Vzlušajte. Jih treba deta. Zdaj nekaj vizitavaj prejšelj, ne bo, da je, da ne vizitajte. Vzlušajte, da je bit... Vizitajte, da je tudi odrečen. Zdaj zelo vse modul je inšta. Vzlušajte, da je intrest inšta. Vzlušajte, da je inšta. Vzlušajte, da je inšta. In če sem vzlušaj. in izgledajte, da sem ti se očetil. Zato si ste kako se skupajte. Zato sem vse nekaj, da so včasni vse. Zato, da smo se skupali ještje, če lahko se stavili, da bomo vse preddjeli kripe in da bomo vse moj skupati! Takaj sem izgleda, da se glavimo. In je to, da je vse vse izpronovate, da je neko vse izpronovate. Zelo je to, da je izpronovate. Vse odličen je, da se skupaj vse nekaj, da je vse izpronovate. In tako, da smo zelo, da smo izpronovati, da se zelo, da smo izpronovati. ⏠ tako, da je mobility sem, vi seациj pomekno da grade in namoči in da so na nekaj pravde, kaj sem semuge. Kaj so vteke iz mnega pravda? To je zelo mjelosti, ki in volume, ki se sve ti semuse. Tako je sem vsovalo tukaj da se rečim vseklju, ma neka bi se na način, če je izgleda in blizda. Adi je naša gra, zato to neki del, vzvebt, da je in večor nečisto, več, kaj pa vzve istotneших. In to je ovo, da imamo. To je nekaj... zนakvorilčne zvečnost. So prvi izgleda, ki smo priberati, je ta, da se bodo komand in na skupin rada. V tom, da smo ne več kažujem, pos envelopečenom ekonomi. Draže pa izgleda na stato priysok. More people would be ready to trade there. It's very basic reasoning. If the price is very high, more people want to sell. OK, some basic economic reasoning said that volume should grow with the distance from... I think I put it something like this. I said P0 is now the price. The further you go away, the more volume there should be in both directions. And so there we came up with the idea. OK, so let's try to model something that we might not see. And then think about why we don't see it. But let's say that there can be latent variables in the world. And let's model a latent order book, which is not what we see, but something much deeper. So we had some idea that it should be somehow like this. Very close to the current price, actually. It should match what we do see, because it obviously has to match. But at further away, things can be different. So this was the idea, I just wanted to stress it. It's a simple idea, but what one has to come up with it. So that you say that there are these... OK, so somehow these are the intentions, right? That might not materialize. It might have already materialized. In that case, you see them in the visible book. They might materialize later, or never. I mean, this is sort of long-term intentions of people. And we came up with a simple model for this. So this deposition-like model. We said that, OK, so let's start with something putting zero thinking into it. I mean, thinking for the agents or particles. And so we said, OK, and see if that can help. If it doesn't work, of course, one wants to put complicated life. But we said, OK, let's do a deposition-type of model. We had a lambda and a nu. So a deposition and an evaporation. Evaporation defines a typical lifetime. And by hand, we added something to it, which was diffusivity of prices. Which is somehow the effect of market orders, which it's not very elegant, right? We had something by hand. It breaks a bit the microscopic description. But, OK, and we had some results from this, is that there is a linear profile close to the price. So indeed you find in a simple model something that behaves linear close to the current price. And another thing, as we'll get back to this later, and another, which sort of comes from this, that vanishingly, small volumes best. At the best quotes, so close to the price. Just for the language. Is it clear what I wrote here? So it's not at best vanishingly, but at the best. So, OK, there's two things that you get out from a very simple model, which are sort of non-trivial. And actually, OK, so we have a linear profile. But, of course, one can think about what's the width of this linear profile. And it will be somehow related to the volatility on timescale 1 over nu. So the only timescale we have in the system is 1 over nu, which is the typical lifetime of this, or the memory time of this book. And so the widths of this profile will be on, well, one can write up property equations, it will be the typical price move on this timescale. Now, of course, here we have to think, OK, there is a model, it gives something linear somewhere, and it is not what we see for the volume itself. So you'll have to think, OK, so what, for a model like this to work to match data, it means that the square root impact type of behavior, so some linearity in an invisible volume, is on relatively long timescales, you can measure it on short timescales as well, but also on daily timescales, or several days. So which suggests that, OK, if you want to map this model to a real world, somehow this timescale that you get has to be the timescale of slow players in the market. So people who are there and who really have an idea of what this price is going to do on one day, ten days, something like this, and not people who are actually, you could call them high-frequency traders, newspaper you can read, who are just there very close to the price and placing on a millisecond scale for minor gains. So it gives you an idea of how you can map this type of model to a real world, OK? So this was what we saw yesterday, and, OK, so, well, from the linear profile it comes, but, OK, let's be explicit, it suggests that you have the square root impact, so you have everything that you sort of want because you have a square root impact, you have very low liquidity close to the best price, meaning that there will be, in a practical sense, there will be octocorrelation of signs because if you want to trade, you have to trade in several steps, but what was, OK, but the thing that was surely a bit missing from this is that, OK, but there was this put in by hand, so there was nothing really to lead to it, and so we had to come up with a numerical model, OK? So I wanted to discuss another way to get to this, and, OK, so I didn't discuss now explicitly the fact that you have this linear profile actually depends on the fact that there is this diffusive motion that eats the volume, which is just sitting there, so there was this model, and what we can do is one, is that what we did yesterday, OK, come up with some numerical enrichment of the model and study that, OK? We can help, it's good to have a numerical model. The other thing is to change the model a bit and add some things that make it more consistent and make it analytically tractable. So actually I will show slides, I think, today for a while to be faster. There's a lot of stuff, so this is what we have seen yesterday, OK? And another comment, of course, that, but it's sort of obvious, time skill in this, I won over this new time skill in the book, but anything that happens on longer times will have a linear impact, for example. So all memory is killed after this time. OK, so I wanted to discuss a more consistent model. So these models, by the way, are very recent, so these are from between 2012 and 2018 in the different models that I'm discussing here. OK, so this is what I just said, so this latent order book type of model gives insights, so you get an idea of what you're looking for, but there is this strong assumption of diffusive prices and the rest we just discussed. So I want to show another type of family, which is extremely, so the idea behind it, so these first three points, actually the first almost four points, are the same, so you want to model this volume in the background, but we change a bit the system, and instead of just having a deposition lambda here of something, you try to model two types of particles. So we want to model a sort of reaction diffusion system, so where you have particles, which are to buy and to sell, which if they meet, they react, they kill each other, and we'll try to get to describe similar systems. So this is the actions that we are discussing, so it's very similar, but slightly more complicated. OK, we have a deposition of rain of new intentions, whatever, they are particles falling. With lambda, again, we simplify this lambda, it doesn't depend on distance. OK, by the way, I comment here that of course we discussed a bit of how lambda has to behave for this to hold, so this is a very wide range of behavior. So just you have rain of particles, you modulate it, actually in this case we'll just use a heavy side, meaning that one type of particles fall on one side, the other part of particles fall on the other side. You could have some other type of function, but of course you want to have some increasing function in one direction. You have a cancellation, so this can be some partial or complete with some lifetime exactly as before, but what we add is these two things. So first of all, you add a diffusion of the particles themselves, and you will add a type of drift, which is this guy. So this could be somehow reassessment of prices. So someone is sitting, a particle fell somewhere, then he can change his position or she can change her position afterwards. It's like, OK, you change the idea of where you want to be, and it will have two parts, so there will be some part which is agent-specific, so each of them diffuses, and this will contribute some diffusion coefficient, but also we will add a common component, so this one needs to do for things to work. So we also add some type of, OK, you could think about it as a collective information, but somehow everyone is shifting in the direction, so some process V, and OK, of course if this V, underlying V is a white noise, then I mean you can set this V whatever so to make the final price diffusive. So this is a bit still by hand, but OK, so you have this two, so you have a drift, which is from V, and of course if V is diffusive, then drift is diffusive, and the agent-specific diffusion, and OK, and you have transactions, so what we do not put by hand some market orders in, but we say OK, so there is some rate, we will consider a high rate, but with some probability when they meet they kill each other, they eat each other, OK, so it's traditional reaction diffusion type of model, and that's it, I think that's clear, yeah? Yes, but I will consider that actually, so this is a drift, I mean this is whatever, V, which drifts everyone, but it need not be a constant drift, so I will define Vt to be a white noise, yeah, so it's a noise behavior that pushes everyone to follow it, noise in time, not in space, exactly, it's different in different time steps, so it's not a traditional drift, but it's collective, exactly. So it's OK, it's not a, I mean it's hard to solve these models, or sometimes hard, but it's easy to understand. So OK, I don't want to go very much into all the details, but OK, so you can write up the equation of motion S we did yesterday, it's not very different. What are the differences? You have two equations, OK, it's different. So you have two equations, you will have an equation for B and for A, so this is again the density of the equation of motion for the density. So OK, let's go from here, so here there are things which are the same as before, these middle three terms. So there will be a diffusion term, OK, exactly as yesterday. There will be an evaporation term exactly as yesterday and the deposition term similar to yesterday, so we have a heavy side here, because, OK. And the two things that are new is one, is that you have this well, this drift component, which we just discussed, which will be the same on the two sides. And, OK, and you have this reaction component, which is exactly the same term, of course, in the two sides. So it's a great kappa. There is this annihilation of the two particles. So this is a hard thing that we have. Sorry? And we'll try to eliminate it in practice for decorations, because it's hard to handle reactions. We set the problem. We set the problem. We are writing the model. So we decide what we say is that it's a... OK. What we will in practice says that K is infinite, so it's probability one, if two particles A and B meet, they eat each other. And, actually, if we put a heavy side here, then it can only be at the front of the two... There is only one reaction in front where they can meet. But one could generalize and make life harder, but it won't change the many things. So, and, of course, there is one... Yeah, OK. So, here we have a Pt. In the heavy side, of course, the definition of this price is where the reaction front, where the density of the two type of particles is the same. This is... Well, in this system, yes, it's mid-price, but, actually, in a system like this, there is no reason to have any other price than one. So, see, if there are particles diffusing and they annihilate when they meet, then, OK, for well-behaved... So, unless new is some extremely... It should be only one point where they meet. So, it shouldn't be aligned. So, OK, this is a bit harder to... It's hard to handle this, because we have the reaction, but, of course, we don't care about the behavior of everything that we care is the price, essentially. So, what we will do is simply... We don't want to do look at the dynamics of rho a and rho b. Is it OK that I use slides and I don't write up here most of the things?  So, phi, so you look at the difference between these two densities, you get this equation. You can... You don't care about the actual details of the reaction anymore, right? What this means is that, OK, you had... We always thought about the... Just to understand, what we had in mind, it was somehow like this type of volumes. Of course, however we define, we will have something. So, actually, this will redefine somehow the densities on one side, we'll call it positive the other. So, what do we have? Well, we have exactly what we had before here, here and here, but we only need the sign. So, I mean, it's trivial what we do here from the heavy side because we get the sign and we eliminated the reaction term, OK? And we can also make life easier that we change a bit the reference frame, so you want to move in the reference frame of this... This is what we call drift, so VT. So, what we do is simply we redefine a new price which is what we had before. So, which is X minus P hat, where P hat is the integral of overtime of this drift, OK? It's clear. Is it clear? P hat T is the integral of up to now of this V, which is so this, let's say, drift term. What we simply do is, OK, there is everyone shifting like this. We take off this collective, we put ourselves in the preference of this collective motion. To čuze... The boundary condition. But at some given point you're somewhere and then on you... There is a collective motion that you don't really care about. What you do is simply take this out of the system. OK. So, what you get is this. This has been removed. OK, now we are back to a system that actually we have seen sort of a system that we have seen before. So, indeed you get something, which is the same that we had yesterday. Right? So, we didn't have to put in by hand what we had and we got back what we... got back a local linear profile. So, I had in mind something. So, what do you have here? So, that the stationary shape of the book, OK, we have it written this way. So, for y being negative, it will be this and it will be this way the inverse on the other side. And so, what is this? It means that that the profile will be linear close to the best. I don't know if I discuss this and there will be... So, there will be a width of the linear region will be so, width linear, I think, I don't know if I use the same notation but it will be something like this. OK. So, again you have one over... It's the same stuff. One over new is the lifetime. This is the diffusion. So, diffusion is the same. And so, OK. So, this is not a... I mean, it's good to show here. This is a known result that the diffusion with some close to an absorbing boundary has this behavior. So, that is good to show, you know. And so, and so I just want to introduce something that is not on the slides but I think I will use the letter later. So, actually, if you zoom in the linear parts, so you say, OK, you will have some linear behavior here. So, just, right? And if you zoom in, you can... you can define somehow the volume, the transactive volume, midtime. So, so, so. You can define, OK, it will be something like this. Is it fine now? Right? It will be something like this. So, you look at the slope locally times the typical diffusion. So, it's OK. Actually, this you can call some type of current. And, and actually, one can... OK, this will be the slope. Should I write this all up? I won't write this all up. So, what you get is that, that actually, so the slope, if you write a bit pay, pay the thing, you will get a slope which is, which will be this, I call it L, which will have the following behavior. Behavior, it will be somehow like this. OK, it doesn't really matter. It's OK. The orders of magnitude are clear. So, this is the slope close to the mid normally. So, this should be coming from this. What is interesting is actually just to a bit, try to connect to what things that we discussed elsewhere before getting further into model. So, OK, you can define some type of slope here, but of course, if you get back, so for example, in a model like in the Kyle, there was a lambda which we defined. Well, it's somehow so, so I think the inverse of this should be something like the lambda there. Of course, we have a completely different model, but this idea how much, how much the price should react to a given volume, so how much the price should change, it's the same type of behavior. So, in a collective language, if you have all these different particles in this type of model, you have a mapping from what there was a lambda to the inverse of this. But, OK, so this is just to say that you can map a bit dismal so the main variables in one model in the other are similar. Yes. So, what you say is there are these stuff falling, they are diffusing in both directions, so there is no explicit current coming from elsewhere anywhere, so they are falling. So, I think the only thing you care about is that they in the middle they annihilate. I don't think you really care about boundaries. OK. I think so. I don't have to think about, because you could have versions of this type of models that you have two ends of the system and there are currents coming in. So, somehow there is a current, but then they are diffusing. I think you don't have to do what did I want to say. So, OK, we have the linearity in a similar model to before, but slightly different. And so what you can actually do in this type of thing, just to show what how this continues. So, of course, what you care about is now, OK, now we have something that we like. So, yeah. Let's say I wrote something. OK, let's talk about this. Here what you say is that I'm one, why is this negative? So, I'm defining one side. So, I'm on this side, let's say. If y is negative, then this is, of course, a decrease exponentially. So, that's OK. And so, what you have on the other side, what you should be having is, well, you flip y in the parenthesis. It should work, OK? And you flip this type of definition. No? Does it seem OK? Yeah? I think. To me, it seems good the way I wrote it, but I could... For the zero, you mean? Or that it's not larger or that it's equal? In zero, it's zero. Everything is zero. In zero, this is one. OK. So, which is the definition, the way you define the price is the point where the two densities are the same. OK. So, we have, again, we have this background system that behaves as we like, that diffuses, that has a local linear profile. And what we want to do is, OK, add a method. So, how would adding a method go to the system? How would that affect? In the same way as before. Exactly, because in this numerical model, we discussed that, OK, it's great to have some linear profile, but if things are fluctuating a lot, it's not that obvious that you can really measure this. So, numerically it works, but there was no analytical solution. So, what you can do is that, you add an extra current. I think it's written here. Yeah. So, you have an equilibrated market. So, you just decide, and you add an extra current, which arrives at the transaction price. So, it's people really know what they do. They are setting a method on the, arrived at the transaction price, and OK. So, we can model it. What we do is simply, OK, there is a delta function here that it has to fall on the current price. And it has an intensity m. OK. It's with another term. Yeah. I mean it could be, if you want to do calculations. Yeah. It's a statistic. We'll keep it general. Then sometimes you say that you want to have it to be constant in time. Yeah. So, OK, and we simplify life a bit. We say, OK, we are close to the best price. That's where we care about things for the moment. Then we will generalize this. What you say is that, OK, locally there on short times, on short physical scales, evaporation and then deposition locally won't change much. So, what we say is that, OK, let's keep only the first and the last term of this equation. Which is actually a nice thing. So, for which there are solutions. So, this is what's called heat equation. Yeah. Is, OK, sorry. Mt has nothing to do with any price. It's just by chance that I call it m. I should have called it, because it's intensity or something. Oh, no, it's m, because it's met order probably. That's why I did it. So, mt is an intensity of particles falling to the point defined by this Dirac. So, it's nothing. Sorry. So, it's not at all a price. And, OK, so we get this equation to which there are solutions. If you put zero here, so if there was no met order coming, then it's traditional heat equation. So, the temporal derivative and the secondary derivative in space. If there is an intensity, you can look up the solution to this. And, OK, so there is some solution. What do I show? Yeah, OK. So, OK, so one can look up the solution. So, what you get is that, actually I would like to simply get to the last line here. It comes from the other, is that the behavior of the price will be some integral on this intensity. So, this met order falling. Is it clear what I'm assuming? This is the Lax equation. You have this one over L here. We just defined on the blackboard. And, what you're doing is, you have an integral of this intensity. And, OK, and actually if you want to be for small impacts, when you're not impacting too much the price, the stuff which is in the exponential can be forgotten. And, you get a model so simple. So, where are we? We simplified life. So, we say that it's a small impact case and we are on small time. So, the deposition of operation locally do not count too much. Right? So, it's a simplified life. But, what we get back actually is some type of propagator model. Just to connect. So, what you have is that, yeah, so there is a linear effect. You're just integrating. And, you have a one over square root of t behavior here. T minus s behavior here. Which would mean that you have a propagator in the language before. This would mean that g is something like, so t minus s to the minus one-half. And, OK, there is four p and stuff in it. So, you get some propagator model. OK? So, when you're small, these impacts add up linearly, but you have a square root decay of impact. Which actually what we have seen is that, what this means is that, on the short terms that we are looking, so we can say that there is diffusivity due to the collective motion. But on the short terms, so this is what we call beta. In other lectures also, what you say it will be a sub diffusive behavior. So, this we discussed. Is it clear? Are people following me? And, OK, on the longer terms, you will have diffusion. So, OK, you have something which is a bit, it's not super good. We have to check, OK, in practice, on what time scales is there this sub diffusion. Actually, there is a solution to this type of problem, which we won't discuss at all here. But you could say that, OK, if nu is not one number, but it is very much dependent. You can get to a model where orders are not diffusing, really, themselves, so there is a fraction of diffusion, but you get back no sub diffusivity on short time scales. If anyone is interested, I can give you the reference to it. It's an interesting paper. And, of course, all this is valid for if you are doing very slowly, so you are not kicking too much the system. If you are more aggressive, then it is not. It is not why it implies sub diffusivity, because sub diffusivity of the price itself. Why it implies, because, well, we wrote it up for the actual market, this relation between the response, this stuff, so this is a convolution. We have this type of relation. Now, in general, here we don't know what C is, if there has to be C, but what you can say is that if G goes with, so there is the relation with, so if you say that this is, so this was the relation that we had, for the price to be diffusive, if beta is one-half, then gamma has to be zero, actually, in practice. So for no finite value of gamma, can it work? So it will always be sub diffusive on short scales. Is this okay? Yeah. The rate at which, probably yes, but I'm not sure to do, I tell you, and you tell me if it's the same that you think. So it's, yeah, the rate at which you make, so you want to do a matter, the rate at which you are making your particles fall in the center. So, so that's why we say that if your rate is extremely, so if you're extremely aggressive, then things break down, then you cannot, well, then surely this, you cannot use this, but you at least have the exponential term. There is also the question if you can eliminate, so the eliminated deposition and the operation terms. YT is, is, so YT is the is the current price, in the price at the moment, T. So we had this P minus P hat, so we are a bit playing going further, but it should be the price, so this stuff here, this equation is the dynamics of the price. So, but anyway, I want to give the main idea, I think these are the interesting things, but it's exactly the equations you don't have to check today for tomorrow. But, but okay, so check them. So what did we say, so this is what we have for when you're pushing the market slowly, and so this is the behavior in time, and actually I wanted to show, so how does the, of course this means that the book is distorted, so all this is understood somehow in my reasoning, but this is what one sees in a simulation, so of course, okay, so now we flip this back for visibility, but so you have a local linear profile in an equilibrium book, and then you start kicking it and you put an extra meta order, and you have this type of distortion that on one side you will have a higher slope, and not necessarily it starts to become nonlinear, so this is what one expects, but it's nice to see in simulation. Yes, sorry, meta order is pushing, sorry, I didn't define it anywhere. Okay, so the meta order is pushing in this direction. So, I mean, it's logical, you have a system like this and you start eating, because, okay, this part is clear that you're eating into it here in the meantime, of course, there is more deposition coming because you break the symmetry, you're pushing in one direction, and the next thing to discuss about this model is, okay, and then you can look at the actual impact of a meta order on it, so we saw the dynamics of the price, but how does it work, and so I won't go through the equations, but actually one can write out that if you say, okay, let's simplify life for calculability, someone was asking this exactly, so let's say that, okay, you have a constant M0, I mean constant M, and you're executing in this way, so just for simplicity, you can do the calculation, that you will come up with, and you find an impact, which is indeed, in all cases, here there is a problem, some other letter, J, this is a 2, no, actually, so this capital Q here is a 2, forget it. So anyway, you have a square root behavior in quantity in both cases, so this is what you were looking for, I just make the statement, we won't study it, and then what's interesting, that you will have two type of regimes, in one case, when you're very aggressive, so this M0 is much larger than the typical current that we discussed, then it's exactly square root, it's obvious, because it's easy to understand, you have a linear system, and if you're extremely aggressive, you will be, I mean, linear profile, you'll be really, nothing else matters, just that you push the price, it will be exactly square root, because it's for geometric reasons. So there are no fluctuations that play a role in the simultaneously. If you're below, so if you're slower, then well, the behavior is there, so you have a square root behavior, but you have some correction to it, which depends on your intensity. But the weak correction, so we can live with it, it might be there empirically. So that's where I, so just to summarize this stuff, is okay, so we recover the square root, so you recover the square root in this model, so it's more an actual okay, microscopic model, where you can do, where you can solve analytically, you don't need to do simulations, and there are a big couple of things in a very rich type of model that you can study, that I don't study here, is how, what does the price do after, so how does the things relax after you start to stop trading. There was all this question of absence of price manipulation that I discussed several times, but only tangentially, is that of course in an actual system you don't want to be able to pump out energy because of the way you push the price itself, so this would be price manipulation, which is illegal as well, but if it works like this, everyone would do this, so you can prove that there is absence of price manipulation, so what does it mean, let's say you buy a lot, and then you sell the same amount, you cannot gain on this consistently, and then you did it. You did it? Not consistently. And there is a last question, of course, which is all hidden here, is that we discuss again getting back to this, so what do we discuss, it's these intentions, so what we call latent volume, of course if you want to, there would be one step from this is get okay, what's the relation between latent volume that you do not really see, but affects the market, and the revealed volume. Latent volume is all the things that people want to do, but they don't do it in the given moment, they react a bit later, so this volume will be here, but only if the price comes closer that it will realize, but so what is the mapping from one to the other? It's an important question, and we won't discuss it here. Of course, if you have any questions, we answered that, and I wanted to summarize a bit. So I remove everything. So I'll try to write up what I have in mind about the relation of all these things. By the way, does it make sense where we went from and where we wanted to go, where we went from, where we got to? It's not an exam question. Is it? You don't have to come out here and do it, because you guys did you have the time to think about this? Is there someone who says yes? You don't have to say yes, it's really... No. Someone said explicitly no, but I think nothing is no. OK, so... OK, so I'll write it up, then you tell me if it's... OK, what did we do? We spent a lot of time, so we had all these empirical results, we spent, I don't know... There were several cases of empirical, but we spent the first two lectures on these. One has to understand what are the empirical things, and so what was it? We said that there is diffusivity. We said it many times, and probably for us this was z t, which is so, unpredictability. Unpred. This is how we think about it. And there were other things, which are important to know, but we didn't follow up too much, so there was... It's extremely important for any model in these fat tails, and we discussed the question of... OK, that... Even on relatively long time scales, the central limit theorem doesn't hold, because you have correlations in the system. Right? And what were these correlations? The size of moves. So there was this what we call the volatility clustering, meaning that volatile periods are... For sure, sometimes you have a volatile period, then you have a non-volatile, so it's correlated in time. These are clear. I mean, we can also go back to the figures if anyone has doubts about these things, but if you want to go back to the figures, you tell me. So there was this type of behavior. We had two types of figures, but this... So what is volatility? It's the size of the moves. Let's forget the sign, typical size, as we use it in English, in everyday language as well. And so that... OK, here you have a 100-year period. There are visibly periods when things are much more volatile and visibly periods when things are much more calm. This would be the case for a random walk. So this is what we call... So volatility is clustered in time. And we saw here that actually... OK, that's on 100 years. This is on 5 minutes, but you have... OK, it's less... Visually less obvious, but you have the same. You have a period here when it's very calm, a period when it's very... So on all scales you seem to have this. You can of course define, this is the autocorrelation of the volatility, which will be a very slow decay correlation. Visually. OK, we'll get to this. OK, we'll get to this in a second. So I just wanted to... So there were these. We discussed something, which sort of seemed... It was a bit side question, just I think it is. So the question of the correlation matrices. It was the only time when we discussed the non-single product behavior. I think it was OK in its own to understand, but... And OK, so essentially there were these things, and I guess the... OK, my reasoning is the following. OK, so there was this main question of... OK, so why are prices really unpredictable? Why are prices diffusive? And so we tried to answer this. So OK, why diffusive? Why prices are diffusive? And so this is where we go to a type of modeling which you hated, which is this type of economic type of models, so say. OK, there is this traditional picture which is fundamental efficiency. So what was this? It was this type of strange model. It's not super scientific, but that's the canonic way of looking at it, is that prices contain information. In a simplified way contain all info. And it's somehow this underlying... So there is an underlying process of information or fundamental price and so it says that OK, there is an underlying process which is unpredictable which some people know. So there are some chosen people who know this and they follow it. OK? The claim is OK. There are critiques of this already in the economics literature so... Well, not very scientific. I don't even... It's OK if I say, you know. I don't want to write too much. That it's not very scientific. There was this reasoning of OK, so let's go to some quantitative ideas. So there was this type of... What he said is, OK, what does efficiency mean? It might mean that the price and this really underlying true value are not more than 100% away. So it's not extremely scientific and we don't have it here. Maybe I will decide it. You can come up on what time scale should prices really follow this and you see that it doesn't work. And there were also... In the economics language there were these puzzles. OK, we actually discussed two of these puzzles. So one was... Because in economics you call these puzzles that one was excess volatility. So if... If prices can only move when there is this underlying... When there is a real news coming in, then why are prices so volatile? So real news comes much less often. Why do prices move so much? In the same language, why do people trade so much? If it's very rare to have real news coming. And something that we didn't discuss but I want to mention that of course we saw that prices behave in a diffusive manner on the time scales that we cared about. But of course on long times we also saw this in the beginning that there are trends in prices. You can see that that on several years typically are going up. But typically they are going up. This doesn't matter. So for diffusivity the fluctuations are much larger than the typical trend. So it's only on a decade or more that you can see this. But there is something that sort of contradicts this idea that it's only when there is a news. If it's only real news that govern the prices this type of behavior shouldn't be there. Also... There are other things which you can use to critique this. But... And so what we said is that we said one type of solution. So one solution at least to why people are trading so much. And this was these... So what we call... Models incorporate information. Ok, so there is this Kyle and lost a milligram type of model. So we can give intuition, but... Is it readable? But so one solution to at least why people are trading so much is that there are noise traders who trade so much. Ok, we found the culprit for one part of the thing. It's strange. We discussed the critiques of all these type of models. Why should be people either noise or informed. Ok, so that's one type of solution in a more economic way. But we went into another direction. Apart from these puzzles that were here we showed another I think quite explicit contradiction which was the... What we call the long memory. So this is what Gabya was asking. Right? So here we... Models incorporate. So it's not just a puzzle there is an explicit contradiction. If the decision of people are made on much longer time scales if people have a big decision but execute it on several days which causes this then if you cannot have all information in the prices. Ok? On micro scale. Yes, so what I say here is that what does it mean that all information are contained in the prices? Well, it's not a super clear claim. But what is sure that if, what you want to do now is buy 10,000 of something. But what you can every day you are able to buy only 30 because there is no one else to sell more than 30 days you will be buying. There is no way that the price incorporates the information 10,000 in the beginning. You could say that on some longer time scales it could be the case but on minute, hour, day time scale for sure not. It cannot be the case. They are different. I'll get to this at the very end. Not really. So when efficiency is different type of definitions of what there is but no, so statistical efficiency so I will get to. Statistically you measure this it will be always the case but it doesn't say the underlying reason. So here we had a clear competition and there, there so in my mind the direction where we went to is there was questions of orders of magnitude here. Why it has to be the case that you cannot have all information. What we did is another way that alternative view to this is the question of impact or the idea of impact which is a completely different idea so it's an alternative view I would say. It's much more physics approach if you want. So what we said is that it's not some of this underlying fundamental value that counts and that people are following but it's actually order flow in the system and regardless of information so this type of modeling says that if there is something you break the balance of supply and demand that you change the balance that pushes the price and it doesn't matter if someone is informed or not you don't have to have a model of noise traders and inform traders and this type of modeling but it also means what the others do what you want to predict is more what the others do than some fundamental idea I don't try this here but it sort of comes and it has a lot of further suggestions it means that things will be more and X so the dynamics will be and the genius if it's the actions of each person that each trader that drives the prices then you expect the system to be and the genius and or mostly, not all has to be and the genius and we will get some other points here and so what this suggests here is that order flow moves prices we didn't say why do we have diffusivity this is where it says this is just a statistical effect so this is what is called more statistical efficiency in economics that it's actually people finding inefficences and trading to exploit these which remove the inefficences you find that you can buy very cheap you will go and buy very cheap and you will be pushing the price up and after a while you cannot buy cheap anymore and that it's continuously people are continuously looking for this so it's a statistical effect it's not some effect like this and it's also important to say what were the facts that we have seen so this is the idea behind it what were the empirical facts that we have seen it was for this all empirically what we discussed is single trade so impact of single trades and trying to model how this can lead still to efficient prices so all these propagator models so this phenomenological models and we saw that there is an effect of conditioning ask me if there are things in the channel I am talking about things that we discussed so I am just saying keywords and then there was the other points the meta orders looking at meta orders it's not the conditioning that counts but it's real true response in the system and there was this square root impact so this is what we did so I think sort of the main claims here that I will get back in a second but this picture against this picture so to have two alternative ways of discussing and then ok so from here we discussed these two types of models so one was let's say phenomenological I don't know what's the good name but propagator and company and there were more microscopic for agent based for another community this agent based models which we discussed yesterday so this was the sort of the map for me I don't know if it makes sense now so I mean if there are logical connections for you yes well it's essentially because of this point that if you say that so what the prices the way prices move is that there is some fundamental news coming and all news is incorporated in the price that's what leads it immediate immediate or on short scales of course of course you can define this does the prices contain information and follow some fundamental value on the scale of 70 years but that's not really I mean it doesn't mean much sure you can always increase the time scale and say this is true on the history of human being but yeah so what it says is that there is this underlying process which is the fundamental value it follows something but that's what's unpredictable it only changes when there is a news coming but so these are weekly scientific claims that's why you have problems with it because these claims are like that that's why we are trying to do another approach so it's completely okay but you would be pushing the price down when you're selling so it's a question not related to this so you want to go and make money now this is what I call price manipulation okay so side remark to answer this what you would expect if some picture let's say you're pushing the price up when you're buying this should be a square root you stop here and when you say okay great I pushed up the price let's sell it now actually you can calculate the average of this somewhere here what would happen is of course sure it would be great to sell here but now you want to sell the same stuff so what will happen is this what you will do to the price in a simple picture this will be the final price in practice probably you would push the price even more in a model like this latent or the book because you it's not balanced in this moment but okay let's forget this it should be somehow the average it would be selling here so you're losing money and this is what you expect from a model like this if your impact were linear you did this at least you would be paying and it would be the same but okay so this is somehow the map that that I had in mind here so think about it a bit if it makes sense and so it's okay what is the final claim that we have two views one is this traditional picture one is a more physics like picture that it's an endogenous process and what the result is that you have actually two sort of self-consistent ideas one is the economics idea the one is the more that it's impact the price prices and the statistical efficiency comes from people using the arbitrage opportunity so you have these two things of course just understand so this means that there is an underlying price coming from somewhere else it's this fundamental information so actually in the language level here you would say that it's price discovery that happens exists but people have to discover it and so that's the way people talk in economics and here you would say that it's more like price formation this is only a question of language but it shades lights on what we are saying and which means that okay prices would exist without trading and prices only exist because you are trading and we didn't give a final answer we sort of showed two views on this and okay what I think is that it's easier, it's much more plausible to have a type of model like this which also matters, it's not just what your predictions do how they work but also it's more plausible but okay this is a one may argue against it and also we have so there are all these different puzzles that one can come out so empirical facts we seem to explain much more empirical facts in this picture than in this picture but okay it's an open discussion and I'm saying this but most of them would believe in this picture and what else did I want to say well okay so things just some keywords that if you come from physics can be important so as you said this is an endogenous process mostly which means that there will be of course feedbacks between the different actors and you can easily have for example model bubbles in a system like this if people actions change the price and people of course can look at each other what the others are doing and there can be hurting effects and feedback loops what we have also seen actually which is interesting but it comes from the model here is that well in these models to find the diffusivity it means that actually you are in a very strange point so you have some state variable that you can look at you seem to be what we call around a critical point for example in this latent order model we have seen that that to have diffusive prices with this autocorrelated order flow you have that there has to be a parameter we can define which parameter exactly if it's the decay of the impact or what which is at some critical value which means that of course you expect the system to be super fragile that the small changes the behavior of the system can change a lot so it is interesting for analyzing I think and it is also important for regulation so it means that understanding more this type of fragility in the system and these endogenous loops and feedback loops can help you devise better rules better fee system maybe so you want to avoid people trading too much without much information there are fees you can trade with me you can have to pay 50 cents so you will only do if you think that you can gain more than 50 cents so there is a strong effect on regulation of all this and so I would say that's it general conclusion from this is like the way I started is that if you have a system in which there are many things that you do not understand probably it's better not making big theories like this and try some simple microscopic models so all the microscopic models we had did not contain some specific intelligence we said what can we explain without intelligence of course you can look at what didn't I explain and think better things but it is a trivial claim but good to keep in mind that if you don't understand things then try to not come up with great big theories so that's what I wanted to say ok if you have 15 minutes I mean if you have questions then try to not come up with them so first about this but if not general then I wanted to say a few points but everyone start this so if you do a meta order or if you do a one shot it's very different it's very different from impact formula we cannot we cannot have a very clear relation between the two so there was this type of picture that we had so the volume is ok we said this later is a latent volume and there is an actual somehow visible volume so what does latent volume mean that there are these people and they might realize if you do not realize to realize themselves it's to come to the market so exactly so if you want to trade a quantity of this much ok say this much so the integral here if you do it in one shot you will push the price here ok you take the volume it's a trivial case you take the volume which is there no one has time to react while in this to get the same amount of volume if you're integrating on the blue you would be I don't know doing this so what you think is that if you do trading slowly you will impact less because people have the time to come there and provide liquidity to you there is no one-to-one correspondence but actually you would in practice never do the white solution you wanted something no? ok so I wanted to say two things one is a very general thing that I don't know how to do it but if you have feedback on any of this or on the lecture it would be good to have them you know my email you can send it I don't know there is no prescribed way doing it here but if you feel I would be happy to get any feedback now or after the exam of course if you like it doesn't matter of course but whenever really and I just wanted to list a few things that we didn't discuss which could be interesting for people is the effect of if exactly so it is this type of that if there is a there is a good word for this which doesn't come to my mind if there is an arbitrage opportunity so if there is an opportunity to gain make a gain in the market by the fact that your trading will move the prices exactly in the direction against your against the arbitrage opportunity so on some scale it will be eliminated of course it doesn't says that it will be exactly diffusive but still it gives a framework why these do not exist for long times and what you say is that so there are all these people in the markets looking for opportunities they can be machines or people so the simple opportunities will be gone and then don't misunderstand me diffusivity here doesn't mean that locally there are no predictable patterns on higher I mean with better statistical measures there are of course they are very hard to exactly and for example for that if you measure so it's a bit tautology because if you know that diffusivity is here and know that long memory is here there somewhere has to be something which counterbalances this long memory and in practice for example you can measure indeed the reacts also so the way the price and also others because the price reacts the way others react so the way the price react to a trade which is very predictable is very different from when it was unpredictable so if you have a strong predictability and the prediction realizes the response in the system is very different from when it doesn't realize it's sort of tautology it has to be the case but you can measure this and you can so the answer is empirical level it's simply this and why it is the case because people know in this case people know that that your actions will affect the market so they have an expectation of what will happen and they will change their behavior if their expectation doesn't realize or they can in practice in these two tendencies sorry, what do you mean by the two tendencies? by loops I mean it's that there is feedback in the system I'm not sure I get the question no, they do not drag you but understanding what's going on can help you come up with good regulation so if I am probably I want a very good example but you know that the market is very fragile so typically the response of the system is in a way but sometimes if there is a small fluctuation it can be completely different and there can be huge jumps in the prices you can come up with an idea in terms of regulation so of course these are particles falling in the model these are people we can tell them if you fell down you have to stay there for 10 minutes or some regulation rules to make the system more stable so it's not auto regulating this research can help you come up with ideas how to regulate the system because then we are back to the original question and of course we can model it in a reaction diffusion model but it's real money that can be lost for certain people and real economy so there is a lot of stuff that we didn't something that I wanted to discuss we didn't discuss at all so of course you can go in the same type of picture further you have an idea of how the price behaves given that you are trading and now let's say that you really have one day to buy a given quantity you can come up with optimizations there is a field which is called optimal execution how should you be trading if you want to minimize and you yourself are impacting the price so you want to put your trades the further apart so that the system forgets you maybe but at the same time you want to be able to sell or buy all you want it so there is a final condition and there are difficult optimization problems that one can come up I thought I would show some things on this but ok, you can live it out for the limit order book there are several queuing models in different orders until when do you want to stay in a queue when do you want to get out because it's too long and you want to do another action stuff like this and then there are some other completely different questions that we didn't discuss I just wanted to mention which I think for physicists can be mass physics can be interesting so there is all a field of derivative pricing that you can consistently do in the market so if if if you have three products A, B and C and then there is some linear combination between them so A plus B gives C or so in a very simple if there is any linear combination of products that gives an other product that gives you a condition on the fact that the price a condition on the pricing that you cannot have the basket same as another product must have the same price so there are some underlying interesting things we didn't discuss about risk measures at all so we discussed only volatility as some type of variance of price sometimes of measure of risk but of course if you have fat tails you can do much better we didn't discuss again so I mentioned here but all these herding models we didn't discuss all and there is a big literature we didn't discuss non-linear correlations actually there was a question once we discussed correlations but only at the linear level so you could, if you have fat tails you could do better to understand interdependences there is a very big many people working on getting more to economics or network systems a system in which well some network interactions and looking at face transitions on these systems etc what else did I write ok, one thing that we just mentioned but we didn't go into the discussion at all so at the correlations we mentioned the portfolio optimization problem it was in a tutorial which ok, it can be quite well mapped to spin glass problems so it's very much physics like approaches and so this is more the finance questions and then there is a lot of things more in economics so studying the distribution of wealth in a society and how you can get what you see empirically and getting to more these macroeconomic models so to model there are many physicists working on there is this general equilibrium models in economics which say that there is an equilibrium with very mild fluctuations around it try to make these models better and via this we have predictions on inflation and monetary policies and it's not finance on the small scales but economics on large scales there is also well, many things to be done so it's a bit similar approaches are a bit like this and the world is very different so ok, I just wanted to list these things if someone is interested to go into other directions yes, on what level do you ask because this question can be like who to work with or to so you're talking about economic research or moving into finance it's which part of it ok, so well, what to study I don't have a good answer to this because I just listed these things there are enormous amount of different things that can be studied so more at the end of macroeconomic systems, which actually I do not know much about which is I think it can be super interesting so to understand how an entire economy behaves and how to model it and as I said there is the other end on the micro scale and in between there are many things and my interest is more on the micro scales I'm more interested in these impact models but ok, that's a question of taste so to the question of what to work on I don't really have good suggestions I can say some groups where I think I don't know if that interests you ok so ok, there are two sides you can work in academia on more like what we call econophysics even if sometimes it's not econo but more the finance and you can go to work in a company doing finance if you work in a company doing finance I guess it's better having at least ok, the place I work at you should have a PhD often it's even good that if you have a postdoc in whatever physics and I think there are two approaches one can go to work in finance in a bank which might be much more mathematical than physical more math than physics it's more mathematical finance and ok, I have a biased view but my view of it is less of a creative work more there are existing models that have to be followed and then you can do calculations on those there are existing models because they are strongly regulated so in a bank you cannot come up with a completely you can come up with a completely new risk model but they don't really want you to because the regulation says that this type of model should be used and so they prefer that you use that while in a hedge fund for example what I work in it's much less regulated and it's much more free so if you come up with new ideas in any type of any type of new ideas if they work and if they make sense then they are good and usable so this is completely to quite different words for me but in any case, probably having a PhD is useful and ok, I don't have a very general remarks on this but ok, I don't know one thing is that if you want to go into so there is the other direction is that you stay in academia but work on these questions most of the things that we discuss here you can do perfectly research in an academic world that's one thing so very good if you move to finance it's hard to get back so that's also why I think after a PhD maybe to even do a postdoc so that unless you're completely sure that this is your direction because it's easy to move in one direction but harder to move back ok, so thank you very much for these two weeks


Hi, my name is Chris Martin with ARM, and today we're showing a demo of a machine learning running completely in software on a microcontroller class CPU. We worked with ST to build this demo, and we're using an STM32, and it has a Cortex-M CPU in it, the Cortex-M7. It's running a convolutional neural network. It's also making use of ARM's CMSIS NN, which is an API that ARM provides free of charge. It's actually open source, and it accelerates neural networks. This particular neural network has been trained with over 60,000 images from the CIFAR-10 data set, and it can detect 10 different classes or types of objects. Here we can show that it can detect a cat. For instance, it's one of the objects that it'll detect. It says a cat. It also can detect other objects like a plane. It says here 100%. It thinks that's a plane. Today with ARM's Cortex-M class CPUs, as well as ARM's CMSIS NN, you can bring machine learning and neural networks to small devices at the edge today. Thank you.


Patriot Prime Reviews is a channel for adult collectors and may not be suitable for children under 13 years of age viewer discretion is advised Hey, what's going on guys? Patriot Prime here once again with another Transformers review, but before I get started I need you guys to do me a favor if you're watching this video right here and are not a subscriber of Patriot Prime Reviews Please hit that subscribe button right now. It won't cost you a thing Will help me and my channel out tremendously. Also, make sure and visit my sponsor toyhacks.com Toyhacks provides reproduction and upgrade decal sets for transformer toys from generation one to the latest modern figures Make sure and stop by the toyhacks armory where they can equip your figures with new weaponry in multiple colors and toy stages provides backdrops for figure displays and photography Each purchase with toyhacks earns you RoboSense that can be used for future purchases. You can check your balance anytime in your cart Toyhacks is a company run by collectors for collectors So check out toyhacks.com and make your collection stand out from the rest and tell them Patriot Prime sent you now onto the review The featured bot in this video is Transformers War for Cybertron Kingdom Inferno. Now I picked up Inferno in 80s toys of Princeton, West Virginia So not only did I add a brand new figure to my collection. I also shopped locally Now let's take a quick look at Inferno in the packaging We got Inferno there in robot mode behind that little tiny clear plastic window and some great artwork of Inferno in Robot mode and fire truck mode trying to put out a volcano He may have bit off just a little bit more than he could chew right there On the back of the box. We got Inferno in robot mode and fire truck mode Back of the box is more of that awesome kingdom artwork. So now without further ado Let's get Inferno opened up out of this packaging and check him out and welcome to Patriot Prime Reviews Now once you get Inferno all opened up and out of the packaging You'll see he does come with a sheet of instructions that as usual are very well illustrated and very easy to follow He also comes with a trading card that is actually something different. I have Megatron and this looks really cool. You got Megatron here in robot mode and you open it up Megatron in robot mode once again Wielding an axe. So that's a new look for Megatron. I guess that's his Gladiator days. So that is really really cool. That's the first Megatron card that I've seen Grapple also includes his rifle To Hose accessories and both these look exactly the same. He also has two Nozzle accessories. You got this one right here, which is more chrome and This one here that is gray. So now let's take a closer look at Inferno himself Now Inferno here is a repaint and remold of Earthrise Grapple and what a fantastic remold he is Great head sculpt. Look at the details. I love the little yellow here upon the horns The silver in the center the blue eyes the silver face He even has the wings that G1 Inferno is known for and these are opposable You can kind of bend those back if you will that's mainly due to transformation But still cool that you can add a little bit of variation to the wings on the figure Most of the details are the same that carry over from Grapple aside from the head sculpt and the wings here I mean great-looking figure. He's got a translucent clear chest right there and right there on the side Let's see that's about it as far as the details are concerned Articulation the head It's not all a ball joint, but it can rotate a complete 360 The arms can go up and down and I love this. It looks like he's got Robot hinges or something there inside the arm that is really cool So the arms can go up and down the bicep can bend the bicep can swivel Let's see. There is no wrist rotation There is waist rotation legs can go up and back out and in there is a knee Bend though very tight There is a rotation there and an ankle tilt so lots of pose ability for this guy Man, I love that face sculpt now with the accessories. You've got these little hose pieces here These attach right there on the side of the leg. So you just match up the peg to the hole Just kind of rotate those into position however you'd like. I kind of like the Nozzle facing down like so and now you have this nozzle here The gray one that has the big hole on one side. This is going to attach Right up here above his left shoulder on his little Head container. So you've got that going on now this piece right here You can actually attach it to either one of grapple's hands to make him look more G1 accurate. You just take the fist and flip around and There you have a hole right there.  I Love this figure and he's not bad looking at all He's got a few gaps right here on the back But isn't it is not as bad as some of the others and of course his backpack here is his Ladder from vehicle mode that you tend if you touch wrong the head section will pop up That's one complaint. I do have I wish this section snapped in a little bit tighter but other than that Robot mode is near perfect. So now let's get grapple transformed into vehicle mode And the first thing we're going to do is we'll go ahead and remove the weapon we see remove this nozzle here and Right here You can leave these hoses on you never have to touch those again. We'll go ahead and flip this fist in Take the wings from earlier and fold back.  So there's a lot of extension to this part now once this is down Now you're gonna bring this body section up bring the arms up like so Okay, and bring the legs up now one thing that they have done Which they vastly improved on the grapple mold is these pegs right here. See how they fluctuate in size Grapple had an issue where you peg these in and the pegs would break off So Hasbro actually learned from their mistakes and it fixed this where these will peg in but it's not too tight And it doesn't go all the way flush so you can pull these out without worrying about the peg snapping off So kudos to Hasbro for actually learning something so you get all this section flat here You want to bring the arms around make sure these gray sections fold into the cab like so And now you want to make sure and match up turn the arms to where The little hole right there is Facing down so they will peg into those holes or those the holes will peg into those pegs right there lock those into place Bring the ladder down. Nope. I am wrong turn around this way. So you have the hollow part Facing down and these will actually clip together. It's been a long time since I transformed this guy Now those will peg in bring the ladder down and you can actually extend the ladder out Which is really really cool. I dig that now you can take this nozzle here and it will attach Right here on the end of Inferno's ladder like so take this nozzle and Just like grapple it attaches here on the back.  Yes, he's just a repaint of grapple with the ladder extension But it really works. You've got the hose there on the side Of course the ladder the nozzle here up top and I do love how the ladder can go in and out That is really really cool. The translucent windows Don't look too bad You don't see a lot of robot stuff on the inside and that's what really bugs me a lot with the translucent collars I dig the hoses big gap in the back. Those that's one issue The front looks really good Autobot logo there the silver lights silver bumper. I Dig it. I really like Inferno's to fire truck mode and now for a quick g1 comparison We've got Kingdom Inferno With his g1 counterpart so you can see a lot of the similarities. I like up here on the ladder He's got that nozzle right there and a little Tip which kind of matches up to the nozzle and tip on this ladder Of course the g1 inferno his ladder can extend as well But he's got a lot more reach than Kingdom. And of course you've got the hoses there on the side And let's see not the wings the wings for the g1 inferno They were separate now a little bit better throwbacks would have been to have the hose and the wings white But it is what it is This is still a great looking vehicle mode and a great homage to that original toy Now of course there is weapon storage as well So you can take inferno's rifle and there's a hole right there that'll match up to the peg on the side of the rifle Just peg that in and now you've got inferno all self-contained And of course the little nozzle is blast effect compatible and that's about the only blast effect I have that kind of looks like water so There you go, and that's pretty much it for inferno in fire truck mode and Now for some quick size comparisons here is transformers war for Cybertron Kingdom inferno With generation one Optimus Prime Generation one inferno and see what I mean I wish the kingdom one had the wings and hoses painted white instead of the gray But still it is what it is And his mold mate Earthrise grapple now grapple here has been enhanced thanks to toy hacks decals So I really can't wait to see what toy hacks does for inferno Why yes, I got a new toy Transformers war for Cybertron Kingdom inferno is a great Figure he's built. Well. He's solid tons of articulation great generation one throwbacks and just an all-around Awesome toy this guy comes highly highly recommended. So there you go guys Transformers war for Cybertron Kingdom inferno So does a transformers war for Cybertron Kingdom inferno belong in your collection? Absolutely, this is a great figure and a perfect remold And what impresses me the most is that Hasbro learn from their mistakes and fix the issue with his transformation pegs So they don't snap off like they did with poor old grapple This is a great-looking bot a great-looking vehicle mode. I love the ladder attachments I mean this guy is awesome. So yeah, you see this one on the shelf pick him up You are not going to be disappointed now guys if you enjoyed the video Don't forget to like don't forget to subscribe and don't forget to click that bell icon to get notified when I upload new Reviews also if you're in any position to help out the channel I offer channel memberships here on YouTube and I also have a patreon page once again guys This is patriot prime signing out


The key concepts that allow horizontal divergence to be converted into vertical motion are that mass is conserved, that the air density and density vertical structure are fairly constant with time, and that the vertical wind at Earth's surface and at the tropopause is effectively zero. This means that the total divergence must be approximately zero, so that the air parcel volume remains constant. Thus, changes in the horizontal area cause changes in the vertical height to maintain the air volume. A key to remember is that the vertical velocity w and its partial derivative with respect to height z do not always have the same sign. The second key point to remember is that the partial derivative of w with respect to z is the negative of the divergence. We will look first at divergent near Earth's surface. If there is horizontal divergence, convergence, then the air must go somewhere and it can not go down, so it goes up. The equation actually says that the partial derivative of w, the vertical velocity with respect to z, must be positive, but if w equals zero at Earth's surface and w is increasing with altitude, then w must be positive. For a divergence near Earth's surface, we see that the partial derivative of w with respect to z is negative, which means that w must be negative above the surface since w equals zero at Earth's surface, so the air velocity w must be downward. At the tropopause, the rapid increase in stratospheric potential temperature acts like a lid on the troposphere and effectively makes w go to zero at the tropopause. If there is a horizontal divergence aloft, then w must be upward to maintain the air parcel volume as the air parcel spreads out horizontally near the tropopause. Mathematically, this means that w must be positive, but we know that it must go to zero at the tropopause. Therefore, the partial of w with respect to z must be negative as it approaches the tropopause, i.e. w is decreasing with increasing height to zero at the tropopause from a positive value in the troposphere. On the other hand, if there is convergence in the air near the tropopause, then the air must go down and the vertical velocity w must be negative. If we look at the changes in w with respect to height above the level of non-divergence, as z increases, w goes from more negative to less negative, which is a positive change in w with a positive change in z. So the partial derivative is positive even though w is negative. Putting these pieces together, we see that if we have convergence at earth's surface, which occurs in low pressure areas for reasons we will see in lesson 10, then at the tropopause, there is divergence. In between the two surfaces, the velocity is upward, i.e. w is positive. If we have a divergence near earth's surface, which occurs in high pressure areas, then there is convergence at the tropopause. In between the vertical velocity is downward, i.e. w is negative. The upward moving air above low pressure creates cooling, which leads to clouds and precipitation. The downward moving air above high pressure region causes warming and drying, resulting in clear conditions.


Good afternoon, cyber traders welcome on back here this Wednesday afternoon May 10th shortly after 2 30 p.m. Eastern here It's been a phenomenal day thus far really good morning.  I'm in right now We'll talk about those in just a moment but great to see later Jeff Dan and all of our students students and members alike inside our live trading room and If you're joining us back on social media Facebook live LinkedIn Twitter YouTube the works all of our social streams Welcome on back as well folks. So with that in mind Hey up start to begin we saw this just in the after hours yesterday I got a bunch of lines mapped out on this personal chart here myself I'm in this right now right off that dark blue line at 1873 just went once it popped back above it So with that in mind, I'm hoping for this to run up higher into the close perhaps making the ultimate move back up towards 20, you know, hey level by level. So right now it needs to stick over 19 here for the time being if not that then definitely 1890 but really like a lot of volume also from consolidation earlier today off the 19 level. So up start UPSD It's liquid. It's just well-known company trades a lot of volume day-to-day coming off quarterly earnings So the fact that it's trending back up. I wanted to give it a shot Thankfully, it's worked out this far, but hopefully more to go for you I'm gonna put this on our list just to begin, but hey, it's been a really good day all together though folks Market ended up making a pretty strong move just early on today on the way down But again, we had a phenomenal list of stocks that ended up running up UPSD being one of them We had another pop just a moment ago on the stock note N. O. T. E. We had AMD run back up again for us Mara. So there's a bunch more that we'll get to I have a couple more that I'm in that I'll share with you in just a moment But let's go from top to bottom here. We'll catch up with them and probably just a moment of actually here So we'll start right from the top Hcdi I don't really recall us trading this one too much here had it has a huge spread right now Looks like the run on this or the pop on this is pretty much over at this point I guess this was more of like a Pop and drop move low float type of move got halter a couple times on the way up I mean if it was called out then I just wasn't really looking at it personally Maybe it was Richard one of our other like students doing it perhaps here But um, yeah, we'll move on from this trade for the time being now CTIC I recall this being on the gainer side for anyone new joining our social streams here like YouTube joining us today I you know, hey, we always like to show this here take like a minute just to show this Well, there's more to it than just this actually let me try and do that there we go Looks very flat right looks very very flat off a big gap up normally it looks like pretty flat to begin with but You know the stock is a buyout CTIC any time It's just gapping up and just very flat not moving much at all in terms of price afterwards That's typically a buyout. So I'm not going to check the news to even check or not, but We'll see what else we got here. I got a bunch of other cheapies here. You fab I am bi to skip over those two And the ST the run on this is over with for the time being but we'll talk about this though for right now I mean ended up trending up pretty nicely after the open hopped and then trended up ended up Rebreaking above to led to a phenomenal entry at the time up towards 250, but from there I mean, hey, I wouldn't have expected a sharp of a drop perhaps maybe looking for higher lows and then for a move back up again, but it came crashing back down So, you know, you know, what happens once this pulls right back down to the level it had broken out from That's a red flag to begin with that was two bucks. Well, look at it since it broke under two It's 15 cents below there now. It looks like it's gonna drop off more. Don't get stuck holding on folks B H I L D Hill. Let me zoom out here on the daily chart Hmm, it's kind of cool. I'm just poking fun right now The artist in me is coming out right now. I'm gonna show you this That looks pretty interesting I'm a modern day Van Gogh. What could I say? So this stock ended up making a nice little push not exactly towards that trend line or to that trend line exactly just yet For right now it had pulled back also below a big volume level seemingly here around 175 also a bit of an order of 78 I'm gonna pass on it for right now. Maybe tomorrow or hey this afternoon if it proves me wrong, then I'm sure Rich will call it out, but What else we got XMTR it's at the highs. All right The stock took a huge hit over the last like six months to a year practically 65 all the way down to sub 15 So I'm not making a strong move up here today, but it's on kind of light volume, right? It's just trading over a million shares Maybe if it was cheaper and if like the ordered book was more liquid then I'd give it a look, but That's what we have comp here for comp is trying to make another push back up I didn't trade this from earlier today Fausto nailed this one from the morning session ended up You know calling out effectively the slingshot off at 350 pushed up close to four Reversal time around 1030 Eastern then from there fell off. So I mean, it's not exactly a four bucks just yet or 395 the high, but it's working its way back up We like that we focus on trends we focus on liquidity. So, you know what? I'll give it a look put this on our main list here comp All right, so upstart we talked about already off 19 It's shaking around there right now I have my stop set to break even if anything so I should have mentioned that as it was pushing up time But if this pulls back and stops me out, it would be a bit disappointing But I really think that there could be a good chance to see this pop higher I'm willing to bet that and if not then so be it. I mean again We always talk about this in class But if you've made even a slight profit on the morning let alone your days pay well Hey, if you jump in another trade and you're up on that trade and then you get stopped at break even Well, do you lose any of the money that you've already retained on the day so far? No So as long as I play good defense now that is like literally what I care most about just at this point So I'm in a couple of other stocks here. We'll get to those in just a minute STSS not one that I traded from today, but it looks like this one ended up having a wacky pop and drop It's trying to squeeze back up here again, but I'm not too convinced on this GRWG, I guess I missed out on this one also I think this is like a pot stock and just one of them that ended up popping up today TCRX well, you could tell I certainly was focused on TCRX this morning at least in pre-market I did not trade this at all after the open though My hands were full. So, you know, you could only trade so much I prepped up for this stock, but I did not trade it ended up ultimately actually pulling back towards the 61.8% fib and moved up. I mean, that's easy to see looking back in hindsight, but Yeah, right now not much to it now Keep going no not much to it now because now at this point the stock note is trending down perfectly off the highs We talked about it before on the other one What was the other one even I think it was maybe the MVST perhaps it was I think it was the MVST, right? I guess we can back up to 91 here But you don't want to get stuck hoping and holding here after a popped up and shook back down So, you know getting back to that other one there at least it's not really looking too good the note All right, I keep it going here folks. I was trying to make sure I didn't lose track of my spot there It's doing good All right, I keep going here EB not much to it Yes, the stock actually we day traded back like maybe like a month ago or a few weeks ago did pretty well on and off just on this trend Up, you know, it wasn't the biggest percent gainer wasn't the fastest mover and that's fine. It's actually a very tradable stock in that regard It's trading a little less volume per minute compared to what it normally does They were what it normally has done for us in the past like a month ago or so So that means like I don't know if it's really going to pick up at any point this afternoon I'll write it down for the side list All right, so folks market is pushing back up back on up right now if there is anything else looking pretty good here type it in the chat board And otherwise actually really quickly before you do that Let me ask Jeff Dan Lita and all this otherwise John or perhaps Ravina that were joining us just before Michael If I was making a call out just earlier on a stock I was looking for a bounce on What stock would that have been I just wrote it earlier before this meeting started about like 20 minutes ago perhaps but There is a stock. I was looking for a bounce on just across the last like 30 minutes 45 minutes What was that stock before I move over to the losers list? I want to show another stock that I am currently in just to give you a quick update Mara good old Mara here Mara is up like 8% so wouldn't have found it on the loser side And it's not up enough to be on the gainer So I Mara is not like, you know making the greatest move right now We talked about upstart already so that one I'm already in John Mara to I got one more I'll show in just a moment, but now as I kind of flat now it ended up making this little pop off the 10 level I was looking for the bounce earlier here, and I ended up taking a 4 cent loss And then when I pushed back over 10 I waited a little bit and then I jumped in like pretty much right around this point here So it's pushed up pretty well just you know, I'm not gonna put it on the main watch list Just wanted to give you an update on that though just for all their traders inside the cyber group room All right now the last one we will find on I think I think it's on the loser side here So we will find it on the loser side Let's see if there's anything looking just as good though. If not better dbln down actually 70% what the heck happened to this today I Didn't notice this in pre-market. Wow. I mean maybe it wasn't down 70% in pre-market But it was down enough to maybe where you look for a dead cap bounce. It was hard to borrow Anybody couldn't have grabbed grabbed the short, but I Mean maybe at some point later this afternoon even I'm thinking I mean it's down so much You thinking maybe like you know should this make higher lows off to 11? I understand there's a chance for a dead cap balance Maybe a small little temporary squeeze You'll look out tomorrow. Maybe otherwise, you know what we don't have too much on our main list so far Why not? Not training a whole lot of volume per minute. I'm not necessarily Expecting it, but I'm just saying what if it's down enough to where you look for a possible dead cap down 70 plus percent We'll give it a look. Maybe if you could build back over this to 11 If it builds back over and then falls back under then red flag, so I'd move on from that move on from that Should that be the case? each cat I don't know looks like kind of in tears Thin order size out there dropped off pretty nicely. So that's what kind of caught my attention there easy to borrow on trade station But man, I don't have a great need to put it on our main list UWMC I feel like we traded the stock in the past recently. Maybe it was this like kind of long upward day I maybe we traded this then but Right now down big the stock actually a little bit more liquid trending down to the lows here pretty nicely You know what head that's why I wanted to skip over one so we could focus on if there's anything looking even better And this does actually I like this. We'll put this on our main list. That was the UW MC as a possible short There was a short that we had yesterday afternoon There were a couple of shorts that we had yesterday afternoon if I'm not mistaken I think we had PayPal and we had dish so actually just really quickly Intermittently, let me just take a look at those right now Just to show you how they did I mean, hey if you're looking at a stock at the highs the day you want to look for a pop higher If you are looking at a stock at the lowest of the day you want to look for a drop-off Lower look what happened on dish yesterday afternoon. So, you know, that's why we have to do a job here folks and just go down the loser's list we're in an upmarket right now, but It could be something kind of going against the grain there Twilio That's what I was moving moving on up right now You know volatile enough. I mean, but I just feel like for The liquidity we could probably trade something a little bit safer right now at this point If you did well on it from earlier, then I'm sure that you're going to follow up on it more So the one I guess it's not on the loser's list or down enough. I reckon plug. I just have a stop set at 782 I just thought it was going to drop off better at this point friend If it were to have I was going to put it on our main list But you know we called this out yesterday afternoon. It didn't make the drop off at the time It made the perfect drop earlier today. It was a really nice short from that perfect 7 97 level There was like a million share black print filled at the ask there and even now it's 780 That's a pretty big volume level just recently 780 780 one So, I don't know. It's on my side list. I'm in it right now But if it moves up to like 82 will likely just take me out on a small loss. That's all All right, so hey for right now. We have four stocks on our main list we got the upstart trade we got the comp trade we got the UW MC short and Bbln that could be a dead cat bounce. So don't rule that out just yet. Actually last one that we said We were going to come back towards the EOS EOS C It doesn't hurt us at this point to slip this on the back ends. There we go. Yeah, five All right, folks, so hey for all of us on social media, let me know if there's anything else looking good here Actually really good timing from one of us on YouTube right now Dave party experience joining us on YouTube right now I appreciate you asking what do you think about PayPal this afternoon? That was the other Short from yesterday afternoon. So actually appreciate you reminded me of that Take a look just from 24 hours ago and dropped off pretty nicely Right after 3 p.m. Eastern broke below the lows and 6650 there roughly and made for a nice drop I mean, perhaps you would have wanted to have seen more given the price Well, you got more today dropped off pretty nicely this morning You know, hey Fausto talked about this in the audio broadcast in our trading room this morning yesterday morning He looked for a long as a swing trade and he got out really fast Well, it's because he realized it wasn't making that big bounce at the time and hey it broke lower So he got out took a small dinky loss But well, hey once the stock breaks below a historical support Historical is the key word, you know less if we're in a really strong market or otherwise I mean this is coming off bad news of its own So it could be a little while before this looks to make a bigger bounce So that's why you're getting a stronger drawback again today So yeah, I mean for this afternoon that's kind of just moving on up off 63 I would kind of pass on it for this afternoon if anything is a day trade at this point now I mean hey the more drops it's gonna be more than my personal price range to follow day to day It's a very liquid stock, you know, just trades a lot of volume So, you know, it's tradable in that regard, but just given the price You want to see it make like a big move and it made that big move from this morning So, you know, I feel like at this point you're pretty much just done with on that trade All right, so Really quickly just to wrap up. I think that I've been Zynga pro up. I hope I do give me a quick second here Yeah, there we go plop it on up here Go So anything that I'm missing anything that I'm missing here Not really because if there was anything up like a lot recently last five minutes, you'd see a big discrepancy I don't really see too much out there. Maybe our cat and that's like a pop and drop stock That's like a known pop and drop stock We'll take a look at those I guess for right now, but I don't really think too much from just yet. Oh Look at that pop and drop What do you know? You car this one's more than hasn't dropped yet. It doesn't mean that it won't so Yeah, I mean for right now this stock actually still has some life left to it Be on the lookout for five to be a big resistance on this trade five dollars as a whole number level It's like a new IPO. I think took a huge move up on day one on day two took a huge move down and That was pretty much it So, you know, if maybe day traders are looking for a stack to pump up on a short squeeze Maybe you know, this could potentially be it But you'd want the order book to fill in a little bit more here the spreads very wide for a four or five-hour stack Jumping back to the our cat trade there. At least this is like more liquid. You know what I mean You know it already dumped off, but maybe this could begin to build support I'll add both of them just to kind of tag along there, but be careful on both Just I feel like they're kind of low volume low float type of stocks and you can get burned on those pretty quick. So All right from Mike saying TGTX Nah too slow. I feel like just a little too slow for a thirty four dollar stock there If you did well on it earlier or if you're in it, I'm always rooting everyone on all right But for right now I want to find something that's more cost efficient in terms of like a move That's worth the time. So I think that we're good for right now though here folks We got two you got four, you know six seven different stacks to work with and then aside from that Hey start and stick with this list and then rich will be on the mic at three o'clock and about 10 12 You know 13 minutes then at that point if there's anything moving well or maybe around 315 Eastern If there's anything else moving well, then you try and follow that. I mean, hey I didn't put matter on the special list So, you know, if that doesn't really make a big move for me soon, I'm gonna pull the plug get out and just focus on something better That's it All right folks. So hey for all some social media Facebook live LinkedIn Twitter YouTube the works We have a bunch of new subscribers on our YouTube channel. We're posting more Shorts lately and will continue to across this week and next so hey make sure that you are subscribed right to our YouTube channel YouTube comm forward slash cyber trading you any questions that you have feel free to shoot me a quick email I always like to post that at the end of these streams as I get a quick border break Josh at cttrading.com. There we go. It's been light or pretty dim in my home office today again I've got to fix my lighting. That's why I'm no webcam today But nevertheless though folks if you're interested in joining our live trading room Go right down to the link on the green banner below just to the stream on the bottom to get yourself set up and started But for Dan, Leda, John, Ravina, Michael and all of us just there live again I'll chat with you folks coming up pretty shortly. Take care


Oh, my current PhD research is about Brazil, the role of Brazil in global postal communications. And what I see is that during the 19th century, Brazil made some effort to escape from the Bibilateral Postal Treaties model and to engage in a multilateral postal arrangement that is emerging with the UPU. And this is the scope of my presentation today and is also the scope of my ongoing research agenda. I don't know if it changed something for the public in general, because as statistic data shows me, the volume of correspondence between Brazil and places in Europe and United States remains the same, but I think UPU set some arrangements, particularly accountability arrangements that implied in an undepthedness, crescent undepthedness of Brazilian postal system in relation to UPU and especially French postal system. That is the thing that statistical and financial data shows me that I need to go further in my research in order to understand this undepthedness.


There's a couple of special problems that show up when we look at factoring quadratics So for example, let's try to factor 8x squared minus 16x minus 64 So if we try to use the quadratic formula on the equation 8x squared minus 16x minus 64 equals zero We start out by writing something like this, which gives us unpleasantly large numbers Now if we wanted to we could grind our way through this and it would be good for building character tenacity Perseverance, but it would also be a lot of extra effort So a useful idea to keep in mind is to try the easy things first And in this case one of the easy things is to recognize that every term has a factor of 8 and so we can remove this common factor and And now we can try to factor x squared minus 2x minus 8 instead of our original polynomial So we'll solve x squared minus 2x minus 8 equals zero using the quadratic formula And we'll get our solutions 4 or negative 2 Which gives us the factors x minus 4 x plus 2 Or how about something like this 4x squared minus 7x plus 12 So we'll solve 4x squared minus 7x plus 12 equals zero using the quadratic formula So we'll substitute our coefficients and do a little bit of arithmetic and Before we go on it's useful to keep in mind two important ideas First computers can't think they have to follow steps until the end no matter how far away that end is And the only reason that it works for computers is they can follow steps very quickly very accurately on the other hand humans can and should think and what that means is you can stop when you see the solution in This case while the quadratic formula requires us to subtract We can look at this 49 minus 192 and immediately recognize that that's going to be a negative number and our Solution will not be a real number, so we don't actually have to do the subtraction We can stop here and say that we have no real solutions and that's important because we want to find factors that are rational and a non real factor isn't rational and so we might say that since 4x squared minus 7x plus 12 equals zero has no Real solutions our polynomial cannot be factored We can run into a similar problem with x squared minus 3x minus 12 so solving x squared minus 3x minus 12 equals zero using the quadratic formula and we get down to this point and Once again the important idea here is that computers can't think people should and The thing to notice here 37 is not a perfect square, so whatever the solutions are they are not going to be rational So the corresponding factors won't be rational either and so again We can conclude since our equation has no rational solutions our polynomial has no rational factors