291 · Sam Miesse - Small Cap Quant Trader Grows $10K into $3.8M in 3 Years
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I lost half a million dollars in one day. That was because I was pushing massive size, but I should be pushing massive size. If I didn't do that, then my 10K might've turned to 50.

or whatever, because my risk tolerance is so low. But at the same time, there's kind of the balance. Because if you're like, well, screw this, I'm going to go, I'm going to mortgage the house and, you know, let's just go crazy on this. And then you have some black swan event. So you see, so there's, there's the temperance that's needed, but it goes both ways. You can be ultra conservative and sort of the paralysis by analysis type thing. And you never want to take the step because you're so scared, or you can be a wild outlaw that gets beat up by the

the law too much because you're doing crazy stuff. I don't think it was too far after making half a million like in a week. You know, there's anomalies that have gone to the upside and to the downside. And this is where the discipline is required is the data said that that half a million dollar loss should have happened. There was no break in the model. Markets, speculation, and risk. This is the Chat with Traders podcast.

Hi, this is Ian Cox, your host. Thanks for tuning in to Chat with Traders. This is episode 291. My co-host, Tessa, is enjoying her vacation somewhere sunny. Well, we've got a great episode for you today. I'd like to introduce Sam Misi. When Sam worked for a tech company, he saw firsthand how predictive analytics boosted sales. He eventually applied that idea with quantitative analysis in the small cap market.

spending nearly two years collecting data and observing how money moved into and out of small cap stocks convinced him that there were repeating patterns which could be statistically exploited. After many back tests, Sam finally had a system which processed over 10,000 small caps and

and produced 20 to 40 actionable daily opportunities. Starting with an experimental $10,000, his rigorously back-tested system gave him the confidence to stick with the signals despite suffering a $500,000 daily drawdown along the way to growing his account to 3.8 million in less than three years. All right, let's get straight to my interview with Sam.

Well, Sam, I'd like to welcome you to Chat with Traders. Thank you. Yeah. So where are you at now?

Um, I'm actually currently in, in, uh, Idaho, but, uh, I'm not a hundred percent sure if that will be a permanent location right now. So we're, there's some in between, there's some transition going on. So, but I did come just before this, uh, from Puerto Rico. So I was in Puerto Rico for a decent chunk of time, but, uh, that was just a little bit too hard on family. So we're back stateside trying to figure out where we're going to land. So.

Oh, great. Great. Well, let's dive into your background. Where did you grow up? And tell us a little bit about your early interests and what drove you as a person. Wow. Growing up, I was a quiet kid. I was the kid that's always just sort of taking it all in. Very observational, very analytical, and just kind of grew up with that sort of temperament. I was one of seven kids, and I was at the tail end of that. So I had the privilege of

watching all my siblings grow up and mess up and succeed. There's a lot of wisdom intake that you can get when you're that far down on the totem pole. So I always felt a little bit out of my element, frankly, as growing up in terms of to the left and my right, my peers, I never really felt quite in the same company. So I was always sort of looking ahead and wanting to grow up fast and wanting to always a very curious mind. Yeah.

With that analytical mind, I got into tech as I became an adult and still love technology. I was always the kid that loved the wizard class of characters. And technology is really the closest thing to having wizardry powers. So I love tech. I develop on the side and technology.

I have some projects in the dark right now, but that I work on. So I really, really love that. And so I did...

to bring it all the way up to current, I did get into enterprise tech sales for a time and that was really enjoyable. And that's actually sort of the launching pad into trading because we were selling data science products that did predictive analytics. And so I was very fascinated by that and wanted to apply that to the markets. So anyway, quick story of the upbringing there all the way up to-

My understanding is you got in a little bit early into entrepreneurship. And tell us a little bit about that. When I was in my 20s, I was always trying something. So something crazy and many, many failures. But I did get into the consumer electronics space.

which was one of my earlier wins entrepreneurially. And I had a business partner at that time and we were 50-50 partners and

that's not a wise relationship. I think somebody needs to be 51 or somebody needs to be the chief, but, but we did grow quite a bit in, we were basically buying and selling used electronics, iPhones, iPads, all sorts of things. And the, the company grew pretty fast, probably too fast. But so that was a decent entrepreneurial win in my twenties and that didn't quite work out. And then I went, got into sale tech sales for a while, project management and some, some,

some, a little bit of pseudo engineering and, uh, did that for a chunk of time. And that was very, very formative and I loved it. Uh, it was a great challenge. So, um, and it was good too, because the company that I was working for at that time was still, they were growing, but they still had a lot of startup culture, uh,

And I'm not one to do well with a lot of red tape and stuff like that and restrictions. So thankfully, I was allowed to a lot of latitude to do what I thought was most helpful for the company and could still kind of satisfy the entrepreneur in me. But always trying some side hustles while I was still there. And one of those took place.

really quite well, which was this trading journey, particularly a quantitative approach to the markets. And that was in like 2019 was when I launched the fund. So what triggered your first early interest in the financial markets? So I mean, I'm a rags to riches type story. Like I came from nothing.

So I'm really, really nothing. So I had to start from scratch and, and I have a lot of ambitions and a lot of desires to grow a lot of things. But when you're in that position, the first problem you have to solve is the money problem, frankly. So I knew that I needed to get capital to, to form a cornerstone to all of these other longer horizon things.

longer timeframe things that I'm seeking to build. The stock market's fascinated me just sheerly by that, by the fact that I don't know what it is today, but I think it's like a quarter of a trillion dollars of value is exchanged every day. And it's such an insane amount of

uh, value that's changing hands. And if, if you can find ways to just skim a little bit of that, then you can be, you know, quite, quite wealthy from that. So, um, so that was really the pure drivers. Like I have a lot of ambitions. I'd like to build a lot of things, but I got to establish a nice, healthy capital base. Um, and the stock markets were caught my eye more than anything, uh, as a expedited path to that.

So did you just save up money from your regular job? And if so, when, when did you open up your first account and make your first trades? I was, I did quite well in the tech world and had a little bit of extra money, not a lot, but a little bit extra money to play with. And so I, I took a small amount, I think in 2016, um,

And I started sort of discretionary trading and just kind of messing around, particularly in the small cap markets. And that's just because that's where most of the volatility is. And I mean, as a trader that's looking for outsized gains, you need...

extreme volatility. So I targeted their discretionary, didn't really know what in the world I was doing, kind of paid my market tuition there, lost some money 2016, I think in 2017 as well. But it was during that time where I'm just sitting in front of screens and studying for hours and learning all of the intricacies of the markets and learning how to read tape and level two and

you know, just all of the nomenclature, all the vocabulary, just basically acclimating to that world. And that was when I really started to recognize repeating just observationally. I started to recognize these repeating patterns that were happening in that at least that segment of the market. And it was so repeatable. And then tying that with the fact that I was literally selling predictive analytics to

A little bit of machine learning with some of that on the back end, but I was selling products that would predict human behavior. And the markets really are just an expression of the human spirit, the collective human consciousness, you might say. Like it's just a window to see that.

And human beings are very, very predictable. They do the same thing again and again and again. They're motivated by the same impulses. And so all that to say, as I was doing this discretionary trading, I'm seeing these patterns. I'm seeing these

Things that very clearly, anecdotally, at least, are repeating. And that's really what start where it got in my mind to the thought in my mind to try to quantify that behavior. So these repeating patterns were you so you were just focused in on the small cap market. Is that correct?

I flirted a little bit with some other segments and large caps, but I needed to be where there was volatility and pretty regular volatility. Plus, I was also wanting to like and then it's one of my filters for how I trade, at least, is I didn't want to go toe to toe with heavy institutional involvement.

They're just well capitalized and the brightest minds are in those worlds. And so it's hard to compete in that if you're under-resourced. So I try to stay away from stocks that have heavy institutional involvement. And I also need that volatility. And that's where small caps really, really showed the promise there.

So what kind of repeating patterns did you see? Like what showed up? Well, that's important. I want to disclose that, right? I'm just thinking about how to answer that. Let me just first say there are abstracted principles to quantitative trading that are universal and aren't

of a specific edge. There's some debate among, I don't know why there's even a debate because it's so stupidly obvious to me, but there's some debate as to whether or not edge erosion is a thing.

But it definitely is. So I can't, I'm not going to share like the exact specific edge because then you just get piled in. And I mean, to be an edge is to not be popular, right? It's to be marked, it's to be distinguished. So I can't say exactly what that is. But I mean, I would say that any of the common concepts

commonly believed patterns, I guess, or there's patterns that are seen in the markets, you know, like cup and handle and double tops and, you know, reversal patterns or whatever. Like there, there are a lot of things that are already recognized, like in sort of standardized within the trading world. And those, I would say those are very worthwhile. They, they, they, they are worthy of closer looks. I would say like, they're definitely worthy of,

of going deeper and seeing if

this is something that could be predicted. There could be earlier indicators that could show that this is what it's going to do after this pattern presents itself. But yeah, so there's a lot of variables that I definitely consider when I'm qualifying a specific stock. But I would say that it starts with screen time and it starts with just anecdotally observing these patterns reoccurring again and again.

And then from there, get an idea and then try to quantify it. And how did you go about the process of quantifying it and getting the data and processing it?

Yeah, so I was manual. So and I think that's still common. I did look at some sources and I couldn't I couldn't quite find like clean enough data. So there were too many anomalies and it was too I was struggling to reconcile it with. It was just so I manually pulled it and I actually just used thinker swims on demand features to look back at historical data, which is a really, really valuable tool.

And I just manually recorded all of the data, thousands of rows and spreadsheets and just mined it, just swinging the ax. So, I mean, I could code, but I didn't find that super useful at the time. So were you kind of learning about statistics on the fly or did you have a background in that?

I mean, I'm an entrepreneur, so I want to question the premise of that question even because I'm not big on certifications or even a lot of training, I think, is frankly a lot of waste. So no, I don't have any background in certifications.

like statistics or whatever, but I mean, it's, you can learn anything. Like you can learn any, especially today with AI. I don't even know why we need education, frankly. Like, I mean, these new models are PhD level in terms of the knowledge they give back. So, so I'm just a very curious mind. I'm very determined. I'll study on my own and I'll figure it out. So I didn't have any background into that. And I wouldn't say that there was, I mean, there's like a mathematical equation or whatever that you can run that,

proves whether or not something is statistically significant, but you can also prove whether or not something's statistically significant just through common sense. And that's the beauty of quantitative trading is it actually is not as complicated as most people think it is. Most people think this is rocket science and it's crazy physics or who knows what. And then they have self-doubt because of that and all sorts of problems. But it's really just

Common sense, you know, it's, it's, it doesn't require any, any expertise or any long term, long form training is, I would say is not really required. How would you define a, how would you define quant trading in the context that you're, that you're in? I think it's helpful to answer that question by defining the, the opposite, which would be qualitative trading.

And qualitative trading is very subjective. It's very much, I like the CEO, I like his vision, I think he knows what he's talking about, or I like the way they're managing this operation or whatever. It's just a very subjective feel that you like the product or you like the company, and so you're going to buy the stock. Whereas quantitative trading is completely agnostic to many of those. It doesn't care. It doesn't know about them, doesn't want to know about them.

it literally just looks at a filtered company and it tracks how, what their performance has been in the past. And then the variables that are involved in there that sort of like brought that

performance to fruition, you track those variables, you sort of try to wrap the behavior in data. And then from there, you start to run some predictive type analytics. Okay, here's all these, it's just like big, long, if then chains, you know, if this, then that, if this, then that. And you just, that once you find something and you're like, oh, wow, this, I just ran the numbers and this thing happens 70% of the time.

You know, that's substantial, depending on your sample size, of course. So in terms of how we define quantitative trading, I mean, it's basically trying to use as much mathematics or statistics or whatever, this data as possible with little to no subjective calls about whether or not to put on that trade or to sell. So it really should be as much as possible. The computer is telling me to do this, and so I'm going to do it.

Did you ever have to hire a programmer to automate your systems or was your system manual, the execution of it? So I looked into developing and automating some of the actual entries and exits and execution. But I decided, I would say that the model that I built is probably like 90% data, maybe 10% subjective data.

So maybe not even that much. And here's an example. So, and I don't mind sharing this, my model, like one of the first filters, one of the higher level filters, and there's a few different levels of filters that the stocks will go through before it, you know, ends up on my desk, so to speak, and I got to do something with it. But the highest level filter is

would be gapping stocks overnight. But sometimes you might have a stock that gaps 100% or whatever, like the data is saying, hey, here's a stock that's gapping a lot and that passes that filter, but it's like a buyout or something like that, you know, or some weird news thing. And so you look at the chart and yeah, it has jumped up 100%, but it's a flat line, you know, overnight. So it's not actually the qualified stocks. So that's a very hard thing

That's an anomaly that's hard to program. It's hard to account for. So to answer your original question, I have certainly looked into that, and I know that's possible. I know people have been successful with that. And it's something I might revisit. I don't know. But right now, I use software to sort of distill what I'm supposed to do down to like, here it is on your desk, and then I will execute.

Still following the plan, like still following what the data tells me to do, but I will enter and I will exit. It sort of tees up the ball and it's really teed up. So, but I still got to swing. So I have not fully automated my particular system. And how long was this process to discover a statistically beneficial plan? I mean, a program that you can execute. So I started, I think, I think in 2017 is when I started collecting data.

And now I was also working at a very demanding job as well. So I didn't have like full bandwidth to give to the data mining and the curation and analysis and all of that. So I think it took like maybe it's probably like one and a half to two years during that time to build. And that's the really hard thing about this is probably where most quantitative traders fail is it's like you're approaching a mountain.

that promises gold, but there's no, there's nothing on the outside. You can't go up and grab it. You have to have like this crazy delusional self-belief that I'm going to keep digging into this thing to get some value, you know, and it almost has to be delusional because you're going against quantitative trading proves most,

I don't know what it's taught now, but my understanding is efficient market theory is still kind of common doctrine in economics. And quantitative trading completely proves it wrong. Like it actually flips it on its head. So you've got to be delusional. But the challenge is once you start digging, you might dig 100 feet in and you have this great idea and the data says, nope, it's not true.

There's nothing here. So you just, it feels like you wasted so much time just eliminating that as an idea. So the proving out of your ideas is extremely tedious. It's very, very difficult. And I don't think a lot of people can really stick through to the end, but if you can there, there, it is there. Like there are, there are statistical edges that you can find in the markets to give you ridiculously outsized returns. It just takes a ton of work.

to actually do that plowing through to get to the diamond. So,

So, yeah, I mean, it probably took me one and a half, two years to build the model. And then once I found it, I was like, this is really crazy if this is true. And so I built it. I actually taught my wife doesn't know anything about trading, but I actually taught her to execute the model because I was traveling all the time and working. So she executed it, not knowing anything about. And that's what I mean by it's only 10 percent subjective because she didn't know anything about stocks. But the subjectivity didn't matter. It was very, very much just do this. So.

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I see. So, so you boil down the model so that it was just simply, you know, buy this stock or sell short that stock. And so it was easy for someone that you, did you have to train her a long time to, to do it? Maybe like a week. I built the model and I showed her when it will tell her to execute. And I just, this is exactly what you do. And I mean, it was really,

And again, she could follow that very well. And it wasn't demanding on her time. She had free time to be able to do it.

Do you feel that your entrepreneurial mindset helped you stick through the process? Because that's a long time because you didn't necessarily know when you would find something statistically relevant, right? I mean, you could keep going for who knows how long. Well, yes. So here's what I would, and I approach this, and this is why I preach self-confidence so much on this.

I approached it with some certainties in my head. I was very certain that I was seeing things that kept happening, like very, very certain. And I was very certain that human beings were the cause of it because the markets are just an expression of that. And I was certain that if you get the right variables and you get the right data, that's sort of like strings attached to the masses, you might say,

And if you can figure out what those strings are, then you can measure it. So you can, you can use those strings, those sort of levers that are stuck in society and behavioral economics type stuff. You can use those to measure the behavior and then predict it relatively decent. You can predict the future. I mean, quantitative trading is predicting the future. That's really what it is. So, and it's crazy, but I will say that to answer your original question,

I am a very, very weirdly determined individual. Like I'm, I will not stop something unless like God stops me basically. So, um, it has to be like a really, you know, it's not just that the front door has to be shut. Cause I'll kick it down. It's gotta be like a brick wall. I can't get through, like, it's gotta be something that, so I'm extremely determined, but I will also say too, I mean, I was selling products, like we were selling products that

knew your purchase history as a consumer and knew that there was an 80% chance that if you go to McDonald's and you buy an Egg McMuffin on Tuesday at 10 a.m. and you're a white male in his 50s, then we need to show you a black coffee because you're a part of a group of people that behave similarly.

And there's a 70, 80% chance that if you buy that Eggman Buffet on that Tuesday and you're a white male in your 50s, there's a 70, 80% chance that you're going to want a black coffee. So we're going to do that. So it's like I was already selling these things and it's minority report type stuff. And it's real. It's very, very real. So, yeah. Wow. Okay. So let's get into that.

Once you found a statistically relevant model for you to trade through, how did you start trading and how much money did you start out with? So I started, I was under, when I traded in 2016, 2017, it was very much discovery and testing and getting my feet wet.

And so it was like under the, I didn't even put enough to be over the pattern day trader rule back then. So it was a different broker at that time. But then when I was serious about it, I switched to a larger broker and they, to get over the pattern day trader rule and start executing this model. And so I think I put like 35,000 or something like that in an account, but I only risked 10. So I exposed 10K to risk.

And that was at the end of 2019. And then I had my wife start executing it. And so in 2020, it grew, I think like 800%. It went from $10,000 to like 86,000 or something like that in 2019. So, which was ridiculous returns. Wow. And so then after from 10K to 86 or whatever it was,

uh in 2019 we kept that same 86k on we didn't withdraw anything and then 2020 came which was

Something that I don't think will ever happen in our lives probably with COVID and sports betting was shut down. Everybody's got STEMI checked. There was so much money in the system. It's insane. So all of that money in the system sort of just gave fuel to the model. And basically, the model was working, and now it's been supercharged like a turbocharged, and all of this juice is flowing.

So just curious. So in the first year that you had that incredible return, what exactly were you? You were just trading these small caps? Small caps, yeah. Long, short or long?

- It was short, I could say it was short, yeah. So it was, I was short selling moves that were, you might consider parabolic. So looking for hyper extensions basically is what it was. - Okay, well, and how many trades did you do that year, if you can remember?

I think it was like 400 round trip. Oh, okay. Yeah. Something like that qualified and was executed somewhere around there. So, and I had built the data, the data that was collected was from 2018, 2017, and a little bit of 2016. Okay.

was my sample size, which was decent. Like it was a decent sample size. So, but yeah, I think it was around like 400 trades, but I think it like doubled at least in 2020 because that 86K went to like 2.2 million. Wow. So, so you're finding things to short one or two stocks every day almost, right? I mean, how long are you holding them for?

This particular model is only intraday, so I don't have to swing any trades, which is nice because this is a very scary thing. It's a very scary thing, especially if you're short selling, to hold overnight. And there was one recently, I can't remember the ticker, but it just went bonkers overnight. I feel really bad for people that are stuck in that. So that's nice on the heart. But yeah. Yeah.

Did you have to deal with borrow fees if you're trading intraday? Yes. And locate fees? I definitely give a lot of money to that. I mean, it's ridiculous how much money I've spent on locates. I don't like to think about it. Okay. Right. Okay. So you had this fantastic year in 2019. And then 2020, did you take any money off the top? Because you had such a fantastic year the first year.

Yes, I did. So 10K to 80, whatever it was, K in 2019, that 86K, I think it was, then grew to just over 2 million in 2020. This is the other issue, too, is there are constraints.

you know, that you can't really resist. You can't go further than them. And liquidity is really the big constraint and makes, that's one of the things that makes quantitative trading quite challenging is, you know, let's say you build a model and you discover an edge that says, hey, you know, if you are, you have a stock and if it dips at the open by 20% or something,

you know, there's an 80% chance that that dip will then spike back up if it holds or holds some sort of line, whatever. So if you did that and it says you must, if that trigger happens, you have to buy the stock right at 1030, I guess we'll just say if you're, if your data is saying it has to be within that one minute window or even smaller, it could be seconds that you have to enter.

then that that's when you got to go. So if you, if you're trying to buy, you know, 10,000 shares, that's one thing. But if you're trying to buy a hundred thousand or 200,000 or 300,000 shares, well, you're going to get to a point where you are moving the market in that instance. And so you're creating something that's actually outside of everything you've back-tested, which is why edge erosion exists because you have a liquidity problem. So, so I couldn't, I had to basically cap out.

in 2020. And we did take some of those, some of those earnings. And we went to Puerto Rico too, to there's tax benefits there, as I'm sure, you know, for finance. So that, yeah. So, but yeah, we, we, we basically hit the liquidity cap and then had to withdraw, take, take stuff off the top when it, when it got over that. So. Does your model actually then the way I understand you're saying actually tell you like a time window to get in or kind of,

parameters to to execute or simply does it process it the night before and then you get a bite you know you say okay go long or go short and then you just you decide um discretionarily like when during the day to do it or i mean it's it's providing you within a time window yeah it's it is a very specific time yeah so it's the only discretion that is used is whether or not

it's truly qualified in the filter. So it goes through two or three, at least how I have it set up, it goes through two or three different filters. And then there's the subjective piece of maybe you're looking at 10 stocks or 20 stocks. Now you just manually check the charts. You manually look and see if this is a qualifying stock. And then you wait for the exact time

that you're supposed to get in. And for me, that's very, it is within a minute I could share. So I've got to make sure that I'm not too, my size isn't too big that I'm going to screw up my entry or my exit. But yeah, the discretion is related, which is very small, but it is related to whether or not they've truly qualified. It's not related to

Do I execute now or whatever? Once it's qualified, then it's just follow the script from there on. I see. And you mentioned that you have to avoid your size being too big. How do you determine that? Like, give us an example. What percentage of the

of the daily volume or what kind of, what are you looking at? Yeah. Well, I mean, I don't have that really super systematized for how to handle that. I just felt it basically. Okay. So I noticed, oh man, I just totally moved the markets there and my slippage is now noticeable. So it was, it's almost like a glass ceiling.

And there's not, if you look up, you know, you can't see the glass. So it's hard to know when you're actually touching it. You just kind of have to feel it. At least that's how I've done it. I'm sure there's probably some other ways to handle that. But I just, I let the account grow. And what I do too for the account is, you know, I went to a special, particularly when it was really small, is I would just keep reinvesting.

And so my risk would be increasing more and more and more every day. So every day I was resetting risk, whether it was down or up, I would reset risk based upon the account size. And so as it continued to grow and, you know, now I'm doing hundreds, hundreds of thousands of shares. That's when I started like, Oh no, I'm touching the glass ceiling. Now I can feel it. I can feel that I just moved that I can see the wick right there that

That's me. So it was, for me, it was just feel. But maybe there's a way to wrap that and, you know, quantify that in some way. So the small cap universe is quite big. Last time I checked, it's in the, what, 10,000, more than 10,000 different companies, right? Typically, how many stocks are you working with on any given day that get filtered down, that make the cut? I would say that that get filtered

The stocks that get to my desk to then be subjectively looked at is probably 10 to 20 on an average day. Busier markets, maybe double that, I would say, which can take a little bit more time to qualify. And then in terms of how many I actually execute,

Out of that, five and under on average. If I'm five or over, then that would be a very busy market, which does happen. It happened in 2020 a ton. 2020 was insane. So that's roughly the numbers that get to me for subjective calls, and then that actually do finally qualify to be traded. And then I just wait for the timing to tell me, which has already been computed.

Here you go. Go ahead and act now. I see. So do you use stops or do you manually exit? I do use hard stops and then I do manually exit as well. So and even the hard stops, too. That's see, that's the thing that makes me scared about giving everything to to an algo is I've watched with my own eyes stops be set and then they just don't trigger.

you know, and that's a, especially if you're, if you're trading a model that's short selling,

Terrifying. Because there's no end. I mean, you can owe money in short. So like you can lose a lot. So if it doesn't trigger it, and I'm, you know, that's super dangerous. And I've watched that happen. So but I typically I'll have a hard stop. And I definitely recommend hard stops. And the data should tell you where that is. So set your hard stops, but still watch it. Make sure it

goes through. But if I don't need to do a hard stop and it's a winning trade or whatever, then I'll actually just execute that. And on my exits, at least for this particular model, I don't have to be like super precise within like seconds. So I can make sure that I'm not dumping all the shares at once, which will give me bad fills. So I can sort of manage that on the exit.

Did you encounter any trading psychology issues trading this way or you sleep like a baby each night because you're not in the market? Well, yeah. I mean, the overnight's nice. There's definitely fears there. I mean, the trading psychology is everything. It's so important. It's so, so important. And that's really what initially what drove me to the quantitative approach is I want to get rid of that. I want to get rid of

I mean, it's a weakness in us. It's every man's battle, really, is to try to control those impulses of greed and fear or whatever. So definitely felt that during discretionary play around time, whatever you want to call that. But then I was like, I'm going to get away from this and I'm going to let the data tell me what to do. And that was super helpful. But there's other fears with those types of things, because and I'd say probably this is this might be one of the biggest fears for a quantitative quantitative trader is

You do know the future to some degree, but you actually don't know if an edge will be around forever, when it will die. You know, those are really unknowns. And so you might find something that's like amazing, you know, but you don't really know how would this how would this behave during like the Great Depression?

Or how would this behave during the housing financial crisis or the dot-com boom, you know, or with AI or with large language models, you know, starting to penetrate markets more like you don't, you just don't know. You really don't know. And there are always people who are trying to find the edge and then that will flatten it and it will make the markets efficient.

So those are the biggest challenges for quantitative traders. And those are fears that you can have, but there's ways to mitigate that. And, but yes, to answer your original question, it's definitely nice that I don't have to, at least this model, I don't have to swing trade. So that helps a lot.

for sleeping at night. And it also helps a lot too for the crazy discretionary traders that I don't even know how they exist. It's just so stressful. I mean, the scalpers and those types, those are very, very stressful positions. How did your model perform in the years afterwards when we had a market regime change and markets started to go down and liquidity started to dry up, at least in the small caps? Yeah.

Yeah. How did your, how did your model fare then? And did you make it, did you have to make adjustments? Well, I'm sort of considering that at this point. And so I, when, when I had this big run-up, which was just insane, where from 10 K to 3.8 million is what it ended up at, at the end of 2021. And then I moved to Puerto Rico and met like amazing people there and incredible, just massive life changes and really, really good ways and really, really

challenging ways just because it was all new at the same time. But after I had that big run-up, I pivoted and I went hard into learning game development and pursuing some AI projects as well. I still had this model just activated, but it went sideways in 2022 and 2023. It was basically a wash.

So this year it's up like 100%, I think, now, which is good. But this year is the first year that I've actually seen maybe a little bit of change. It's my conviction right now that we're in something of a quiet recession. So our monetary policy is so different now than it used to be with Keynesian economics kind of

starting to have more and more influence. And we printed so much money. I mean, it's an insane amount of money that was printed during 2020. And you got to pay for that. So it's my philosophy right now is that everybody's having a party back then, like everybody. And we should have felt some pain, but we didn't. And so now,

Probably at the end of 2021, for the past couple of years, everybody's been paying for it through inflation. Nobody has extra money in their pockets. There's not a lot of volume in the markets. People are not doing as well, frankly, in my opinion. So I think what happened is it was kind of like you basically are robbing from the future.

2020 shouldn't have been as big as it was for me. In other words, in my opinion, it should have been like a 2019, which was still crazy, you know, 800% or whatever it was. That's crazy. But if we didn't have the insanity of the printing press and STEMI checks and everybody's pockets and all this madness, you know, the paycheck protection program and all that stuff.

If we didn't have that, then I probably would have seen much more moderate returns that would be like 2019 or a little bit smaller, but it was like thousands of percent. So I think that was robbing from the future. And now the volume's dried up. And if you look at the active traders, the small cap market, there's a lot of traders that have really struggled the past couple of years. It's just things haven't been there. So this is the first year that I've seen a little bit of hope. But that does, I'm interested in getting back in and

And, you know, potentially building some more models. I miss the markets being away for a little while. So but thankfully, this year, it's you know, it's up, like I said, about 100 percent now, the account. But but yeah, you got to be you got to be willing to pivot. I mean, every the only constant is change, you know, and that's the way it is in business. That's the way it is in life.

That's the way it is everywhere. So you've got to be willing to pivot. You've got to be willing to polish. But I still have the exact same model activated. My hunch is that, like I said, if the economy starts picking up and people start finding themselves with some extra cash in their pockets, then you're going to see a lot more volume. And I think that spirit will return and the behavior will return with it, which is a behavior that can be predicted.

So you mentioned the small cap market has underwent a tough time there and people weren't doing so well. But how is it that the regular stock market now is hitting all-time highs? Do you see a great disconnect between what's going on in the broad big market and what goes on in the small cap market and how much of a link there is between the two? Yeah, I mean, I have ideas, but it's weird. Like it's really...

a bizarre difference. And my gut thought on that is that large cap markets, mutual funds and institutional stuff and all this stuff, I think they probably live behind a different gate than a lot of people that participate in small cap markets. Small cap markets is much more retail

It's much more Robin Hood type traders or it's your average, maybe middle class or upper middle class or even lower, I guess, wanting to get involved in the markets. The sad reality is that group of people has been suffering. They're the ones that are not having extra money in their pockets.

But the ones that are behind those, the different gate, the institutions and the big tech players, the big names in the markets, they're doing crazy well. They're doing very, very well. I held real estate during this time, and that was a wonderful place to put money.

I mean, the cost of food and cost of everything is going up. But the fact that I had it in this pretty expensive house and nearly doubled, like basically made a million dollars again from a house, most people can't afford that. So that is my suspicion, is that the participants in each of those different sectors, large caps, which are

continuing to grow and small caps, which have suffered, they really have the past couple of years is that that's probably happening because of the type of people that are in those

those different, different spheres and the rich get richer type thing, frankly, uh, that's the feel of it. So on your blog, uh, you have a quote here. Uh, you said, quote, I'm a firm believer that there are countless statistically significant edges to be discovered in the markets that will destroy the returns of any of the wall street, big boy hedge funds. So, um, so

A lot of these hedge funds and quants have been around a long time. If we look back at, say, James Simons, who was an early quant, started Renaissance Technologies in the 1970s. Wouldn't all these statistically significant edges be already found by now? I mean, they have a lot of computers and a lot of smart people working on this.

Why do you think that you were able to find what you found when so many players have been working on this for decades? Yeah. How do I answer that question? I mean, I would say that, so the conviction that I think that there are edges that exist is,

that could still be found, it's probably the same type of conviction that I would apply to believing that there could be a next unicorn startup in the tech world. So, you know, the brightest minds are in Silicon Valley, for example, and they're, to get out of finance for a moment, and they're building like mad and they're smart and they're driven

But we still get, you know, an open AI that pops up, you know, that's changing the world right now. We still and for a long time, people just didn't even think that that could get to where that sort of critical cross that critical threshold, which it has now. So, I mean, machine learning has been around for some time, really. So I would say there's a similar spirit in terms of how I would approach tech.

to be hopeful that you can create something that is going to be insanely outsized. Now it's a unicorn, like it's a unicorn to go from 10,000 to 3.8 million in three years. That's a huge unicorn. Yes. And just like the tech world, you know, if you look at a VC firm, I think of the statistics are like, they expect nine out of 10 to fail. Same type of thing I would say in the data science world and not just in the stock markets, but really everywhere.

you can keep studying because human beings are complex they're extremely complex and we learn psychological truths like psychology is constantly evolving our understanding of our mind for example is really limited like it's a blue ocean you know we don't really understand a lot about the psyche of human beings there's a lot to discover there so i would just say that

It's a very similar approach to tech that I would have to data science in the markets. And that gives me a strong conviction to say, no, don't quit. Work at it. Yes, there are Harvard grads and whatever. There's the Ivy League guys that are hedge funds now, and they're searching everything. But they get it wrong, just like they get it wrong in tech. I mean, they have huge misses. Like there are tech titans that hugely miss big time. Like why was Sears, for example-

Why is Sears bankrupt and not Amazon? You know, they were established, but Amazon came up and disrupted. And that requires that type of conviction that says, no, there is a diamond in the rough. Like I'm going to find it. I'm going to find it. I'm going to disrupt this entire sector and look what happened with Amazon. You also have an interesting quote. You say that, quote, a man's journey to wisdom is learning discipline to find the balance. Care to comment more on that?

I would have to see the context. I haven't read that blog in a while. I haven't read that blog in a while, so I'm not sure what I was referencing there exactly. So the balance, what it needs to be balanced. Balance as in possibly knowing when to curtail your greed and knowing when to step up to the plate, not being too shy to put on a position, being stuck in fear and not...

getting too greedy. That's yeah. Yeah. Yeah. I think so. Um, I mean, I think we're all prone to maybe not all of us. Some of us might have more temperate demeanors, but, um, you waste a lot of energy if you go up and then all the way back down. Like if you're trying to go on a linear line from left to right, whatever, you know, the shortest distance is a straight path. Right. So, um,

if you are approaching something you want to get to that that end point but the way that you get there is by shooting way up and then shooting way below and then you're just this big wave with high range then you're wasting energy you're wasting your time you're wasting resources and you're never going to get to true understanding or wisdom or whatever the end point is so so yeah i mean i

that's, that might be more personal for me than it is for others, because I definitely can be a bit manic about things and get super excited and, you know, want to conquer the world. But that's also served me well to break down walls that everybody said I can't break down. Um, but I've also, I've also paid the price. I have definitely gone through a lot of pain and suffering, uh, trying to discover that. And, and I'm finding more and more, you

you know, that there's great wisdom and constraining yourself and balancing those things and not letting these wild exaggerations to the left or the right knock you off your path. So, yeah. Did you discover any of this as an entrepreneur or was this all done strictly through trading? Oh, definitely both. Yeah, definitely both.

Yeah, I mean, it's just like it's one of those things where, you know, if you're a wild weed that shoots out of the out of the ground, you're going to be the first to get whacked by the weed whacker. You know, you're going to be the first to get hit. And that's the only way that you can learn to go back is to get cut, like to get burned out. So I think I was listening to NVIDIA's CEO the other day and he was wishing suffering back.

upon the young audience. I don't know if he was at Stanford or where he was, but he was giving a talk and he was just saying, you need to learn to welcome suffering, basically. You need to learn to welcome that pain because you've got wild shoots coming out of you that are messing things up. Whether you like it or not, suffering will help burn those. It will help temper you. It'll help

make you stronger. It will help allow you to endure more because you need that. I mean, to bring it back to trading, like you should put on more size, for example, if you've got an edge that works. But when you're talking about, like when I was, when I was trading, my biggest loss was half a million dollars in one day. I lost, I lost half a million dollars in one day. And that was because I was pushing massive size.

But I should be pushing massive size. If I didn't do that, then my 10K might have turned to 50 or whatever because my risk tolerance is so low. But at the same time, there's kind of the balance because if you're like, well, screw this, I'm going to go, I'm going to mortgage the house and let's just go crazy on this. And then you have some black swan event.

So you see, so there's there's the temperance that's needed, but it goes both ways. You can be ultra conservative and sort of the paralysis by analysis type thing. And you never want to take the step because you're so scared. Or you can be a wild outlaw, you know, that gets beat up by the the law too much because you're doing crazy stuff.

So how did you feel after you had that half a million dollar loss in one day? Did you modify anything after that? Or were you confident enough in your approach that you figured, well, sometimes those things happen?

It was, I cried, but it, I mean, it hurt. It definitely hurt. But I don't think it was too far after making half a million, like in a week, you know, there's anomalies that have gone to the upside and to the downside, but the, the, the, and this is where the discipline is required is the data said that that half a million dollar loss should have happened.

So there was no break in the model. So it's like, this is it. And that's the thing too, is it depends on your type of model too, that you've built. I mean, if you think of like a casino, each of their, the house is always going to win in a casino always, but the games that they, that they, you know, allow people to play, they have different risk structures. You know, if, if, if, if they have a, somebody coming and pulling slots,

you know, they're probably going to lose 90% of the time and win 10% of the time or whatever. And so it's going to be as big win with lots of small losses. Whereas like maybe I don't, I don't gamble. I think gambling is really stupid, but I'm sure there's other things, maybe a blackjack or something else. That's more of a,

The win rate is for the house is not as high, but the loss when the house does lose is not as big. Right. So for my for my particular model, it was one of those cases where when I lost that half a million in a day was, oh, man, the slot machines all just happened to win the jackpot today against me. You see what I'm saying? So it's like, but they should they do. They are meant to win the jackpot at some time.

It just happened to be a black swan. It just happened that 10 people won the jackpot today instead of just one. And that does actually bring up an important point about quantitative trading that I think could be helpful is that's another big risk is sector risk in quantitative trading, because especially in small cap world where there's a lot of

adjacent stocks that will just, you know, the rising tide lifts all boats type thing. Like even today, I think we had a really big mover today. That's probably going to spark more volume in other sympathy plays.

So now if you have a sympathy play that's super strong, it could be really risky because it's like, oh, no, here's one stock that just won the lottery. But now it's carrying five others with them that won the lottery. And that's a big risk for quantitative traders. It's hard to mitigate. It's very, very hard to mitigate because it doesn't happen that much. And it's just a difficult thing to account for. So.

Excuse the last interruption here. This is Tessa. We hope you're enjoying this episode so far. If you love the podcast, please give Chat with Traders the best review you can on whatever platform you're listening from. This will help us to keep the episodes coming. Also, if you haven't subscribed to our email list, please hop on to chatwithtraders.com and click on subscribe so we can keep you posted of information that may be of importance. Thank you. Now back to the chat with our guest.

Were you ever tempted to get into the meme stock trading back in 2020 and 2021? Plenty of volume there. For sure. Yeah, yeah. I watched with popcorn in hand most of that madness. It was bizarre. It did trigger some thoughts that I have considered, like maybe some sentiment analysis, perhaps like,

you know, using Twitter's API or something to just track like trending names and potentially catch early

trends and stuff like that. There might be some data work that could be done around some of those types of things, but no, I didn't. And plus to all of those stocks, AMC and GameStop, they were all too large cap. So I just sat on the sideline because they didn't qualify for my model, even though they were insane runs, just a wild story. It was a really wild story during those times. So, but I was just watching. Yeah. Earlier in our call, an

privately you mentioned about what you see is commonly see as fraud in the industry and i was curious to kind of get your take on what you're seeing what have you seen on that yeah yeah i mean well that that was getting started in trading especially in the active market small cap markets you know call it day trading call it whatever you want um that world is

It was really difficult to discern signal from noise. It's really, really difficult. There are so many people that are claiming things that aren't true while they stand in front of a Lambo or on a beach or these types of things because they can make a ton of money in financial education market. And so that's what they do. And it's really frustrating to me because

You know, I basically if I share what what I do with someone, I'm immediately encountering stigmatization. Like, I mean, I'm immediately stigmatized.

As a fraud, like as a fake or whatever, that type of thing, or or as like a fool that's being taken advantage of. Right. Because that's all that's most of what you see. You see these very hyper charismatic, flashy promises and commitments being made by all sorts of types. And they're just lying. They're just they're flat out lying many, many times. And sometimes there's SEC enforcement. Most of the time there's not.

And there's lies in the companies. The companies themselves release information

garbage press releases all of the time. It's like the direct stock manipulation, but that doesn't get prosecuted. Then crypto instead gets chased by Gensler, but anyway, which is frustrating. So I'm just saying there's so many lies, like there's so much noise, there's so much lies. And I think it's very, very important that people, this is actually one of the big reasons that I'm wanting to start to share my story because I'm typically pretty private.

But I think it's a worthy story to tell and can give hope is I want to be a signal voice that cuts down all the fraud. It makes me sick that people are getting burned again and again and again by these liars.

that have hot, hot stop tips that are just completely bogus. So couldn't some say that you had a fantastic return there over a couple of years and maybe these other people who are frauds, but the public doesn't know about it. They could say, well, look, you know, I can get those kinds of returns too. I mean, you had fantastic returns. So is it the level, is it the level of returns or what,

What are the tip-offs that they are frauds just because they use bait like the Lamborghini to promote their service? Well, that's the challenging thing is how to discern it because I don't have a problem if someone is – let's say someone was truly successful. There are people. There are definitely traders that I know who have rooms or whatever and have subscription services.

And they're legit. Like they are the real deal. And those are people that you should even buy their products. You should listen to them. But man, they're like probably 10% or less, in my opinion. The difficult thing there is how do you discern? And yeah, you could still have a charismatic person that's like, I did really do this. I am going to promote it with a Lambo or whatever.

I don't necessarily hate on that, but in terms of discerning this, one of the reasons I would say I like chat with traders, I like you guys because you require broker statements to verify the validity of these claims. More of that needs to happen. And I don't know...

talking with their own broker, seeing if their own broker, I know they're careful about what they can kind of partner with or like vouch for, but there needs to be receipts. Like there needs to be more receipts and more honesty and more transparency. But at the end of the day, I mean, you just got to get better at smelling this stuff. Like it's a scent thing, you know, it really is like discernment is a smell. It's not, you gotta like, okay, that doesn't smell right. Something smells off. And so I think that would probably be like the quick,

tip is what do you smell when you hear this person talking what do they smell like and in some people you can i could see that when i first started trading like to give a name drop like like tim grittani was somebody that i followed who's kind of well known in um in in small cap world and then in penny stocks before that but um for otc stuff and you could just you listen to the guy talk you listen to what he's saying you can kind of like this

I don't smell fraud here. Like he smells like he's legit and he is, he's a legit dude. So I would say that, you know, some people, I don't think you have to share profits, but some traders, you know,

You know, like Matt as is another Twitter handler that is legit. He's a good dude. He shares his P&L every day. And he's been struggling the past couple of years, too, because of the markets have been tough in small caps. But he's real as it comes. And he's a scalper and whatnot. And he's successful in his own way. But so that's helpful. He's sharing profits. But you don't have to share profits every, you know, all the time and stuff like that. But so I would just say, like, get better at your scent.

Like get better at smelling these guys. So, uh, so to wrap things up, uh, what's next for you? Well, I'm, you know, I'm, I'm reengaging. Uh, like I said, I, after I had those big wins up to 2021, I'd been pivoted to game development and I'm still, uh,

That's definitely a big bandwidth piece for me. I've got a real estate property now that has done quite well in Puerto Rico. And since we've moved back, I'm going to flip that to a vacation rental and try my hand at some real estate plays there. I'm excited about that.

And then giving more bandwidth now to trading. There have been models that I've had in my head, like observations the past few years that I just haven't had bandwidth to play with. So I'm excited to get back in there. And then I've got some AI stuff that I'm working on as well, unrelated to finance. So yeah, I've got a lot of projects that I'm spinning, but still trying to make sure it all fits and I don't overwhelm myself. Yeah.

Yeah, yeah, yeah. Well, Sam, thanks for coming on Chat with Traders. Thank you for having me. Yeah. How can our listeners reach you? I mean, the best place would be just to find me on Twitter. My Twitter handle is apathetictrader.

meaning, you know, I don't really care about the emotions. It's trying to be data driven. So apathetic trader would be, you can just find my Twitter profile there. And my, my, my messages are open. You can, you can message me there if you're interested in talking or whatever, learning more. So yeah. Fantastic. Great. Thanks for coming on the show. Awesome. Thank you.

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