izgledaj smo, da vam začnem svoj skrin. Zato je ozretil in zelo čutno vzvečenje, nači tezavstvih a zelo skerznih tezavstvih. Zato sem zelo vzvečenja, kaj je izomor, tako še je dobroj čutno v Valeria de Paiva. Zato bi se začnem, da se čutno tezavstvih je, iz niriже nege začujem zelo bolj nekako začutiti komendlje, nekako začutiti komendlje, nekako začutiti komendlje, nekako začutiti komendlje, nekako začutiti komendlje. Mislim, da ne bom bilo pohvarjovan, svojo solucije je, da pošliči vse teorems na zelo, zelo pošliči, in pošliči vse neuralne netore, kaj je universela proksimator, da se da se začela si vauti, v holjnjehaske formule je začela. Na Substituciju isomorphizmu pa tudi danes stačnje obrov'jo. Tudi, ta logicallyja, ki povzaj kogako je istrihodnog propozistika in tudi tudi line teria premajno위čno zaj, da je spetak, ... protikaj vz 94 toga, kaj na dvej vse je, da pronekimo, ne lahko je postajniti v pustilite vsega. Pokretim, dobro ju nečem opetke, dialetje, kaj ležite, je jeo ta je razstavena, a je več neč delajovana, začalala mi, da je zelo brzivna, neč vzelo, neč zelo neč delajovana, neč začal. Selo bilo, The last part of the lecture is aYes, I will give you a brief presentation of the questions and topics in the future. And now we will continue, but here we will have a little break in the lecture. We will be back soon with Q2, and after this cycle, I will give you a brief presentation. Thank you for yourrant. Thank you. OK. Thank you. I will present a few questions upgraded features are 50Cr in 60RN. So, the somewhat surprising thing here is that we got very, very high successor rates.  Tako, najbolj je, da je to nekaj logičnih, zelo smo videli, da je tukaj tukaj izgleda, kaj je zelo izgleda. je to skupnje proverbovačno, in je poznane početno. Zato je skupnje je zelo, dobe zelo, da je bolj početno. Zvom, da je to izgleda, da je bilo, da je skupnje, da je zelo, da je izgleda, da je dobe izgleda, da je dobe izgleda, da je dobe izgleda. the fullProPositionally intušenstike linear logic is story complete even in its propositional form, but then if we restrict it to the implicational fragment, that is decidable in intuitionally propositional logic. That's not the case, they stay p-space kompleten, in actually Richard Stadmann provejte p-space kompleten, as a result on that fragment, and that's equivalent with other things like QBF, quantified Boolean logic, in terms of complexities, and allegedly it's above NP, we kind of believe that. And what we want here is to have polynomial algorithms for generating the theorems, because when we turn them into test sets, then by combining tautologies and their proof terms, that can be useful for either correctness or scalability tests of linear logic theorem provars. When turned into data sets, of course, they would help training neural networks that are focused on those neurosymbolic computations, which are getting very trendy in the neural net conferences. They are somewhat related to either theorem proving, or more specifically to reduced fragments of logic programming, like data log, for instance, and some reasonably good practical combinations of neural network and symbolic computations are happening in the last two years, NEAPS conferences. So, let me show you very quickly, instead of writing down the spec, how formula looks in this implication of fragments for linear logic. This is the lollipop operation, which is dash and o approximated here. So, there's the formula. And this is the lambda term corresponding to it, with l marking the lambdas and a marking applications. Not the brewing form here, just plain variables. And variables here, but denoted as numbers. So, zero is x, one is y. And then this is a binary tree with leaves in a set of variables. And there is a canonical form for that that's relatively restricted from putting all the combinations here for variables. We can just restrict it to something that's counted by the bell numbers. We will get to the sequence in the encyclopedia of integer sequences. So, not all those formulas are as nicely mapped as these two here. For instance, for the formula here, we have lambda xx, the identity function, and this one has here some redundancy. So, they are not necessarily mapped one-to-one, unless they are in normal form on the two sides. So, the Curry-Howard correspondence is a correspondence between computations and proofs. It's partly because the axioms are just isomorphic on the two sides. In its simplest form, it connects the implicational fragment of propositional intuition and stick logic. We'll call it IIPC here for easy pronunciation, with types in the simply typed lambda calculus. A low-polynomial type inference algorithm associates a type when it exists to a lambda term that requires unifications with occurt check in general, but in the case of linear logic, that's not the case because the mapping is one-to-one between the binder and the variable it binds. On the other hand, we have harder p-space-complete algorithm that associate inhabitant to a given type expression with the resulting lambda term that's ideally normal form. If it's not, we can normalize it, serving as a witness for the existence of a proof for the corresponding tautology. So we can use then combinatorial generation of lambda terms plus type inference, which is easy, to solve some type inhibitation problems, which are in that sense harder. Now the formulas and the generators that we ended up, that's covered in our ICLP20 paper, that's about a month ago, basically we had a few steps to get where we are, and we will see where we got in the next slide, but the process to get there was basically deriving better and better programs for the purpose, and first we start with the IP formulas directly, so we have binary trees of size n, and they are counted by the catalan numbers, and then we label variables using set partitions, so we do not have too many redundant combinations of variables. It's reasonably smaller than the exponential explosion if every variable can go in every position in the situation, and that's counted by the band numbers. We have n plus 1 leaves for binary trees with n internal nodes. So linear lambda terms, which would be the proof terms, will be derived first by introducing something called a linear skeleton of mod skin trees, binary unary trees with constraints enforcing one to one mapping from variables to their lambda binders, and then we derive a closely linear lambda terms, then we derive them in a normal form, and after that we eventually end up with a very good generator in the next slide. Pierre Lescan had a very nice way to count how many of those terms of a given size exist, but his high-skill generator will go about two orders of magnitude below this, because of high-skill's memory inefficiency. Here we generate them on backtracking, and then we can go as far as we want, and we can even parallelize the generation, so we can compute those terms relatively easy. That's a good size for a neural network. Billions are usually enough for training them, even less, and then of course we have also their types, because types are easy to infer, so that's then the dataset that we will eventually generate, and the generator itself is relatively compact prolog code. The way we do it, actually, I'm just giving the main ideas here. We do not need to use unification with occurs check for checking that two variables corresponding to the same lambda binder would have the same types, unifying them and not forming cycles, because there is only one, so that's relatively easy, and what we need to check is exactly that there is exactly one binding, and this is after we pick a binding, we have a check binding constraint here, that will be very easy to test. You initially use a variable, and then you bind it, you can check if it has been or not bound by using null var, so with that you can find relatively efficiently the correspondence between the binders, the lambda binders, and the variables, and for that you don't need to use De Bruijn force, so this is the final derived generator, and now if we look a little bit at lambda terms that we have here on the left, we have some similarity that kind of shows up, that's at least empirically how we kind of found it out that they kind of look very similar the term and its linear time, and then the same applies for a few larger terms, you can notice already some symmetries here, and if the size definition is fine tuned, it looks like, that's first empirical conjecture here, that they would have the same size, the type on the right, and the lambda term on the left, so this is the little Eureka moment, it looks like we see some interesting symmetries in the pictures, there are exactly two occurrences of each variable, both in the theorems and their proof terms, of which they are the principal types, theorems and their proof terms have the same size, count it as the number of internal nodes, so that's the size definition that we need, and then if we can solve the problem of generating all the IPL tautologies of size n, what this reduces to generating as I showed in the previous slide, all the proof terms of size n, and then the bijection between those two will make it extremely easy also for neural networks to eventually learn that because this is a very simple correlation. Now, after reading a little bit about the theory behind everything here, Zilbargar attributes this observation to Grigori means, it is that there is a size preserving bijection between linear lambda terms in normal form and the principal types, but he has nice little paper where he sketches a proof basically for that, and it's by exhibiting a reversible transformation of oriented edges in the tree describing the linear lambda term in normal form into the corresponding oriented edges in the tree describing the linear implication formula, which acts as its principal type. So, with that we have obtained the generator for all the theorems of implication linear indiciality, propositional logic over a given size as measured by the number of lollipops, and we did that without having to prove theorems, which is definitely good, because our first attempt was to reuse my intuitionistic propositional logic prover, which is a p-space-complete algorithm, and then restrict the resulting proof terms to be linear, which works, but compared to going the other way around via the Kirihauer isomorphism is much easier computationally. Now, this is a Goldilog situation that points out the very special case that implication formulas having linear logic and equivalently linear type theory, and probably that's why a lot of talks study it because it's a very, very nice mathematical mapping that also relates combinatorial maps and types and lambda terms all together. So, the data says that we generate now. They also have latex formulas, by the way, as comments, so if someone wants to display or have a very large example set of lambda terms, linear lambda terms, and their corresponding proof terms that's usable, the few billions of them there, it can use for correctness, performance, and scalability testing for the theorem provers, and then they have this structure here formula and then proof term, those are the pairs, and they are good for also testing deep learning systems on theorem proving tasks. Now, to make this a bit more interesting, some of the tests that I will show you also add non theorems, because you would like to see it distinguishes between theorems and non theorems, rather than just providing the proof of a theorem when it exists. So, some encoding issues here always needed for easily feed the neural networks. The encoding is a prefix form here, lollipop is zero and so is application, lambda is one, variables would be uppercase letters on the two sides and we'll use a separator between them, and then the formulas look a little bit like this in prefix form with that encoding. So that's now relatively easy, because they become strings, but they have some structure, and the neural networks that we will need to use for that are neural networks that are good at processing sequences, mostly borrowed from natural language processing tasks. So, let me very, very quickly overview the minimal background in neural networks that we need for what's coming next. So, theorem provers are computation intensive search algorithms basically, some of them are touring complete, some of them are p-space complete, or NP complete, the languages on which they work. There are two ways that neural networks can help for the proof search. Well, first would be to fine tune the search by helping with the right choice points. There are plenty of choice points, basically, which of the rules should be applied first, and there is quite a bit of work on using them for that purpose. Another thing is to use them to solve low-level perception intensive tasks. For instance, when you work with learnable ground facts labeled with probabilities, and the deep pro-block is a system that does that reasonably well, where they're invited to by look there at ICLP that described how well they perform on some vision task, computer vision task. So, the third way that we are looking at here is can neural networks replace the symbolic theorem prover given a large enough training data set. So, that's really cutting to the problem as directly as possible. And some of the key machine learning concepts that we need to watch for, of course, honesty, that matters in everything. So, when we split the data set, we need to keep it very clean. That's also for medical trials and so on, okay? Training and validation. So, training is on which the neural network trains. Validation is a little bit of feedback independently. They're usually randomly split, but then a completely clean and untouched test set is also good to see from an outsider's perspective how well the neural network performs. So, that's pretty standard in machine learning. Two things to avoid overfitting. Works on training but fails on validation and then even more on testing. And then something else, which is relatively deep and serious. If we have high Kolmogorov complexity, so, generally random data, then hopes for neural networks to work on that are very low because they are an approximator that tries to encode regularities in the data. And if there is no such thing, it's pure noise, then they won't work. So, the neural network's key element is a linear matrix times the state of input at the previous stage plus some kind of bias. So, that's a linear operation. They cannot be trained if they're just linear so called activation function needs to be applied to it. This used to be sigmoid or hyperbolic tangent, but these days a very simple one seems to perform really well. It's a max of zero and x vector wise. So, apparently that's also very friendly to the GPU training of neural networks. It's very, very simple to compute in a vectorized form. So, another very important thing is that we need differentiable functions. The gradients that are computed on back propagation, they would be the feedback that we send based on the loss function that measures how far we are from our ideal result that needs to be back propagated stage by stage and it's the derivative plus some kind of learning time, some learning rate that needs to be used to make sure that the back propagation is not too aggressive because then it might flip or go into a local minimum and otherwise it might not converge too fast. So, if we start training our theorem provost, we have the Karihauer dizomorphism, what we do, because both of the types and the lambda terms are trees, we represent them as prefix strings and we adjust the size definition as I mentioned before to have exactly the same size. Now, more than available neural networks, one of the very good ones is sequence-to-sequence networks. We used originally in the translation French-to-English-English-French natural language, so a sequence on the left, a sequence on the right and then training them on a parallel text, very large data sets, translated text, aligned sentence by sentence. So, in this case it would be aligning the proofterm and the formula together as if it was a translation from French-to-English or something like that. Another important thing is to use a recurrent neural network, LSTM is a particularly good one in this case. What they do is that they have the long distance dependencies and that's of course important also in natural language processing. Paul, could I just ask a question here? So, you're trying to learn a function from formulas, applicative formulas to the proof terms, which are linear lambda terms. That's in the high-level function for giving an applicative formula, try to produce a proof term. And just a question, and just a technical question, are the formulas, do they have to be balanced in the sense that the variables occur exactly twice or, I mean, exactly once negatively and once positively, or can they be arbitrary, can propositional variables occur arbitrarily often in the applicative formulas? One day occur arbitrarily often, so the mapping is not a bijection between the lambda binders and the variables, then we get into IPC, implication fragment of IPC, and it works on that too. I will show you the graphics in a minute for the two cases actually, how well they perform in the two cases. So, let me then proceed with the thing itself, because I think we are getting a bit close to be out of time. Let me show you right away the type of neural network. So, I said it would be LSTM, it's a recurrent neural network that handles relatively well the internal dependencies between the, well, in a trezine prefix form, and they also, as I said, have some other properties that are good, for instance, vanishing or exploding gradient as you propagate those derivatives over 100 steps backwards in a deep network. Two things can happen, either the gradient explodes or it vanishes, and then one of the tricks that LSTM uses is that they are feeding also unchanged values, so they have internal loops and they have a memory where they keep the previous state. So, it's like an extra feedback from the previous state of the memory, and that propagates over the deep chain of connections. So, let me, those are some links here in the slides for where those are on the web, the data sets, and also the actual code for everything. And this is the very positive surprise for the accuracy curve over 100 epochs for IP, for the intuitionistic propositional linear logic. And here is the loss function, usually they go opposite, the loss is diminishing and when the value loss and the loss are very closely following each other, it means that honest generalization has happened. Now, this is a very easy case, so I said, well, let's look at making it a little bit harder in which we would throw in also unprovable formulas, and then it has to recognize which is provable and which is not, besides generating a proof type. So, that went also very, very well, same convergence over 100 epochs, relatively quickly maybe one hour of GPU, and this is now the harder case. We went to intuitionistic propositional logic, where a lambda binder can have multiple variables that it covers, and this is the general case. It performs also reasonably well, we had a surprise here, when probably the size of the formulas changed radically and then it dropped, but then it recovered relatively well, and it recovered both in the validation set and the training set. So, that's the dual, the loss curve for the tool, and my conclusions are that we obtained the generator for theorems of a given size, without needing theorem prover. We sketch the use as a data set for training neural networks, turning them into reliable theorem provers for the harder inverse problem given a formula in IP, find the proof for that. This is the data set that we have, and now as future work and experimentally speaking, open problems, can we extend these two full fragments of IPC or LL, because IPC and LL in implication form seem to be well covered, and of course, also experimentally something that needs to be seen, how they perform on the larger human-made formulas, the repositories of those, and also how it works, if we use random generators for formulas, either as trainers or as test sets for the results. Let me show you very, very quickly how this actually works here. So, what you see here is the honest test on the test set for the relatively easy set, which is the one for for the linear case. This is the intuitionistic case here. Well, you will see that things are not as great when you do the final testing, but they are reasonably good nevertheless. So, now this is the neural network working exclusively in inference mode. The training has happened, and we are just doing the forward pass. We are not back propagating anymore, so that is relatively good. The pluses mean that we actually did well. We found the proof for intrinsic formulas. If you look here, you would see that the sizes do not much anymore, so we have definitely more difficulty here, and the formulas are a little bit larger as well. The correlation between the size of the type and the size of the lambda term are not predictable. Well, there are some worst case scenarios in which one is exponentially bigger than the other. It doesn't happen so far this size, but we got a reasonably good success rate in this case as well. So, that's my talk, and ready for questions. Thank you very much. It was very interesting to listen how this most modern tools, like neural networks, can be used in research and logic. Are there any questions or comments or suggestions? If I'm here, I would have a question for more a comment, actually. Paul, I'm wondering if you are aware of this line of research where people try to synthesize tactics for S&T solvers, and they use neural networks to train and to get to automatically find out those tactics to solve different S&T problems. Have you looked into that, or how does it compare with what you are trying to do? That's a very interesting aspect when you have the proof assistants, like Agda or Koch, in which you have to find as automatically as possible the next step, rather than having to feed the proof assistant with that, and tactics are the macros that help you doing that. So, then if it's the choice between tactics, and there may be a dozen of those, that's a reasonable thing to try to train the neural network with, now synthesizing one, that definitely looks like a harder problem, and I believe that the way to do that would be similar in terms of training data. The training data would be then the proofs that they have in terms of which tactic succeeds for what kind of formula pattern. So, I believe that if you have a very large library, then they are building some of the libraries for mathematical knowledge, then if you correlate the formula type, the path, some kind of patterns in it, and then the sequence of tactics used to prove it, there is something where neural networks can help, but I didn't see yet very, very strong results, even in the easier case of logic programming, the famous family program with inheritance, there are some papers trying to get close to that Joffrey Hinton started that in the 80s, and then once in a while someone else comes back with a different method, they are all grounding based, in which you replace variables with all possible constants that you can have, and then you try to train on those. Other very, very interesting things are related to trying to mimic unification, for instance, instead of having actual constants, you would have embeddings, vectors representing those constants, and then some kind of similarity between those vectors, not Euclidean distance, usually it's mostly Cosino similarity, would be what replaces unification, and there is a lot of work centered on that as well, but what we are doing here is going directly when we are in the propositional world, right, like with linear propositional logic or retitianistic, it's no need to bypass any of the simplicity that you can have, the equivalent of grounding would be generating the formula and proof pairs directly. Now what's open here is how well that scales for more difficult problems and larger problems. If you train on small problems, would that naturally scale on gigantic ones? Okay, are there any other comments or suggestions or questions? About neural networks, do you know about neural networks that could work directly on trees instead of sequences? Yes, there are some very good attempts, not just trees, but actually so-called graph neural networks, in which there are encodings for graphs. The tree-to-tree algorithm, for instance, is an alternative to sec-to-sec here, but for propositional formulas, it's, I think, probably an overkill to use, for instance, graph neural networks. Now the tree encodings, the idea would be when you need them, when you have interesting labors on the trees, reaching properties, like you would have for, let's say, a chemical formula or something like that, but in this case we do not have, and when you do a tree-to-tree mapping instead of sec-to-sec, you would try to propagate from the leaves towards the root neighbor by neighbor, some of those properties associated to each of the nodes, but because here we do not have rich properties that we would propagate, that would not be needed for this case, so the simplest encoding in just prefix form worked relatively well. If I can ask a second question related to the first one, do you measure the part of the effort of the neural network to guess the right tree structure behind the sequence? I mean, how many times he missed the tree and finds another tree? This is the honest measurement only on the test set, and this is for the harder one for the intuitionistic logic, so the plus is here, would be when it finds the right tree, the minus here would be the case when it gets it wrong, so for the intuitionistic logic we get about 90 out of 100, right, but for the case of the, let's say, the linear logic, or even the linear logic with non theorems added, and just marked with a question mark, the results are also almost as good as the ones for the formulas that are just formula and proof pairs, so here it has also to know that this is a not a provable term when the question mark is generated, and it's still the set to set algorithm, the non theorems correspond to the question mark symbol in the training set, and then I think it might get also 100% on this because it's the easy set, the linear logic set. Okay, thanks, any other questions, comments? If not, let's thank Paul.


The early Prussian school system provided eight years of instruction in reading, writing, music and religious studies. It imposed a strict ethos of duty, discipline and obedience, and that worked. The model turned into a big cultural, economical and military success. Not long after, state officials all around the world started writing curriculums and public education became both a child's right and duty. In 1938, the Germans then went one step further and made school attendance compulsory. Ever since, it's not parents but the state that makes sure kids go to school. Today, 85% out of all adults worldwide are literate. Should government officials still have a monopoly on how we learn, a growing number of parents don't think so and want to take control over their own child's education. They advocate for what's called unschooling. . . . . .


And so I really loved Boutique Fitness in New York at the time, it was 2017, it was exploding all around us, you know there were different modalities and I was taking a lot of different classes and I felt while I loved taking those classes I felt like the class in the community that I really wanted and really needed at the time wasn't there. And so 2017 came really just the realization that I wanted to create the class in the community that I wanted which was the Swap Society so blending everything that I really loved in fitness was my own spin on it.


Login Digital Asset News is like a top story in cryptocurrency and digital assets. Man, I'm breaking down a bite-sized piece, as today we've got some important stuff. First up, Tether printing presses and overdrive as regulators watch closely. And what this really comes down to is the lawsuit that is going to move forward against Tether and its associated partners, such as Bitfinex or Ifinex, and how this is going to play into the cryptocurrency and digital asset market as a whole. Also, the dominoes continue to fall as big bank UBS to charge negative rates on holding above 250,000 for some of its members. And this is just the start of what is going on for negative rates as banks are charging customers to hold on to their cash. Amazing. Also, looks like the SEC is going to have a new chairman. His name is Gary Gensler. I'm going to look at how bullish or bearish he is on cryptocurrency and specifically XRP. And finally, we'll round it all out to a little tax news. Everybody's favorite. Kenyan crypto firms, I noticed, as digital tax law takes effect. And you can see this going throughout globally from Kenya to South Korea and, of course, to the U.S. of A as IRS to transition from education to enforcement. So we'll take a look at all that great stuff. But first, let's take a look at what's going on the market. So today is January 13th, 9am, El Paso, Texas time. And what do we got? Well, Bitcoin continues to slump just a little bit. We had gone from 40 to 30. Now we're up to 35. Now we're down to 343. Quite the roller coaster. And if you watched the video from yesterday, it was me and the OG, one of the original gangsters, Diddy from the Bitcoin family. We've just talked about how, you know what? Hey, this is normal. Things are going to happen. You're going to go up 10, 20%, 30, 40%. You're going to go down 40, 50%. And just hold on for the ride because that's what the all-time high years are, which is what we're in right now. And that's what's going to happen. So just be prepared. Ethereum above 1,000. I like that. 2.3%. Ted is tethered. Well, actually, everybody cares now. I can't say anymore. Everybody's really interested in this. And I'm going to take a look at what's going on legally and what could potentially happen if things just really fall down to the wayside. XRP up 4.6%. Hey, watch out. Polkadot in the fifth spot up 18%. Wow, almost 10 bucks. Pretty good. Litecoin's doing well. Cardano's doing well. Everything's doing well. What else? Anything great? Synthetix, one of our plays from yesterday. We did a Trinity Trading and hopefully, you know, makes up to that $18 spot. That's what we're, that's our goal. Go ahead and check that out. It's a link at the very end. And what else do we have? 20% for Aave. Wow. That's pretty good. But again, not, not it's all-time line. So let's take a look at how we would have done if we just would have invested into Bitcoin. That's all I like to do because, you know, what's the point, right? So if you would invest in everything else except for, well, Polkadot, you're doing pretty well. Litecoin 0.1. Wow. 2.7, 3.4 for Chainlink. 6% for Bitcoin SV. Congratulations. Synthetix again. Great play. 18%. And really why we do this is just to see like how we would do if we just would have invested in some all coins versus Bitcoin. And today looks like a pretty decent day actually. 14% for Zcash, 2.4. I don't know what Solana is, but apparently it's making the big rounds. We'll see how that goes. So that's what's going on. And today, let's just break into the top story because this, to me, is pretty fascinating. Okay, what's going on? So, Tether, I get this question a lot and it's been asked a lot throughout everywhere. And the thing is, we've got to break this down and just see where we're going and does this really even matter? So, this is what it states. This is from CoinGeek. This was from today. It says, Tether and his associated partners approach January 15th. Hearing with the New York Attorney General Office, it appears the infamous printing press that produces new USDT tokens has been more active than ever. And I'm going to show you how active it actually is. I'm just going to skip ahead. This is how active it is. This was from just a day ago, 400 million USDT printed, 400 million USDT printed. I mean, this is like reserve bank reserve type of action. That's amazing. So the real question is, is it being backed by anything? Do they really have the dollars or not? So with a large portion of the money flowing in digital asset markets recently and inflating asset prices coming from USDT, any regulatory action against it could have a largely negative impact. And it could and it couldn't. Really what it comes down to is, look, if this is a stablecoin and you're saying that it's backed by the dollar, where's your dollars? Show us the dollars. Show me the money. And everything's good. And that's it. I mean, there's some other different things in play, but that really is the big thing. So there's an ongoing injunction that prevents Tether Holdings Limited from loaning funds to the Bitfinex exchange. Tether Holdings Limited shares a parent's company called DigiFinex with Bitfinex. So what was going on here was that they were loaning out money or loaning out USDT to Bitfinex back and forth, and it looked like they were covering some losses. And that's not good. And this happened a while ago. This is 2018, 2019. And it looks like things, they probably shaped things up a little bit. I mean, look, if you get called before the New York Attorney General and you're like, look, we got to make sure you guys are legit. If you just go, well, we got some things in the back works and we'll, you know, we'll get it to you as soon as possible. Maybe behind the scenes, you clean things up. That's pretty much how it's done, right? You got caught. Maybe you were doing something a little bit crazy. You had a year, a year and a half to fix things. So if you didn't fix things, if there was anything to be fixed, then we got serious problems. So this is actually the letter that was put out December 9th. And what they were asking for, they were saying, hey, show us the documentation that proves all these things that were going on. They said, you know what, we can do that, but here's the thing. What they asked for was, they asked for an extension. They asked for an extension on September 17th to say, hey, we don't have the information, but we'll get it to you in 90 days. Then 90 days came up, they go, hey, we still need some more time. And we gave you some things, but we're going to need some more time for some other stuff. And now we're just waiting for that deadline to pass, which is going to be on January 15th. So they should have everything. And however, usually how these types of things work is they might just say, hey, we need another extension. Look, if you've ever been sued like I have in the past, it's one of those things. You just, you go through the whole process and it takes years and years and years. That's why this, this whole thing with the XRP and the SEC, unless they drop it, I see it to drag out quite some time. We're going to go over that in a bit. But I think really what it comes down to is how big is this going to affect everything? And I'll go over that just after this part. But the question is asked really is, because of regulatory issues, is Tether a stable coin, a security, a derivative or a decentralized blockchain asset? That's what this piece asks. So it is a stable coin because it's peg of the dollar. We can just say that that's what it is. As far as the security, security, as far as I understand it, is that when you are issued a security, it means that you're trying to gain some type of economic response or some type of revenue from the work of a third party. So in this case, really it's peg to the dollar. There's really nothing to that. It's a stable coin. So that does really make sense. And as far as a derivative, it really comes down to what is the underlying asset that you're talking about? Well, the underlying asset is the dollar, and that just really goes down. So that's really not a really good thing to say that, yes, this is a derivative or a security in all honesty. And then the last piece is, is it a decentralized blockchain asset? Yes. And right now, the way that the government classifies cryptocurrencies, it's property and not really a currency per se. So really what I just see it as is stable coin property. And that's really it. Now, I'm not a lawyer, nor do I play one on TV, but that's just how I seize it. So there's a couple of things. First, first of all, is how is this all going to play into how banks use it? Well, if you remember, just a couple of days ago, the OCC says banks can use stable coins in payment. So if they're looking for clarity or regulation, they've already said, Hey, you know, you can go ahead and use that, whether it's a stable coin derivative, a blockchain aspect, you guys can use it. And we'll kind of figure it out. So everybody, but it seems like everybody's kind of playing loose with the rules. And this was just one of the many articles that came out about it. Now, nothing has been done because banks are not known for innovation. So I don't see them, you know, picking up the torch going, Hey, thanks, we're gonna do that right away. They're not here for innovation. For Pete's eggs, they've been using Swift since like the 60s and 70s. I think they're gonna change too much anyhow nowadays. So the next question comes out of this is, what's going on with tether? So this is a very odd thing. And hopefully, somebody can help me out in the comments section. But we're taking a look at tether here. And the market cap is 23 billion. And of course, the story, including supply is 24 billion. So essentially, you know, pretty much one to one, right?  And that's what's going on right here. And it's peg the dollar. So, you know, 24 to 24, or actually, right now, it's 0.98. So yeah, that makes sense, right? Maximum circulating supply, everything's out there and so on and so forth. Here's the thing, here's my question, the 24 hour trading volume, 96 billion. So you got the market cap of 23 billion, and the market, the trading volume of 96 billion. And we take a look at every other thing that's out there. So that doesn't happen anywhere else. I mean, look at Bitcoin, you got 64 billion, the trading volume 24 hours, 646 for the market cap. Gotcha. 33 for Ethereum, 122, 88, 24, totally inversed. XRP, 3 to 13, and on and on it goes. The other stablecoin like USD, it's not, that's not how it is. It's got 1.7 for 24 hours. And then the market was 4.7 makes sense, right? Because you've only, you've got a max market cap and then 24 hour trading volume, okay, we're underneath that. The only one that I could see where it was kind of closely inversed, there was, it was sushi swap. And that is down here somewhere. Sushi's got 594 million, market cap is 590 million, which is pretty close, right? And the next one I think was Qtum, which was, you know, pretty close as well, but that's about in the top 100. So the question I have is, why are they inversed? So that's what it really comes down to. And then the next question I really have is this, is let's say, let's take the worst case scenario, is Bitfinex and Tether and the subsidiary, so you know, we're totally don't have anything there. We have nothing in Tether, so it's just going to collapse. So if you did collapse that one, let's just take the market cap. So the market cap is 24 billion. And people are saying, well, if that's what's propping up the whole market, then everything's going to collapse. Well, 24 billion is quite a bit of money, right? But that could wipe out some people, and that would be pretty awful if you store it in a Tether. But if you take 24 billion off the table, what is the total market cap right now? The total market cap is 960 billion, almost 1 trillion. And we're going to go above that. And then people say, well, how does that affect Bitcoin? Well, Bitcoin's market cap is 646 billion. So let's just take away 24 billion. Well, that happens all the time. Now, psychologically, this will be a huge factor. But in the grand scheme of things, what will happen? Tether will go away. It'll be replaced by a stablecoin that will be regulated by the government. All stablecoins will be regulated. And they say, you know what, we're not going to have what happened to Tether. We're going to stop that nonsense. And here we go. And now banks can use it, blah, blah, blah. So I think that could definitely happen. I just don't see what the bigger question though is, if you had a 24 hour volume of 88 billion, where's this all going into? And then if 88 billion is gone from the total market cap, that's quite a big chunk of a number. Again, I would, me personally, I don't use Tether. I don't know why people want to use Tether. I will just use USDC, is the ERC 20 token and the fees. But so is Tether. So I mean, in some exchanges, you have to use it. And I will talk to you about like, if you're on Binance, that is a huge amount of the volume. This is the total volume for all Tether right now that is being used across all the different exchanges. And if you take a look at Binance, 4% is just on Binance. I don't know why there's two. Oh, sorry, Bitcoin, USDT, Ethereum, USDT. That's like 5%, 6% of all the Tether that is being used in just those two on one exchange. And then you can go down the list. So you take all this different volume off, what are people going to go to? Well, some other kind of stablecoin. Me personally, I wouldn't have it in Tether. I don't know why people have it in Tether. That's just me. Let me know what you think in the comment section. Let's move on to our next piece. Next up, this is a quick piece, negative rates. We had heard about these. I was like, isn't that going to happen? And here we are. So this was today in Zurich, UBS, pretty big bank, will begin charging clients with cash balances above 250,000 Swiss francs or $280,000, almost 1% interest rate fee from July. Switzerland's biggest bank said, it's becoming encouraged and clear that we'll have to contend with negative interest rates for years to come. That's why we decided to lower the threshold for deposit fees in the end, less than 5% of our clients affected, which is true. So, but this is where it all starts. This is a slippery slope. At first, we're going to start with, you know, the 250,000 people. And I'm like, you know what, maybe we'll go down to 175. And then before you know 100,000, then 10,000, then 5,000, then everybody's got a negative interest rates. If you want to keep your cash so we can protect it and have it insured, well, guess what?  And also, when you keep it here, we're also going to do fractional reserve lending, which whatever you put in, we're going to lend it out. So we're going to double dip. And I mean, look, it's a business. It's a business. And that's, that's fine if you want to do that. But here's the thing.  Why don't you just put in the crypto, which is, this is the year to do it 2021. I can't tell you what to do. But if you listen to MicroStrategy, Michael Saro, the CEO, he's like, we're taking out of cash. And we're going to put in the crypto, especially Bitcoin, well, actually all Bitcoin, and it's going to go up and we don't have to have it on fire. And also we've actually doubled our position in five months. So that is what's going on. Let me know anything in the comment section next piece. This one was interesting. So with the new President-elect Joe Biden coming in, I know people are still holding out that Trump might win. I don't think it's going to happen, but okay. Look, there's going to be a new U.S. SEC chair. And what's going on is, it's this guy, Gary Penciller, will blame chair of the U.S. SEC by President-elect Joe Biden, said two sources familiar with the matter and appointment likely to prompt concern among Wall Street firms of tougher regulation. Why? Well, Gansler was chair of the CFTC from 2009-2014. So when I read this, I'm like, ooh, that doesn't sound too good, you know, tougher regulation, maybe for Wall Street because they know him. But the question I had is who is he? Because also, you know, Brian Brooks is potentially stepping down. Nothing has been said. Nothing has been said by anybody, but this is just an expectation because Brian Brooks, head of the OCC, was appointed by Trump. And usually when new administrations come in, they like to clean house and get all their people in there. However, Biden has said he was willing to work with Republicans and was thinking about keeping Republicans or nominating Republicans to his cabinet. Let's see if that happens. Brian Brooks is a great guy, hope to heaven he stays there, that would be fantastic. But the real question is, you know, who is Gary Gansler? And I think this statement, this two minute video will kind of clear it up for you if you have any apprehension. Let's take a listen. Let's say Mexico, if it's between the U.S. and Mexico. So one idea that's been around, it's widely associated with Ripple, but it's not only associated with Ripple, is this simple chart. What if I move fiat to crypto and crypto to fiat? This is called a bridge crypto or bridge currency. I can sort of say I can go from U.S. dollars to Bitcoin or XRP, you fill in the middle, and then move over to the other fiat. Mexico pays so in my example. And might that take some cost out of the system? We have Sean's issue earlier, volatility. If the crypto is fluctuating a lot, that causes some issues. If there's a lot of cost or friction, because now you're doing two currency exchanges, not one. I'm calling crypto a currency for this purpose. I know that crypto is not technically a currency. But for this moment, let me just call it, you have two currency exchanges. And that's pretty much it. And he goes into some different details. Now, he does make a distinction before everybody XRP army goes, ah, that's our savior. Look, he does say that there is a difference between Ripple, the company, and XRP, the product. And he talks about transferring and ODL, on-demand liquidity, things like that. And he said, it's very different. And I was like, well, that sounds pretty good. But then this was actually a response from Jake Czerwinski. If you know what Jake, good guy to follow. He is the lawyer for compound finance. And he said, look, Gary Gensler deeply understands crypto and has strongly supported Bitcoin for years. Selection as SEC chair signals a policy shift in favor of Bitcoin. However, he also won a record in 2018, saying there's a strong case that XRP is a security signaling no shift on that issue. So I know when we see these things, we get very enlightened and very excited, but we have to withdraw and get all the facts. So thanks, Jake, for putting out that tweet. All right, let me know what you think in the comments section and let's move on. Lastly, taxes, everybody's favorite topic. He only likes this stuff. I only talk about it because I have to talk about it because I got to pay it. And I think that if you've ever done anything with cryptocurrency assets and you've done in KYC or AML, you know it too. And also, I want to thank the supporters of this video, which is CryptoTrader.Tax. They have been friends of the show for quite some time and they've actually sponsored this video. So I appreciate it. And let's talk about taxes because this is what is going on, not just in Kenya. This is going on in South Korea. And guess what's going to happen with the IRS? They're going to transition to enforcement. So real quick, Kenya's plan digital service tax or DST came in effect at the start of 2021. That's right now. Part of the country's 2020 revamp finance act focuses on the digital service markets, e-marking transactions, including crypto payments, now attract a 1.5%. Levy, the new tax policy, prescribes a 1.5% tax on the gross transaction value of every crypto sale, which means for everybody outside of Kenya, you're going to have to pay for that. And then inside, it's going to be a different case. However, that's just one of the new tax laws that are coming in effect. And I think why this is happening here, also in South Korea, because they're going to have a 20% tax. And also in America, where they're going to just transition going, hey, Bob, you know, there's a question on there that says, did you engage in any virtual currencies? And you said no. So we're going to write you a letter. They're going to go from that to going, hey, Bob, here's a fine because you lied to us. And that's what's going on in this article. We've covered this a couple of times. I want to really go over it because it's the same thing. Just know this, you're going to have to pay taxes. And that's the thing. So you really got two options. Well, you got three options. You can evade taxes and go on the run. You and Wesley Snipes have fun with that. Or the second thing is you can pay taxes and you can go through and do all the different things with each individual exchange that you've gone through and get all the forms and fill them out and convert and all that stuff. Or you could use CryptoTrader.tax. This is exactly what I use. Everything that I talk about on this show, I use. I use them last year. Between the time that I signed up, actually the time that I got logged in to actually figure out what the heck I was doing to send it over to my CPA was 30 minutes. I saved $16,000 in tax loss harvesting. It was fantastic. And this was, I did 2017, 18, and 19, because I had never really done them. So I kind of combined them all together and it was fantastic. So right now, if you want to use that program like I do, which I will be doing again, there is a link in the description below looks like this. Viewers of Danica, 20% off. Also, you can enter to win for a free report, which is a $300 value, which is an unlimited report, which is pretty awesome. So just put the first name and last name, they draw it every week. We've already got one from last week. So fantastic. And this is what I recommend and what I use. Also, if you want to pay any taxes, kinda, you can use a crypto IRA from iTrust. Now, if you got a traditional IRA somewhere else, an old employer plan, like a 401k, 403b, military TSP or a 457, then you can move them over tax and penalty free to a crypto IRA at iTrust. So I have an account there. I've had it now there for eight months or so, six months, eight months, it's been a while. And everything that I put in is going to be tax free. Now I get tax on the income that I have, but if Bitcoin goes from, you know, when I put it, when I started there, like 8,000, and it goes up to, well, it's already 35,000, or it made some pretty good money. And I don't have to pay the capital gains tax using this program. Again, that link is also in the description. And that is all I really have for today. So look, I know this went a little bit long with a lot of things going on. This is the year. This is the year and just expect massive volatility, but just be prepared that we should hit some all-time highs. Again, not investment advice, it's just what I'm doing. What I think is going to happen. All right. So thanks for watching. I appreciate it. I will see you on the next one.


Judge Bork, if you don't know already, has made a statement to the effect that he is going to remain as the appointee. I'm just going to say to you that we are going to fight on for an independent judiciary. And we'll stay in the fight and see that we get our story told. Thank you very much. Thank you. Thank you all.


Presentation of TFNN. The Tom O'Brien Show is produced every business day. Tom takes your phone calls toll free at 1-877-927-6648 internationally at<PHONE_NUMBER>. Let's go to Eddie and Bookarton. Hey Eddie, what's going on? Hey Tom, how are you man? I'm doing great man, yourself? Good, good. It is a treasure to have TFNN every hour during the trading day to be there to help you to guide you and even to give you some peace of mind or like that somebody else is there with you while you're trading this crazy market, these are up or down. Well listen, we appreciate you growling and following us out here because we wouldn't be out here folks if we didn't have all you guys gals, tigers and tigers as clients and you know the market teaches you every single day man. Now, Tom O'Brien. Welcome folks, this is Tom O'Brien of TFNN. We go five days a week, we go seven hours a day, we go 24 hours a day on the internet at tfnn.com. Always remember folks, whatever you think about, you bring about whatever you focus on grows so everyone's having a great day, safe day, it's making a great night folks. Don't take anything personally, transform your life. When you refuse to take things personally, you avoid many upsets in your life. Your feelings of anger, jealousy and even sadness will simply disappear if you don't take them personally. Alright, let's take a look at it out here. We have the Dow Industries up $2.39, Nasdaq up $1.63, S&Ps up $45, Gold, Gold Contract trading down $41.60 at $19.58 and Outs, we have Silver off $0.22, $22.42 and Outs, LightSuite crude up $1.69, $69.33 a barrel, notes and bonds. So 10-year note, down $29.6, trading $1.14.05, the 30-year down a full point, plus $12.6 at $130.02 and $Kingdala. $Kingdala right now, down $33.60, trading $103.248, the euro is at $107, the yen is trading at $132 and the British pound is at $122 to $1 at U.S. dollar. Our phone number is<PHONE_NUMBER>, give us a call folks, want to know what's going on in your world. The world of the S&Ps, let's take a look at them. Now this is going to be really interesting folks to watch how this shakes out and specifically what I'm going to talk about is this. So we have intraday here, right, is a high volume low and you've just tested the high with tremendously lighter volume. So the high that we're talking about that we're testing here is the 4,031. We got the 4,000, check this out, this was amazing actually, 4,031.50 was the high of this morning. Now we just tested it and we're laying right there. That high at the first time we're up we had 450,000, yeah let's take it, 658,000 contracts. Bottom line, we just went into that with 206,000 contracts, let me see this. So yeah, we got one minute left. So the real question is is it going to basically close under that level and if it does close under that level and the next minute what's game is that you can see down the very bottom here at 4,003 you have a high volume low. So when you see something like this what can happen is that we can come right back down there before the close. That's the first part, the second part gets more intriguing and okay so that's on a daily basis right there. The next part goes like this, when you look at the spy what we have with the spy is this, the spy you can see that the correlation the correlation is the contraction of volume here is phenomenal folks okay. Number one you're going into 111 million shares that was the downdraft that was created out here on the 9th of March. We only done 58. Then you get the swing point that we're taking out right now, the swing point we're taking out is the 29645. At that particular level now watch what we have here. So what you're going into is 112 million the swing point that you're taking out is 149 million but watch the consistency here. So on Thursday, no on Monday on Friday we did 140 million shares yesterday we did 93 million shares and you're at 58 million shares there. So that's telling me two different things it's telling me we're either going to see that failure out here today with the future or when the Fed comes out tomorrow with their statement bottom line we're going south once again because we have a high volume low inside the spy out here at the 380 as well as all the way down here at the 374 as well as all the way down here at the 348 as well as your high as your ABC structure, larger ABC structure on the way down and the X100. Let's we'll put up the NQs first you take a look at the NQs what we have with the NQs out here I suspect it's going to be the same number okay so the number that we took out on the NQs the NQs have been stronger than the spy that's for sure and the Q I mean the S&Ps so the number is 837 and right now you're 13 points over that level now we went over that level with light of volume inside the futures market we had 244,000 contracts we just took that out with 90,000 the Qs also though the the futures also have that high volume low that's laying out there at 12,712 then if we go into the Qs you're going to see the same setup inside of the Qs meaning that we take a look at the the dailies and what you're going to see with the dailies we just took out the swing point of 309.15 and you know there is volume up in this 313 okay so well let me go through this one first so what you have there is that you're taking out volume of 89,000,000 at 35 you're going into 63 so either way okay this is where this gets really cool either way folks okay is that I suspect when they come out at 2 o'clock in the afternoon where are you going to basically fail today on this 309.15 which is pretty easy to do all what you'll end up happening is that we get the statement tomorrow at 2 o'clock and we get the news conference at 230 this may be spikes this high up here but my take we're going south that's that when you put it all together we're going south gold we look at the gold contract bottom line inside the gold contract gold just loves to do this man this just never ends bottom line it took out the swing yesterday has the volume taken out in the swing we take a look at gold if we just go from the bottom to the top right now you're approaching a point three a two I suspect what we're going to see here inside the gold market let's put this up let me see where let's see get this volume and then we had rolled on this contract so even we're even in the role there it is right there so there 1935 right now you're 1958 1935 is game in 1935 let's see what that is probably like a 50% or just over 50% retracement from the move this is the 50 1932 is a 50% retracement of the move and I suspect guess what that's where it's going to go doll was going to get intrigued here with the dollar is this is that the dollar came back to you know where I suspect it was going to come back to and what that is that's its strength how we came off the bottom and the high of that strength was the 103 006 well we got to 103 002 today and right now you're still hanging up you have a couple ticks from that but you know bottom line I suspect you're gonna get a rejection of lower price like that and the dollar once again wants to go topside stay right there folks who come right back our phone number is<PHONE_NUMBER> there's a call folks currencies commodities and bond markets are as important as ever right now with how they're driving the volatility in equity markets across the globe which is why it's a 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Dow industry is right now trading up 265 we get the magic up 171 S&Ps are up about 48 and a half. Let's get over to our Mammus the Basel Chapman as we do each and every Tuesday at 20 past the hour and don't forget folks Basel is an outstanding show here every trading day 10 to 11 Eastern standard time also is a great newsletter the opening call now it's very easy to get Basel's opening call come over to our website folks at tfnn you go into newsletters you're going to see it right on the left hand side the opening call you can get the opening call for one month for $149 you can get it for six months to 6.95 which is the savings of $199 or 22% and you get it for one full year for $1195 which is the savings of $593 and 33% now they all come with a 30-day money back guarantee folks okay when you get Basel's newsletter you're going to get approximately 12 separate archives so you can see how Basel looks at the market each and every day and how you can understand how to ride the Chapman wave. Basel how you doing? I'm doing well Tom how are you? Good good. A lot of grief lately and I just want to say that at least on my behalf I think that we lost a really dear friend and there's very little we can say the speed of with which it happened is just unbelievable and what I can say is I know there's you know the toughest thing about death folks is there's always a void the void is as heavy as you can get and what happens is that they it affects everyone differently and you know there's there's to me there's always this void and you know no matter what words you say they're never enough man I mean that's that's like wow man okay yeah it's like are you kidding me man? At least at least he's left us with not just memories but a lot of technical indicators which is really cool I know a lot of expressions that throughout the day we repeat them you know don't wait wait for a loss to increase just get out when you can it's just the best thing that you can do have your choice so what I wanted to show you is that within the context of all that I look at and I use nine-period moving average 14-period moving averages the MACD that's the moving average convergence divergence the slow stochastic the on-balance volume most importantly what I look at is how prices move down and how if there's a divergence between the indicators and then I also like to look at other areas of the market so what's really fascinating for me is that this little cluster this is on the left side here is the Dow chart okay we have long positions going all the way back from 2020 the low of 2020 we try to keep those and we trade around them and we add to them so on the very short term for subscribers we've been training for quite quite a long time now the three times long Dow and even though the trend has been down we've been able to get some really nice big spikes to the upside and we've gone to profits there and then if we take it out we're taken out of it holds it holds but we treat it as if it's a trading possession so we added a trade we've done it twice now from the very low actually the day before the laughing was last Wednesday 31,000 for 29 but what I really wanted to talk about is you what you're talking about the negativity but what's really important about this is that there's been price movement if you look at the Dow and you see here on yes the 200-period exponential moving average at 32,900 we are still way down the 32,522 so there was a period where the Dow and you can see the weekly chart there was a period where the Dow was absolutely the leader and then it started to pull back and if I show you the S&P you'll see that it's very oops I typed it into the den by mistake let me just do that again you'll see that the S&P right here and I'll show this in my show tomorrow in the Tiger Technicians hour at 10 o'clock I show some of these techniques in greater detail so here's the 200-period moving average this time the S&P is right on the 200-period moving average plus there's these two little trend lines here the green and red I call the Chapman Wave inside track repellent zone how many times look how many times the price has been pulled back from that level if we break above it for whatever reason next week we can get into the 4,000 and 32 area I think that'll be an important breakout but now look at this the QQQ this is the NDX100 the 200-period moving average which was like a magnet before is actually now moving like a propellant to the upside so the 313.38 high that was made the very beginning of February look how nicely the NDX1 this is the daily chart if you look at the week the monthly chart it's not looking good at all but on the daily chart this is good and if you look at the SMH's and I always like to think that the SMH's in many ways kind of a predictor of the general market trend they do very well they actually went above the high of the second of February at 255.64 so I just wanted to put things into into perspective to say this is a very diverse market whereas before we had the Dow leading then the S&P then the Q's this time we've got the Q's that are actually doing very nicely the S&P is actually lagging the Dow's lagging the those two so there when we're looking I don't think this is one market and I think that's the reason why whatever the Fed says tomorrow yes it could impact the financials but I'm starting to see and this is for subscribers we're looking at certain areas I call them under the radar that are starting to work very well besides looking at the market if you look at those sectors if you're able to isolate that isolation can keep you in a trade a lot longer than you you'd expect because they are not as volatile on the market pullback so I think this is a period where stock selectivity is going to be really important it is it has been already and you know I'm always looking for Pd's if you look at the gold that's the fourth highest peak when you start to buy signal to a buy mode gold did that it did that Pd back in February I think was the second and pullback very sharply and we just got that Pd because today is a very sharp pullback so I do see a some digestion there but the weekly chart is in the leg DC if you put it together there is a chance that whatever the Fed does the market can interpret it tomorrow afternoon or Thursday by saying wait a minute this is very selective because maybe the financials find some stability and gold is just telling us that I always think of gold as the place to go to when there's financial crisis and that just might say that at least for the moment we might be able to get through the next few days without any major crisis so I'm looking at both sides of the coin I can see the downside but when I go deeper in I think I'm looking at very selectivity selectively the upside says you know some things are ready very sharp you off the bottom so even if there's a pullback there's still much higher than they were just even a week ago and that I think is important yeah there's this this is always a interesting time as it Chinese would say right because the bottom line is that you know this they save the banks and you know they have to save that's the bottom line there's a great we know that there's a great cost of what they've just done and the cost it'll unfold over a period of time but when you're saving everybody which is really kind of now what they're saying we'll see what happens this is this is a complex period I don't think you need to be very aggressive you don't need to be you don't have to put yourself on the line you put your stops in I think you put your position well we know there's not enough money to save everyone that's a fact so the real question here is is that you know if we have four top banks and you know you think that that's going to be it well then you got to hope without one of the four top banks don't make any bad trades and you never want to get to the position where you've shrunk down to three or four of anything right that's not the way it should be right so tomorrow I'll discuss the night period moving average that David and I discussed so a year ago being great detail we'll do it tomorrow folks it's on 10 o'clock in the morning check it out Basil thanks so much have a great one a safe one thank you time stay right there folks to come right back and now is the time to subscribe to my gold report the gold report is a comprehensive look at the metal sector as well as the markets that move gold which is the currency and bond markets new subscribers get a 30 day money back guarantee so you have nothing to lose every Monday morning I published a gold report with coverage 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Dow industrial is right now up to $2.62 nasa except $1.72 s&p's are up $48 check this out folks this is you know this is about like the silicone valley bank knew that they were going out of business listen why do you see this man the stuff that's going to start coming out here is like a pretty amazing okay so the silicone valley bank to insiders tripled tripled check it out to 219 million before it failed okay this is like crazy man this is wow okay as silicone valley bank deteriorated late last year regulators began internally flagging flaws and risk management the lender ended up opened up a credit spittock to one group insiders listen this this is sick loans to offices directors and principal shareholders and their related interest more than tripled from the third quarter of last year to 219 million in the final three months of 2022 now watch how this shakes out this is where this comes it really gets wild the amount of doll loans issued to insiders going back that's a record amount by the way of loans to insiders going back 20 years the surgeon loans to high figure up to the surgeon loans to figures may draw scrutiny of course it's going to be a better draw scrutiny the firm one of three lenders to fail this month collapsed after investors depositors tried to pull 42 billion on a single day now watch here we'll get this the the federal the fed takes enforcement action or refers violators to other regulars if it finds problems with these loans set of spokesmen for the central bank which oversaw svb before it's collapsed before regulators seized silicone valley bank on march 10th it had a reputation as the go-to lender for tech companies in the venture capital firms that seeded it the federal interest rate hikes last year took a toll on the lender whose liquidity was tied up in longer-term government bonds now this is not over because i'm going to show you what's really going on here and the most recent proxy statement silicone valley bank the parent company of silicone valley financial group the parent company of silicone valley bank before it's collapsed said it made loans check it out last year to related parties including companies in which our directors are their affiliated venture funds are beneficial owners of 10 percent of more of the equity securities of such companies the bank issued loans in the normal course of business and with similar interest rates collateral as other customers around the around the same time according to the filing still loans in other categories such as real estate and commercial grew at a much lower rate just over three percent then those issued to the insiders the loans to executives and other figures surged as the bank weakness came to light last year the fed federal examiners identified a critical problem the bank needed how to to improve its tracked interest rate ricks the firm had about 15 billion and unrealized losses the bank also directed its lending business increasingly toward larger borrowers and this is where this comes in let's check this out including private equity and venture capital clients 63 percent of 46.8 billion of the firm's lending portfolio was comprised of loans to clients who received at least 20 million according to the end report so what you have here is this this is what they did they lent money to themselves they lent money to the companies that they own and it gets you know I mean common sense as this is the most most most they did in 20 years guess what they knew they were going down the tubes if they didn't know they were going down the tubes they knew it was going to get pretty close that's pretty intense man and what does happen though okay so that's i'm gonna dial this back again for a second and what i'm gonna dial it back on the way that regional banks work folks is that most regional banks and community banks will open for the founders that do it and there is self-dealing on a continual basis that's that's how the banking business works okay it doesn't supposed to work like that but that's how it works okay you know if you look at any community bank you're gonna see that the community bank they're gonna be in the development business they're gonna be in some business like that okay and because then it's a much easier way in order to get loans at a price that you know will not necessarily tremendously low on interest rates but you get the loan that's that's the that's the key when you start looking at this that's what it comes down to yeah it says as uh you know the the difference is is that the gangsters got to go into a bank with a gun the white collar criminals uh don't have to the bottom line is that you know you just open it up and you say okay baby how are we gonna divvy this up you know and of course i'm making that a lot more facetious than it is but it's the same thing i'm telling you i've seen it a million times man i mean yeah so there's gonna be a lot of trips that are coming down so pitches so even with that bank down the bottom line is that you know they their companies have the money they have the money and then we'll see where the whole rest of this goes some of the high volume equities out here today we take a look at it what we have out here you got first republic is up four and a half bucks you got tesla up 13 advance mic was down 63 and video is up four bucks amazon's at 276 you got uh child swab up 310 google's up three and a half you got a new york community bank that's up 53 cents you know they and was it was also happening too if you go look at a new york community bank right here let's do this new york community bank because when you see this this is like pretty cool um if you if you're the bank this is pretty cool meaning that watch what they go when they took when they took signature folks right signature bank partial takeover by the competitor is no for what it doesn't include it doesn't include 11 billion dollars in loans against the class of new york city apartments whose values have tumbled in recent years they have they have a loan portfolio part of this loan portfolio is 11 billion dollars and they're it's lent folks on a bunch of apartment buildings that uh inside of new york city that you can't raise the rents on and you know they're dead in the water it as one of the uh the advisors say the crystal whale and it's toxic waste and there's no doubt so you know the the we'll see if this gets you know to the taxpayer um but this is something everyone should remember okay uh to the aspect of you know do you really want to stick up for banks and you know in in the aspect of um more or less regulations because one of the one of the mind blows that you know has been in the marketplace after the debacle of 2007 2008 it took about seven or eight ten years it always blows my mind when this is right across the board folks that have nothing to gain whatsoever from banks regulations being tighter than lusa because if someone says that oh oh they're strangling them to banks like that these people pile on says yeah that's terrible they're doing this and doing that it blows my mind man because it's like how do you how do you forget something in the in the course of even 10 years but it happens continually it just happens continually that people stick up for big corporations and the corporations are taking them down it's like are you serious man because they they think that the big corporation itself has something to do with being a republican or re-independent democrat like folks you gotta think that true you know don't go protect someone that has nothing to do with you particularly yeah it doesn't even make any sense now that was up 277 nazics up 180 s and p's are up 50 stay right there folks are coming right back if you're looking for potential trading setups in the stock market then rocket equities and options report is a newsletter you should try tom yo brian delivers options and equity trades when the markets present them using a combination of fundamentals and technicals sign up for rocket equities and options report today with a 30-day money back guarantee so you have nothing to risk for all the details and to start your subscription today visit the front page of tfnn.com tfnn educating investors you might think that if you want to be successful at trading in the stock market you're going to need a crystal ball after all it's impossible to predict the future right like any endeavor in life before you decide it's impossible get some advice from the experts you might find that it's not so impossible after all for daily market overviews that give you direction on the key indices selective stocks and commodities subscribe to the opening call newsletter at tfnn.com the opening call newsletter is written by basal chapman creator of the trading methodology known as the chapman wave the chapman wave up down sequence gives you an edge in identifying price turns finding the peaks and valleys and stock prices get the opening call newsletter by basal chapman and your inbox every day first time subscribers also get a 30-day money back guarantee if you're not satisfied let us know and you'll get a full refund within 30 days of signing up tfnn.com educating investors biotech is booming but for how long whether you think the biotech bull has room to run or has run its course trade labu or labd directions daily s and p biotech three times bull and bear etfs visit direction investments dot com slash biotech today an investor should consider the investment objectives risks charges and expenses of the direction shares carefully before investing the prospectus and summary prospectus contain this and other information about direction shares to obtain a prospectus or summary prospectus please contact direction shares at<PHONE_NUMBER> the prospectus or summary prospectus should be read carefully before investing an investment in the funds is subject to risk including the possible loss of principal the funds are designed to be utilized only by sophisticated investors such as traders and active investors distributor foresight fund services LLC tfnn has launched the tiger's den hosted at discord tfnn has been educating traders for more than 20 years with live programming hosted by a variety of professional traders during market hours the tiger's den available to all tigers and tigers for just one dollar for the year there's no catch or added costs when you join our community of traders sign up today and become a part of this educational community of traders just visit the front page of tfnn.com this program is brought to you by vista gold traded on the nyse american and tsx under the symbol vgz welcome back folks to dow dow industry is up 313 asex up 193 sps are up 56 we had a couple questions and what the question is that these are these are great questions because it's like you know if you have more than 250 grand in a bank in one account okay the bottom line is that then you are susceptible to basically losing that money if the bank goes down that being said i don't see you know everything going out of business so if you're in the light with the with the fed and treasuries trying to do right now is have the money stick in regional banks instead of basically going to the bigger banks because everyone knows that the bigger banks the probability of the bigger banks not getting covered by the fed of the treasury probably you know much higher than a regional right so if you're if you're at a morgan stanley if you're at a goldman if you're at a city group if you're at a bank of america if you're at a walls fargo um you know that's really the only place that i would say that your probability of not losing money which over over you know 250 grand um is a much better probability and as one of our tiger says it's 500 000 if you're married with a joint account so what you absolutely want to understand is that when yellen comes out and says that don't worry you know we're going to have you all covered well she can't do that that's the there's not the way our banking system is set up folks okay is this is that it's a a fiat system that everyone businesses have to go bankrupt and we have to have these downdrafts because when you do the math behind it it's impossible mathematically it's impossible that you can have leverage 10 to 1 leverage and we give up under the bank they get 10 to 1 leverage it's absolutely impossible that you don't have white boats that's that's the system that we're in okay so in that context it's absolutely impossible that she can turn around and say that when in fact we know the lawmakers okay that that's not the law the law is 250 000 so let's pitch this i give a i'll give a scenario the scenario would be that a couple of the big banks okay watch this couple of the big banks basically get in trouble right and then three or four of the regional banks are in trouble also now if the fed or the treasury comes out says okay we're going to guarantee all of it right what would happen immediately is that our note and bond market would get imploded so they wouldn't do that that's my take on it there's no way what they would do they'd wipe out the regional banks that's how it would go and it's happened before they listen we that Lehman moment folks okay was and that's why that uh dick i forgot his last name that's why we so flipped out like i can't believe it you're saving everyone else want to save me well they just decided that they're gonna get a burn someone so you know the key to this is to stay with the bigger banks that's the bottom line you know so i don't think it's going to stop and as i said yesterday i think fluid thanks you thanks man dick fluid so i think what you we're gonna see i mean if i go back watch okay so 1998 and at 1998 um that's the first that was uh long-term capital then you had the 2007 and we're 2023 right let's like every 15 years and what probably will end up happening is that that time frame will keep going down and down and down level you know at small levels you know maybe it will maybe it won't but i suspect that when you have when you have to be between 12 and 15 years in this cycle in general those seems to be the cycles that you know basically our economy goes on meaning and what what tends to happen and what's going to happen now that's why when people say that okay we're going to come into a recession if you haven't been through this before what ends up happening is that banks themselves immediately will start lending less money and that this is the mind blow because we're not there yet but let's say that we get into a recession and you're going down and the prices are less expensive that's when the bank should be lending money because they they lent a million let's say that's it's they meant let the million dollars at the height of the market right now and then the asset goes down to 700 000 well then they won't even lend on the 700 000 that's what that's so convoluted about the system that we're actually in but that's the way it works and that's why you see at the bottoms of recessions why whether it's funds whether it's individuals if they have any cash they can make a much higher rate of return because the fact that the matter is is that that's when you're buying there's not as much lending going on but if you can buy in then all of a sudden they start loosening up i'll use the same thing with 700 000 then you go to 8 850 oh they banks that lends a little bit more they get to a million oh they're not gonna lend and lend and lend that's how our system works man you know so it's the aspect of uh being safe err i would say that you know the big banks are where it's at man you know and yeah and it's sad for the small banks sad for some of the small banks not gonna feel bad for a bank i'll tell you that man because the bottom line is that you know silicone valley bank um yeah that what you're talking about inside is now this this statement cat kathy wood man i wish i heard this i knew she was coming on bloomberg but listen to this one you're gonna love this one especially world traders kathy wood sees a silver lining and her two billion dollars in losses okay kathy wood highlighted one silver lining and her brutal run uh exchange traded funds through last year those billions of dollars and losses will help offset future tax gains well first off how does she know she's not having to gains the founder chief executive officer of our investment told bloomberg tv she has over two billion losses from selling stocks during the market route by selling stocks at a loss those losses can now lower or potentially offset the tax the tax bill woods funds could receive on future capital gains this is like freaking kind of god man blows my mind man i'm telling you you know if she's hearing me i i there's this there's the still house folks she hangs at the still house the still house is actually where i develop and palmetto park and it's so weird man i mean it's like okay it's over two billion right now which against we can take further gains and then concentrate towards the highest conviction names listen you know but the bottom line is is that in her case guess what they just keep plowing money into it would explain that her a flagship fund fell from february 2020 high it reduced its holdings from more than 50 to just 28 stocks selling stocks at a loss to offset a portfolio gain is a popular strategy used during market downturns to soften the blow on the road yeah give me a break two billion you know anyway you know the the sad part about this is that we are definitely in a um air that probably a lot of younger people that are just getting into the market believe this crap okay because the bottom line is that yeah we know we all get losses but you know that's not good good thing it's not a good thing to have a loss and then oh i'm going to make more money in the future so you know it's going to go against those losses it's you know it's it's insane that's what it actually is um yeah wow dow dow is up 303 nasaq's up 192 s&p's up 55 stay right there folks you come right back are you looking for a way to consistently add winning trades to your portfolio tom o brian is here to help tom o brian has been successfully trading markets for over 30 years a frequent contributor to td ameritrade network and cnbc tom o brian found a tfnn over 20 years ago to help educate investors just like you tom's daily market newsletter market insights is published every morning when the markets open to give you the competitive informational edge you need to succeed these newsletters are packed full of tom's advanced technical analysis and are geared to deliver comprehensive strategies for a successful portfolio get tom o brian's newsletter market insights today and try all of our products and newsletters 30 days risk-free with our money back guarantee at tfnn.com tfnn educating investors you might think that if you 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that's tfnn.com then hit watch tiger tv welcome back folks uh doubt does up 316 as except 191 sps up 55 and we have a question about credit unions credit unions folks are awesome beyond belief okay um the the way that credit unions got put together okay our work is getting together so that they can take care of their constituents that's the bottom line okay so there's a there's a huge difference between our credit union and regional banks okay community banks are close community banks should have been like that okay but my take on credit unions is that they're awesome okay um that's the bottom line because what what tends to happen is that we're a credit union let's say that you know whatever profession you're in whatever that type of credit unit is you need a car you need it you get your house fixed up there to me they're in a whole different ball game that's how it was supposed to be by the way okay that's a community bank supposed to be you know regionals are a little bit bigger but that's how it's supposed to be you get a bunch of people that are together they pull their money and they turn around and then they help each other i'll give you a perfect instance of why i saw this myself because i did a huge amount of business with the vietnamese when they first came over here i had i had shops all over the country this was 1980 okay and the way they work check this out man this is amazing i saw it myself they would come in families and you open a restaurant they would pull all the money in okay and they just give it to the person to open the restaurant and bottom line they paid it they paid money back okay and a bigger context that is how credit unions work and they still work that way okay so the bottom line is that yeah it's the 250 grand maybe they get you know up it but the bottom line is that i would say credit unions are in a whole different ballgame than regionals community banks are close they're probably community banks as i i said a little bit earlier is that you know whether it's the developer the opening and all that there's a lot of inside of taking that's the bottom line so it looks like what we're gonna have here you're gonna have higher prices you're gonna have less volume uh it's gonna be a nice tomorrow because it's uh this thing's gonna get spiked always remember folks the back and claw your heart out the bull can run you over and thank god there's always another trade health app is in prosperity you have a great night have a safe night come visit tom in the morning kicks us off 9 a.m and then we're gonna have the fed announcement at 2 and how old beyond at 2 30 have a great one folks have a safe one oh yeah i'll get them folks