The Man Who Cracked The Market Algorithm - Samir Varma PhD

a rocket scientist turned trader ranked officially the fifth best in the entire United States. >> You need to understand two things. How to lose money and what your reaction will be to the loss and how you're going to act. And the second is you need to understand what it is about your logic that went wrong that caused you to lose money and be able to distinguish bad luck from bad process. >> Introducing Samir Varmmer, a PhD particle physicist turned 8-figure futures trader, consistently beating the market for the last 30 years. In this episode, Samir reveals how in designing algorithms, he discovered that market manipulations are just hidden iceberg orders and how a leech on the whale strategy is the best way to catch a ride alongside the market movers. The liquidity of the stock market at any given instant in time is not very big. An actual retail stop order can move the market even though you wouldn't expect it to because at that moment in time there isn't much liquidity. Liquidity exists over periods of time. At instance in time, it doesn't really exist. So I give you a perfect example. Um, if you're holding a position overnight, do you get nervous? Can you sleep? If you've had a bunch of losses in a row, do you freak out? I think I'm pretty good at You're pretty good at handling that. Just those two questions already suggest you could probably do intraday trend following. The place that you would probably want to do this is opening range breakouts. And you could basically find like a 10, 15, 20 minute range, wait for the breakout, and trade in that direction the rest of the day. It is funny. In two questions, you've described my approach. just describing that price signature of a predictable area. Stops are likely below. Price goes, grabs the stops and then goes in the direction they predicted. A stop loss hunt. It's a dynamic I've explored a lot with many guests and I never come to a clarity of what's going on here because it gives the illusion that there

is a higher power in the market and they are coming to the inferior and exploiting them. What is the mechanics of what is going on here? Cuz I see the signature all the time. That's a superb question. Here's the answer to that. I wondered about that for years.

>> Hello ladies and gents. Welcome back to another episode. I have to say so it's it's been a long time since I've been this excited for an episode more because I know I'm going to have I'm going to have a journey today with you and we're going to explore a lot of things. Uh I think it's a cocktail of a lot of insights and original thought uh your career credentials not only going from physicist to then publishing things to then being a leading I want to say hedge fund manager but then also being a non-conformist which is is a wonderful mix. >> I'm the proudest of the last. >> Uh so it's going to make for a very vibrant conversation. I want to I want to kick off with a little bit of context on your journey because you you've taken a career that is not a typical route the the physics background that you have that you're still very much involved with with publishing and connecting that to finance and then obviously having success whether it's algorithmically and your strategies I want to explore but you can give insight of what's happened over the last few decades to arrive to this point. >> Yeah. So, huh, I I actually started off uh as an electrical engineer. Um, and I got my bachelor's in electrical engineering, but I honestly didn't enjoy engineering very much and I really wanted to do physics. So, I switched back to physics, which is really what I wanted to do in the first place. I became a particle physicist at the University of Texas. And then the uh uh we were building this thing called the superconducting super collider, which is going to be a real physics experiment which where we might discuss uh discover something new. uh the US Congress uh as I say Congress critters are infinitely wise uh in their infinite wisdom canceled the project uh which meant I didn't have a job and had to find something else to do so I became a trader um in October 93 I read this uh very beautiful article written by Matt Ridley in the economist called the

mathematics of markets and basically the article said that there's a bunch of people now all of a sudden on Wall Street that are using mathematics to make short-term predictions in the financial markets >> and I said oo that's interesting and then it used the buzzword chaos theory and I said ooh double ooh because I'd published in chaos theory as a graduate student and I said ah this is fun um I'd read a lot of economics uh so I said well this sounds like nonsense we know the efficient market efficient markets hypothesis this can't possibly be true and of course I didn't have a job uh so I started playing around with chaos theory in the financial markets and very rapidly discovered that far from it not working it worked very nicely at least theoretically so since I didn't have a job I figured hey I into this. And so I started my own trading company. And so the first thing I ever traded was uh the S&P 500 futures. And I did that using chaos theory. And as far as I know, I was the first guy to algorithmically trade the S&P 500 futures using some advanced mathematics, not just like you know moving averages or something like that and that worked quite nicely quite some time. So that's where I started as a futures trader. >> Wow. I I want to I want to begin with an open paradox I want to say which is how can a trader the endeavor is obviously to make money. How can someone achieve consistent outcomes from chaos theory an inconsistent or random market? So there are there are really two aspects to that. One of them is psychological and the second one is your edge. So the first thing you have to ask yourself is everyone and his uncle is trying to make money in the market. So what exactly is it that you have or that you think or that you can do that is different than other people? That then becomes your edge. But the second problem is that you can once you get reasonably good at this, you can find more than one edge. But that edge then has to be congruent

with your personality. >> If the edge is not congruent with your personality, you will never be successful with the with the trading strategy even if it works. So I give you a perfect example. Um by and large you can break trading strategies up into two groups. They're either counter trend or trend right? So breakouts are trending, moving averages are trending. Uh on the other hand you have things like MACD and so on which are technical indicators that are counter trending. If something goes up you short it, something goes down you buy it. That's counter trend and vice versa. But uh the thing is that when you have counter trend strategies typically you make um a lot of positive gains and the occasional big loss. >> So you have a lot of positive trades. So maybe you're 65 70% positive trades. On the flip side, if you're if you're trading trend trading, you maybe make 30% positive trades or 25% positive trades, but the positive trade will be like 19 times bigger than your losing trade. But some people can't deal with being wrong five times out of six or four times out of five. So it wouldn't work for them. So you know they'd get whipsawed in a moving average strategy for example and they just say no the seventh time I'm not going to take the loss and that's the time it goes up 200%. >> So you have to find the edge and then you have to find an edge that is congruent with whatever it is that you can live with. And it the the the reason the trading journey is so hard is that you have to lose a lot of money to begin with to learn what A works and B works with your personality. >> Mhm. With this word congruency, what are the variables of types of trading personalities? Let's say swing trading, scalping or fundamental technical, all these variables, but also then personality types of risk aversion or the ego to not not being liking to be wrong. How would you connect these? So to know okay, I'm X kind of person. I'm I'm Y kind of person. What should this

lead me towards in terms of behavior? >> So the first question you can ask yourself is if you're holding a position overnight, do you get nervous? >> Can you sleep? >> Yeah. So then you would need to scalp or day trade. >> The the second question is if you've had a bunch of losses in a row, do you freak out or can you handle that? >> I think I'm pretty good at handling. >> You're pretty good at handling that. So you're probably in that case just those two questions already suggest that you could you could probably do intraday trend following. >> Yes. >> And most likely the place that you would probably want to do this is opening range breakouts which are fairly successful in the equities >> and um we actually traded this for quite some time with Joe Richie in Chicago. um which was uh opening range breakouts on stocks and you could basically find like a 10 15 20 minute range on many stocks wait for the breakout and trade in that direction the rest of the day >> and you'd be making more pro I mean you'd be on average profitable it's funny in two questions you've described my approach >> which is pretty interesting uh what are the let's describe your personality type with because I know you're pretty much the opposite you you're doing trades that are one year in duration or maybe even longer >> um what is your personality and how have you connected that to your trading behavior. >> Yeah, that's that's a great question. So, my personality is that I actually hate having to make decisions, which is a strange thing for a trader to say. Um, and the reason I hate having to make decisions is that I can always think of the nth variable that is not part of the n minus one I just thought about. >> So, I decided years ago that a I'm a physicist, b I like systematic stuff, so I'd need to be a systematic trader. And so, I've been a systematic trader forever. I started off as a short-term trader. uh uh but the issue is that I realized back in 2003 that alpha from

short-term trading is going to become harder and harder and harder to achieve because there were more and more people trying to do it. So >> efficient markets >> efficient markets and and there's only a limited amount of alpha you can get anyway and of course the shorter term your uh your trade is the less money you can run through it. >> Oh because you create the alpha >> decay. Exactly. You create the alpha decay by just trying to take advantage of it. So I decided that I would try to become a um longer term trader and then I said you know I'm I I hate being having a majority opinion on anything on any topic. It makes me uncomfortable if people agree with nonformist. >> Non-conformist. Exactly. So what is it that a a systematic or a quantitative trader equities trader would not do. And the answer is twofold. One increase their time frame to beyond a year. Nobody does that. >> And the second is stop looking for alpha. And so I did both and that's really what >> let's begin with why is that the majority consensus to not hold trades for that duration and so forth? Because by and large when you're trying to build a um a trading model as opposed to an investment model um what you're trying to do is you're trying to find a dislocation in the market of some kind and then you're trying to find a signal of that dislocation and then you're trying to find um whether you can put sufficient capital through that dislocation so as to be profitable. So I'll give you a perfect example of this by the way which I think is still true. Um if you take say the S&P 500 uh ETF spy >> m >> and you divide it up into its daily um into its intraday return open to close and its overnight return close to open you will find that more than 100% of the return of the spy takes place overnight i.e the the on average the spy goes down during the day. >> Interesting. >> Yeah. Exactly. But taking advantage of that is pretty difficult and the reason is you can certainly buy you know market

on clo open and then yeah no buy market on close sell market on open you can do that >> but there's only a limited amount that you can do that's the first issue before you start to move the market too much >> and the second is of course that you are then subject to the reason it's positive of course is that you're getting paid for taking overnight risk. >> Mhm. So that's that's an example of an edge that exists in the market that's pretty difficult to arbitrage and yet it's right there and you can see it in the data. >> Interesting. >> So on the other hand, if your trade is lasting more than a year, whether it takes you an hour to get in or two hours to get in, the whole day to get in, you don't really care. And that's that's the arena I wanted to be in. Um we started to discuss with Joe uh light speed limits which is to say the amount of time it takes light to get from your computer to the trading arena right and when we started discussing that I said I don't want to do this anymore okay let's take a moment to talk about a partner of the show a leading prop firm that is funded next it's important for me to listen to our community to see who are you working with and how can we make your experience better and the main feedback I heard is trusted payouts quick payouts ability to scale and afford affordable prices and Funday Next has ticked all of those boxes. Not only being a top three prop firm in the industry, but also having on demand payouts and every 10% you gain on your account, you will double your capital for free. And because in this industry, trust and reliability is the most important factor. An important guarantee that they have is that if you do not receive your payout within 24 hours, they will gift you an additional $1,000 to your payouts just for being late. So to unlock all of these benefits and work with a leading prop firm in the industry funded next, check out the link in the description or use the code toot. Exploring this of of extended time

horizons. I'm not sure the correct way to phrase it, but I want to basically say that as the time horizon extends, the degree of variance also extends to the analogy of if I put a gun to someone's head and said, "Where is price going to be in 10 minutes?" You can have a 90% confidence it's a box about this big. If I say where's it going to be in one year, it's a much larger box because a lot of things can happen that are valid today and you couldn't foresee as time as time goes on. >> Um how do you account for that? Okay, so that is an absolutely brilliant question and uh in fact that's that's really the crux of in my opinion all of trading. What I real I started off like essentially everybody else under the sun trying to predict things >> short-term market movements using chaos theory. um alpha generation strategies for scalping um uh breakout systems whatever these are all predictions what I realized in my old age with all this gray hair is you need to stop predicting things you need to start reacting to them and so my system is actually reactive not predictive I don't predict anything but you're reacting to today's information >> and hoping that that today's information carries through for maybe a year Yeah, walk me through that. So, what what tends to happen this is more true in the equity markets than anywhere else. What tends to happen in the equity markets is the following. This is this is this is not a well-known fact, but it should be. Um, take any line that follows the uh price of the equity market at some distance. Um, and you could, the easiest is to just take a 200 day moving average because everybody else under the sun does. It doesn't matter what you do. You can take any long-term line you want as long as it follows the the price action. >> If you now ask what percentage of the return of the market takes place when it's above that line, it'll generally be around 2/3. >> Okay? If you ask what percentage of the risk takes place above that line, it'll

horizons. I'm not sure the correct way to phrase it, but I want to basically say that as the time horizon extends, the degree of variance also extends to the analogy of if I put a gun to someone's head and said, "Where is price going to be in 10 minutes?" You can have a 90% confidence it's a box about this big. If I say where's it going to be in one year, it's a much larger box because a lot of things can happen that are valid today and you couldn't foresee as time as time goes on. >> Um how do you account for that? Okay, so that is an absolutely brilliant question and uh in fact that's that's really the crux of in my opinion all of trading. What I real I started off like essentially everybody else under the sun trying to predict things >> short-term market movements using chaos theory. um alpha generation strategies for scalping um uh breakout systems whatever these are all predictions what I realized in my old age with all this gray hair is you need to stop predicting things you need to start reacting to them and so my system is actually reactive not predictive I don't predict anything but you're reacting to today's information >> and hoping that that today's information carries through for maybe a year Yeah, walk me through that. So, what what tends to happen this is more true in the equity markets than anywhere else. What tends to happen in the equity markets is the following. This is this is this is not a well-known fact, but it should be. Um, take any line that follows the uh price of the equity market at some distance. Um, and you could, the easiest is to just take a 200 day moving average because everybody else under the sun does. It doesn't matter what you do. You can take any long-term line you want as long as it follows the the price action. >> If you now ask what percentage of the return of the market takes place when it's above that line, it'll generally be around 2/3. >> Okay? If you ask what percentage of the risk takes place above that line, it'll

generally be 1/3. And then vice versa, if you look below the line, the risk will be approximately 2/3 and the return will be approximately 1/3 more or less. These numbers are all approximate because depends on the line and so on. What that tells you is that the risk versus return trade-off is not constant. >> Okay? And the mistake that I think large numbers of equity traders make many times, by the way, they know better, but they're forced by the risk management committees to do it anyway, is that they are forced to take positions regardless of what the risk outlook is. And so you may say, what does that mean? So I'll give you another example. Um I wrote a paper on this gosh 20 years ago now. Um supposing you take the correlation of all US stocks to the S&P 500. So you take you know stock one and its correlation stock two and its correlation stock three it's correlation and you average it and you the let's say the correlation is over the last I don't remember what number 100 days it doesn't matter and you average the correlation today over the last 100 days and then you average it tomorrow and average and so on. As that average correlation goes up, the rate of return of your long short strategies goes down automatically >> because the S&P >> Yeah. As the average correlation of the average stock to the S&P goes up, >> the rate of return of any long short strategy is going to go down. >> Why? Because it's baked in. Basically, >> it's baked in because when you you're you're trying to make the difference in the return between the two. As the correlation goes up, the difference in return comes down. Mhm. >> But nevertheless, you are forced very often by your investment committee or by your um what is the I forget what the term is by the people that allocate money to you. Okay. >> That you're basically told no no you can't take advantage of this fact. You need to have your exposure on at all times. >> Similarly, the same thing happens with

mutual fund managers that are constantly frightened of being behind the index. Right? So they can't do things to actually actively manage the risk in that way and so they don't. >> Does this imply a negative correlation to the S&P is favorable? >> Yes. If you could find one >> just trade non-correlated things. >> Yes. So the so the ideal thing would be of course to find a negatively correlated asset and stick it in your portfolio along with the positively correlated asset and the two correlations will will offset and in fact the negatively correlated asset could even lose money. >> Yes. But as long as it doesn't lose too much money, you're still better off. >> But that's very hard to find. Very hard to find. Um, so you try to find an uncorrelated asset, which is also, by the way, pretty hard to find. What about this idea of being a non-conformist in strategy? What I mean by this is not necessarily a non-conformist in price because then you're just trying to pick the top, but more let's say looking at the CO2 reports, commitment of traders, and seeing, okay, everybody is long. 95% of people are long. Therefore, who is going to carry that trend forward if everybody's already long? Therefore, using positions as a reversal format, which would be by definition non-conformist. It would work if you could get the data uh fresh enough. Number one, and number two, if the uh uh imbalance was extreme enough that you would feel pretty confident about taking the opposite position. 955 probably still isn't good enough. If I had to guess, >> okay, >> um, I would probably want I'm making this up right now because I haven't tested it. I'd probably want it to be like 982 or something like that. >> Does that happen in real life? In >> very rarely and in those very rare circumstances like George Soros used to know this in his gut. Um, you take the opposite position >> like you know when he quote broke broke the bank of England remember the famous trade that's exactly what he was doing

in effect. I I want to explore the idea of self-fulfilling prophecy when it comes to the coot report where is the market truly random or is it a law of cause and effect of human psychology or is it a law of cause and effect of the large money managers who if they position a bias that bias must play out by virtue of the capital they put in. So all of the above >> is the answer and so let's go through that in some detail. So the first is you have to um um there's a guy um Joe Stiglets who won a Nobel Prize in economics. I forget when now um anyway he uh came up with a I think Sanford Grossman I forget. Anyway, Grossman the Gman Stiglets paradox and the Grossman Stiglets paradox is a statement that the market suppose the market was completely efficient. Well then nobody would trade in it because there'd be no point in doing so. So then no one would trade the market which means it would be inefficient which means of course everybody would trade in it. So there has to be a balance of inefficiency. Yeah. There's a pendulum and the pendulum will presumably then settle in some equilibrium where there's just enough inefficiency to make it worthwhile for you to do the research to find the inefficiency. >> Right? That's the Grossman Stiglas paradox. So if you take that to its logical conclusion the answer is for your question all of the above. You have to do all of those things. What what is your thoughts on patterns specifically where not necessarily pattern traders but uh in the randomness of the markets there is predictable pockets because human nature at certain extremes or certain situations will act in a predictable way. >> Um is our job to find inefficiencies or is our job to find pockets of predictability? >> Both. >> Okay. >> It's it's our job to find anything that makes money. >> That's my my my position. >> Which one do you prefer to exploit? Um, I generally try to prefer to exploit um, uh, situations where I'm pretty sure that the statistical odds are in my

in effect. I I want to explore the idea of self-fulfilling prophecy when it comes to the coot report where is the market truly random or is it a law of cause and effect of human psychology or is it a law of cause and effect of the large money managers who if they position a bias that bias must play out by virtue of the capital they put in. So all of the above >> is the answer and so let's go through that in some detail. So the first is you have to um um there's a guy um Joe Stiglets who won a Nobel Prize in economics. I forget when now um anyway he uh came up with a I think Sanford Grossman I forget. Anyway, Grossman the Gman Stiglets paradox and the Grossman Stiglets paradox is a statement that the market suppose the market was completely efficient. Well then nobody would trade in it because there'd be no point in doing so. So then no one would trade the market which means it would be inefficient which means of course everybody would trade in it. So there has to be a balance of inefficiency. Yeah. There's a pendulum and the pendulum will presumably then settle in some equilibrium where there's just enough inefficiency to make it worthwhile for you to do the research to find the inefficiency. >> Right? That's the Grossman Stiglas paradox. So if you take that to its logical conclusion the answer is for your question all of the above. You have to do all of those things. What what is your thoughts on patterns specifically where not necessarily pattern traders but uh in the randomness of the markets there is predictable pockets because human nature at certain extremes or certain situations will act in a predictable way. >> Um is our job to find inefficiencies or is our job to find pockets of predictability? >> Both. >> Okay. >> It's it's our job to find anything that makes money. >> That's my my my position. >> Which one do you prefer to exploit? Um, I generally try to prefer to exploit um, uh, situations where I'm pretty sure that the statistical odds are in my

favor. Whatever they are, it doesn't matter to me how I find them. Um, and also I prefer not to exploit patterns that disappear when I exploit them. Let's walk through what a pattern is first of all because a pattern can be correlation causation but on top of that it can also be something that is optically a pattern but behind the scenes in the orders the behavior may be different to another time that pattern appeared. >> Sure. >> What what is your first thoughts on pattern traders? >> So the first thing to think about is um as you just said human psychology. >> So it is true and this has been studied quite a lot now in economics. Um people like to place trades at round numbers. >> So you'll find more trades at zeros or fives or 2.5s than you will at you know 2.1 or you know so 99.17 is going to have a lot less trades than 100.00 just just as an example. That's something you can exploit. No no no question. >> The second thing you can exploit is what the CEO of Renaissance called patterns that seem to repeat but have absolutely no good reason for existing. um Peter Brown, is that his name? Um he said these these patterns are so completely illogical that if you try to get logic out of them, you would never trade them and that's why we do and that's why they work. >> Now maybe he's blowing smoke. I don't know. But it actually sounds >> Does that Does that imply a job of a trader is not to know why the market moves? It's simply to react and not understand why it did. >> Yes. Because I think that we make a mistake as traders and my biggest losses by the way which we can talk about some of them have come from thinking I understand things um is from thinking that we need to understand stuff whereas it's a complex system computationally irreducible in the terms of my book and because because it is that way it means that trying to understand it is a futile quest and you shouldn't try >> as a scientist as a physicist who explores for answers was this quite the

favor. Whatever they are, it doesn't matter to me how I find them. Um, and also I prefer not to exploit patterns that disappear when I exploit them. Let's walk through what a pattern is first of all because a pattern can be correlation causation but on top of that it can also be something that is optically a pattern but behind the scenes in the orders the behavior may be different to another time that pattern appeared. >> Sure. >> What what is your first thoughts on pattern traders? >> So the first thing to think about is um as you just said human psychology. >> So it is true and this has been studied quite a lot now in economics. Um people like to place trades at round numbers. >> So you'll find more trades at zeros or fives or 2.5s than you will at you know 2.1 or you know so 99.17 is going to have a lot less trades than 100.00 just just as an example. That's something you can exploit. No no no question. >> The second thing you can exploit is what the CEO of Renaissance called patterns that seem to repeat but have absolutely no good reason for existing. um Peter Brown, is that his name? Um he said these these patterns are so completely illogical that if you try to get logic out of them, you would never trade them and that's why we do and that's why they work. >> Now maybe he's blowing smoke. I don't know. But it actually sounds >> Does that Does that imply a job of a trader is not to know why the market moves? It's simply to react and not understand why it did. >> Yes. Because I think that we make a mistake as traders and my biggest losses by the way which we can talk about some of them have come from thinking I understand things um is from thinking that we need to understand stuff whereas it's a complex system computationally irreducible in the terms of my book and because because it is that way it means that trying to understand it is a futile quest and you shouldn't try >> as a scientist as a physicist who explores for answers was this quite the

confrontation when you came into the finance world. No, because I started off like everybody else thinking there were the explanations for things. So, so I should actually tell you about my very worst trade. >> Please do. >> Um, my very worst trade took place during the dotcom bubble. Um, and I may get the numbers slightly wrong, but the idea is is roughly correct. Uh, when I was tra this was for my own account. Um, because I was trading futures at the time and this stocks was my hobby at the time. Um, so I I actually made 10% on my very worst trade. And you'll say, "How can that be your very worst trade? What a ridiculous thing to say." Here's why. I bought Sevil Systems, symbol SEBL, at something like $5 split adjusted. I held it to, I think, 120. I may be wrong, but something like 120 split adjusted. And I sold it at 550. Yeah, that's right. I was greedy on the way up and then on the way down I kept saying, "No, no, it's going to have to bounce. I'm just going to wait for it to come back up before I sell it again." >> So, I sold it at 550 when I essentially, to use the common term, puked it out because I couldn't take it anymore. That was the very worst trade I've ever done. I I've had losing trades which are not as as stupid as that one. >> Are you Are you saying it's the worst because of the opportunity cost of what could have been? >> Exactly. And and also from the fact that psychologically I did everything wrong. >> Everything. I identified the correct stock. >> I more or less identified the correct time to sell it. >> I then didn't pull the trigger to sell it. Then I had regret over the fact that it didn't pull the trigger to sell it. >> And then I kept having regret over the fact that the price was higher the last week or two weeks ago or 3 weeks ago. And as it plummeted down, I kept saying, "No, I'm going to wait for it to bounce." And it didn't. >> A very relatable story. >> Absolutely. I' I've done it >> for the last two years. A proud sponsor

of the show is a topranked leading prop firm, Alpha Capital. And for the years that I've been working with them and the thousands and thousands of viewers, you guys that have been working with them through the discount codes of Titans of Tomorrow, it's clear for me to see why they are top ranked prop firm in the industry. They have also reached a monumental milestone of $100 million in payouts. And with the multiplestep plans and the multiple package types they have, there's going to be an option catered specifically for what you're looking for. So, you can buy an evaluation account catered to your needs at the most competitive prices. And with our discount code toot for titans of tomorrow, you're able to get the most unbeatable unmatched prices in the industry with a leading trusted prop firm. And with that being said, let's get back to the episode. I want to explore therefore and on the back of this an area that people don't maybe consider. >> Obviously, everybody talks about risk and they talk about my final take-profit level, but the the what happens in between. >> Maybe it's a bell curve in the sense of it might go 5% to my target and then reverse. It might go 99% and then reverse. uh and and maybe it's a normal distribution. Hence, people take partials at a certain point. How do you eradicate or minimize um opportunity cost in a system? Because it's very easy to say it's break even, no loss, no no harm. But then if you have these pockets of you know 2% 1% 2% that all went back to break even. At the end of the year, you've got a huge amount of money that could have been >> yes. >> How do you navigate that? >> So the answer is you can go broke taking a profit. >> So that statement is is false. has always been false. I don't know why people think this is a good idea. I think it's very stupid. Um, what you need to do is you need to I'm a systematic trader now completely. So, I know exactly what's going to get me in and I know exactly what's going to get

me or rather my computer does, right? But even if you're not a systematic trader, you need to know exactly what your exit conditions are going to be before you enter your trade. You you do not get to make it up as you go along. That's the error. So anchoring back to what you previously said, does this not now bring undertones of predicting, not reacting at a level? No, you're reacting. You're you're saying in advance if if you're trying to make discretionary trades, these are the types of conditions that if they prevail are going to make me pull the trigger to get out. >> Mhm. >> So the perfect example of this, I don't trade like this, but the perfect example of this is William O'Neal's Can Slim System, >> which is in a book, I think, called um what is it called? how to make money in stocks. I don't trade like this. It's not something I could do. But basically, they have very specific patterns they look for when they get in and very specific patterns they look for when they get out. And they have very specific rules for taking profits and so on and so forth. Um the point is that if you have specific rules, even if those rules are things that you do discretionarily, you can look back at your trade diary or whatever it is, you should always keep one um to see what worked and what didn't work and you can at least somewhat optimize what it is you're doing or not doing. >> The the the the problem is always when people don't have a clear plan. So I this happens with people that I advise all the time, you know, friends, um, where they'll buy a stock and it goes down and they say, "Oh, well, you know, I bought it for a trade, but now it's an investment." No, I mean, dude, it went down. It shouldn't have. Sell it. >> Sun cost fallacy. >> It's cost fallacy. >> Earlier when we were we were speaking before we started shooting, you were explaining how going through data uh and sifting through financial data when you were a physicist and and through your

career had to take that step. you uncovered certain things patterns within the data. Can you share to me what that was? Yeah. So, so interestingly um the original um uh system that I mentioned was was using chaos theory and in chaos theory what you do is you make the assumption that a physical system if it has behaved in a certain way historically in the recent past then in the very near future it's going to behave the same way. It's a form of pattern matching actually >> and um for chaotic systems which is a very specific definition which I won't get into but for systems that are technically chaotic that actually works reasonably well >> for short-term predictions and that's what I was doing in the uh financial markets on Australian futures. Now from having looked at large amounts of financial data for more than 30 years it becomes instinctive where you can look at the data and you can say hey you know that's not noise that is probably a pattern. So like for example uh the example I gave you earlier about the overnight versus intraday returns of the spy that's a pattern I mean that's not noise. Um uh it's it's just not another one by the way is the congressional effect which has weakened over the years but for the longest time almost the entire return of the S&P 500 took place when Congress was not in session >> which tells you something about politicians but that's another story um but that was a real pattern that was not something that was noise again >> now noise would be for example that again for the longest time stocks whose whose symbols began with a vowel outperformed the market as a Wow. >> Yeah. That's not explanation. >> No explan. And that's not a pattern. I'm giving you simple examples, but if you do this long enough, eventually you learn how to see a pattern. >> Yes. >> And so it turned out, this is what I was telling you about earlier, that I have a physics paper coming out in a in a proper physics journal, European physical journal, C particles and

fields, um, which I'm pleased to say will be out in the next few weeks, accepted already. um where I put my finance eye looking at a very peculiar set of data which is the mass of quarks. Quarks are the fundamental constituents of matter. They make up the inside of protons and neutrons. >> And um the pattern of their masses, the ratio of the masses of one quark to another. There are six of them has never been satisfactorily explained by anybody because they look at it and it's crazy. The heaviest quark is I think 13,000 times lighter than heavier than the lightest one. No one knows why. and finance I believe it or not I looked at the pattern and I said I think I get this and it turned out because the paper is now being published and it's being peer- reviewviewed I was right so I've actually found a pattern in the masses of quarks as a physicist yes trained as a physicist with a PhD but with a finance eye applied to physics so I think that's pretty cool >> fascinating and first of all congratulations there's no small feats >> what about the other way around taking the physics mind and applying it to finance any unique or certain >> insight you've had. So the most important thing that you need to do as a physicist coming to finance well you need to do two things. First is you need to be relatively humble which many physicists me included are not. >> Why? >> Um well because you see the reason you get into physics in some cases is because a you love physics but b it's because you are convinced in the very fiber of your being that this is the highest intellectual challenge of mankind. >> And so you think of yourself as being a pretty damn smart guy. and you probably are for that matter, but the financial markets have a way of humbling people that are pretty damn smart. >> Um, and the reason is that there's a what you would call technically a lot of noise in the financial data. What looks like a pattern isn't. And so you need the humility to be able to understand

that unlike in physics where if if there's a pattern it probably is real, in finance, if there's a pattern, it probably isn't. And so you need to study really hard >> to understand how to identify patterns that are real versus patterns that are not real. That that's the biggest thing that I would tell um physicists getting into finance. That's the place where you really have to be humble. >> How do you also segment or or come to the answer of what is a real pattern versus not if optically they are the same? >> Yeah, that so that requires an enormous amount of learning and background. So uh you need to read a lot of books on um uh economics and you need to read read a lot of books on finance. But then the most important thing is you need to read them with a very skeptical eye to see where they're wrong. And again some of the stupidest things I've ever done in my life is when I've read an economic theory and said that makes a lot of sense. Got it. I'm going to use that in the market. And it blows up in your face every single time. Um every single time. So >> interesting. >> You know, for example, people love to use risk models. So there's all kinds. There's Gach and Echot and ARMA and Arima, and I could go on with all these silly uh names. And what they're trying to do is they're trying to predict what the risk is going to be. See, we're back to predict again. Um and so basically, they're trying to tell you that the annualized risk of the S&P is going to be 23% in the next month. Or they'll say, "Oh, it's going to be 6% in the next month. There are two problems with this. Even though everyone and his uncle uses them, all the hedge funds use them, uh the proprietary funds use them, the investment banks use them, the commercial banks use them, it's crap. And it's crap for two reasons. It's crap a because those risk models work until they don't. That is to say, until the, you know, hits the fan, >> they work great. When the hits the fan, they blow up and they don't react

fast enough. That's the first problem. And the second problem is that they are mistaking a exact prediction for reality. And I think it's Kanes who said this. I I'm not sure but I think it's Canes. These models would prefer to be exactly wrong than approximately right. >> Interesting. >> It's a lot more important to be approximately right than it is to be exactly wrong. And what you the way to deal with risk is to classify it, not to predict it. It doesn't matter. The analogy I like to give is um way back when when Tiger Woods when his was in his heyday, number two was generally Phil Mickelson. Question, was Tiger Woods twice as good as Phil Mickelson? Four times as good as Phil Mickelson or eight times as good as Phil Mickelson? Does it matter? No. He was just a hell of a lot better than Phil Nicholson, >> right? It's the same thing. Is the risk high? Yes. Okay, be frightened. Is the risk low? No. I I mean, yes, great. Be aggressive. That's what you need to know. You don't care whether it's going to be 26% or 28% or 29%. So, technically speaking, what these people the mistake they're making is that they're trying to optimize what's called the root mean square measurement of error. >> And that's just as silly because that's the wrong metric by which to measure things. Because what will happen then is you're optimizing for getting things right 90% of the time, 92% of the 95% of the time. But those 95% of the time don't matter and the 5% of the time that do matter, you get wrong. >> That's the issue and that's just the wrong way to do things. When we speak about risk and and off camera you gave me a nice analogy of the 7% of funds and and how in spite of that things happen. Yes. >> But I want to explore >> risk you can also look at as each independent trade has an independent outcome. It does. So therefore risk is um just it's a collection of things that happen. But then when I bring the human element into it, well then there's confirmational bias. There's a gambler's

fast enough. That's the first problem. And the second problem is that they are mistaking a exact prediction for reality. And I think it's Kanes who said this. I I'm not sure but I think it's Canes. These models would prefer to be exactly wrong than approximately right. >> Interesting. >> It's a lot more important to be approximately right than it is to be exactly wrong. And what you the way to deal with risk is to classify it, not to predict it. It doesn't matter. The analogy I like to give is um way back when when Tiger Woods when his was in his heyday, number two was generally Phil Mickelson. Question, was Tiger Woods twice as good as Phil Mickelson? Four times as good as Phil Mickelson or eight times as good as Phil Mickelson? Does it matter? No. He was just a hell of a lot better than Phil Nicholson, >> right? It's the same thing. Is the risk high? Yes. Okay, be frightened. Is the risk low? No. I I mean, yes, great. Be aggressive. That's what you need to know. You don't care whether it's going to be 26% or 28% or 29%. So, technically speaking, what these people the mistake they're making is that they're trying to optimize what's called the root mean square measurement of error. >> And that's just as silly because that's the wrong metric by which to measure things. Because what will happen then is you're optimizing for getting things right 90% of the time, 92% of the 95% of the time. But those 95% of the time don't matter and the 5% of the time that do matter, you get wrong. >> That's the issue and that's just the wrong way to do things. When we speak about risk and and off camera you gave me a nice analogy of the 7% of funds and and how in spite of that things happen. Yes. >> But I want to explore >> risk you can also look at as each independent trade has an independent outcome. It does. So therefore risk is um just it's a collection of things that happen. But then when I bring the human element into it, well then there's confirmational bias. There's a gambler's

fallacy where the human connects the the string of losses to then affect future behavior. How do you how do you factor that in? That's hard to factor in um unless you have a reasonably intelligent risk framework that other people i.e. not the trader are running. Uh-huh. >> But that has to be intelligent, not stupid. And this is what I was telling you off camera. I have a paper coming out, I think this month, in the journal of portfolio management where I show that the most popular method of managing risk at these pod shops like, you know, Millennium is one of them and other places that hire pods of traders is just simply stupid. Um, and it's stupid in the sense that it's statistically stupid. They're leaving profits on the table. And um I demonstrabably show so this is not even my opinion that in effect the reason they're successful is in spite of their risk management not because of their risk management. And so the issue is this. So the simple-minded way of managing risk with traders is to say you have a draw down limit and your draw down limit is pick a number 8% 10% 7% doesn't make any difference and if you exceed that you're out goodbye. You can show statistically that this is just dumb. And I do this in the paper um by setting up a scenario in which um there is every advantage given to the rule and every disadvantage given to my statement that this is a stupid idea. And I still show it's a stupid idea. And so what I did in this is I took um pairs of ETFs. I found specific periods with specific investment thesis for which ETF to be long and which ETF to be short. And I picked the precise period where it was the the most profitable period for this pair of ETFs to be long one and short the other. >> So you just had to hold the trade for whatever period of time it was profitable. Trade it and you would have a profit. And remember this is in perfect hindsight. >> And then I showed that if you imposed any kind of a draw down cutff on it, you

turned what was a certain profit because you know it's going to be profitable into a loss more often than not. Why does that happen or why is this a repeating thing? The reason it's a repeatable thing is that you need to divide you need to understand why a draw down occurred. So let's say for example uh uh some um you own a stock and some piece of really terrible news came out >> right and the stock fell. Well now as the risk manager you need to understand is it because this guy didn't do his due diligence the portfolio manager >> is it because the news was genuinely unexpected? Is it because this was some bizarre thing that happened to the supplier and the person could never have anticipated it etc. So I'll give you another example. Just uh not very long ago um we had these you know strange tariffs that got imposed right all of a sudden overnight seemingly by typing them into chat GPT and then chat GPT gives some numbers and they presented them in the rose garden and the stock market of course collapsed. >> Now if you've been long the stock market then and you hit your 10% draw down were you a bad money manager? The answer is obviously not. All of this needs to be nuanced. The problem is that the way these people run their portfolios is not nuanced. It's silly. What I also mentioned to you off camera was this perception that I and I think a lot of retail traders have is that the hedge funds have got it all figured out. They have the insider information. They have all the resources. They have the top talent. Therefore, they know better than us. As I've spoken to more and more traders and people that have the insight like yourself, the more I realize it's simply not the case. if you can shed some light like you have here on on irrational behavior. What frameworks or disadvantages do these large institutions have that work against them and that things that we don't have to have to deal with as an individual trader? >> Yeah. So that's a very good question and

I I put this if if somebody buys the Kindle edition of my book in the back of it is a uh is three presentations one of which is about the future of AI in finance >> and in one of them I say that one of the problems with uh with um traditional finance large hedge funds and so on is that they hire the same people trained by the same professors at the same schools on the same strategies at the and do the same thing at the same time and then they complain that they don't have anything unique. That that's the fundamental problem. What's happened to the hedge fund industry is that it used to be run by people like Michael Steinhart and George Soros and people like that that A were risking their own money, b understood something about the markets and c um were very clearly people that had to and want to take a specific position for a very specific reason. and they made concentrated bets and they basically bet that they knew what they were doing and they applied good risk management and they were smart and all the rest of it. What it's turned into is effectively mostly not always but mostly a mechanism by which large pools of institutional capital are locked up into vehicles that charge very high fees and do not produce any significant set of returns. And uh simultaneously what has also happened is that you now have this quote war for talent unquote where you know PMs are getting these very large guaranteed you know sums of money. But in most cases um you as the retail investor are way better off than any of the institutions because all you have to do to beat essentially all of these hedge funds is buy the spy and sit at home. And in the even medium term, you will beat essentially almost not all but almost any hedge fund out there by just buying the spy. So why even bother investing in them? And that was that famous bet which I've forgotten the details of George Soros and some hedge fund manager. You remember not George Soros sorry Warren Buffett.

>> He bet some hedge fund manager 10 years ago, 12 years ago, 15 years ago. Okay. >> Some sum of money. >> I'll get the details wrong. You can Google it. that uh this manager could pick any group of funds and uh uh uh Warren Buffett would pick the S&P 500 and then the bet was that over the next 10 years this group of funds would not beat the S&P 500 and of course the S&P beat it by some ridiculous margin >> and this just keeps happening over and over and over. So the larger hedge funds are essentially now vehicles for marketing and sales. They are not vehicles for actually making any money. So an interesting inflection point I've reached in my career off the back of this is I've been training for next month will be a decade and I've been through the whole journey and the whole human experience of the market and everything it brings out of you but uh you know after after some time I I found a sense of consistency but my approach as as we explored earlier is lower time frame it's scalping it's in certain pockets of time and it removed all the freedoms that I wanted from my life and I thought this is the game this is what what has to be done as I've got more financially mature Sure. I've I've I've taken the arm this year at investing quite significantly. >> Yeah. >> One step that I did was let's say I had $100,000 in a trading account because I'm taking one trade at a time. I'm never in two trades at once cuz I'm not swing trading. >> I was not utilizing the full margin and therefore I realized 80 or 90% of my account is just dead weight. It's dead capital. Y >> So I thought this year around the tariff time, why not take this portion out, maintain my lot size or position size? So as a risk on the 10% that is left obviously I'm risking more but as an overall of my portfolio I'm risking exactly the same differences I take this money and put it into the S&P and Tesla fortunately gold and just diversified. >> Yeah. >> And now that five months later after the

tariff thing I've realized on my 90% of my account that I just put into investments >> I'm up like aggregated about 25%. Obviously, it's a it's a a fortunate window, >> but it just makes me think why bother? Like all the effort that now I've also been a bit hands off in my trading because I'm making this money here. I no longer chase the markets and and as I extend it out to the conversations that I've had and I reflect on certain conversations I've had with others. The largest of money managers are not in the lower time frame or in the, you know, scalp intraday. They just take position trades. Yes. And then all of this after you said all of this just makes me think why bother even attempting to day trade or why bother even to manual trade when you have the the beauty of indexes and such forth. What is your reflection on that? I agree with it essentially 100%. Um and this is why um in my own um you know in the funds that I run um we must be the only hedge funds maybe that don't care about alpha. Um so you know it's pretty pretty funny but it is true. What do you care about? >> Risk and only risk. So basically my idea way back when was um 20 more than 20 years ago now that um what is being arbitrageed away is alpha. What cannot be arbitrageed away is risk because risk is generally a pylon. A sells so B gets a margin call. B cells so C gets a margin call. C sells so D gets a margin call and so on. You can't there's no way of stopping that from occurring. So there's no way of arbitrageing it away. All you want to do really is figure out periods of time when that's likely to happen and be out of the market if you can. And so my idea was I'll use all of these risk models that I just post spent 10 minutes criticizing and I'll basically see what the risk is and if the risk is high I'll be out of the market and if the risk is low I'll be long and levered. That was the idea and it would work every you know work two three four years in a row

and then it'll blow up. So I after a while I got fed up of things blowing up and I would talk to people on Wall Street constantly saying hey dude this blew up and they would always say always always literally always there wasn't a single exception to this they would say yeah that's a once in a lifetime event and I would always say to them the lifetime of who a lab mouse I mean seriously and that's when the realization hit me that this is the wrong way to do things you shouldn't be forecasting risk you should be reacting to it classify >> so first of all you're referring to black swan events as the once in a lifetime thing. >> Well, they're not really once in a lifetime events. They're like, for example, every so often the S&P will drop 30% for no reason at all, right? >> Well, okay. So, you can identify reason, but like for example, the COVID crisis hit, right? So, you can say it's the COVID crisis, but on the other hand, it's not like we didn't know that there was an infection that was about to go around the world. So, why did it drop 30% in 10 days? Why didn't it drop 30% over two months? Why didn't it drop 40% over six months? I mean, it could have been anything. It just happened to drop 30% over 10, you know, 20 days, I think it was. Happy to say I was out of two-thirds of it. Um, but that's the point is that if you had if you had had a risk model and you had been running it every day and you had been basically trading through the COVID crisis, you were dead. And even the very best hedge funds, Renaissance is a perfect example, um, were not able to trade through the COVID crisis properly. They got out too late and they got in too late and that's again because they're trying to forecast risk. They're not trying to react to it. >> You mentioned a moment ago of something that is low risk and high risk which is a sense a prediction or how are you correlating high and low? >> I'm I'm in the case in in in my specific case what I'm worried about is the risk

of a draw down >> risk of a large draw down in the S&P. So because that's what I worry about. Um but obviously if you're trading something else, it's the risk of a large draw on whatever asset you're trading. Um so it it turns out that risk is pretty predictable in the stock market in the in the sense of not I can tell you what it will be tomorrow. But I can tell you that if certain conditions obtain then the chances that the S&P will fall a significant amount have just gone up. >> And if those conditions don't obtain that the chances that the S&P will fall a significant amount have just gone down. >> And that's predictable and that's consistent and you can see it throughout history. And that's sort of my edge is I know what those conditions are. >> Can you talk me through the dance that people do where they bring in psychology and then they bring in mechanical systems or something objective and then human discretion, subjectivity, intuition, it it creates a whirlwind of differences of opinions. >> Uh what is your take on it specifically because you are quite systematic in your trading? >> I don't like it. In my opinion, you need to either be a discretionary trader that has certain inputs that you look at and then you go with the psychology um from you know there are lots of people that teach you how to deal with trading psychology, how to deal with losses and so on. >> So you look at those indicators and you try to be consistent whenever these indicators are in a certain mode I'm going to do a certain thing and then you just do that. >> That's one way of doing things. The other way of doing things is the way I do it, which is you write down very specific rules. And those are rules you follow. And that's the end of the discussion. And if you don't like the rules, well, then you got to go back and change them. >> You don't get to say at the last second, I don't like this trade. I'm not going to make it. That's not acceptable. And I've never done that.

>> So why have a system that is still manual but systematic as opposed to completely automated and remove human elements? >> Yeah. So well, it depends on what you mean by human elements. So in my case the system is it tells me what to do. I just have to actually enter the trade to do it. >> But there there is that last thought of like should I and the human filter. >> Yeah. I don't and that's that's a lens of emotion. >> I don't do it. >> Okay. >> I I absolutely over the years I've trained myself out of it. If the system says to do it, I just do it. >> If I have deep misgivings about what the system is telling me, I'll still do it. >> Then I'll go back and I'll do the research and see if my misgivings are justified. If what I'm saying is correct, if there's a way of improving the system, whatever. That's a different question. That's a research question. It's not something that you're allowed to do at the moment of making a trade. That's wrong. >> So, easier said than done. >> Easier said than done. >> How do you have the discipline or the mental frameworks to be able to act when maybe your gut is saying otherwise? >> Very, in my opinion, it's very simple in one sense and very difficult in another. The the the very simple aspect of it is you have to write down the rules yourself. You have to program the rules yourself and you have to test the rules yourself. And you have to test them every single which way you can think of. Try to break them in the nastiest way you can think of. Um, and I'll give you some examples of how to be nasty to your own trading rules in a second, but you need to be really nasty to them and try to break them and just keep trying to break them until you really throw your hands up and you say, "I can't take this anymore. I can't break them." That's what you really have to do. So, I'll give you I'll give you some examples of how you try to break trading rules. So, one easy way of trying to break trading rules is to add noise to your

inputs. So, essentially, let's say that you've got um I don't know, three pieces of data. You're going to take uh the difference between the Fed funds rate and the 10-year Treasury. Uh you're going to take the current um trading volume in the S&P 500. And you're going to take uh oh, what's another good one? Uh the distance from the 50-day moving average. just making this up right just on the spot. Um, now all of those require inputs. Take the inputs which which are presumably sitting in CSV files or something like that. Write a little program that adds noise to each day, random noise from some reasonable distribution. >> Okay? >> Right now you've got a data series that has noise added to it. Now run that through your system. What you should find is that your returns should be be um by with small amounts of noise it should be unaffected and with large amounts of noise it should start to degrade and what you should see is that there's a curve that degrades as you add more and more noise to the system. That's what you really want to see. What you don't want to see and this is what happens with most systematic systems is something like this where some amounts of noise produce a good result and other amounts of noise produce a bad result. That doesn't fly. Why would that happen? >> From the fact that you your original system is not real. You fitted noise, you didn't fit data. >> You fitted noise, you didn't fit data. >> Yes. >> Which means you never really had an advantage. So, uh the classic example of this which people still do, beginning traders particularly, is they'll take say some agricultural commodity and they'll say I'm going to trade a moving average system, a moving average crossover system. and then they'll try every single combination of of the two moving averages until they find the quote optimal moving average. That is almost never going to work, >> right? And you can see that from the fact that if you just added some noise

inputs. So, essentially, let's say that you've got um I don't know, three pieces of data. You're going to take uh the difference between the Fed funds rate and the 10-year Treasury. Uh you're going to take the current um trading volume in the S&P 500. And you're going to take uh oh, what's another good one? Uh the distance from the 50-day moving average. just making this up right just on the spot. Um, now all of those require inputs. Take the inputs which which are presumably sitting in CSV files or something like that. Write a little program that adds noise to each day, random noise from some reasonable distribution. >> Okay? >> Right now you've got a data series that has noise added to it. Now run that through your system. What you should find is that your returns should be be um by with small amounts of noise it should be unaffected and with large amounts of noise it should start to degrade and what you should see is that there's a curve that degrades as you add more and more noise to the system. That's what you really want to see. What you don't want to see and this is what happens with most systematic systems is something like this where some amounts of noise produce a good result and other amounts of noise produce a bad result. That doesn't fly. Why would that happen? >> From the fact that you your original system is not real. You fitted noise, you didn't fit data. >> You fitted noise, you didn't fit data. >> Yes. >> Which means you never really had an advantage. So, uh the classic example of this which people still do, beginning traders particularly, is they'll take say some agricultural commodity and they'll say I'm going to trade a moving average system, a moving average crossover system. and then they'll try every single combination of of the two moving averages until they find the quote optimal moving average. That is almost never going to work, >> right? And you can see that from the fact that if you just added some noise

to the system, that optimal moving average wouldn't work anymore and you'd get something that looked like this. So that tells you that the system has a problem. That's just one example. So you you have to stress you have to really say, I'm going to try to break my system. I'm going to really try to destroy it in any way that I can. And once you've run out of ideas for ways of how to destroy your system, you probably have something that will work. >> I think off the back of that leg work that you have to do, you're only left with confidence because you've got so much data behind you. You've got so much uh testing behind you that when it comes to an opportunity in the market, you probably won't think twice because you've done the foundation work, probably what most avoid uh and therefore fuel the emotions in the moment. Have you done a lot of things specifically for your psychology or is it all the data behind you and all of the numbers behind you that enable you to act in a sound way? It's it's entirely the fact that I've done I've done the work. >> It's like I think it's sort of like being a professional athlete. You go into a match, you don't know whether you're going to win or lose, but if you've worked hard enough on whatever it is that you're trying to play at the end of it, that's all you can do. >> And whatever's going to happen is going to happen. >> Um you always have to be careful how much you risk. Obviously, that's that's obvious. You should never be risking 100% of your portfolio sorts of things. That's stupid. All the people do it. Um, and you have to be willing to basically be very upset. I mean, I've had losing periods. I had losing period in 2022. Uh, in 2022, I had seven straight trades where uh it looked to me to the system, I say me, but it's the system that it's time to get back into the market. >> We missed the first drop, by the way, in 2022. It was great. Um, it looked seven straight times like this was time to get

back into the market. We took a small position and then the small position would get hit by a 7% drop the next day or 5% drop the next day and it happened seven straight times. I was tearing my hair out by the end. But I wasn't I was just upset. But it wasn't like I was not going to take the next trade >> because this is a point where the beginner trader and someone with experience shines. And that is it's better to do the right thing and get a bad outcome. >> Yes. >> Than do the wrong thing and get a good outcome. >> Yes. >> Explore why that is so dangerous because I know even myself I used to do it all the time. I took a trade I shouldn't have taken. Ended up being a win. Confirmational bias. I'm going to do that again because you're incentivized to. >> Yes. Actually, you just said it. That's that's the real reason. Um the thing is that you have to understand that that trading is meant to be a business. you. It's meant to be something you make money from. It's not meant to be exciting. >> I want my life to be as boring as possible. I I hate excitement. I don't want any nothing. Thank you. Nothing. Um I don't want to be happy. I don't want to be sad. I don't want anything. I just want to essentially be able to ignore everything as much as I can for mental equinimity. >> And if you have uh not put yourself in that state, that's when you're going to run into problems. You have to be in a state where your your system is set up, your risk controls are set up, your uh your trading rules are such and so on that you basically just trade it >> and move on. >> Do you believe there can be an idea of a trade idea that is setting up systematically in your case that you can call a high conviction play and would you modify your risk in a high conviction play? Yes. But what I would tell you is that you should have already built that into your rules >> and I I and I have. >> So you would have um categories of trade types say this is this is my A+ and so

forth down. And how do you do you maintain risk and allow large numbers to play out >> or do you say high conviction equals higher risk >> worse offset is still has an alpha uh less risk. >> Yes, that's exactly what you do. So basically based in on the expected reward and the expected risk, you have to do your position sizing. And so the most important thing there is figuring out based on whatever it is that you're doing, what your sizing is going to be. And generally speaking, the best way to do this is um um is to use the the Kelly criterion, the which is the optimal trade sizing criterion, and then take a very small fraction of that. >> What is this? I'm not. the the Kelly criterion is basically um if you have a series of trades, a series of wins and losses, what percentage of your capital should you risk on each trade to get the maximum growth. >> The problem with the Kelly criterion is that if you actually follow it, your draw downs are like 95%. No one's going to live through 95% draw downs. But the nice thing about the Kelly criterion is that it is in some sense statistically reasonable. And because it's statistically reasonable, what you can do is say I don't want 95% draw downs, but I'm willing to live with 45% draw down. Say, right, if you're long the stock market, you're willing to live with 55% draw downs actually. So, um, you say fine, I'm willing to live with 55% draw downs. So, I'm going to have my position set up such that my maximum expected draw down when lots of things go wrong is 55%. >> Say, so that's how you size your positions. So you you've already thought about your worst case series of trades going wrong, everything not working out the way you want it, and you've sized your position such that you're not happy, but you can live with it. >> And why not take the opposite approach, which is just standardized risk for every trade type uh to kind of even out the highs and lows. So that's effectively what you're doing when you

forth down. And how do you do you maintain risk and allow large numbers to play out >> or do you say high conviction equals higher risk >> worse offset is still has an alpha uh less risk. >> Yes, that's exactly what you do. So basically based in on the expected reward and the expected risk, you have to do your position sizing. And so the most important thing there is figuring out based on whatever it is that you're doing, what your sizing is going to be. And generally speaking, the best way to do this is um um is to use the the Kelly criterion, the which is the optimal trade sizing criterion, and then take a very small fraction of that. >> What is this? I'm not. the the Kelly criterion is basically um if you have a series of trades, a series of wins and losses, what percentage of your capital should you risk on each trade to get the maximum growth. >> The problem with the Kelly criterion is that if you actually follow it, your draw downs are like 95%. No one's going to live through 95% draw downs. But the nice thing about the Kelly criterion is that it is in some sense statistically reasonable. And because it's statistically reasonable, what you can do is say I don't want 95% draw downs, but I'm willing to live with 45% draw down. Say, right, if you're long the stock market, you're willing to live with 55% draw downs actually. So, um, you say fine, I'm willing to live with 55% draw downs. So, I'm going to have my position set up such that my maximum expected draw down when lots of things go wrong is 55%. >> Say, so that's how you size your positions. So you you've already thought about your worst case series of trades going wrong, everything not working out the way you want it, and you've sized your position such that you're not happy, but you can live with it. >> And why not take the opposite approach, which is just standardized risk for every trade type uh to kind of even out the highs and lows. So that's effectively what you're doing when you

do that. So so, so the issue is the is the following. When you're a long only money manager, for example, or even if you're a long short money manager, one of the problems that you've got is you're not sizing your gross position size based on what the market risk is. >> So, if you're a, you know, if you're a mutual fund manager, you expected to be 100% invested all the time. So, there's no position sizing taking place there. All you can do is move your your stocks around. >> Mhm. >> That's sort of the wrong way to do it. You have to actually be free to vary your position sizes. And the reason you're varying your position sizes is what you're trying to keep constant in some sense is the expected draw down of your portfolio. So to give you a perfect example of this, remember I said two-thirds of the risk roughly speaking is generally when the market is below some long-term line. >> Mh. What that means is that even if you have a positive expected value trade below that long-term line, if you're trying to keep your draw downs limited, you should actually be limiting the size of your position when you're below that long-term line. So if you were going to invest say 50% above the line, you should be investing I don't know 25% below the line because what you're trying to do is to keep the risk roughly speaking constant. So, so there's a there's standard deviations on where price is how you modulate risk. I try not to use standard deviations. Okay. >> And the reason I try not to use standard deviations although sometimes you're just forced to is because the the uh that assumes that the uh returns are normally distributed >> and they're not. >> Okay. >> And they they are most certainly not. They are what's called in the in the industry leptocrotic. And what leptocautic means is that the if you if you overlay the distribution of returns over a Gaussian distribution, the peak will be thinner and the tails will be fatter. >> Got it? >> That's leptocratic. Extreme things

do that. So so, so the issue is the is the following. When you're a long only money manager, for example, or even if you're a long short money manager, one of the problems that you've got is you're not sizing your gross position size based on what the market risk is. >> So, if you're a, you know, if you're a mutual fund manager, you expected to be 100% invested all the time. So, there's no position sizing taking place there. All you can do is move your your stocks around. >> Mhm. >> That's sort of the wrong way to do it. You have to actually be free to vary your position sizes. And the reason you're varying your position sizes is what you're trying to keep constant in some sense is the expected draw down of your portfolio. So to give you a perfect example of this, remember I said two-thirds of the risk roughly speaking is generally when the market is below some long-term line. >> Mh. What that means is that even if you have a positive expected value trade below that long-term line, if you're trying to keep your draw downs limited, you should actually be limiting the size of your position when you're below that long-term line. So if you were going to invest say 50% above the line, you should be investing I don't know 25% below the line because what you're trying to do is to keep the risk roughly speaking constant. So, so there's a there's standard deviations on where price is how you modulate risk. I try not to use standard deviations. Okay. >> And the reason I try not to use standard deviations although sometimes you're just forced to is because the the uh that assumes that the uh returns are normally distributed >> and they're not. >> Okay. >> And they they are most certainly not. They are what's called in the in the industry leptocrotic. And what leptocautic means is that the if you if you overlay the distribution of returns over a Gaussian distribution, the peak will be thinner and the tails will be fatter. >> Got it? >> That's leptocratic. Extreme things

happen more often than you expect and little things happen less often than you expect >> versus the normal distribution. >> So that's the issue. That's why you have to do it that way. So I try not to use standard deviation because standard deviation is really a measure of the width of a Gaussian distribution. It's not the measure of a of of a good measure of a leptocautic distribution. And there's also some good evidence by the way to suggest that the standard deviation of the stock market is not defined. It might be infinite. >> Should I go there? What does that mean? What it means is that the the there are there are there are all kinds of technical mathematical points that come with trying to define what the width of a distribution is because you know it's it's like this and there is a certain point at which you say what the width is. But if the width can go out almost any distance, right? Then it's not clear what its width actually is or what you should really be measuring. And so a stock can only go down 100%. >> But it can go up >> infinitely. >> Infinitely, right? If you were short Tesla all this time, you're crying, right? Unless you got the timing exactly right. >> So what was the what good would standard deviation have been to you when trying to measure the risk in Tesla? The answer is it wouldn't have done you any good at all. when you are operating a hedge fund, there's a lot of things to do and and in this city where we are right now, there's the flash offices and the excessive teams and and everything that goes on >> and that's how they justify their management fee, I'm sure. >> Um but then obviously it means you can't be as adaptable. Changes come slow. There's a lot of bureaucracy. >> The approach you've taken is it seems like it's a very lean team. Dare I say it's just a handful of people, maybe less. Yes. explore to me your philosophy on being a hedge fund and competing in the same arena but doing it in such a

different way down to team and resources in terms of you could hire the best talent and you you could do a lot of things why do you choose not to well for multiple reasons the first is I'm frightened of group think and as I said to you earlier I get very uncomfortable when people agree with me um it's just my nature um I'm and and then if somebody says to you well I'm also contrarian so we we you know we'll agree to disagree, well then I'll want to disagree with that, too. So, I like to disagree with myself as well. Um, so put that aside. Um, that that's the first thing. I I'm very scared of group think. The second thing is that to launch a hedge fund in that way requires, as you just said, a fancy office, $200 million of capital, a large team, you know, huge expense on Bloomberg terminals and all the rest of it. And at the end of it, no great guarantee that you're going to succeed. Mhm. >> So what ends up happening in these cases is that essentially what people are doing is they're playing with what I call OPM, other people's money. If you're good at raising OPM, then essentially what you do is you pick up pennies in front of streamrollers. Now what does that mean? What that means is you try to make bets such that the upside is all yours and the downside is the clients. >> So effectively, you know, let's say you're down for the year. Swing for the fences. Who cares? Um if you're down 10% or you're down 30% doesn't make any difference to you. you're not getting your incentive fee anyway. >> Um, and if it's, you know, down 50%, you shut the fund and go raise the money from somebody else the next time, right? That's what they all do. >> That's a way of maximizing in some sense your own some sort of expected utility, but that's not the way of somebody who's a sort of lifetime trader like me or some of the people that I, you know, worked with like Joe Richie. For us, this is what we do. I mean we're traders to sort of fiber of your being

sort of thing and we can't do that. So I don't want to be in a situation where um I have to do things because a risk committee said I have to do them or because there was some mandate that said you need to be 100% invested or whatever. I want to do that which makes sense is statistically palatable where the risk is tolerable where I'm not doing anything insane. And I want my clients to think that because I'm in the same boat with them, my capital is at risk, too, that I'm never going to do anything that's going to be nutty. >> Um, and so far so good. I mean, it's worked very nicely. But that's why I don't want to do it that way. Should trading or let's say should optimal trading be a lone wolf sport where you don't allow an echo chamber of of group thinking? um or or should you and can you benefit from uh accountability or externalizing risk and the kind of benefits that may come from a trading floor environment? Depends on your personality. >> Okay. >> Um very much um it doesn't fit my personality. Um the reason it doesn't fit my personality is that um in the past um I I worked with some people where um they were very smart people. I liked them. We got along very well. But they liked to argue. >> But the problem is that when you have a trading idea, even if it's going to be a systematic trading idea, it's very nebulous. It's not at the moment well formed. It's somewhat intuitive, whatever it is, until you've tested it and stress test it and all the rest of it. And the argument for me doesn't help. I don't need somebody to be skeptical. I'm already being skeptical. I don't want to lose money. Thank you very much. Um, what I need is somebody to tell me if I'm forgetting something important >> or something that, you know, I may not have thought of. Yeah. Blind spots. >> And so, one of the problems with the institutional structure is it's actually designed for cookie cutter outcome. It's not designed for really nuanced thinking. And so, what my business

partner and I do now is very different is we we I can't remember the last time we ever had an argument. We're basically just trying to think through everything in the most rational way we possibly can and then do the best we possibly can and then move on and that's it. That's really all you can do. And I don't think the institutional environment for me anyway is conducive to that. The other problem is that pretty frequently in one of the institutional environments you are made to do things that you know are a bad idea and that would drive me bananas. I couldn't take it. I'd scream. Can >> can you elaborate on that? So u a perfect example uh is uh a set of papers written by Andre Schlifer uh in the 1980s early 1990s um economist at Harvard. Um for the longest time uh there was this company Royal Dutch Shell and I if I I'll get this wrong again. Uh Royal Dutch I think perhaps was traded in the Netherlands. Shell I think was traded in London. I think that's right. I could be getting this wrong but the idea is right. Um you would have situations where the two prices and it's the same company were massively different. So you had would look like a free arbitrage. But here's the problem. Many people did the arbitrage. Many people had their heads handed to them. Here's the problem. Just because it looked like a free arbitrage doesn't mean it was one. because you needed to ask the question, why did this arbitrage open up in the first place? What are you missing? >> And in an institutional environment, that doesn't happen. And so, another great example of this is long-term capital management. You remember in 97 when they blew up and basically blew the world up, the reason that they blew up despite having a Nobel Prize winner on their team is that they were arbitrageing on the run and off therun treasuries. And basically what happens is the treasury issues say 30-year bonds and issues them periodically. And whatever one just issued is the fresh

bond and that's called the on ther run treasury. >> Mhm. >> The instant a new set of bonds is issued that's the bond now that everybody trades and everybody stops trading the old bond even though it's essentially identical asset. Yes. >> And so what happens is that the two assets diverge in their yield. >> And so the idea in long-term capital management was these are two identical assets. Financial theory tells us they're exactly the same. They're trading at different prices. We should arbitrage this and leverage it at 40 to1, which is what they did. What they forgot is that there's a liquidity risk. >> And the liquidity risk is that because no one's trading the off the run bond, it can go off to some other random price and there's nothing you can do about it. >> And if you're short it, you're dead. >> And and so that's that that's exactly the point. And so one of the problems with institutional type money management is that because they have cookie cutter processes, they are not able to deal with the nuances of actually how you would handle real life risk situations. And that's why my paper in the journal of portfolio management, you have a 7% or 8% or 9% draw cut off because you can't be bothered to actually do things properly. >> And if it works, it works I guess but it's silly. What is the difference between a trading plan and and the systematic rules you may have versus what you're referring to here as cookie cookie cuts and mentalities that is holding you back? Um, yeah, that's a good question. Uh, the the the the answer is that trading rules are set in a specific time frame for a specific asset in a specific way to produce a specific outcome. The cookie cutter rules are not like that at all. The cookie cutter rules are not specialized for a given market. They're not specialized for specific volatility. They're not specialized for a given market. They're not specialized for any of this. They're just cookie cutter rules. >> And so what the other thing that of

course happens, as you would imagine, you know, people rationally say, "Okay, well, I can't lose more than 7% and they've allocated me, you know, $500 million. So I tell you what, I'll pretend the portfolio is $50 million. So then I'll never lose more than 7%." But that's just dumb again. >> Mhm. >> Why would you do that? I've spoken to a variety of guests on the show and a unanimous common denominator between all of them is the emphasis they put on data and actually knowing the inner workings and the insight of your edge and your performance. That's why I'm proud to bring a partner of the show, Tradzella, the number one journaling, back testing, and all-in-one insight experience created by traders for traders. What Tradezella really gives you is deep insights about your trading that would ordinarily not be visible. Whether it's through understanding your trade types and playbooks or even insights powered by artificial intelligence through Zela AI. Whether you trade forex, futures, cryptos, the stock market, it all seamlessly connects to Tradzella. So there is no additional work. You've seen me reference it dozens of times and all of the benefits I've had in my trading from the insights I found from my Tradzilla. So join myself and thousands of other viewers of the show. You'll get the best discount using the link in the description or code toot for Titans of Tomorrow. Can you give me some examples of what could be deemed a cookie cutter approach? But in terms of a retail trader, just to give an insight of what maybe is coming to my head of, you know, you're told always do 1% risk or buy and demand, sell and supply or you know, these certain rules that you hear very often which seem like they're embedded in logic or truth >> but that can actually be holding someone back. >> Yeah. So the problem with all of those truths is what is the indicator of demand and supply? What are you going to look at? What is what is it that's going to tell you that there's an excess

demand or an excess supply? >> Previous previous price points, >> maybe, maybe not. Have you tested it? Do you know that it's true? Have you looked at a thousand charts? Have you made sure that this always is the case or frequently is the case or whatever? So that's the first question. The second question in terms of of don't risk more than 1%, don't risk more than 2%, ruins, whatever is again you need to let's say that you're going to be a a chart reading trader. Then you need to have a thousand charts, 2,000 charts, 5,000 charts, and you need to go through them by hand, each and every one of them, and without bias, which is hard, figure out what your entry and exit points would be, and write down why you thought that was the entry point, why you thought that was the exit point. >> Don't worry about the P&L yet. then take the thousand charts and then look at what the sequence of trades was, what could have gone wrong, you know, what your reasoning was, all the rest of it. That's hard to do. That's why it's better to do it via computer. But if you want to do it by hand, you can absolutely look at a thousand charts. William O'Neal did uh you know, how to make money in stocks, the guy that I mentioned. I don't do that, but he looked at something like a thousand charts probably to figure out his trading system. >> How can someone use now AI to their advantage to do this kind of heavy lifting? >> Yeah, so that's a very good question. Um, it's a little it's a little hard to get started, but once you started, AI is a massive help. So, what do I mean by that? You have to have some direction in which to point the AI. >> So, what are you going to ask the AI? Number one, and number two, how are you going to judge if the AI's answers make sense? Right? Those are your two conflicting goals. Those are your two conflicting problems. And to to be able to even ask the AI questions and judge the answers, you actually need to know something. Now, how do you know

something? Well, there's only two ways of doing it. First is to read lots and lots and lots of books, which I did when I started and um and and economics papers. And the second thing that you need to do after that is you need to start actually trading with real money in the market. There's no substitute for that. And expect to lose money. Period. For what game? Just market experience. >> Market experience. You need to understand two things. You need to understand how to lose money and what your reaction will be to the loss and how you're going to act. >> And the second is you need to understand what it is about your logic that went wrong that caused you to lose money >> and be able to distinguish bad luck from bad process. And this is why we come back to what you just said asked earlier. This is why bad process is such a disaster is because you didn't learn anything from it. In fact, you learned the wrong thing from it. >> And undoing things is much harder than doing them in the first place. >> Or you you you may focus on building good psychology, but if you're building good psychology on a flawed system, >> you're corrupting yourself in essence. >> Yes. What about the the third one that came to my mind here of like a valid loss where you have wrong behavior uh right behavior wrong outcome and then just a valid loss. H how do you how do you navigate that in a system or how do you identify that compared to the other two? So the way you would the way you identify a valid loss is by asking did you follow the set of rules that you were supposed to be following and if the answer is you did then really you just have to be upset about it but move on. So what's the point of market experience to then then try and train an intuition or notice subconsciously patterns but then stop yourself to actually act upon it in any way because that would introduce an invalid loss. No, it's more that you want to train your intuition to figure out what the valid patterns are.

something? Well, there's only two ways of doing it. First is to read lots and lots and lots of books, which I did when I started and um and and economics papers. And the second thing that you need to do after that is you need to start actually trading with real money in the market. There's no substitute for that. And expect to lose money. Period. For what game? Just market experience. >> Market experience. You need to understand two things. You need to understand how to lose money and what your reaction will be to the loss and how you're going to act. >> And the second is you need to understand what it is about your logic that went wrong that caused you to lose money >> and be able to distinguish bad luck from bad process. And this is why we come back to what you just said asked earlier. This is why bad process is such a disaster is because you didn't learn anything from it. In fact, you learned the wrong thing from it. >> And undoing things is much harder than doing them in the first place. >> Or you you you may focus on building good psychology, but if you're building good psychology on a flawed system, >> you're corrupting yourself in essence. >> Yes. What about the the third one that came to my mind here of like a valid loss where you have wrong behavior uh right behavior wrong outcome and then just a valid loss. H how do you how do you navigate that in a system or how do you identify that compared to the other two? So the way you would the way you identify a valid loss is by asking did you follow the set of rules that you were supposed to be following and if the answer is you did then really you just have to be upset about it but move on. So what's the point of market experience to then then try and train an intuition or notice subconsciously patterns but then stop yourself to actually act upon it in any way because that would introduce an invalid loss. No, it's more that you want to train your intuition to figure out what the valid patterns are.

>> Okay, it's in the building phase. >> It's in the building phase. And >> you don't want to build your building on shaky foundations. Let's put it that way. And the purpose of losing the money is to find out where the foundations are shaking >> cuz you never you never learn anything really from making money. You only learn it from losing money. That's what I've learned in my years. >> What is your thoughts on traders that you often use justification of a trade or probably you see it in the professional money management space too. They justify behavior and say it was my market experience or my intuition led me to take that behavior which is why I can't fully explain why I took that behavior and it was a positive outcome. So what does it matter? Um that's most well it depends who it is depends why they're saying it depends very context dependent but it's not necessarily a wrong answer. It could actually be the right answer. You see that's the thing I was saying about nebulous trade ideas. It can be something you can't really explain but you do it anyway. Like George Soros used to say that when his positions were bad he got a backachche and whenever his back hurt he knew he had to exit his positions. >> This is his famous story. Um but to get to that state you need to have had a lot of practice. It's not something that is going to come to you without having done a lot of trading and practice and learning and so on. >> In general life in pursuit of mastery you end up arriving at subconscious competence. You end up in a flow state some may call it >> where you like I'm having this conversation with you. I'm not necessarily focusing on how I move my hands. It just intuitively happens. If is this something you can arrive to in the markets and if you do have any subconscious competence is that not also a way to describe it's a gut feeling that I can't explain. So I haven't bought or I haven't got enough clarity on my thinking process because if I

can't explain it maybe I don't truly understand it. What is the difference between flow state subconscious competence and you haven't quite understood it enough yet to define it. That's essentially unanswerable except by looking at your returns. So, it's exactly the same problem that say a tennis player has when they're trying to climb the ranks. Um, are they ever going to be good enough to be world number one? They don't know until they're world number one. There's just no way of knowing. Can't be done. >> Um, you don't know that until you look at your results. And the only thing that tells you if you're any good is your results. And um if your results are good and they're reasonably consistently good then you need to develop the confidence that hey my intuition does know kind of what it's doing. And then what you have to do is you have to be very careful of two things. You have to be very careful of uh overconfidence which will lead to overtrading and too much leverage. And uh uh the second is um allowing that confidence to become fragile. What is what is the place of ego in the market where as you just mentioned overconfidence overzealous overrisking, but then there's also the side of ego which is self-preservation but then it's also identity of I want to be right. How do you harness ego as opposed to letting it be to your detriment? >> That's a great question. I forget who said this but it's one of the market wizards interviews. Um and he said uh everybody gets from the market that which they want. And I think that's really true. So your ego has to be focused really on what it is you want from the market. Are you looking for excitement? Are you looking for a diversion? Are you looking for a gamble? Are you look what are you looking for? Or are you just looking to make it as literally boring as you possibly can? >> And my personality is I have 100 other things I would like to do. I want to make my trading life as literally boring

as it can. On any day, you should not be able to tell whether I'm making a loss or a profit. Um, I shouldn't even be able to tell whether they're making a loss or a profit and I should just be able to ignore what's going on completely. That's the state you want to get into is if you're going to make trades, forget about whether they made a profit or a loss. Just make the trade because it made sense. >> How long how long have you spent building your system? Because you mentioned earlier to me off camera the idea of you spend years building it, testing it and trying to break it and then you have a viable product and then you're continually adding and refining until it's like marginal gains and you know you reach you reach a plateau thereafter does the market evolve? Have you seen market changes or alpha decay or you know certain things that means you have to now go back to the building blocks and if that is the case upon what time horizon versus this is a valid loss versus no this is my edge decaying. Yeah that's a good question. So because I'm really risk focused as opposed to edge focused that's much easier for me than it would be otherwise because alpha DK is the hardest thing in the world to figure out by the way. It's really difficult to know, particularly when you're running a long short portfolio, which I' I've done obviously in the past, um, why it's not making money. It's really hard. You can make up stories, you can do tests, you can run regressions, you can do all kinds of things, but at the end of the day, it's just a story. You really don't know. >> That's the truth. Um, it's a little bit easier when it comes to figuring out risk. And the reason is that the drivers of risk more or less remain the same in the market more or less over the long term. they don't really change. Um the only uh thing that I have seen recently over the last couple of years where you sort of scratch your head and say huh is uh is the yield curve. So um um I think

it's Campell Harvey that discovered this like 25 30 years ago. Um whenever the yield curve is inverted a recession invariably follows >> and also because a recession invariably follows the S&P invariably goes down. We've had an inverted yield curve now more or less for two years. Not a whole hell of a lot has happened. >> Yes, we did have the market decline, but that was identifiably because of the Trump tariffs. Nothing to do with the recession. So, that doesn't count. Um, so and then the COVID decline. Was that a recession decline or was that a COVID decline? I don't know because the yield curve was inverted at the time. So, is that a success or a failure? I'm not sure. that is the one indicator which is a risk indicator that we do look at where you look at it like that and say h I'm not entirely sure. So that's one we're just seriously looking at and thinking about as hard as we possibly can. Um the safest thing to do is to not ignore it. So we don't ignore it but that that is one where there's been some change in the market for which I can give you a hypothesis but it's merely a hypothesis. >> Here you're basically trying to differentiate correlation and causation. Yes. Was it co or was it the yield curve? >> Yes. >> Okay. How do you in general what is the process to differentiate in in many for example in in forms of technicals correlation and causation usually economics okay you you need to really uh focus on what the economics and I mean economics proper economics academic economics what the rationale is from economics for whatever statement it is that you're making and you also need to understand which particular school of economics it comes from so like you'll have people that are very good traders that are Austrian, you know, follow the Austrian school. >> Um, you have very good traders that follow the Keynesian school. They're very good traders. They're neo, you know, classical or whatever. But what they've done is they've taken whatever

insights you can get from that form of economics and then they've used that to inform their view of the world, but still understanding what the limitations of that view are. Mhm. >> So you you really you really have to use economics to figure out whether something is real or whether something is not real because finance will not tell you. >> I think it's very clear to say that fundamentals or economics, geopolitical factors on a long-term horizon will drive price. On a short-term horizon, can technicals in a self-fulfilling prophecy kind of way determine what price will do? >> Define technicals. >> We came to a level of support and therefore her mentality. A lot of people may buy on that support or a trend line or a demand area. Uh and therefore that will happen not because of fundamentals but because of the technicals. Yes, it can. Uh studies suggest that it does but it is as best one can tell a fairly short-lived effect. So for example, if you find a channel and there's been a lot of you know times when the price has bounced off a certain level, there's a very good chance that there's going to be a bunch of stops below. >> So if the market drives through those stops, it's a nice short-term scalp to short here. wait for it to fall a bit and then buy it back. Great. Absolutely. That that's true. It's much harder to justify that as being a long-term statement. I finally have a special offer to share with all of you from the US or my futures traders, which is over 20% of the listeners of the show. And that is Alpha Futures, a leading futures prop firm that is working with Trader of 8 and Ninja Trader that are compliant with CME regulations with the largest end of day balance drawdown in the industry and 90% profit split and same day payouts and with the most competitive pricing in the industry with accounts starting at just $79. On top of that, just by being a viewer of the show, you get up to 40% off all evaluations. So why not get

started with an evaluation right away trading $50,000 $100,000 and you already know the power of prop firms and larger capital. So go ahead and use the link in the description or code toot for the best prices in the industry plus the best discounts in the industry to make this a home run offer if you are a futures trader. So ju just describing that price signature of a predictable area stops are likely below price goes grabs the stops and then goes in the direction they predict any stop loss hunts. It's it's a dynamic I've explored a lot with many guests and I never come to a clarity of what's going on here because it gives the illusion that there is a higher power in the market and they are coming to the inferior and and and exploiting them. >> Yes. >> If we are to assume that that premise is it even worthwhile to then for them to put the liquidity to move the price to grab the crumbs what it seems of what those stops could be. What is the mechanics of what is going on here? Because I see the signature all the time. >> That's an question. Here's the answer to that. I what wondered about that for years. Those advantages are taken by trade placement uh algorithms. >> So now you ask what does that mean exactly? So now imagine that you um you have um how do I explain this? Le let's say that you are a person that's writing an algorithm that is going to do VWAP volume weighted average price and you are given um a buy order for a large number of shares and it's going to last a few hours. What you're going to try to do is is if you have a smart algorithm, you're going to try to find those regions where there's this weakness and you're going to try to use those to do more buying >> because since you can be relatively certain higher than 51% whatever that some of the triggering of the stops is going to produce the price going down, you can use that as a way of getting a better price than you otherwise would. So, it's a way of improving your

execution. who who's creating the cause like the cause is somebody an institutional guy that wants to buy million shares of IBM because he wants to invest in IBM >> right he doesn't care about stops and things going up and down and all the rest of it but there is that demand but that demand is not all going to come in at one moment >> so is is it a a matching algorithm to minimize slippage >> yes it's a and to get the better price and so you know that you could that if something is collapsing during that period of time you're getting a better price than you would otherwise. So get in there and take advantage of it as much as you can. >> And and therefore, how do they engineer it? Because the logic makes sense. There's a bunch of stops. I got a guy with a big order. Might as well drive down price. A lot of things happen. Liquidity is found. But who is creating or engineering that? >> I can tell you how we did it. When I was helping Joe with his u um trading algorithms, what we were doing is we were actually using what we call trader logic, Joe's term. And the idea was to do exactly that. Write in a computer program what a trader does as visual identification of you know areas of supply and demand or areas of support and resistance and so on so forth. And basically say we're going to adjust our trading algorithm where you know we're getting orders from customers and we're going to adjust the trading algorithm to take advantage of these particular periods where something interesting might be happening in the market. So another one for example was I mentioned earlier was opening range breakout. So you knew for example that statistically speaking if there was a breakout from the opening range the stock was likely to go that way for the rest of the day. So you speeded up your VWOP >> on the all day VW. >> Okay. >> So I I want to speak to you in my language and hopefully you understand but I'm a purely technical trader and I'm down in the lower time frames. I'm

execution. who who's creating the cause like the cause is somebody an institutional guy that wants to buy million shares of IBM because he wants to invest in IBM >> right he doesn't care about stops and things going up and down and all the rest of it but there is that demand but that demand is not all going to come in at one moment >> so is is it a a matching algorithm to minimize slippage >> yes it's a and to get the better price and so you know that you could that if something is collapsing during that period of time you're getting a better price than you would otherwise. So get in there and take advantage of it as much as you can. >> And and therefore, how do they engineer it? Because the logic makes sense. There's a bunch of stops. I got a guy with a big order. Might as well drive down price. A lot of things happen. Liquidity is found. But who is creating or engineering that? >> I can tell you how we did it. When I was helping Joe with his u um trading algorithms, what we were doing is we were actually using what we call trader logic, Joe's term. And the idea was to do exactly that. Write in a computer program what a trader does as visual identification of you know areas of supply and demand or areas of support and resistance and so on so forth. And basically say we're going to adjust our trading algorithm where you know we're getting orders from customers and we're going to adjust the trading algorithm to take advantage of these particular periods where something interesting might be happening in the market. So another one for example was I mentioned earlier was opening range breakout. So you knew for example that statistically speaking if there was a breakout from the opening range the stock was likely to go that way for the rest of the day. So you speeded up your VWOP >> on the all day VW. >> Okay. >> So I I want to speak to you in my language and hopefully you understand but I'm a purely technical trader and I'm down in the lower time frames. I'm

in and out within a couple of hours. >> Y >> and what I'm looking to exploit is what we're describing here. Market open where I see more liquidity, more orders. And then I'm looking for predictable areas. Could be an Asia low range. Could be a support level. I love take taking out a demand area. When I see that is getting swept and then in the right time, usually open, I see a sense of confirmation. Could be as simple as a break of structure in my direction. >> I'll look to long uh stop loss at the low of that sweep and look to a session high or something like that. >> Yeah. >> And very easy or very often I can get a 1 to three or 1 to four risk reward in the space of an hour and that became my system. >> Y >> can you help me understand the why of what I do? Because I've seen it a million times. So, I can believe in it because I've tested it, seen it, but I I rarely understand why it's working. And therefore, the valid losses that I take that optically appear the same. I don't understand why it was a loss. I just accept it was a loss. Think of it as an iceberg. Um, basically, when there's an institutional order, what you see is some number of shares, but in fact, there's a giant iceberg below them >> that trigger upon the first one. trigger upon the first one and that's why you get the drift in that direction and all that's happening to you when you get losses is that something else happened and either you made a mistake or you something else looked like the iceberg but it wasn't the iceberg. >> What you're taking advantage of is you're saying listen I'm the uh you know what is that little fish that eats the the parasites on top of a shark. >> Uh I know you're for let's say like a leech. >> Yeah. Right. So that's the idea. So that's what you're doing. You're basically saying, "Okay, I know there's a whale out here. He's going to be trading in this direction the rest of the day. I want to be trading in his direction and I want to just basically

whenever I can take some Yeah, exact catch a ride." And what you're trying to do is you're trying to identify when that ride might occur. >> And what you found in your trading rules is when that ride might be occurring, presumably more often than 50% or whatever. And you're basically making a nice profit from it. And one thing you could do, by the way, to help with your um P&L is don't put your stops at the obvious places. Um am I not safe to assume that the area where the predictable stop losses were that then got taken out that for some time that should be the protected low of the day? >> Yes, that's correct. >> And therefore, is that an obvious place to put it or not? >> That's not an obvious place to put it. But an obvious place to put it is um you know, here's the market going like this. here's a you know line I can draw across the lows support level fine I'm going to put it you know one tick below the support level bad idea don't do that >> or if it's a round number please don't put it on a round number okay what's the story though because when I when I try and understand it further I feel like I'm getting into conspiracy world where they are they are hunting the stops of the retail >> no they don't have to hunt the stops but visually that seems what's going >> it seems that way but they don't have to actually hunt the stops because they know they're there >> and so what they're doing >> but they're Stop losses everywhere in the market because they are concentrated. >> Yes. Because people psychologically put them at specific levels. >> And this is I'm I'm I'm really going to explore this because I with every other guest I never get an answer. >> These predictable areas are retail predictable areas and therefore are they not a drop in the ocean in terms of the stops and liquidity that >> Yes. But the oh that's a very good question. So let me say two things to you about that. Um uh fractional shares if I forget let's get back to that. But

let's talk about that issue first. So the thing to understand is that the liquidity of the stock market at any given instant in time is not very big. >> So an actual order, an actual retail stop order can move the market even though you wouldn't expect it to because at that moment in time there isn't much liquidity. Liquidity exists over periods of time. At instance in time it doesn't really exist. I forget there's a guy Olsen and associates way back. He used to call it a camel going through the eye of a needle. >> Okay. >> So basically the camel is a big order >> and you got to push it through the eye of the needle at at the eye of the needle itself has very little liquidity. >> Mhm. >> That that's why those orders matter. And the second thing you want to do, by the way, is when you're putting your orders in to go in the direction of the flow of the of the whale, please don't use round numbers ever. >> Mhm. >> Use something really stupid. Make yourself look like an idiot. So, put an order for 96 shares or, you know, 221, some really crazy number. Use prime numbers. That's even better. um that don't end in five because prime numbers and also numbers that don't end in five and zero. When I this signature that I'm describing where we get predictable area sweeps it >> Yes. >> bounces up, gives me a confirmation, I get in. >> Yep. >> A lot of the times which is happening more now than it ever used to. Y I wonder why >> that protected low that is ordinary protected low for me gets swept once again. So it comes into a where the stop losses should be, taps once, taps once again, and then goes. >> Yep. >> This didn't used to happen. I didn't see it 5 years ago or 10 years ago. I'm seeing it more and more now. What is going on here? >> Same thing. Targeting the expectation is that that area may have some stops. And so what you what you're doing then is as an algorithm, you're basically slowing down your buying. And if you slow down your buying, of course, the stock's

going to fall. >> Mhm. >> Because it's your buying that's been propping it up. And you wait to see if you can get it at a cheaper price by slowing down how you buy. Can >> Can this be victim to spoofing? >> Absolutely. >> No question. >> And nothing to be done about it. it. The game is the game, guys. >> The game is the game. >> What was the other thing you wanted me to to bring back to about fractional? >> Yes, fractional shares. So, so the the the the thing is one of the things that gets you picked off is um is when people think you're an institutional order. So, what they're going to do then is they're going to try to get in front of you. So, don't um look like an institutional order. So, you you want your order to always look like a non-institutional order. What does that mean? >> So, um, an institutional order will generally be with round numbers. It will be steady. It will come in at regular predictable intervals and last an entire day or whatever because they're not honestly that concerned about uh uh slippage because they're more or less expecting that they'll have slippage and but what they do is they they give the algorithm, excuse me, they give the order to an algo provider and then they rate the algo provider on how good their execution was. So the algo provider is basically trying to execute that algorithm in the market with the least slippage they possibly can which is where the trader logic comes in. >> So if you on the other hand are a retail trader right you want to advertise you're a retail trader you want to advertise that you're not somebody to be picked off. >> Mhm. >> And so you do that by having round numbers uh non sorry non- round numbers. >> Yes. >> Super fascinating conversation. Honestly I think I could go on for hours but um I want to just wrap up with a open question. maybe taking a a a portion of your book because I wanted to bring this into the conversation uh that would be particularly relevant for a viewer

especially if we can try and bridge the the wealth of knowledge you have towards a a beginner trader. What kind of advice could you give to to bridge that gap? >> Okay, so the book is called the science of free will and uh basically I go through and this is relevant to trading actually. Um, you're made of atoms. I'm made of atoms. This microphone is made of atoms. That camera is made of atoms. All those atoms are following a mathematical law. We know that mathematical law. And if every atom in your body is following a mathematical law, then what's the difference between you and a machine? That's the question. And the answer in the book, which you can read, is that it all comes down to a a a something called computational irreducibility, which is a mouthful, but it's a very simple idea. And it's the simple idea that um if you see complex things going out in the world, you would expect that the rules that generate that outcome are also complex. But what computational irreducibility says is that no, in many cases, the very simplest possible rules you can imagine can produce output that can never ever under any circumstances be predicted. So I like to say that these are rules that even a 5-year-old can follow >> but whose output is not predictable and I go through in the book to show how that might might occur. And so the most important thing that a beginner trader I think needs to understand particularly in the context of the book >> is that a lot of market movement genuinely is random i.e. it is unpredictable. Even though it's coming from a deterministic process, it's not predictable. It can't be predicted. And so your job as a trader is not to try to outthink the market or outf fox the market or whatever. It's to do what you just aly mentioned in the last five minutes is that you need to find an area of the market where you can be reasonably confident that you have some edge and you need to be able to identify what that edge is and you need to be

able to say something sensible about the edge before you ever trade that edge. That's the most important thing. And you also need to be very careful that you don't train yourself on um randomness and uh basically as you said earlier have a bad process that luckily produced a good result because if you do that you're never going to learn how to trade properly. It is always better to have a good process and a bad result and a bad process with a good result. Always >> that that I think is the key. Find a process, find an edge, find where it is that in the market there is some element of uh um it's not even necessarily predictability. It's it's an element of consistency. That's an easier way of thinking about it. Something tends to consistently happen. Find that and trade that and you can exploit that. That's the best way to do things, I think. >> Beautiful. Honestly, I think that was one of my favorite episodes. This this was a blast of very stimulating conversations. So, I just want to thank you for the opportunity and your time. >> Thank you. I enjoyed it. Thank you so much. That's great fun.