023: David Bush – The Transition From Discretionary to Quantitative Trading & How to Optimise Your Strategy

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chat for more information. Tasty Trade Inc. is a registered broker dealer and member of Finra, NFA and SIPC. The biggest secret of the best traders in the world is that they're just like everyone else. However, they've worked hard to learn the markets and discover what works and what doesn't. How can you hear about these? Here's your host, Aaron Feiff.

Hey there, what's up? Welcome back to episode number twenty-three of the Chat With Traders podcast. I'm your host, Aaron Firefield, and thanks so much for tuning in. Now this week I had a very interesting discussion. I was fortunate enough to speak with David Bush, an extraordinary seasoned trader with 20 years experience in financial markets.

David comes from a non traditional background, and what I mean by this is he has no formal education in the field of finance. In fact, he is a music graduate and performed as a professional musician for many years. But as you're about to hear, David changed paths during his twenties to become a trader. After overcoming the initial challenges that all new market participants endure, David did well for himself as a discretionary trader for many years.

But with an urge to optimize his trading approach, he gradually transitioned into a quantitative trader and went searching for new ways to exploit opportunities within the market. From there, David has gone on to take out the number one spot of Battlefin's Sharp Ratio Shootout, an international quantitative finance tournament with over 3,000 competitors. During our interview, David brings a really insightful take to topics such as the transition from discretionary to quantitative trading.

how to eliminate a single point of failure by trading multiple systems, and how the Monte Carlo tool can teach you a lot about how robust your strategy really is. And I must give a special mention to Zach Hurwitz, who was the guest on episode eleven, for making the intro to Dave. So thanks a lot, Zach. I appreciate it, man. All right, guys, I'm Aaron Feifeld, host of Chat With Traders. Here is this week's guest, David Bush.

Dave, what's up man? How you going? Uh doing great. Good to speak with you, Aaron. Likewise, thanks so much for coming on the show. It's great to be speaking with you, the uh the Battlefin champion. Uh a battlefin champion. There are there are uh there are a few at this point, but yeah, I was probably number

Four? That that might be right. I'm not sure about that. But uh yeah, it it's certainly uh you know, that's been a great relationship um and uh you know, fun experience. Nice. What was the category you took out then? Uh let's see. If I recollect correctly, um there were uh I think in the email there were there were thirty two hundred applicants from forty one countries or a bit more than that applicants uh wise. But Um they they chose I think it was Nine or ten. uh strategies in

uh in three different divisions. Uh I couldn't speak to everything about the divisions, but uh, you know, one division had an audited track record, one uh simply a live track record. One might have been just, you know, back tested results. And, you know, I had a live track record and um, you know, verified or audited uh, you know, results. So I was in that that so called the lead category. And uh, you know, I think the strategies also if I remember correctly, were across

broad asset classes. So it was not like equities in were in one bucket and futures were in another and and so on and so forth. I I think I'm right about that. At any rate. Um, you know, they had a proprietary formula and um, you know, which took uh took volatility into account. So in other words, it wasn't just a return base type of um formula which you know is flawed in the sense of one can just

juice a strategy for a tournament or something like that. So that that I don't believe that was anyone's approach anyways. But um You know, that that was that was the the gist of it at a high level.

Yeah, very cool. Well done. So let's let's get this started by giving us sort of bit of an intro about yourself. So take us back to where it all began for you and tell us how you discovered trading and w what were you doing at the time. Yeah, that's that's a lot to talk about there. Uh and I'm sure everyone's story is is is probably Uh w differ different in uh in the details but but similar in the sense of uh

um a a lot to talk about at the early stages. Uh what I was doing at the time was I was a professional musician. I had been for uh a number of years. I got started playing very early uh you know, in uh grammar school, I guess. Uh I took to it very uh very deeply. You know, after a couple of years, I I wouldn't say I was bitten by the music bug immediately, but after a couple of years I started playing drum set.

that's ultimately what I played professionally. And that uh it's just it just took over. Uh you know, and as I look back, you know, music and markets are uh are both pattern driven. They're both uh cyclical, uh one has to tune in, you know, you have to tune in as a musician to those around you.

Um literally if you're playing a pitch instrument, you know, to pitch uh obviously drums to uh to groove and tempo dynamic, you know, on and on. There are many layers uh that are happening simultaneously. Markets are very similar and they both are you know, they both have that behavioral component, you know, um markets are are certainly uh not the same in terms of investor sentiment behavior from

you know, year to year, day to day, whatever. Um so anyways, to answer your question about, you know, how we got into this, you know, I'm I'm working essentially uh, you know, in the n in the evenings, you know, and and at night and occasionally obviously rehearsing during the day. Uh but day days were, you know, largely free. And, you know, I how you know, I I don't remember the exact initial trigger, but I believe it was

Watching I believe I was at my father's house and he might have had C N B C on, you know, in the very early days. This would have been nineteen ninety four, nineteen uh early nineteen ninety five, but I this was probably would have been nineteen ninety four before I started trading, which was in ninety five. And I remember probably on C N B C possibly Bloomberg seeing a piece or uh you know, some kind of interview, and whoever was being interviewed talked about the market said,

You know, valuation can change overnight. You know, I mean yesterday's close can be dramatically different from tomorrow's open. based on uh just market crowd sentiment, you know, however you wanna phrase it. And for whatever reason that just fascinated me. And plus the ticker You know, the scrolling ticker, which is not something that I use, you know, at all unless it's some kind of watch capacity, I you know, which is uncommon for me.

You know, that fascinated me. I I knew that there were patterns in there. Obviously you could just visually see some patterns. And then also, uh, you know, I obviously I knew there was opportunity in there and you know, I was uh You know, I'm not shying shy about saying after all my training I was a very, very good musician. I played, you know, at a high level and so on, but

uh, you know, it wasn't the most fruitful um, you know, career uh monetarily, uh, at least, you know, in in my years, you know, as a professionalist. So um You know, so I there was a perfect opportunity to to look at something new and, you know, see if I was really interested and see if I could do it.

Uh you know, so that that was the genesis of the idea. You know, from there it was really uh starting to pick out some books, uh go to the bookstore. There were a lot of futures books at that time, which is interesting'cause There are not as many when I go to a bookstore now, um, you know, a physical bricks and mortar bookstore, but

There were just tons of uh tons of, you know, Larry Williams books and and so forth. But uh, you know, ultimately it was stock that I started trading and um You know, it was uh actually um, you know, picking up investors daily and seeing charts and going, Oh, okay, charts, these are interesting, you know, and that led me into uh

uh, you know, investigating you know, what technical analysis was. Um so that that really is uh I probably missed some part of your question there, Aaron, but you know, that was really the very, you know, the very early stage.

Yeah, that's excellent. So I mean I obviously wasn't trading around that point in time, so I believe this was before electronic trading sort of really came in. So Talk to us about how you were maybe receiving data at the time and analysing the market and sort of placing trades, I believe. it sort of involved a fax machine of some sort. Well right. Uh and it's funny now to me anyways. Uh it it did the fax machine was later on. That was an incredible uh upgrade, actually.

Uh you know what it first began was um was the newspaper literally um reading stock tables uh Barron's investors business daily. Um, but I quickly learned through investors, Mrs. Daly, that they had a service at the time and they probably still have it n now electronically, but Back then, uh to me it was an amazing service because it was called Daily Graphs, and you could get this graph book sent. Uh

pr produced after Friday's close. Uh so, you know, after four PM New York time, uh probably to wait till that, you know, even after that to get official closes and so and so forth. But that would arrive at my house and you know, I lived in New York City for a long time, but my girlfriend and, you know, then girlfriend now wife and I had moved up to uh the Catskill, so we were uh north of um north of the city and you know kind of uh remote in a sense.

This chart book would show up on Saturday mornings um with all the latest charts through Friday's close. And that was You know, that was just awesome to me because, you know, the the uh FedEx or whatever it was would truck would drive in and I'd I'd get this chart book, I'd rip open the envelope, and you know, I knew that that weekend and for the next week I'd be staring at that.

You know, and I would mentally update things as to, you know, throughout the day or throughout the week rather, you know, this this stock had gone up, this had gone down, whatever. But I mean it was very crude. Uh and yet um It was uh you know it was definitely formative and my first trade was based off of uh you know, based off of what I knew to look for, which was limited, but uh was based off of chart pattern.

And um and I think it was that very first trade I'm I'm almost sure uh which was Xylogics X L G X. I'm I'm almost sure of that without digging uh through um ancient trade confirms. That was um that was my first trade and and I got woken up. I didn't, you know, I didn't have data coming in. I did not have um Any kind of uh

you know, active real time anything. Uh so I got woken up by my broker, uh and he was a full service broker and it was probably I probably placed about two trades with him when I realized the cost of them, which was I think maybe a couple hundred bucks a trade back then. Uh, I realized that was insane. Um but he woke me up one morning, actually, uh pretty early before the market was opening, and he said, Hey Uh, you know, it's Charlie. Uh y you making money in your sleep, you know, and

I don't know whether I was asleep or whether I just was not quite uh up and running, but I I I do remember answering the phone and listening to this and going, What the heck is he talking about? And that stock had been taken over. Uh, you know, b uh uh two or three weeks after I I had placed the trade. So that was in hindsight probably like the worst thing that could that could happen to me because here you've got this

this uh process that looks incredibly easy, right? We you know, you just get you just get a chart book, you pick what you think's the best. Uh, it gets taken over for, you know, whatever percent gain it was, probably, you know, somewhere between thirty, forty, fifty, sixty. I don't think it was as high as sixty, but I it was probably in the forty or fifty percent.

gap up range, something like that. At any rate, um, you know, above my entry and Uh, you know, that that was the first trade and you know, from there it was uh it was a hard slog, you know, basically f uh, you know, speeding up my my path a little bit, you know, I did uh get uh trading quotes that was That was the beginning of some pain for me because while I was looking at charts initially I got quotes, but the quotes didn't have charts and the quotes came through

cable. So you had to have a cable connection. They came into a little box that God forbid if it ever got unplugged. It took about two hours to reload that box, the OS. But anyways, uh I I got quotes, so now I had quotes, no charts, and I started, you know, tr trying to trade based on that. Um, you know, I had some success, some uh obviously some losses, uh, but it was very um I was I was uh as people will say when they're uh new at something powerful, you know, I was I was most

dangerous to myself. You know, I I had a loaded weapon and and uh I I was uh uh you know constantly you know i it was constantly pointed at at myself, you know. Um I I ultimately um placed a lot of trades, uh went away to uh play music for a couple weeks. And the market had a very normal pullback. Uh this was now in nineteen ninety six. And uh I basically um you know, not watching charts, uh just working off of quotes. You know, I I lost a lot of money. In fact

the the screen, uh, every morning. Uh I would just look at the screen and of course, you know, in a in a pullback that's broad, you know, uh the screen is essentially red. There might be a few defensive names that are that are green, uh, you know, at the top if you have it sorted that way. But Basically, you know, day after day, the market was just pulling back, pulling back broadly, everything highly correlated, my stocks all going down.

And eventually, you know, I was getting very afraid and uh r you have to remember I knew v almost nothing. I was completely self taught. Uh, had some success, but uh this was my first real pain moment. And, you know, I said, if this happens, you know, if the market is down again tomorrow, I'm just I'm gonna sell it. I can't take it.

And uh and I remember that that next day, uh it it was it opened, you know, timidly, maybe a little bit higher, uh, and then the market just sold off and it was an absolute knife twist. uh bottom, you know, uh I I sold out of everything. market reversed, uh ended up positively. That was, you know, the bottom for probably months, actually, you know. I mean it was it was the classic um

uh reactionary traitor psychology. And uh that that was when it w that was my gut check moment where it was, okay. Uh, you know, am I gonna really do this? Am I am I serious about this? Because clearly I'm not equipped to do this properly and uh, you know, in terms of education, terms of equipment, anything. And uh, you know, uh I'm not one to give up easily. So I just buckled down and that's when I started really reading. That's when I started

uh you know, I I had an aha moment of uh, you know, every trade needs a plan, every, you know, risk needs to be redefined. I mean, really it's it's so basic. Uh however that was um that was the beginning of really becoming a trader was was you know that first um you know, l large painful loss uh, you know, account drawdown and and uh you know and and then going, all right, what am I what am I gonna do about this?

Okay, so from that point, where were you learning from? Did you land a mentor or did you continue to teach yourself like through books and study, etcetera? Yeah, e up to that point it had been exactly that. But then comes the fax machine. You know, I I g I started uh subscribing to a couple of newsletters.

Uh this was, you know, again, this was still before email or, you know, at least any uh you know, widely spread email. I'm sure some governments were were in some early uh uh you know, had some early iterations and so forth. But Uh, you know, so I got a fax service. Uh I got a fax machine and I started subscribing to some fax services and they were just daily trading sheets. You know, I'm doing this, I'm doing that, here's my stop, here's our target.

You know, here's the kind of order. Very basic. Uh so I I subscribed to a couple of those. Or at least trialed them. I subscribed to one of them and you know, after a couple of months I called the guys up. I said, Hey, you know, I'm not that far from you. Uh can I come in? I'm a subscriber. Can I just see your operation?

And, you know, they said, uh, all right, you know, come on in and And it was a couple of guys with a secretary, uh, a money management business, um, but also that was just making a little, you know, small foray into education and essentially it became an a uh w n no one ever used the word internship, but it essentially became an internship where I uh was able to just you know, help them around the uh uh around the office, do this and that odd, you know, job, whatever.

And uh meanwhile I was often behind them, uh listening to them talk about the markets, listening to them look for opportunity, uh place trades, whatever. Uh, and I started to obviously I picked that up.

Okay, excellent. So give us a bit of an overview of sort of your trading approach and sort of the method that you've adapted today and sort of how you navigate the markets. Sure. Uh it's certainly evolved. Um, you know, my But early trading once I had education, once I had really a couple of mentors um clarifying things for me, uh showing me subtleties of patterns, showing me, you know, uh how uh How to get a deeper insight into a daily

chart, for instance, by by looking at your day and and lowering risk and and so on. It really was discretionary technical trading, uh trading primarily uh on a swing time frame. Uh so looking for, you know, that kind of two or three day to one or two week type of trades. Um

So working typically off uh you know daily chart, but uh looking at various time frames, uh and trading larger concentrated positions. Uh, you know, they might be, you know, twenty, thirty, sometimes greater percent of the accounts. Um, you know, which uh is something I don't do anything resembling that now.

Um but at the time, you know, that that is that is how I how I started. Uh, you know, flash to the, you know, present day, which is twenty years later, um, not quite to the month, but in a few months it will be. Um you know, and I have A couple strategies. Uh, you know, I'll still play some discretionary trades, certainly, but my my primary activity is quantitative.

and I have a flagship strategy, uh, one that's been live for uh over four years, you know, and has an extensive back test with, you know, survivorship bias free data and and much more that I could speak to. But That is Um, you know, that's my primary uh strategy that is very similar in a sense to my early trading style, but it's quantified. All the positions are typically small. There are no concentrated positions.

um you know nothing nothing ever over ten percent and and uh in fact the max is lower than that. Um and the typical position is much lower than that. So it really uh, you know, I see the evolution of my trading as you know, managing a trade or a few trades at a time to managing strategies. Uh, you know, the strategy I just spoke to, another strategy that is in the final stages of development is also quantitative, but it's very different. It's a machine learning based strategy.

uh it will be up to uh you know, sis remains to be seen, but somewhere between thirty and fifty most likely. strategies all or or systems rather all running together as a meta system, you know, under one strategy. So uh that that's yet another thing and and that'll be primarily intraday. Uh but

You know, bottom line, now I'm really the manager of strategies. Uh the trades themselves are less significant. Obviously they're important that overall they work, but um It's really making sure that the risk controls are in place and are working, um, that the live trading is um Is in line with the back test. Uh there's lots of ways to back test poorly. So, you know, that's um probably more than you asked for, Aaron, but that's, you know, the kind of the the quick uh

evolution of my trading. Yeah, that's excellent. And I want to sort of get into that a little bit deeper. But before we do Um, if you were to give sort of a discretionary trader an overview of quantitative trading, how would you actually sort of explain this? Just if we can be clear on this before we sort of get too much deeper into it? Sure. Well it's a great question. Um and deserves probably a a compact elegant answer. Um, you know, and let me try to do that in a second. Let me this might be

This might be helpful because I was that discretionary trader and why did I get into quantitative trading? It's because I had this question, which was I'm in a trade, right? And I've I've I've entered this trade as a discretionary trader. What does that mean? It means that uh you know, I'm using charts, I'm trading on principles, right? I'm trading uh this trade because uh you know

Volatility contracted, n there were narrow range bars, uh the trend was clear, whether up or down, uh on and on and on. Bunch of principles that you know formed uh enough of a case. You know, they built enough of a case to justify this trade. But now I'm in it and I know my risk and so forth. And, you know, I'm hip to proper position sizing of which there are many approaches.

But what I never knew the answer to was, you know, what what's or what I was always asking was what's the optimal exit? Like when When should I get out?

you know, is it when I feel like getting out? You know? Or that's so there's so many behavioral biases that work against the discuss discretionary trader or your average discretionary trader. You know, your your fantastic discretionary trader can overcome all or most of these biases of of not selling too soon and not holding on to the loser, the disposition effect and all these kinds of things.

Um But bottom line, I just I knew that there had to be You know, based on my approach, if I made those, you know, conditions, if I made them rules. There must be an optimal exit. You know, even if the exit evolved later on uh or was a little bit different in the past, right at this moment there probably was an optimal exit and I didn't know it. So that was that was the the germ or the seed that was planted.

that uh you know that I carried for a while without doing anything about it. Uh but ultimately, you know, ultimately I did uh you know make the move in into finally um getting over my fear of learning to code. and and diving in. Um so I certainly can, you know, speak to any of that more, but in terms of you know, the benefits um you know of of quantitative trading. Uh one can really um

aim to be optimal or at least near optimal. That's how I think of it, uh, rather than than trading suboptimal. I mean, uh what it whatever area of business you look at, you know, or uh you know, let let's take um you know com com uh conservation, for example. Uh, you know, let let's say uh you know uh a company is is trying to, you know, um Uh shave uh expenses and and whatnot, you know, efficiency is really

you know, is is almost like the go to area. How can we be more efficient? Uh, you know, UPS drivers, uh I I think uh somebody was telling me about a report where UPS um was able through, you know, efficient uh data analysis shave something like thirty million miles off uh there. their driver's routes uh in a recent year. I mean don't quote me on that, you know, but that's that's the idea. So, you know, as a discretionary trader, you have to be left wondering, what could I be doing better?

Uh, what's the optimal exit? Um, you know, gee, maybe maybe this this pattern, this reversion pattern, this pullback would have been better entered, you know, a percent lower. Uh, you know, these are the kind of things that you just never are gonna know as a discretionary trader, whereas a quanti you know, quantitative trader, uh, you can know. Uh and then you have to of course be sensitive to um

Do you really believe in your in your your back test? Um is it signific is it statistically significant? So that you know, that's another area of there are a lot of pitfalls to become a quantitative trader if you don't do it right. Um But um, you know, that that's that's the the gist of it in my opinion. Okay. Awesome. Thanks for for breaking that down, David. Are you ready to get serious about trading? Then join Tasty Trade, Investopedia's best platform for options trading in 2026.

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So what were some of the first steps you actually took in transitioning to a systematic sort of slash quantitative trader? Right. The the first step, I believe, was having a huge list Of things I wanted to test. So um, you know, as an often pattern based trader, uh there were a lot of patterns, whether it was, you know, a you know, technical uh

uh, you know, triangle pattern, you know, symmetrical or ascending or descending. Um obviously I wanted to uh have conditions that surrounded that. So I just started, you know, obviously

taking my discretionary approach and putting it into a list which essentially were conditions. I mean I don't think I would even would have called it that when I was creating that list, but I put these conditions in a list and um you know and then I I found somebody who was willing to help me code up just kind of like a master template.

And uh that was invaluable. Um So that that was really the you know, the the the first foray was was having somebody on the other side of the bridge uh code this up for me, you know, at a pretty reasonable rate. and um you know and and then give it back to me and, you know, with a f with a couple notes and then it was I was off, you know, and it was just a matter of um you know, of of starting to experiment. Um and I certainly um

Did not have any great testing process at that point. Um I was uh um I was smart enough to use out of sample data. Uh that's a that's a big thing. So I don't wanna go all into, you know, those in proper testing procedure, but um, you know, that that's what I started to learn next. W you know, apart from the coding, you know, I had a head start on the code itself, at least in that first program. But um You know, f from there it was really well, you know, what's

How many rules, uh, you know, how many data points do I should I have? You know, uh, how many rules do I have? Do I have too many rules? over too little data. You know, these kinds of things you start to learn. And um, you know, I started to read about trend followers, even though I'm equities Uh and uh you know, one strategy is reversionary for the most part, uh, another strategy is quite varied. Uh, but Uh, you know, futures trend followers are really some of the earliest systematic

programmatic, you know, quantitative traders. Um and I started really reading about them and reading about the turtles and uh, you know, um really just the history of that early generation of systematic traders. Uh, many of whom, you know, could write their systems on the back of a napkin. You know, simple. Okay, sure. So I don't know if this sort of this question may be sort of beyond the depth of this interview, but

You mentioned earlier that you were sort of looking for your optimal exit, right? So what sort of process did you actually go through to discover the sort of the most optimal position to place stops for your actual trading method? Right. Well, you know, it's funny. It's a great question. And uh stops are one thing that of course as a discretionary trader trading larger concentrated positions Uh I found Um we're we're getting in the way.

of of some of the best uh the best edges. I started to, you know, create a bunch of edges, you know, a bunch of systems.

um that uh you know m most of which I did not ultimately keep, uh the best of which did end up in my, you know, in my meta system, which I still trade. Um But um, you know, but stops were were something that when I traded a lot of positions with very small size, I got rid of because often the uh at least with my systems, the the greatest probability of a reversion would happen essentially um

you know, at uh uh at at at the stop at the stop point. So that that's something that uh you know would be easier to to see with with some data. But in terms of of how how I determine the optimal exits You know, in forgetting about stops for a moment, just simply uh let's say uh a limit exit or even um you know, in any ways you could think uh of of a swing style exit, whether it was, you know, end days later, n bars later.

um uh various uh you know, various exit styles based on indicators, um, you know, on and on. Uh pattern based uh um exits and and certainly I tested stop based exit two trails and w and whatnot. But um It w the I would say the process of of finding that optimal exit was was really just testing all of them. Uh and you find out early on as you get into quantifying trading that and and systems and edges and so forth.

that you you you absolutely need to be incredibly organized uh with with your with your with your record keeping uh of results, uh, because you you don't wanna just constantly change indicator values, um you know, and and not keep track of of what you're really learning. You're not gonna learn enough as you combine con multiple conditions together. If you don't understand each one separately and how it's starting to interrelate with the other,

um, you know, you're you're gonna be lost, you know, as you try to quantify. So um it was really breaking down Uh entries with a dumb exit. a so called uh you know naive exit. I I would eat even in my code I would I would call it dumb exit. You know, I would comment out, you know, some uh some text that that you know wouldn't be active code, but uh that's what they were called dumb exits. So I would trade you know, I would I would test Entries over dumb exits. I would trade exits.

over uh random entries, you know, rand randomly generate an entry. Let's say, you know, um day one uh has a bar uh in that low value and high value, you know, the the whole range of that bar, any any point on that bar, for instance, could be

uh could be an entry and that that's that's a random entry test that or one iteration of a random entry test. So Just basically uh to be more simple and answering your question, you know, tr testing things separately, keeping good records, and finding out, at least based on the ideas that I was testing, uh what really was optimal.

Uh and it was typically a shorter hold time than I had anticipated. I I anticipated that um actually with my approach a longer hold time uh you know just some number of days more was probably going to be a boon. You know, it was gonna be uh superior, but actually a shorter time, a whole time was was typically superior um, you know, in in my findings and, you know, in my approach. So it was it was illuminating and it was very um some cherished notions tested well.

other cherished no s notions had to be uh tossed into the fire. Sure, sure. That's really good, Dave. So now that you are sort of, you know, pretty much one hundred percent a quantitative trader,

What would you say this has allowed you to do that you couldn't do previously as a discretionary trader? Like has it freed up sort of more time? Um are there any sort of benefits in in that regard?

There are a lot of benefits. Uh I would say, yeah, time was not the first one that came to mind, but that is one. Um geometric um Geometric sizing Um, you know, while I as a discretionary trader was familiar with position sizing and and had written you know, had read rather uh, you know, all the latest and greatest uh, you know, research and and so forth, had built risk calcs and

and so on, um, I still did not feel like I was managing my capital um as efficiently as I could, whereas once I was quantitative, I I have, you know, a set of code for the signals, uh, all aspects of the signals, uh, you know, which I could go into deeply, but There's another set of code that's just the position sizing and risk management controls. So really being able to control these and study them ahead of risking dollar one with this new approach was was

Fantastic. So I would say time is a benefit in the sense that, you know, I am not um You know, the uh discretionary traders, you know, have that that twofold job of whether it's the during the daytime, you know, you're working during the daytime, often, you know, even if you're not actively trading major decisions, you know, you're typically monitoring things, maybe looking for other things.

Uh, you know, and then the markets close and your day begins all over again with looking for opportunities and and watch lists and alarms and on and on and on. So that process um i is is incredibly streamlined now, uh, where it's really literally, you know, collecting uh more data and and you know, pressing some buttons and uh you know, and then I have a flat file I can upload uh the next day.

um or or I can simply uh in another strategy um you know simply deploy uh you know it it uh no no order uploads n necessary. It's it's um uh it's gonna be interactive throughout the day. So, um, you know, I'd say time is a benefit. Another benefit is risk, uh, in the sense of Uh Being able to study All of these systems, um, you know, together. Uh I I do believe in multiple systems uh you eliminate any single point of failure.

uh that's that's crucial. Uh so multiple systems together, uh you know some some people will say that is the uh you know that that is the one free lunch, you know, a form of diversification in the market. That uh what was I gonna say about that? Basically the being able to study the interactions of those systems from a risk level. In other words

Uh and and you can do this as a as a discretionary trader as well if you have enough historical trades. You can look at all your trades and you can input them into some Monte Carlo program and essentially Monte Carlo uh is uh going to give you alternate trade histories based on those trades.

but randomizing them into a different order. And there are some versions of Monte Carlo where you can add substitutions uh that are statistically similar, uh, you know, and there are all sorts of variations. But the bottom line is Monte Carlo analysis allows you to look at your strategy, whether it's real trades historically or uh hypothetical trades.

and scramble them all up, look at alternate trade histories, and you see not one theoretical equity curve or one h historical equity curve, but you see um many. You you can see, you know, two thousand, twenty five hundred, five thousand, ten thousand if you want to go just absolutely nuts with the Monte Carlo And you can learn a lot about your approach. And you can learn that wow, you know, actually Uh let's say it's historical trades.

Uh I might have had a drawdown. Uh this is just hypothetical example. You know, I might have had a drawdown of you know, fifteen percent, uh, you know, during the uh you know, last X number of years I was trading the strategy. Well, you know, you do Monte Carlo, you might realize that you have actually a you know you have a ninety five percent confidence of um Of a 40% drawdown, you know, that you didn't know about because uh a Monte Carlo analysis shows you that if your trades in the future.

are statistically similar to the trades in the past. then uh you might be assuming more risk than you knew about. So that's that's just a beautiful thing about quantifying um trading is that you can look at a risk level and you can say, okay, you know, what what should my position sizing be? What should my approach be uh if Um uh you know, if I want my uh my max drawdown, my max expected drawdown to be no more than let's say fifteen percent, you know.

in all of those uh all of those trade histories, you know, one hundred percent confidence level, uh or you know, or nine ninety-five percent confidence level. So one one in twenty years, uh, you know, uh, I might get a um uh you know, a a drawdown that bad or or possibly worse. I mean, so uh the benefits are so numerous. Um and, you know, quite frankly, I just I love data. This is

This is already being called the decade of data and we're only halfway through it. You know, when people came up with that term a couple of years ago, at least I'm sure, probably earlier than that. But Um You know, this this is an incredibly I think this is the beginning. This is an incredibly fertile time in terms of um data markets, um data streams that are becoming standardized. Um and and usable as a trader. So uh I I'm really glad that that I did make that switch. Uh it's not

that there's any flaw with discretionary trading. Uh I will still, like I said, occasionally make a discretionary trade, uh, something that I don't have quantified perhaps, but I see the opportunity and that's very compelling and I like the risk. However, um, you know, I there just just so much that I wanna do um, you know, in in terms of quantification uh with financial data and, you know, and sentiment data and so on.

Yeah, there were so many good points that you brought up there, David, so that was really good. Um obviously a good system or a strategy is one that is robust. So how do you actually determine whether or not a strategy is robust? Right. Uh it's it's uh it's a great word. Um I I would say that Statistical significance is is really then the test that it that it must pass um

you know, a high information ratio. You know, they're really they're really metrics. So uh let me let me give an example of a couple. First of all, You know, there there are some trading systems that people have or or you know, systems where I've seen in in books, people write a a book about trading systems. You know, and they'll include some systems and and you know, maybe they you know, in a couple cases they'll they'll have a system that they they put out there and it has

You know, it has a a hundred trades historically. Uh, you know, it has a number of rules. Um, you know, and there's no mention of statistical significance. Um that that's important. There needs to be in any good system, in my opinion. Um a lot of degrees of freedom. So in other words, the number of data points, uh number of trades, um compared to the uh the number of rules um in uh embedded in the system, uh all the conditions, all the decisions from entry to exit and so on. Um

There ha there there there have to be mostly degrees of freedom. Uh there cannot be too many rules. Uh so a robust system will be statistically significant at a very high confidence interl interval. Uh, you know, and there are programs that you know, will enable you to uh you know to do that within uh the software or you can just do that within Excel. I mean there are a lot of ways to do that. Um certainly there's plenty of literature on that.

Uh you know, another um another aspect to a good system, a robust system, is um You know, there's so many. I w I would say ab ability to uh to translate to other tradables. So um, you know, it it should work across not necessarily a lot of asset classes. I'm I'm not one who says that, you know, this equity strategy should work on

uh you know, on on energy futures, you know,'cause'cause there are a lot of structural differences. Um l a lot of differences between uh the business of money management of equities uh and the institutional sponsorship uh and demand that that can drive reversionary strategies and equities, whereas

that uh doesn't exist in the same way in you know various commodity markets. But but I but I do think, let's say within equities, um a a good system or a great system, a robust system should work across uh most equities of the same class. You know, most uh you know, mega caps uh you know uh strategy should should be um significant across them. Uh you know, another aspect which might be uh too um obscure, Aaron, so you can, you know, shut me up if you need to, but uh survivor survivorship bias.

Is something that the quantitative trader has to face. In other words, if you're developing an equity strategy across stocks. and you are testing across today's S P five hundred index components, you know, those five hundred stocks that exist in the index today. Uh and thinking that historically that's what you would have been trading, that is just not even close to the case.

Um you know, w where's Lehman Brothers? Well it's not gonna be in today's S P five hundred. Uh where's Enron? It's not there. Where's WorldCom? You know, so you have to know as an equities quant trader, where are the tickers that live in infamy? You know, though those are delisted. And so if you really want to um

to go the extra mile. And in fact there was a point, kind of another gut check point, uh when I was developing uh, you know, one of my uh flagship str my flagship strategy, one of my my early uh you know, meta systems. Where I said, you know, what would Steve Jobs do? You know, like would he would he go

Uh, yeah, you know, it's time to build a Survivorship bias free database, you know, because'cause that's gonna give me more information. And the answer, you know, in my head anyways was, well, of course he would. You know, he's he's he's a perfectionist. He's gonna want to know the absolute best approach to um uh, you know, to building this this product, this this strategy.

Uh and uh and therefore go the extra mile. And so you know, that was one of those things I did um not from the very beginning. I developed a strategy and then I said, you know what, I don't believe it enough. until I add in the blow ups, you know, I add in the Enrons and the WorldComs, uh, you know, or the shorts that might have been taken over.

uh acquired and gapped way up that aren't in the index anymore. So, you know, that's to me another real key for an equities trader um to uh to consider and hopefully test across.

uh are those delisted symbols. And I'll tell ya, that's a real pain in the butt because um you do if you're really gonna do it to the daily level uh or even beyond, but certainly let's just say the daily level, uh, you you do need to know what are the constituents on any given day, you know, um, you know, uh April third, uh, nineteen ninety seven, you know. uh if that was a business day, you know, what what's in the index that day? Well it might not be the same the next day.

So um that that's quite a process to put something like that together. Um but uh but I think worth it and uh you know yet yet another way to to ensure that you have a robust system. Yeah, again that was a really good point you brought up there. I was actually gonna ask you about survivorship bias, so yeah, that was that was good that you mentioned it.

Um, have you ever come across a situation where sort of trading a particular strategy live performed nothing like the results it produced when going through your back testing process? Um, you know, happily the answer to that is is mostly no. Uh in fact, it's А мор ріцен ферма феноменон. of uh of of not having had that experience happily, uh really having my uh uh you know my performance live be what I expected um in in the uh you know from the back test.

Uh however, uh when I have uh altered execution um even after study, I have found that's an area where um you know, where the the live results have not um have not, you know, emulated uh the the back test uh to the excuse me, to the degree I like So that's something that you know, when when I experience that, you know, I have to take that offline, revert back to, you know, the the original, uh, you know, proven uh methodology, you know, for execution and then

um you know, and then go to the woodshed on uh on what I was experiencing with you know, with the change. So Th you know, there there can be some surprises like that. Uh I'm certainly not immune to them, but happily, uh my you know, I I took s really two years to develop my my strategy, which, you know, included um, you know, many systems together, uh, the Striversify strategy and that um

when I rolled that out, you know, it was really after about a two year process. Um you know, so so I uh I try to look at every I tried to see every facet of that diamond, if you will. And um you know, once I rolled it out I was I was happy with uh you know with how it uh how it performed. You know, it brings up the point though of what uh how how does one measure, you know, performance

uh, you know, with a quantitative strategy. And, you know, th that is something that, you know, traders will differ on uh in terms of, you know, how um you know, how best to manage a strategy obviously depends on the strategy and the nature of the strategy. Um and it could be as simple as having sliding time windows for something like winning percentage, you know. uh if you see uh that your winning percentage is um you know dramatically different by some standard deviation from your expected

winning percentage over a certain number of trades within a certain time window, you know, that can be something to examine. You know, so uh having some kind of dashboard, some kind of uh method at least for uh checking your system health. uh, you know, uh that that's important as a quantitative trader rather than assuming, well, you know, hey, it's it's worked, so it's always gonna work. Yeah, yeah, too right. So that's that's really great.

Um another topic I'd like to bring up with you and that is uh fractal patterns. Now I don't know too much about fractals, um, but I believe it's something that might be relevant to your trading. So would you be able to explain this a little bit? Sort of about h what this is and how it's relevant? Yeah, it's well that's a huge that's a huge question. It's a fun question. Uh I am no uh fractal expert, you know, but I I take inspiration, uh

from you know from those who are are those experts. In fact, if uh one of my favorite quotes is from the late mathematician uh Mandelbrot who said that bottomless wonders spring from simple rules repeated without end. And, you know, I can imagine I don't know the actually the context in which he s he said stated that But I can imagine if it was a lecture that, you know, he was uh displaying uh, you know, his uh one of his fractal sets or

uh, you know, looking at uh a a coastline, you know, and expanding and y you know, you go closer in on the coastline and it keeps getting longer. You cannot quite measure it because uh it just keeps uh growing, but nevertheless the um the patterns are are repeating almost on, you know, from any perspective, from any dimension.

So certainly in trading, whether one's a discretionary or quantitative trader, I mean in fact as a discretionary trader, you start to see that. You say, Oh, well look at this weekly pattern.

Um, gosh, I've seen that on the daily charts before, you know, and then you look intraday and you say, Well, gosh, here here's that same pattern. You know, if I did not have uh, you know, date levels at the bottom of my chart, if I did not uh have you know year uh markers separating, you know, uh this year from that year on this large time frame, I might think this is an intraday chart. Um, you know, so there is a definite widely recognized fractal nature to patterns um you know in in markets.

And uh in terms of my own uh uh you know, my own coding and so forth, that is something that is you know, largely it's a great interest to me. Uh lots of books on the subject. Uh as you might tell I I I love my trading books and I I try to read far outside of just trading, uh, you know, which this would be one of those one of those subjects that that applies in so many fields, disciplines and and so on. Um

But that's something that I that I am um you know, it's kind of in in I wouldn't even call it development phase. It's more the um you know, wood woodshed type of phrase. That that's a that's a That's an expression from my music days. You know, i if you hear some, you know, great musician playing something and you go, Wow, you know, I cannot do that. What is that? You go

lock yourself in the woodshed, you know, and you you stay there until you figure it out and then you you know you methodically work on it over time to you get it. So, you know, fractals and incorporating uh fractals beyond my conceptual understanding and, you know, certain aspects which maybe they're already, you know, um

coded to some degree. Uh you know, that that's something that's uh kind of in the woodshed. I I I do expect uh uh you know, maybe have a production strategy that is uh largely based primarily on on, you know, uh some some fractal formulas uh as as the key driver.

Okay, great. Yeah, that's a really good answer. So again, Dave, thanks very much. But um Now I couldn't have you on without asking you about um a quote that Zack Hurwitz mentioned on episode eleven which he learnt directly from you and that was uh the eagle, the donkey and the mouse or the concept of those three things.

Right. So would you like to talk us through what this actually means and how you've used that to view trading? Well that's funny. I um I love Zach and and certainly uh respect you know, respect his uh um you know, his approach and and and unique ideas so much. Um You know, in terms of that that quote, it's funny that is, I believe, from, you know, an older interview I did with um with the Tab Group, uh, Quant Forum and

You know, uh uh I was probably speaking off the cuff. You know, I'll tell you how I relate to that though, which is Um certainly as as a trader, uh, whether uh you know, or this could apply to anything, running a business. Um, but certainly as a trader, I think you have to be

you have to be all of these things, right? I mean, as a quantitative trader, for example, um you know, you you have to have the vision, uh, you know, you have to have that that that perspective, that that high, you know, eagle like um vantage point, uh where you're looking at markets and you bring some kind of concept

Uh you know, two, let's say you're coding. Um And, you know, that that is uh uh you know, in one sense, um being the eagle, you know, i is is having that uh that vision of, you know, what your strategy might look like uh or or some concepts you're you're you're gonna take uh you know take take to the woodshed so to speak and and code up and and see if they're really viable. Um, you know, the mouse, you know, y that the dollars in the details is an expression that uh

Uh, I don't know if anyone else says, but I say to myself all the all the time, you know, rather than the devil's in the details, you know, as a trader, the dollars in the details, you know. Um, you you gotta be a mouse and I mean you care. as a quantitative trader anyways, about every uh period, comma, semicolon, forward slash and so on that's in your code. It's all relevant. Um and if it's not relevant then then you're not a very elegant coder. But anyways.

You know, so y you really do have to pay attention to the details. I I would put, you know, uh survivorship bias obsession with with that and eliminating that from ones uh back testing. I would put that in the mouse category, you know, in the dollars in the details uh bucket because it really does uh does matter, you know, what you're testing over. You know, these kind of obsession with details uh also make you the donkey, you know, because

Uh it's it's a lot of grueling work. I mean it's um It's easy to test to back test poorly. Uh, you know, we could talk about that for an hour. Um but i you know If if you want to test well, you really do have to uh put in those hours that are uh you know, mentally backbreaking hours, if I can, you know, uh use that phrase. And uh and and and that's where the donkey comes in. So

you know, uh probably using it a little bit differently than I've did the first time and probably differently than Zach put it, but uh, you know, that that's how I relate to, you know, to those roles. You know, there's l there's a lot of roles, whatever kind of trader you are. And Um you know, it's a demanding it's a demanding job, whether you're quant or whether you're discretionary. Yeah, no doubt. That's that's really well said. So

Let me ask you this, what would you say to someone who is perhaps interested in adapting a quantitative approach with their trading, but maybe has little to no knowledge of actually how to code? So I mean, is it fair and for good reason that many traders may find this daunting? Yeah, yeah. Well certainly I found it daunting. Um and I don't know if it would be easier today or not. I you know, I c i uh I feel a little glib when I say I think it would be easier.

To cross that bridge today from a discretionary trader to a quantitative trader. Uh, and I think that is probably the case. There are so many. uh even sites now that are giving you the opportunity to, you know, uh have access to some some very unique uh approaches to to quantifying, you know, financial data. So I do think it is easier today, but nevertheless it is daunting because uh it's it's not something uh typically where you're gonna dive in and you're just gonna end up with um

with something fabulous uh that that is, you know, y y just the ap optimal expression of of everything you had hoped for, you know, as a as a trader. Um but I would say that if you've traded enough to have experienced pain uh as well as some success. Uh, even if you are uh you know, alternating, you know, in in those experiences and you're essentially breakeven, but you do recognize um you know, you have the ins and outs of the process of trading and so forth.

You know, y you could uh you probably have an advantage over somebody becoming a quant with no concept of what the market is like, um, how behavioral uh it can be and how driven by emotion it can be. So I don't know if that answers the question, but um I I would say that it's always gonna be daunting. However, the tools today, you know, they're they're out there uh and you just have to um go in with it.

uh go into it with the attitude that this is gonna be a process. This is not gonna be something where, you know, in in in a month I have my strategy. You know, it it's probably gonna be some months of of diving in. Uh and last thing I'll say about that is, you know, I've spoken to some um Or uh a specific example. I remember speaking to one uh young kid, real bright, who had just graduated

from a pretty significant uh university uh with a with a recognized uh you know economics department. And He uh had quantified uh a strategy that um that used uh weekly data And in had no account in its risk or for for anything that came between these these weekly data points. So you can imagine uh what can happen in a week in, let's say, the equities market. Um, you know, uh a drop of of ten to twenty percent can happen inside that week.

uh that doesn't even appear in the weekly data. Um, you know, if if those kinds of um dislocations are okay with you, you think you can continue trading them out, you know, as they're happening, then then, you know, maybe that's for you. But um but it does help to come to the uh you know, quantification process to the coding process with some idea of how markets work, some experiences, rather than, in this kid's case, uh a real bright guy but

Um, you know, yeah, he hadn't experienced drawdown. He hadn't hadn't uh seen um, you know, his savings, his account, his hard earned money, whatever, uh start to evaporate um, you know, uh intraday. And that's you know, that's as I'm sure you know, as any trader who's traded a number of trades knows.

you know, that's a formative experience. You know, it's a gut check moment and your insides become, you know, it's it's like alchemy. Your insides, you know, become uh made of different material after that happens. uh you know, at least uh they're going to be if you're gonna continue trading. Yeah, for sure, Dave. So This has been incredible. So again, thanks so much for coming on. Let's just go to a couple sort of shorter questions and then we'll start to wrap things up.

So from what you've seen, what would you say sort of causes the majority of traders to never reach a high level of success that they sort of generally intend on reaching when they set out into trading? Right. That's wow. That's y you have good questions, Aaron. That's surprisingly. Uh That is a great question. I I would say that um You know, we all come Or I'll certainly speak for myself. I I imagine many other people um are like this. We come to the market with

with these rose colored glasses, right? You know, uh I I still remember looking at my father's T V and seeing the ticker tape, and this is like probably twenty one years ago now. You know, and and I certainly went into markets thinking, wow, this there's so much opportunity. uh once you get, you know, thrown down a staircase or two and you're hurting

uh, you know, you realize, wow, you know, this risk part is pretty important. You know, I that was unfun. I do not want to live through that kind of trade or that experience or that drawing or whatever. Again. Um, you know, so so your your lenses constantly change as you develop as a trader. And, you know, some people are uh are just gonna say, you know, I I just don't want to dig that deep. You know, uh I it's just

Uh it it's too challenging. Uh I I I'm gonna have to look at my psychology or you know, my my own belief system. I'm gonna have to uh examine that. You know, come on, that that uh that that's that's nothing to do with trading, but of course

Uh it's everything to do with trading, especially as a discret as a discretionary trader where you're more prone to succumbing to to biases and usually with larger positions. Uh so um You know, I would say that it's it's uh just a a matter of of of digging deep. You know, I have met so many bright people. I work with some incredibly bright people, uh, and I like, you know, I believe in fun just like systems can diversify um one's strategy, you know, by being uncorrelated, for example. And

uh you know and and s removing a single point of failure. I like surviv surrounding myself as much as possible with people who are brighter than me or think differently than me. And in this way I'm getting exposed to um you know to more uh more ideas, um, more knowledge, uh more perspectives. Um and uh my larger point with that is what I guess um It it's it's a matter of uh you know uh uh of of digging deep and and constantly exposing yourself to um uh new ways of thinking.

Um and and I think that that's more than some people, you know, really really are up for. Uh, you know, to me that that's probably the the number one reason. Um it's it's overcoming a lot of biases and taking a hard look in the mirror. You know, it's uh it it can it can be rough in the early going. Uh and it can be rough later on, but i you know, in just as a trader, you know, there are bad periods for any approach

trader strategy, what have you. Uh, but at that point later on, you should be very well prepared for them and you should know, especially as a quantitative quantitative trader, you know, is am I in my risk parameters? Is this drawdown normal? Um, you know

on and on. So uh more more more than you asked for probably, but um I I I would say the the digging deep, if I can put it in two words, that's what's required. Um and, you know, some people are are gonna leave it at a certain level and and um and and and not, you know, push to be where maybe they could be.

Sure, yeah, those are some really great points. So now as you mentioned, you're a you're a big fan of of reading a good book. So are there sort of maybe one or two books you'd maybe suggest as a good read for upcoming traders?

Maybe one that it uh perhaps it isn't even directly, you know, a typical sort of trading book. Right. Yeah, boy, that is so um that's so great. You know, I don't know if I can pick one. Let me just spit out, you know, a few. Um I thought that Fortune's Formula, it's not a trading book, but I I found that to be fascinating and um really helped drive me to uh to want to quantify finally. So this is some years ago, but

Um, you know, I found that to be excellent. Uh I'm not a trend follower uh in the classic sense of that first generation trend follower, systematic trader. um, the turtles and so on. But uh I found that the book Trend Following, uh, by Michael uh Covell, you know, I found that to be uh fantastic. Um so many great anecdotes um of the developers, you know, of the actual traders behind uh behind the systems, so to speak. Um I thought that was a very well done book. I I like that a lot.

Um, there are so many. Um, you know, the physics of Wall Street, that's a fantastic overview uh for someone who just is interested in the, you know, evolution of you know, of really quantification largely. Uh and some of the different players from different fields who have um who have come into the field uh you know o of trading and and uh and so on. Um, you know, I could go on and on, but uh those come to mind um

Off the top of my head. Okay. Sure. Yeah, those are excellent. So I'll be sure to put a link to those in the show notes. Um all right, Dave, well we should probably wrap this up. I mean I'm sure I could keep going on and on, but um your answers have been unreal, so thanks again for going into so much depth with all of your answers. It's been awesome and I've no doubt listeners are gonna take a lot away from this interview.

Aaron, I I sincerely appreciate it. It's great talking to you. Uh uh you know, I hope we can do it again. You've come to the end of this episode of Chat with Traders, but don't worry, more great episodes are on the way. with each great new episode. And we'd like to- Yeah.