a little kid I'd I'd I'd note things that were symmetric and things like that. So it's just it was the who I was. It was in my blood, so to speak. And so for me the the natural inclination was anyone doing anything really quantitative. I and And so the the the the the discretionary traders who had ideas they talk about in companies, I was less interested in that.

But I'd I'd go sit down next to people who are running big spreadsheets and uh doing some other forms of programming and try to understand what they're doing and how they're viewing it. And uh so if yeah, the easy answer, high level was quantum. Um, and then the more granular, the more mathematical it was, the more it intrigued me. And I remember that one of the early models that got my attention was was a a a a quant model in StatArb. And that was the first strategy that I fell in love with.

was statistically underst or looking at a statistical view of the market to understand what was um kind of normal or or or abnormal, I guess people would call an anomalous behavior uh versus the normal. And using statistical methods to derive that or to extract that. So that that fascinated me. And of course w uh therefore I went down that road. What sort of products or assets were you trading the arbitrage between?

Uh so at the time uh my initial entry was through equities and I was I was just trading just as simple as you call it stocks, right? And uh uh I I I think that's certainly the right place to start. It was a good place for me to start just to get familiarity with markets. Um I was happy that I learned both a long and the short side early on. Um a lot of traders start out more long biased uh or or tilted. And I I think for me the the idea of not of being around a lot of arbitrageurs who are

who would take on trades of course that were both long and short, especially in Statarb. Um there's always a pair. I mean in fact they called it pairs trading uh as a nickname. So with with that initial take on the market, I didn't have this uh early on get get brought up so to speak with a directional view that the market had to go up. And so I was equally looking for long or short trades in equities. And then over the course of time, I started to get intrigued by option.

And of course, once again the mathematical nature of optionality um and the the mathematical modeling behind uh converting uh an underlying uh inequity into an option was all just You know, of course Black Shoals is the is the standard model, but there the the the the math behind that and how something could be a derivative of something else would just pulled me right in. And so I got into options trade.

And between the two, between equity and options, I that's where my career has has gone from there on uh with those two uh instruments.

Okay. Did you still go through the same stage of trial and error like many new traders do? I mean I guess it was a great advantage that you were in this particular environment where you were surrounded by so many uh more advanced traders, but um did you pick it up pretty easily? So yeah, my to the to your first question, did I go through trial and error? I I I cannot say without any like, or I'll say with absolute

uh uh certainty is I I yes I I did and and have continued to. I think trial and error is the is the crux of everything. And it I think all we do is as we age and get wiser in the markets and and learn more. It's it's going through those trials. and and quicker and finding the errors better. But n trial and error will never stop. That's the the the bane of of markets always changing on you. But um so that was to answer that question, yes, I went through trial and error and and yes I had a

a a leg up in in the sense of the wisdom that was around me. I sorry, I forgot. What was the second part of your question? Was Did you pick it up? Yeah, yeah. So I did. I did because um I I I I guess you know I'm a smart guy, so that that helped. Uh, but particularly on the quant side, I it was the stuff was really intuitive to to me. Uh I was

um call it a fish in water. And so I I just I loved it. I I I picked it up quickly and the more I picked it up, the more I wanted it. I I I think there's this, you know, common um uh among people who are get good at stuff is having a passion for it uh because that drives you to want to put in the time.

And so for me I I was right away passionate and I remember, you know, half the trading floor, if not more than half, the majority of people would leave after the close by five PM Eastern, everyone's gone and I stayed and I I was out there using all the computers and the the the the systems. I'd stay till late in the evening. I was like one of the only people there at pat you know when when it got dark.

It just because I've so immersed in wanting to understand and wanting to know. And yes, it was easy for me. And yes, I was passionate. So put those two things together and you get um you you get better at stuff. Yeah. It's like when you're good at something you kinda want to see how far you can take it. Exactly. Exactly. And it's enjoyable. Like p you know

I remember at the time I like people telling me you you work too hard. I they still tell me today and I'm like I'm like yeah, but I I don't actually I don't feel like I'm working. I'm I'm having the time of my life doing this stuff. So I'm not really like I don't know what they mean when they say that.

Right, right. So let's jump forward uh many years. I know we're missing a lot out here, but fast forward to I think it was around two thousand eleven, uh, when you formed Logica. Can you just briefly describe uh what is Logica today? Yeah, Logica today is I'll say the culmination of all my trial and error over over fifteen, twenty years. Um but the the thesis was uh primarily to to

use I'll call mean expansion investing or long volatility investing. And it's the the the way I understood markets from an early standpoint is it go back to the statistical arbitrage and all the different uh ARBs that people do is Th there's a lot of reversion trading going on. And you know, whether it's value investing, which is uh buying a a a position that people expect to revert.

back to the upside of course and then or or or arbitrage trading where there's a pair that's on and you know something that is out of whack should come back together, quote. Um so all all of this was one side of the broad of the big market that a lot of people were doing. And I early on took this approach of the the or the other side of that, which I call mean expansion. So

that world fits into, for example, momentum or trend following. It's where you you expect things to continue. It's the exact opposite of of of of a reversionary trade. So all all my education into um investing in I I I don't want to call it momentum'cause that really dilutes the the it's such a broad category.

But uh things like stocks that would break out to the topside or the convexity of options that you know, the ways to participate in in uh through option structures, th these these versions of long volatility or mean expansionary investing.

um all came together for me in my mind and i with starting logica to say is I wanna come up with a a better mousetrap for for that type of investing. And the w one of the problems With long vault or trend following and all the all the things that fit into the mean expansion category is that they tend to have a low hit rate. meaning, um, they can do well uh for a a period of time, but you need to wait for that that swing. It's like you you have to be patient and then a big swing comes.

And and but you could wait a long time and and people will know that in in options world you can lose money month after month and then eventually you have this big payoff. It's like the lottery, right? And Of course, you you get paid a bundle when you get paid uh because you have such an asymmetric payoff structure. But you it can cost you either in actually losing money or just in not making any money and being flat.

for in in the period of waiting. And so I I understood this. And I I love long volatility, mean expansion, investing. That's where all my career had been. But I understood its flaw or its void, which is having to wait or lose till you get that that big payoff. So Logica was set up to say, How can I fix that hole? How can I be a a long vault investor, but make money along the way? Like like they do in mean reversion, like they do in arbitrage. And that's what's so

is so widely loved about the whole other side of the world, the mean reversion side, is and all this arbitrage is that it's consistent. You can the people pump out, you know, fifty, a hundred bips a month like clockwork, right? Because it's it's

i it that that allows for it in that type of investing. But then to to their void, they have this once in a while big drawdown. And so it's got uh what what's called negative skew. It it has a lot of little ups and once in a while a big down. I like the idea of

of either flat and once in a while a big up or being up a little bit and then once in a while a d uh an even bigger up, right? And so that became the the core ideas. How do you achieve that? How do you take something that does have those big ups? but layer on it an ability to make money along the way. And that became, of course, many years of research and work.

Okay. Well I'd love to drill down into this further and and spend a bit of time here talking about trading long vol. So just in very simple terms, what does it mean to trade long volatility? Like what is the what is the objective here? So going back to I guess what I just said in that last paragraph or however long I was I was talking, um, w was that e So something is gonna take off in in a direction.

Um, and so I I guess being I that that's one side of it. I'll answer it in two different ways. So it it's that pop or or uh expansion that you wanna take advantage of. That's one version of long volatility is you wanna be long a big change. If you if you wanna

take the word volatility. Volatility is just, of course, uncertainty. So that's another way to think about it. But it's it's change, right? Low vol is less change and high vol is more changing. So to be long change is to say, I expect a bigger change to occur. It's that simple.

So if you expect a big change hooker, buying a lottery ticket is a is betting on a big change. You have a dollar and you're gonna make a hundred million, right, on this one exchange or on this one transaction. Uh that's a that's a long vault payoff. That's one way of thinking about it. Also being long vault is saying, I'm I believe more that there's gonna be more drastic change in the future than there has been in the past. Uh so you're you you in a way bet against stability.

Uh you you you bet against uh an environment that is just status quo and say, well, something drastic is on the horizon. Uh and there's a big difference. People often associate long vault with being more focused on market downside. Uh but for me it's not. There's you can be long vaul to the top side too, meaning uh the easiest version of that actually just happened.

month and a half where we came off the March bottoms to now being basically at, you know, flat on the air in the S P uh with the the V recovery, that that yeah, vol itself crush. But you could belong that expansion in the market if you had of bought the bottom on March twenty

second or whatever that date was that that was the absolute bottom and held that through today, to me that's a mean expansion position in the market. You you rode that whole thing up. Uh and so v vol it's it's a little bit of a conundrum because volatility itself That the way it's measured in the market

did come down over this period. But that that would be that payoff structure in by let's say had you bought a long call in the market, would be a long vault payoff because you're there's an extension to the to the to uh an extreme of the distribution. So it it I mean the people talk about it in different ways and I guess more of the world is focused on volatility being uh to the downside. Um, but I I see it on both sides and so we're trying to take advantage of both sides of volatility.

Okay. So just some To save any confusion, the way you position uh you are market agnostic, like you have no bias on direction. Correct. Okay. I th yeah, uh to me it's dangerous to have that bias, but yes. Right. Right. So can you give an example for like a long vol model? Like how would you express a long volatility trade idea?

Well, I mean long volatility is in in is the simple simplest way to think about it is through an option, right? If you if you're long vol and you wanna be and you wanna take advantage of the market going up, you buy a call. It's i that's as simple as it gets'cause you're by owning a long call, you're long volatility. You're you're in a volatility instrument, uh i.e. a call option. Um and but you're betting on the market's upside. You're betting that the upside will be much more extreme.

than uh other people think. And you know, so what do I mean by that? I mean as if you think the market's gonna just cruise its way up. over the course of time, you you would want a linear uh pay or not you'd want, you'd you'd you'd be okay with a linear payoff to that upside movement, to that trend. If you believe the market's gonna take off rapidly.

You know, that that's there's convexity in that. You you'd want a better payoff if you knew that information, if you knew that was gonna happen, that'd be this huge upside move. You bet you want to take a bet on that right tail payoff. So you do that through a call option and then hence your long vol for in an for what I'd call an up cap.

Uh and it's it's different than say I I I don't I I we don't use the words long or short because in w we are long all options. But if you're long a call, you're long up capture, and if you're long a put, you're long down capture uh is the way we talk about it.

Okay, so I think it might be interesting to pick up on one of the things you highlighted a little earlier. You spoke about how there can be kind of long periods where you may lose a bit of money or you certainly don't make much money, but then every now and then there's a huge payout. Sure. So how do you hedge your portfolio to minimize the bleed during those periods of what I presume would be low volatility?

Yeah, so that's that's the magic, I'll call it. Um, and that's that's what was spent many years, uh we spent many years trying to figure out'cause that's the whole problem is how do how do you make money while you shouldn't be making money, while you should be losing. Uh so if in that easy example, if you were betting on market upside, you can just get long a call. And if you did that naively,

you'd say, okay, I'm gonna buy some SP calls and you're gonna hold them for the month and and roll them till the next month. And if you if the market doesn't do much that month, you're that's gonna cost you. You're gonna pay theta, you're gonna pay for that cost of ownership. for that uh long vault that you're buying, for that convexity that you're trying to own, um that that's gonna cost you, of course, with the price of the option and and the decay of that option over time.

The the easy answer for me, uh, and I uh I I guess first generally I'll say that the way people traditionally finance that trade would be to sell optionality elsewhere. So if you want to be long in the market, you might say, okay, I'll buy a long call. at this level at this strike. I'm gonna short a much further out call to give me some income to pay for this call because I don't believe I believe the market will go up maybe ten percent, but it there's no way it's going up.

twenty five percent. So I'll short some up there where I think it's highly unlikely and that'll that that income will pay for my for where I wanna belong. So that that's the traditional approach. I I did not like that approach uh because I viewed it as uh being a counter trade. It's a you're kind of going against what you want to be doing. Uh and so my my version or my method to paying for that ownership was uh I realized could be through

trading. That was one of the components. Meaning um instead of just buying that call and holding it for the month and waiting for the market to do what it's going to do, one could trade around it, right? The way you might think a a market maker could trade, right? So My my experience on a trading desk was learning to trade.

So the the the cool kind of realization for you me was being able to lay over or or or combine trading with holding long volatility. And so how do you do that? You say, Okay, instead of buying and holding this call for a month, I'm gonna I do I believe that I can, you know, the market's gonna be a bit down over the next few days, let's say as a swing trader, I'd say, okay, it sell it looks a bit toppy here, so it's gonna pull it pull in. So I'm gonna sell some of the calls I have.

And the market pulls in over the next few days and then you buy them back and you buy a bit more because it looks a bit oversold. And then the market runs up for the next few days. And but you had more than you needed because you you bought you bought a bit more when it sold off.

And so after it runs up, you take some off the table. You're taking a little bit of profit, but still keeping some of the calls you have. What you've effectively achieved, or if you can do that well in in a in as a trader. you you've you've maintained this long volatility exposure.

scaling up and down your your holdings, you're you're modulating your inventory, but by doing that, you're scalping, you're pulling out little profits out of the market, all the while holding on for this big pop for when it's going to happen. Right. So if that lasts for two months, you're you're you're trading the noise while you're waiting for the pop. Uh and that's the idea and that's what ends up working out I I guess fairly well. And in fact it has for us.

Uh i and and even more so is aligned with volatility because volatility itself tends to be a more uh mean reversionary asset, meaning Um, like it it it it it moves up and down a lot, right? So if you if you think about a a low volatility environment, uh and I'll I'll use our easy example of this, the year 2017.

most people would agree was a really quote low vol year, right? So you'd say to me, Well, there's not much volatility there, Wayne. It's that's so much it's it's the lowest vol it's ever been. Let's say Vic's down at ten. Yeah, sure. It it was low vol. It was down at ten, but it didn't just sit at ten every day. Uh in fact it moved from let's say, you know, ten to thirteen. Let's just give it that small range.

That's a small range, 10, 12, um, 11, and it's bouncing up and down with a little bit noisy around there in a few points of all up and down. But, you know, ten to thirteen is is thirty percent of movement. That's that's a ton. That's I mean, if I told you a stock's gonna move thirty percent tomorrow, that's an incredible thing to trade as a trader, right? That's like trading Netflix or Google or whatever it might be.

So or probably Tesla, right? That's the kind of thing that moves a lot that you want to trade if you're a trader. So That idea that even in a low vol year, you get the a lot of a lot of noise. And so if you could buy that vol every time it dipped down to nine, you bought up the VIX and then it popped up to 11.

and you sold some and then it popped back down to nine and a half and you bought it back and popped up to eleven and a half and you sold some, you're you're you're long the VIX the whole time, but you're trading the noise. You're you're scalping In in and all that requires is being a good enough, I guess, swing trader to be able to buy those lows, sell those highs while maintaining your long vault inventory. And it even does exist during low vol years. That's the beauty.

When you are trading around in the noise, are these directional trades you're placing? Uh they're directional uh partly uh in the sense that, yeah, if you buy more calls, you're you're buying more up cap. Right. But so th there's a there's a bunch of answers to that question. So the first answer is yes, they're directional in the sense that you're adding or subtracting to a direction of the market. At the same time, um Uh there there's b the instrument itself has a a convex

Of course, upside and a concave downside. So go with the example that you you buy some calls. If you're wrong the next day and the market continues going lower, your your delta shrink. Right. Because you're you're it's like a built-in stop.

you're the amount you're losing is shrinking really quickly. All you could lose at the end of the day is your premium. So if you had a hundred bucks and you spent only a dollar or one percent of capital on this long call, sure you could make ten dollars if you're right, but if you're wrong, you could only lose a dollar. So you're you in a sense you're b you're you're going more into a direction, but you've still as a trader capped your down

So that's one one kind of piece of the of the more complex answer. The other piece is of that is that w at at logica, because we're market agnostic, we're gonna do that same thing on both sides. So as much as Vol is coming down, I'm I'm buying some more calls, I might be buying some more put.

Effectively we're we're we're adding up capture and down capture at different times. So we are definitely tilting, but then you end up trading not one side, but uh I guess the the position would be called a straddle. Right. And a once again, a naive straddle would just be saying I'm gonna buy a hundred calls and a hundred puts from at the money and and so you wait for this V shape to pay off uh your long both tails.

uh or your long up capture your long down capture equally. The way you do it as a trader is say, I want to do that same thing, but I'm trading around the noise, but on both sides. So I might be adding a few puts, selling a few calls, adding a few calls, selling a few puts. Therefore All the time you you you you're anti-fragile in the sense that if the market goes extremely in one direction, you you have convexity to that direction and concavity in being wrong.

Right. You're gonna lose your premium on your puts and you're gonna make a bundle on your calls or you're gonna the opposite's gonna happen. You're gonna make a bundle on your puts and your calls go to zero. It doesn't matter because you're cause you're along the other side.

But you could trade the noise on both sides and then each side is quote paying for itself and collectively the whole thing is paying for itself. That's to me the most beautiful trade there is because you are along the extremes, you're gonna participate in both directions. But you're you're managing yourself, you're making money while you're waiting for those big pops.

Now, just another question on this still. Trading around and the noise, are these trades model driven still, or is there some discretionary which comes into play? Yeah. So there there's a beautiful interaction between the two. And I I I always um struggle with this question, not not personally, but as as a like to me that it's a it's it's such an uh interweave uh in the sense that

You know, a model is discretionary because we the human built the model. I don't mean to be all philosophical here, but in a way I do because The the like I yeah, I I I build a model and it's a model and everything that went into that model was how I understood as a discretionary trader how that thing should go. And all I've done really is

codify the rules in my head. Okay, so now that's done and that's a model. And I say, okay, it's fully systematic. And then it starts trading. And and you know, let's say a month later it does something that I'm saying, well, wait a second. That's not exactly what I would have done there. uh you may i it it really bought too much on that dip and I don't like that. So I go back to the model and I and I tweak it for not being as sensitive to certain moves or changing its its rate of

uh uh uh modulation or whatever it is that I'm gonna tweak as a parameter, now it's a new model or it's a refined model based on my, once again, discretion. So For me, the the idea of being systematic is nothing ever that's gonna be you know plug it plug and play or let's plug it in and leave it alone. It's gonna be forever infusing my discretionary understanding of markets and Yes, I'm trying to codify all the rules.

And and we wanna just be able to plug it in and let it go. But that it never works out that way because markets are always a little bit different because things change, because this V recovery was unprecedented. Uh the the the rate at which the market went back from its lows to now is just I mean I I think it's uh definitely on record as one of the biggest.

uh moves in history. So had you built a model to all past behaviors, well you wouldn't have captured this. So models work and then they don't work. And so I I'm a big believer in a systematic approach. to uh I'll say set the boundaries of your trading and to and to give you a framework with which to trade, but we'll never stop. uh every day I'm I'm in the market understanding what it's what the trade that's going on and and asking myself, is that what I would do here? And if if if yes,

All good. If no, go back to the the model. And therefore, you know, is it is it systematic or not? I'm not sure. It's both. Okay. That's a great answer. And I have quite a few questions for you around modeling, uh, which we'll get to in just a moment. Just one last question before we do. You spoke earlier about, you know, the insurance costs, I guess you could call it, you know, the bleed.

um which can go on during low volatility periods and how you try to hedge against that. I guess that is one of the risks of running a long vol strategy. Are there other risks which are inherent to these types of strategies? I mean, I think that's a good thing. surrounds the cost of ownership, you call it the cost of insurance or the bleed. It's just how much of that risk, right? So in other words, if um, you know, the

the market can go I guess you can be directionally wrong, but in in a straddle that's not gonna happen. So if you you know, if if one gets tilted too much to one side, if you only bought calls or only bought puts, you can be in a much worse situation than if you're buying both. Um because uh uh of course you can be directionally wrong and you can bleed. But given that you're in uh or that you take a market agnostic approach.

you can't really be totally directionally wrong. So then it just becomes about the bleed. But the bleed itself isn't just a, oh, you can bleed. There's there's many ways in which it can bleed. Right. So

You can bleed in uh uh of course, just the market being still, right, and not moving much. Uh and you can bleed in being whipsawed, where you you can I guess that this would be a really m not as much a bleed, but more a directional uh m uh misjudgment where it goes w one direction, you get tilted to one side and then it goes the other direction, you get tilted to the other and you keep on getting it wrong, you know, because the market's just not

doing what it's quote supposed to in your mind. Um right. So that that's a kind kind of directional mismatching. But the other form of um bleed is in vol crush where uh the the market can go up and vol could drop a lot or you know

i th the the vault doesn't always totally associate with what's going on in the world. I mean, right now, um, we'd say we've we've come t back to all time highs. I guess the NASDAQ's made uh you know, the NAS uh QQ Q's have made all time highs. S and P is right about flat on the year.

But Vol or the VIX is still up around twenty five. That's still two times the historical or close to two times the historical vol on the S P. So um i th th there're still i with extended vault you can be right in a direction, but if tomorrow the market goes up percent up a percent and VIX crushes, let's say, seven percent, you could have been right in buying your call, meaning your direction is perfect.

But you lose more because vol crushed more than the the the delta made, right? So your your short Vega is worse than your positive delta. That's the problem. So there's there that's what I mean when I say there's different types of bleeds. there's there's you're more subject to bleed when the overall environment is at higher vol. Uh and so you have to be more right on your directions or you have to do something else to manage that.

though that that height of bleed. So I I I guess I can call it the the magnitude of bleeding changes. Uh and that's relative to where vol is and how choppy the market is around that position or around that vol. Mm. Mm. Okay. Okay. Gotcha. Yeah. Have you ever watched a stock explode and thought, if only I had the capital, or sat on the sidelines because your account balance felt too small to matter? Good news.

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Uh Wayne, let's focus in a bit on modelling, just in general terms, uh and perhaps some uh words of wisdom for aspiring quants out there. My first question around this might be, you know, what what are some of the mistakes or the flawed practices which you made early on the beginning of your trading career? I think, you know, I I mean I I'm very happy to say that I didn't make one of the worst mistakes or flaws, which is overfitting. I was really

I I understood that from a the very beginning. I understood um that if you you search enough in data, you can find anything. Uh and I think that's so I that's not something that I was subject to so much. But I believe that that's something that is very um prevalent i or uh w or widespread. Um and so that's what I caution everyone about is is uh the the the ability to you know, find something. It's so easy to find something. Everyone's out there searching for

different data sets and looking at different things at different angles and and and trying to find a pattern in data. And if you do that enough, you're inevitably there's you're always gonna find something. It just doesn't have any meaning. uh and it may not last very long. And so that's my biggest caution is the whole curveing mining uh concern. And I but I think that's that's known generally uh as as a risk by people. I think the problem is

not understanding in the in testing how easily that can creep up on you. And I think that's where so as I guess going back to myself, I knew intuitively that I can't just I don't want to just search for a pattern. I want to have an idea in my mind. And I wanna look into the data to try to confirm that idea, right? So it's not it's not seeking a pattern, it's seeking

validation of something that my mind thinks it makes sense. Uh so that that that was step number one. And there therefore I had curve fitting or data mining licked from from a top level. But where it went wrong for me, I guess where trial and error helped to learn was you can make those same curve fitting mistakes even though you start with that premise, right? What I mean is that

Let's say you have an idea and you test you test for confirmation or validation of that idea and it it doesn't come out exactly as you hoped it would, right? And then you say, okay, well let me let me let me turn that like let me shift, let me, you know, instead of buying it every 10% down. I want to buy it every 9% down. And you test that. And so what you've done is you've started out with a good thesis that was independently hypothesized by you and it's got a sound approach.

But once you start testing and not getting the the outcome you'd want, you start tweaking until you get the outcome. That's terrible, right? So that's That's like it's terrible because you started out with a with a good thesis. You started out trying to not be a fitter, but then you became a fitter in the testing process.

that's something that I probably made mistakes not probably, I definitely made mistakes with early on and learned very quickly how just e um How incredibly aware one has to be on every test you do and every iteration you make to know that you're not. biasing your your test. It's just it's it's almost unbelievable how we check ourselves today before I I'll say the time it takes, uh the biggest caution is taking time to set up a test. Today when we have an idea, we don't just

go do the test. We we talk for hours like brainstorming, like is this the right test and what are we missing? And and like you you want to go through all this brainstorming and discussion just in setting up the experiment. to know that you haven't done anything wrong in the experimental phase, then you go test. And if the results don't look right, then you move on to something totally different. That's I guess that's the biggest

um learned or trial and error learned lesson I've had over decades is uh getting better at that feature of testing. And um it's just so easy to do. We're so we so easily fool ourselves as humans, especially when we're

of course, biased to want to find something, right? We all when when we're sitting there as quants testing, we want to find something. We wanted to be to find the, you know, dig for the gold and there's a little sparkle in the ground. Oh my God, I found it. And and and we want to make money. And so we're so inclined to believe quickly. Uh and it just it's much harder than people think to find things that work.

Uh and so most things don't and and therefore spend more time being sure and spend more time setting up one's experiment to and expect it not to work. Uh and the anomaly should be when something does work out really well in testing. Okay, that's interesting. So you spend a lot of time, as you said, brainstorming these ideas

all before you actually go to running the test. And if you run a test and the first iteration of that test shows uh, you know, undesirable results, you pretty much just abandon that idea altogether.

Yeah, that's a it's a it's a good qu I mean, that's a great follow-up question to what I just said, but it's a the problem is there's nuance there, right? So it's would would I say I abandon that idea altogether? Sometimes yes. Other times I'd say If I if there's something about the idea that struck me. Yeah. I I may not abandon it altogether. I might I might sit there and

think or discuss and talk about what did we miss? What I what I will not do is say, okay, let me let me just shift this one parameter by seven degrees and retest. That's the big no-no, right? So there's somewhere in between tweaking parameters of a model and going back to the drawing board and saying, Okay, we believed and, you know, whatever what what's you know, s I some silly thesis, you know, we believe growth outperforms value, right? You know, and and get into that big debate. But um

Yeah, yeah, we and that's not the kind of things that we we do at Logica, but just to talk about that one as an easy concept, right? So you could do that and say, okay, and and you do a test and it doesn't work, say, okay, let's try instead of big caps, let's try small caps. And well, well, it worked over there.

Right. So that's interesting because there's a we understand the difference between big caps and small caps and why growth would be more powerful in small caps makes sense'cause there's perhaps more room to grow if you're small versus when you're already big. So in that example, there's there's a

when you tweaked that parameter to look at not small uh large caps but small caps in in trying to identify the the benefit of growth or the alpha in growth, you're you're you've you've not just tweaked it by some

mathematical metric. You've you've sat at a table and said, wait a second, it maybe large caps don't have as much room to grow versus smaller companies because they're, you know, they're they're by by definition, there's there's more more room. And of course We'd say this, you know, this is not true of Amazon or some of the bigger boys, but but put put the anomalies aside is that there there's a sound reasoning behind changing a parameter now going to a new test.

where you've you've you've deliberated and you've understood what was wrong in your first experiment and why in your next experiment. Th th there's you've stuck to the core idea that there's alpha in growth, but you've changed a parameter that has a a reasoning for it to change and a logic behind why the new one should work. So that's the difference between just saying I I'm gonna run this array of tests and and and find something. Does that make sense or is that helpful to that answer?

That does make sense, yes. As you stated that most or in all cases, before you run any test, there's always a goal and a brainstorming process which uh takes place beforehand. For newer traders out there and aspiring quants in particular, Where do those ideas come from? Where do th how do you form those goals? Yeah, that's a great question. Um I th th the the toughest part is the the the paradox, I guess, is that you said for aspiring

Quant' is that for me those ideas have come from experience. So how do you how do you put experience in in a in a new trader, right? That's that's tough. Um I I uh on top of that though, I I I think there's if I had to say like the the merging of three different things. So one is of course experience. Two is reading and um just absorbing from other

great uh investors slash traders out there. I mean I when I was young I I was just, you know, when when I I stayed late at the trading firm and then I went home and read a book, right? That's that's all I just it's just absorbing yourself in what All the w wisdom before you has had to share. Reading and and taking in all you can from those who have done it. The third thing is.

trading on your own capital. I c I can't stress that enough. I like I th this idea of simulating and idea of paper trading and all that is just absolute nonsense because there's all of it all of it comes down to how you emotion where I you know, people try to b uh build systematic approaches because our our our emotions can do us wrong and and you know as much as they're systematic, people go in and try to trade a systematic approach and they end up

overtrading it or or or disagreeing with it and it because the emotions always come in and you say, Oh, this doesn't feel right and you you do something different. And so for me, the biggest gain in ex in in in ideas has been just

literally to put money in account and start doing it. And I tell this, um, I get into a bunch of conversations. I do a a lot of tweeting and uh I guess I I like the Twitter environment. And a lot of people ask me, they DM me, new traders, no, how what's the best way to start? And my answer generally is open an account and put in some money and start trading. Like that, I I can't think of a better version of gaining real life experience on your own capital to feel what it feels like.

when when you're sitting there and you think something's a good trade and you get in and then it just f the floor just drops and it just starts falling rapidly and what do you do? And your you know, your your body freezes up and and and uh understanding how that how those

behaviors work and how you feel and how the market does it and where so many times traders will say like that s something goes wrong against them and they stop out and then two minutes later it it reverses, right? And it's like this common

not uh ta it's a common thing that people talk about is being that that being shaken out. That's because everyone behaves the same. And so unless you go in there and do it, you don't learn that behavior. And once you learn that behavior by doing it, then you could start to model

You know, okay, now I can see like uh it's where my emotions are coming into play and where where behaviors need to be overridden by certain rules. And so you can better build the mousetrap by knowing what it is that you're that that that's going on. So that that's where my advice I guess culminates. Yeah, great.

Are there any testing or modeling methods which you use that are unique in some way, you know, from common practices in the industry or common practices used by, you know, quants just generally speaking. Uh yeah. I mean I'd say that we we generally don't like to use just what's out there, the the the off the shelf models. Um there's a lot of stuff that you can I mean, I I know people read and I I do too. I you the the white papers, there's

new ideas every day and being published. And I guess I have the general feeling that if it's a white paper and it's being published, especially if it's published by AQR, uh, who are putting out so many like

that it's like the market knows that. Like you're not gonna learn anything there that no that people are I I'm sure sure it can continue working. I'm not that's you know, maybe it will. Maybe that's just enough of the way the market works. But if it's a white paper, you're not the only one who has seen Right. And so if there's some mathematical approach that, you know, in in volatility investing, people use Garch as a very typical model um for the way volatility behaves. Uh it's uh and and so

Yeah, uh what we say is we we're not gonna use any of that. We're not gonna do something off of a white paper. We're not gonna do something we're not gonna use garch. We're gonna develop our own stuff based on how we understand it. And so you the the for me, the beauty of that in in using uh I'll call it proprietary mathematics is that you yes, you can be wrong, but by definition, you'll be different.

Um, and if you can figure out something that has alpha where you can be quote right and you're using different mathematics, then then people won't come upon it. Right. And don't go issue a white paper on it because then it will be known and it'll be and then, you know, you'll you'll be competing with yourself and and all the others that that figure that that saw it from you. Right. So

You know, I I I think my um uh broad answer will be that yes, we we we we don't we we do all our own stuff. Um we use interesting mathematical approaches. Uh I like integrating um like uh I guess m more disconnected ideas in mathematics. So, you know, for for example, the the PhD that we have in-house is a um algebraic geometer, right? So what does that mean? He is his PhD is in the combination of algebra and geometry.

Uh and those two are beautifully intertwined. Um the you know it's a little bit of math back history here is the first uh formula or algorithm that that combined algebra and geometry was the Pythagorean theorem, right? That you could take this A squared plus B squared equals C squared, um, that there's this geomet uh the algebraic formula

um that perfectly fit this geometric sh shape that the hypotenuse would be the square of the two sides. Right. That's that's that's a cool way that geometry and algebra came together. And that was the first one where they did come together. So Building on that approach, now come shoot forward thousands of years in markets, people can use algebra, but you could also use geometry. And so for me, you know, w when somebody's looking at, for example, standard deviations.

I wanna look at that topologically. I wanna look at that in in geometric space, like what are the distances between two things? That's more interesting than how much they, you know, specifically deviate as a percentage. And if you can uh come up with distance relationships and how that what that means and do do things take on certain shapes or symmetries.

These are these are ways of using mathematics and um and doing things differently than most other people are. And I think they help if you can figure stuff out using such approaches.

Just out of curiosity, what is your level of mathematical education? Like how much of this is self taught versus formal education? Yeah, so it's a nice mix. Uh probably uh not probably, most definitely more self taught because I I I'm so much older than I have been out of

school now for um twenty five years. So by definition, I've more time out of school than in school for four years. Right. So um therefore I I I definitely have more self teaching. Uh I so the the the background for me is I I certainly had a strong mathematical understanding or or uh as I talked about earlier, as a kid, I just I saw things numerically very easily. And so I had that to intuition.

Um in fact I so much so that I I got more advanced in math as a as a youngster. And um in high school I was so advanced over my peers. I I started uh university level math and getting through, I remember I was like starting I guess ki college calculus in in tenth grade of high school. And so I was really uh leaping ahead

But then the funny thing happened to me is because I was taking all these college classes when I was in high school, by the time I actually got to college, I was kind of sick of math. All right, or I was just like I had been that that youngster in the college classes, the high school kid in there. And so

When I got to college, when I actually uh went to formal university, I I thought to myself, or I I I at that point I didn't want to do it anymore. I didn't want to be that I had kind of negative experiences with being the younger kid in those in those college classes d while while in high school. So I said, you know what, I'm not doing any much math and I was able to transfer all my credits'cause I did so much university work. Uh and so I I actually uh

took on an English degree and did English and creative writing out of Berkeley. So Um in in that way, I didn't formally go further with my math education, but I was this advanced math kid. So when I came out of college and then got into the real world, of course my love for math was quickly pulled back in. But

it what it gave me the opportunity to do is to understand things really quickly in in in the mathematical realm and to be able to pick up where I left off and then self-educate for the next twenty-five years. So my my love, my passion, my intuition for math. has has grown a lot of education in me but

Formally it it wasn't actually even that much other than um this advanced stuff I did early on. Uh so but it's it's i I I love the way it came together for me because I it allowed me to see things at di in different ways. than people learned who who did the I'll call it the formal route. For someone who doesn't have a strong mathematical education, but is uh very interested in quantitative trading. Do you have any advice for someone in that situation?

Yeah, I mean I I would liken that to to myself, right? And the is that if if you If somebody has the um the interest uh and not just the interest and passion, but the intuition in math. If you're d intuitively good uh with numbers and with understanding that that sort of thing. then self education's amazing. I mean, I there's uh it's incredible what you can get online now. And I mean it's

Uh right. I they didn't even have as much of it was when I was uh I mean and they did uh of course twenty five years ago it wasn't it wasn't it was there was still uh Wikipedia was still around, right? Or was uh already around I should say. Um but

Today there's even more. I mean the classes that you can get through Coursera or any number, I mean through Khan Academy, it just goes on and on and on. And you can, I mean, there's online classes out of out of every major Ivy League put through different um online entities. So one can fully educate. I mean, you can give yourself a a PhD if you want to by just taking online stuff. The the answer is if you have the passion and the basic understanding and intuition, then go

just get out there and do it. All right. Grab everything you can and and learn, learn, learn. And I mean I that's that's what I did for myself over over decades and you just keep on go. I mean, if y again, it's like that thing where we were saying earlier, is if people say you work too hard, but no, not if you love what you do, then it's not really work. So same for me. It's like on the weekend I I um Uh yeah, I I would I read math stuff'cause that's

I'm so just interested in it. I in fact I remember there's a funny story. I was out um when I I I'm a big cyclist. I go biking, it's my kind of sport of choice. And uh for many, many years I was listening to math lectures in my iPod while I'm biking. I was bike I was out somewhere biking and I was uh

I I I ran into somebody I knew and he saw my my my I had my iPhone, I had my little earphones in and he he uh he knew me. He walked up to me and he said, Oh, what do you listen to? And he grabbed my ear thing and he stuck my the one to the left, pulled the left and he stuck it in his ear.

And there's some like, you know, partial differential equations or whatever. What the hell is this? Right. And that's but that's what I wanted to do when I was biking. And it helped me to like take it in as I you know, cause I was concentrating on listening, it I I could climb the hill better, right? So that's my answer is if you love something then do it and you could learn all you w you you want. Um and um and if you if you have the intuition then it then it'll come to you.

That's a great little anecdote there. Um Yeah.

Just jumping back into uh modelling. Before jumping on this call with you, Wayne, I spent uh more time than I expected to scrolling through your Twitter feed. It was just fantastic. Thank you. One of the things I pulled from it was you had a tweet about trade frequency and the importance of it and

uh I think you may have called it mastering the art of trade frequency. So I just thought that would be an interesting question to ask you uh to to get your thoughts on The importance of trade frequency, I guess. Uh trade frequency is uh I I'll say I I love that idea in the sense that there is no uh right frequency, right? There's not absolute version that that works. Um and so the the new the important point

Is that one has to use multiple frequencies and the market actually shifts. A lot of the time the the general behaviors don't shift, but the frequency shifts, right? So markets run up and down, but

um at different frequencies. And so to one, it's very, very hard to time. Just like it's hard to time a regime shift, let's say from a value to a growth regime or the on the broader level of factors, it's equally difficult, if not more, to to really hone in on on the right frequency and or shift in that frequency. So my general approach, and that's something that we do in in Logica, is to have multiple frequencies running.

you know, at the same time. And it's like it it it it it I I guess I liken it to hedging, right? So let's say I have one frequency in which we do, which is which day trades, right? It makes a trade per day. And another frequency, let's say which uh buy uh twice a month trades and another which is monthly, right? So you have slower and slower at at different rates. So what could happen there is on a day trading level, the market's overdone.

And so you you take some off the table, you sell some SP, let's say, but the next day the, you know, of course, unemployment comes out, the number on Friday, much better than we expected, uh uh right, still drastic, but of course much better than the world thought. or the US thought. And so boom, the market gaps up two percent. So as much as the day before was extended and you in your day trade sell some.

s took some off the table on your on your on your two week frequency trade, you you still were net long, right? So you you're you're hedging not being wrong. Um and so it it's not that you It's not that one was wrong, or not that the day trade decision to take some off the table was incorrect. It's that both were correct.

that each is correct under its own time frame. And I can't stress that enough. And the market, people use these words of of, you know, that you're uh uh profit or loss being right or wrong. It's not really right or wrong. It's that there's there's a lot of different

uh things going on. And a a day trader is right to have taken an overbought position off the table. But if you're a month long trader, you want to h hold on to that mean expansion longer. And so by having both, you're hedging your own ability to to time and you're you're you're taking both approaches that over time can be successful and and of course merging them to have a a more I guess noise cancelling the downs.

And of course, hopefully keeping the alpha associated with your with whatever edge you have in each frequency.

Wayne, let me ask you one last question. As someone who mostly ignores macro, more focused on the math and in their trading. What have been the greatest observations for you from this recent market crash and recovery, like As you said before, it's probably been the the greatest rise in markets which we've seen for a long time. And I think very few people

expected to see this and people have been making predictions everywhere and almost everyone's been wrong about what's actually happened. What has this taught you or what is this, if not taught you, what is it kind of uh enforce? It's enforced to keep on ignoring macro. Yeah. Oh yeah, so yeah, no, I I I I it two things. I I wanna say that and then I wanna actually c contradict myself in a in a second.

Um so the first thing is that m m what's long been my premise is that nobody has any idea, right? It's just it I I I w the the funny thing is I I actually did a interview um a a while ago Um, which was after the December eighteenth sell-off and and and the in January nineteen recovery. And when I talked about on that um on that cast was how wrong people got it, right? In December of eighteen, as the market's selling off, people are saying our economy's done and this is it, the bubble has burst.

And then not three weeks later in January of of nineteen, it's everything's back to normal. The market's right back up, new highs and like nothing happened. And you know, I was say I was talking about how wrong everybody got it on that you know, the V and how how You know, doomsday the the world begot and it became on on the initial initial weeks in in December, and then suddenly how optimistic everybody was in late January.

And lo and behold, the exact same thing happens again, right? In February, March, l the world was ending. This was worse than oh eight. This was gonna be the, you know, the the the gloom we've all been waiting for. And here we are again. you know, uh making having made new highs on the Nasdaq once, you know, and and close to it on the S P. So

I i th the the highlighted point is once again, nobody knows. Once again, uh people get the the emotional behavioral side really always just falls into the what's happening now. And if if you if you look at even the the you know the Twitter environment or the FinTwit or the media in general, most of it and the the panic is associated with what happened in the market today and yesterday.

And that tells us so little and and and where all the all the people that follow the media end up getting immersed in what's being said now, which is just a function of what just happened, which inevitably tells us nothing about the next two weeks. So I double, triple, quadrupled down on that theme.

Okay. So that's that's where everything stays the same. On the other side of that, I I've had a really amazing new experience in the in the last year where I actually Logica took on a uh a new partner, somebody I funnily enough, met on Twitter, uh Michael Green. He he was

uh at the time he was working at Teal Macro um and he had traded in the vol space for a while and and uh so we we came together and just having a a a a a common respect and understanding of what each other was doing. I guess uh having found each other on Twitter. and met and started talking about some stuff that was for I I didn't know about, but he was sharing with me that there was this big reasoning from a macro perspective.

of why the trades I was putting on were were working really well. And you know, he he he basically said to me as Like, yeah, you're producing alpha, but it's not all yours, right? And in a very polite way, like yeah, maybe you know, you have something for sure, but there's there's more alpha associated with what's happening in the market as a function of uh pass it the growth of passive. and how that's distorting the market, where you have this um uh I'll call it agnostic

buyer, so a long only buyer who whose whose basic model is if cash then buy, right? It's a it's a um and and every two weeks everybody's paycheck dumps more money into this this this premise if cash then buy and here's every two weeks ne more cash in everybody's uh r uh r retirement and of course uh uh corporate matched plans and all the rest, trillions of dollars that's going into this stuff on a regular basis, just buying, buying, buying. And by definition,

exacerbating momentum because you're uh the the more the stock is represented in the S P, the more it gets bought. So inde passive indexing is becomes the self fulfilling prophecy. And so all of his work around from a macro perspective, how that has been influencing market structure over the last ten, twenty years and

um really opened my eyes to this whole new framework of macro, which is not to say we're gonna use macro to predict the future or to make a forecast about the economy in six months. It's that we're gonna use macro to understand the structure of the market. and therefore how we can better profit off of that. And so integrating Mike's work.

with what I was doing, we now had a better mousetrap because we could say, okay, that does make sense. That's why a straddle, you know, the the a lot of short vol exposure out there is helping push down the pricing of our of the vol we're trying to get long.

Uh and so this call overriding is bringing down the price of of calls that we're trying to get along. That's why there's a little bit more alpha there. Um so yes, I had a good idea, but I was being helped by this larger structural dynamic of the market.

And so I I now have come to a new found appreciation for macro in its in its ability to uh help explain what's going on and into it to help I guess uh d structurally uh build a model and or to to design a model to uh what what could be more of the thesis behind why something's working. Uh and so again, we're not using it for making predictions other than saying, hey, if this is gonna continue, which it looks like it will, uh as as uh as uh just in line with the US population and and growth.

then uh w the market w should continue to do this. Therefore we can make certain bets. Therefore uh we could bet more on extreme outcomes. Therefore that what happened in February, March and now April, May, this insane V in the market. Uh was to be more expected given the influence of passive. This explains it better.

Therefore, our tra going back to what we were trading talking about before, trade frequency, if that's the case, then we have to slow down our or perhaps speed up or make changes to our trading cadence. because this is the market will take on more extremes. based on the influence of passive. So it it just adds this whole new dimension to what we're doing. And so for me, it's a way to infuse a macro view without actually being a forecaster, which is what I'm always going to stay away from.

That's brilliant. Wayne, thank you very much for coming on the podcast. Very grateful you for your time. This has been an incredible conversation. So um yeah, once again, thanks a lot for coming on the podcast. Uh if somebody wants to follow you or connect with you online, uh, where is the best place to do so? And I know you also have a non profit as well, so would you like to give that a mention?

Uh sure. Yeah. The nonprofit. Wow, that's I started that back in 2003. Um, I just uh that's called Informed by Nature. It's uh I I I just love love uh the I guess scientific thinking and rigorous experimentation and um this mathematical view of the world. And so I as a believer in that, I wanted to fund some projects that did that type of thing. And um

At one point we were we had this little solar cars and that we're had kids were building in a classroom and things like that. It's done different things. Right now we work with some uh journalists who are promoting some um kind of innovative scientific ideas.

um and another group that does a a science camp um for budding scientists, uh younger scientists or scientific thinkers. And so it's just a way uh to give back to the world for for me, what has helped me so much in along my path and the w the way I think.

Um, so that's what that does. And then how to get a hold of me. That's pretty easy. I I guess I'm on Twitter, right? And and uh to your point, I've got my a lot that I I I like to say stuff often and I've got a lot of thoughts that I like to share. So there my handle is just my first and last name at Wayne Hemmelshein. Um and so uh and from there my my firm logica of course we're at logicafunds.com, so that's easy.

And um I guess, you know, if one were to just Google me there there'd be in a lot of stuff, I'm sure. That's what a lot of people tell me. These days I think anyone's easy to find, right? It's there we're we're a Google away. But yeah, I I I definitely think that if someone's interested in hearing more, then my my Twitter is a great place to start because I I I love sharing my my thoughts as they come.

Yeah, folks listening to this podcast, I would certainly encourage you to follow Wayne on Twitter. Uh, you know, there's very little fluff, very little feel good stuff, it's just a lot of really wise thoughts. So well worth your time. Wayne, thanks once again and hopefully uh we'll chat again soon. Thank you. You've reached the end of this episode of Chat with Traders, but rest assured there are more episodes. Yeah. Love it if you leave a-