Trend following. The dynamic systems & strategies that make up a modern CTA, w/ EMCs John Krautsack

From his days as a clerk in the S&P pits to becoming Chairman and CEO of EMC Capital, John Krautsack is here to tell us about his journey in the futures industry — and nothing is off limits! From his wild west pit days to working for one of the original turtle traders, the late Liz Cheval, to what it’s like implementing a new vision and everything in between.

With their Classic program having been around for 40 years, John gives us a glimpse into EMC’s  recipe for long-term success. We’re talking about building robust systems for trend following (EMC is featured in our latest Trend Following Guide here), various strategies, automated research, AI and machine learning, plus more. Hold on to your seats; this episode will take you on an adventurous ride!


Find the full episode links for The Derivative below:


Before you go, check out these items mentioned in this episode:

Blog post: Liz Cheval: From Turtle to Titan

Podcast: Trend Following Turtle Tails (and Tales) with Jerry Parker

Whitepaper: Newly Released Trend Following Guide


About John Krautsack:  John directs all investment activity at EMC and started his career in the futures industry in 1985 as an assistant to a prominent S&P 500 trader at the Chicago Mercantile Exchange. From 1989 to 1995, he managed trading operations for De Angelis Trading/Crown Capital Management, JPD Enterprises, and ALH Capital. He joined EMC in 1995, overseeing trading and managing the portfolio until he assumed the role of Chairman in 2013.


Check out the complete Transcript from this weeks podcast below:

Trend following. The dynamic systems & strategies that make up a modern CTA , w/ EMCs John Krautsack


Jeff Malec  00:07

Top of the morning value trend followers out there. We’re digging in deep with one of the longest tenure trend followers around in today’s episode trend appears to be coming back in a big way with bonds selling off and commodities taking off and we’ve got John crowd sec, Chairman and CEO of EMC capital who’s been trend falling in one manner or another with clients hard earned money since 1985. I was 11 Wow. So how do you stick around that long in the cutthroat hedge fund world? How do you stick with trend following through all its fits and starts over the years? We’re finding out send it This episode is brought to you by our Sam’s new white paper on way for trend following almost like we plan that check the link in the show notes or go to our CM Altstadt COMM And right up top on the menu there click education and white papers to download the newest paper where we get into what trend following is why it tends to work in inflationary environments. Why commodity exposure mostly sucks unless you do it smartly via trend. And highlights on five trend managers we recommend. Many of them have been on the pot here so you’ll recognize the names. So go check it out today. And let us know what you think. Now back to the show. Okay, we’re here with the main man at EMC Capital Advisors. John crowd, Zach. How are you, John? Hey, good. Good to see you.


John Krautsack  01:39

Um, see you as well. Yeah,


Jeff Malec  01:42

hopefully we’re warming up here. And you’re in the northern Chicago suburbs. Right? That’s right. Yep. So hopefully we’re done with the winter, but I think it’s supposed to snow like a foot on Thursday, right.


John Krautsack  01:55

Geez, I didn’t hear that. Yeah,


Jeff Malec  01:57

yeah. Um, so let’s start off a little background. So you started as a clerk in the s&p pits that right?


John Krautsack  02:04

Yep, that’s right. I had a I had a good friend of mine whose brother in law worked as a friend of mine whose brother in law was a big s&p trader, big local trader in the pit. And he asked if I wanted to go down to the mercantile exchange with them and so I I headed down there with them and you know, we went down we got onto the floor. I’ve never been on the floor before in the this is in the 80s. And just total chaos in my eyes. His brother in law comes rambling out of the pit. We go up to the merch club for lunch. The guy’s not even paying attention to us he’s looking at his cards looking at the ticker Finally, he he looks across the table and he tosses me his trading cards. He’s like, you know, cut my position he’s like by side on one side sell side the other. So I go through his cards and I’m like, you know your your long at s&p big contracts. And he just he looks at me goes, Do you want to work for me? I wasn’t even looking for a chop I would I just was going down there in a meter. So it was a really a lucky started to the business.


Jeff Malec  03:24

Yeah, for those watching on YouTube, I’ll show a little right. This was basically the trading card, just a simple two by two grid, right. And then I bought five I sold 10. And then I was a clerk down in the bond pen and then our job was to go find the right to match up. Okay, you did those 10 Make sure get the other guys little piece paper.


John Krautsack  03:46

That’s right. And because he was such a large trader, my role was to literally get into the pet. And I was probably one of the only clerks that was in the bottom of the pit for the whole day, just getting cards from him and giving him his position because he was trading he just traded such huge size. He needed account like continually all throughout the day. And then I I’d move about in the pit and match the trades with guys who, you know, who traded with them because he was just so crazy.


Jeff Malec  04:21

And tell the listeners if you can what they what that trading floor was the life guy was saved people would lose their minds if they saw the amount of paper on the floor, which we just talked about a little bit but how do you describe it to people who never got that experience?


John Krautsack  04:34

Yeah, I you know, I I kind of equate it to, you know, a sporting event where you know, the bigger the bigger the guy is, the stronger the guy is. I mean, you’d have so many people packed into these pits. That if one guy swayed, the whole row guys would sway and yeah, and paper all over the place. The phone clerks would be sure cards out to, to the to the brokers on the you know, it was just it was it was crazy. I know the first couple days working down there. I was you know, I was so exhausted coming home on the train just it was just really intense.


Jeff Malec  05:20

Yeah, I was sad. I didn’t make it. You get like dip spit spilled on you, fistfights every day was it was the wild west to be sure. Really was what you got any What was your craziest story from one of the on the pit days?


John Krautsack  05:34

Well, one of my one of my favorite stories is there’s this one big local trader in the pet. And he would just over trade and just get himself into into trouble. So one day he shows up in the pit. And you know, if you want to do big size 100 Lots of whatever, you got your hands over your head. So he showed up in the pet. And he had a shoestring and he tied his wrist to his belt loop. So he couldn’t get his hands over his head to over trade. And I just thought that was so funny. I mean, just all sorts of


Jeff Malec  06:16

things. But he could still do a bunch of fives with the one hand he just Yeah, yeah.





John Krautsack  06:21

Yes. So there’s so many stories down there. I mean, it was it really was a it’s wild that, you know, business gets accomplished down there. It’s just, you know, so much chaos. It doesn’t seem like it would be that organized. But right.


Jeff Malec  06:37

And then clerks like you and me back in the day, right? And I’m showing up at 6am to go meet the other clerks and settle trades out trades. Literally, I know like there are millions of dollars, right of like, Oh, I’m missing a 20 lot in bonds. And it’s from two basis points higher or something.


John Krautsack  06:53

Yeah, I remember right before Black Monday, like that morning. My the guy was working for he was he was out a whole bunch of contracts. And I was in he wasn’t even in town. He was using Arizona. And I was running around trying to square him up before that opening. And that chaos and give myself a heart attack on it right now. I finally got a hold of him. And he he left a couple of his cards in his pocket in his jacket pocket. Didn’t didn’t turn those cards in. So he was square, but I didn’t know that.


Jeff Malec  07:38

Your run around for no reason. And so moving on a little bit of the firm background. So EMC was founded by Elizabeth Shivam. who’s one of the original turtles, right? That’s correct. Yeah. And so we’ve covered the turtle program on here before, we’ll put a link to our pod with Jerry Parker in there, you can learn more about it. So you don’t need to go too deep into that. Um, but if you could, like give us the personal side of Liz, what she was like, how she viewed the markets, kind of what you learned from her as you were getting into this?


John Krautsack  08:15

Yeah, I think number one, she was, she was a wonderful person she was, she was probably the best listener I’ve ever met in my life. I mean, she, she could meet the janitor and the janitor could tell her what she’s what he’s interested in. And she would go and like buy a book and give him a book about like that particular subject. She, she always wrote letters, she, you know, she was just, her. Her attention to detail was unbelievable. But I think probably one of the best things I learned from her is, is her discipline with, with the systematic style that the quantitative side of, of how we do, how we research, how we, you know, don’t change the portfolio just because things aren’t working. All those disciplines to our, to our style, is really what I learned. I think that’s why we’ve been around for so long is because we’ve been so disciplined with when we’re back testing our models, making sure that we’re not overfitting over optimizing our strategy. Those disciplines, she, she constantly wanted us to replicate what our research was producing.


Jeff Malec  09:43

And then what did she What was her background before joining the turtle program?





John Krautsack  09:48

So she, she had a a math degree. So, you know, she just, you know, she responded to that and And I think it was only it was her and just one other person who, who? One other woman who, who chosen. Yeah.


Jeff Malec  10:13

The and then, tragically, she died within 10 years or so, while on a business trip in China, right?


John Krautsack  10:20

That’s correct. Yes. She she was there to, you know, we started using our models and Testing, testing our models on Chinese commodity markets, and the performance coming out of those markets was just fantastic. So she was, she was pitching a Chinese guy who owned an FCM, basically, pitching, you know, how we started trading money for Morgan Stanley as an FCM. And then you know, how they grew that whole managed futures business out. So she tried to form a partnership, and she went into the meeting, and she had a brain aneurysm before really, the meeting started. So that was in 2013 2013.


Jeff Malec  11:12

Yeah. Um, so let’s move on to happier topics. But um, her her spirit lives on in the firm, right? Oh, it really does. And so you guys have been running the classic program since 1985. Is that right? That’s correct. Wow. 40 years. Um, so before we get into the nitty gritty about your programs, and all that, just let me ask sort of like, ask a married couple that’s been married for 40 years. What’s, what’s the secret? What’s the MC secret for haven’t been around so long? Right, there’s probably been 40,000 hedge fund firms that have launched and died in those 40 years?


John Krautsack  11:50

Yeah, I really think the secret is, is our discipline, our entire team’s discipline to our researching, to sticking with our models, I think that’s the most important thing, because it’s so easy. When you’re a quantitative firm, to if things aren’t working, you start changing it to what, what, you know, the markets doing at this current time? And, and, you know, I think we see that currently, that there’s a lot of, you know, we are in a, in a 40 year bull bond market, Bull Stock market. And it’s very easy, you know, there’s a lot of firms who, who basically have, like this long only element in their portfolios. Now, I think, you know, our discipline, there’s some tough times, not too long ago, in the commodity markets, no big trends going on, it’s very easy to to want to kick those markets out of your portfolio, it’s very easy to want to change the your models for trading those strategies, and we just stay disciplined with the way we research, the way we run our optimizations. We stay disciplined with the diversification in our portfolio, whether that’s with markets or with systems. And I think I really believe that that is the reason why we’ve been around so long, because when it’s time to, for manage futures to perform, we usually perform


Jeff Malec  13:30

the and that’s interesting is usually people hear discipline, right? And I’m thinking like, oh, I only risked so much on a train, I don’t write I don’t get crazy on the wrist side. And that’s why we’ve been around so long, because we don’t blow up. So that’s also true, but it seems more like you’re saying no discipline to stick to our knitting within a framework of you’re evolving and whatnot, but right, it’s all too easy. Like I did a, we did a blog post on silver trend following silver, and I think it had 19 Straight false breakouts over like 11 years, right? Like nothing. And then last year, had this big move, or it might have been been 2020. But it had this big move it finally paid out. Um, so that’s the discipline you’re talking about, right of like, in order to take that losing trade over and over and over knowing at some point it’s going to pay


John Krautsack  14:18

out? Absolutely, that obviously that was you mentioned silver, and oh, man, we wanted to kick that mark out of our portfolio. But, yeah, but I also mean, the discipline of you know, it’s all embedded in our, in our optimization process, the risk you take per trade, the weightings of each market, you know, your your correlations between each market, everything is built into that research optimization process. So sticking with, you know, the quantities that you’re supposed to buy, it’s, you know, the disciplines of taking, you know, putting on trades at you know, Are you buying it at all time new highs are selling it? Lows? Yeah, that’s a very hard thing to do.


Jeff Malec  15:08

The and what piece of advice would you give to some startup guys applying that longevity, right? Like, it’s so hard because if I’m a startup, if I don’t have the the assets behind me, if I don’t have the perhaps your guy’s track record and, you know, capital built up, it’s going to be way more tempting to pivot and say, We got to stop trading this, we’re going to go out of business, we’re going to lose our clients. Right. So how do you kind of weigh those two things? If I’m a startup hedge fund, wanting that longevity?


John Krautsack  15:38

Yeah, I mean, I think it’s, that’s what’s so hard about it is that, you know, you have to make sure that you’re, you have a good research process. And you, you stick to that process. Even even through hard times, and I agree with you, it’s it’s a tough hurdle right now. And the cost of just running a business right now the price, you know, the cost of exchange fees, and getting live live quotes and getting enormous amount of data, like we’ve paid so much money for data. So it’s, there’s a big hurdle, a big hurdle for sure.


Jeff Malec  16:26

And that’s been a refrain on this podcast for a long time of like, hey, what if you have the choice, go out of business or change your model? Most everyone’s going to change their model, right? They don’t want to go out of business, right? It’s coming from an investor, I appreciate the right I’d rather have you sticking in one lane, so to speak, right of like, Hey, I know what I’m gonna get with these guys. I’m allocating, this is the profile, they’re gonna write, if you start changing all this stuff, then as an investor, I lose confidence in that not knowing what I’m going to get.


John Krautsack  16:54

That’s exactly right. And, and we’ve seen that we’ve seen that with other managers, all of a sudden turn into, you know, they turn it to a completely different trader. And, you know, we owe it to our investors. I mean, we have two large investors who have been in the classic program, one since 1989. And one other since 1991. So they know what you’re getting from us.


Jeff Malec  17:20

Yeah. And they’ve probably that’s compounded rather nicely, right?


John Krautsack  17:25

Sure, yes.


Jeff Malec  17:31

So let’s dig into the strategies a little bit. The classic program that we mentioned, the core of that is trend following, right?


John Krautsack  17:40

That’s correct. Yeah, we kind of bucketed into two areas, we do a technical trend following we have two core systems that are under that. And then we have a two systems that are more statistical momentum, we’re trying to capture the same, you know, big outlier moves, whether up or down. But we’ve categorized and just a little bit different


Jeff Malec  18:04

and expand on that. So what’s the difference between that momentum signal in the trend phone signal, like a breakout versus a relative value?


John Krautsack  18:13

Yeah, we don’t do really breakout anymore. You know that that whole style degraded? You know, quite a bit. So what we’re doing like for the, for the trend filings slice of it is is what we do is we have, we have three core look backs, and we’re trying to get confirmation on these look backs. So we’ll look back maybe on the first parameter, anywhere from 100 to 200 days, and then there’s a threshold at that market either has to be above or below in order to take a buy or sell, then there’s a medium term, looked back with another separate threshold. So we need confirmation on all three, three look backs in order to go to the next level. So the next level would be once we get confirmation that lets say the long term look back, medium term and short term look back, are confirming that there’s direction upside directional movement, then we have a volatility filter, and basically that’s looking at current vile, and comparing to pass file. And if it’s over a certain threshold, we don’t take the trade at all. If it’s under it, we’re allowed to take the trade. And then we have a little volatility move that must happen from the previous close, has to move in the direction of that trend in order for us to put that trend on. So we have two of the two systems that have that same core logic, but in recent The way we the way we make those systems different from each other is we pair each of our systems with a unique super value. And that super value gives that system direction and how we want that system to contribute to the portfolio. So an example of a super value for our shorter term systems will pair A Sharpe ratio with a accelerated return numerator. And that will allow that system to be it creates a shorter term system. And it also is a more nimble system, so it’s responding fast. So


Jeff Malec  20:47

super value is higher, you’re gonna get a, you’re more likely to get a trade or you’re going to trade it a little larger.


John Krautsack  20:53

Well, it’s it basically is using that super value across all that market data to have to fit those parameters, genetically have to be we populate each year new parameters for this core logic. So it gives, it gives that system a roadmap to how we want it’s performed, we want it to be really, you know, a real good risk adjusted return from that system, versus those same core parameters on our longer term trend following system, we might pair that to a return and a Sortino. And then that system becomes way more accepting of volatility, and becomes a much longer term system. So each one of our systems has a unique mathematical super value. And it’s, it tends to be weighted to the closer to the most recent data, you know, so there might be a Sharpe over 10 years, times a, you know, return numerator over two years. And so during that optimization process, that’s how these systems get new parameters. There’s no wholesale changing in the parameters, but and the parameters should be okay, the first look back, maybe it was 150 days look back, but next year, it might be 180 day look back, and the parameter that indicates it’s trending could move as well. So we’re optimizing to get those parameters, which make the systems quite different from each other.


Jeff Malec  22:42

And that’s all machine learning AI based. Are you run that into your hit a button? Or is it ongoing? It’s like a rolling 12 month process?


John Krautsack  22:51

Oh, no, it’s a that the RE optimization is an annual process. And the genetic algorithms take place. So the, you’re basically populating the strongest gene in in each parameter.


Jeff Malec  23:07

Yeah, hit on that a little. Why do you call it genetic algorithms?


John Krautsack  23:12

Because what it is doing is it’s learning over time. Through our genetic algorithm, it’s learning what the best gene or Paramor that is, that will go to the next generation. So as we do a forward walk, every time, we will. So forward walk is basically, we don’t just optimize a system to the whole set of data from 1980, let’s say until present, yeah, we walk through that optimization. So So for example, will will back tests from 1980 to 1985, we’ll come up with the core logic of a system. And in the sixth year, we trade those that core logic in that so we’re trading out of sample we’re building an out of sample track record, basically, what we’re forced to do in real life as a manager much easier. So


Jeff Malec  24:16

if you could trade insane not possible without the Time Machine.


John Krautsack  24:21

Yeah. So basically, each time we hypothetically have to trade those new parameters on data that we are privy to just yet that’s that gets, those parameters are like get populated. So those are the best parameters. And every time we do a rolling optimization, the genetics of what comes to the top is the best parameters keeps on moving along. So


Jeff Malec  24:57

each of those, each of those timeframes each Those systems. Yeah, it’ll change it on the longer term on the shorter term. And then is that changing the portfolio construction as well, or that’s static? Or you might add them over time. But it’s not the machine learning side saying, and you should add carbon credits or something,


John Krautsack  25:16

right? No, we would, we would have to just include that data into our optimizations in order to you know, add markets.


Jeff Malec  25:29

I love it. And so what is Divi ever seen? What’s that? When is that period? Are you allowed to say? And like, does it work really well, right after the parrot and start to degrade? So you have to switch it over?


John Krautsack  25:39

Are you asking? When’s the period where we add a new marketing?


Jeff Malec  25:43

No, when you re optimize basically. So if you do it annually, said or periodically, but


John Krautsack  25:49

yeah, so we have four systems in classic. And so we, we have a set calendar date for each one of them. So it’s, we don’t do all four of them at the same time. So we might, you know, one each, each quarter is when we optimize them.


Jeff Malec  26:05

And but they’ve used over the years, like five years ago, did it take three years to degrade? Now it takes one year? Does it degrade at all, you just find the next the new best one?


John Krautsack  26:16

Um, the, the part of re optimizing is is basically so we don’t degrade? Yeah. So it’s just it’s creating parameters. And really, looking forward. More than backwards? Basically, we’re trying to, we’re trying to, you know, understand what, what are the changes in the market?


Jeff Malec  26:39

Do you ever do that? Look at the one that if you’d kept it, just to drive yourself crazy? Or just take the new one and ignore what happened?


John Krautsack  26:48

No, we definitely do we look, we look backwards and see what changes have been made. The best thing about, you know, back 15 years ago, we would run an optimization and see what move what parameters moved, and then try to hone in on like, a, you know, a range. So for example, the first look back is anywhere from 100 to 200 days, we would, we would like put a low look back and a high look back and had to optimize within that range. But now when we do genetic optimizations, we can just let everything we don’t have to put like ranges on these optimizations, we could just open up the parameters as wide as we want. And just let those parameters get populated, you know, from a natural selection process.


Jeff Malec  27:50

And how crazy could that be? So could it jump from 50 days to 550? Or something? Are there still some?


John Krautsack  27:58

Yeah, no, they never, we never get wholesale changes, mostly because we’re optimizing the systems to that specific unique you know, Sharpe ratio, basically, yeah. The Super Value is always geared towards so it always wants to, it always wants to stay within its, you know, its, you know, longer term parameters, shorter term parameters. And that’s kind of like the beauty of it, we know what kind of contribution each one of our systems is going to give us. I mean, obviously, over time, we can look back and go cheese, if we only traded the momentum system, the one momentum system, we would have been doing way better, but we want to diversification within the systems, because they all go in and out of favor, just like markets go in and out of favor. And, you know, we just want to be around for that.


Jeff Malec  28:57

And speaking of that, over time, and in and out of favor, like what is the model today look like? In terms of what it was in? 85 9505 15? Right. So, like, is it 50%? The same 5% the same would it? What does that evolution like? But


John Krautsack  29:14

well, I would say it’s more like 5% the same? Yeah. These models are a lot more, you know, sophisticated models. Back in the day when you know, was started the firm. It was a it was one it was one system and one risk management like strategy that overlaid the portfolio with very little components to them. Now we have multiple systems. Each system has, you know, multiple core logic to it. You know, probably one of the most important things we’ve done is our so each system has its own risk mask. element to it, where it gets in where it gets out. But one of the best things that we added to the portfolio was this whole risk overlay. And it’s, it’s built with components very similar to our system components, we take that those components, and we run it through an optimization every year. So the components would be, it would be like open trade equity, a trailing p&l, a scale factor. And so what we see what happens in in classic is we optimize those core parameters to a utility function. And a utility function is really like a, like a satisfaction, you know, how much are you willing to make? And how much are you willing to give back on that. So we’ll see in big periods of time on when MCs making money, and that trailing p&l builds up. It once it gets past the scale factor, we start lightening up the portfolio across the entire portfolio. And we can like get to a point where we’re cut back in classic, you know, 50%, because the trailing p&l has really kicked in. And, you know, this is something that our clients have really noticed over the past, you know, 10 years or so, we implemented this in 2007. And then we continue to reoptimize it every year. And add more, you know, more components to it. But what happens is there’s you know, you’ve been around this space for a long time, you see huge monster that CTAs all do really well. Yeah, and like the next month, it’s given all back, this component really helps us, you know, retain the, you know, the p&l. It lowers our volatility, it lowers our drawdowns, and it helps us capture more of the more of the trade,


Jeff Malec  32:12

does it take you right? Does it like move you less positive skew, like us, so I can see where you’re coming from? Right. That’s the number one complaint about trend following. Great, but I’ve made, you know, I rode crude from $30, up to 90, and then it sold back down and it didn’t get out till it went back to 50. And people like you and me are like, Hey, you still make 30 to 50. That’s a great trade. But misters like, Well, I had to report that loss from 70, down to 50. And it was, it was painful to report to my end investors or to my family office or whatever. So that your utility factor solving for that?


John Krautsack  32:49

That’s exactly right. It’s like a transformation function. And so what it what it basically does is it’s looking at, it’s like ranking monthly returns, using an arc tangent. So basically, what it’s doing is, when you’re looking back at your, your research, and you’re looking back at your monthly returns, it’s going to it’s gonna reward up better negative return to an improved negative return more than it will reward an improvement in a positive return, if that makes sense. Yeah, really, it’s like a satisfaction thing. So you know, we would go to our clients and go, Hey, you know, would you rather us, you know, make 25% or 20%, and only half of, you know, a couple percent, you know, give back a satisfaction how much is enough?


Jeff Malec  33:54

Right? So to do do that with an eye towards the behavioral finance, right, like, that’s a famous test, and like, Would You Rather, you know, make $100 or lose, I can’t remember what the math is, right, the famous experiment, but people would rather avoid the loss and get the gain, even if it’s economically in their favor to go for the larger game. They want to avoid equity.


John Krautsack  34:16

Right. Yeah. I mean, that’s, that’s that was what that concept was put in place for the utility function.


Jeff Malec  34:25

I just reread because in your lab in the scientist lab, you’d be like, who cares what the investor thinks just I’m making the best product possible. Right? And then that kind of goes into like full Kelly betting and all that stuff, right. I’m like, Well, the best product possible might be I fully run this thing at 80 Vol, and draw it out. Right, and I’ve drawdown to 80%. But over 30 years, I compound the highest grade in the classroom, not so great for real world and real investors. Right?


John Krautsack  34:54

That’s right. That’s right.





Jeff Malec  34:57

Um, but that also leads me to like, do you so how much is Do you give up? Do you give up some of these? Right? You’re not You’re in that scenario, you’re probably never gonna have 100% return or like the huge outlier move.


John Krautsack  35:09

And yeah, I mean, it, you know, it was in place during Oh, eight, you know, and we had, we had a really big year and classic. And oh eight, even though we scale that portfolio dramatically, you know, so let’s say, you know, if we, if we made 50% know, it, we probably left 20%, you know, further in return on the table, probably, I mean, it’s, yeah, we’re cutting that thing pretty, pretty dramatically. And we don’t just do like, all of a sudden a 50% cut, it’s like, as that p&l builds, we’re, we’re doing 5% 10% Cut. And, you know, eventually, when that p&l stops building, we stopped cutting, but it’s across the whole board, you know, we’ve had so many different, different strategies to reduce that give back. And, you know, one of them was just look at the market that’s trending and making money cut that. That’s a bad idea. So terribly bad idea.


Jeff Malec  36:18

Yeah, I was just gonna ask next. Right? So that’s on the portfolio level? That’s correct. So it’s going to be in does it look at each month. So if I, if my best three month return, and historically was 40%, and now I’m at 38, it’s going to start peeling back.


John Krautsack  36:34

It’s a it’s actually different than that it’s, if if, but let’s say we, we have a trailing p&l over X days, that gets above, let’s say, 9%. So once a trailing gets above 9%, and it goes to 10%, we have a little scale factor that’s built in. So what it really does is, if we have an open, open equity, we don’t trade in our in our classic strategy, we don’t trade that open trade equity. So the scale factor is just using a multiplier against that open trade equity. So it makes the open trade equity bigger, which makes us take money off off the table, basic guy,


Jeff Malec  37:25

right, which is the classic from my days running a trend fine, right, you would hit a big trading corn net, right? Say you started with a Million Dollar Portfolio, you make 200, grand and corn. Now, right? Open trade equity, now you’re at 1.2 million and you get a trade and silver. Do I size it off the million or the 1.2 million, inevitably, you’d size it off the new 1.2 million. Now the corne p&l goes away and you lose on silver and you write it just increases volatility across the board.


John Krautsack  37:56

That’s right. That’s right. And we I was just gonna say and from a standpoint of sizing trades in the portfolio, we do look at that markets, volatility, short term look at volatility, to know how much we want to risk in that market. So each market has its own risk weighting. But then we take in consideration, the volatility of that market, the current volatility of that market, when we put a position on so the more volatile the market is, the smaller the size, and vice versa. If markets, you know, doesn’t have a lot of volatility, something like Euro dollars, we’re able to put on a bigger position,


Jeff Malec  38:40

right, which is classic trend phone one on one, right I’m I’m doing 20 Euro dollars, one palladium. And if there’s the big outlier move, right, if it’s four standard deviation move, I want to make the same amount. It’s right to those markets, I don’t want to unknowingly have like most of the portfolio’s profits, because this markets more volatile. And then you said something interesting before, if the volatility is too high, is that in each market or portfolio wide? If the volatility is too high, you’re not going to take the trade.


John Krautsack  39:12

So that components within within the system got it. So we’ve tried to find we really tried to get rid of that parameter and all of our systems, but it screens out so many bad trades, that if we needed at some level, each system has maybe a different threshold. So some, some of our longer term systems are more accepting of expanding out expanded vial. And we want to make sure that there’s not a trend and just because of volatility, we’re not a part of it. So that’s the diversification within all the systems as well


Jeff Malec  39:53

as my experience as far as when volatility is expanding. Oh 708 Right. 20 2022 2122 here. That’s when trend find us better. A year. This is a not just increasing vol. But if it’s really high absolute level of vol. You’re going to get whipsawed, you’re going to get stopped out too soon. Yeah. So on to the other program. So we’ve covered classic anything else to add on classic?


John Krautsack  40:23

Um, yeah, I could just go to the other types of systems. Yeah, that we have is the statistical momentum systems. And so those are quite different than having confirmation on three levels, like the technical trend following. So the statistical momentums are basically we’re looking at a shorter look back period of time. It’s, we’re, it’s all time weighted to the presence. And one of the systems is looking at a close to close basis. And so we do this analysis where we’re looking at today’s closed versus yesterday’s close, versus the day before closing, and we walk back and forth. This, what we call count against, so we’re looking for closes in a in a certain direction, and a lack of closes in the opposite direction. So almost like you look at a manager where where you have, you have a run up. And then you just want to make sure within that run up, you don’t have like these drawdowns, these big drawdown. So a lack of, you know, a lack of counter closes, will allow us to put that that momentum trade on. And then our longest term system is more of a looking at a shorter term look at the market, but really looking at the drift and the magnitude of the drift. So that would be a market that, you know, if you looked at a chart, you just like, use, you’d go oh, it’s, you know, it’s definitely going higher. But it kind of measures that on a short term. So the system, you know, is more accepting of volatility. And it allows his trade to get put on, like, awfully fast. Over the last five years, that longer term system has been a momentum longer term system has been our most successful system in the portfolio. But all four systems, we equally weigh just for diversification. And, you know, we, we want to just make sure that we are, you know, we’re not making changes just because of the results of our optimization,


Jeff Malec  42:56

which is weird, because you’re like, we’re going to do optimization in this, these, this parameter set we think gives us the best chance. But then at the same time, sort of ignoring optimization, they’re on that on that level. So it’s an interesting, yeah, ying and yang, they’re like, Okay, I’m optimizing, but not over optimizing.


John Krautsack  43:14

Right? Right. And that’s, that’s critical in our research process. I mean, because it is really easy to say, Oh, this system works great with this market. And this system works good with, you know, another market. So trade, just those systems for those markets. That’s just a real overfit strategy, then, what we do is we equally weigh all of our systems across all of our markets. So once we run an optimization and come up with parameters for, let’s say, our short term, trend following system, those parameters that we come up with, we trade every market with those same parameters. So we’re trying to build a robust set of systems that trades successfully in sugar as it would in Euro dollars as it wouldn’t SMPS. So we’re not customizing the systems to each individual market or to each sector,


Jeff Malec  44:12

which comes back to the silver common, right. So even even to the point where you would add something that’s lost 26 times in a row, and no one in their right mind would ever trade on a standalone basis. Right.


John Krautsack  44:24

Right. Right.


Jeff Malec  44:27

Which is crazy. But that’s the power coming back. It’s interesting to me, right? Always the momentum factors usually talked about in stocks, right. Single name stocks that really in futures markets, where we tend to call it trend. So like for you what are those correlations look like? Are those models point 8.9 correlated or they you’ll see much different trades.


John Krautsack  44:50

Yeah, they’re there. They still have a high correlation. Yeah, because just like CTAs trend following CTAs have high correlations because we’re trying to chase the same outlier moves. And so that just moves the correlations, you know, up pretty high, because we want all these systems to be able to capture trend or, you know, momentum type moves, I guess the correlations would probably be, you know, between our longest term system and our shorter term system, that would probably be the lowest correlation. So the longer term system goes out to about 120, some days hold average holding period, and the shorter ones more like a 15 day. So there’s plenty of times where those systems are actually on the opposite side of the market. So one could be short and one could be still be long. So did you know that probably more like a point six correlation with each other?


Jeff Malec  45:54

I guess another way to ask it, like you’re capturing the momentum factor, even in the trend? model, right. Like, is that the scientific explanation for why why these things can work over time?


John Krautsack  46:07

Yeah, I would say yes.


Jeff Malec  46:14

So circling back, so you guys have a few other programs. So let’s talk through those quickly.


John Krautsack  46:22

Sure. So the the other programs that we have the first one, we call AMC alpha, and we really custom built this program. For a client of ours, who wanted to start a 1948 mutual fund, which is a game changer in our business. You know, basically, people want to invest in our programs, it was typically, if we had a fun vehicle, then they could come in for a lower threshold. But if they just wanted to do a manage account, and classic, the minimum was $5 million managed account, there’s not a lot of guys who have, you know, 5 million that represents a small portion of their total assets. So now this mutual fund structure is a completely different game changer. Number one, yet, you basically have a lower threshold in order to get into this alternative investment. And you have, you know, hundreds of brokers selling this to their clients versus, you know, one sales guy, or two sales guys, you know, pitching it. So we’re the sub advisor to that, this, this program called the Alpha program. And where it differs from classic is we have, we have 10 systems in the, in the portfolio. And then we have sub strategies for sub strategies. One of the sub strategies is just short term interest rates, call it unconstrained rates, we optimize a few systems to that core strategy. So that differs a lot from what we do in classic, whereas we’re customizing systems to certain sub strategies. And then we have a commodity only sub strategy that we optimize core systems to. And then we have a long short global financial, which is basically currencies, fixed income stock indices, and we optimize some more trend momentum systems to those sub strategies using different super values as well. And then the very final sub strategy is a long only rebalance sub strategy of stocks, bonds and gold. And that really helps smooth out the advantage futures like returns. So, but the the fixed income and the stocks, they can all get, like they can all get flat and can get short. So right now, in that strategy, we are we are short fixed income. When there’s


Jeff Malec  49:19

a long there’s a long only bucket but then there’s also the dynamic bucket that could go Yeah, be long in the long only in short in the dynamic net, correct?


John Krautsack  49:27



Jeff Malec  49:31

And how do you get that’s like to give you basically a positive carry that will keep you above water until the kind of alternative piece pays out, or do you view it as the give you positive beta with protection via the alternative piece?


John Krautsack  49:48

Yeah, I mean, quite honestly, the white man. Yeah, the whole idea of his portfolios, we always, you know, when we just get classic, we always said you know, you Want to combine this product with your other, you know, your other holdings? And that’s usually long stocks, long bonds, long gold. And so, you know, it was that’s, that’s a hard sell because when a when a investor invest in the classic program, he doesn’t look at it as a combined investment with his, you know, their current investments, their long investments totally looks at it separate. So sometimes, you know, we go through periods where they’re not great. They’re not great periods and so


Jeff Malec  50:36

red line item risk we comment. Yes. And you see in that line item when if you bundled it, it’s like, oh,


John Krautsack  50:43

yeah, exactly. So it’s a, so we just decided that, you know, it’s hard to sell people on just the standalone. So why don’t we build like, a portfolio? That’s, you know, it’s more balanced? As is just a, you know, a complete investment. So what’s interesting is that that mutual fund started out with 5 million in 2013. It’s, it’s over 500 million now. And really, where they’re selling that that product is, is basically two brokers fixed income slaves, they started out sonnets is like it these these futures and yeah, managed futures or even in, you know, someone’s stock bucket. Now, where the big attraction is happening is on relates to income. Yeah. So it’s shooting more towards single digit returns with single digit volatility, very different than what classic shoots for.


Jeff Malec  52:01

And then what else other programs,


John Krautsack  52:03

and then we have, we have another carve out within that EMC Alpha program that’s in the mutual fund, we also trade ETFs ETFs, in that, in that portfolio, we have an EMC alpha plus strategy, which is a levered up version from that alpha strategy. And it’s all futures No, no ETFs in that portfolio. So it’s, it’s very similar. We built it very similar to alpha. It’s just a levered up version of that.


Jeff Malec  52:37

Jumping around with a very touched a little bit on the AI machine learning. Just give us a little bit of like, what’s your take on all that indispensable to you should have done it sooner. It’s just a fancy spreadsheet, what what’s your take on how you guys use the machine learning and how you value it?


John Krautsack  52:58

Yeah, I mean, we really value it a lot. Because it’s, it’s an, it’s an automated research process for us now. I mean, we used to set it sit at a at a conference table, everyone in the management group, and just, you know, spitball spit ball, because figure out what change in the system is a good should we allow it to change by that much. This is a natural selection process. That, you know, coming to these conclusions, were able to come to them a lot faster, because of the genetic optimization that’s happening. There’s a lot of different interpretations of artificial intelligence. You know, there’s, you know, artificial intelligence, where people just throw a whole bunch of parameters, you know, into the, into the code and let it try to figure out what’s the best parameters? We don’t do it that way. We basically have core lot logical parameters. And then we’re directing the systems to get the best parameters within that core logic. So it’s quite different than some of the artificial intelligence we’ve had.




Jeff Malec  54:12

Yeah, I think I view it as like, outsource manpower, woman power, right? Like you’re just instead of a room full of 1000 people crunching through all this, you have the machine do the work.


John Krautsack  54:22

That’s exactly right. Yeah. So we really believe in it from not only from a system building idea, but also from that overlay risk management. That’s really, that’s really helped us outperform a lot of our peers, especially in good times.


Jeff Malec  54:43

That’s easier to tell investors about to instead of like, well, I don’t know why we went on that. But the AI did it. Yeah. It seems to have worked over time. And the classic AI right is like, going to optimize to what’s working over this x period where you’ve kind of said like, no We’re gonna have discipline and even if something’s not working in that period, we believe in it philosophically, and we’re gonna keep keep it in the portfolio.


John Krautsack  55:08

Right? Right. Yeah, we look at the whole portfolio, you know, when we run these optimizations, we’re not, we’re not just singling out markets, we will, we will take some outlier performance in our back test and remove those markets. So they don’t skew our results by too much like if, you know, the central bank, you know, devalue their currency or whatever. And that had a huge move, like, way back. When Mexico had their big devaluation, it really spiked our our p&l, and so we kind of take some of that noise out, just so it doesn’t skew the results.


Jeff Malec  55:54

Take out the largest loser to I’m thinking of the Olympics here, the largest loser largest winner, scored judge scores get thrown out.


John Krautsack  56:02

That’s right. Yeah, just some of the ones that can really skew.


Jeff Malec  56:14

If trend falling starts to go really back in Beaucaire. REITs had a great 21. It’s doing great so far this year. If hundreds of millions of billions start flowing back into the space, do you have any reservations that it gets too big that that’ll degrade the core signal there? What are your thoughts on the old trend fallings too big angle that was prevalent? Five, six years ago?


John Krautsack  56:38

Yeah, I, you know, I, I personally, you know, it’s I think you can get skewed when, you know, if you look at if you look at the 40 years that the bond market has been, you know, has gone up, and you you customize your, your strategy, to have a long bias or you customize it to, you know, have a long bias in stocks. I think that that could definitely hurt, you know, you know, if you if people have adjusted their strategies to, to that pass market environment, I think, then it could terribly degrade. But from our standpoint, we we build the same parameters that gets you in a long trade, or the same parameters to get you in a short trade. And then shorter versions of those parameters. Get us out of those positions. So shorter look backs and thresholds get us out of the position. So I, you know, I personally don’t believe in like, you know, that the more money that comes in, you know, the less success we’re going to have. We would have to probably weigh certain markets, like lumber and orange juice and some of those smaller contracts, we probably have to wait them too low to actually participate in them. But at this point, right now, they are lightweights. But they can still trade those markets and add value.


Jeff Malec  58:23

When asked another way, what would you have to see in order to be like, this has gotten crazy like these? As soon as there’s a breakout, the thing spikes this much, we’re not able to get in sooner. No wonder like, what kind of things would you have to see to be like there’s too much money chasing these trends, which is a odd question in and of itself, because what timeframe? Is it on a 10? day time frame? 100 day a 200 day? Right?


John Krautsack  58:47

Yeah, I mean, we, we would definitely have to, you know, obviously, we have to look at slippage, you know, type effects. We’d have to look at all our custom algos that we, we built for putting trades on, we’d have to look at our market weights, there’d be several things, you know, if we start seeing, you know, moves that we can’t get in and timing can’t get out in time. We hope that through our optimization process that those parameters will all adjust to that current environment, right. So but my


Jeff Malec  59:26

my counter argument would be get the more the merrier that’s going to drive more liquidity and more people into the market. And if your models are better than the next guys, there could be opportunity there.


John Krautsack  59:37

Opportunity. That’s right. Yeah.


Jeff Malec  59:40

And so you mentioned everything being the same on the long side and the short side. The bond trade we talked about, right? So bonds basically went straight up rates down for 3040 years. Two things there. One back in the day, everyone said managed futures returns reduced because they could hold them bills, but we’ll ignore that for now. But just right if they were a participant in that huge 30 year bond trend. Now, simplistically, we can say, Oh, if that reverses, if rates go back to 15%, whatever, there’s going to be a huge downtrend and bond prices rates up. But I’m Roy Niederhoffer, some others have pointed out like that won’t look the same, because of the cost of carry, because the curve will be different. So you what are your thoughts on on the rates up bond prices down? Is that going to be a mirror effect? What’s it going to look like for for your programs and managed futures? In particular?


John Krautsack  1:00:37

Yeah, I mean, we trade any trade across, you know, the whole curve. So we, you know, we’re trading short term interest rate products, we’re trading medium term, we’re trading long term products. And so we’re obviously, we built these systems based on on the data that we know, that we had, which is really skewed to the upside. You know, but for, for us, in particular, I think, you know, we don’t really look at it can is it going to be a different type of trend, we’re just trying to capture directional price movement. So I mean, the amount of p&l We’ve already picked up and the euro dollars over the last, you know, couple of years, signals, it’s a, it’s a pretty good trend right now, I mean, almost better than the amount we picked up on the upside. So, you know, it all depends, I think it depends on the volatility of the market, that doesn’t, you know, Euro dollars vol is just picked up big time, and they’re so our strategies could miss if they get screened out by VA, but we got in such so early that all our systems already positioned in those markets. So, you know, they could be right, that, you know, this is going to be a different type trend. But, you know, we we basically are just building the whole set, catch, you know, try to catch outlier moves, and however big that is, it is I mean, we’re hoping that our risk management overlay, you know, ends up capturing, you know, scaling back when we do capture these moves, and, you know, like right now we’re, we have a pretty decent scale going on and the classic program. So if those Euro dollars reverse, we’re gonna capture a big piece of that already.


Jeff Malec  1:02:47

What’s in sounds to me, like you’re saying, like, yeah, maybe that trend looks totally different, you’re not gonna make as much as you made on the way up, but unless you set your whole model up, as like, that’s the main factor, right? That you wanted, that your whole performance is based on it looking exactly the same? Right? If you don’t do that, then it’s not necessarily a problem. You’re just gonna get what you get, and have a nice day and move on to the next one. Right.


John Krautsack  1:03:13

I mean, look, I think, probably most investors didn’t think that interest rates could, you know, go negative, and, and could have, you know, put into their models don’t you know, you can’t you can’t buy bonds when the rate is, you know, under a percent or whatever, you know,


Jeff Malec  1:03:38

and we’ve talked about that before, but what do you do with balance at the zero bound? And why would you go long there? There’s no more room to go. But yeah, yeah. 2019? Was it right, where there was tons of Mad Money, man, boons and bubbles, and all that German stuff of going long at the zero bound in it, right. They say went more negative, and you could make money.


John Krautsack  1:04:00

Yeah, and the only data that supported you know, was prior to this all happening was Japan, you know, the Euro Yen, the rates, rates went negative, and the volatility just completely died out. So it was almost like a flatline movement on a chart. What we did as a manager, just, you know, not being in that type of environment before, as we just we just lowered our risk or market risk in there because obviously, with no vote, no ranges, we would be putting on massive positions based on the volatility of that market, which is a very dangerous thing to do. So we, we pretty much cut the leverage in that market. And that’s sort of like that. That’s definitely a discretionary move when it comes to you know, our research but we just felt that, you know, without a data supporting what could happen here that we would be best off protecting ourselves.




Jeff Malec  1:05:11

Um, and then let’s talk to we’ve mentioned lumber a little bit. We just had a party a couple weeks ago on lumber, talking about the supply chain issues, the Beatles because of climate change all this stuff. So it’s always amazes me, right? Like, no offense, but you don’t know anything about all that stuff. But you might say, Oh, no offense taken it, I love being able to make money off something that I know nothing about. Right. So to me, it’s like a feather in the cap of trembling, like, you would need an army of analysts and all this exposure to be like, I’m, I researched all these lumber, I bought into this lumber mill that’s publicly traded or bought this timber. Right in here, it’s just part of the portfolio, and you get that exposure in a measured way. And it’s like by, you know, it’s a long call option on things like that happening, right?


John Krautsack  1:06:02

Yeah, that’s right. You know, it’s, you’re right. I mean, even if you, you think you got all the fundamental reasons for, you know, what a market is going to do? Or what it should do? Sometimes, you know, the markets completely go the opposite direction, and you put your hands up, like, why? Yeah, I’m, you know, that’s the greatest thing about the disciplines of being, you know, a trend follow systematic managers, you don’t have to, you don’t have to know why.


Jeff Malec  1:06:40

You don’t have to know why. And then something, another one carbon credits, are they part of the portfolio yet? What are your thoughts on it? Like, to me, I think it’s, it’s a weird thing to think about, right? Because by definition, they should go up, they reset plus 15% A year or whatever. So it’s would seem to be a perfect thing for a trend following portfolio.


John Krautsack  1:07:01

Yeah, we do not have any of our portfolios right now. And we don’t we also don’t have like Bitcoin in our, in our portfolio as well. There’s a couple reasons for it. From a standpoint of the mutual fund, the the, the, the oversight of that distribution of the mutual fund, they disallow it to being in our portfolio, so that one program can’t have that. And then we, you know, we’ve gone in front of, you know, several of our investors in the past, to add the VIX or to, you know, some other markets, and they don’t, they don’t tell us we can’t, but, you know, we just sort of look at some of the returns from some of those markets. And we’ve, we feel like it’s a little bit of a dangerous move to get in, and especially the VIX that, you know, what you think where you think you’re gonna get out and where you do get out of some of those markets is not, you know, it’s not the same. So, you know, we’re staying away from some of those markets. We still have a lot of diversification in our portfolio. And we commodities and financials currencies,


Jeff Malec  1:08:29

in the crypto always seems like a perfect fit for a trend follower, right? Like just take a little bit of risk and you’re going to get some of these outlier minutes and rinse and repeat have the builder do go short. So I’m sure you guys will revisit that down the line? Absolutely. Yeah. As the futures become more entrenched to and the liquidity gets better let’s finish up with two truths and a lie this year. What do you got three three things about you one of which is a bit of a stretch


John Krautsack  1:09:05

hmm I’m a good golfer you’re good at these podcasts


Jeff Malec  1:09:25

making it tough now. And yeah, three that you’re not gotten the sun beaming down from behind you there is just the normal sun. Can you see that on your screen? That sounds like


John Krautsack  1:09:39

Oh, yeah. No, I you know, honestly, uh, you know, I think I’m good at what I do here at EMC from a standpoint of keeping, you know, the philosophy of of what we do from a research standpoint, and, you know, managing the company. But I’m not the smartest guy in the world. I mean, I surround myself with some very brilliant people at the company here who’ve been with us for, for, you know, several decades. Yeah. And you know, and I’m loyal to them. That’s Lewis was very loyal to me. I’m loyal to them. I, you know, I pay him what they’re worth because because I want him to stick around here. Yeah.


Jeff Malec  1:10:37

Teach him well enough to leave and pay him well enough that they won’t want to. I think it’s how this right is how the line goes. Right? It’s right. Um, well, thanks, John. Any other last thoughts before we let you go? Tell them where they can find you and all that good stuff.


John Krautsack  1:10:53

Oh, sure. You can find us at www dot EMC


Jeff Malec  1:11:00

Emc. Cool. And then we’re doing a white paper on trend vine, which you guys will be highlighted a little bit in. So check that out. And thanks for listening. Thanks, Joe.


John Krautsack  1:11:14

Hey, thank you.

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Limitations on RCM Quintile + Star Rankings

The Quintile Rankings and RCM Star Rankings shown here are provided for informational purposes only. RCM does not guarantee the accuracy, timeliness or completeness of this information. The ranking methodology is proprietary and the results have not been audited or verified by an independent third party. Some CTAs may employ trading programs or strategies that are riskier than others. CTAs may manage customer accounts differently than their model results shown or make different trades in actual customer accounts versus their own accounts. Different CTAs are subject to different market conditions and risks that can significantly impact actual results. RCM and its affiliates receive compensation from some of the rated CTAs. Investors should perform their own due diligence before investing with any CTA. This ranking information should not be the sole basis for any investment decision.

See the full terms of use and risk disclaimer here.