In todays episode, Jeff Malec (@AttainCap2) sits down with Bill Gebhardt (@BillGebhardt1) , the founder of 10dynamics, to discuss his interesting career journey and the development of his unique systematic trading approach. Bill shares his background, from working on the floor of the CBOE in the early 90s to earning a PhD in finance and transitioning into the energy trading space. He provides insights into the evolution of the commodity markets, including the rise and fall of Enron, and the challenges of maintaining an edge in fundamental trading. The conversation then delves into Bill’s transition to systematic trading, sparked by his exposure to a successful systematic team at his previous firm, Trailstone. This led him to develop the 10dynamics model, which is built on 10 core signals that closely mirror Bill’s own decision-making process as a trader.
Bill explains his “allergic to optimization” philosophy and the benefits of using multiple time frames to generate positions. He also discusses the importance of risk management, operational efficiency, and adapting to changing market conditions. Throughout the episode, Bill shares his unique perspective on the markets, the role of human behavior, and the future of systematic trading. This insightful discussion offers valuable lessons for both aspiring and experienced traders looking to navigate the complex world of alternative investments. Buckle up, because this conversation is about to “send it” into the world of alternative investments.
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From the episode:
Whitepaper 10Dynamics: “The Freezer” – Automated Risk control systems for systematic trading
Check out the complete Transcript from this week’s podcast below:
Trend, Prop, and Being Allergic to Optimization with Bill Gebhardt of 10Dynamics
Jeff Malec 00:06
Welcome to the Derivative by RCM Alternatives, where we dive into what makes alternative investments. Go analyze the strategies of unique hedge fund managers and chat with interesting guests from across the investment world. Hello there. Well, the kids are back at school. It’s 98 degrees in Chicago, and I’m not wearing sunglasses, and if you didn’t get that, please go watch the Blues Brothers. But anyway, let’s get to the pod. We’re sitting down today with Bill Gebhardt of 10dynamics, so named because of the 10 core trading signals Bill systematized to build out his multi time frame model. We go from Bill’s days in the Chicago option pits to his energy trading days to his work as an allocator of sorts, hiring traders into a Deutsche Bank prop trading arm in between all that we touch on quantum trading, prop versus multi strat, discretionary versus systematic, and one of my favorite lines of late being allergic to optimization. Send it. This episode is brought to you by RCMs managed futures group, which helps match up investors like you with unique managers like Bill and 10dynamics. Go to RCMalts.com RCMalts.com and call our group to see how we can help build a portfolio of unique managers to meet your unique risk and reward parameters. All right, everyone, we’re here with Bill Gebhardt of 10 dynamics. How are you, Bill,
Bill Gebhardt 01:39
I’m great. How are you?
Jeff Malec 01:41
I’m great. Beautiful, sunny, 60 degree day here in Chicago in August, which is super rare. This is I was seeing the pop up at the coldest on record. I think I was gonna
Bill Gebhardt 01:52
say I lived in Chicago for a bit. That sounds awfully cold for August. I remember sweating in bars in August. That’s
Jeff Malec 01:59
the way you’re doing, if you’re gonna be sweating doing in a bar. Yeah, how’s London this time of year? Lovely.
Bill Gebhardt 02:07
Oh yeah, it’s London’s pretty nice. We’re cooler than you guys. We’re probably, you know, 6970, well, actually not you right now, you’re, we are, but, but typically we don’t get really hot, like, like you do in Chicago, but
Jeff Malec 02:19
it’s how long you’ve been in London?
Bill Gebhardt 02:22
Uh, 23 years now. Wow,
Jeff Malec 02:24
it’s been a while, yeah, all right. And before that,
Bill Gebhardt 02:29
uh, well, originally from Colorado, but, and I moved around a lot for jobs. I was in Chicago, as I said, for a while. I worked, I worked on the CBOE for a bit, way back, you know, in the early 90s. So got my floor trade. Not floor trading. I was a clerk, but but got the experience of being on the floor, which is, I think, one of the coolest examples of complete chaos resulting in order that you’ll you’ll ever see. And it’s a shame that kind of the open outcry has gone away with technology. But yeah, that was interesting. But yeah, moved around a bit, and ended up at one point, down in Houston, working for an energy company, and they, they did a joint venture that brought me to London. So been here since then. I was coming. I came on a two year expat deal, and just never left, never left.
Jeff Malec 03:11
And my listeners know I’m not gonna let you say Colorado without talking skiing for a minute. So where were you from in Colorado?
Bill Gebhardt 03:17
I grew up in Fort Collins. I went to school in Boulder at CU, awesome. Yeah.
Jeff Malec 03:22
So you’re a Dion fan. You liking what’s happening with prime? Yeah, I
Bill Gebhardt 03:26
think, I think it’s pretty interesting. I mean, they’ve, you know, I mean, my, my this old, I’m, I was in the McCartney era, right? So my, my first year at CU, I think they went one, and whatever the number of games was, they’re one in 10. And then McCartney came in that next year, and by the time I left, they won the national championship. So it was a, yeah, it was a pretty good run, but yeah, it’s been kind of up and down since then. So
Jeff Malec 03:48
good. Love it. What were your favorite ski areas?
Bill Gebhardt 03:52
Well, my brother lives in Steamboat, which, which I like quite a bit. And I mean, as a being in Boulder was just really easy to get up to, sort of Winter Park, and Mary Jane and I was back in the day. I was obsessed with modal skiing. So Mary Jane was my, my go to
Jeff Malec 04:09
that’s a fun one. I love it. So not here to talk about skiing, unfortunately, but 10 dynamics. You guys are doing some cool stuff in trend, 10 different signals, 15 different time frames. So want to dig into that. But let’s first you’ve got a bit of an interesting background too. So give us a little bit about how you got to 10 dynamics, how you founded the firm, and sort of the financial background, yeah,
Bill Gebhardt 04:39
yeah. It’s a little bit of a long story, since I’m been around for a while. But yeah, I worked for a bit, as I mentioned. I was on on the floor the CBOE for a while, and then I went off and I worked for Chase, back when Chase was separate, worked in their mortgage bank in Florida, and did some finance stuff, and ended up getting a PhD in in finance from Cornell. So I went back. School for for a few years, and
Jeff Malec 05:01
didn’t kill yourself. You didn’t jump off the Cornell bridge. No, I did
Bill Gebhardt 05:05
not. I did not push it’s, yeah, they, they work you pretty hard there. I’d say it’s, it’s, it’s a great school, though. I love Cornell’s. It was really, really good. Um, finished my PhD in 2000 and then one of my actual former professors from Colorado, worked at Koch Industries and convinced me to get into the energy trading business. And so, yes, I moved to Houston and worked, this is kind of even far back in times as the Enron days. And my first job was on The Weather Derivatives desk. So that was a very quantity sort of derivatives product at the time, and that’s where I got my first exposure to energy trading. And
Jeff Malec 05:47
it was incredible. Was and run at the time like just the elephant in the room. Is it hard to actually get trades off and do things with them being such a big player? Or they were,
Bill Gebhardt 05:56
they were a great source of liquidity. I mean, I think they were, they were obviously super successful in what they did, but they were really important for the liquidity in the market everywhere they went. So, you know what? When, when I joined, within a year, I ended up coming to London because coke sold their energy trading business and put it into a JV with Entergy. And so Entergy, Coke was this, this joint venture, and Entergy had an office in London. And so when I came over, there were already, I think, 21 US energy companies active in Europe at the time. And that was that was led by Enron, right, they came in, they sort of structured the market. They got liquidity. I mean, there were existing markets here, but they did a lot to to bring liquidity and change contracts and, you know, bring a help the market development develop. And that’s why all the American energy companies were here, and then Enron went under. And within, I think, was two years, there were only two left. And it was, it was us, Entergy, Coke and Sempra. So the 21 there were only a few smart and that was just because both of us had higher credit ratings. So everyone else was kind of triple B plus, and they couldn’t survive the implosion,
Jeff Malec 07:02
I derailed you, but Enron has lots of questions, right? Yeah, yeah,
Bill Gebhardt 07:07
exactly. And then from, from, from there, we got bought by Merrill Lynch and worked at, worked at Merrill and in the energy business, and was head of at the time, I was head of entity, Coke’s fundamental trading or fundamental analytics group. So we had a very close one of the things that that we did, that kind of Enron did, too, is we had a very close pairing with a fundamental analyst, with Prop traders. And that was a it was a really good model at the time. And at the time, that’s that’s really where I started my my trading career was, was developing fundamentals and trading off the back of that, so kind of bottom up fundamental modeling. And, you know, in 2000 it was a really great time to be doing that, because there was, there’s a lot of information that wasn’t available at the time, so you could actually find information to feed into models that other people didn’t have. And a lot of the models were pretty new, so you could do a better job of forecasting demand and supply for just on the pure modeling side. So, you know, we had an incredible run. And
Jeff Malec 08:10
I think some of those inputs are, like storage numbers that people didn’t know. And things well, things like,
Bill Gebhardt 08:15
I mean, we had, you know, in Europe, you had things like hydro production and the, you know, the governments would produce the the Hydra numbers, but they almost, at the time, there wasn’t this sense of, like, everything should be open. So even though they would put it on the web, they wouldn’t make it easy to find it, and they would only allow you to download it once every so many, like 24 they had all these restrictions on getting the data, and so you could find data that was just because the market hadn’t developed enough that people weren’t using or hadn’t really thought of, you know, with particularly in the power market, the more granular you get, the more accurate you get. And so initially you just say, Well, let’s look at temperatures and try to forecast demand. And then then you got to figure out, okay, well, yeah, there’s a lot of different kinds of power production, but you’ve got, at the time, there’s hydro production run a river, which is basically driven by precipitation. And so there were lots and lots of things, and you could add to your to your model, to make it more accurate and and that was, that was a super successful approach. And it was an approach that led to, eventually, I ended up at at Deutsche Bank in 2007 and was ended up heading the European trading energy trading group there. And so trading power and gas and emissions credits came along at the time and and it was really that it was that bottom up fundamental crop trading approach. And one of the things that was was clear, though, is that more and more people were doing the same things and getting access to the same data. And so you were losing your edge, if you will. Are you losing a bit of diversity of opinions? You had lots of people coming to the same result, and the result that was it got harder. So, you know, I think if you look at performance over my career for fundamental trading, it’s been in a steady decline in the commodity space. I think it’s still, you know, some people are still making money there, but it’s gotten way harder than it was, and you have to invest a lot more to get there to do it than than we did at the time. So
Jeff Malec 10:09
isn’t that always the story? No, nobody ever comes on the podcast and it’s like, it’s way easier now than it was back then. It’s always the case of like, oh, it was all this info was just right there. You could grab it, make,
Bill Gebhardt 10:20
yeah, yeah, for sure. It depends too on what you’re, you know, if you’re trying to, particularly if you’re going after mispricings, right? If you’re going after mispricings, then, you know, regardless of what you think, markets are truly efficient or not, they’re efficient enough that, if enough people are doing the same thing, it’s not gonna, it’s not gonna work forever, right? So it’s, it’s harder to find those, those places where you get, you get mispricings and, and so, yeah. So, you know, from there, you know, I ran that, ran the desk for, for, I guess, about six years, and
Jeff Malec 10:51
at Deutsche for six, yeah, yeah, Deutsche and started in oh seven. Didn’t the stock go down 90% probably right after you started. It did. It did.
Bill Gebhardt 10:59
I had one of my best years ever in 2008 as a desk we had we, we used to joke we made more money than Deutsche Bank because the bank as a whole lost money, like everybody did in 2008 and we had it. We had a great year.
Jeff Malec 11:09
So you get netted out, right? Like, a lot of these people leave because their bonus like, oh, sorry, you did great. But
Bill Gebhardt 11:14
I’ll tell you. You know, people make a big deal about being uncorrelated, or particularly about being a hedge like, let’s say you run a hedge business. That sounds great in theory, but the reality is, when the hedges pay off, nobody has any money or nobody has any capital, so bonuses don’t go along with being the great performer when you’re a hedge. And it really is, it’s I’ve seen it happen in my career a couple times, where you have an outperformance when everybody else is not performing, and unless you have some contractual guarantee with somebody who’s got deep pockets, you’re probably not going to have a great year yourself, right? So it’s, it’s tough, it’s tough, yeah, in that spot. So
Jeff Malec 11:50
that’s super interesting, right? It’s like misaligned incentives, because it’ll create this thing within the banks where everyone wants to be on the correlated trade so they get paid
Bill Gebhardt 11:59
completely. You’re way better. The people who were on, riding the tide, they made way more money than the people who were there trying to, you know, be the, be the ones who, you know, performed in bad times or or, and I’m sure there’s lots of people with similar stories, depending on what they were doing, where they they were, they had their one great year, and, yeah, didn’t work out.
Jeff Malec 12:18
So yeah, a lot of the traders that become good CTAs, good hedge fund managers are right. That was the trigger that got them out of wherever they were. And said, Yeah, this is crap. I made all this money. I didn’t get paid. I might as well go on myself
Bill Gebhardt 12:29
Exactly, exactly. Yeah, that was pretty interesting. Interesting time too, to see, you know, just all the things that can go wrong. You know, I think when you in the business for a while, you start to see, you know, how easy it is to be overconfident. You know, one of the things we could talk about it too, like it was sort of the foundation of one of my philosophical principles, is I am allergic to optimization. That’s my that’s my saying allergic to optimization in all of its forms. So I really, we don’t really optimize our system at all. That’s kind of one of the core things that we don’t, we don’t have any parameters that we optimize. And I think that’s that comes from seeing how easy it is. Even really smart people can fool themselves through optimization, thinking that they know a lot more than they do. And then, you know, in the case of 2008 correlations all go to one when supposedly they don’t, or, you know, you have things that never dislocate, and suddenly they’re 400 basis points apart when they’re usually five, you know, things like that. And you know, if you optimize too much, and you can really get yourself in trouble. And we saw that, you know, a couple times my career. You know, we’re sort of try as hard as we can not to do that.
Jeff Malec 13:39
I call that statistical correlation and fundamental correlation, right? Like, yeah, cool. All this is statistically non correlated. It looks great in the back test, but what are they actually doing? And is what they’re doing going to become correlated in a crisis? Yeah, you gotta, you gotta have both pieces, right?
Bill Gebhardt 13:56
And I think that’s one of the big difference, one of the big things you learn being in commodities that you don’t get from financial markets. Financial markets, people that I meet, they are way more confident in correlations and covariance matrices and things like that. And and I get why, because they’re there. They are more stable we’re in in commodities. You know, if you start doing bottom up fundamental and modeling, you have switching points. You have things where the correlation can be close to 100% and then you switch a substitution point, and suddenly the correlation is negative. So you can’t that’s why you don’t see people in commodities doing the same types of things you see in like equity space and whatever, with, with really relying heavily on on correlations, because they’re they’re highly volatile.
Jeff Malec 14:41
So you survived the first downdraft in Deutsch, yeah, then the second downdraft you said,
Bill Gebhardt 14:47
Well, no, I think, I think it was, you know, you had the you had the crisis, right? And then every one started to figure out what was going to happen. And just like, I think the same thing happened, you know, around the Great Depression, it took a. Four or five years before the SEC and the CFTC and all those rules got put into place and change everything. So you just had Dodd Frank and some of the other changes in Europe. You had limits on bonuses that are based on salaries. And so you had a lot of changes. And then the big thing was accounting. You had changes for how derivatives were accounted for in banks, which really increased the balance sheet charge that you got for for trading and and energy was still a very prop business. Even inside a bank, we didn’t have lots of customer flow. The one one, again, big difference for for commodities inside a bank is typically, you’re dealing with people who know more about the commodity than you do if you’re if you’re trading oil, you’re trading against VTOL and other oil traders who are as smart as you are, where, if you’re selling a financial product to a corporate that’s hedging their you know, price risk for some input, they probably don’t know as much about the market as you do, so that you’re unlikely to be taken advantage of. Where, you know, in the energy space, if you’re offering solutions to clients, a lot of times you could be on the wrong end of that. So, so it was really a prop business. And as you know, prop trading and banks sort of went away for all the reasons, you know, everybody’s aware of and and so then 2000 left in 2012 and started with some partners. So the the management team at the commodity business, at Deutsche, or our head of our head of commodities, started trailstone, and myself and the head of oil and the head of metals, we all left uh Deutsche to join trailstone And and so started a kind of new commodity energy merchant in 2012 and I was responsible for the European business. And it was the same, it was the same basic approach, bottom up, fundamental training. But we had a an idea, at least in Europe, our strategy was around assets, because assets were super cheap power assets at the time. So we wanted to, you know, looking to buy power assets and then build a fundamental kind of trading, I mean, like owning the actual power plant, yeah, owning the actual power plant, yeah, that’s a really good model when you can do it at the right time of the cycle. And that’s one of the, again, I talk a lot about commodities energy, because I think it’s such a great market. But one of the reasons why there’s such good markets to trade is because of the huge investment cycle. So you go from oversupply to under supply repeatedly. You can look back over the last as long as you want to look, and you just see these cycles of excess supply and then and then price tightness, and that is driven by the investment cycle is so long, if you want to build a new power plant because prices are too high, it takes you a while to do it. So, so you get these, these nice cycles in energy in particular, that that I think are kind of what leads to, you know, the we’ll talk about a bit, kind of the money that you can make in terms of trend following Same, same sort of cycle you’re catching, really, and
Jeff Malec 17:41
then was that tough? You’re competing with, like, Glencore and those kind of groups of, like, big, huge, yeah,
Bill Gebhardt 17:47
we were, we were pretty we were a bit more niche, because we were just doing power and gas to time so, and we did oil as well. We owned an oil refinery in the US for a bit. And so, yeah, we were competing with kind of the big names, and in that space, had our own, you know, one of the other things they did, we had a very renewable energy focus, which, which they still have at Charleston. That’s a big part of their their business, they’ve just been acquired recently. But so we were, we were, you know, pretty I think we’ve successful over the time building that business. We didn’t end up buying any assets, though, and, and that eventually led to, kind of changing the business at trailstone. And that led to me leaving and, and starting, starting 10 dynamics trailstone,
Jeff Malec 18:32
you guys, were launching a few different managers and things of that nature as well.
Bill Gebhardt 18:36
Well, trailstone was, you know, we had a big prop trading team. And one of the things that, over my career, I’ve seen many, many attempts at systematic trading and quantitative trading, usually driven by quants. So it’s, you know, tends to be more the technical. And I don’t mean technical analysis. I mean more technical in terms of mathematical type of approach to to that business. And it was never successful. And and over, you know, many years and many different attempts and for various reasons. And and then we ended up hiring a team of systematic traders that had been successful over over quite a long period of time, and they had a great track record, and convinced us they could do it. And, and so we hired those guys into trailstone, and they were successful, but we realized pod
Jeff Malec 19:24
chat before it was a thing. Yeah,
Bill Gebhardt 19:26
I mean, we didn’t really want to spin them out, because we didn’t know we’d need to. We didn’t realize how broadly applicable their strategy was, I think, at the time, and how much more capacity they could manage than we could ever fund. Because we were, we were an energy trading company. We didn’t want to be a systematic hedge fund, right? So, so we realized these guys, you know, we could, would probably be better for them if, if you know, we help incubate them and spin them out, so we spend them out into their their own business. But, you know, it was really influential on me, because, you know, for the first time, seeing what a good systematic team does. And. Kind of how they approach it and and what I learned from that, you know, was, was that some of the things that I because I had a PhD in finance, over my career, both trading and managing traders, I built up a lot of tools that I was using for my own trading to help, mostly around timing for fundamental type trading. Because the problem you have with with fundamental trading is in this sort of the bane of fundamental traders. Well, we’re into early, which is I AKA, we got stopped out, right? So, and that is a thing because you you because, at the end of the day, most fundamental trading is counter trends. So if a market’s low, you think it’s cheap. It goes lower. You think it’s even cheaper, so you tend to like it the more it goes against you. And so, you know, timing becomes if you’re not paying to paying attention timing signals, you end up, you know, getting stopped out, or having big drawdowns or whatever. So, so I was using timing signals to help alongside the fundamentals. But then what I saw with our systematic group was that, you know, that the competition on the fundamental side had gotten so tight that actually the systematic systems were outperforming, you know, on an average year, were outperforming most people in, you know, on the fundamental side now that you’d have a great year fundamental training, where you have some shift in fundamentals, and you’d have an amazing year where the systematics wouldn’t be able to keep up, necessarily, but, But by and large, you know, it was, it was a it was as good, or potentially better, in a lot of different circumstances than fundamentals. And so, you know, I, I’ve been pretty I got to the point with this tools that I was using at the time that I thought all I needed was the tools. I didn’t really need the fundamental inputs anymore, and I but I needed to, you know, make them more systematic, as they were. You know, it’s tough to do everything by hand. You can only watch one or two markets at a time, and and to me, you know, the idea of being able to be diversified across a lot of different markets is really valuable, and that was kind of the seed in my mind. I guess, to well, maybe I should take the tools that I’ve gotten and see if I can put them into a system and apply them to a broad them to a broader range of markets. So,
Jeff Malec 22:04
and then that became tenure main
Bill Gebhardt 22:08
10 dynamics, yeah, and
Jeff Malec 22:11
so we’re 10 dynamics is because, yeah,
Bill Gebhardt 22:14
it was because we have, there’s, there’s 10 basic signals that I’ve, I’ve developed over time, and, and, and these are kind of old and tried and true. I mean, you know, the first one of these signals actually wrote in 1992 so it’s been around for for a while, and I have, my approach has been maybe different than what, what a lot of systematic people do, and a lot, a lot of the approach that I’ve seen from systematic is come up with 1000 ideas, test them all, see which ones work, keep the ones that work and assume they’re going to keep working and throw out the rest right, without any real connection to the market itself. And because I had spent my career looking at the market and working with traders and that kind of stuff, I wanted a system that traded like I would trade. So so as I was developing this, the signals, if I would get a signal that I would say to buy, even if it made money, I wouldn’t want that signal, because it’s a trade I wouldn’t take. So I wanted to strip out every signal that the system would produce that I would not believe in, and not take hand on heart. So I’d like try to do the, you know, put your hand over the chart and say, would I take this or not and try to ignore what I was doing,
Jeff Malec 23:21
which is sort of the opposite of classic trend volume, which is like, I have no idea why this market’s breaking out. I don’t care. I’m just going to get in line exactly,
Bill Gebhardt 23:29
exactly. So what I wanted was a system that modeled my decision making as closely as possible. So that’s, that’s really what we we we built was something that that. And now I would say, you know, I used to think, you know, as I was developing the tools over the years, that there were times where the tools would be kind of like, ah, you know, maybe should buy here. And I’d be, Nah, that doesn’t, that doesn’t look right. Occasionally, my intuition would be right. But probably, I would say, it’s been, God, at least six years since I felt like that. I feel like now the system is always right. If I’m trying to Outcast the system, it’s a mistake, you know? It’s, it’s, I feel like the system, the system, is a better version of me. Somehow, it’s, it’s taking all the trades I would take without any emotion or conflicting signals or other things that can influence is the
Jeff Malec 24:13
point of systematic in the first place, right? Take all that away. Yeah.
Bill Gebhardt 24:17
So that’s, that’s really what, you know, we try to say. Look, what we’re trying to do is we’re trying to model a prop trader. Is what we’re effectively trying to do. No, it’s true. We’re using prices to do that, and we’re using, you know, things that that aren’t what we necessarily would like a fundamental trader would use, but it’s that same concept. So, you know, even how we do risk management, you could talk a bit of all that stuff, but there’s, there’s things that we do specifically that are modeled on how I think a good prop trading desk works, and how you manage risk and traders and that sort of thing. So, so
Jeff Malec 24:48
you wouldn’t, you consider yourself a trend model or no? Yes, because
Bill Gebhardt 24:51
we follow the trend for sure, so we’re definitely trend following, but we don’t know. We, you know, talk a little bit about the signals, you know, basically
Jeff Malec 24:58
as a prop. A trader would follow a trend, not a Yeah, systematic, but follow a trend,
Bill Gebhardt 25:03
I guess, in just the Yeah, I guess the thought process around Exactly. So I used to have this way of looking at a market and saying, oh, or even looking back and saying, Well, here’s where, here’s a reasonable place where you should have gotten in. You know, what signals would you get to get in there? You know that you’re not going to pick the bottom, you’re not going to pick the top, you’re going to pick some reasonable place to get in. Well, what would make you get in there, you know? And so kind of thinking through how the signal should work as a whole, to try to identify those, those areas and and I started with a couple of philosophical things. One is my PhD. I was doing behavioral finance at the time. So I was sort of a big believer. And one of the early, you know, this was, this was in the sort of, you know, early 90s, right? And that was just at the beginning of the behavioral finance movement. So we were still a heretic, and in the eyes of anybody from Chicago, if you start talking about behavioral finance, but yeah, I so I did some research in that, and really started to believe that the markets, at the end of the day, are just driven by human psychology, and there’s kind of, you know, underlying factors that push it one way or the other, and that, you know, I think, you know, unlike efficient markets, where it’s like, the market moves exactly where it’s supposed to go, and then something happens, and it moves exactly where it’s supposed to go. And I think it’s the opposite. I think it’s like, you know, we use waves as this analogy all the time. I think, you know, I think I always view it like a pond, and when a pebble drops in a pond, that’s like, news, it’s news hitting the markets like a pebble dropping in the pond, and then there’s this wave that propagates, you know. And I think that’s the way markets really work, is this sort of propagating psychology that repeats itself and, and so that’s kind of had that in mind when we were, you know, when I was developing the the signals over the years. And the other thing that I got experience with and in the 90s, that was when sort of mental brought and fractal mathematics was kind of a hot topic, right? And, and I get interested in that. And I also had, in my PhD, I had a class from a kind of famous professor who’s big market pricing guy, and he did this thing that all, I think all efficient market guys do, they put up a chart of unlabeled chart of a stock, or whatever, quote, unquote stock, and they say, you know, can you guys guess what stock this is? And go around the room, and everybody has their guests. Oh, it’s whatever, Microsoft, whatever. Oh, no, this is a random walk. See how you guys were all fooled a random walk looks exactly like stock prices. But then what I did is I went, I generated 100 random walk paths, and what I realized is that was cherry picked. If you generate 100 random walks, they don’t look anything like stock prices. 90% of them look nothing like stock prices, right? So, so it was okay, yeah, statistically, it’s great for kind of mathematical finance, from a statistical point of view. But it didn’t really look to me the way markets behaved. And then, you know, taking kind of the fractal idea and what that kind of stuff, metal bra doing, you generate price, pass from that, and it looks a lot like what markets do, and you’re suddenly like, oh, wait a minute, this has to be somehow closer. And so I spent a lot of time trying to figure that out. Never figured anything out worth, really worth using, but because the math didn’t, didn’t play out, but it worked in my mind in terms of thinking about how the market is structured, right? So, yeah, yeah. Exactly, exactly. So. So kind of those two things, like keeping call, it’s not a it’s not a mathematical fractal, but keeping this fractal behavior kind of in mind, along with market psychology, and kind of putting those two things together to come up with, with ideas for identifying when, when a market is is behaving in a particular way.
Jeff Malec 28:37
And in layman’s terms. What you mean by fractals? It’s going to jump there’s going to be jump points. No,
Bill Gebhardt 28:46
I think, I think, well, a couple things for me that mean, like fractals. One is that it doesn’t matter what time frame you’re looking at, right? You should see the same type of behavior on a weekly chart because you do on a 30 minute chart. Really, you know that, and there are limits to that. Like, if you go into the really short term that I think all bets are off there, it’s kind of a different, different arena, but, but in general, you know, you should be agnostic on time frame. And I used to have this with with traders that I work with, you know, somebody come up, go, oh, look, look at this 60 minute chart. If you would have bought gold here at this price, and then look at what the 60 minute chart did. It’s like, Oh, wow. It’s amazing. And then they come, you know, two weeks later, and they’d say, Oh, look at this daily chart. Look at this daily chart. Views this daily chart on this market. You’d have done an amazing I’m like, well, when do you use the 60 minute? When to use the daily you know, when do? How do you choose what time frame you’re picking? And what I came to the realization is, you can’t. There is no way. I don’t think there’s any way. So maybe they’re all right. So maybe all the time frames are right. Maybe they’re all telling you something different in a different level of abstraction within the market, right? So, so that was kind of the basis for why, why we use all the time frames at the same
Jeff Malec 29:59
so, no. Other way to think of that is, we can’t know which one is right, so we might as well participate in all of them, so we’re sure to get the one that will be right. Yeah. And I
Bill Gebhardt 30:08
think it came back from, you know, in my, in my finance career, you know, you have this, have you heard the fundamental law of active management and that kind of stuff, which basically, basically, you just want to, if you have an edge, you want to bet as many times as you can, like, make it as broad as possible. And that kind of idea made sense to me, that if I, if I have a system that works on every time frame, why not bet on all the time frames at the same time? And if it works in one market, why does it work in all the markets? Why shouldn’t I be betting in all the markets at the same time and trying to, you know, really make as many different trades as you can, because you think you have an edge, right? And that’s, that was the, I guess, underlying basis for, for 10 dynamics, and how we got to go to where we eventually got to.
Jeff Malec 30:50
I’ll real quick, the far end of that logic, right, is that you’ll just earn the T bill, right, that you’ve removed all the risk, and you just get the risk rewrite. If you did that across the world, in every single market, every single market, every single timeframe, are you just getting the right? Well,
Bill Gebhardt 31:05
I don’t know. It depends on if you can lever that or not, I guess. But what you get is, what you get is you get an ever increasing Sharpe ratio and so, and that’s what we see in our portfolio. The more markets we add, the more assets we trade, the more the Sharpe ratio goes up. So it is kind of that that, you know, I guess, implementation of that, that idea,
Jeff Malec 31:32
talk to me a second about these time frames. So we’re talking, what’s the shortest, what’s the longest? We
Bill Gebhardt 31:38
We started a 30 minute timeframe. And we go, we go all the way out to weekly, basically. But then we divide the market under lots of different timeframes. So we don’t stick to necessarily what people would consider the normal timeframes or whatever. We just want to make sure we have enough timeframes that are different, enough if you divide it too much, then you end up with chart, you know, signals that are exactly the same across a lot of different time frames. So you don’t get any diversification there. But we’ve kind of arrived at like between 12 to 15 time frames, depending on how many hours the market trades, because we do trade down to intraday. So it
Jeff Malec 32:16
could be some like 1.75 days time frame, right? It’s like, custom, yeah,
Bill Gebhardt 32:21
yeah, exactly. And even, even, you know, you can even get different looks. We haven’t really done this with some of the things. I think, you know, everybody just uses the clothes for their daily chart. Well, why not use 12 o’clock? Or why don’t use three o’clock? You know, get in before everybody else here. You know, there are some different behaviors around the clothes. But, you know, there’s different ways of, if you think about, you know, the the difference between kind of precision and accuracy. I’d rather have three different charts that are all slightly different, giving me the same trade, right? But if one, if, if one out of the three is giving me something different, then that makes me think, is there something weird about just that very special pattern that, you know? So, so again, to me, it’s like having lots of different, lots of different signals,
Jeff Malec 33:06
like a robustness is like a voting machine. So you have seven, say, Buy, six, a sell. I’m long gone. Yep, exactly. And it’s netting out. Are you placing all 13 trains? No, no
Bill Gebhardt 33:16
at all. We just net it all out. So, so we boil everything down to, I mean, our process is, we, we have a risk. We basically start at like, what, what volatility are we targeting for, for a given, you know, account or fund, or whatever, we start with annual volatility. We back that into a daily volatility target, and then we scale the portfolio to try to achieve that target. And that amounts to allocating a risk budget to each asset. So you might say, okay, to hit a 10% vault depends on how big the account is. But let’s say, you’d say, Okay, well, we can. We want to have at most 100,000 risk when we use var. So 100,000 var on, say, corn futures or whatever, then we can back that into a number of contracts based on the volatility, and that tells us our max and min contracts. And then what our system does? It just scales between those based on all these different things moving around. So, so we’re always scaling in and out. So we have our 10 signals that we apply to each time frame independently.
Jeff Malec 34:17
Is it ever flat or it’s always scaling at some it
Bill Gebhardt 34:21
can be flat, sure, sure, but it’ll, but it’ll pass through that. So we tend to scale in increments of five to 10% so we will, we’ll take all 10 signals and apply them to a given time frame, that’ll give us a position on that time frame. And then we take the positions on all the different time frames and add them up, and that’ll give us kind of between minus 100% and plus 100% type of number, and that that gives us our scaling for the for the position. So, so the model is just constantly sort of scaling in and out based on how the confident, I guess, it would be. We call it. Call it a confidence measure on the on the trend. How
Jeff Malec 34:55
better than a confidence game? Confidence measure? Yeah. Exactly,
Bill Gebhardt 35:02
trading might be the same thing. We don’t know. How,
Jeff Malec 35:04
how often is it 100% right? That everyone’s voting in the same direction across all the time frames? Yeah. I
Bill Gebhardt 35:12
mean, it happens in really strong trends. Obviously, you know, Coco’s been the, the darling of the only part of the year, and orange juice. So there you were. You know, you were running at 100% for weeks at a time, but, but overall, I’d say it’s pretty evenly. It’s, it’s not quite an simply not a normal distribution. So it’s something like the tails are probably 100% long, 100% short, maybe eight to 10% of the time, something like that. And then kind of up from there. So it’s, it’s, um, and you know, it can be, it can be flat for for significant periods of time too. So it’s just depends on how the model is looking at the different trends and the different time frames, and how it’s all netting out,
Jeff Malec 35:53
and then how, how many markets. So it’s not just energy. Energy is your root? No,
Bill Gebhardt 35:59
- So we do, we do all the, yeah, we do all the futures and, well, all the liquid futures, I guess, in the US, we do European energy futures. We do some financial futures in Europe, we do, you know, treasury bonds and notes, and we do currency futures, equity indices. So pretty much the whole, what, I guess would be the traditional, sort of broad CTA kind of space. We also do, but we also do on the equity side. We do large cap, single name equities. So we’ll do, you know, kind of the big the big companies in the S, p5, 100. We also do ETS, Nvidia. Yeah, do Nvidia and the system. What’s interesting about the system? And this is probably true of all trend following in the equity space, it’s kind of what you’d expect, right? That the more behavioral something is, the better it works. So the big like tech companies and things that are hot, so your high volatility, your large caps that are really moving, system works great. When you get down the small cap space, you’re basically talking about beta and earnings announcements, right? So that’s like, jumps and, and sort of, you know, beta, which, which, you know, is not what you’re trying to capture. So, so the system doesn’t really work for for smaller cap stocks, because the the dynamics are different.
Jeff Malec 37:13
Um, yeah. See, I know Jerry Parker chesby is big on single name equities, but the rest of the trend world that’s been hesitant to go there for whatever reasons.
Bill Gebhardt 37:24
Yeah, yeah, it works. Well, the hardest part is, is talking to investors about it, because it’s not normal, and they already have that exposure. They think, right? Yeah. They think that there’s a lot of things that go into it. A you know, if you’re talking to the big allocators, they have teams. They have a kind of CTA type team, or systematic team, or quant team. Then they have a market neutral equity team, and then they have a long only team. And then when you say, well, we got a we got a model that trades equities, it can be anywhere from 80% net long to 80% next short. So you’ve got beta timing in there which equity guys typically don’t like,
Jeff Malec 38:01
right? Your head explode. Yeah, exactly.
Bill Gebhardt 38:03
So. So it’s a tough that’s, that’s been a tougher education hurdle. I think then, you know, we’re on the future side. People are much more used to kind of what we’re doing. So it’s so
Jeff Malec 38:16
talk about the allocators, sort of saying, Where are they putting you in that trend bucket? You guys correlate with the trend indices and whatnot we do. We’re probably,
Bill Gebhardt 38:23
I think we’re about between 40 to 45% correlated with with trend, something like that. So there’s definitely trend exposure there. I think one depends on what we do. You know what? What markets you include? Because if you want to get rid of trend, you can take out some, some some of the markets. I think one of the things that adds to trend a bit is the we trade rates. So rates exposure, that gives you some pretty significant weight on the on the trend at the end of the day, but also equity indices as well too.
Jeff Malec 38:53
Do you allow investors to do that? Like, I just want the commodities? Yeah.
Bill Gebhardt 38:57
So that was one of the things that, because we have. We didn’t really talk about it. We just mentioned how we don’t we don’t optimize. We use the same model for everything. So that means that we’re happy to customize to any portfolio. If somebody wants to do a managed account, if it’s if they have enough capital to do it, they can customize to whatever exposure they want. So somebody can, and that’s what our current managed accounts do. So we will have people say, we just want, you know, these markets, whatever it is, Ags and energy, or we just want, you know, we’ll take all our futures, or we don’t, we can even do just equities, if we want to do that. So we’re happy and kind of set up to be able to manage to different benchmarks, we call it.
Jeff Malec 39:36
And then on the risk side, you will dig into that for a minute. But do you view it? That’s probably where your prop trader roots help the most, right? In my experience, prop traders hate losing even for 10 minutes, right? Versus systematic guys will be no, it’s in a 30% drawdown that was in the back test. It’s all fine. So Right? Have you that part of what you built into the motto of like, well, yeah,
Bill Gebhardt 39:59
I mean, it’s. For sure some of it. I mean, one of the things which we kind of talked about is the system scales in and out. So one thing that we don’t do is we don’t try to make sure that the portfolio is hitting a specific vol target every day, right? Because sometimes it could be an environment where there’s not a lot of trends. And so one of the things that we learned from from
Jeff Malec 40:20
risk, just for the sake of putting it up exactly,
Bill Gebhardt 40:23
and you could see it, you know, with it’s one of the the kind of psychological challenges for being a non systematic trader, is, when do you know something, and when do you think you know something? And the reality is, if you’re going to be a fundamental guy, you know, generally, big fundamental changes in a market might happen once or twice a year, if you’re lucky, and if you’re a good fundamental trader, you make a lot of money, and you tend to outsize your bets when you’re confident, like that. But then what happens? Well, then what happens is nothing. You think you have an idea, and then you get bored. And, you know, there’s very few traders who can make one trade a year and then sit on their hands for the rest of the year and and usually what happens is, they make a lot of money and then sort of bleed for the next, you know, six or nine months until the next big idea comes along and and so we built the system specifically so it would scale like when the market, when the model doesn’t have a lot of confidence within an asset, it will be close to flat. And when there’s a lot of assets that there’s not a lot of signal, you’ll be close to flat. So, you know, for, for a 10% annual vol. For us, we in our VAR model terms, it’s like a one and a half percent daily var that that’s kind of our target var, but, but our var actually ranges. We could be at 50 pips. So we could be at, you know, less than a third of the target var for for weeks or even a month. But we can also go as high as two and a half to 3% var. So our var range is actually 10x in terms of, you know, we’re low deployment versus high deployment So, and that definitely adds value. So I, I’m, I’m amazed that people can scale up their systems all the time and have it be successful, as if they allowed it to sort of reflect some sort of confidence in the signal. And, you know, we can, we can do it in our if we want to. We can force our model to be fully deployed. What we see is it deteriorates the returns pretty significantly. So, so that that being fully deployed when you have confidence is and not deployed when you don’t, I think is a is a key lesson from the prop side that works in our system, for sure. And
Jeff Malec 42:24
it’s funny. How do the investors feel about that? I’ve seen plenty of times where an investor quits a investment a CTA because it’s flat. I’ll be like, well, its peers are all down 10 to 20% it’s like, it’s like, yeah, but I just wanted to be doing something. I want it to be so I think it’s not just the trader, it’s the investor too, who hates seeing just or if nothingness
Bill Gebhardt 42:44
for sure. And I know for a fact a couple of the big multi strats, if you go work for a multi strat, they force you like you need to be full. They want you to deploy risk all the time. And if you don’t, it’s not the shot for you, right? So, so there are people like that, and you know, not every shoe fits every foot right? So that’s not our that’s not our approach. And you know, if it usually people like that, they’re looking for, I don’t know, looking for something different than we are, right? My our thing is we want to deliver the best risk adjusted returns that we can, right? So, and that’s in that, and that’s without benchmark, and without, you know, where other people have correlations they’re worried about, or they want, you know, different things out of their their investments.
Jeff Malec 43:31
So talk to that for a minute, since you’ve kind of been on all sides of this, and have some insight into the like. Do you think you can? Doesn’t sound like you’re trying, but do you think people can recreate a pod shop multi strat, right? With systematic or quantum mental is the new term, right? If you can create that same kind of look and feel and return profile with models instead of actual traders, well,
Bill Gebhardt 43:55
so our experience at trailstone was that our systematic guys were slightly negatively correlated with our prop guys, which in a way makes sense, because our systematic guys were trend following. And as I mentioned, you know, fundamentals, by nature, are counter trends. So you’ve got guys doing counter trend trading alongside a trend followers. What does that mean? That means in market extremes, you tend to be flat as an organization, and then you tend to make good money in the middle, usually, so, and it’s super efficient from a, from a firm level, return on risk. So, could you replicate so, you know? And I, again, my, my experience with systematic is purely on the, you know, we are trend, trend following. I don’t. And this is a, this is a, I think it comes down to your philosophical makeup. I I really have a hard time trading counter trend. I can’t do it, and, and, but people who are quants, they tend to love counter trend, like, it’s cheap. Look at the model says it’s cheap. You know, let’s, let’s
Jeff Malec 44:53
did it for if you like that at 40, you’ll love it at 20. Yeah.
Bill Gebhardt 44:57
And that’s why, you know, you see a lot of quants doing mean reversion. And strategies and, you know, pairs and all that kind of stuff. That’s, that’s really, you know, that that kind of mean reverting idea, and that’s the problem I really have with mean reverting strategies. They tend to be negatively tailed, right? So you have negative tails and distribution. And again, that goes so against my this goes the proper thing, not wanting to lose, right? The idea of having a tail event. So our our returns are positively skewed, right? And that means that at the end of the day, we probably lose on more trades than we win on but, but our winners are, you know, far outsized from our losers. So that’s how we, you know, that’s, that’s by design, right? So,
Jeff Malec 45:34
yeah, but it sounded when you were talking about building the model, you sounded more like a quantum mental right? Of like I’m taking everything I know from being a fundamental trader and putting it into the system, which is what this new quantum mental is trying to do. Right? Of like, hey, now that everything’s digitized, we can take all these inputs that used to just be the trader and a bunch of screens and using them, put it into a model and trade off it. Well,
Bill Gebhardt 45:58
I think the the quantum mental combination works really good for mean, reverting, systematic with fundamentals. Because, if you think about it, took me a long a while to sort of figure this out, but, but let’s say you’ve got, let’s say you’ve got a system that where you lose on nine trades, but you make a ton on one. Okay, so you’re gonna you have this sort of trend, yeah, classic trend. Now you’re gonna use a fundamental filter to say, well, I want to, I only want to trade when the fundamentals are in my favor, because that’s going to eliminate all my losers. The problem is, if you eliminate that one winner by accident, you turn a profitable system into not making anything right. So, so it’s quite it’s quite risky to combine a filter with a trend following strategy where you have a few winners, because you can easily wipe out the winners. And if you do that, you kill your returns right the other way around. If you have a system that wins on nine trades and only loses once on the 10th, and it’s a big loss, yeah, if you’re trying to filter that out negative skew positive, yeah, if you can filter out the negative skew, that’s hugely valuable, right? So, so that kind of combination of of mean reverting and fundamentals works pretty good, because you’re going to filter out those big losers. And if you can do that, that’s a good combination, right? Where I think it’s it’s a lot tougher if you’re, if you’re in the trend following space, to make that combo,
Jeff Malec 47:23
yeah, which is most trend followers like, Hey, we’re taking all the losers, but they’re small, right? They just, they know that it’s an option profile. We’re buying all these straddles, buying, buying, buying, and then, boom, it’s going to pay off eventually, exactly, exactly. Um, so what’s next? More markets, more stocks. Yeah. We just, well,
Bill Gebhardt 47:44
we actually interesting. We just today. I just finished. We had a had an investor who was interested in the Chinese domestic futures. Yeah. And one of the things I really love about our model is how we look at back testing. For us, back testing isn’t anything to do with looking back with the markets. It’s give us a new market. We’ve never seen it. Let’s see how we do. So, yeah, so we are perfect at a sample test. It really is, right? So, you know? So I just took out of the box, you know, the same model we use for anything. We loaded up the sort of 30 plus futures contracts, domestic Chinese futures contracts, and ran it, and we get basically the identical Sharpe ratio that we get in the other global futures with with less correlation of the trend factor. So it’s like, oh, well, this is, this looks alright. So we did the same thing a few years ago on the on the crypto side, somebody was interested in crypto, and we said, well, let’s have a look. And crypto works fine. So the model seems to be pretty robust to as long as it’s a normal market, you know, and we, and we even, you know, one of the things we don’t do is we don’t cherry pick the portfolio. So I mean, Coco was a good example. And I don’t know how if you, if you mess around with a trend following stuff, but I can remember doing some trend following testing, you know, in the 90s. And the one market that stood out is really difficult, was Coco, yeah, but what is really not very trendy. And I think it has not been trendy for 30 years. So if you would have not put it in your portfolio, you know, you wouldn’t have caught this trend. Now,
Jeff Malec 49:08
as we’ve seen, I’ve seen that in a lot of portfolios, exactly.
Bill Gebhardt 49:11
So you know, if, as long as a market is a normal market, just because it’s never had a big move doesn’t mean it won’t. So we tend not to throw things out of the portfolio as long as they don’t, you know, behave in a way like, like, or very jumpy. So the model doesn’t do good with jumps, right? And no, trend fall. I
Jeff Malec 49:26
think can really do good with jumps, because it’s so, like, power doesn’t work. And no, no power does. So
Bill Gebhardt 49:31
this is, this is one of the other funny things that we’ve, you know, given my background, everyone who’s a power fundamental analyst, like, oh, trend fall, I can’t possibly work in power. And it’s like, well, it actually works just fine. Doesn’t seem to have a problem. What doesn’t work, though, is like we do some commodity spreads, so things like Brent ti that sort of move over time, or gold, platinum, or things like that that are almost behave like a normal market. Those are great. What doesn’t work is spreads that are tightly linked, that then tend to have dislocations. So like. Certain regional spreads will be very tight. Like, what are the there’s the wheat wheat spread in Chicago. What is it? The Minneapolis
Jeff Malec 50:06
versus Yeah, yeah, versus Chicago, yeah, exactly.
Bill Gebhardt 50:08
So you wouldn’t, you wouldn’t trade that with trend following, right? It, would, it would. It’s kind of a jumpy sort of shot, because it’s all, it’s a nice straight line until it isn’t exactly, exactly. So we exclude anything that’s that’s too kind of like, that that’s that doesn’t move, doesn’t move or and we also exclude anything that’s really too illiquid to follow. But yeah, so we tend to be, we don’t want to. I don’t like the idea, like, when investors will say, Well, what you know, if we only wanted to trade 20 futures, what would you recommend? I’m like, Yeah, you tell me which ones you want to be in, because I can’t pick them. I don’t know which. I don’t know what co what’s going to be Coco next year. Yeah,
Jeff Malec 50:45
we did a blog post, we put in the show notes, I think silver. It was maybe five years ago, but we It hadn’t made money on a trend trade in 15 years or something. Yeah, right. Was like 30 losers, and then had this huge winner. It’s like, well, that’s that’s what you have. This one, exactly your point. If you try and silver doesn’t work, we’re leaving that out. Yeah, gold Exactly.
Bill Gebhardt 51:08
And volatility is a bit like that. So we trade the VIX just from the long side, because, again, we don’t want the negative tails in there. And so we trade vix and some of the volatility related ETFs, and we do make money with them, but it’s not over time. It’s not a lot of money, but it really pays in certain times when you know trend in particular is getting smashed, right? So it’s like the hedge benefit, if you can, if you can, have a market where you’re not steadily losing money, even though this is it’s not trending, that’s a great market, right? It just tells you, when it does trend, you’re going to make a lot of money in it. So, so yeah, we tend not to. And then do you try to cherry pick that way to that point?
Jeff Malec 51:46
Do you have this crisis period type profile you believe you will in a, oh, wait, yeah, long drawdown at 22 type drawdown? Yeah.
Bill Gebhardt 51:53
I mean, one of the best years for the for the model, was during covid. It was a fantastic, fantastic year for the model, where everything was was trending dynamically, and volatility was paying off and all that. So 2008 would be, would be good as well, because you had to remember, back then you had huge commodity trends rate, oil went to whatever it was,
Jeff Malec 52:11
yeah, yeah. I get, I’ve gotten in that debate on Twitter and elsewhere, like, you’ll, people will think about a tail hedge and be like, Okay, I’m willing for it to pay 200 basis points a year, whatever, for this tail hedge. But then they look at a trend follower, and it’s only say it averages 3% a year. Positive averages, like, God, I don’t like that. It only I’m like, but it’s providing that profile with a positive carry. If it can hedge with a positive carry, that’s the whole rail, right? What do you do? Yeah,
Bill Gebhardt 52:39
I know that’s, I know they don’t get, like, our, you know, we don’t sell. We don’t, I haven’t talked to anybody, but just our volatility portfolio, it’d be a great thing to add. And like said, it doesn’t cost you any money. If things, you know, go derail again, it’ll, it’ll, it’ll be there for you. So it’s, um,
Jeff Malec 52:58
why not do that in the VIX futures versus the ETFs. We do both.
Bill Gebhardt 53:01
So for people who just want futures, they’ll just get the, obviously, the futures exposure, we do the ETFs as well. The other thing, it’s interesting. I don’t know if other people do it, but the triple levered sector ETFs like er why? Which is the energy triple that actually acts exactly like the VIX. It decays through time, because the trend is because of the costs of being triple levered, plus the fact that it’s generally up, right? But then it spikes. And so the system, our system, works just as well in triple levered, some sort of sector products, as it does in the VIX, and they tend to spike it well. In nice thing about energy, it has different spikes, right? So you get those different chances to capture spikes. So it’s kind of a interesting thing that we found.
Jeff Malec 53:51
Anything else we should know? Well,
Bill Gebhardt 53:53
the one thing that I think is interesting, and we mentioned a little bit about, if you’re trying to capture mispricing, I don’t think that what we’re doing is capturing mispricing. If I look at our like, if you can look at systematics as two types of camps, you have the alpha decay people, and if you’re an alpha decay person, if you believe it’s constantly decaying, then you’re constantly re optimizing to try to catch whatever’s changing, to catch that alpha decay, right? And that’s your your system. So that is a miss. To me, that’s a mispricing basis. But for us, you know, we’re using the same system, and if you look back to the 90s with the same system across the same markets, you don’t see any alpha decay at all. What you see is clear cyclical behavior in either look at how you look at whether you look at sharp ratios or alpha, we tend to look at Sharpe ratios. So you have years where the Sharpe ratio is, is really good, and then you have a pattern of years where the Sharpe ratio is, is, you know, not great. And you think about what you know, we’ve kind of dug into, what’s driving that well, from a trend following basis, right? After 2008 what happened? Well, the economy kind of contracted a clock across the globe. What did that meant? I mean, you had oversupply in almost every commodity market, which meant you had downtrending or chopping around masses, right? So trend following during those years on a global basis wasn’t working very well, because what was happening from a macro point of view. So there’s this tie back to what systematic is catching. It’s catching these big investment cycles in energy. Let’s say it’s also sensitive to these big macro changes. There’s times where trend following works well and times where it doesn’t. That’s not mispricing, right? And I you know, people want to argue that it’s a factor, but if I look at what people call trend the return on the trend factor or momentum factors. It’s not very it’s not great, right? The Sharpe ratio is not great on that. And what you know the good trend followers are delivering is much higher. So I know what it is. Is it smart? Trend? Is it smart? I don’t know what it is. I just know that it’s it’s not, it doesn’t look statistically like mispricing, and we’re not chasing alpha decay. So I don’t know what it is out there, but there’s something to what we’re doing that’s that’s kind of core to the way markets work.
Jeff Malec 56:06
You rang my bell of what I was gonna ask, which ties into what you just said. Do you think it’s because of the multiple time frames gives you better monetization, right? Like, are you get x thing Coco at a better place, for example? Are you doing things like that because of the multiple time frames and the multiple Sure, the
Bill Gebhardt 56:23
multiple time frames definitely helps. For sure, I think what really happens in our model is the long term time frames generate the overall positioning, and they generate the overall return. And the short term time frames for basically, like hedging, they’re like, they’re like, smart stops, basically. So they’re stopping you out of longer term positions when the short term is turning against you. And so they work kind of collectively, in a together really well, and
Jeff Malec 56:50
the weeks long. So you’re using basically weekly bars, as we would have caught it, called it back in the 90s, weekly bars. So that trend may hold for 18 months, or something like, what’s Yeah,
Bill Gebhardt 57:00
yeah. We we look at like we look at two different kind of measures of our holding period. If you look at when we adjust our position from, say, 10% long to 20% long, that typically lasts about two weeks. So that’s relatively short holding period. But if you look at how long we’re net long or net short, that’s much longer. That’s closer to two and a half, three months on, on average. So, so, you know, if you look at, you look at, I was using the example of Apple, you know, Apple stock. It was, it was basically an uptrend for how, I don’t know how many years was that it was an uptrend for a long time. So the system was never net short Apple for, you know, eight years. But it was also flat a few times. So it got to flat, and then it got 100% long, a flat. Long flat. So you’re, you kind of have this long position that you’re always just sort of delta hedging it based on what the short term is doing. And there seems to be some nice synergy there.
Jeff Malec 57:51
That’s another cool way to look at it, of delta hedging these long term positions Exactly, exactly. Awesome. Well, I think we’re going to name the podcast allergic to optimization. I like that line. Cool. All right, yeah. Well, thanks for being with us. Where can they go find out more website? Yeah,
Bill Gebhardt 58:13
we’ve got our 10 dynamics.com We’ve got an increase tab you can reach there. We also have a lot of content on there. Now we just put out a a white paper on how we handle risk management and operational risk from a systematic Fund, which is really important. One of the things we’ve kind of learned by doing, I would say we hadn’t really, and I think a lot of people don’t, don’t think about it. There might be some interesting stuff there for people. How do you view that
Jeff Malec 58:38
operational risk in terms of getting your signals off. You
Bill Gebhardt 58:41
mean, well, just all you could think about if you’re trading systematically, you know, in the you’re really doing it from where you’re basically the system’s doing everything from selecting the trades to automatic execution and everything else. If you have a problem, there’s a lot of ways that that can go wrong, right? So how do you what’s the best way? What’s the best framework to make sure that you’re managing that risk, but also that you’re not just, like stopping the system because one little thing went wrong, and then you shut everything down, and, you know, how do you keep from doing 1000 trades in a day? And, you know, all this sort of stuff. And I think we came up with a pretty I’m pretty proud of it, I would say, in terms of what we do, and the system really runs, really runs well now, and we have a whole framework for how we how we manage that. So I
Jeff Malec 59:27
think, did you read the Jim Simons book? I can’t, let’s call the I think I have it right over here, the man who solved the market. It’s about Renaissance, and Jim Simon, oh yeah, somewhere in there, early on, they were losing money with this model, and he couldn’t figure it out. And he couldn’t figure it out, and they had basically the sign flipped, I think, for or, like, multiply it by the value of the E Mini, for example, something, and they were like, oh, and switch that, and it just started, yeah, so yeah, that’s that kind of operational risk. That’s more model risk. But yeah, yeah, there’s
Bill Gebhardt 59:57
all that plays into it. You’ve got, you’ve got. Basically your pure risk management in terms of, you know, any limits or metrics that you have, like we have, we have limits and metrics that we manage, but that go along, that goes alongside operational risk things like, you have a database, and then you need the data from the database to feed into the model. Well, what happens if the database gives a missing value for something? Then you know, what are you going to do? Are you going to shut down the whole fund? Are you know, just isolated and, you know, can you solve it automatically? And all those kinds of things or things that we we sort of dealt with over the years. So, yeah,
Jeff Malec 1:00:28
if na do what? Yeah, yeah, exactly, exactly,
Bill Gebhardt 1:00:31
yeah, yeah. We’re lucky. We’re, um, we’re lucky we are where we are today in terms of technology, because I think it’s way what we’ve done, we couldn’t have done with the time and people that we have. It would have taken so much more 10 years ago than it did today. And just even having Python to do everything in this process, this this operational and market risk management process that we have, Python made it conceptually, much easier to deal with, I think, than had we been,
Jeff Malec 1:01:00
you know, then, did you use some your team, use some AI tools to even write that Python we Yeah, that’s
Bill Gebhardt 1:01:07
kind of, that’s kind of new. Most of what we built, we have, and I we do use it a little bit every once while, but it’s, it’s interesting. It’s very good at writing boilerplate, yeah, like, you know, give me a function that does this okay, but, but most of what we do, it gets really confused. Really
Jeff Malec 1:01:24
not boilerplate. Yeah, it’s not boilerplate, yeah, I keep I use it to write some market commentary stuff for some groups, and it can’t get past and I even have it in the script, don’t make this error, but Right? If the S P is down 1.5 and the fund is down 1.8 Yeah, it’ll say that the fund did better than the s, p, oh, really, yeah? Like, no, it was more negative. More negative is not better, yeah? And you can have it in the script. Don’t make this more negative mistake in an example, and it keeps good, yeah, exactly. So, yeah, it’s not quite there, right? So you don’t want that in your trading model to be like, whoops,
Bill Gebhardt 1:02:01
no, no. And we’ve we, I guess we’re as just philosophically, we’re as far away from AI as you can get, given that we don’t do any optimization. So we’re, I don’t know if we’re dinosaurs, and don’t know it yet or what, but we’re fighting the fight. Yeah,
Jeff Malec 1:02:14
I have some personal worries about that, that as more people coming into the space have grown up, so to speak, on the AI, they’re just gonna unknowingly optimize by using the
Bill Gebhardt 1:02:25
- Yeah, well, and it goes even further to you know, what I mentioned about how the traditional approach to at least, that I’ve seen with technical trading, was come up with 1000 different indicators with 1000 parameters and see what what with no connection to the market, or what the market’s doing, or how the market trades, or what’s a pattern that makes sense, you know? And this kind of stuff. So then
Jeff Malec 1:02:44
the next iteration of that, though, is, okay, all of these don’t work. I’m going to trade the ones that are working now and then rotate right like you keep moving the ones that are working to the top and moving the others to the bottom, and the others can come back in. Yeah. You
Bill Gebhardt 1:02:57
have 1000 AI traders, and then you have one AI trading manager deciding which one of the AI traders to bet on. You know, this is the this is where all this is going to go. I don’t know. And
Jeff Malec 1:03:07
then you need the AI to write the investor letter every month, because no one will know what happened.
Bill Gebhardt 1:03:12
Yeah, exactly,
Jeff Malec 1:03:15
exactly. All right. Bill, thanks so much good talk. Thank
Bill Gebhardt 1:03:18
you. Yeah, appreciate it.
Jeff Malec 1:03:19
We’ll look you up when we’re in London. You’re going to go to the Bears game. You
Bill Gebhardt 1:03:22
Bears game. Actually, that’s not a bad idea. Yeah, haven’t seen a Bears Bears game since the 90s. But, yeah, they’ll
Jeff Malec 1:03:29
be there at Tottenham stadium, I believe, against Jacksonville. Yeah,
Bill Gebhardt 1:03:33
the problem that was have here is the the football walls. They call them the football pitches here. They’re not used to such heavy guys, and so the turf is like, every time Wembley, the guys are like, slipping and sliding over the place, because they’re just tearing the turf up. So hopefully they sort that for the game. But yeah, they’re big,
Jeff Malec 1:03:50
yeah, uh, awesome. Good talking to you. All right.
Bill Gebhardt 1:03:55
You too, Jeff, see you take care. Yep. Thanks. Bye.
Jeff Malec 1:04:02
Okay, that’s it for the pod. Thanks to Bill, thanks to RCM, and thanks to Jeff Burger for producing. We’ll likely be off next week. Have yourself a good Labor Day weekend, but back soon after that peace
This transcript was compiled automatically via Otter.AI and as such may include typos and errors the artificial intelligence did not pick up correctly.
