In this episode of The Derivative, Jeff Malec sits down with Mike Stendler of O’Brien Investment Group to explore their history and evolution of trend following strategies. Stendler shares insights into their innovative approach, blending traditional trend models with machine learning techniques across multiple time frames. They dive deep into the evolution of quantitative trading, discussing everything from the O’Brien family’s century-long history in commodities to their latest strategies in futures, including a unique cryptocurrency trading program. Learn how modern quant managers are adapting to challenging market conditions, diversifying their approaches, and seeking alpha in an increasingly complex financial landscape. SEND IT!
________________________________________
________________________________________
Check out the complete Transcript from this week’s podcast below:
Beyond Traditional Trend: Leveraging Experience, Short Term, and Crypto with Mike Stendler
Mike Stendler 00:07
I’m not actually a believer in that. In that term trend following is a first responder, it’s kind of a second responder. And so the trend following models that we have in place are not going to kick in the day something occurs in the market. It really needs those prolonged trends.
Jeff Malec 00:23
Welcome to The Derivative by RCM Alternatives. Send it!
Mike Stendler 00:29
Thanks for having us. I’m Mike Stendler, Managing Director of O’Brien Investment Group.
Jeff Malec 00:42
You Hey, Mike, how are you good to see you?
Mike Stendler 00:50
Jeff, good seeing you again. Thanks for hosting the podcast today. No
Jeff Malec 00:54
worries. We’ve crossed paths at a few different events. What was it talking hedge, Austin and Nashville last year we down there.
Mike Stendler 01:03
Yeah, that is correct. And we’ll be a talking hedge again in Nashville this fall.
Jeff Malec 01:08
I’ll be there as well. I don’t think after I left you quick sort quick side stories, but I left you at that Nashville one with my suitcase, and someone’s like, Hey, let’s go to one of these little honky tonks and grab a quick beer on your way to the airport. I’m like, fine, but I have my suitcase, so I get it’s fine. So I two bars are like, No, you can’t come in with a suitcase. The third one’s like, Sure. Come in. I wheel it into the bar, and the music stops. The the singer’s at the front. She’s like, look at this city slicker I had to blazer on. She’s like, look at this city slicker with his suitcase and his blazer. Music stopped as a little embarrassed, but anyway, well, we’ll go down to one of the honky tonks down there again. There you
Mike Stendler 01:47
- Maybe, maybe you thought you were starving artist,
Jeff Malec 01:49
exactly. So give me a little personal background, and then we’ll get into a Brian investment group. So how’d you get started in all this crazy world? Sure,
Mike Stendler 02:02
I started my investment career, I guess, unfortunately, 40 years ago now, and started working at a brokerage firm, later went to work at a small cap value shop, and in 2003 moved to the quant trend following side of the business. So it’s been 22 years on the quantitative trading side.
Jeff Malec 02:25
I never knew that you were a stock guy. Yeah, small camp. You were on the other side.
Mike Stendler 02:31
I was on the other side, correct in 2003 again, got into the managed future side over I met some of the O’Brien family members throughout my career, talking to to John and Mike Durkin and some of the other family members is kind of the origins of O’Brien Investment Group, which, again, started in 2016
Jeff Malec 02:53
got it. And so you mentioned the name a couple times. So let’s talk about who the O’Brien family is and what the O’Brien Investment Group
Mike Stendler 03:01
is sure the O’Brien Investment Group was, was started as commodity trading advisor. It is part of the O’Brien family. That’s the ownership on it. We focus on trading, quantitative trading of diversified portfolio of futures. The O’Brien family has a long history in that the origins of that go back 100 plus years with the creation of RJ O’Brien. That’s been kind of the family business. Recently, he’s been acquired. They are the family, the legacy family even goes back to John O’Brien’s grandfather, Robert O’Brien, who was the two term chairman of the CME. In fact, he was on The Tonight Show in 1964 if you kind of dig through the or our PowerPoint presentation, he was on The Tonight Show with a live cattle, introducing live cattle futures. Then it was called the Merc, but it was launched on the Merc. So again, a long one that’s on YouTube. We could go pull that up. I’m guessing you could find it all right, we’re gonna try and find that. Put it in the show notes for a couple of our PowerPoint presentations as well.
Jeff Malec 04:08
Who was the host back then, 1960 joy? Bishop, Joey. Bishop, Yes, you. Colbert just got canceled, right? But like those tonight shows now, they would get canceled if they brought an FCM person onto the ship.
Mike Stendler 04:25
Yeah, yeah. I guess if you bring it alive cattle, because you kind of implying that this is live cattle, but eventually it won’t be live cattle.
Jeff Malec 04:31
Exactly. That’s correct. We just did our you’re following the podcast last week as meats, trading meats and cattle and all that. So good timing, but so that. But before that, 100 years ago, the O’Brien family was involved.
Mike Stendler 04:46
That’s, that’s correct, yes. So I work with John O’Brien, but his father, his grandfather, his great grandfather, were all part of the RJ O’Brien at futures clearing firm, headquartered in Chicago. So it’s again, the law. Long, long line of people. And what’s as
Jeff Malec 05:03
they must have been one of the founding members of CME are in there pretty early.
Mike Stendler 05:08
They were, they were there early, right? And it was especially in what I’d call the Midwestern type commodities and and that includes milk and eggs, but also all the grains. And, you know, they’ve seen the evolution of all the trading of that from the old days in the pits to now. Of course, it’s all electronic.
Jeff Malec 05:26
Yeah, I think right. Wasn’t it originally the Chicago egg exchange or something? I think its first iteration was something to do with eggs, which is ironic with egg prices in the news, and nobody really trades egg futures. So and then when you flip from the stock side to future side, that was with Clark capital. No, no. That was predated that. Sorry.
Mike Stendler 05:49
That was another CTA that I was working with. And again, that was quantitative trading. It was Chicago. So that kind of started my career working on that side of the business,
Jeff Malec 06:01
and what do you think coming from the stock side bunch of crazy Yahoos? Or you were intrigued.
Mike Stendler 06:08
It was, it was intriguing. I talked about this maybe in a little bit. But one of the things going back to, you know, when my early, early, early days of working was the crash of 87 and again, I’ll talk about in a second. But post the crash of 87 I was kind of young in the business. I started hearing about managers that had made money on that particular day, and it kind of stuck with me, kind of an intriguing part of who can make money when, in that particular case, a crisis is happening. And that was always intriguing. And as I started exploring different managers and strategies, etc, I started saying, yeah, there’s, there’s a group of managers, commodity traders out there that can trade both long and short, the stocks, the currencies, the bonds, commodities, etc. And I found it always intriguing, as compared to being on the long only side, which is kind of one, a one directional type trade, correct?
Jeff Malec 07:01
Yeah. And did you, did you know of the term managed futures? Were you like, I’m going to go check out managed futures? Or you were thinking more commodity traders?
Mike Stendler 07:07
No, it wasn’t necessarily commodity trading, but it was, and I didn’t know the term managed futures
Jeff Malec 07:13
either. Yeah, we made that up, maybe somewhere around that, that era. Yeah, some
Mike Stendler 07:18
managers also. You heard about some managers that were making money, and then particularly some of the European managers that were doing some interesting investments in, you know, kind of driven out of Europe, kind of the old AHL group that a bunch of spawned out of that, etc. So Winton, it was, it was interesting. And as I kind of pursued some of that, you know, I found it. So this is, this is more interesting than it is just being on the long side of trading.
Jeff Malec 07:44
And then how? So I didn’t know that you were there at the beginning, yeah, of O’Brien Investment Group, correct? Yes, and that’s different from the family office. So the family office said, Let’s start our own manager as well. Well,
Mike Stendler 07:57
it’s Yes, completely different than the family office. The overlap is that they are the ownership of O’Brien Investment Group, and my colleague is John O’Brien Jr, who I work with closely at the O’Brien investment group. And of course, he’s part of the family. Yeah.
Jeff Malec 08:14
And was that weird at the time of like, hey, let’s start a CTA when were other CTAs like, Hey, you’re competing with us at the FCM, or was there any weirdness to that, or you were kind of isolated and insulated enough that it it worked out?
Mike Stendler 08:27
No, no. I think the transition probably occurred prior to the start of O’Brien investment group. They had acquired Clark capital. Clark Capital Management, again, another Chicago CTA that was run by Michael Clark long, long line of being a commodity trading advisor going back to the 1990s so through their acquisition of that which, of course, came with people and systems and so that transition from them being a family office and O’Brien invest the RJ O’Brien being a FCM, they kind of already had that transition occur. But I think they wanted to kind of start something that was kind of new and different and and use some of the legacy stuff of Clark capital, but kind of go in a different direction moving forward. So that was kind of the start.
Jeff Malec 09:11
Yeah, I’ve actually traded with probably one of the first CTAs ever put client money with, was Clark capital back in Yeah. Oh, two of three ish. And our frustrating thing with him was always he would launch a bunch of new programs that had different portfolios and like, Oh, now this portfolio is working. This portfolio is working. So there was, yeah, it seemed like good models in there, but I was always frustrated with the portfolios. But he got out of the business. He sold that to O’Brien Investment Group, and you guys just basically took the IP,
Mike Stendler 09:42
Yes, correct. I mean, it was an ongoing business. And so we were operating the ongoing business. Michael was going to retire. And so we kind of took, you know, it took it over. It worked out for, for both parties, you know, Clark was an aggressive kind of old school trend. VIP. Manager. And you know, we wanted to use take some of that good part of that old school trend following, but then also kind of turn it into something but more our own, if you will.
Jeff Malec 10:09
Is he still alive? Yes, yes, living in retirement.
Jeff Malec 10:21
So all right now talk about so what you guys have created since then.
Mike Stendler 10:26
Sure we have, we have three strategies. When we first started out with our legacy, our flagship strategy, which is the quantitative global macro program, and that was available in a managed account. And we also did it in a fun format that was kind of the where we started on it, that later, as we started developing newer model groups that added into some machine learning and some other types of things. So we later have introduced a later in the sense that we just did it this year, a short term machine learning program the family office had an interest. And of course, the evolution of cryptocurrency started happening. So we ended up having a third strategy, which is, which is focused on trading CME cryptocurrency futures, or Bitcoin, Ethereum, and now, more recently, Solana and XRP. So, but going going back to the flagship strategy. We really tried to take what was good about the old legacy, trend following systems, medium term, long term, trend following systems, all quantitative trade, along with short term breakout. So that you know, ultimately, we know that works. We know that it has that crisis Alpha built into it, etc. The biggest difference that the evolution had started only five years ago was the addition of machine learning models. The machine learning models tend to be shorter in time frame, and they they’re predictive type models. So we have three different classes of machine learning models added to it. So it really kind of diversifies away from the the trend based models, and ultimately gives us a different source of alpha, if trend isn’t necessarily working, but we still, we still believe in the trend side. We just want to make we wanted to enter some models that ultimately could potentially make money if the trend you were having big trends occurring in the market.
Jeff Malec 12:10
You were prescient to name it global macro instead of trend back in the day. But you knew like, hey, we want to add some more stuff to it. Want to make this more of a macro program. We
Mike Stendler 12:19
wanted to make it more of a quantitative macro program, instead of having a just a traditional trend following model. Because, again, we believe in that, but we don’t believe that, we believe that you can do some other things on it to make it a little bit less volatile or less lumpy return
Jeff Malec 12:34
stream. Thank goodness. Given the past 12 months in trend for the classic, right man, the more classic, the more old school you are, the more pain you’ve endured. And is what I’ve seen across.
Mike Stendler 12:47
Yeah, I would certainly argue that trend obviously had a good 2022, but long term trend following hasn’t done a whole lot since that, and it’s been kind of two and a half years now, and it makes it difficult for investors to kind of absorb just kind of mediocre returns. They’re looking for some big trends, and it’s been a little bit more challenging on the trend
Jeff Malec 13:07
side. And this is the second time they’ve been duped. I feel like, right? It was like, Oh 80708, great returns, oh 910, 1112, even lackluster, right? Same thing here. 22 great returns showed its stripes now, 2324 25 lackluster, yeah, so yeah, we’ll fix that on another podcast. But they’ve been duped twice, so they’re like, but to me, the reason trend works is because you get paid for enduring those those flat periods, right? Like for the investors who are willing to endure those flat periods can get paid out on the outliers.
Mike Stendler 13:44
Yes, yeah. The thing about trend is that, again, I threw out the term crisis alpha, which is kind of a came out of the 2008 correction. I’m not actually a believer in that, in that term, because I don’t think trend following is a first responder. It’s kind of a second responder. And so the trend following models that we have in place are not going to kick in the day something occurs in the market. It really needs those prolonged trends. But what I say we’re believers in it is that we know that history has proven to us that there’s going to be some period of time where crude oil is going to move 40 or $50 could be up, could be down. We know that, you know, gold is going to go to 5000 or one, you know, whatever that might be. But ultimately, there’s going to be a big trend, or multiple trends in the marketplace where the long term trend models can take advantage of it. It’s that sometimes, is that it have periods of time where it trend doesn’t necessarily work as well. And that’s, that’s a challenging part for investors,
Jeff Malec 14:40
yeah, which is the billion, million, billion dollar question, whatever. How do you balance those two things? Everyone knows they need to add things to trend and make it more livable experience, right? So those drawdowns aren’t as sharp. But how do you do that without, you know, throwing the baby out with the bathwater, so to speak. How do you how do you lessen those. Down periods, without taking away the big outliers to the upside.
Mike Stendler 15:04
And our approach has been, yes, we, you know, we have the trend, but we want to make sure we’re diversified. I think just just in this, in diversifying itself, helps out a lot of the big trend followers, because of their size, are really forced to do a lot of big financial markets and less in the commodity space, particularly smaller commodities. So we want to make sure that we have the exposures to the coffees and to the soybean meals and to the Cocos and and rubbers markets like that, so that if indeed there is a movement in some of those, hopefully we can capture that. So diversification certainly helps out a lot, particularly in the commodity side. I think you know, that’s where you kind of first start. But then you also want to also want to look at multi time windows as well. So we have medium term, long term, we have short term breakouts. And again, all the machine learning models tend to be on the shorter side. So you hopefully that’s not taken away from when the big trend occurs. And I think it probably to some degree it will, but it’s certainly smoothing out the return stream that you have, and I think that’s the big benefit there. But ultimately, if you do have a trend going on, we still want to be able to capture that.
Jeff Malec 16:07
And what did you guys consciously make that like, Hey, we’re adding these three time frames, these three machine learning. It’s bringing the return down, but it’s bringing the drawdown down much more, or something like that, right? So the MAR ratio, if you will, is going up, even if the returns going down, right.
Mike Stendler 16:25
So I mean, every time we have introduced a new model, you know, first of all, it’s back tested. Sometimes it’s been traded with Prop capital. But ultimately, we’ll take a look at it, and we look at it multiple time windows going back to the last 25 years, then we could certainly go back further than that. And then we kind of slice and dice and say, What did, what did this model do for this, this window of time, this window of time, this window of time. And then collectively, so what did it do to the absolute return of the portfolio? And then ultimately, what did it do on a sharp basis, so risk adjusted basis? And ultimately, you know, if we’re looking at various different new models, will say, Okay, is it? Is it benefiting both of it? Because we want, we definitely don’t want to sacrifice absolute return to have a better Sharpe ratio, because we kind of want to have both. And so it’s a methodical process, and
Jeff Malec 17:13
each sharp, as they say,
Mike Stendler 17:15
but once, once a model is introduced, it doesn’t, it doesn’t just stop there. Obviously we’re monitoring closely, and we’ll take a look at it and and, you know, sometimes it did me a situation, we’ll say, Hey, we’re going to put on the watch list. And sometimes it could be a little tweaking. You go from a one day bar to a two day bar, or something along those lines, without getting too wonky in this conversation.
Jeff Malec 17:35
But wonk away. We like it, yeah, but they know that’s kind of
Mike Stendler 17:39
what we’re gonna look at. And, you know, push come the shove. We might say, You know what? It doesn’t seem to be performing as good in live trading as it did, kind of back testing trading. So, right? But the other part, Jeff is that you got, we were very slow on our risk budget. So every one of our models, we have 45 different quantitative model, 45 plus different quantitative models. Each one of these models have a special risk budget. We’re divided into 13 groups. So we introduce a new model, we’re going to start with a low risk budget and kind of see how it works. And then if it seems to be doing exactly what we think, we might bump up the risk budget on it. And that’s and again, even if we’re bumping up a risk budget, it might go very small incremental. So we don’t want to be introduce a new model that just dramatically changes the overall results of the overall program.
Jeff Malec 18:25
And what? What say those numbers? Again, 45 models. 45
Mike Stendler 18:30
plus models. Yeah, 13 different we have. We have them divided into 13 groups. So we have multiple medium term trend following. We have multiple long term trend following. And then in the machine learning, we have three different classes of machine learning, and then those are kind of separate model groups, and then different time windows and mixed in there. So it’s a lot of different models that are operating on a day, on a daily basis. So these are all quantitative.
Jeff Malec 18:52
And then each of those, and I want to get into the machine learning in a second, but each of those is doing upwards of what 6070 markets.
Mike Stendler 19:00
Yeah, our universe of markets that we trade is 70 plus. And you know, in general, you know, some of the markets are big notional exposure, big margin requirements, so you have a little bit less sizing going to it. But yeah, the platinum market, something like that, yeah, I would say on any given day we’ll have 45 to 50 markets on
Jeff Malec 19:23
got it. But I was getting that you could have all 45 models across these 13 groups could be operating on any of the 70 markets. Or you’re saying some of them might be too big notionally to Yeah,
Mike Stendler 19:35
yeah, there’s, there’s some nuances to each one. But if a if a market is moving in our favor. It’s not uncommon to have, you know, 12 or 13 different models working for us. Now. We do have a risk overlay on top of that that will size a position. So if we’re getting too many models kicking in and building our position side, then we are. We have an overlay that will actually shrink the. Next trade, it might be down and a half, and it could be to zero too. So
Jeff Malec 20:04
yeah, but that’s a I like that model because it’s a voting machine basically. Hey, all of these models are agreeing on the same thing. They may all be wrong, but unlikely and unlikely they all be wrong at the same time. I Yeah.
Jeff Malec 20:25
So the machine learning piece interesting for you to call it machine learning instead of AI. Did you? Did you predate the term AI or what I
Mike Stendler 20:34
think we were we weren’t cool back then. So we just appreciate learning. We started the our first model that we introduced to the program was about five years ago, so that was before everyone was calling it AI. I guess if we probably did it today, we’d probably call it AI, to be cool, but yeah, we machine learning models. That was the first one, the first class of models that we introduced. And again, we’ve introduced a couple different classes, now three different classes in total, and again, multiple time frames on it. So what makes it unique? And I think what people in a traditional trend following and use, kind of a 200 day moving average as something simplistic, if you want to call it that. But you know, if you’re if the if the market is above the 200 day moving average, you go long. If it’s below, you go go short. Kind of a real simplistic, you know, quantitative type trading in machine learning, it’s substantially different using predictive windows. So you’re kind of looking at what’s the return over some period of time. So let’s use 10 days as an example. So what’s the return over a 10 day period of time, and then you can kind of rank it according to the returns, right? And so you kind of start looking and saying, Where were the what were the returns for this period of time on this ranking? So you’re ultimately looking and saying, What, what 10 day window was positive. And then, and then, once you have that, that result, you can look backwards over the last X number of years, 25 years, 10 years, whatever you’re looking at, and say what happened during that, the time preceding that that produced those results. So it makes it it’s a completely different look versus saying it’s above a moving average or below moving average. I think the other thing that is unique is that trend following a traditional trend model would say, I’m going to go long crude oil at 70. I don’t have a stop at 62 and if the crude oil moves up, I’m going to just keep moving to stop up, and I’ll have that trade on it could last for a day, and it could last for two years. Machine learning, we actually have to revisit that window. So if we’re doing 10 days, if that 10 days is up, and let’s just say that market didn’t do anything where zero ote, we would then have to run the model again and say, Should we have that position on again? And it could get us out. We could be at a power we could be at a positive gain. Could be at a loss or even flat. So you’re
Jeff Malec 22:52
correct. So it’s almost the antithesis of trend at extreme levels, right? Because you’re like, we’ve all done that being trend followers of why it feels so wrong to buy it here, right? It’s come up so far, so fast, right? It feels wrong, but the model is telling you to buy it there, when it feels like, yeah, the machine learning could be like, Well, if you bought it here, nine out of 10 times that actually wasn’t profitable. So your your intuition was right, yeah, the problem is, the 10th time is insanely profitable, right? It’s this huge outlier. Is why trend does it, but Right? Putting the both together,
Mike Stendler 23:24
yeah, even with that, though, all, even the machine learning models, all of those stuff stops in place. So we want to make sure that we’re always protected on, even on those models.
Jeff Malec 23:33
And then are you letting it do? Like, how are you informing it? So how many machine learning models are there 1313, so all you started with one, and you’ve been adding incrementally, that’s and then you say, Okay, this one is looking at this way. What if we tweak these three inputs basically? And said, look at it a little different way. Or is it all time based? No.
Mike Stendler 23:56
So the first class that we introduced, we had a certain timeframe, the model was working well for us. We introduced the same class of machine learning with a different timeframe. Once we introduced another machine learning model, then we said, you know, let’s, let’s take a look at a different timeframe as well. Same with the third class of it. So they’re completely different. They’re neural network, deep learning type machine learning models. But we want to have a little bit different focus to each one of them. The ultimate goal certainly is that there’s there’s a low correlation, and the correlations between many of them are less than 50% so ultimately, that’s kind of what we’re looking for as well. We don’t want to keep introducing machine learning models that, you know, they could have the same time frames, and then you have a correlation of point eight or point nine that doesn’t add a whole lot of value.
Jeff Malec 24:42
And then those are relatively they’re short. How short term are we talking like you’re in and out of those trades inside a day or the couple days? No,
Mike Stendler 24:50
no, they’re not that short. We’re really kind of focused on kind of one to two. We could do what we call short. And I know there’s periods of time in the market. It, and this year is probably one of them, given the all the announcements coming out of the Trump administration, but which makes it a little bit more challenging, you’d probably be better off trading in one hour increments now, but ultimately, we set it up to trade in kind of one to two week some of them are longer than that, but kind of one to two week timeframes.
Jeff Malec 25:18
And has it gotten easier for you guys to run and test that with the boom, boom and AI and all these different models that you can use now, are you just still using your same systems you use to build it in the first place?
Mike Stendler 25:31
We’re using the same same systems? Yeah,
Jeff Malec 25:35
yeah. It’s funny to me the whole AI thing, I’m like people, yeah, trend followers, quants have been doing this machine learning basically for years and years, right? I think the the difference now with AI, to me, little sidebar is the they’re using text, right? It’s basically doing what trend followers and quants have been doing for decades, with numbers, with prices, but with text. So that is different, but in terms of, like implant, putting it onto a quant model, unless you’re just letting it unsupervised, say, Find me some any way to make money. And it says you should trade only on Tuesdays or something right, which you’re not doing anything of that, sort of like unsupervised. Tell me a way to make money with this data that,
Mike Stendler 26:19
though we’re not using any of that. Keep in mind with it, when we look, when we’re looking at it, it’s strictly the price side of it. So we’re not there’s any large language models. There’s a lot of it. Really interesting ideas floating around on large language models. And I think as as time goes on, and probably faster than both of us would imagine, there’ll be some interesting large language models that we could probably tap into that might provide some interest, you know, some some trade ideas that would be just pure price. You know, the right now, we haven’t seen anything, or anything that’s reliable on it. There’s been some interesting studies on it. I think it has worked out. In many cases, it’s worked out for a day. That’s, that’s, that’s driving sentiment, the sentiment of the price of of gold or crude oil, or whatever it might be. But it seems to be very, very short term in nature. It doesn’t, it can’t. It doesn’t necessarily produce the sentiment that’s going to drive something for a week, two weeks, three weeks, four weeks, five weeks, so, but, you know, I have no doubt that we’ll be looking at at some of that stuff over the next couple of years.
Jeff Malec 27:23
My pet theory, my worry is, we’re gonna write the big back testing problem in our world is, don’t curve fit. Don’t trust the back test. Make sure you have all the things like over optimize. Don’t over optimize. My worry is, like, all this AI is we’re basically creating a new generation of optimizers, right? That they’re going to over optimize. They don’t know those lessons of don’t curve fit. So all of this large language model is basically curve fitting answers and text and copy and going to make movies basically, that are all curve fit, whether, whether that has as much pain in the in the real world as in the financial world. Who knows? But yeah, actually,
Mike Stendler 28:04
the interesting note to that is that when we first started looking at the machine learning, we the research was curve fitting right. It was say, let’s, let’s, let’s do machine learning for soybeans, let’s do machine learning for the euro, etc. And we started doing that, and it worked good, and back testing, but the more you kind of dug into it. So if you looked at a three, three or five year window, 2002 to 2005 whatever window you picked, and it worked really well, but you could curve fit it. But then when you start looking at different time frames, the performance wasn’t there. And ultimately, when we we started introducing and we said, You know what, if they’re more successful when we weren’t curve fitting, and we laid one single model across all of the different markets that we trade. So you got away from the whole curve fitting, so that what you just said is 100% positive. It still works today. Get away from the curve fitting and and some models on a specific market. So, you know it worked. Sometimes it works. Hey, works Hey, works really good for two weeks and then doesn’t work for you
Jeff Malec 29:04
anymore. What that my words like, the people are going to get so used to using the AI, they won’t know when it’s curve fitting or not, right? That’s another podcast topic. So this worked out so well that you guys separated out as a separate program, or it was doing some it was different enough that you said, Hey, let’s separate it out. It’s a separate program.
Mike Stendler 29:26
Well, one, it was, it was working well for us. So that’s first and foremost. But secondly, because we took us the shortest of the window time windows, and we thought we were diversified enough, but we wanted to focus on kind of the short term nature of it versus the longer term side. So we took and introduced a new program. It’s called short term machine learning, and that’s all kind of one to two weeks. That’s, we don’t go in any longer than a two week time window. One, it’s all machine learning, but two, it’s a shorter timeframe. And ultimately, we were hoping that that would would. Obviously both these models are all used in our quantitative program, so it’s not going to be completely different, but ultimately, we wanted to have a little bit of more diversification and hopefully a lower correlation between the two strategies. And of course, there’s less models that are in it as well. We could offer the program at a lower minimum. And again, I think the machine learning makes it kind of special and unique as well. They’re both diversified, so that that’s that’s definitely a positive, does
Jeff Malec 30:24
it? So what? It runs super low correlation with trend, not just with your trend, but overall the trend indices and
Mike Stendler 30:32
whatnot. It runs a low correlation to trend, yeah, but it’s going to have a little bit higher correlation to our flagship quant program, because overall, the model is already being used in the correct but yes, and I would expect that if we have a really big trend, some of the models in the short term program will pick some of that up, but we’re not going to have a if 2022 happens again, these models will probably they’re certainly not going to produce the dynamic returns that a trend manager will have, yeah,
Jeff Malec 30:58
but that’s what you want out of that space, right? You’re like, hey, grab as much of that upside as possible and as little to zero of the downside as possible.
Mike Stendler 31:06
Yeah, because all these models have relatively tight stops, besides what I mentioned before about revisiting the model and re running the model, so they might be getting you out. But ultimately, the stops are modestly tight, certainly compared to a trend manager. So yeah, if you have a big trend reversal, you’ll be out relatively quickly.
Jeff Malec 31:31
Last piece you got into crypto, so that’s a separate program. And how did that come to be? Where have you always been a crypto guy
Mike Stendler 31:40
on a personal basis, no, but maybe that’s a demographic. It could be a level of interest. But so the family office started investing in some different crypto projects, not necessarily specific spot cryptos, but in some in various aspects of the crypto industry. And we started having conversations at our firm. Really started probably back in 2018 and we started looking at the crypto market. Obviously it was evolving, and we’d have these huge runs, and then you have these crypto winters and that. And so we said, You know what, let’s there’d be some interest there if we started looking at the crypto space. The Evolution kind of helped the decision process as well, because along the line, it wasn’t just spot cryptocurrencies. The CME started trading Bitcoin futures. Later, Ethereum and again, most recently, solarium and XRP. The our level of comfort with counterparty risk with the CME skyrocketed. We were futures traders. That’s that’s the origins. That’s our history of us, right? So we felt very comfortable trading the future side of it when we first started. So we started the fund in January 2021, and ultimately we were doing both the futures contracts and some of the spot. A year later, we just said, Let’s just trade the futures on it. You know, it’s keep in mind, this is, this is cryptocurrency. So just by nature, it’s a lot of volatility to it. Then, if your diversification is Bitcoin, Ethereum, and, you know, a couple other cryptocurrencies, you’re kind of lacking diversification. They kind of trade together, right? So you have a lot of volatility to it. It’s a fun and interesting fund. It’s done well. But you know, to some degree, and it’s long, short, right? So we can trade it both short. It takes a special investor to want to look at at a cryptocurrency, but there’s a lot of dynamic returns to it. One thing about cryptocurrency, if you’re if a quantitative trading, one of the things you want to like the most is something that that moves a lot over a win, over periods of time, it has a lot of volatility, because that’s where the opportunity set is.
Jeff Malec 33:45
And then, real quick, back to the trend and the machine learning. Do they include the crypto futures and their universe? Or it’s only in the crypto program,
Mike Stendler 33:56
the we actually do trade Bitcoin, the the micro contract in our our main program, yeah, it’s one of 70 markets, though. Yeah, if a individual investor that wanted to manage account with us said, Please don’t trade it, we would be happy to take it out. It’s not a big deal. It’s one of 70 markets. So the impact on that particular program’s results is going to be pretty modest, yeah,
Jeff Malec 34:20
but to me, it’s always when, when trend followers. Like, no, I don’t want that in the portfolio. Like it’s, it’s an asset that moves up and down. Like, check your feelings on it if it’s real or fake. But, I mean, I guess some would say the word is, it goes to 01 day, just automatically.
Mike Stendler 34:34
But, yeah, but yeah, we were trading it long and short, so yeah, exactly. The opportunity to make money, if I look at it, this year, we’re down a little bit in bitcoin trading, a bit. If you follow Bitcoin, it kind of peaked at the end of the year. We had a kind of bottomed out in February. Has moved back up, but it’s kind of still, you know, it hasn’t had a huge move this year. Some other ones have big but Bitcoin hasn’t had this huge move. So it’s been a little. The tougher market to trade. Again, if I looked at it today, we’re probably close to breaking even on Bitcoin this year. But it’s again, one market. So even if it has Bitcoin goes at 200,000 Yeah, we’ll make some money at it, but it’s not going to be the key driver in the portfolio,
Jeff Malec 35:16
right? In the main program, in the crypto program, that would be precise out, yeah. Well, that’s yeah. So in the crypto programmer, is there a are you trying to have a beta to Bitcoin, or to any of them, or is it purely absolute return? No correlation.
Mike Stendler 35:31
Well, we so we have, we trade all of our quantitative models, and we actually have a discretionary, kind of semi static, long position that we use kind of for risk management. So we have a little bit of beta there, but we could in that probe, in that it’s called Pecos, but Pecos could be, we could be net short, and we could be 100% plus net long. So we do have both sides of that to trade on it, but there’s, there’s definitely a bias, I think you, if you trade Bitcoin on you and your investor in Bitcoin, you probably have a bias to the upside, but given the fact that we are traders at heart, we’re certainly going to take advantage of the trading mentality, and we could certainly be short Bitcoin as well, and Ethereum and the other ones.
Jeff Malec 36:16
Yeah, it surprised me. I guess there’s a couple out there, but surprising me, there haven’t been more pure crypto programs like that, using the futures. I guess, until very recently, it wasn’t quite liquid enough. You’ve got a huge roll cost. How do you guys handle that? Or because you’re trading, it’s not really, you’re not trying to replicate the futures.
Mike Stendler 36:34
We’re definitely not trading. But I think your first point is probably more valid when Bitcoin, you know, when Bitcoin first started trading on the CME again, it added a lot of value from a counterparty risk, but margin requirements were high. It was difficult. A lot of The FCMs didn’t want to trade it.
Jeff Malec 36:50
If only you knew someone at the FCM
Mike Stendler 36:53
Yeah, probably helped out. Yeah. I mean, I think The FCMs dragged their feet, but eventually did it. There’s not a ton of volume we, you know, if you look at the options, it’s still pretty thin. Ethers probably, you know, came along next. There’s still not a ton of volume there. It’s getting better, sir. And if you look at the recent ones, they’ve introduced Solana and XRP. Solana, the volume is pretty small there. So, you know, if indeed, you’re talking about, you know, some, you know, gigantic managers stepping in and wanting to do some strategy as a stand alone, it would be a little bit challenging, you know, there’s, there’s, there’s plenty of liquidity for us. But you know, if you were going to talk about a, you know, billion or 2 billion or $3 billion you’re going to start running in some, some issues there.
Jeff Malec 37:41
Yeah, the the you Yeah, the the allowing the ETFs has helped out the space, right? A lot of them are using the futures, and that’s provided the liquidity, yes, for us, for the traders. Um, so what’s next? Got more more programs on the list, or you guys are set for a little bit?
Mike Stendler 37:58
I think we’re definitely set for a little bit, we just introduced the short term program, even though we’ve been trading it for a couple of years. But, you know, that’s new. We kind of want to keep working that one. You know, we’re always going to be looking at new model opportunities, etc. We get together once a week as a as a group, and we chat about what’s happening with our models. We what’s happening with our with our different markets that we trade, etc. We’re all pretty open about saying, Hey, we should look at this particular market, or that we don’t trade lumber. We don’t trade, you know, several other markets. So should we introduce a lumber to it? You will talk about it. Is there liquidity there? Does it make any sense? Etc. You know, a lot of those type of ongoing conversations most of the time. You know, it doesn’t take a step forward, but I think the three programs that we have now are good. We’re not looking to expand that, certainly for the balance of the year.
Jeff Malec 38:53
Yeah, and that’s we’ve had some on this podcast of the new frontier for trend following seems to be in these smaller, esoteric markets, maybe they’re traded over the counter and those kind of things, but so nothing like that at this point.
Jeff Malec 39:15
A little segment we do with all our guests here, if you could travel back in time to any market event, just to witness it, to trade it, to do whatever with it. What would it be and why? Yeah,
Mike Stendler 39:26
it’s an interesting question, and I think I’m going to answer it if I know, if I knew, then what I know now, it would probably be a lot more interesting. But I mentioned I had started my career in the mid 80s, I was working at a brokerage firm when the crash of 87 came along, I was responsible for the mutual fund and annuity department. Anybody that was familiar with the business back then knew that most annuities were fixed annuities, fixed rate interest rate annuities and most of the mutual fund. Business was all government bond funds, right? That was kind of the big thing, certainly in the back in the 1980s so I’m in my news fest, yeah, I’m in my office both the Friday before and then the Monday crash. And we had a little box in the in every office, which was in Chicago. No, that was in Milwaukee. It was a little box called the Squawk Box. And if anybody not familiar with the the show in the morning at CNBC called Squawk Box, we all had this little speaker box at our in our office because, of course, we didn’t have emails and and internet and all the other stuff. And so every day, we all the department heads would have to get on and talk about what’s happening for the day, right? So that and plays all day. You could turn it on or off. It kind of plays all day. And people are talking about what’s happening in the markets, etc. We also had quote Ron machines, which was kind of before Bloomberg machines. The interesting thing about it was the quote Ron machine for the Dow Jones Industrial Average only had two digits when the market was down more than 100 points. You didn’t know, unless you had to do, you had to do the math to say, okay, that the Dow Jones is down x, right? And so, and you, my entire day was spent
Jeff Malec 41:14
on the phone, right? There was no showed it was down 99 points, basically, yeah,
Mike Stendler 41:18
- So you look around and say, Okay, it’s the markets down 25 points, whatever. Not a big deal, right? And because of the what I mentioned about the departments I was responsible for, my phone really wasn’t ringing that day. I’m talking about, you know, keep in mind, Friday was down big and then Monday, of course, was, it was the crash, but I was only a couple years out of school, you know, kind of kid not really fully understand what’s what’s happening. And as time kind of went on, on Monday, I was like, it’s kind of a boring day, kind of slow, and I just heard a lot of yelling and screaming, and, you know, this 1980s and a lot of expletive words, but going on, but went over the OTC trading desk, and it was just a zoo. My overall point on my story is that, you know, I was kind of just so naive about the business and what potentially could happen in a day like that. And then, you know, it was just, it was just really fascinating to watch, but I kind of really didn’t appreciate it at the time of going through it, of what was actually happening that particular day, you know, melting down to the market. It was, it was, it was, it was, it was interesting, Dave, but I we kind of wish I knew, you know, I had the hindsight to say, Boy, look what’s going on and really kind of absorb a lot more of it.
Jeff Malec 42:29
Yeah, where did you have mentors went out like, hey, take every dollar you earn from here on out and just plow it into the market. Or were people spooked?
Mike Stendler 42:37
Oh, I mean, they definitely spooked, definitely, yeah, yeah, yeah, it was. It was kind of an interesting time, because people were spooked. And
Jeff Malec 42:49
you’d be like, the stock market, are you crazy? If you think
Mike Stendler 42:53
of 2008 I mean, there was plenty of times to be spooked, and yet it took another, you know, several months before, you know, even though all the kind of market was collapsing in August of September of 2008 you know, we kind of bottomed out in March of 2009 right? So if you jumped in, you kind of, you had to have staying power to be able to do it. Turns out, you know, those are great times to buy. But unlike the crash of oh eight, yeah, it took a while. Oh eight, 2019 88 was kind of a rough year. Of course. You know, 1989 came along and things kind of went back to, you know, the good times again. But you know, and I guess people look at the results of the stock market, s, p5, 100. Dow Jones back then, you know, it was a good year in 2000 and excuse me, 1987 from January to August, it was a really good year. So when it started correcting, it’s kind of like it’s kind of taken profits, but wasn’t really an ugly year, and it didn’t turn out to be an ugly year overall, just that, unfortunately, the drawdown will happen. And of course, of a two day period of time,
Jeff Malec 43:55
I think that helped the futures markets right, the whole portfolio insurance. And everyone saw the flaws in that. And said, hey, maybe we should edge with actual futures and futures options and options, options probably help Chicago, that, that event.
Mike Stendler 44:10
It probably did. But, I mean, if I think back of the, you know, the term portfolio insurance, a lot of people were going, I heard the word, but I’m not exactly sure what it does, yeah, and when the market keeps going up, as I said, through the bulk of 1987 it was kind of like, I don’t know what insurance you need. Everything’s
Jeff Malec 44:27
great, yeah. And I actually don’t even know I got it looked at what, what they were actually doing with that portfolio insurance piece. Awesome. What else we had to talk about? Anything?
Mike Stendler 44:39
No, I think we covered it. It’s, you know, I think as we look at, you know, this year and kind of going forward, I mentioned, I kind of touched on it before, this has been a challenging year for I think most managed futures managers, regardless of if your machine learning trend means. Same term, short term trend, relative value. It’s been a very, very difficult year. Part of it has been headline driven, certainly the tariff announcements back in April, the budget bill, there’s been plenty of kind of headline things. I think we’re slowly getting beyond the tariff scare. I mean, we were signing agreements with India, and I think this morning, etc. So there’s, we’re getting past a lot of that, and I think ultimately that’s going to, you know, start putting some some movement and trends and a little bit kind of, you know, less chaos into the into the market driven by the headlines. That’s usually good for trend following. You know, might it might take the rest of the year on it, but ultimately we know that eventually the markets are going to trend. It’s not going to be, you know, we could go in recession with it, with the all the tariffs, and ultimately, or we could start seeing the dollar that just started moving down. If you look at the euro, euro went from 104 or 105 to 118 now it’s back to 114 handle, yeah. Who knows if the if the if the dollar starts weakening against some of these currencies, where you start seeing a Euro going from 114 handle to a 140 handle? Yeah. So those are the type of moves that ultimately we know that as managers, we can make money on. You know, same with gold. Gold’s obviously moved up a lot. It’s been kind of sputtering around this 32 3300 level. Ultimately we think we could make some money. But, you know, gold could certainly go to $5,000 and that’s where we can make some money on it. So, you know, those are the opportunities. I think we’re always optimistic that you have to, kind of have to be, but you know, we think we can get generate some big gains again. And you never know, there’s always that one off market again last year was coffee, a little bit cocoa. Some of the soft markets really helped out. We made a lot of, a lot of a fair amount of money on the grains. Last year, being weak, this has been a bumper crop for grain, so kind of, we’re see where that goes as we get into fall. So yeah, there’s a lot of
Jeff Malec 46:49
property. Yeah, yeah. But I was holding guys up right. Trend following is dead. All this, like, this bad period. I’m like, Well, there’s a few guys that have bucked the trend. Pun intended. So yeah, keep doing what you’re doing. Those models seem to be covering the trend downside for now, keep doing your show. And I prefer, if you’re European, you say cocoa instead of cocoa. Cocoa. Okay, I’ve always loved when I’m at a panel and these guys are like, the cocoa trend you and you know, a lot for a quant guy, right? Most of these guys can’t tell you what any of their markets prices are at. So do you actually? How do you reconcile that you follow these you’d like to, like, take in the news, follow what the markets are actually doing, even though your model doesn’t really care why they’re going up
Mike Stendler 47:35
or down. It’s, it’s both professional and a hobby. You kind of find it what’s what’s happening. I get up early and kind of turn on the CNBC or Bloomberg and they’re talking about the various markets. And of course, our industry puts out a lot of information. The FCM has put a lot of information about what’s happening on the fundamental basis. And so you can kind of hear both the fundamental side and the quantitative side. And ultimately, when, normally, when there’s a big movement in a market, it’s usually a combination of quantitative and fundamental everything’s kind of in the same direction. So if your corn is going down and the quants are shorting it, and it’s been a it’s going to be a bumper crop, and it looks like it could be, you know, supply is gonna be fantastic. You ultimately, usually those are going to be the better trends. You know, the models will dictate. It doesn’t really matter what I think. If the models are dictating us to be short, we’re going to be short that market, and vice versa. You know, I’ll have my opinion, but we don’t. We don’t overlay our opinions on any of our trading.
Jeff Malec 48:38
I tell clients, I’m like, You need to learn how to talk the market, because you’re going to sit in investor meeting, they can understand that it’s a quant model, but they’re really going to understand when you talk about, there’s a huge crop in corn, and that’s why it’s going down, right? Like, so to me, my advice is always like, hey, learn how to talk the market, whether you care what it’s doing or not. You need to learn it. Do you think you could be a discretionary trader?
Mike Stendler 49:04
Yeah, probably, probably. But I mean it, you do what you want to do. I think the best, the best times, is when it again. It’s a combination of quantitative trading and fundamental trading, where you can kind of look at both sides of insane, because, you know, it’s the worst thing about any discretionary trader is I’m right, I’m right, I’m right, and markets going against them, and they keep thinking, I’m right, I’m right, I’m right. So you, you kind of have, you want to have the technicals behind you to say, You know what, you’re not right. So, but you were, you were just talking about the the fundamental side. There was a FCM that put out a interview with a Mississippi cattle farmer, and he’s a farmer, and he’s kind of saying, well, there’s less cattle and supply sound, but demand is still there. You know, forget all the everything. That’s just kind of somebody saying, you know, prices are up. And, yeah, meats is one of the area, both live cattle and lean hogs that we’ve made some money on this year. It’s been a. Been a good trade. It’s too small markets. So yeah, we made some money on it all time. Huh?
Jeff Malec 50:06
Awesome. Think we’ll leave it there. Thanks. Mike, Jeff, appreciate it. Thanks. Great. Talking to you.
This transcript was compiled automatically via Otter.AI and as such may include typos and errors the artificial intelligence did not pick up correctly.
