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 All right, Ryan, how are you doing today?
00:17 - I'm good, Aaron.
00:18 How are you doing?
00:18 I like your little opening there.
00:21 - I'm good, I appreciate it.
00:22 Yeah, it's got, I don't know,
00:23 I think it's important to just,
00:24 I sometimes forget to zoom out
00:26 and just do a quick overview
00:27 of what the general market is doing.
00:30 You know, I think it's helpful.
00:31 It sets the stage, if you will.
00:34 - Sets the stage.
00:36 Generally weak, marginally neutral open.
00:40 - Exactly.
00:41 Which I should say, you know,
00:43 I mean, we got through August.
00:45 We had a little nice rally at the end of August,
00:47 but historically August has not been a great month
00:50 for stocks and then September, October,
00:53 or no wait, maybe, I don't,
00:54 I think September also hasn't been great historically
00:57 for stocks, but then October, November, December have.
01:00 But I don't know.
01:01 I mean, Ryan, I mean, like, is something like that,
01:02 I know this is kind of, you know, off topic,
01:04 but is that something that you would even ever consider
01:07 or like look at how stocks do historically
01:10 in months in the past?
01:12 - Sure, I mean, because that's tied
01:14 to behavioral patterns, right?
01:15 I mean, fine, we focus on AI-directed trading,
01:20 but that doesn't mean that you ignore
01:22 the behavior patterns that you're trying to trade off of.
01:26 So, you know, you're used to quiet summers.
01:29 There's a reason for that.
01:30 There's a reason that the last week in December
01:33 is basically dead and you have a little flurry
01:35 of confusing rebalances right before the end of the year.
01:38 There's a reason that usually this week is pretty active
01:43 because people are back after Labor Day,
01:45 kids have gone back to schools and they're hungry, right?
01:49 You're right in between earn cycles.
01:51 So, you know, people are, this is the hottest time
01:54 for IPOs, for example, every year, you know,
01:57 you've got now, you got February, you got May.
01:59 So, it's not like you get to ignore those cycles.
02:03 They're important to be mindful of,
02:05 especially if you're recalibrating a portfolio.
02:09 - Yeah, that makes sense.
02:13 So, Ryan, let's hop into it, but before we get started,
02:16 and I know I would, if I had to guess,
02:19 I'd venture to say about 99% of our audience
02:22 or 90% of people watching right now
02:23 are familiar with Kaiju at this point and yourself,
02:25 but there may be just a couple stragglers
02:28 that are popping in for the first time today.
02:29 Welcome, if that's you, by the way.
02:31 But do you want to just give us a quick rundown of Kaiju
02:35 and maybe the dip ETF before we hop into these questions?
02:38 - Sure, sure.
02:39 - Set the stage again, if you will.
02:41 - You set the stage again, set the stage.
02:43 - Uh-oh.
02:47 AT.
02:51 I don't know if it, oh, nevermind.
02:54 Gone, it froze on my end for a second, but you're good now.
02:57 - Who knows, you know, technology.
02:59 I can't really denigrate it,
03:00 seeing as we've built like a global ecosystem
03:03 of companies on it, but whatever.
03:05 So Kaiju's been involved in artificial intelligence
03:08 for half a decade.
03:10 Some of our team members have been doing this for 30 years,
03:12 some for 10.
03:13 We've largely done this on the private fund side.
03:18 And then last December,
03:20 we decided to bring our expertise to the public fund side.
03:25 We launched the first AI curated and directed ETF
03:30 on the New York Stock Exchange called DIP
03:32 mid-December last year.
03:36 And we've been running that since.
03:37 We have plans to bring out additional AI curated
03:41 and directed ETF.
03:42 So again, like it's not thematic exposure
03:44 and it's not AI informed.
03:45 We're not using it to, you know,
03:48 cull a field down for us to make manual trade decisions on.
03:53 It is end to end artificial intelligence.
03:56 And yeah, it's been a good ride.
03:59 And obviously the last six months
04:01 have been hugely validating to us.
04:02 You're sort of, you know, working in the shadow.
04:05 No.
04:13 - Got it.
04:16 Yeah, hopefully that's just on mine.
04:17 You cut out again for a second, but yeah,
04:21 I think that might just be on my end here.
04:23 All right, so Ryan, let's hop into it then.
04:27 I know you built a proprietary portfolio management
04:30 and trade visualization platform at the genesis of Kaiju.
04:35 Can you describe this platform?
04:36 I mean, it sounds just from that description,
04:38 like, you know, building a proprietary
04:39 visualization platform, I'm sure,
04:41 is not an easy thing to do.
04:44 What did you see in the market
04:45 that led you to building it?
04:47 And is any part of that tech still in use today?
04:50 - Yeah, I mean, it was actually called Kaiju.
04:55 We were operating under a different name at the time,
04:59 which was Synergis.
05:01 And we switched to Kaiju because when we started
05:04 our kind of global expansion and things like branding
05:08 and trademark were elements that we had to consider,
05:14 you know, our advisors came back and said that Synergis
05:17 just wasn't brandable, really.
05:19 There were too many close company names
05:23 and we really needed to find something different
05:25 to call ourselves.
05:26 And based on what we were trying to accomplish,
05:28 this sort of monster system that we were building,
05:33 we appropriated the name from this proprietary tool set
05:38 that we'd built, which was Kaiju,
05:39 and we just named the ecosystem,
05:41 all of our companies Kaiju.
05:43 And really we built that.
05:46 I mean, I'm sure anybody who's watching,
05:48 who's been trading for a while,
05:50 I'm sure there are tools, there are functions, features
05:55 that you wish you had access to,
05:57 that whatever platform you're using doesn't do.
06:00 And you've customized these things,
06:02 whether it's your OMS, EMS,
06:04 or your visualization software, charting, analytics,
06:08 to the extent that you're able to.
06:10 But you get to a point where the developers
06:14 just didn't build the specific tool that you want.
06:17 And so most sort of investment managers at our size
06:22 or larger when that happens,
06:24 will build your own internal system.
06:26 So you can work with companies like Bloomberg
06:31 and they'll build custom panels for you
06:34 up to a certain point.
06:36 But really, if you're doing something that's very unique,
06:39 in our case, there was a system that we wanted to use
06:44 to visualize complex options trades.
06:49 And we collaborated with SIBO via LiveVol
06:54 and what is now LiveVol Pro used to be LVX.
07:01 We collaborated, we tried to collaborate with Silex
07:04 that they weren't able to do much for us there.
07:07 And no, again, no criticism of them.
07:09 I mean, you're like one shop saying,
07:11 "Hey, can you build me this weird thing
07:13 that nobody else has asked for?"
07:15 So we just started building it internally.
07:16 Actually, when we were a shop of just five people
07:20 at the very beginning, our CTO, David Schooley,
07:24 and some of our developers started building this thing
07:27 and it just grew and grew and grew
07:29 until it really became this sort of
07:31 all consuming monster system
07:33 that seemed appropriate to call Kaiju.
07:36 And yeah, it's still in use today.
07:39 We use it more for scan distribution
07:43 and order execution instructions from the AI system.
07:48 So our traders are watching the outputs from this system
07:53 to determine what adjustments they should make.
07:57 Not for dip, but for the private side fund strategies
08:01 that we run.
08:02 - Got it, that makes sense.
08:04 So Ryan, I understand that you were working
08:07 with AI back in 2018.
08:09 In a recent interview, you described the view
08:12 of what you were doing as practicing the dark arts.
08:15 You were like you were Professor Snape over here.
08:18 What made you make the leap then?
08:19 I mean, 2018, we're talking about five years ago.
08:22 I'm sure there were a lot of people who would be skeptical
08:25 of making that move to such a new technology
08:28 that's not established yet.
08:31 - Yeah, I guess for us, it really,
08:34 it addressed one of the fundamental challenges
08:39 that accompanies quantitative trading and investing.
08:43 Quantitative trading has been around for ages.
08:46 There's nothing new about that.
08:48 The challenge with quantitative systems
08:50 is that they're static.
08:52 There's no machine learning layer to them.
08:54 So you're sort of trying to avoid curve overfitting.
08:59 You're looking back through historical data
09:01 and you're asking yourself,
09:04 what could I have done in these environments
09:06 using this strategy, that strategy, these assets
09:11 in order to get a favorable outcome?
09:14 And you randomize the testing as well as you can,
09:19 but you still have this inherent challenge.
09:22 You can't forget what happened, right?
09:24 So you know that the pandemic is coming
09:27 at the end of Feb, 2020.
09:30 And so you're specifically looking for positions
09:33 you could have taken that would have
09:36 safeguarded against that.
09:39 But in reality, would you have applied that
09:42 not knowing that this global pandemic was coming
09:45 at the time?
09:47 I mean, we were right at the top end of a historic melt up,
09:51 and we were running systems at the time,
09:55 thank God that helped us take a downside defensive position
09:59 prior to the pandemic.
10:01 But like, it's not like the AI identified the pandemic.
10:04 You know, we just identified imbalances
10:08 that we couldn't explain
10:10 and indicated we should take a protective position.
10:13 So prior to using AI, you know,
10:16 you would build a quantitative system,
10:19 it would work for a while,
10:20 but when market participants change,
10:22 conditions change, et cetera,
10:23 these things tend to break
10:25 and they require human intervention,
10:26 you go back and you're like,
10:27 "Well, this isn't working anymore, what am I gonna do?"
10:30 And because of the machine learning layer
10:32 that's common to all AI systems,
10:36 we don't do that anymore.
10:37 The machine teaches itself.
10:39 So as things change, it's a quantitative system
10:42 that's able to constantly update
10:44 and at a scale frequency and level of refinement
10:47 that we could like never do as a person.
10:50 But you're talking about a collective of scientists here.
10:53 Right, so for us, it just, it made sense.
10:56 It was an obvious thing to start building.
10:59 It was a lot of heavy lifting.
11:00 You know, Dr. Aitor Muguruza,
11:04 who came on at the onset
11:08 and Nicholas Sabryan, our director of AI
11:10 had a tremendous amount of work to do,
11:12 building these systems from the ground up.
11:15 We started pulling assets from Imperial College in London
11:19 and, you know, it grew from there,
11:21 but you turn around and you talk to investment advisors
11:25 that are sort of very comfortable with fundamental analysis
11:28 and investing in that way,
11:29 same way people have been investing
11:32 for literally hundreds of years.
11:34 And, you know, they just look at you like you're nuts.
11:38 You're gonna hand this decision-making over to a machine.
11:41 And especially when you start making statements like,
11:44 you don't care what the company does.
11:46 You don't care when it's reporting.
11:48 You don't care what it makes.
11:51 You know, people, well, you know,
11:52 why do you think the AI added Disney
11:54 to the portfolio last week?
11:56 Because it saw a pattern
11:58 that it thought was gonna be profitable.
11:59 It literally doesn't care about Disney
12:01 or what its, you know, potential future profitability
12:05 might be.
12:07 And this is just, it's so counter
12:09 to how a lot of people invest
12:12 that there's a lot of closed-mindedness around it.
12:14 And we're not saying it's better
12:16 or it should simply replace that way of investing.
12:19 We're saying that there's room for both to coexist.
12:22 And people are starting to now see that that's true.
12:25 - Yeah, and I'm sure back then, you know,
12:27 like you said, there were people
12:28 that were probably very skeptical saying,
12:29 I can't believe you're giving away this.
12:31 So you, it must feel at least somewhat vindicating
12:34 that, you know, everything that's gone on this year
12:36 where now everyone in on Wall Street,
12:39 whether they want to or not,
12:41 has to at least, you know, focus on AI a little bit now
12:44 and what's been going on.
12:45 So, like I said, I'm sure that just feels good
12:48 for you to be like, hey, like I was onto something
12:50 five years ago and all those people
12:52 were telling me I was crazy.
12:54 But so Ryan, so this is, you know,
12:57 kind of been a new, interesting, recent development.
13:00 And I think a lot of people have anticipated
13:02 some sort of types of regulation surrounding AI,
13:06 but the US Copyright Office issued a notice of inquiry
13:11 in the Federal Register seeking public comment
13:13 on questions about copyright law
13:15 and policy issues raised by AI systems.
13:18 This comes after the news recently
13:20 that the New York Times, amongst others,
13:22 were considering legal action against open AI
13:25 on copyright grounds.
13:26 How do you view all of this?
13:28 What responsibility, if any, does AI
13:31 or the companies that create them
13:32 have toward the human creators behind the material
13:35 that AI is referencing?
13:37 So real quick, I'll try to explain it.
13:38 Like basically, if you go to any of these image generators
13:42 on AI and you say, hey, paint me an image of a dog
13:45 on a skateboard, the AI are using thousands of pictures
13:50 and art that's already out there
13:53 to come up with these things.
13:54 And obviously that art had to come from somewhere.
13:56 So all these different photographers, artists, et cetera,
13:59 whose art is being used by AI are coming together
14:02 and saying, hey, we never, you know, signed off on this.
14:05 We never signed off on this.
14:06 So what are your thoughts on kind of this issue
14:08 that's being raised by the US Copyright Office?
14:11 - Yeah, it's sort of a key area of focus right now, right?
14:16 And it all boils down to model training.
14:18 So when you build an AI system,
14:19 and it doesn't matter what it does,
14:21 but when you build a system,
14:22 you have to train it on an enormous number of data.
14:27 And the question is, where are you getting that, right?
14:34 And for financial services, we're lucky in that,
14:38 you know, these questions never get directed towards us
14:41 because there have always been mechanisms for us to buy data.
14:45 We all buy data, Benzenga buys data.
14:47 You know, you subscribe to real-time data feeds.
14:50 You can buy from Oprah, you can buy from ARCA,
14:55 you can buy from NASDAQ, you can buy from Morningstar,
14:58 Zax, it doesn't matter.
14:59 You know, like you've got these data aggregators.
15:02 You could go out, we use ICE,
15:05 and you know, you buy these data from these institutions.
15:09 You've paid for it.
15:10 So now you could do whatever you want with these data.
15:15 The challenge with things like LLMs, like ChatGPT,
15:20 MidJourney, Lensa.ai, DALI, you know,
15:26 the image generators that you're talking about,
15:30 is that they're scraping publicly accessible information
15:34 on the internet, but for profit,
15:39 and without the permission of the person that created it.
15:44 So just because you're an artist and you put your art up
15:49 on, you know, DeviantArt, or it's a stock image
15:53 in a big stock library or something like that,
15:56 does not mean that somebody can come along,
16:00 train a for-profit AI system on it,
16:04 and then make money essentially
16:07 with systems with proprietary content, right?
16:12 Like you're teaching it how to write, how to talk.
16:24 It's a mimic, but you're now mimicking thousands potentially
16:28 of New York Times journalists who were never compensated
16:32 for the training of this model.
16:34 And that's something that, you know, my personal opinion,
16:38 if it's for profit, then you should absolutely.
16:40 Music program and Apple TV, Netflix has done it,
16:51 et cetera, et cetera.
16:52 Like content licensing isn't new.
16:55 So you can do it, it's just a,
16:57 obviously it's a lot cheaper if you build
17:00 this monster system.
17:02 (indistinct)
17:04 And yeah, that's not great.
17:14 Where you get into the gray area is in something
17:18 like the medical field, right?
17:20 You're training these systems to root out a chronic disease
17:24 and discrete illness and things of that nature.
17:28 Are you compensating the companies and institutions
17:33 that paid money to do the studies that you're scraping
17:35 to inform the machine?
17:38 You probably should, but there's an argument to be made
17:41 for, well, this is to provide better medical care.
17:46 So if you're doing it at like the federal level,
17:48 the government level, maybe there's a case that, you know,
17:51 you should be able to access that information for free.
17:54 If you're doing it at the private level,
17:56 again, it's the same thing.
17:57 You're making money off of the system you're gonna sell.
18:01 Somebody else ultimately paid the price
18:04 for your AI to get so smart.
18:06 That's where we're at right now.
18:09 - Yeah, it's very interesting.
18:10 I mean, I think it's kind of a byproduct of any time
18:13 you have a brand new technology
18:16 or brand new industry developing,
18:18 there will be these kind of gray areas where,
18:21 hey, look, we don't have the guidelines
18:23 or regulations in place
18:24 'cause we've never had this before.
18:25 We've never had a chat GPT or open AI, you know,
18:29 doing these things.
18:30 So it's kind of for the first time, you know,
18:34 on the run, I guess, you know, the regulators
18:37 and people in charge are gonna have to try to figure out
18:39 what really the best route is.
18:42 And like you said, in the case of the medical stuff,
18:45 there might be arguments that say,
18:46 hey, actually this is for the better of society to let,
18:50 you know, so it'll just be interesting to, I think,
18:52 see how this plays out.
18:55 - But so Ryan, let's continue on
18:57 'cause we've only got about five minutes left.
18:59 You're headlining a panel at Future Proof next week
19:02 called Breaking Barriers featuring CEOs
19:04 who have changed the game, in your case, using AI.
19:07 For those of us who can't attend,
19:09 can you give us a little sneak peek,
19:10 a little preview of what you guys will be talking about?
19:13 - Yeah, I think this is,
19:15 it's a panel that's mostly focused on breaking barriers
19:21 in traditional investment management,
19:22 but largely in ETFs and there's panelists,
19:27 panelists representing systematized trading,
19:32 alts and alternatives to active management, et cetera.
19:37 So for my part, what I'm just gonna probably try to speak to,
19:41 I think there's like 3000 RIAs that are there.
19:44 I'll be just trying to speak to why it's,
19:49 why one should keep an open mind
19:51 in terms of AI making end point
19:53 investment management decisions.
19:55 There's, we and you and I have talked about this
19:58 a lot before, there's still a lot of skepticism
20:01 coming off the back of the crypto implosion.
20:04 You have yet another new technology
20:08 that's a little hazy for the lay person to understand.
20:12 And you have a bunch of experts saying,
20:14 "Don't worry, we got this, you should trust it.
20:17 This is why."
20:18 And you have a lot of investment managers and advisors
20:22 that are like, "Yeah, I'm just gonna wait and see.
20:24 I'm not going through that thing again."
20:26 So for me, it's about discussing questions
20:31 that they might wanna ask.
20:32 And this goes for any investors,
20:34 some folks that are watching,
20:36 you wanna know what type of AI is being used
20:40 and whether it's black box or rules-based
20:44 and then determine whether or not if it's rules-based,
20:47 the rules that are in place
20:49 line up with your investment ideology.
20:52 That's really sort of key.
20:54 So hopefully I'll be able to build a little confidence
20:59 in my short time on that panel
21:01 and start the long road back to trust.
21:06 - Yeah, I mean, I mean, and it's,
21:10 I think a good time for you to be doing
21:11 those types of panels and whatnot,
21:13 because I've said it before, but right now
21:16 it does seem like kind of all attention,
21:18 not all, I shouldn't say all attention,
21:19 but like that's been the biggest thing this year so far
21:22 on Wall Street is people just wanna know more about AI.
21:24 And it's basically every facet of it really too
21:28 that I think people want,
21:30 yearn for more understanding of whether it's,
21:33 okay, how is this dip ETF actually working?
21:36 How is the AI actually picking these stocks?
21:38 What is going to happen next with AI?
21:41 What should be the right regulations
21:43 when it comes to copyright?
21:43 All these questions, all these things,
21:46 I think are things that people,
21:48 like I said, wanna have a better understanding of.
21:50 It's hard because the technology is so new
21:53 and it's, for a lack of a better term,
21:56 kind of complicated, as I'm sure you know,
21:58 some of the AI machine learning stuff.
22:00 And so if, Ryan, if you're not someone like yourself
22:03 who's been in it for five years
22:05 and you're kind of seeing it for the first time,
22:06 you're trying to understand how it relates
22:08 to the financial world, what impacts it's gonna have,
22:11 it's kind of overwhelming to be honest.
22:13 So that's why I've been very grateful
22:15 to have you come on the show every day
22:17 or every Tuesday for the past few weeks.
22:19 'Cause now I do feel, I'm not gonna turn around, Ryan,
22:22 and say to anyone that I'm an AI expert,
22:25 but if someone now has questions about AI,
22:28 I feel like after our conversations,
22:30 I'm in a better position to answer those questions
22:34 or at least to maybe help them
22:35 have some better understanding of it
22:36 than I was two months ago before we started talking.
22:40 - You probably feel more comfortable with it
22:42 as a technology, as an investment tool, right?
22:45 In the very beginning, you're probably like,
22:46 wow, like, why should I trust this?
22:49 And how is this just not gonna run amok?
22:52 And now you probably have a better sense of,
22:55 well, it's, they'd actually have to program it to go nuts
22:59 for it to go nuts.
23:01 - Right.
23:02 And that's, I think, what the biggest thing is.
23:04 I mean, I think for, you know, a lot of people see AI
23:07 and think that it's already kind of this sentient,
23:09 you know, thing and it's got AGI, all this stuff.
23:12 When in reality, it's like, okay, if you're a human
23:16 and you touch a hot stove and you don't know,
23:19 chances are you're gonna register that
23:21 and then not touch the hot stove again.
23:23 And that's basically what the AI does,
23:24 it uses things like that, then learns from it.
23:26 So it's not really any different, you know,
23:28 it's not doing anything crazy.
23:29 It's just kind of recognizing patterns and going off of it.
23:32 And then, like you said, doing what it is programmed to do,
23:36 which is the biggest thing,
23:37 because I think, like I said, a lot of people
23:39 in their head think, okay, it's a sentient, you know,
23:42 whatever, or maybe it will be eventually.
23:44 But really, it's not.
23:46 It's similar in a lot of ways to basically
23:48 all the other computer programs and stuff,
23:50 that it's doing what it has been programmed to do.
23:55 So it shouldn't be that scary, at least not yet, Ryan.
23:58 I mean, but we'll see, we'll see.
23:59 - Yeah, I mean, we've got a long way to sentience.
24:02 So, and long, and there's lots of opportunities
24:04 for us to screw it all up.
24:05 But for right now, no.
24:07 It's just, it's pretty well contained.
24:10 - Yeah.
24:11 Let's knock on wood and hope it stays there for a while.
24:14 But who knows, maybe in five years,
24:16 the conversation we're having about AI sentience
24:19 is a lot different, who knows?
24:20 All right, but let's get to the Kaiju kicker, Ryan,
24:23 and then we'll wrap up for the day.
24:24 A popular question from new traders is that,
24:28 "What size of portfolio could a retail trader
24:31 "responsibly expect to support themselves
24:33 "through trading their own account?"
24:35 I believe you mentioned a few weeks ago,
24:38 if you wanted to start trading,
24:41 start with a paper trading account for like six months,
24:43 is that right?
24:44 - Absolutely.
24:45 Like I've sort of always kind of,
24:47 I get asked this question a lot, obviously,
24:49 from like friends and family kind of thing.
24:52 Hey, you were a trader for years,
24:54 and a portfolio manager, and whatever,
24:56 and I wanna start trading, how much money do I need?
24:59 So it's just kind of a loaded question, right?
25:00 And I always say, like, look,
25:03 your broker probably offers a paper trading account,
25:06 if not, companies like Worden, TC2000,
25:11 you've got a charting program,
25:13 and it comes with a paper trading account built in,
25:17 you can message them and say,
25:19 "Hey, can you make it a million dollars,
25:21 "make it a hundred grand, make it 50 grand,"
25:22 and they'll change this for you.
25:25 I think you can even add paper trading accounts,
25:27 but it's been a while for me.
25:28 So yeah, I always say six months on a simulator.
25:33 If you can't, first of all, if you can't do that,
25:36 if it's like, I don't have six months to wait,
25:38 I wanna get into this,
25:39 you don't have the discipline to be a trader to start with,
25:42 like you're just not gonna make money,
25:43 so save yourself the hassle.
25:45 You should be able to paper trade for six months,
25:49 you'll probably go through at least two market conditions,
25:52 so you're not gonna get a distorted view
25:57 of your profitability because you just happened to start
26:01 training during a favorable market condition
26:03 for your strategy, and you're like, "I'm awesome,"
26:05 and then you blow up.
26:06 So it lets you see at least a couple market conditions,
26:09 and if you're profitable on the paper trading account
26:12 after six months, yeah, sure, go for real money.
26:14 If you're not, you should probably continue practicing,
26:17 like it's gonna be worse when it's real money.
26:20 So that's, you know, bucket one, that,
26:22 and then getting started any amount, right?
26:24 I mean, you could have like two grand, $1,000, whatever,
26:27 you can start trading really small amounts,
26:30 you will not be supporting yourself at all at that size,
26:33 and you have all kinds of restrictions,
26:35 you will not be able to open that many positions,
26:38 and you won't have access to sort of like prosumer
26:43 or semi-pro brokerage accounts like Interactive Brokers.
26:46 That's, I think they're, they used to be 15 grand or higher,
26:49 maybe it's 10 now, I don't know, but you can look it up,
26:52 then you get like a proper semi-pro brokerage account.
26:57 So that's retail and small funds are packed into that.
27:01 But anything under, you know, 21 or 25,000,
27:07 the pattern day trader rule is again gonna limit you
27:11 to five round trip trades a week, right?
27:14 So you could still trade
27:15 if you're practicing position trading, then that's fine,
27:20 you know, you're gonna adjust your portfolio weekly,
27:22 probably on Sunday anyway for execution Monday,
27:26 doesn't matter how much you have,
27:27 but if you wanna intraday trade,
27:30 which you really shouldn't do,
27:32 but if you wanna especially swing or momentum trade,
27:35 then you need to be outside of the pattern day trader rule,
27:38 which is over like, whatever it is, $25,000.
27:42 And then finally, over 100 grand
27:44 gets you into portfolio margin and out of reg T margin,
27:47 which means that you'll get,
27:49 your money will go a lot farther
27:51 in terms of the leverage that you can use,
27:54 especially with options.
27:55 So, you know, you got these different buckets
27:57 and it depends what you wanna do.
27:59 If you're looking, you know, if you want to,
28:03 I don't know, supplant a $200,000 a year salary
28:06 and use your own account,
28:08 I hope you have a million dollars or more to stuff in that.
28:10 You're just not gonna make that over,
28:12 I'm gonna make 100% per month every month.
28:14 No, you won't.
28:15 And I wish you could,
28:17 I wish I could say that you could, but you can't.
28:20 If you're looking to supplement an income, you know,
28:23 work one job instead of two,
28:25 work part-time instead of full-time,
28:27 you don't need a ton of money to do that,
28:29 but you probably need more than you think you do.
28:31 I mean, I started with like a $15,000
28:34 home equity line of credit,
28:36 and I was fortunate enough to grow that very quickly,
28:40 but that's an anomalous result.
28:42 I mean, really at that level,
28:45 you can expect a couple hundred dollars a month in profit.
28:48 If you wanna get into the thousands,
28:50 you're gonna need more money.
28:51 But again, depends on the trader,
28:54 depends on the goal, et cetera.
28:56 I know that kind of answered
28:57 and didn't answer the question at the same time,
29:00 but that's something we can maybe begin to later.
29:03 - I think more than what you probably think you need
29:08 it sounds like, 'cause here's the thing, Ryan,
29:11 is I think a lot of people, and like you said,
29:12 you've had a lot of people reach out
29:14 and ask you that question,
29:15 "How much money would I need
29:16 "if I wanna start trading for myself?"
29:18 And I'm just gonna venture and guess,
29:20 maybe you were getting that question a lot more
29:22 a couple of years ago, during COVID,
29:23 when the markets were basically just going up
29:25 and it seemed, "Hey, anyone can do this."
29:29 But I think people, a lot of us are optimistic
29:32 and we're wishful thinkers.
29:33 So if you make a couple of good trades
29:34 and you're doing some math in your head
29:36 and you're like, "Well, I made 6.5% last week.
29:40 "If I just do that every week, then I'm at 30% a month,
29:43 "and then I'll be at 360% on the year."
29:47 And then, so I think a lot of people
29:49 have this very optimistic thinking.
29:51 So they think, "Okay, I can start with 10 grand
29:53 "and we'll have 100,000 by the end of next year."
29:56 And it's like, "Okay, yeah, maybe you had a hot week
29:59 "or maybe you had a couple of trades,
30:01 "but no one's getting 6.5% a week,"
30:04 or whatever it is.
30:05 - Exactly, right?
30:06 You have a good bet, you catch a gap,
30:09 your trade returns 18%, you're like,
30:12 "Okay, so if I do that just 20% of the time."
30:16 And you're like, "Yeah, that's just not going to happen."
30:18 - Yeah, I mean, look, Ryan, I'll be the first to admit,
30:21 I can be kind of dumb sometimes.
30:23 I think we all can.
30:24 I think that's just part of the human condition
30:25 is we're, as humans, our brains aren't computers
30:28 and we just are dumb sometimes.
30:31 That's one of my big core beliefs
30:33 is that we as humans are not perfect.
30:35 And I remember right when COVID hit,
30:37 so I was actually, my brain was actually being pretty smart
30:43 and I bought a bunch of puts on the market
30:45 'cause I was like, "Okay, this COVID thing
30:46 "seems pretty serious from everything I'm seeing
30:49 "from the medical doctors I follow on Twitter
30:52 "and everything, but yet the stocks
30:53 "hadn't gone down that much."
30:54 Either way, made a bunch of money.
30:57 There were a couple of days where off $800
31:00 in my checking account, or $800 in my Robinhood account,
31:03 made three grand in one day just buying puts
31:06 on the spine and cues.
31:07 And I was like, "Shit, if I make $1,000 trading options
31:10 "every single day, it's like, no, of course,
31:13 "of course you're not gonna be able to do that."
31:14 But I was like, "I was like 20."
31:16 - 'Cause you forget, it's not just the drop, right?
31:18 If you're buying put options,
31:21 it's the VIX going to frigging 80.
31:24 - Right, I literally timed it.
31:25 - It wouldn't matter what happened.
31:26 - I timed it. - You needed that
31:27 to happen too.
31:29 - Well, here, look, I can just, oh, hold on.
31:32 - Like the Vega component with that,
31:34 I was sort of debriefing with my cousin
31:38 who runs a fixed income desk at Merrill.
31:42 And we were talking kind of two weeks
31:44 after all the halts and the collapse.
31:46 And it's like, "Did you ever think you'd see a VIX at 80?
31:49 "No, you, no."
31:51 It's like, "If you'd told me last week,
31:53 "I'd think you were nuts."
31:55 And yet there it was.
31:57 - Yeah, so this is my Robinhood all time.
32:00 First of all, up $500.
32:02 So let's just put that out there, not in the red.
32:05 But February 2020, you can see right here, boom.
32:09 Here's where the market crashed March 16th.
32:11 I think was like the lowest point in the market.
32:13 I was up 11 grand.
32:14 Guess what happened within two months of that, Ryan?
32:18 Bam, back down to up 640.
32:20 So that was a good learning experience for me.
32:23 It was kind of my first time trading options.
32:25 I was like 22 and I was like,
32:26 honestly, hindsight's 20/20.
32:31 If I just would have sold all my options,
32:33 just like put it in the SPY,
32:34 I'd be up double that right now.
32:35 And I would have had, it would have been less work, but I-
32:38 - But you didn't know, right?
32:39 You didn't know.
32:41 - I didn't know, now I do.
32:42 - When you first trading options
32:46 and you've got your directional component
32:48 that's working for you,
32:49 or it's like right before expiration, the move happens,
32:53 you get a gamma kick there,
32:56 you're not really sure the Vega component.
32:58 So you're not paying attention to VIX at all.
33:01 And you have one of these,
33:04 I knew a friend who started trading options,
33:09 did a little course.
33:10 And then during the first threat of the trade war
33:15 in 2016 and 2017,
33:21 market lurches, VIX pops,
33:23 guy had some put options and just like he was like,
33:26 "Whoa, that was easy money, 20% a month."
33:29 But then the next time that happened,
33:32 nothing really moved.
33:34 He kept waiting for it to happen.
33:36 Theta basically ate his premium away.
33:38 And he got out under where he bought in at,
33:41 even though the market had trended down
33:44 and was like, "I don't understand what just happened."
33:46 It was like, "Okay, here's a book on options trading
33:50 you might consider reading."
33:51 So I mean, you have these experiences, right?
33:53 And it's easy enough to extrapolate out and go,
33:57 "Well, I'll just repeat that every time."
33:59 Without realizing that you had sort of a perfect storm there
34:03 that made that what it was.
34:05 - Yeah, I don't know if you ever golf, Ryan,
34:08 but it's like if I hit a drive
34:10 just right down the middle of the fairway,
34:11 I can be like, "Oh, I'll just do that every time."
34:13 - I'll just do that.
34:15 - I'll just do that.
34:16 Whatever I did there, I'll just do that.
34:17 - That's what keeps you coming back, right?
34:18 Like that's the part of golf that keeps you coming back.
34:22 But I don't get it.
34:22 - You get a few holes and it's a disaster
34:24 and you're like, "I hate this stupid game."
34:26 And then you do something magical and you're like,
34:28 "Well, now hold on, it's not so bad.
34:31 Maybe I can just do that again."
34:32 You totally can't, but yeah.
34:34 - Yeah, exactly.
34:35 Well, Ryan, got a few minutes over today,
34:39 but very happy to do so
34:41 because we've been having a great conversation as always.
34:43 Thanks again for hopping on with us today on Benzinga Live.
34:47 I'll drop that Kaiju link in the chat
34:50 if you guys wanna go check out more.
34:51 Again, the ETF is DIP, ticker D-I-P.
34:55 So go check that out if you guys want to.
34:58 - Great, I'll see some of you guys,
35:00 I think at Future Proof next week.
35:03 - Yeah, I don't know if that panel's gonna be recorded
35:08 or whatnot, Ryan, if I can watch it.
35:09 Otherwise, you'll have to just give me a recap,
35:12 give me the lowdown next week after.
35:14 - I think Scott and Mike might be there.
35:16 Look forward to catching up with them in the sunshine.
35:20 - Beautiful.
35:21 All right, Ryan, well, enjoy the rest of your Tuesday.
35:23 Thank you again for hopping on Benzinga Live with us.
35:26 - Thanks, Aaron, take care.