291 · Sam Miesse - Small Cap Quant Trader Grows $10K into $3.8M in 3 Years

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Trading in the financial markets involves a risk of loss. Podcast episodes and other content produced by Chatwith Traders are for informational or educational purposes only and do not constitute trading or investment recommendations or advice. Yeah. I lost half a million dollars in one day. That was because I was pushing massive size, but I should be pushing. 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 gonna go, I'm gonna 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. Uh, 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.

Hi, this is Ian Cox, your host. Thanks for tuning in to Chat With Traders. This is episode two ninety-one. 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 Messi. 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 produced 20 to 40 actionable daily opportunities. Starting with an experimental ten thousand dollars.

His rigorously back tested system gave him the confidence to stick with the signals despite suffering a five hundred thousand dollar daily drawdown along the way to growing his account to three point eight 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 uh Chat with Traders. Thank you. Yeah. So what where are you at now?

Um, I'm actually currently in in uh Idaho, but um I'm not a hundred percent sure if that will be uh a permanent location right now. So we're so there's some in-between, there's some transition going on. So but uh 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 gonna land. So

Oh great. Great. Well let's um dive into your background. Kind of where did you grow up and tell us a little bit about your early interests and what drove you as a person. Wow, uh growing up, th I was a quiet kid. I was the the kid that's always just sort of taken it all in, very, very observational, very analytical, and uh just kind of grew up with that sort of temperament.

Uh, I was one of seven kids. So 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 or, you know, there's a lot of wisdom intake that that you can get when you're that far down on the totem pole. So so I always felt a little bit out of my element, frankly, as growing up in terms of, you know, to the left and my right.

my my peers, I never really felt quite in the same company. So I always was always sort of looking ahead and wanting to grow up fast and wanting to always a very curious mind. With that analytical mind, I got into tech as I as I became an adult uh and still love technology. It's I was always the kid that like loved the the wizard class of characters and tech technology is really the closest thing to to having wizardry powers. So so I I love tech and I I develop on the side and um

uh have some projects in in the dark right now, but um that I work on. So I'd I really, really love that. Um I and so I I did to bring it all the way up to current, I did get into uh enterprise tech sales for a time and that was really enjoyable. Um and that's actually a a sort of the the launching pad into trading because we were selling data science products that did predictive analytics

And so I I was very fascinated by that and wanted to apply that to the markets. So anyway, quick quick story of the upbringing there, all the way up to Yeah.

Um my my understanding is is you you got in a little bit early into entrepreneurship. Um and w tell us a little bit about that. When I was um in my twenties, um, I was always trying something. So something crazy and many, many failures, but I did get into the consumer electronics space. uh, which was one of my earlier wins entrepreneurially. And uh I had a business partner at that time and we were fifty fifty partners and

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

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 really quite well, which was this this trading journey, uh, particularly a a quantitative approach. uh to the markets. And that was in like 20 2019 was when I launched the fund. So so what triggered uh you f your first early interest in the financial markets?

So I'm I mean I'm a rags to riches type story. Like I came from nothing. So um I'm really, really nothing. So I 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 uh to to form a cornerstone to all of these other longer horizon.

you know, longer time frame things that I'm seeking uh to build. The stock market's fascinated me, uh I mean, sure just just surely by that, by the fact that you know, I don't know what it is today, but I think it's like a quarter of a trillion uh dollars of value is exchanged every day. And it's just 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 it that was really the pure drivers. Like I have a lot of ambitions. I'd like to build a lot of things, but I gotta 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 twenty sixteen, 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 as a trader that's looking for outsized gains. You need extreme volatility. So so I targeted there discretionary, didn't really know what in the world I was doing, law like kind of paid my market tuition there, lost some money 2016, I think in 2017 as well.

And 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. uh repeating just observationally, I started up to recognize these repeating patterns.

that were happening in that's at least that segment of the market. And it was so repeatable. And then tying that with the fact that I'm I was literally selling predictive analytics. Uh a little bit of machine learning with some of that on the back end, but I was selling uh 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 it's just a window to see that.

And human beings are very, very predictable. Um, they do the same thing again and again and again. They're motivated by the same impulses. And so all that to say, like 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 where it got in my mind to um the thought in my mind to tr to try to quantify that behavior.

So these repeating patterns were you um so you were just focused in on the the small cap market, is that correct? I flirted a little bit with um 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.

Um, they're just well capitalized and you know, uh the the brightest minds are in those worlds. And so it's hard to compete in in that um if you're under resource. So so I don't I try to stay away from stocks that have heavy institutional involvement. And I also need that volatility. So, um, and that's where small caps really, really showed the promise there.

So what kind of repeating patterns did you see? Like uh what showed up? Well that's a important Yeah. Want to disclose that, right? Um I'll just think about how to answer that. Well, let me just first say it. Um there are abstracted principles to quantitative trading that are universal and aren't revealing of a specific edge. There's some debate among I don't know why that's even a debate'cause 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 so I can't I'm not gonna share like the exact specific edge because then you just get piled in and I mean to be an edge is to is to not be popular, right? It's to be marked, it's to be distinguished so. So I can't say exactly what that is, but um I mean I would say that any of the common commonly believed patterns, I guess, or or there's are pat patterns that are seen in the markets, you know, like cup and handle and

double tops and, you know, re reversal patterns or whatever. Like there there are a lot of things that are already recognized, like and sort of standardized within the trading world. And those I would say those are very worthwhile. Uh th 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, you know, could be predicted. Um there could be earlier indicators that that could show that this this is what it's going to do after this pattern presents itself. Um, but yeah, so I the so that there's a there's a lot of variables that I that I definitely consider when I'm qualifying a specific stock.

But I would say that the it starts with screen time and it starts with just anecdotally, you know, observing these patterns reoccurring again and again. And then from there get an idea and then try to quantify it. And and how how did you go about the process of of quantifying uh the quantifying it and getting the data and processing it? Yeah, so I was manual. So um and I and I think that's still common. I I did look at some sources and I couldn't I couldn't quite find like clean enough data.

So there were there were too many anomalies and it was too I was struggling to reconcile it with there was just So I manually pulled it and I actually just used uh thinkorswim's 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, uh, thousands of of rows and spreadsheets and just just mined it, just swinging the act.

So no I mean I could code but i I didn't find that I didn't find that super useful at the time. So you were you kind of learning about statistics on the fly or or did you have a background in that? I mean I'm I'm an entrepreneur so You know, I I I wanna like question the the the premise of that question even because um I I'm not big on certifications or, you know, even a lot of even a lot of training I think is frankly a a lot of waste. So no, I don't have any background in

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

proves whether or not something is statistically significant, but you can also prove whether or not something's statistically significant just through common sense. You know, it doesn't really 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. You know, they they think it's and then they 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 um it doesn't require any any expertise uh or any long term long form training is I would say is not really required. Mm-hmm. How how would you define a uh how would you define quant trading uh 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 the you know they're managing this operation or whatever. Like it's just a very subjective feel that you like the product or you like the company and so you're gonna 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 uh to fruition, you you you track those variables, you sort of try to wrap the behavior in data. And then from there, you you 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 um 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 that's that's substantial, depending on your sample size, of course. So I in terms of how I define quantitative trading, I mean it's it's basically trying to use as much mathematics. Or statistics or whatever, this data is 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.

D did you ever have to hire a programmer to automate? uh your systems or was your system manual, the execution of it? So I I looked into developing uh and automating uh some of the some of the uh the actual entries and exits and execution, but but I decided Yeah. I would say that the model that I built is probably like 90% data, maybe 10% subjective. 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 gotta do something with it. But the highest level filter would be gapping stocks overnight. But sometimes you might have a stock that gaps 100% or whatever. Like that the the the data is saying, hey, here's a hu 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 a hundred percent, 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 like account for. So I I do I uh oh to answer your original question, I 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 um 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 teased up the ball and it's really teed up. So but I still got a swing. So I I have not fully automated my particular system. And how long was this process to uh discover a statistically beneficial plan? I mean a program that uh you can execute? So I started I think

I think in twenty seventeen is when I started collecting data. And now I was also working at a very demanding job as well. Um, 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 to 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 gonna keep digging into this thing to get some value, you know, and it almost has to be delusional because you're I mean 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 his head. So you so you gotta be delusional. And but the challenge is once you start digging, you might dig a hundred feet in and you got 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 could find in the markets to give you ridiculously outsized returns.

It just takes a ton of work um to actually do that plowing through uh to get to the 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 I build 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

And working. So she executed it, not knowing anything about, and that's what I mean by it's only 10% subjective, because she didn't know anything about stocks, but the subjectivity didn't matter. It was very, very much just do this. So Have you ever watched a stock explode and thought, if only I had the capital, or sat on the sidelines because your account balance felt too small to matter? Good news.

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I I see. So so you boil down the model so that um it was just simply, you know, buy this stock or sell short that stock, uh, and so it was easy for uh for someone that Did you did you have to train her a long time to to do it? Was it Maybe like a week. I built the mo 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 she could follow that very well. And and it wasn't demanding on her time. She had free time to be able to to do it. Do you feel that your entrepreneur entrepreneurial mindset helped you stick through the process? Uh,'cause that's a long time because you didn't necessarily know when you would find something uh Statistically relevant, right? I mean Yeah. Yeah. You could keep going for who knows how long.

Uh well, yes. I so here's what I would and I and I approach this and this is why I preach self 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 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 match. So you can you can use those strings, those sort of levers that are stuck in society.

and behavioral economics type stuff. Um, you can use those to measure the behavior and then predict it relatively decently. You can predict the future. I mean, that quantitative trading is predicting the future. That's really what it is. So and it's crazy. But and I will say that m 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 a brick wall. I 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 eighty percent chance that if you go to McDonald's and you buy an egg McMuffin on Tuesday at ten AM and you're a white male in his fifties. Then we need to show you a black coffee because you're part of a group of people that behave similarly.

And th and there's a seventy, eighty percent chance that if you buy that egg McCuffin on that Tuesday and you're a white male in your fifties, there's a seventy eighty percent chance that you're gonna want a black coffee. So we're gonna do that. So it's like I was already selling these things and it was and it's it's minority report type stuff. It's and it's real. It's very, very real. So um yeah.

Wow. Uh okay, so let's get into Uh once you found uh a statistically relevant um uh model for you to trade uh through, what how did you start trading and you know how much money did you start out with? So I started, I was under I I only when I when I was traded in 2016, 2017, it was very much discovery and testing and getting my feet wet.

Um, and so it was like under the I didn't even put enough to to be over the pattern day trader uh 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 um they uh to get over the the pattern day trader rule and start executing this model. And so I I think I put like thirty five thousand or something like that in an account, but I only risked 10. So I expose ten K to risk.

And that was at the end of 2019. And then I had my wife start executing it. And so in twenty twenty it grew, I think like eight hundred percent. It went from ten thousand dollars to like eighty six thousand or something like that in in twenty night in twenty nineteen. So, which was ridiculous return. Um wow. And uh so then at 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 dropped, what it was 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. Like like it basically just the model kept was working and now it's been supercharged, like a turbocharged and all of this juice is flowing.

Yeah, so just curious. Uh so in the first year that you had that incredible uh return, wh what exactly were you you were trade you were just trading these small caps? Small caps, yeah. Uh long, short or Um it was short. I could say it was short. Yeah. So it was um I was short selling moves that were you might consider parabolic. So uh looking looking for hyper extensions, basically, uh is what it was. Okay. Wow, and and how 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 it's in the and I had built the data the data that was collected was from 20 um 18, 2017, and a little bit of 2016 was my sample size. Uh, which was decent. Like it was a decent sample size. So um, 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 um so you're finding things to short one or two stocks every day almost, right? I mean, you and how long are you holding them for? This particular model is uh is only intraday, so I don't have to swing uh swing any trades, so um, 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 I know there was one recently, I can't remember the ticker, but

just went bonkers overnight. I feel really bad for people that are stuck in that. So that's that's nice on the heart. Did you have to deal with borrow fees if you're trading intraday? Yes and and locate fees? 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. Oh okay. Ha ha ha.

Right. Uh okay, so you had this fantastic year in twenty nineteen and then twenty twenty, did you take any money off the top? Um, because you had such a fantastic year. First year. Yes, I did. So ten ten K to eighty, whatever it was, uh K twenty nineteen, that eighty six K I think it was, then grew to just over two million in twenty twenty. There these the this is the other issue too, is there are constraints.

you know, that you can't really resist. You can't go you can't go further than them. And liquidity is is really the big the big constraint and makes that's one of the things that makes quantitative trading quite challenging is um 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 twenty percent or something.

You know, there's an eighty percent 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 ten thirty. I guess we'll just say. If your 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 you that's when you gotta go. So if you if you're trying to buy, you know, ten thousand shares, that's one thing. But if you're trying to buy a hundred thousand or two hundred thousand or three hundred thousand 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.

Um, because it you just you have a liquidity problem. So so I couldn't, I had to basically cap out. um 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, um, for finance. Um so that yeah. So but yeah, we we we basically hit the liquidity cap.

And then had to uh uh withdraw, take take stuff off the top, uh, when it when it got over that. So Does your model actually then you 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 buy 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 or 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 uh it's true it 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 chart. You manually look and see if this is a qualifying stock, and then you wait for the exact time. so that you're supposed to get in. And for me, that's that's very it is within a minute, I could share. So um I've got to make sure that I'm not too my size isn't too big that I'm gonna, you know, screw up. my entry or my exit. So um but yeah it's

The discretion is 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 you're then it's just follow the script. I see. And you mentioned that you uh have to avoid your size being too big. How do you determine that? Like like give us an example, um, what percentage of the Of the daily volume or what kind of what are you looking at?

Yeah. Well I I mean I don't have that really super systematized uh for how to handle that. I just felt it basically. Okay. So um I notice, 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, that you can't see the glass. So it's hard to know when you're actually touching it.

uh uh you just kinda had to feel it. At least that's how I've done it. Um I'm sure there's probably some other ways that that to handle that. But I just I I let the account grow and what I do too for the account is you know, I when it especially particularly when it was 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's me. So it was for me, it was just feel. Um, but maybe there's a way to wrap that and you know, quantify that in some way. But

So the small cap universe is quite big. Uh last time I checked, it's in the what, ten thousand more than ten thousand uh different Companies, right? Uh typically, how many stocks are you working with on a on any given day that that get filtered down uh that make the cut? I would say that that that get the stocks that get to my desk. to 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. So um and then in terms of how many I actually execute. Out of that, five and under on average. If I 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 like the the the numbers that that get to me for subject for subjective calls and then that actually do finally qualify uh to be traded. And then I just wait.

for the timing to tell me which has already been computed to here you go go go ahead and act now. Mm-hmm. I I say so d uh do you use stops or do you manually uh exit? Uh, I do use hard stops and then I do manually exit as well. So uh and even the hard stops too. That's the see that's a thing that makes me scared about giving everything to a 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 uh there's no end. I mean you can you can owe money. And short, so like you can lose a lot. So if it doesn't trigger, then I I 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 I'll actually just execute that. Um and on my exit. At least for for 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. Um, so I can sort of manage that on the on the exit.

Did you encounter any trading psychology issues um trading this way or You sleep like a baby each night because you're not. Yeah. Not in the market. Well, yeah, I mean the overnight's nice. Um there's definitely fears there. I mean

The trading psychol psychology is everything. It's so important. It's so, so important. And that's really what uh initially what drove me to the quantitative approach is I want to get rid of that. I I wanna get rid of I mean, it it's it's a weakness in us. We it's a it's 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 gonna get away from this and I'm gonna 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 the housing financial crisis or the dot com boom?

you know, or with AI or with large language models but 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 uh 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. Um, and it also helps a lot to for the the crazy discretionary traders that I don't even know how they

exist. Like it's it was 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 you know, markets started to go down and liquidity started to dry up, at least in the small caps? Yeah. H how did your how did your model fare then? And did you make did you have to make adjustments? Well, I'm sort of considering that at this point. And um so I c when when I had this big run-up, which was just

insane where from 10K 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 at incredible. massive life changes in really, really good ways and really, really you know, challenging ways just because it was all new at the same time. So but uh after I had that big run up, I sort of pivoted um and I went hard into learning game development and pursuing some AI projects.

as well. And so I still had this model just like activated, but uh it sort of went sideways in 2022 and 2023, basically a watch. So this year it's up like a hundred percent, I think. Um now, which is good. But this year this year is the first year that I've actually seen. Maybe maybe a little bit of change. It it's my conviction right now that we're in something of a quiet recession. So we have our monetary policy is so different now than it used to be with, you know, Keynesian economics kind of

starting to have more and more influence. And we printed so much money. I mean it's just an insane amount of money that was printed during 2020. And you gotta pay for that. So it's my my philosophy right now is that everybody was having a party back then, like everybody. And we should have felt some pain, but we didn't. And so now Twenty probably at the end of twenty twenty one for the past couple of years, everybody's been paying for it through inflation.

Nobody has ex 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. Um, so I think what happened is it was kind of like You know, you 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 north 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 STIMI checks in everybody's pockets and all this madness, pay pa you know, the the pay paycheck protection p 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 twenty nineteen or a little bit smaller, but it was like thousands of percent, you know. So I think that was robbing from the future.

And now people now the volume's dried up. And if you look at the active traders in the small cap market, there's 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 I'm interested in getting back in.

And, you know, potentially building some more models. Uh, I miss the markets uh being away for for a little while. So but thankfully this year it's you know, it's it's it's up like I said, about a hundred percent now, the account. But um But yeah, you gotta be uh well, you gotta 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 gotta be willing to pivot. You gotta be willing to polish. But I still have the exact same model activated. I my my hunch is that Like I said, if if the economy starts picking up, then we s people start finding themselves with some extra cash in their pockets.

then you're gonna see much a lot more volume and I think that spirit will return and the behavior will return with it, which which is a behavior that can be predicted. So you mentioned uh that the small cap market is underwent a a tough time there uh 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... Mm-hmm. It's weird. Like it's really a bizarre uh I my my my my gut thought on that is that large cap markets, you know, um 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.

My small cap markets is much more retail. It's much more Robinhood type traders, or you know, it's it's kind of your your average um maybe middle class or upper middle class. or or even lower, I guess, wanting to get involved in the markets. So and the sad reality is that group of people has been suffering. Like they're they're the ones that are not having extra money in their pockets. And It but the uh but the ones that are behind those the the different gate, you know, the institutions and

the big the big tech players, the the big names in the markets, like they're doing crazy well. They're doing very, very well. So and that's the thing. Like I I held real estate during this time and that was a wonderful wonderful place to put money. Uh 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. you know, nearly doubled, like basically made a million dollars again from a house.

Most people can't afford that. So so I that is my like 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 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 A lot of these hedge funds and quants have been around a long time. If we look back at say James Simons, uh who has an early quant uh who started Renaissance technologies in the nineteen seventies.

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

is that 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, we have there's the brightest minds are are in Silicon Valley, for example. And they're to to get out of finance for a moment. Um, 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 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. I mean, machine learning's been around for for some time, really. Um, so I would say it's th there's a similar spirit in terms of how I would approach tech.

To 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. Yeah. And just like the tech world, you know, if you look at a V C firm I think the statistics are like they expect nine out of ten to fail. Same type of thing I would say in the data science world and not just in the stock markets, but really everywhere.

uh 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 that'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 at hedge funds now and they're they're searching everything, but they get it wrong just like they get it wrong in tech.

I mean, they they have huge misses. Like there are tech titans that hugely miss big time. Like why is 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 that requires that type of conviction that says, No, there is a diamond in the rough. Like I'm gonna find it. I'm gonna find it. I'm gonna disrupt this entire sector and look what happened with Amazon.

You also have an interesting quote. Um, 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 vlog in a while. So I'm not sure uh what I was referencing there exactly. So Yeah. The balance.

Um balance as in uh possibly uh knowing when to curtail your greed and and uh knowing when to step up to the plate, uh not being too shy to put on a position uh being stuck in fear and 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 the shortest distance is a straight path, right? So If you are approaching something, you want to get to that that endpoint, but the way that you get there is by shooting way up and then shooting way below, and then you 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 gonna 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 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 know, that There's great wisdom in 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 um as an entrepreneur or was this all done strictly through trading?

Oh, definitely both. Yeah. Definitely both. Um yeah. I mean, i it's just like it's one of those things where it's, 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's he was wishing suffering.

upon the the the young audience, you know, I don't know if he if he was at Stanford or where he was, but he was giving a talk and he was just saying, you you need to learn to welcome suffering, basically. You need to learn to welcome that pain. Because you've got sort of wild shoots coming out of you that are messing things up and

Whether you like it or not, suffering will help burn those. It will help temper you. It'll help make you stronger. It'll help allow you to endure more because you you need that. I mean it it to bring it back to trading. Um 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. Whoa. 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 If I didn't do that, then my ten K might have turned to fifty. Or whatever, because my risk tolerance is so low. But at the same time, there's kind of the balance. Cause if you're like, well, screw this, I'm gonna go, I'm gonna mortgage the house and you know, let's just go crazy on this. And then you have some black swan event.

You see, so there's there's the temperance that's needed. Um, 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

Uhhuh. So how did you feel after you uh had that half a million dollar loss in one day? Did you modify anything after that? Um, or were you uh confident enough in your approach that you Figured, well, sometimes those things happen. It it was it I cried. Uh but uh it mean it hurt. It definitely hurt. Um but uh 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 it 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 and that's the thing too. It's it depends on your type of model too that you build. I mean, if you think of like a casino Each of their the house is always gonna win in a casino. Always.

But the games that they that they you know allow people to play have different uh risk structures. You know, if if if if they have a somebody coming and pulling slots. You know, they're probably gonna lose 90% of the time and win 10% of the time or whatever. And so it's gonna be a big win with lots of small losses. Whereas like maybe I don't I don't gamble. I think gambling is really stupid, but um I'm sure there's other things, maybe Black Shack 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, 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 where world where there's a lot of uh adjacent

stocks that will just, you know, the the the rising tide lifts all boats type thing. Um like even today, I think we had a really big mover um today. Um that's probably going to spark more volume in other sympathy plays. So now you if you if you have a sympathy play that's super strong 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 within 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 uh because it doesn't happen that much.

And you 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 Chatwith 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 chatwithraders.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 guests. Uh 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 I wa I watched with popcorn in hand most of the most of that madness. Uh it would bizarre. It it did it did trigger some thoughts that I I I have considered.

Like maybe some sentiment analysis put perhaps like you know, using Twitter's API or something to just track like trending trending names and potentially catch early. uh 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 two, 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 in insane runs. Just well, wild story. It was a really wild story during those times. So, but um I was just watching. Ha ha. Yeah. Um earlier in our call um privately you mentioned about what you see as 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 It's 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, it was really difficult to discern signal from noise. It's really, really difficult. There There's a there are so many people that are giving uh are claiming things that aren't true while they stand in front of a Lambo or on a beach or, you know, these types of things. J because they can make a ton of money in financial education market. And so that's what they do. And it is and it's really frustrating to me because

You know, I basically, if I share uh 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 uh 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 they're flat out lying many, many times. And sometimes there's SEC enforcement. Most of the time there's not. Uh and there's lies in the companies. The companies themselves release garbage press releases all of the time. It's like the direct stock manipulation, but that doesn't get prosecuted.

And crypto instead gets chased by Gensler. But anyway, um, which is frustrating. So I'm just saying there's there 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. It could give hope is I want to be a signal voice.

that cuts down all the fraud. I I 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 vogue. So could couldn't some say that uh you had a fantastic uh return there over a couple of years and maybe these other people who are frauds

Um, but the public doesn't know about it. They they could say, Well, look, you know, I I can get those kind of returns too. I mean, you had fantastic returns. So is it the level is it the level of returns or what Kind of 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. Um, is how to discern it because I don't I don't have a problem if someone is let's say someone was truly successful. Like there are people, there are definitely traders that I know. who have rooms or whatever and have uh 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. Um, 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. So 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 I like chat chat with uh with trade traders, I like you guys because you require state broker statements to verify uh the validity of of of these claims.

More of that needs to happen. And I I'm you know, I I don't know uh 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 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 gotta get better at smelling this stuff. Like it's a scent thing, you know? It really is. It's 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. to give a name drop like I like Tim Gratani 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 or OTC stuff.

And you could just you listen to the guy talk, you listen to what he's saying, and you can kinda 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. Um, you know, some people and I don't think you have to share profits, but some traders.

Uh, you know, like Matt Az is is a is another Twitter handler that is legit. He's a good dude. Uh he shares his PL every day. And he's he's been struggling the past couple years too because of the markets have been tough and small caps. But he's real as it comes.

And he's a scalper and um whatnot and he's successful in his own way. But so that's helpful that he's sharing profits, but you 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 twenty twenty one, I'd been uh pivoted to to game development and I'm still That's definitely a b a big bandwidth piece for me. Um, I've got a real estate property now that I'm that has done quite well in Puerto Rico. And since we've moved back, I'm gonna flip that to a vacation rental.

um and try my hand at some real estate plays there. I'm excited about that. Uh, and then giving more bandwidth now to um to trading. I'm there there 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, uh, unrelated to 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. Well, Sam, thanks for coming on uh 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. Uh my Twitter handle is apathetic traitor.

Apathetic, meaning, you know, I don't really care uh about the emotions, just trying to be data driven. So apathetic trader would be if you just find my Twitter profile there and Uh my my my messages are open. You can you can uh message me there if you're interested in in talking or whatever or learning more. So Yeah. Okay. Fantastic. Great. Thanks for coming on the show. Thank you. You've reached the end of this episode of Chat with Traders, but rest assured there are more episodes.