- 3 years ago
Kaiju ETF Advisors builds, trains, and employs robust artificial intelligence (AI) and machine learning technologies designed to improve fund management decision-making. By empowering these innovative technologies to curate and provide direct management of our ETF, we’re striving to go places no Registered Investment Advisor has gone before.
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00:00 (upbeat music)
00:02 - What is up Zinger Nation?
00:04 Welcome back to another episode of Benzinga Live.
00:06 Happy Tuesday, everybody.
00:08 (upbeat music)
00:10 Ryan, welcome back to Benzinga Live.
00:16 How are you doing on this Tuesday?
00:17 - Thanks, Aaron.
00:18 I was about to say happy Monday, but it's not Monday.
00:21 - No, it is not, but even though it might feel like one,
00:24 maybe a little bit for some people.
00:25 - It's Monday, I don't know, it's showtime, so.
00:29 - Yeah, how was your weekend?
00:31 Anything fun, anything exciting?
00:33 Any sharks? - I don't think so.
00:34 No sharks, no sharks this weekend.
00:36 I'm in the mountains till the end of the summer
00:39 in the West Coast of Canada,
00:41 so we don't have any sharks here.
00:44 - Okay, so Ryan, let's hop right into it.
00:47 I kind of mentioned the dip ETF and whatnot.
00:52 So I'd imagine for a lot of people who are maybe curious
00:56 about investing with AI,
00:59 one of the things that might be top of mind is risk.
01:02 A lot of people might think, okay, like, you know,
01:04 my money, my hard-earned money,
01:05 I don't wanna just turn it over to this computer
01:08 that I don't know what's going on with it.
01:09 So can you talk a little bit
01:11 about dip's risk containment strategy
01:14 and kind of, you know, is it different than other methods?
01:18 Do you have a practical example of this risk management?
01:21 - Sure, yeah, no, and that's a really good question.
01:25 We normally get asked about whether or not
01:27 it's a black box system, which it's not,
01:29 but I don't think I've been asked yet
01:32 to substantially dive into how the risk is managed
01:36 within the system.
01:37 So I'll just, I'll back up and just explain
01:39 a black box system would be an AI system
01:44 where the system is connected to tradable instruments.
01:48 It's given a capital allocation
01:50 and then no guidelines other than go make money.
01:53 And so that system has the autonomy
01:55 to do anything it wants to do.
01:58 It can overweight, it can underweight,
02:00 it can manage risk or not manage risk.
02:03 We've seen that previously in testing black box systems
02:07 when given those types of instructions,
02:09 tend to just gamble, like they're not dissimilar from humans.
02:11 We call it going to Vegas.
02:13 You know, what is the most capital efficient,
02:15 highest paying bet you can make,
02:18 way out of the money calls and puts and penny stocks,
02:22 low float, illiquid, and it will just bet
02:26 on all of these remote probabilities.
02:29 And over time, it may win money,
02:32 but what you see happen more often than not
02:35 is that there are these drawdown periods.
02:37 They're like 75% or 90%.
02:39 Like it just drives off a cliff, like it'll be fine.
02:42 It's gonna work out.
02:43 And then it gets like some 300 to one hit.
02:46 Now, obviously nobody wants to invest in an ETF
02:48 that looks like that.
02:49 So we put a lot of guardrails around
02:52 how we select the portfolio for dip.
02:56 So for starters, we're not trading
02:58 the entire universe of stocks, right?
03:00 There's no OTC BB, there's no pinks, there's no low float.
03:04 These are S&P 500 components largely,
03:08 and a subset of those components mostly.
03:10 And then we've got some large index ETFs thrown in.
03:14 So right off the bat, these are highly liquid,
03:16 you know, massive cap companies.
03:19 A second thing is that we have specific
03:22 diversification criteria and weighting criteria.
03:25 So we don't let the system just go like all in on tech
03:27 because it's hot and it likes it.
03:29 It doesn't work that way.
03:31 We make sure that we're diversified
03:32 across industries and sectors,
03:34 so that when you get a tilt that can't be predicted,
03:38 we're probably already present in some of that inventory.
03:41 And on the diversification side,
03:44 we're spread across a suitable enough array of candidates
03:47 that again, you're not piled into one or two stocks
03:50 because the AI cannot determine what's going to happen
03:55 you know, a week from now, two weeks from now,
03:57 a month from now, like the rest of us,
03:58 there's no crystal ball.
04:00 The last thing I'd say is we probably,
04:02 should probably be included in that we do have
04:05 a hard exit of seven days.
04:06 Like if it's still going strong after seven days
04:10 of position, we'll take it out.
04:12 Because again, that's probably exceeded our price target
04:15 at a responsible amount of profit.
04:17 And we have no idea whether the next day,
04:20 the CEO of that company is going to get arrested for fraud
04:23 or this is horrible negative news.
04:24 So very much a take the money and run scenario.
04:27 All those things together, you know,
04:29 seem to so far be proving that capital can be kept safe
04:33 autonomously within the system.
04:35 Yeah, so here on, I mean, I'm looking on Benzinga Pro,
04:39 you can see over the last couple of months,
04:41 I mean, this is going back to May,
04:44 the S&P 500, what we're comparing to is up about 8.2%.
04:48 Dip is up about 12.2%.
04:50 So you can see some correlation here,
04:52 but in the last couple of weeks,
04:54 it looks like dip is actually outperforming the SPY,
04:57 where, you know, SPY's kind of dip,
04:59 I'm saying dip, SPY's dipped,
05:01 but the dip has not dipped as much.
05:03 Is this kind of that risk, you know, strategy at work here,
05:07 or what's going on here, Ryan?
05:09 So really a couple of different things.
05:10 One, you're looking at the trading ideology itself, right?
05:14 Like there's correlation
05:15 because we're using the same components, you know,
05:17 anything that's S&P correlated, we're gonna correlate too,
05:22 but we're trying to do, obviously, it's not buy and hold,
05:25 but we're opting in, opting out for little bumps in profit.
05:29 So we do expect, you know,
05:32 a regular outperformance over time,
05:34 but you're also seeing here the impact
05:36 of the machine learning really kicking in
05:37 in this current market condition.
05:39 So we don't manipulate this at all.
05:40 We don't step in and say, oh, let me help you out machine.
05:43 This is just self-evolving.
05:45 And so as it gets better at identifying the patterns
05:48 that we fed it to start with,
05:50 evolving its balance and weighting of criteria
05:53 that we use to make the buying and selling decisions,
05:56 it's expected to do exactly this.
05:59 So it's validating for us, for sure.
06:01 That's awesome.
06:03 Well, you know, Ryan, I mean, I know right now,
06:05 you know, 2023, it's kind of all been about AI
06:08 on Wall Street and maybe for a lot of people,
06:11 you know, on the outside looking in might think,
06:13 oh my God, this is crazy.
06:14 You know, people on Wall Street
06:16 are gonna be using algorithms and computers
06:19 to buy and sell stocks.
06:20 Well, that's been going on for a while.
06:22 I mean, you know, you talk about quant trading
06:24 and different algorithms that are running.
06:27 So, you know, what makes, I guess,
06:29 what Dip is doing right now that much different
06:33 from what's been going on in terms of algorithmic trading,
06:37 you know, kind of like the big differentiation there
06:40 between quant trading, algorithmic trading, and then AI?
06:45 Yeah, it's really the,
06:46 and this is a question that we do get asked a lot,
06:48 you know, the quantitative versus AI component
06:51 because quantitative component is part of AI,
06:54 but AI is sort of an evolution of that.
06:55 And really it boils down to the autonomous learning part
06:58 of it, the machine learning part of it.
06:59 So quantitative strategy,
07:01 you're looking back over 15 years of historical data,
07:03 20 years, whatever your strategy window requires,
07:07 and you're putting together a system
07:08 that you then get a wash, rinse, repeat,
07:10 and trade over and over again.
07:12 And it's probably valid up until the present day.
07:14 You've tested it up until today and it would work.
07:18 So you use it going forward.
07:19 And it's not unusual for quantitative strategies
07:21 to have some success right in the beginning, right?
07:24 Because you've tested it up until this point,
07:26 done your homework, and so it's going to perform well.
07:29 But as market conditions change,
07:32 nobody's changing the quantitative strategy
07:34 without some serious heavy lifting.
07:36 So then you start to become,
07:38 you start to have off-model performance.
07:40 It's not working as well anymore.
07:42 With an AI strategy, completely autonomous,
07:46 curated and directed AI system,
07:49 it's able to learn as it goes along.
07:52 It can rewrite portions of its own code
07:54 so that as these market conditions change,
07:57 as it finds greater efficiencies in the criteria it's using,
08:01 it's like, yeah, this one has stopped being as reliable
08:04 as it used to be.
08:04 This one over here that we just examined
08:06 for databasing purposes is actually becoming
08:09 a pretty decent indicator of whether this will succeed.
08:11 I'm going to weight it more heavily.
08:13 So that sort of makes these strategies
08:16 theoretically evergreen.
08:17 Whereas the quantitative systems will run for a while,
08:20 and who knows, two weeks, three years, 10 years,
08:24 whatever it is, not 10 years.
08:26 So some period of time, and then they'll probably break.
08:30 And then the vendor is either gonna determine
08:32 whether or not a substantial update can save the system,
08:34 or they're just gonna scrap it,
08:36 which is why it's tough for ETFs,
08:38 'cause you can't fold an ETF.
08:39 - Yeah, that makes a lot of sense.
08:42 I mean, I guess that when quant trading came out,
08:45 Renaissance, Jim Simon, all this stuff,
08:48 I mean, he was a forefront genius at the time.
08:51 Maybe he should have been working on AI, not quant trading.
08:54 He should have been looking so far at--
08:56 - Yeah, well, they actually were.
08:58 Rentech is one of the godfathers of machine learning and AI.
09:02 I mean, they're super secretive,
09:03 so you're not getting any output from that shop.
09:05 But what started as a purely quantitative process
09:08 became by 2008 autonomous learning.
09:11 I mean, they survived the '08 collapse
09:14 because of the autonomous learning component
09:16 of their systems.
09:16 I mean, the famous story out of that is,
09:19 first week of the collapse,
09:20 they lost like 20% of their capital value.
09:23 And then the second week, they lose another 20%,
09:26 and the whole firm just goes nuts.
09:28 And it's divided into two camps.
09:30 One camp wants to shut it all off.
09:32 It says, "The machine doesn't know what this is.
09:35 It's just like trading blindly."
09:37 The other camp wants to give it more money
09:39 because they figure it'll figure it out.
09:42 And Jim comes in and he splits the difference.
09:45 So he gives it half as much money, but he leaves it on.
09:48 And by the end of the year, they posted like 126% return.
09:51 So it did figure out what was going on.
09:54 It did learn to put it on that.
09:56 And then it continued, yeah,
09:57 it continued their crazy streak of,
09:59 this is why you have a shop that's 67% net of all fees
10:02 for the past three decades.
10:04 297 scientists and five traders, go figure.
10:08 - It's interesting, I guess,
10:11 once probably they realized like how much money
10:14 it could be potentially made with this technology,
10:16 like that's kind of the impetus
10:18 to then push that technology further.
10:20 And you probably wouldn't have seen a lot
10:24 of the developments of AI and quant trading
10:26 if there hadn't been like,
10:28 oh, wow, they're doing really well.
10:30 They're making a lot of money.
10:30 We can do it.
10:31 So yeah, I mean, that's just,
10:34 it just seems like that's the natural progression
10:36 of technology, how it started with quant trading.
10:39 And then now, you're seeing it more to AI.
10:41 So it's kind of cool to see that development,
10:43 these developments happen real time.
10:46 - Yeah, I mean, it leaves breadcrumbs, right?
10:48 I mean, Renaissance Technologies is super secretive
10:50 because it has these incredible returns
10:52 and it's 100% employee owned.
10:54 So there are no outside investors,
10:57 at least in the Medallion Fund anymore.
10:59 And so you can imagine,
11:00 like if you were making that return every year reliably
11:03 as a secretary, a janitor, a quant, a trader,
11:07 whatever it was, how likely would you be to talk
11:11 about that outside of the walls of that firm?
11:13 Probably zero, right?
11:14 So almost nothing has ever leaked out of that shop.
11:18 But the sort of key things that leak out are one,
11:20 that their win rate is slightly better than 50%.
11:22 So you know, they have a huge asymmetrical return on risk.
11:25 Two, their average trade window is three to eight days.
11:28 So you understand as we have that AI
11:31 for making trading decisions works the best
11:33 in the immediate to short term.
11:35 It's not very good at 90 days out.
11:37 And so that's the kind of thing that you can sort of
11:39 take away as, okay, like if they're doing it
11:42 with all of this power, we can emulate part of that.
11:44 We can start researching in these areas,
11:46 which is what we do.
11:48 - Yeah, and it's always been fascinating to me.
11:50 I mean, I think for people in finance
11:52 or for people that pay attention to this stuff,
11:55 they view Jim Simons as this kind of rockstar figure.
11:58 But for most people, you go walk around the street,
12:00 99 out of 100 people probably never heard of him.
12:02 And yet he's sitting on 30 bill or whatever.
12:04 And I mean, it's just, it's a fascinating story.
12:07 So if anyone out there is like interested in this stuff,
12:09 on the history of this stuff,
12:10 I know there is a book or something about the,
12:14 about Rentech, but it's, I don't know,
12:16 like you said, how much information
12:18 is really available about it.
12:19 But it is, again, just a fascinating story.
12:22 So Ryan, right now, as I mentioned,
12:25 AI has been the story of Wall Street 2023.
12:29 And a lot of it is obviously inherently soft.
12:33 The conversation is about the software
12:35 and about the technology,
12:36 but you also have this hardware aspect
12:38 and you've seen a lot of companies benefit
12:40 from this increased demand in AI,
12:42 whether you're talking about NVIDIA, Taiwan Semi.
12:45 Do you see any sort of bottleneck in our ability
12:49 to produce the hardware necessary
12:51 to keep up with the market's demand for AI?
12:54 Will our technology outpace our ability
12:56 to deliver on its promise in real terms?
12:59 - I think it's actually the other way around.
13:01 I think the limitations are gonna slow us down
13:03 at some point.
13:04 You know, right now it's able to explode
13:06 at like an exponential rate.
13:08 And what would drop that to like a geometric rate
13:10 of development would be limitation of raw materials, right?
13:14 So the more chips we create,
13:16 the more rare earth minerals and materials
13:18 that we need to mine.
13:20 There are limited places on earth where they're available.
13:22 The conditions in those places are suspect
13:25 and it calls into ethical questions
13:27 or ethical questions around it.
13:29 And it's difficult for us to keep up
13:33 with the computing needs.
13:34 Right now we can, but you're starting to see that crunch.
13:36 Right?
13:37 I mean, like if you're reading quarterly days of semis,
13:39 it's difficult to keep up with the demand
13:42 and the demand will keep increasing.
13:43 So on the hardware side from a rare materials,
13:47 yeah, you got a bottleneck there.
13:48 And then power consumption,
13:49 which was the same with crypto, right?
13:50 I mean, for these machines to do what they do,
13:52 the power consumption is just insane.
13:55 And at some point you say, okay,
13:56 there's just not enough power to go around.
13:58 So where do you turn your attention to?
14:00 Efficiencies in the technology itself.
14:03 So this is where, you know,
14:04 sort of bleeding edge quantum comes into things
14:06 with the minimization of the technology,
14:10 efficiencies in power consumption, et cetera.
14:12 So if we want this to continue,
14:14 at some point we can't just keep doing same old, same old,
14:16 let's just build bigger, badder, better.
14:18 You gotta turn your attention to, okay,
14:19 how do we build this more efficient
14:21 so that the bigger, badder, better can continue?
14:23 And that's where I think we're at the very beginning
14:25 of that.
14:26 So, so far, no, no limitation,
14:28 but it's walking up the driveway.
14:30 It's right there.
14:31 - Yeah, that makes sense.
14:33 I mean, I think, you know, right now,
14:35 like if you saw it during the crypto mania,
14:39 I mean, there was that kind of shortage
14:40 of semiconductors and whatnot.
14:41 So if we run into any supply chain issues like that,
14:45 while simultaneously the demand is increasing
14:48 for these things,
14:48 that's kind of where you can run into those sort
14:51 of bottlenecks and problems.
14:52 So Ryan, a few weeks ago,
14:56 we talked about you diving with sharks.
14:59 I've got, I got some more inside information passed off
15:04 for me from a producer that you can also fly a helicopter.
15:07 I mean, this seems like kind of a theme that, you know,
15:10 you have some dangerous hobbies.
15:11 Is there anything like, you know,
15:14 is there a trend here we should be concerned about?
15:16 - I can see how that conclusion might be drawn,
15:20 but I am, and I was about to say like,
15:23 I'm not an adrenaline junkie.
15:24 And then I started like mentally going through my hobbies
15:26 and thinking it's gonna be difficult,
15:30 but really like, it's just,
15:32 I guess if I was gonna encapsulate it all
15:36 under one umbrella,
15:38 it would be like just effective risk management, right?
15:40 I mean, it's why I do what I do in capital markets.
15:44 It's, you know, diving with sharks is risky
15:47 if you have no idea what you're doing
15:49 and you just pay no attention to current conditions, right?
15:52 You stick your head underwater
15:53 and you've got a pack of reef sharks
15:55 and they're clearly in a feeding mode.
15:57 Don't jump into the water.
15:59 I'm not gonna go in there.
16:00 I've done that once.
16:01 And I was like out in two seconds.
16:03 You're like, okay.
16:05 You know, they take a couple of runs at you
16:06 and you think bad decision, out you go.
16:09 Helicopter, same thing.
16:10 Helicopters do not like fog.
16:13 They don't like bad weather.
16:14 So you go out, it's low rolling fog.
16:17 It's high winds.
16:18 It's whatever it is, don't fly.
16:21 And I guess you could,
16:23 that could correlate to, you know,
16:25 anything for trading, right?
16:26 I mean, you look at a stock, it's the wild west.
16:29 It's low float.
16:30 It's sketchy, don't trade it.
16:33 You know, you see a market condition
16:34 that you're just not sure about,
16:35 which was like basically all of 2022, right?
16:38 It's so volatile, so unpredictable.
16:41 You know, you're probably gonna roll into strategies
16:43 that you're more comfortable with,
16:45 that you're gonna preserve more capital.
16:48 I guess that's gonna be the ideology you follow,
16:50 capital preservation,
16:51 rather than rah, rah, let's make some money here.
16:54 And so I think you could boil
16:56 my odd choice of hobbies down into that.
16:59 And just, you know, just to be clear
17:01 on the helicopter front,
17:03 yeah, zombie apocalypse, I'm your guy.
17:06 It can, you know, get us from A to B
17:08 and out of a hostile situation.
17:09 But I wouldn't like recommend that we load up the families
17:13 and go on a sightseeing tour.
17:14 Like I fly helicopters passively,
17:17 but not extraordinarily well.
17:20 - Got it, yeah, I've never been in a helicopter.
17:22 I actually got offered to cover a story like a year ago
17:26 where, you know, it was at some remote,
17:29 they were opening some facility like in Canada
17:31 and you would have to go here and then take helicopter.
17:33 And I was like, I don't know,
17:35 'cause it was kinda, you know, I don't know.
17:38 There's been some news stories in the past couple of years
17:41 about different helicopter crashes that have determined.
17:44 But I think about that stuff a lot, Ryan,
17:46 I guess just like inherent risk versus reward.
17:49 Like for example, for me, like skydiving.
17:54 I know a lot of people who have gone skydiving,
17:56 looks very fun, sure.
17:58 But for me personally,
18:00 like, and I know it's not very risky whatsoever.
18:02 You look at it like, you know,
18:04 I don't know the exact percentages,
18:05 but like you're not likely to have any accidents
18:08 or anything happen while you skydive.
18:10 But with that said, I think about,
18:12 would I personally like get enough enjoyment
18:16 out of that activity to even take
18:18 that minuscule risk whatsoever?
18:21 And I don't think I would, like, I don't think,
18:22 I think there are people out there
18:24 that like, that will love skydiving so much
18:26 or wanna do it one time so badly
18:28 that they're willing to take on that
18:30 even just tiny minuscule risk to go ahead and do that.
18:33 But I think for me, like, I just, it wouldn't,
18:36 the reward wouldn't be high enough
18:38 to even take on that tiny little bit of risk.
18:41 - Exactly, and that's gonna be the same for everybody, right?
18:44 I mean, if you boil down like risky scenarios,
18:47 you're gonna find some extenuating circumstances every time.
18:51 You know, this person got bitten by a shark,
18:53 low visibility, high shark content,
18:56 swimming around with seals, like their food source,
18:58 they do not have fantastic vision.
19:00 If there's a lot of surge,
19:01 then your visibility is like two feet.
19:03 Okay, so there you go, you got bit.
19:05 You know, the news stories about the helicopter crashes,
19:08 inclement weather, hydro wires,
19:10 like these are not the friends of helicopters.
19:12 Beautiful blue sky day, you're flying around,
19:14 like people don't generally crash their helicopter.
19:17 And same, I guess, you could take that
19:19 all the way through to the trading, right?
19:21 I mean, you know, if you're a trader,
19:22 you know that something you're about to get into
19:25 is probably sketchy, and it's that risk reward balance,
19:30 the two little voices on the shoulder
19:32 that you're gonna be trying to find balance
19:35 between, I guess.
19:36 - Yeah, and that, yeah, no,
19:40 it just all makes a lot of sense, I think.
19:42 At the end of the day, trading like, you know,
19:45 a lot of other things, you gotta weigh the risks and reward,
19:48 and then make your decision based on your scenario, right?
19:52 Like some people might, for some traders,
19:54 some portfolio managers might have a higher risk appetite
19:57 and say, yeah, I'm gonna buy this, you know,
20:00 penny star, whatever the risky speculative asset is,
20:04 versus someone else who might, you know,
20:06 who maybe you're investing with all your retirement money,
20:08 and you're close to retirement,
20:10 you're not willing to take on those, you know,
20:11 you're not gonna spend 50% of your portfolio
20:14 on a penny stock or something if you know,
20:16 okay, I gotta use most of this money
20:18 in the next couple of years.
20:19 So Ryan, for the past couple of weeks,
20:23 we've ended our conversations with a Kaiju kicker,
20:25 kind of a type of, I like to think of it
20:29 as like some sort of inside information
20:31 from your professional finance world to our audience,
20:34 so we can get some kind of inside baseball,
20:36 some inside tips from people
20:38 that are doing this professionally.
20:40 And I thought this week would be interesting
20:41 to talk somewhat about indicators.
20:45 I mean, you know, if for a lot of our audience out there,
20:48 people kind of day trading, you know,
20:49 you're looking at charts, you're using different indicators.
20:53 What are your thoughts on like,
20:54 what are some good indicators to use for a retail trader?
20:57 - Yeah, that's a really good one, Aaron.
21:00 And I think, you know, when you were discussing tools
21:03 a couple of weeks ago, and you know,
21:04 we were saying that a software package
21:08 is just not gonna make you a more profitable
21:10 or better trader.
21:11 It can help you with your analysis,
21:13 but it's not like you're not unprofitable
21:15 because you don't have the right tools.
21:17 And the same thing goes for indicators.
21:19 Like there are hundreds of indicators out there.
21:23 They're all basically using iterations
21:25 of the same three pieces of data, right?
21:27 You know, price, time and quantity.
21:29 And what I can for sure tell anybody who's watching
21:33 is there is no red light, green light indicator.
21:37 Like there's no magical combination
21:40 where if you trade something on a crossover,
21:43 you're gonna make money.
21:44 I'm gonna buy this on this cross,
21:45 I'm gonna sell it on this cross,
21:47 and I'm gonna consistently make money.
21:49 You may have had some positive outcomes from doing that,
21:54 but I promise if you take selection of 10,000 trades
21:57 applied to that, it's a money losing proposition.
22:00 That said, indicators are really good to help.
22:04 Their primary role I should say
22:05 is to help you confirm something you think you see,
22:08 something you think you already know.
22:10 So, you know, your number one indicator
22:12 is your chart layout, right?
22:14 I mean, like whether it's open high, low closed bars
22:17 or candlesticks, like that's an indicator
22:19 of sorts right there.
22:20 It's giving your brain a pattern
22:23 that you can build up over time to recognize.
22:26 And then underneath that, you're probably running,
22:29 you know, volume bars, but you know,
22:31 they're useless if you don't apply like the proper period,
22:35 exponential moving average, right?
22:37 Because you need to know whether this volume
22:40 is above or below what's normal
22:42 for how this stock has been running recently.
22:45 You see like strong white candle,
22:47 you think there's a momentum move to the upside,
22:49 but you got a green volume bar
22:52 that's below the 20 day exponential moving average,
22:55 front weighted probably, that's not moving with momentum.
23:00 That is not actually underpinned by heavy volume.
23:02 That's just working up through liquidity pocket.
23:05 And so the likelihood that it's gonna whipsaw
23:07 and collapse is much, much higher.
23:09 So that's something you can do.
23:11 You know, add the proper period,
23:12 exponential moving average to your volume bars, one, two.
23:15 You probably wanna see what we call
23:18 volume pressure at price.
23:19 Like, you know, I always say that
23:21 if you're looking at stock price, that's the symptom.
23:24 That's not the underlying disease.
23:25 It's not the cause, right?
23:27 It's a cough.
23:28 You know, you have a cough because what?
23:30 You've got lung cancer, you got COVID,
23:32 you know, you've got a tickle in your throat,
23:34 you ate spicy food, same cough,
23:36 very, very different underlying reasons.
23:39 And so volume provides the underlying reasons.
23:41 So to see like volume pressure at price,
23:45 you're gonna wanna run something like
23:46 time segmented volume, TSV we call it,
23:49 within exponential moving average.
23:51 I don't know which charting companies provide TSV.
23:56 I know Worden does, TC2000 has TSV.
24:00 If you're a momentum trader,
24:01 you're probably using a period of like 18 or 20 for that.
24:05 You'll want a strength of price trend indicator.
24:08 RSI is usually the fan favorite,
24:10 only I do something a little differently with it.
24:12 Most people run RSI and with an EMA as well.
24:17 I actually run the short term over the long term.
24:21 So I'll run an RSI 15 over 75.
24:24 And that shows me the current strength
24:26 of the short term price trend
24:28 over the long term price trend.
24:29 And I tend to set overbought and oversold to 80/20.
24:32 Some people use 70/30,
24:33 but in the current market condition,
24:35 a little too many touches for my liking.
24:39 And again, like altogether,
24:42 none of that is like this crossed,
24:44 this is, I'm gonna buy it.
24:46 But you have a strong up move on your chart.
24:48 You have above average volume associated with that move.
24:53 And it's been increasing leading up to that move.
24:56 So you're seeing like quiet accumulation
24:58 that happens unlead off exchange.
25:00 You see that the time pressure is like substantial,
25:04 it's moving upwards, it's over a moving average.
25:06 These are larger block aggregated orders
25:09 and compressed time periods.
25:10 Okay, cool.
25:11 You see that the short term
25:12 is over the long term crossing over it.
25:14 So you have a swing in price sentiment for sure.
25:18 All of that together matches with your analysis.
25:20 You can feel more confident about making that trade.
25:23 And the opposite is true.
25:25 And the only thing that I would add
25:27 as a caveat at the end is a warning.
25:30 If you use MACD, please stop using that indicator.
25:34 That is, it's terrible.
25:36 I know it's a fan favorite of retail.
25:38 I got a good MACD crossover, I'm gonna buy that.
25:41 One, it is incredibly laggy.
25:43 You're not seeing something that's happening right now.
25:45 You're seeing something that happened
25:47 and is now showing up in your indicators.
25:49 So laggy, number one.
25:52 Number two, the professional side programs algorithms
25:56 to predate on that crossover
25:58 because they know retail will buy
26:00 with market and limit orders.
26:01 What do I mean by that?
26:02 So I don't know, we're holding, I'll use your example.
26:06 We're holding a big Disney position
26:08 and I go to the PM and say, yeah, I want that unwound today.
26:13 It's no longer meeting its price target.
26:16 We've met the price target.
26:17 Risk envelope is too large, whatever reason.
26:21 First thing that guy's gonna do
26:22 when he goes to his executing trader and says,
26:24 we need to unload this position by EOD
26:27 is that executing trader is going to see
26:29 whether or not there's a MACD crossover that's close.
26:32 And we'll unload into that crossover if they can
26:37 because retail will take it all at the offer
26:41 and it'll just bid right up
26:42 and you get an extra point out of it at close.
26:45 Like don't willingly throw yourself into a piranha tank.
26:48 It works on both sides, HFTs predate on it
26:51 because there's this love of MACD.
26:54 These institutions manipulate flow around it.
26:57 So don't be part of that.
26:59 You don't need it, it's not gonna help you.
27:01 Just leave it on the side of the road.
27:02 That's my final piece of advice on indicators.
27:05 - Yeah, and I actually, it was funny
27:07 'cause I mean, when trading kind of blew up during COVID
27:11 and everyone was buying and selling stocks
27:13 and Robinhood and all this stuff,
27:15 MACD was one of the ones that like,
27:17 everyone was using, everyone had MACD on their charts
27:20 and was talking, and so it's interesting, I guess,
27:24 that one of the most popular indicators
27:27 that I saw being used among this new wave of retail traders
27:30 is actually one that as a professional trader,
27:34 you're saying, hey, like, don't bother with that.
27:36 So that's exactly, these are the exact types
27:39 of like inside baseball tips that I expect
27:42 and love about the Kaiju Kicker.
27:44 So Ryan, before I let you go, I wanna ask the audience
27:46 if they have any questions for Ryan,
27:48 anything about the dip ETF, about AI,
27:51 and I also will mention, we will be back
27:54 with another one of these segments next Tuesday,
27:56 same time, 11 a.m. Eastern.
27:58 If you wanna continue to learn more
28:01 about how Kaiju is using AI to run their dip ETF
28:06 and just in general about, you know,
28:08 like it's not just about the dip ETF or this,
28:11 like we're getting all these types of insights
28:13 about trading in general that you can take away with you.
28:16 So we will be back next Tuesday.
28:18 Ryan, any other thoughts while we wait
28:23 for some questions to file in?
28:26 - No, I just say, you know, adding onto what you said
28:30 about the popularity of MACD,
28:32 I'm probably gonna get like angry peer BBMs on my terminal
28:37 once if anybody that I know is watching this to be like,
28:40 why did you say that thing about MACD?
28:42 I mean, you're not gonna see people who profit
28:45 off of the use of this indicator coming out and saying,
28:49 don't use this indicator.
28:50 I mean, you know, I say that because one,
28:54 years ago I was a retail trader
28:56 and finding out after the fact some of this stuff,
28:59 it's irksome.
29:00 So you wanna save as many people as you can
29:02 from plunging down the right thing.
29:05 It can seem like it works.
29:07 It's almost self-supporting.
29:10 You know, you run a 81224 MACD and you're looking at it
29:14 and you're looking at price and you're like,
29:16 man, like all of these crossovers line up.
29:19 Yeah, but remember that it lags and it adjusts, right?
29:22 So did it look that way on the day?
29:24 No.
29:25 Would you have made the same decision?
29:27 Probably not.
29:28 When did the crossover occur?
29:29 The next morning or during the trading day.
29:32 And then you go back far enough,
29:33 you're just not going to see that correlation of profit.
29:36 I know it looks that way, but it really isn't.
29:39 And it's not worth it at the end of the day.
29:41 It's much better to like develop good pattern
29:44 recognition skills, get a core set of indicators.
29:48 Don't add like 20.
29:49 One or more will always contradict the others.
29:53 So you'll end up confused.
29:55 You know, me, when I actually sat at desk,
29:58 I probably used five, you know, and that was it.
30:01 And I got really comfortable with my analysis
30:04 and my five indicators for confirmation of my bias.
30:08 And that worked out really well for me.
30:09 Ultimately, again, there's no like,
30:12 it's not like you're just trading a crossover.
30:14 They confirm the execution part of another strategy,
30:19 like strategy that you're running.
30:20 So I have this strategy goal.
30:22 And when this happens,
30:23 I check this to make sure what I'm seeing is correct.
30:26 And then I'm going to execute on my strategy.
30:28 Like, it's not like, hey,
30:29 I got green, green, green across the board.
30:31 I'll just buy this for no other reason.
30:34 That also probably won't work over time.
30:37 Just add that at the end.
30:37 - Makes sense.
30:39 Yeah. And I mean, you know, it's right now,
30:42 I feel like with the, in general,
30:45 just AI has become such a big part of the conversation
30:50 that it's like, I feel like a lot of traders now are not,
30:53 I haven't heard as many people like retail traders
30:55 that started trading.
30:56 They were talking about all these indicators and stuff.
30:57 And now everyone's talking about AI.
30:59 It feels like, I don't know,
31:00 maybe a lot of these traders have gotten washed out
31:02 over when the markets got, you know,
31:06 a little bit less friendly, I guess we should say,
31:08 over the last couple of years.
31:10 I do see some comments flying in from the chat.
31:14 Marky wants to know,
31:15 are there any sectors that the ETF focuses on?
31:17 - It's actually broadly focused.
31:21 So it doesn't drill down to, you know,
31:25 an overly focused tech sector, for example.
31:29 Like, you know, our general overview
31:34 is that there are already sector ETFs.
31:39 If you're interested in that, you know, you've got Qs,
31:41 you've got the Xs, you've got, you know, broad market.
31:45 So what we're trying to do is give you a little bit
31:49 of more refined price positivity and profit
31:53 within that S&P universe.
31:55 I mean, you know, the momentum side of dip will skew
31:59 while still holding its diversification.
32:02 So, you know, if tech dips across the board and tech pops,
32:07 it'll push as far as we're comfortable pushing,
32:11 which is about the same way that SPY has for tech,
32:14 into those areas.
32:16 But no, it doesn't really focus on a specific sector.
32:19 - And then I see someone else asked, what ETF do you manage?
32:24 I think I can handle this one, Ryan.
32:28 Dip, ticker D-I-P.
32:30 I'll throw that in the chat as well.
32:34 But Ryan, all right, I didn't realize how long we've gone.
32:36 I know you're a busy guy.
32:37 Now it's 11.36 a.m. Eastern.
32:40 So I got to let you go, but, you know, always great
32:42 having you on again.
32:43 We'll be back next Tuesday.
32:45 Everyone in the audience, you know, this is,
32:49 we're so lucky here on Benzinga Live
32:51 that we actually get to talk to Ryan,
32:52 someone who not only has like inside knowledge about AI,
32:55 but also investing at this kind of, you know,
32:57 crossing point that we're at right now,
32:59 where, you know, it feels like, all right, AI's here,
33:02 AI's coming to Wall Street,
33:04 and we're not really turning back.
33:05 It's not gonna, you know, be like, okay, yeah, yeah,
33:08 we tried that.
33:09 No, we're not doing AI anymore.
33:11 You know, it seems like it's here, gonna be here to stay.
33:13 So we're really lucky to be able to talk to you, Ryan,
33:15 and get some of this inside knowledge.
33:17 And, you know, I truly appreciate your time
33:19 and hopping on the show.
33:20 So with that, I'll let you go.
33:22 I'll let you get back on with your busy Tuesday
33:24 and looking forward to next week.
33:26 - Thanks a ton, Aaron.
33:27 I'll see you guys next Tuesday.