111: David Bush – Strategy Objectives, Statistical Significance, and Market Behavior

Chat with Traders is brought to you by Trade the Pool. Did you know that every decade the market reinvents itself? Online brokers opened the doors. Mobile apps made trading seamless. and commission free trading erased barriers. Now a new era has begun. Meet, trade the pool, limited risk trading. And now you also have unlimited time to reach the profit target. From now on, your trading risk is capped. and your trading opportunities are limitless.

Trade the pool funds home-based stock traders with up to$200,000 in buying power. That means you can trade larger positions and scale your strategies without risking your own savings. It's time to trade with more capital, making it truly worth your time and effort. Ready to trade the pool? Click the link in the description and join the stock trading revolution today.

Are you ready to get serious about trading? Then join Tasty Trade, Investopedia's best platform for options trading in twenty twenty six. Options, futures, and more. Tasty Trade has everything you trade all in one platform. Get low commissions, including zero commissions on stocks. So you can keep more of what you earn. Trade smarter with advanced charting tools, a pre-built strategy selector, risk analysis tools, and more features. Visit Tastytrade.com slash.

Tasty Trade Inc. is a registered broker dealer and member of FINRA, NFA, and SIPC. Podcast.

What's up crew? Welcome back. Welcome to episode 111. This week returning to chat with traders for a second time is David Bush, who was first on episode 23. David first began as a discretionary trader more than 20 years ago, but over time he's developed into almost purely a quant trader, and he's exceptionally good at what he does. David's been the first place winner of two real money trading competitions in recent years.

Last time David was on, we spoke fairly extensively about his path as a trader and a high level overview of his process. This time around, we covered plenty of new ground, exploring David's process in greater depth. Also, I particularly liked David's comments towards the end about intensity, not time. That'll probably make more sense when you get to it.

But anyway, uh full show notes for this episode can be found at chatwithraders.com/slash one one one. And I really hope you can gain something from this. Please welcome to the podcast, David Bush.

Hey Dave. How's it going man? Good. How are you doing today? I am doing very well. You're a bit under the weather, are you? I am, yeah. It's uh it's been a stretch, man. It's been a stretch, you know, just uh busy and pushing it so under the weather, but you know, just uh I sound horrible but Otherwise I'm all right. No, you don't sound too bad. All right, good. What is it? Just a a basic cold? Yeah, it's a basic cold. Yeah. I uh

Normally I normally avoid it or it's very light. This you know, this year th or this winter I've been uh You know, I've been sick uh twice like this in the last like three weeks. So it's just this one was self induced, I think, from just uh trying to fit this R workshop in at the end of the week. I mean, you know, I'm glad I have that.

knowledge now, but uh I'm paying for it, you know what I mean?'Cause I had to fit everything around it and you were you were caught in my whirlpool of rescheduling and anyways, so Yeah. Self-induced problem. We've you've got me interviewing you at seven AM on a Sunday morning. Oh, well, you're the man. I'm so sorry. I was about to ask. And then I yeah,'cause I didn't do the calculation in my head, but wow.

No, not a worry. Not a worry. I appreciate it. No place I'd rather be. Have you ever seen this time of day before on a Sunday morning or uh Of course, of course. I'm usually up well before this anyway, so I'm just teasing good. Excellent. So how was the uh workshop? How was that? Yeah, it was uh you know it was an introduction, uh, but it was two days of an introduction, you know, so very two very full days. Um my

You know, there's there's two there's two ways I look at it. There's my goal, which is first of all just to learn about R, you know, I'd really knew not not a lot. And then, you know, other than yeah, statistics focused and whatever. Um but I my goal is really just to be a better manipulator of data. I mean, we might have talked about this the other day briefly, but you know, just uh have my data set up uh in a much more um, you know, usable, uh automated

you know, updating way. And then to be able to actually hands on just more quickly manipulate it. So so basically I feel like I right now have the skills. I mean it would I'd be very slow at first, but that's how I'm gonna cut my teeth. I have the skills to, you know, just import and join a ton of Excel files. And it's you know, it's so quick, you know, in R. It's just like it's instant, you know, and you can hyper thread if you want and all that stuff. So

So that's you know, I feel like great, mission accomplished. I'm gonna you know I'm I'd need to sit down and spend hours doing it, but if I can just become awesome at mutating and joining Excel files Um, that'd be great. You know, I mean and then I gotta peek into the the the other levels which are like, well, you can do anything, you know what I mean, just like most code, you know, it's w you can do insane machine learning, you can use it

for graphic uh portfolio analytics, whatever, you know, and and obviously there's a lot of packages that people have already created, which is cool. So I gotta avoid the temptation to go deep into the deep into the weeds, like, you know, trying to solve the world's problems uh through R and just like have that one skill, you know. So I I thought it was great.

Oh, that's excellent, man. That's very good. Yeah. So you hadn't coded an R previously before this, like at all? No, exactly. Exactly. No, I've never you know, I didn't have R installed until, you know, like twenty four hours before the thing. So no, didn't know R and uh but I feel um You know, like that that data manipulation thing, I feel like

I mean I I know I'm gonna run into ball into walls, and you always do. When you're coding, you know, uh anything y y it's always longest at the front, which makes it tough to go through, you know, through the barriers, but We we did in class, you know, it was a very hands on thing, you know. Um basically the guy would talk for like five or ten minutes and then be like, All right.

Here's what you you're gonna do now, you know, and that's the way the whole two days was, which was good, you know. So I already succeeded at you know, doing probably seventy five percent of what I wanna do anyways. Now it's just getting good at it and, you know, setting up some scripts and Yeah, yeah. Just putting in the work. Yeah, I'm excited. Yeah, man. Oh, excellent. So I know you've got a programmer who you work with. Yeah.

Do you d prior to this, did you have any programming knowledge yourself? Like were you coding in a different language beforehand? Well what what I've done, uh I always think of it like he's a capital P programmer. I mean like he's trained, he's that's his whole schooling, that's his PhD, he knows Probably.

tons of languages and and is facile and most, you know. For me, you know, I I started coding in just proprietary trading platform languages like trade stations, easy language or uh mechanicas um language, um, you know, what else? Uh trading blocks. You know, so in other words, they're all just kind of proprietary, trading specific. Language is uh a pure language, something I I have not

ever learned. I mean I I have some Python scripts and so forth, uh, and some utilities that that use Python, but that's because the PhD programmer wrote them for me. Um so but uh so I'm excited. I'm I I actually, you know, I mean that's another level of just well learning R. and be able to do things. Obviously it's much easier to grasp this language um

you know, having known the language of other platforms and syntax and and stuff like that. So it becomes, you know, it felt like familiar actually, which was kinda nice, right? I mean there's a first language, it's just a purely its own thing. Um with many applications, not trading specific and yet

You know, it felt familiar. So it's pretty cool. Yeah, absolutely. And I think R is quite similar to Python in many ways. So if you've seen a few Python scripts uh previously, then I'm sure you know you could probably interpret um are reasonably well like sort of on a basic level, I'd presume. Interesting.

I was a little disturbed uh when somebody asked a question that was in my head. I mean I just wanted to ask it, but I wasn't gonna ask in front of the class and waste everyone's time. But you know, one guy who's

was really pretty good. He was picking up everything quickly. He obviously had a lot of programming knowledge already and you know, he at the very end he's just like, So what's the difference between between Python and Ard in your opinion? You know? And the and the instructor's like, I don't really know. I don't know Python. So you know I was like, really? You know, it's like are you n just so uninquisitive?'Cause I was wondering. Yeah. But uh No, many similarities I think. So what was your

What was your uh preference to learn R? Like why did you decide to pick R? Uh you know, if this um business analytics center, uh University of Cincinnati If it had uh offered uh intro to Python two-day workshop, I would have done that. You know what I mean? They just did intro to R for s for whatever reason. So um that was it. I just either one I would have gone to. Um it wasn't like I saw

out are I knew that I'd get a really quality experience'cause I've took a two day tableau, you know, data data vis workshop. So

Now also in other news, um, since we last spoke, I think that was episode twenty three. I meant to check before we actually got on the call, but it was a early twenties anyway. So for anyone listening if you want to go back

Check it out. Early twenties when David Bush was first on the podcast. I was speaking to you not long after you'd just won, I think it was Battlefen, if I'm getting that correct. Correct, right. Now Correct me if I'm wrong, but you've also won another quant trading competition since then, is that right? It is right. Yes. And I'm trying to think now. Yeah, thank you. I appreciate that. Yeah, it was uh first place in this

uh NAME, which is an acronym, National Association of Active Investment Managers. They had a uh strategy competition. So actually I I trying to think whether it was purely a systematic or quantitative contest. I actually think it was not, but um certainly that That was certainly the focus of those who were presenting. And uh yeah, that was that was a great um it's a great group uh you know, great organization, great

very welcoming uh people and and the competitors actually were you know, were very impressive. I was uh Certainly pleased to win, but uh, you know, happy to meet the other guys. So how many other people were you up against? I remember Battlefin was something like between two to three thousand people. That was that was a lot of people. Yeah. They had a l tons of applicants and then

it got narrowed down into three groups of maybe nine competing over a few months, something like that. And it involved the presentation in Manhattan as well. Uh this was quite different. This uh and I don't know how many applicants there were but there were present there were a couple of series of presentations and Uh the first uh presentation was uh in the fall of um maybe twenty fifteen, I think. And that was uh maybe

uh fifteen to twenty people. It might have been sixteen rings a bell, so I'm not exactly sure. And then that got narrowed down to maybe six, if I'm remembering correctly. And then we the six or so of us um presented in uh in the spring of last year and um yeah it was an interesting group. I mean you had individual um you know traders uh with a strategy they had created. You had

uh, you know, small money management firms with proprietary strategies, a group of strategies. You had uh I think two actively managed mutual funds um which Alone, uh, you know, it's funny, because on one hand, some traders so say, uh, mutual funds, you know, what what you know, what good is a mutual fund? I'm you know, trade the market in my self-directed way, um, so on and so forth. Uh but to actually launch and you know take a

active strategy, put it in the mutual fund wrapper and get that out to market and and make it out of viable reality is actually quite challenging. So, you know, I was impressed with all of them, uh, bottom line. Right. And during the competition, was it all trading real money? That's a good question. My strategy uh is real money. It has a model, but it uh it's approaching six-year anniversary of real money trading. in March of this coming uh of this year now.

Uh in terms of the other strategies, obviously the mutual funds, uh real money naturally, and um, you know, millions of dollars and and then um you know, there were a couple other strategies. I I you know I'd say predominantly there there might have been one that wasn't. I think that was Something like that. Okay. So the strategy that you traded during the competition was the same strategy that you've been trading for the past, like you said, six years.

That's correct. Yeah. It's an equity strategy. So it's blue chip stocks, uh, megacap equities, and that's been live since twenty eleven in the spring or uh late winter spring, March twenty eleven. Right. Very interesting. I always thought that I don't know why I thought this, but like a lot of uh traders who enter into these competitions were trading like purpose built strategies to try and win that particular that particular competition, but um obviously not the case.

Well, it's interesting. I think there are I think you're spot on that there are these competitions that are exactly that where Uh they're essentially orienting the whole approach, the whole investment approach or trading approach to winning. Uh, you know, drawdown be damned, uh, whatever long term efficacy, who cares? You know, just

win uh the competition. But you know, these competition it's funny'cause I I never set out to be a competition winner. I'm I'm thrilled to to be one a couple times now, but Really um These just kinda fell in my lap in the sense that I learned about'em uh by uh one means or another and decided to enter usually at the last minute, uh often right

Right at the deadline I think maybe maybe in each case. And uh, you know, why not? You know, uh this this could be interesting. Um, you know, they were not that kind of competition. They were really about um the whole picture. You know, so to to the credit of each of these groups, battle fan and name, their competitions were rounded in the sense that there's a presentation, you had defend

Uh you had to defend your uh returns and risk and your thinking, uh your por your model. Um you had to defend all of that and much more and of course uh you know had to have uh you know good returns or I suppose for those who didn't have real money returns, you know, uh had to really defend their hypothetical models. Uh but it wasn't about yeah, it wasn't about gaming the competition and just winning. You know, it was really about um

the strategy and the integrity of the strategy. So uh maybe that's a bit different in the competition world. Yeah. No, I I'll I mean I think that sounds much better. It's much more

realistic is probably one way to describe it. So y you know how you said you had to like defend your strategy? Was there like a panel of judges that you presented to and did they ask you any questions? Oh yeah. Yes. Yeah.

There uh there was a panel. So in the uh you know, in the first competition, um you know, the battlefield uh competition, which I'm sure has evolved. I don't know how it's done right now, but Uh at that time it was, you know, uh go into a Wall Street shop and uh there's a meeting room and you know, you walk in and put your slides up and you're on the hot seat, you know. And um you're just gonna get um you know make a little presentation and then you're gonna get grilled. Uh and it was similar

for name, although it's a bit more formalized. You know, there was a whole um, you know, hundred or two hundred or however many people were there. Uh in the uh you know, in the room in the theater or whatever, and then there was a panel of uh maybe four or five judges, something like that. And uh uh people I've come to know actually um since and you know some sharp some sharp people for sure. and uh very um Very probing questions. You know, really just um

How and I'll tell you, here's my here's my takeaway from watching others. Uh in one case I got to watch others present. Uh in in the other case it did not. But where I got to watch other um you know strategy creators, traders, managers, whoever they were present uh and defend their strategies really where people would fall. uh from from uh from a great height, maybe they've been doing great up to that point. It was always related to risk.

Every single time. They would just unwrap because if you started if the if the panel would start to peel that onion.

around how they thought about risk and this kind of risk and that kind of risk and maybe sector risk and market risk and correlation risk and on and on and on. You start to peel back that onion And in some of these uh very, you know, sharp guys uh either just weren't as prepared as they could have been because they probably have answers but they didn't articulate them well, which, you know, didn't work in their favor, or um or, you know, in some cases may have been a little light on

the risk defense, uh and the risk knowledge and really having thought through every facet of that diamond. So that's something that I think about a lot is uh I think that that did help me because I've uh Uh I've thought a lot about that and and certainly that was part of my development process. Uh made my development process longer actually uh for that strategy because I was constantly looking at another facet of the at uh of the diamond to to use that same

Analogy. Yeah, that's really interesting actually. That's very cool to hear. Um I've made a note here to uh ask you a bit more about risk controls um as we get going, but um yeah, I'm definitely gonna um pick back up on that.

So last time when you were on, Dave, we spoke mostly about your path from Starting out as a discretionary trader to becoming a quantitative trader now. Uh this time I think we're going to pick up on a few of those topics and go a little deeper um as well as cover some new grounds. So one of the first things I'd I'd really like to talk to you about or ask you about.

is uh things around strategy development. So starting out right at the very beginning When you come to develop a new strategy, do you have any predetermined metrics uh for what will be a good strategy in your mind, like goals or objectives before even trying to find something? Right, great question. And that is that's something I if I could go back, uh I'm I'm perfectly happy with my strategy, but there are times where I've thought, hm. Uh you know, I wish I could go back and maybe rethink um

you know, rethink a couple uh aspects of the strategy. Um, you know, obviously with a lot of perspective, uh having managed it now for almost six years. Again, m mostly I'm I'm very happy, but you do learn, you know, you do gain a perspective on uh, you know

what uh a great objective function might be and so and and s and many other metrics which I t I can get into. Uh basically uh one thing that I think that uh you know a a new programmer, a new developer Uh and I've certainly talked to a number of uh of people aspiring to either make that transition from discretionary to systematic. uh or simply uh finally uh deepen their uh their knowledge of uh quantif you know quantification and and develop a model and so on.

Um the some questions I think that that those people should ask is first of all, who's the model for? Is this for pure

uh personal or personal entity or family uh trading, for example, or is this for other people's money? Uh that's that's a huge question right away. Uh and one that uh uh really should be answered because if it is uh you know for other people's money uh then one has to be thinking about um a broad regime of of questions from uh regulations to, you know, that that can inform what asset classes uh one might choose to model.

Uh and that's usually not a question because most people have uh you know some pro you know leaning towards uh you know forex or uh you know futures uh or stocks or whatever it is. But um You know, from regulations to, you know, drawdown tolerance, your tra your personal drawdown tolerance might be way have a much greater threshold for drawdown than

uh you know, than a client, for example, uh for those people who are other or are managing other people's money. So, you know, that's one consideration. Um more very specifically

uh having a um an objective function. You know, this is kind of the other extreme of now just the strategy itself. How are you going to determine your um your end result, let's say you've gone through you're towards the end of the development process and you're choosing between um, you know, a couple of different sizing methodologies or you're stepping across uh your your parameter stepping, various sizing approaches and finding where uh what sizing of your portfolio of systems, for example.

Uh is optimal for uh for you. Well, how do you you know how do you do that? That can be a number. Uh one can develop a um a specific objective function or formula is all that is. Um, you know, a simple formula that's just scores essentially all of these results

from various sizing approaches and therefore is rankable according to your objectives. So for instance, to make it a bit more specific, If one uh is looking for a um an annualized return that is um Some multiple of the max drawdown across all years of the back test, for example. you know, that's a number that every uh strategy run is going to have. And uh that could be that formula could be part of

a an objective function with maybe two other similar metrics, right? And it all gets rolled into one formula, it output is one number. That's then your uh objective function. That's what you're trying to maximize. And of course you're trying to maximizing it without burning up the past. That's a whole other issue. Uh burning up your data. You know, finding a clever way through the past instead of a robust

um set of rules and a strategy that can generalize well going forward, walking forward into the future. Obviously that's the that's the real objective. But just in terms of of your question and hopefully I'm answering it at least in part. You can have an objective function uh that actually is a metric and it's a a formula that can be pretty simple, usually made up of a few different metrics, and essentially it's a score and you can score your uh score your end results.

Okay. So in that in in your response there you gave an example about um a metric of drawdown compared to return, um something along those lines. Let's say you also had a couple other um metrics that you wanted to achieve from a strategy. So let's say going into this, I want to achieve a sharp ratio of one or greater, you know, max drawdown of twenty percent, something like that. How much would you be willing to vary on these metrics? So how much would uh

I'd be able I I'd be willing to vary in terms of well, I have this great output here that I feel great about, but it has a really crummy sharp and I wanted a higher sharp. Is that yeah, yeah. Exactly. Okay. Yeah. Right. That's a really interesting question. Um, you know, and these are uh you know, very great questions and not always ones that I was thinking about. M my number one

Um you know, my objective function was essentially pretty simple. It was pretty much what I articulated along with a couple of the things, which essentially was, you know, positive in all years, um, minimal drawdowns. and an average annualized return that was a multiple of the worst drawdown figure. Uh average drawdown is not really something you should look at in my opinion. Uh perhaps there's a case where that is important, but max drawdown uh in related drawdowns, drawdowns that were close.

Uh those are what you're gonna be living through in the future and you're probably gonna be living through worst. Your worst drawdown is always gonna come in the future. So um that was part of my max my my objective function. Uh in terms of Other um uh other factors um you know that

you know, other metrics that maybe it didn't live up to as well. Um, you know, I didn't experience that, but I didn't set out with a a schmorgis board um of requirements. I really set out to do what I said, which is without having full exposure to the market, to be able to uh stock market in this case, you know, benchmark being S P five hundred total return index, uh, you know, including the dividends and so forth, uh

With that as a benchmark, you know, obviously I don't want to be well, maybe not obviously, but I don't want to be uh fully exposed to that all the time. Uh some correlation is okay in in in my approach. Uh other approaches are, you know, non correlated and they strive for no correlation. That's a different s you know, whole approach and discussion. But I didn't have a big list of requirements. So um

I think it's a discovery process though. In other words, one should lead um you know, one's development I there's gonna be discoveries along the way. um that are probably gonna be surprising. And especially if you're coding a cherished notion like uh, you know, every time this makes a thirty day high, this market and, you know, this other factor and this is

other other factor are happening simultaneously, well that's definitely uh you know, that that results in a a reversion to the downside. You know, and then you test that and you really start to see, you know, that's a ridiculous notion. It really um it's you were subject to a confirmation bias where you you thought that's what was happening, but you were only noticing the times it happened and actually there's so many other times. Uh in fact the majority of times.

uh you know, it it it didn't work or uh the way you have um the uh you know the entry and exit and the risk management isn't working. So that the discovery process may lead you to abandon Uh some cherished notions, it may lead you to abandon some uh previously cherished metrics to

Are you ready to get serious about trading? Then join Tasty Trade, Investopedia's best platform for options trading in 2026. Stocks, options, futures, and more. Tasty Trade has everything you trade all in one platform. Get low commissions, including zero commission on stocks. so you can keep more of what you earn. Tasty Trade is packed with advanced charting tools, backtesting, a pre-built strategy selector, risk analysis tools, and more features to help you trade smarter.

See equities and derivatives with high trading volumes, dividends, upcoming earnings reports, and more with their pre built watches. Or create a custom watch list. To keep an eye on the companies and sectors that matter to you. Manage your positions with speed and precision using Active Trader Mode one click trading and smart order tracking. Plus Tastytrad's Stellar Trade Desk team offers live support during trading hours if you need it. Visit Tastytrade.com slash chat for more info.

KC Trade Inc. is a registered broker dealer and member of Finra, NFA and SIPC. 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. With Trade the Pool's limited risk platform, you don't need millions or even thousands to start trading the U.S. stock market. Bypass the PDT and tap into over 12,000 U.S. listed equities.

From penny stocks to big caps, ETFs, even the newest IPOs, and short anything you like, with zero locate or hard to borrow fees. Start your evaluation, get funded with up to$200,000 in buying power so you can go big without risking your own savings. And now you can also have unlimited time to reach the profit target. It's a game changer. Not ready to trade yet? Trade the pool offers a free demo and educational resources.

Practice on live data, master the platform, and build confidence risk-free before you even pay a cent. Click the link in the show notes to start trading with Trade the Pools Capital.

When developing a strategy, you know, still during this development phase, can you speak about how you think about simplicity and is there a place for complexity in what you do also? Right. You know, mm simplicity and this probably is touching on our last conversation, but simplicity is a huge thing for me. Because uh

My for two reasons. First of all, that's just generally philosophically resonates with me that simple things are more robust. Uh you know, there's a Mandel Mandel Bratt quote which is very beautiful in this regard, um, related to simplicity. uh simple rules um and so forth. But Also, um, you know, my study of you know kind of first generation commodity trend following uh traders. Um and there's some great books out there on this group uh really is

emphasize the simplicity as well. And so I was influenced by that, even though I'm not in this strategy, I'm not doing futures and I'm not doing trend following um in the purest sense.

I'm doing equities and mostly reversion. So almost the opposite in a sense. Nevertheless, I was influenced by their emphasis on simplicity. So that that has served me well because the the thing about complexity is What ends up happening is if you're uh I mean, in all power to those who can have really, really complex uh conv uh maybe convoluted is not the right word, but ornate code and complex rules and if thens, you know, out the wazoo. If they can get that to work.

That's fantastic. All power to them. However, what typically happens with most people who try to do that is they create an artifact basically a really, really elaborate way through the past, right? Because all financial models rely on the past. And so we want to avoid complex travel routes.

that are overly uh tied to one place in time. You know, they're they're overly specific. That that is too complex. What you really want is, you know, going forward, you want to be able to generalize well because Data, you know, markets are not stationary. Um the uh the data ch can change, regimes can happen, you know, decimalization can happen in the stock market, uh regulations can change. uh markets. Uh you know, there's just so many things

that that change. Uh obviously technology, you know, market microstructure is is hugely changed uh in the last ten years. You know, I think the average shares

uh stock uh stock trade was like two thousand shares maybe in two thousand five or two thousand six and you know ten years later or w thereabouts it's like two hundred shares. Might even be less, but you know jet that's a general idea. Mic micro uh structure. Um So basically um long winded way of saying that, you know, it's important to uh to really have simple rules that can can really walk through and navigate

markets that are going to be different in nature in the future than they are on now and on the data, which is yesterday that you tested on. Um if you're overly specific, overly complex, you're probably gonna

just break apart in the future. It's gonna come unglued because it was just um um too brittle. Uh you know, it's funny, you can even see this uh on certain parameters, for instance, Uh maybe this is too much detail but You can you can uh find, you can do a parameter step, let's say on one piece of logic, one variable in the test, and you can find uh a parameter shelf.

like a think of it, imagine like a ledge. You can find this narrow little ledge, you know, above a, you know, a thousand foot precipice that you can walk successfully. And boy, that would have been great in the past if you had used that parameter. And then you can step. right um you know one parameter um to the right, so to speak, of the one you found that worked and you're falling off into the precipice cause it didn't work. That's not a robust

uh piece of trading logic. You know, if if you are narrowly finding the the rule that worked and you're not seeing that one wrong move and you fall off a cliff. So that's that's the overc that's the risk of overcomplexity. All right. So I just want to pick up on that. So I I think just to sort of summarise what you're sort of saying there is Let's say your strategy uses a a moving average of twenty for some parameter, it's just one of the trading rules. It it involves a moving average of twenty.

If that moving average was nineteen or twenty-one or twenty-two, what you're saying is that if that produced completely different results, like uh vastly different results, yeah. It's it's quite a brittle strategy. Am I understanding you

Correctly there. Yeah, perfectly said. Perfectly said. In fact, you can just replace what I said with that, Aaron. That was perfectly said. And I actually remember when uh when you were on last time you described these uh Uh these commodity C T A um uh futures traders. As their their strategies were so simple that they could actually be written down on a napkin and followed and they made a lot of money doing that. So, um that's one thing that that sort of stuck with me.

Also when you're on last time, you use this term uh degrees of freedom. And I think I kinda glossed over this. Um I think that's maybe quite an important point which um we didn't really go into too much.

Can you talk to us about what you mean when you talk about degrees of freedom in a trading strategy and and how it's a positive thing? Sure. Yeah. Now that relates to uh statistical significance. So the idea is And it relates exactly to what we just discussed. So if one has uh twenty Data points, let's just make it real simple, rather than you know, thousands of data points. If one has 20 data points and has 19 rules, or 20 rules,

related to a system that works over those twenty data points. Uh or let's uh you know put it in another way, you maybe you have two thousand data points but you have two thousand rules. There are no degrees of freedom. Um and you you have created most likely uh An artifact rather than something that fact in a sense that can

live on its own and work and generalize well going forward. So the idea is to have, let's say, those two thousand data points, but have a minimal number of rules Um so that therefore there are greater degrees of freedom You know and at the risk of being completely non quantitative and non statistical. I always think of um of Robert Frost and I might butcher this, but he said something to the effect of um

I don't know whether it was poetry or art, but I I think he it was specifically to poetry. I think he said poetry is moving easy in harness because When you're developing this logic, uh, it is a harness, it is a constraint. And the rules are uh are, you know, they're they're fastened, right? They're hard coded and they're fastened on the horse, if you will.

And yet you you ha they have to be able to you know, the strategy has to be able to navigate the different terrain, you know, you have to be able to move easy in harness if you're um If you're too um confined, you you have no degrees of freedom and and the strategy is not gonna be viable. So kind

uh a mixed answer to that, you know, partly uh referring to the statistics of it, um, you know, of statistical significance, uh and you know, and partly just uh the way to imagine that, or at least the way I imagine that. Mm-hmm.

Um also just going back to when you were on last time, you You mentioned that uh back testing poorly is very easy to do. Um, and I think that kinda leads us into the next step after sort of thinking about your strategy idea. How do you actually go about back testing that and what are some of the ways that one can back test poorly? Wow, there's so many, Erin. Uh how long is the interview? As long as you want. Well I would say having

Poor data, that would be a bad starting place. You want high quality data. I met somebody brief anecdote. I met a young guy who had an economics degree from a uh from a prestigious school and he was talking about a system that he would just started to develop on weekly data. And I talked I you know, I asked him about well, weekly data. Okay, you know, what about the

You know, in between those those weekly data points? Uh are is there any anything that have you done any research there into that massive gap of time between these weekly uh closing data points and you know, and he hadn't. Uh so You know, you have to think about your frequency and what's what's missing from your data. Uh you know, if you use end of day data, well you you don't have that intraday low or high that maybe um blew up your system or was too uncomfortable to keep trading. So

One has to think right away about the data. What's the data I'm using? Is it good quality? And is it what's it missing possibly? So that's the first thing I would I would say. Uh secondly, um if one creates a strategy and it looks phenomenal. But that developer really

really couldn't articulate what's going on. You know, back to the panel discussion that, you know, we we had early as you were asking about these um you know these competitions. Uh if If one couldn't be grilled and come up with really cogent answers as to what the logic is, why it's working, be able to articulate one's investment process and one's edge, if you can't do that, that's

probably a sign that you just created something that uh you don't really understand and and that probably has some flaws. So uh maybe not necessarily a way to develop poorly, but that that would be an issue to me. One really has to understand each aspect Uh, you know, I have a strategy that has it's a it's a singular strategy, but it has nine systems within it and a tenth is being added related to adding a positive volatility element. And

You know, each of those systems was um it you know, is very familiar to me. Uh it's it's was not a hodgepodge of uh uh of rules and logic that I don't really understand. So, you know, in my case that works for me and I think that's probably important for most developers. Uh you know, a machine learning process is is uh is different. One should really understand that. There's a lot to understand there. Maybe um you know the the the um

the whole process is finding things that you may may never have come up with. That's a different thing, but If you're doing kind of traditional development, I think you should be familiar with uh the

the ideas um and the logic and be able to articulate it. Um, you know, so those are those are two ways, uh I would say um not being statistically significant. So, you know, if you have a uh if you have thirty trades and it's a phenomenal system that works on the lumber market and yet you only have thirty trades uh in your model And

you know, is that is that meaningful? You know, i that that's a statistical significance question and a degrees of freedom question and a robustness question. You know, better you have thousands of data points, not just thirty. Uh so I could go on, but th those are a few. I'll probably think of more later and circle back.

Yeah. So just to pick up on that last point there where you said, um, you know, it needs to have statistical significance. You know, if if there's only thirty Trades thirty data points in your uh back test, it's not very significant. It doesn't really tell you that much. Every trade is different in this sense, but for you, what's sort of a rule of thumb for something that is statistically significant?

Well, it's a it's a good question. I for for my strategy, uh, let's see, I think the essentially um Generally speaking, there were about maybe twelve to fifteen thousand data points and um you know, m under uh under uh fifty rules perhaps in t total. You know, that's something actually I'm I'd have to go back and and quantify. I might be off there a little bit. But in other words There's is I have a very, very low rule to uh data point uh

you know, number and um ratio. So that That that was important to me just to have a lot of data and to have uh a lot of degrees of freedom, you know, back to that discussion. Somewhere between twelve to fifteen thousand. Um obviously that's very relevant to you. You're trading a universe of of many, many stocks. I'm not sure how many exactly. So you've naturally there's gonna be a lot of data points in that.

Um for someone who might just be starting out, let's say they've got a strategy that only trades on uh one particular product, um or across, you know, a very small universe, would you say maybe something like a few hundred trades is going to give you some significance? Yeah, my my answer to that would be uh most likely yes. And secondly, is Does the logic work well on related markets? So if one has a great uh ten year um, you know, note futures

Strategy. Uh does it work on the thirty-year futures? Does it work on the five-year notes? Um uh you know, that this is um uh you know, this is something that I would uh I would investigate. Um That would you know go for stocks obviously if one had a a stock specific strategy. Can it work over a portfolio of stocks, not just a single name? Um you know, I I'm a believer in in the portfolio approach but but it really differs. Uh

you know, uh with stocks it's that's quite helpful. Um and working the sectors within uh within the stock market, that uh rotation in and out and so forth, even if it's fairly short term. But I I yeah, I would think a few hundred data points and does that same set of logic, the same rules. Uh do they work on related markets? Probably not as well, but do they at least have a positive expectation?

Uh that that's something I'd look for. If it falls apart on every other market except the one that you uh developed, that is um probably a red flag. Okay. I'm glad you added that last bit at the end because that was gonna be my next question. I was gonna say like what if it doesn't work on, you know, like you said there, the ten year uh note and then you try it on the 30 year note um and it sort of falls apart, then that is an obvious red flag. So um yeah, thanks for adding that.

Now, here's something I wanna ask you a few questions around, um, and that's curve fitting. So we've probably already touched on some things that are relevant to curve fitting already, but Yeah, let's just let's just break this down. So what measures do you take to reduce curve fitting? Well, uh you know, I think we've already discussed nearly all of them, actually, because it's uh avoiding

a too high rule to data ratio, right? So in other words, minimize the rules uh and the logic, uh avoid complexity. Uh you know, simplicity doesn't have to be simplistic. It can be sophisticated yet simple. Uh most most formulas you know, that are really powerful are incredibly elegant, like the fractal formulas. Um I have a book on and I'm going a little maybe a little, you know, um tangent here, Aaron, but Um you know I'm I'm fascinated with really complex um or seemingly complex

things, uh, you know, phenomena and nature and so forth. And and the fact that many of them can be reduced to really elegant, simple formulas, uh obviously not not all the time, but um That is often the case. Uh I think we have talked about a lot of them. You know, it's really avoiding the complexity, um, embracing the simplicity, having simple logic that can generalize well, um, quality of data. Uh in and in terms let's see more specific, maybe something I haven't said. Um if one is

uh not reserving data. And that's something we haven't talked about. So this is this is critical. If one is not reserving data, Um that never gets touched. The the trading logic, the whole development process. Never cease this data. This data is completely quarantined like it has the bubonic plague, you know, just sealed off. Never gets seen. That data should be seen once and only once, purely out of sample. Most people

uh developers don't you know they don't do that. Obviously sophisticated, more scientifically oriented, you know, modelers are gonna do that every time. However Um you know, you're maybe average trader who just says, Hey, I wanna get into, you know, trading logic now and develop something quantitative or systematic might not uh be rigid about that and that's that's crucial. So

That's that's very important. One has to develop and develop and then flash that data once essentially across that out of sample data and you know if it uh If that doesn't work. Um, you know, you really have to go back to the drawing board. Um, and you also have to realize that if that doesn't work and then you go back and tweak it some more on your, you know, your development data, your testing data, your training data essentially.

Uh and then you go, you know, you you are now that data is not out of sample anymore, uh, because it's informing your changes. Um, you know, maybe maybe the strategy was great on the out out of sample data, except for whoops that, you know, nine eleven or something, you know, in market terms that was pretty horrible in other terms, of course, horrible, but Uh and then you go, Well gosh, if I just uh you know tr change the holding time, I could have have avoided holding in, you know, to that day.

and avoided that drawdown and suddenly now my results look great. Well, you know, y you just use your out of sample data to um

you know, you just burned that up too, because that's no longer out of sample. It just informed your further development. Now what data do you have a second set of data you can test on? That would be a good idea too, in that case. So I I would say that's a that's Uh something we haven't talked about that's really important to avoid the curve fitting, uh or at least to verify that you most likely have not curved fit.

Yeah. That's a very good point that you do highlight there is, you know, once you have um run your strategy or your idea over your out of sample data. Um and then you go to make some changes and do it again. That is a essentially all now in sample data. Um so you know, it's just something that you need to be aware of for sure. Are there any telltale signs that you have overfit? Well...

That that that summary or that equity curve when you go and and go to the out of sample data and it looks completely different. than your very smooth uh development equity curve. Obviously that can be a glaring hard, you know, impossible to ignore. um sign that you have uh developed a curve fit model. You know, obviously uh real performance, you you l you um launched that strategy on uh on real money. uh now and uh you are not seeing you you're immediately breaking

um metrics that shouldn't have been broken. Uh maybe you you have uh maybe the worst data po the worst drawdown perhaps in all of your uh back test period um was uh you know was five percent or ten percent

And you're you're breaking that within the first month of trading. That's um that's certainly conceivable that that could happen, uh if uh perhaps it's a similar market condition type of scenario. But That that could also be a very bad sign that, you know, you you have a curve fit uh model that was just too fit to the past and and now that you're running uh through the present and future um, you know. It's it's simply not generalizing well and and uh it's it's falling apart. Okay.

So with that in mind, how do you begin trading a new strategy? So you've let's say you've gone through all your development process, you're happy with how it looks. How do you actually start trading that in a live market? Do you start with small size? Do you trade it Uh Still on a demo account for a certain period of time or do you just go all in? Yeah, well that's a good question. Obviously that's

that's gonna differ by individual. I don't think there's there's one right answer to it. Obviously there's just degrees of conservatism versus um, you know, gunslinger, really. Uh I I'm never a gunslinger, so um I I would weigh in to the water and you know, and and test it uh test it that way. Um on the other hand, uh if one is f paralyzed by fear, uh one you know just is still afraid that the model maybe is is not uh gonna be effective. Um

you know, you could find reasons to delay for a really long time, uh and and that you know, that that's obviously not a good scenario either. So you know, I I'd say it's personal preference. Um, you know, perhaps the smartest way to go though would be to uh Depending on the nature of the demo, I mean

you know, markets are gonna be different and the demos can be different. You know, if it's spot four X and suddenly you have the greatest spreads where they really don't exist in uh, you know, where you'll be trading that strategy then your your demo results are are meaningless perhaps.

Unless you can build in those cons those assumptions. And that was another thing that we didn't touch on, but have very conservative assumptions. Don't assume that you'll get filled at your limit if you have limit orders in your model. Uh look for those limit order prices to get exceeded.

and then count that as an entry, especially if you're looking to develop something with high capacity, that would that would be trading a lot of money. So anyways, short tangent on that, but I wanted to mention that.

So uh in terms of rolling out, you know, a demo is a good idea, but just be honest. Look at the look at the nature of the demo. Uh is it really modeling uh what your fills and commissions will be? Um if it's not a situation where you need it to um be handling a lot of capacity, a lot of uh money, assets and so forth, then um that

maybe not as important, but uh of course if if you're d if you know one is developing a hedge fund strategy, for example, and it's a high capacity strategy, uh, you know, how do you model market impact, right? Your your very order uh in r in the real market could uh displace, you know, like elephant in the bathtub kind of situation, it could displace the marketplace, it could have a market impact and your actual fill therefore it would be quite different. So if you're in a really illiquid market

Uh you have to think about that or if you're trading huge size or you intend to be, then that's a factor that you have to take into consideration. Now most people are probably not gonna be, you know, modeling uh very for very illiquid markets or necessarily trying to develop a you know one billion uh you know dollar capacity strategy, for example. Um but those are factors and uh you know as you th my approach

in those different scenarios would potentially be different, um, in terms of the rollout in those in you know, in those scenarios. Yeah, absolutely. Both are really good points. Um

Now I don't think I've ever asked this question before, but I think it's a good question. Do you have a life expectancy for your strategies? So once you start trading them, do you have Yeah, do you have an expectancy? Like, do you think that this is gonna last for at least five years? Do you think it should last forever? Um, or are you with the sort of thought that you know, are you with the thought that an edge

will not last forever? Yeah, that's that's a great question. Um and somewhat unknowable. So I've met uh I'll answer the question in a second. I I I have met I you know, I divide'em into two buckets. Um you know, other traders with quantitative systematic strategies. There is one group, um the fact the hedge fund that is quite um sophisticated and pretty large where essentially uh they are constantly um adding new edges, retiring edges, um modifying uh you know

Uh updating parameters, so on and so forth. So it's like a constant living, breathing, evolving uh strategy. And um that would be hard uh potentially for uh you know, for a small player to um less very capable with with uh modeling and and uh

and data to keep track of that model over time. But but that's one bucket, that's one approach, you know, and I've met I've met some groups like that and some individuals like that. Then then there are other groups or individuals that really stick to the you know, to the uh hey, uh uh this is it, this is my logic and I I I stick with it.

And that really haven't modified it, you know. There was that one tweak uh back in, you know, nineteen uh ninety two. But since then, you know, and there are those people out there and I've met them as well. So I d there's not one approach to um to how frequently one um There's I don't think there's one set of rules uh that say you have to never alter your your model or you should constantly alter it. There there are diverging philosophies around that. In terms of my own life expectancy for

the strategy. Uh you know, Mike's based on my model, um, my expectation is in in its performance over um almost six years.

is is that it should navigate, um, continue to navigate markets well because uh again, back to simplicity, the simplicity of the logic. Um, you know, in my in my studying uh of in of markets over long period uh you know, other periods of time, you know, um data that goes way back, uh, you know, sixties, fifties and uh and earlier on than that where when you can get that data.

You know, I have reason to um to expect that you know that the strategy should continue to perform well. Uh but I don't necessarily think that it will be without some revamp. You know, there just to just to take one example, uh, you know, obviously all stock traders uh know or should know of you know the twenty nine crash, the eighty seven crash. Uh you know, I've met many people who were wiped out in the eighty seven crash uh or just took their puts off.

right before the crash. And you know, that was a life changing decision. Um trade you know, the biggest option trader in the pit. who um you know lost it that day. Um great guy and you know and the th these were life changing events. But essentially uh I was I was gonna mention eighty seven. Essentially that crash There there is a change. Um there there was a bit of more propensity for momentum prior to that.

um, fall of eighty seven and after the crash, um, reversion uh worked a little better in equity. So, you know, there are there are these obviously it's a dramatic event, eighty seven crash, but you know, there are these changes in markets that can produce new tendencies. And so I think to put it simply that I will adapt to those hopefully. But I expect it Um in lieu of something like that, I expect it to continue to uh to perform over time, just due to the simplicity.

Yeah. And I just want to throw in there, guys, if you want to hear more about the eighty seven crash and someone who actually made a lot of money on that day, uh, listen to the interview with Blair Hull, who uh bought the the lowest tick of the day on the eighty seven crash.

Um yeah. Pretty incredible. Good rec good recommendation. Yeah, it's phenomenal. Um so with all this being said, how do you monitor your performance when live trading? Like how do you know everything is doing as it should be? Right. You know, I think that's a keep it simple um approach. In other words, what what metric or metrics should one monitor. Um, you know, there there are the obvious ones such as um, you know, rolling return over various windows of time.

Uh how you know, how is that stacking up over time? Is it deteriorating? Is it uh maintaining? Is it slight deterioration? Um, how does that compare to the model? Uh they're uh the same with drawdown. Um you know, Dr. Howard Bandy has some great books on trading. I you know I highly recommend. And uh you know, in uh at least one of them he talks about Uh you know, winning percentage can be uh can be a good monitor, for example. Just um and he doesn't employ portfolio approaches, I don't think.

Uh, you know, on a trade basis for the system, you know, what's what's the winning percentage uh over um various slices of time? You know, is that deteriorating? That could be a uh canary in the coal mine saying, you know. one's edge is decaying. And so I so I I don't think there's a one size fits all there, but um I I don't think it needs to be uh incredibly complex either. So there are some basic measurements to essentially look for um for edge decay.

And if I could rant if I could rant real briefly for a moment, there is this um There's this objection to systematic and quantitative trading. Sometimes you hear people will say, hey, well, I've never seen a bad back test. Ha ha meaning, you know. It's a survivorship bias free thing, you know, uh or or a survivorship bias thing where uh you know you only s only the good back tests win and every all the other back tests are deleted.

or sitting in a you know in a hard drive somewhere. So um yeah, there's something to that. Um the obviously uh no one pursues a strategy with a negative mathematical expectation. It has to be positive. That goes without saying. Um so that objection bothers me uh sometimes like just because uh the fact that people pursue winning models is is somehow inherently flawed. Uh but what is flawed and whatever.

quant or systematic trader has to have an answer to is um how your question, you know, how will you know when it's not working anymore? Um, you know, because some people do object. They say, Hey, I you know, I had a quant, uh, you know, I I um

had an experience with the Quan strategy and uh you know it worked until it didn't and then uh it blew up and you know uh I'll I'll never I'll never touch one of those again. Um you know obviously had the negative experience uh and uh you know, it's really crucial. for uh I think every systematic or quantitative

uh you know stratag uh trader or manager to have an answer to to that edge decay question and you know it wor it works until it doesn't, you know, what's your answer to that? Because if you don't have an answer to that. then uh that that could be a problem whether you're just trading it for yourself or whether you're, you know, trying to, you know, go out into the world with it. Good run. I like it. Is there any other misconceptions you'd like to get off your chest?

Wow. Uh you know, probably, but they they don't they don't come to um to mind at the moment. Um so uh no, I'll I think that's my singular rant right now. Okay, okay. Now we were speaking uh the other day prior to doing uh this interview right now.

Um, one of the things you you mentioned to me was that you were thinking about sort of doing things a little bit differently this year. You'd like to kind of explore some new things. I'm not saying you're gonna ditch your your your strategy that you've been running for six years. Obviously that's gonna continue, but you're You you're looking to explore and look into some new things as well uh this year moving forward. Can you tell us a little bit about what some of those things may be?

Yeah, absolutely. Um And that's always exciting. I I think you have to be uh fascinated with markets um to really succeed in them. Uh maybe that's not true. Maybe there are people who are just cynical and just Uh, you know, develop strategies and and then they make a lot of money and they they don't they care less about them. But I you know it hasn't been my experience. I think most people are.

Deeply interested and and that fascination serves you well, especially especially in hard times when uh maybe you're in a drawdown. But and so you know, in terms of my development uh list. This year it's it's something that I've really started to codify uh in the last couple of weeks for the rest of the year. Is um Uh relates to c you know a couple of different areas. One is just having a better data infrastructure. Um and that uh you know, this is the decade of data as a lot of people refer.

um, you know, now call it, have been calling it for a few years. Uh where you know, I forget the stats you might know, Aaron, but I mean, you know, just essentially the bulk of the world's data has been created in the last

you know, few years essentially. It's really just a phenomenal fact, however close to the uh truth my summary is there. But So essentially a better data infrastructure um and uh more tools to manipulate data in uh essentially making uh you know, because data prep is really a lot of testing, um whether you're doing doing data visualization.

Um, you know, maybe it's non market related, you know, doing word clouds or um, you know, web scraping and then trying to find some intelligence through that or obviously in the trading uh realm. um simply uh looking at correlations and looking for other relationships that might be interesting with um you know new volatility indices related to uh you know a strategy you already have. I mean There's just so it's just so there's such a richness of possibilities. So having the data uh uh

you know, a b a better infrastructure. It's kind of a boring topic, but that's something that that I'm working on uh this year. And really that that will enable me to work through just many ideas uh more quickly. Um so uh a a lot of them are related to um There are other aspects. of classes that I'm developing uh edges in. And that's usually how it starts, which is um develop an an edge. It it works in a lot of currency pairs or it works across related futures markets.

And then from there one can, you know, delve further into You know, how can I uh exploit this? Um, you know, what capital would I apply to this? Uh you know, obviously it raises a lot of questions. But uh so I have uh uh a deep list there, slash far out stuff, perhaps is just uh looking at um various uh Whether it's, you know, fractal formulas or uh formulas uh related to uh

you know, biology and so forth. Uh th those are those are things that I'd like to um go deeper into, but they're a little further down the list. So, you know, it's um I think we might have talked about it, Aaron, uh just casually the other day, but the um separating one's um you know isolating oneself uh away from distraction is really I think crucial for development, at least for me. So uh that has to be a um Very uh you know, specific uh time of day.

um has to be uh the right day, it has to be uh or a regular schedule, whatever it is for for everyone's gonna be different. But I I I'm a believer in intensity, not necessarily time. Having the schedule is good, having the time set aside is important. But then intensity, uh being n undistracted so one's you can have long thoughts. I think that's really important. And I just mean uninterrupted thoughts where one can just go through a hierarchy of logic in one's mind. As one's coding.

keep that all there, you know, cached in one's mind as one's working. I that's really important to my process because if you allow the constant interruptions, then uh you can you can lose some really phenomenal things that you might be on the verge of um You know, of of developing and and codifying. So um kind of a long winded answer perhaps to your question, but

You know, th those are just a few of the areas that I'm I'm really looking at and and how I think about them. Yeah, no, I like that. I like that. I think um we'll have plenty to talk about uh next time you're on as well by the sounds of things. I'm blanking on the the the man's name at the moment, but uh he wrote a book called Deep Work and it sounds as though I haven't actually read the book, I've read like a few reviews and a bit about the subject but

It sounds as though that's kind of like what you're talking about there. Um another word for it is uh deliberate practice. You know, it's it's one thing to spend a lot of time on something, but are you actually spending um that time in the most efficient way? Like are you actually you know, totally engaged with what you're doing or if you've got distractions uh coming left, right and centre, you know what I mean?

Yeah, I don't know that book, but um but you'll you can tell me about it later. I'll Google it, whatever. But yes, that's absolutely uh I'm a believer in that. And you know, I'm older than you. Um you know, I've always had a serious bent But when you see your own theta, you know, your life as time decay, which sounds pretty dark, but just putting in the option terms there for a moment, but you you know, you

you you do dig deeper. Um I mean I do anyways. Um just dig deeper and go, all right, you know, uh this this and this other thing and this other thing, they have to fall away. because they're just they just don't rank anymore. Uh, you know, I thought I would do them, I thought I'd get to them, but you know, I'm jettising them. because I am doing this and I'm doing this other thing and that's it. And

you know, you said uh deep work, I think you use use that title. Um, you know, I just think of it as long thoughts and I also that phrase intensity, not time, uh, because you can have a block of time and do very little with it. Um But you can have less time and do tremendously well with it. You know, one trick, uh I think uh this I saw this I've been using this trick for a while and I don't use it every day, but I get into periods where I use it where I had set set a little

kitchen timer, uh a little egg timer. And um that's a nice trick because you can set that for, you know, twenty five, fifty five minutes, whatever it is, and that's your focus time. I mean you g let nothing go. accept your task at hand for that period. Um it's a little you know, it's a just a little uh kind of a crutcher device. Obviously, um for longer things it's not gonna work. Uh but for for tasks staying on task, it it can be uh it can be a a good device.

Yeah, that's called something, that tactic, isn't it? Is it Pedora? Something like that. I probably just got that really wrong, but I don't know. I I did see a um I I saw an article uh within the last year, you know, it's like came, you know, in my inbox uh and and uh it did have

It did have a name, um so I guess it has a name. Yeah, well you spend like twenty five or fifty five minutes or a certain period of time, um, and you just purely focused for that amount of time and then you have a ten or five minute break. um, where you just do absolutely nothing. You just kinda zone out and then just smash it again for another 25, 55 minutes, whatever it is. Yeah, I I've read a couple of things about that actually. But um just going back, um, the the man who wrote

Deep work. I can't believe I'm uh blanking on his name right now, but um actually trying to get him on the podcast. Not a trader whatsoever, but obviously I think you know, a lot of what he talks about, um and is knowledgeable about will be very relevant and beneficial to uh traders and obviously listeners of this podcast. Um just speaking of um Um talking about your life in terms of uh theta or decay um and being kinda dark.

Uh you might appreciate this as you appreciate uh data visualization, but there's this very, very simple chart which I've seen floating about online. And across the X axis it's got weeks and a year. Okay, so it goes from one to fifty-two. And then down the y-axis vertically. It's got ages, so going from like zero to I don't know, hundred or whatever you live to. Um and each little circle on that chart represents one week of your life. And um I just think it's so powerful I've

You know, I said girlfriend's heard me go, uh, you know, mention it and she thinks it's really dark and and kinda strange. But I think it's like I think it's really motivational. I think it's um very powerful to look at and see things displayed like that. I I'm gonna look for that, Aaron. You know, it makes me think of uh you know, I like to sit in uh Zazen, you know, just a traditional simple sitting zen practice. And Dogan

a Zen master from oh gosh, uh I you know, twelve hundred I think. Uh I'm probably off, but at any rate he's kind of revered just for his writings. Um And I think it was him, although maybe it was somebody else, but I maybe I Dogen was was quoting him, uh whoever the other um, you know, Zen practitioner was. But bottom line, just said, you know, sit like your head's on fire. You know, which to me is

You know, sit with intensity and practice now. You know, it it see you know, carpe diem essentially. So that can be applied in all all aspects of life. So I think about that a lot. I think about um just solutions. You know, there is so many obstacles to anything great. uh, you know, trying to be great, um, that you have to push through obstacles. So, you know, solutions, solutions, solutions is just another mantra. I think about that every day at some point. I just go, you know.

uh solutions, man. Just gotta find the solution to this. Uh and it might not be trading related. It might just be, you know, life. But that um These things are important, I think, for the trading mindset. I mean, we're talking about quantification and it in you know, in data and logic and so forth. But um there are there are times that are really challenging as a trader and you really have to have a resiliency.

mentally. Um that can be, you know, multifaceted from staying fit to um having a you know, just doing the things you need to do. I chill out incredibly deeply, unless I can't, but on Sunday f at least for the afternoon into the evening and I I don't look at screens usually. Um and you know, that's just like that's absolutely it's like a refresh thing. Um so I can start Monday full force

Uh and I I completely um you know, slowed down. So, you know, they're just little tricks like that. Everyone has their own way, you know. But I I think they're important to to have that healthy mind and and so on. No doubt. No doubt.

On that point, Dave, let's sign off. Um, where can listeners go to find out more about you? Sure. Um I would say, you know, alpha Tative dot com. That is uh a website that um uh people can check out. And um, you know, uh I'm on Twitter at Alpha Tative. Uh I'm not super active, but you know, those those would be good places to look. Okay. And do you just want to spill out Alpha Tative for us so that listeners can easily find you on Twitter? Sure, it is A-L-P-H-A-T-A-T-I-V-E.com.

Or Alpha Tative. There you go. Yeah, at Alpha Tative on Twitter or AlphaTave.com. Um and what's the go with your site? Uh I've been to it a few times. You've got to put uh you've got to sign up uh to to view it, is that correct? Yeah, that's right. And and that's something I can't really talk explicitly about, but in other words, you know it it is not available unless you read through that um and and agree to those terms because uh you know the just the nature of of um

You know, the alpha alpha tetative activities, exactly. So unfortunately that that's what I can say. So um you know, just uh just meeting meeting requirements and regulations there. Yeah, it's there because it has to be. Yes, it's a thank you. Perfect. Dave, I'm very grateful for having you back on, man. It's been an absolute blast. Thank you very much.

Aaron, a pleasure as always. Uh love what you're doing. Keep it up. And uh, you know, thanks for the opportunity. Sure thing. We'll talk soon. Beautiful. Thank you. You've reached the end of this episode of Chat with Traders, but rest assured, there are more episodes.