Do you ever have an analysis where you're repeating the same function over and over again with maybe not much changing between those functions? And you've thought to yourself, you know, there's no reason that I have to run these in series. It'd be great if I could run them in parallel. Yeah, me too. Well, in today's episode, I'll show you how we'll do just that using the fur package in R. Hey, folks, I'm Pat Schloss, and this is Code Club. In each episode of Code Club, I apply principles and practices from reproducible research to interesting biological questions. Over the past couple dozen episodes, we've been looking at the sensitivity and specificity of Amplicon sequence variants, a way of analyzing 16s RNA gene sequences that's all the way of these days in microbial ecology. Now, don't go away. Even if you don't have a clue what microbial ecology or microbes or 16s or ASVs or any of this jargon is, stay tuned. Because in this episode, I'm going to show you something really useful in R, which is how to parallelize your analysis. Over the past couple of episodes, we've been trying to build a confusion matrix and found initially that it was going to take days, maybe weeks, to run that analysis. And who has time for that, right? And so over the past several episodes, we've systematically gone about trying to speed that up. Now, I want to make something crystal clear. Most of the time are as fast enough for what we want to do. We're in a situation though, right now, where we're comparing tens of thousands, I think 20,000 sequences to each other at different regions within those sequences and using different thresholds. So we have like 56 different combinations of thresholds and regions within the 16s gene against again, that 20,000 sequences. And oh, yeah, we need to repeat this analysis 100 times, because we're taking one genome from each species, because we're not really unbiased, let's say, in how we sequence genomes across different bacterial species. So this gets really slow. Where we left off in the last episode was that we used RCPP to generate a function to build a confusion matrix for one combination of threshold and region. And then in the last episode, we went about trying to deploy that then across all 56 combinations of regions and thresholds using nest, mutate and unnest. Well, now we need to go to the next step and repeat that 100 times. And so there's not really much that changes between those 100 times, except what genomes are being sampled from those different species. So we could run this in series one after another until we've gone through all 100 iterations, or my computer has about 16 processors on it. And why not take those 100 iterations and divide it, distribute it across those 16 processors, so that those then run in parallel, that we would run those different iterations in parallel. And then in the end, pull it back together. Well, this is actually something you can do in R pretty easily using something called the fur package. So in today's episode, we will do just that, I will build up going from doing it in series to then doing it in parallel. Along the way, I'll show you some tricks, some problems that we actually run into using our studio. And we'll see if we can't make this faster. Something to note is that if we do it in series, I think it should take about 16 hours to run. So if I've got 16 processors, it should take about an hour to run these 100 iterations, which is far, far faster than what we've done before. Of course, along the way, we've learned a lot. And you might be questioning, you know, was it worth learning all this extra stuff to get, you know, maybe 15, 20 hours speed up in how long it takes to process. I would say it does because in the long run, you might use these tricks again in the future. And that those that savings or the expense you might say of learning this now, you will then realize again and again in the future as you use this again. So let's go over to our terminal. I will move to our project root directory and go ahead and open our project. Over here, we will then go to get rock data. And we again, if I don't know why it doesn't just put me at the top of the script every time I come into it. But you'll see at the top here, we have our libraries, we have the function that we use to build the confusion matrix, get confusion matrix, that is a C plus plus function. We set our seed, we read in our ESVs, we get our metadata. And you'll notice that in this metadata step, we use slice sample to get one genome from each species. We then join those together generate that confusion matrix using nest mutate on nest. And then that's where we're at. And so now we're ready to replicate this again 100 times. We've done something like this in the past, where we read in a bunch of files and we used map DFR to do that. We're going to do the same thing to get going here before we transition to doing things in a parallelized format. Okay, I'm going to come back up here. And I'm going to create a function that builds my confusion matrix. And so what we will do is I will do get iteration as our function name. And it doesn't need any arguments. And I'm going to then indent all this other good stuff, a spot, so that I know it's part of the same function. And I'll close that with a closing brace. And then I'm going to let's see, I'll highlight all this and load this into my our session, so that I can then show that my function works, this get iteration function works. And we can maybe time it to see how long it takes to run. So we can do system dot time confusion, get iteration. And that should be good. And so I'm going to run this, I think it'll take about nine or 10 minutes. And so I want to see a couple things I want to see how long it takes to run. And then I want to see what the output of confusion looks like to make sure that looks good. And then we'll probably run it again to see that we get slightly different numbers. Because again, we're using that slice sample to get a different genome from each species. And so we'll do this for two iterations to see how things look. So that ran wonderfully, let's see how long it took in minutes, 549 seconds, it's going to be under 10 minutes. So it's about just over nine minutes. So great. So again, we then iterate this 100 times, that's going to be nine. So that times 100 916 divided by 60 minutes in an hour, get us about 15 hours, right? So again, even if I got something else running, I could theoretically get through this whole thing in 15 hours. But if it's paralyzed, I could get through it in about one hour, which would be really nice. All right. So the other thing I wanted to show is if we do confusion, we can see what the confusion output looks like. So we will save this. And I will run it again. And we'll see that the numbers change ever so slightly. Because again, we're taking a different genome from each species. And again, that variation is why we want to do this 100 times, and then kind of look at a median or a mean over those 100 iterations. So let's go ahead and rerun the system time. And I'll see you in about nine minutes in wall time. But in YouTube time, it'll be about one second. So we finished running at the second time, we're at 564 seconds, which was pretty close to what we had before up here, 556. Right. So within eight seconds, really good, still right around nine minutes. And if we look at confusion, kind of pull this back up, we see that at least for like this last row, it's easy to always look at the last row of a data frame to compare things. We see that the numbers are pretty close to each other, subtle differences. And again, that's because we're taking one genome from each species. And each time we do this, it's because it's using a random number generator process, we're going to get subtle differences, which again, is the whole premise of this because we want to do 100 iterations across running the same function, this get iterations 100 times, and then we'll merge all the data together. Okay, so the next thing we want to do is start to build up to do those 100 iterations. And I will build a little pipeline here. And the function we're going to use, we've seen before called map DFR against, we're still doing this in series, we're going to use this to help get us to doing things in parallel. We've seen map DFR several times. And what we're doing here is actually very similar to what we did in a previous episode, where we had a bunch of files that we wanted to read in and concatenate together. And so we used map DFR with I think read TSV or read CSV over all those files to concatenate them together. So what we're ideally going to do eventually would be something like one to 100, get iteration, and we could put it in a formula format with this tilde, get iteration. The other thing we can add for some eye candy, it doesn't really matter, is a column called iteration with the argument id. And so when this runs through the first time, it'll create this data frame with an extra column that's iteration for the first time through to be one, the next time through, it'll have two in that column the next time through three. And by the time it gets to the 100th iteration, that column value will be 100. I call it eye candy because art doesn't really care if you've got the same values, you know, the same threshold and region repeated over and over again. It's more useful for me as the programmer to see how things are changing over different iterations. Anyway, so we can run this and I'm not going to do 100 iterations though, because I'm still testing things out. So I'll leave it at two iterations. And what we'll do is we'll see that this runs in series should take about 18 minutes, and that we should again see different values for the same threshold and region within our data frame. So we'll run this should take about 18 minutes. And then we'll look at the results. As this is running, one thing I want to point out to you is that if I look at the top output, so again, if you go to your terminal and you type top, it outputs all the processes that are going on in your computer by default in order of descending order of amount of CPU usage. And so you see the first line here in my top output is the R session that I'm running. And that it's been running for almost an hour now, not just on this process, but overall, since I fired up our, but you'll see that it's using 100% of the CPU, and that there's only one row here. So later when we use the fur package, we will see multiple lines here. But I wanted to show you what this looked like with one process running. Wonderful. So it finished 1116 seconds. Let's divide that by 60 to see the minutes. Yeah, 18 minutes. Again, right around what we'd expect for running a nine minute process twice dead on. And if we look at confusion, we will see that we have a column for the iteration, the region threshold, and then our confusion matrix. And if I do something like confusion, count iteration, we see that we've got 56 twice, we could have seen that from the number of rows in our table of 56. And if I do something like confusion, filter, region, v four, four, and threshold equals 0.03, we'll get two rows. And we can see that the values are slightly different. Okay, now, if I were to again, take that nine minutes, and then multiply it out 100 times, it can take about 16 hours as we saw. What we'd like to do now is take the analysis that we've done in series, and to do it in parallel, using the fur package. And so what we've done here already with the map DFR function, as you've seen, is we've run two iterations in series, one after the other, what we'd like to do is to take each iteration and run them on separate processors. So that instead of running it in 18 minutes, we run it in nine minutes, because we're doing it twice as fast because we're doing it on two processors. Unfortunately, the fur package just is really painful to use in our studio I've found. Instead of getting super frustrated about that, we're going to see this as an opportunity to learn how to run R from the command prompt in the terminal. The downside of this is that I think this might be where we lose some of our Windows users. I will show you Windows users how you might be able to get this to still work inside your terminal. But I think that if you use the Linux operating system within Windows 10, I think it should work. Let us know down below in the notes, whether or not any Windows users have been able to get fur to work with parallelization on Windows using that embedded Linux system. Before we leave the cozy confines of our studio, let's go over to packages and make sure that we have fur installed. If you type fur into the finder window here, it should pop up. If it's installed, if it's not installed, click on that install button here in packages, then type fur, three Rs, not two, and then click install to get that going. I've already got it installed. So we're in good shape. I've got my GitRock data are saved and in good shape. So I am going to quit our studio. I've opened up my project in Adam. And you can see my GitRock data is yellow. And that's something nice that Adam does for me because it's aware of the repository in the state of the repository and that it knows that I've been making changes since my last commit. And if I come down, I can see the yellow band here on the left side of the screen, telling me that this is where I've been mucking around, if you will. Okay, and so you'll see everything we have here is what we had in our studio. It's a little bit different. One thing I'll let you know that I have installed that's really nice for working with R in the command line is a package called R exec. This allows me to highlight lines and then hit hit command, enter and run them into the terminal. This is a lot like what we did in our studio. Okay, and again, we're doing this in the command line, because there are problems in using for an unfortunately, in our studio. So let's go ahead to the terminal. I will start R, R from the command prompt, we have our nice cozy, comfortable R prompt there, even though it's not in our studio, it's the same prompt. And I'm going to scroll back up to the top and add library for to the top here. And I'm going to highlight everything. And if you don't have that R exec package installed in Adam or in your command in your text editor of choice, you could always do command C, command V into the terminal. But again, I can do command enter. And this then runs everything in R. It should load up without any problems. And we're right back to our prompt. And again, what I will now do is come back to my system time confusion map DFR. And we're going to modify this for use with per. And so one of the things that you might be interested in is running available cores. And this is a function from for that it tells you how many cores how many processors you have available. So I have 16 available. Depending on your computer, you might have more or less. The other thing I need to do is to tell for my plan. And so I'm going to say multi core. And so this is without any quotes. And so this tells for to look at my system to see how many cores I have and get ready to do what's called a fork. And so if you think of a fork has a bunch of tines at the end. And so what you can think of the fork is doing is that each processor gets a different time of my fork to run the different things that I'm going to map to it. Okay. So we can then run map plan multi core. And then here in my system time, I can then do future underscore map underscore DFR. And it should just work. And so again, we'll highlight and run this and see if we get any error messages. But if not, this should take about nine minutes to run. One thing I'll do is I'll open up a new tab here. And I will do top. And what you'll see at the top is that I have to our processes running. And I'm not doing any sleight of hand here. These are those two processes you see that they just started 21 seconds ago. And these are my two iterations running in parallel. And later when we scale up to the full 100, we will then see 16 our sessions going because I have these 16 processors. So we'll let this run for another eight, nine minutes, and then we'll come back and look at the results. Wonderful. That ran through in 591 seconds, which again is under about under 10 minutes, 9.9 minutes. So a little bit slower than what we'd seen before for an individual run. Again, that could be because I had other stuff that I was doing while this was running. But who knows, it's still much faster than 18 or 19 minutes, we do get these warning messages, unreliable value expected unexpectedly generated random numbers without specifying an argument for the seed. So you'll recall in our code up here, we do slice sample, which draws from our seed set seed up here. So what we want to do is let's look at the arguments for future map DFR. And there's an options argument that allows us to set the seed within that argument. And you'll see down here in this first example, you can give dot options equals for options seed and then the value of the seed. So I'm going to copy that because that's something that I don't use very often. And I lose track of things. It's hard for me to remember things that I don't use very often. And let's go ahead and put each of these arguments onto their own line. And I will put this options argument on its own line. And I'll use my preferred seed of my birthday, 1976, 0620. That is good. And we can then remove this seed from up here. And we will run this again. And we now should not get the error message. The other thing that I'm going to do is output this to a right TSV. And we'll put it in processed. I'm sorry, data processed, forward slash rn db dot RC dot TSV. And so this should then output the large table that we're generating out to this rock TSV file that we can use for aggregating in the future and for making plots and all sorts of other analyses. So let's go ahead and rerun this. And now we should not get that error message. So this time it completed without the warning messages were in good shape. Again, if we look at confusion, filter region before and threshold equals 03. We see that we've got two iterations and slight differences in our accounts for the confusion matrix. So we're in good shape. One other thing that I wanted to show you is that if you're on Windows, it might be possible to use multi session instead of multi core. I think it runs a little bit slower because of how it does the it doesn't do forking it runs, it creates more sessions in the background. It's supposed to run a little bit slower because of how it does that parallelization as well as how it handles memory. Multi session is also supposed to work in our studio, but like I said, I just couldn't get it to work reliably. So I'm going to run this and let's just see how long it takes relative to using the set the plan multi core. So does it take nine minutes or longer or maybe even shorter? We'll see. Let's look at what the what top says. It complained. Yeah, something weird is happening that that I don't really understand. With with multi session, maybe the multi session is the problem that I was running into in our studio. So I'm not going to worry about that. Yeah, this is the same error message I got in our studio. I'm not really sure what it means, because the code works as is. And and I'm not I'm not going to worry about that. There's more that you can do with a future map DFR, you can figure out how to put in a progress bar and all sorts of other bells and whistles that are nice. I feel a little bit bad that I've pulled you out of our studio to run this in the terminal. At the same time, though, remember, we're going to embed this in a make file so that it's going to get run from the command line anyway. So we're actually gaining functionality by moving for to the command line like this out of our studio. And that's really the environment that we're going to try to run things to make things reproducible anyway, right? It's not reproducible or automated to highlight a bunch of lines of code and then run it in our studio or in the terminal. What we've got set up again is our our script or our script, right? And so I'm going to go ahead and remove this system time and put this back to multi core, right? And I will get rid of that closing parentheses. We'll bump this over. I'm going to up this to 100 iterations. And I will get rid of this last step of the plot. That should work. Oh, one thing I want to double check is this file to make sure we got output there. And let me open up a new tab. Move to my project route. Look at that. That's there. I look at the head and we see all our great values. I could do WCL on the file and there's 113 lines. So one for the header, right? This this line counts. And then 112. So 56 and 56 gets us 113. That's great. And that is our target. So I'm going to create a rule for this in my make file. So it's been a long time since we looked at our make file. And we can come down here. And that's going to be my target. And it's going to be code forward slash get rock data dot r. And then the input to this is going to be these two files actually that we've been using in our exploratory data analyses, our genome ID taxonomy and our R and D, B, ESV count table.  So I will quit out of R and I'll do chmod plus x code get rock data dot r. That should be good. So let's see what happens when we do make data processed R and DB rock TSV should work and it should take just about an hour to run. So we'll see. So that's firing up. Loading the packages. I'm going to go look at top and see if bam, you see all these R sessions fire up, right? Screen flow is what I'm using to record the screen here. And that looks like it's taking up about like two or three processors maybe. So this isn't going to be completely at 100% using the processors, but it should be pretty good. Maybe it'll take an hour and a half to run with me for recording the screen in the background. Okay, so I'll be back in just a bit and we'll figure out how long this actually took to run. All right. So the day got a little away from me, but the job was running in the background as I was on my zoom calls and various meetings today. Surprisingly, it took three hours to run. I'm not sure why it took three times longer than I thought it would take kind of doing a little bit of Google searching on the future map functions. It seems like it does take a little bit of time to get up and running, but still, that seems like a lot longer to get up and running. Regardless, it's still a lot faster than the 16 hours that I anticipated it would take if I used a vanilla map DFR without using the future map DFR. So I'm happy that it worked. It works. And that's good enough for me. Maybe it is one of those packages that's still a little experimental. I don't know. But it worked well. So something I remembered was that I need to update my readme file to say that I used library for and to go ahead and add that as a dependency that I had used here in my are my readme file. And maybe I'll put this up here with our CPP. That is good. And I will go ahead and make my readme. If I spell it right. It's the end of the day. My fingers are tired. So again, this is building because we updated some dependencies that were in there. If we look at get status, we see we've updated the make file, the readme's, as well as our code get rock data.r. I also want to go ahead and commit that output file. Let's go ahead and do a WC-L on data processed. rndb.rock. So 5601 lines. So one of those lines is the header. And then we had 100 iterations of those 56 combinations. So this makes sense. I'm going to go ahead and add this data processed to the repository. Maybe I should double check how big it is. lth data processed. So it's 228. That's pretty small that we can post that up to GitHub. We just want to be careful about posting really big files up to GitHub. But I think this would be a really nice file to have that other people might want to work with. And that I certainly want to keep under version control. So I will do get add data processed rndb rock.tsv.  So that dash F forces get to add it will add readme.rmd readme.md. And then the code get rock data.r. And that's all good. I guess I forgot to add the make file so get add make file. And that's in good shape.  And so deploy generation of rock or confusion make sure tricks for 100 iterations. Great. So again, that didn't turn out exactly as I expected, but it's still it was quite a bit faster. Again, the goal is to get the right answer, not necessarily in the fastest time possible. So in this episode, as well as two episodes ago, where we talked about RCPP, we're kind of going a little bit into the weeds of our, you know, you don't always need to optimize things to be as fast as as is possible on your computer. But it's nice to know that you have these tools like RCPP, and the future map functions to speed things up with art with C++ code, as well as to parallelize things using those those future map functions. So give these a look next time that you're doing a map function where you think it might take a long time to run. And perhaps you'll find that by parallelizing it on your computer that you can get it to go quite a bit faster. So anyway, thanks as always for all the kind comments that people have tweeted at me or emailed me, those definitely make me feel really good. I got a grant rejected today or really bad comments and instantly I got an email from somebody telling me how much they really enjoyed these videos. That made me so much better that I almost don't care about the grant proposal. Anyway, so please keep practicing, tell your friends about Code Club, be sure that you subscribed and you've clicked on that bell icon so you know all the exciting things that are coming our way. I'm really excited about this series and I think it is going to culminate in some type of manuscript that we're going to submit. And I'll show you all that process as we go through for those of you that have never perhaps submitted a paper and don't know what it's like. So and we'll do this all with a mindset of being reproducible and keeping things automated. And like I said, reproducible as much as possible. So keep practicing and I'll see you next time for another episode of Code Club.


Right, yes. It's very much a team effort this one. I'm going to be presenting the work of the modelling team. And I just want to give a very brief introduction to the modelling team and who's done what. An overview of the role of modelling within the project and whiz through three of the more complete models that we have produced. And then a little bit of talk about the future of computer simulation of submerged landscapes, because I think there's a lot of research of submerged landscapes still yet to do. And there's a lot of computer simulation still yet to do. So a little bit of a look at the future. As Ben said, the modelling teams, Tabitha, Eugene, Miho and me, Tabitha did an awful lot of the research behind the first two models that I'm going to be presenting here, sea level change and duration and deposition. Eugene has done work on an advanced piece of infrastructure for doing large, large models in a high performance environment. Miho has been working on the forest growth dynamics and I've been doing the rest of the stuff, which is I suppose mainly the design and programming of models one and two based on Tabitha's data. And as far as the role of modelling within the project goes, and one of the key elements is that of data integration as you've seen from today, there are a lot of disparate sets of data with their own strengths, their own limitations. And the computer simulation environment was seen as suitable wanted to bring all those together. And because it's not just incorporating the data from the project, which incorporating research data from other projects, and also processes that are available from other projects, and this is one thing that computer simulation allows us to do is it allows us to treat data and the processes behind that data equally. And, and that's particularly important for submerged landscape research because the data is really difficult and expensive to get and sometimes it's under 10s of meters of marine sand and sometimes it's been eroded away. And so the difficulty of getting primary data is to a certain extent compensated by the other things that you can bring in with a computer simulation environment, and more than any other archaeological tool certainly it treats a four dimensional dimensional environment in a four dimensional way dogland to land change from the last place you maximum through to the inundation is constantly changing. And as a result, we need a tool that can deal with change over time and computer simulation is a lot better in that way than traditional tools such as GIS. Also, one of the things one of the things I particularly an enthusiastic about with computer simulation is asking the question, what would that look like. And it's important to draw the distinction between asking that question about dog land what would dog land look like. And because that recreation is not what we're trying to do. Basically, it allows us to look at, for instance, the data in the course to produce a hypothesis regarding how that data got there whether it's taffanomic or whether it relates to the environment around the core. And, and when we produce a mental model to explain that we can use computer simulation to ask a question. What would that mental model look like. And if it was a simulated system that we could play around with that way what are the implications of our hypotheses when it comes to the landscape as a whole. And that's a very important point with the computer simulation is we're simulating hypotheses, we are not recreating dogland as a complete environment. So, and just to illustrate that, and the first model that was produced and deals with sea level change, and there are a number of long term sea level change models out there produced by a variety of different research projects for a variety of different purposes based on a variety of different data. And what they do is they provide a model of how the sea level changes over time. And our question, what would that look like is what would the results of those look like. Those models look like if we applied them to the Southern River Valley, and if we applied them to the timescale of that would be noticeable by humans. And what would those long term sea level change models look like what the implications of them to the inhabitants of dog. And so we created a model that produces two main inputs, one of which is kind of a simulated inundation history of a particular point in the landscape, and the other is a look at the landscape as a whole. And this is the kind of input data that we have. This is a model of sea level change over the last 21,000 years produced by Sarah Bradley and her collaborators within and without the British ice project. Sarah has been very helpful to us over the process of putting this the data together for this model. And as you can see here you can you can look at this graph relative to sea level change over 21,000 years, and you can see flatter areas you can see steeper areas whether the sea level rises relatively quickly. But I mean, what does relatively quickly mean on a humans guy what do do the inhabitants of Dogland know that they are living in an inundating landscape. And, and there have been people who have expressed opinions about this, but actually we have with us within computer simulation a tool to provide a part of the, of the picture that might lead us to an answer it certainly won't give us an answer, but it will give us an extra bit of data that we couldn't elsewhere. And this is, I'm not sure how well this is going to come over on zoom but this is a little animation of this model in action. And this is the Southern River Valley and they see it just plots where the sea level would be based on the long term sea level curve and plausibly modeled short term processes such as ties and whether so you can see that that kind of pulsing the tides, that kind of thing, and it lines up quite well with with modern sea level data. And we can produce things like this, which is a, it's a model of the inundation of a particular spot in the landscape and the spot in our virtual landscape corresponds to the location of core and left up by which is in the Southern River Valley. And basically, we used the outputs of nine different models for long term sea level change, which are all plotted here. And the, the, the, the kind of wiggliness of each line of the graph as it goes from 0% inundated before a wave has, has lapped over that particular point to 100% inundated which is kind of the last time he sees the sky as it were. And as you can see, the old nine of these are different in some way. And, and there's a wiggliness to it that represents the random nature of the effects of the weather. But from, from the inundation process as a whole from starts to finish within this model, that's an important caveat that we'll get back to soon, within this model takes about 400, 450 years from the first wave to the last one. And we can include incorporate other kinds of data. So for instance, we have two bars here, bar a is the, is the last radio carbon date from core e left of low five from a pre inundation context, and Barbie is from intertidal models. So, were these long term C level curves to be an accurate representation of the inundation of this location you would expect, and they a to be before the start curve that B to be during the curve. And we can also, and this is the output of the landscape as a whole based on just one of these curves. And we can split the landscape up into a series of different categories that people areas or areas with zero percent inundation the blue areas or areas with 100% inundation. And the different types of green are different percentages of inundation over a certain period of time so that the green areas are basically the intertidal zone. And they're listed on the pie charts as mud flats, low marsh, mid marsh and high marsh, but don't take those categories to literally, and other factors are involved obviously in those environments. And as you can see, you can start to look at the southern river valley area and you can, you can start you can maybe even draw conclusions about how you think the inundation may have progressed over a period of time and over 1500 years it goes from very little marine to almost completely marine dominated. And it's important to point out what this model isn't and what it is, it's certainly not a recreation of the inundation of the southern river valley there are very important processes in the real world that aren't in the model. And it's not a conclusion in and of itself you can't present one of these maps as the answer to anything. And although we can do this, we can model this very precisely. And that doesn't mean say it doesn't give you any indication of accuracy. However, it is something that we can compare to all the other types of data that we have seen throughout the course of this day. It is its own thing. It is what happens when a static landscape surface interacts with the output of sea level change models, combined with plausibly modeled short term processes, it's no more or less than that. And, and has to be taken as part that is one of the tools for interpreting landscape, not the answer to interpreting landscape. And it can also use it for examining what's called data downscaling. So, for instance, the curve that sea level curve is, is only a curve because of the resolution of the data points mainly the data points every 500 1000 years. And it may well be that the actual process is more of a stepped processing which you get periods of much quicker inundation followed by periods of slower inundation or maybe there's inundation and regression that kind of thing. Maybe it's more of a weekly one. And we can't give the answer to that, but we can model each of the alternatives and we can start to look for indicators that may be present in the landscape that would point towards one of one or other of those options. Model one has a very static landscape doesn't change in any way shape or form it's like a like a like it was carved in stone as it were. And model two looks at processes of erosion and deposition and it takes the same seismic data as a base. And it adds a rainfall model based on modern rainfall data that you can you can basically on anything you like. And it allows us to introduce tokens into the system. These are simulated bits of 10 represent environmental proxies, for instance, basically what they do is they move with whatever thing they're in, whether it's water or whether it's sediment. Whether they can they can flow over the landscape they can end up in sediments they can be and they can be buried by other sediments that could be eroded out shifted so it uses some way of simulating environmental proxies. And this model produces three different types of outputs. It gives us the opportunity to pick a location in landscape and do a virtual core so it's and we can call a particular location this landscape in the same way that we can call the real landscape. We can produce a map where it tells us which areas are lower than they started which areas are higher which have net erosion which have net deposition, and we can also track the tokens as they go through the system. And just like the last one this is a video of this model in action, which doesn't do anything for a short while, but the blue areas are where water is but there's no indication of death. And then at some point it will rain and that rain will flow downhill. You notice there's there's no sea involved in this one and there's no explicit modeling of the southern river, although if you leave it running long enough the river kind of creates itself. And as a result of that we can get virtual cores. And one of the interesting things about this model is that the processes involved in landscape are really simple. It rains the water runs downhill and based on how that how that war moves either stuff gets eroded or it gets really positive. And however, and that leads to really quite a large variance in in in output of virtual cores. And you don't get much happening in some locations. These are just the core locations which feature any deposition. And you get the core location else 36 which is pretty regular over the amount of time that this takes. But the other cores they have they have periods where lots of deposition occurs and then they have areas which not much deposition occurs. And this is is purely a reflection of the local and the upstream system itself so series of kind of feedbacks that creates from a very simple system actually quite complex inputs. These are the cores that feature only erosion and this is and the three cores in this particular simulation that have both erosion and deposition. And the interesting thing about these because is we can display them as virtual cores so they're analogous to the real cause. And but of course just like the real cause we're missing data from that. Whereas if we if we show them here as the change of height that location over time, you can actually see what bits of data you're missing. And that's one of the good things about computer simulation is I believe it's very important to have a simulation produce inputs that are analogous to the real archaeological data that you have. However, we can go beyond that and we can go back to the system we can see which behaviors have created those. And this is just a map of 10 tokens in one of the simulation, the tokens start on the red dot and end at the green dot, you can see some of them move quite away, some of them don't move pretty far at all. And you can you can track them over time so you can see when they're moving those a lot of them move quite a way to start with them when they stop and then whether they move on from there depends on whether they're eroded out of the deposits where they land. Again, not a conclusion on itself, and certainly not a comprehensive model of all Tafanomic processes it's not even a sophisticated model of the Tafanomic processes it involves. And, but it's necessary beginning because Tafanomic modeling is is likely to effectively have now and, and it's in the process of modeling and the process of doing things seeing the result changing things as the endless iterative process of Tafanomic modeling, where the usefulness is what Georgian is called developmental utility, it's not the necessarily the results that are directly applicable with the process, which is the learning process. And just like the data and scaling and you, you can use this model for examining Tafanomic processes themselves you can change the Tafanomic processes and and see what the result would be. Now, this is the basis of me holds PhD work, and this is his model on forest growth dynamics and basically what this does is it takes a series of models that have been around for 40 or 50 years now called forest gap models and they used in in forest management and environmental science. And, and it changes them from stand based models to agent based models so each tree is modeled individually, and you can then incorporate and fairly a climate data with that. And so you can see how the changing climate effects forest dynamics, and you can also simulate human and animal activity and the removal of either a sense of trees as a whole or a different sense is different species. And as you can see here from the images in the in the lower left corner this is just a series of snapshots from one another simulation over nearly 1000 years. After 50 years you have actually quite a homogenous environment after 300 is starting to change up and the stand dynamics of self thinning and stand density and competition flight result in over a period of time quite a heterogeneous environment. And you can see this yourself if you've been, you've been walking through a, an area of woodland that is a plantation for the timber industry, for instance, compared with an ancient area of woodland, you can see the difference kind of instantly the make up of the trees and the, the uniformity or lack there on the trees. And what this is is it's the results of a series of simulations, but this number in the top left hand corner starts at 0.0 and that is just a an environment with three different species of trees that are all competing against each other using natural dynamics. The, there's an ever increasing amount of thinning that happens on year 50 of this 1000 year simulation and going from 10% up to 80%. So at 0.8, then a year 50 in the simulation, 8% of all trees are removed. The interesting thing about this is this obviously causes an effect in the environment, but the knock on effect, the effect on the dynamics of the growth of the trees in the environment can be seen four, five, 600 years into the future. And this gives us confidence that we will be able to working back from things like pollen analysis of archaeological sites. What we may be able to do is detect markers of human activity actually quite some way into the future, based on the very indirect traces of human activity. Again, it's not a recreation, but one of the good things about it is because it's based on forest gap models and a lot of research has gone into forest gap models and a lot of research has been done on looking at woodland to validate the forest gap models. Then, then this is a very, it's very well validated, particularly for an archaeological model. It's also expandable. I mean, the model itself has far more than the three species that we've just shown, but you can theoretically use this anywhere. And it's able to be integrated with future models. And if we combine models one, two and three, which is the plan, and it would give us a complex system that is very complex despite being based on simple individual behaviors that produces things in a format that we archaeologists can already understand and can relate to our primary data so we can produce virtual cause with virtual proxies in them. It also gives an almost infinite parameter space to explain that is that the number of potential combinations of variables that go into it. However, and this is one of the medium to long term aims of the computer simulation community project is if we make these models as accessible as commercial software as utilities as games that you download for the PC. That means that that parameter space can be explored by many more people. It's not just me and it means that those people can explore these models for the questions they want rather than the questions I envisage. And that drastically changes the way that simulation can contribute to archaeology and you know to facilitate this this Eugene Chung has produced an infrastructure that is able to run models that you normally have to use a high performance computer cluster on on a desktop PC that has a commercial graphics card in and this slide alone could have 25 minutes spent on it but we unfortunately we have to shoot past it. So as far as the future of computer simulation in submerged landscape goes on to highlight two things. And the first of which is, we need to deal with data integration it should be obvious at this point, and that, and even though we, we only have a fraction of the data that's available for dogland we still have more data than we can comfortably switch together very easily. And, and we need to deal with data integration that includes processes that includes change over time but that also allows us to include introduced processes from from other disciplines and and computer simulation is the best tool for that. But the second point I want to make is is possibly more important in the long term, and it's to do with the accessibility of the models, because this relates to the accessibility of the results of archaeological research, research, and of archaeological research itself. Dogland is equally inaccessible for all of us, unlike the vast majority of archaeological sites, some of us can get Stonehenge, some of us can get to various places.  And, and as a result, accessibility has to be built in from the very start to the tools that we use to research dog land, and that means that people now who are currently find it hard to access both the results of archaeological research and the tools archaeological research will get the benefit of our tool building endeavors. So, we can produce these models, we can make these models accessible. And so, people who might otherwise struggle to get involved in archaeology will have access to cutting edge tools that they can ask their questions, not ours about the archaeological data, and that has significant implications for the medium to long term future of archaeology not just submerged landscape archaeology. And just like everyone else has said, wouldn't have been possible without all the different members of the team and although the modelling team is four of us, and lots of other people here have been involved with various aspects of this. And that's about it. Phil, that's some, some wonderful time keeping that extremely nice by that. Okay, so while everyone just gathers themselves. We have a few minutes for talk for questions here. I've got one here for you off Becky Bryant. Two questions. The first is about uplift in model one Martin mentioned there was evidence of uplift uplift in Southern River Valley. Are you able to include this in your model. First question. Do you want to answer that first and then we'll have the second question. Yeah, can do. And uplift not involved in any way model one. And as a result, we have to, we have to involve it in the way we deal with the results of model one. And so we use what we have as a model with no uplift at all what we have in the real world data is potentially some uplift and certainly some movement and there's a the, the, there's lots of lots of research has shown that the landscape of uplift is is is much more wibbly wobbly over the medium to long term. I don't that's probably a scientific terms that but yes uplift and other movement is a factor. And so having a model that doesn't have that is one way of trying to filter out the effect of that in the real world so you can look at individual data points. And so the, the static model says the sea should be a particular time you look at where it actually is based on the, the dates you've got and that is a potential explanation for that difference and that's where they that's where all the, I mean, I would say computer simulation is a terrible answer generating machine but great question generating machine and that's one of the, one of the questions that would naturally come out of that. Okay, I think if we can do the second part of the question and the virtual cause remind me of synthetic boreholes we create and we're looking at sediment movement in the FEN basin. This is very useful approach to integrating field and model data. Could you say a bit more about your initial conditions did you try to scale rainfall in relation to climate data. And how did you decide how much sediments on the landscape initially did this vary spatially. Do you have different particle sizes in your sediment. Though, again, very relevant to our questions and I'd be interesting in looking at the fan based stuff and the landscape is is treated as if it was a homogenous unit. So it erodes and deposits but there is there's there are no bits of it that erode quicker or slower than others and and the the rainfall is based on contemporary data. And, but like I say that's just that that is, and that's just the easiest thing that was to do at the time that's that can certainly change model tools the one that I've had least time to do work on actually model ones and and yeah the how much sediment was on the landscape initially basically the landscape is entirely made of sediment was no bedrock so it is infinitely erodable although it doesn't it doesn't in effect it doesn't particularly much and so and no no no particle sizes or anything like that but he's certainly and it's only something that can be included and is something that I'm aware isn't included and has to be taken into account with with how you use data.


The following is a presentation of TFNN, the morning market kickoff with your host, Tommy O'Brien. Good Tuesday morning everybody, I'm Tommy O'Brien, coming to you live from TFNN just after 9 a.m. Eastern time as we come in after the long weekend and we got a little bit of a reversal as negative market action kicks things off. Just look at the last couple of days, right, Thursday action. We talk about some volatility, man. Friday, intraday, we spiked to a high of almost 48.38, let's see what we did get to just after the open on Friday, 48.3650. Right up at that level, we've seen a little bit of volatility to the downside this morning, back to a 50 minute chart. We were as low, about 6 a.m. when I woke up this morning, 48.17, you were down there at about 3 in the morning. Look at that acceleration, right, let's go back to a 5 minute, because 3 a.m. Eastern time, man. It doesn't look like much and it is interesting in context, right, just extreme volatility on Thursday and on Friday, and you had quite a drop out of bed, man, in terms of 3 o'clock in the morning, you'd go from 4800, I guess it's only 15 points, but pretty dramatic in the overnight session as you get Europe opening up, you trade up above 4800 and right now we're trading at 47.94, you're negative by 22 points in the S&Ps, that's in the red by almost half a percent. You jump over to the NASDAQ 100, we're off by about half a percent as well, you trade lower overnight, you see the volatility in the volume at about 3 a.m. Eastern time, 16,820, just like that, we're up a bit off those lows, still off by 78 points, Dow off by 160 right now, you're negative by 410th percent, see a little bit of a rollover in the Dow coming back into those 3 a.m. Eastern time lows. Crude, you talked about volatility last week, we're trading with the 72 handle yet again, 72.73, you're basically flat, you're positive by 5 pennies, Gold accelerates to 2067 last week, you're trading at 2043 right now, Gold off by $8 right now, and we jumped in notes and bonds and what do we got? We got higher price, lower price, higher yield, lower price, higher yield, where are we? We're right back to where we were Thursday, which is just remarkable in terms of the volatility we had since then, you get the 10-year, 4.03, 4.03, not bad on the 10-year right, back above 4%, what were we last week? When we hit 112.26, we're approaching 3.92 I think, something like that, just huge swings across the board, anytime you're getting some action yields, you might get some action in the dollar, and there it is man, dollar, 103.21 as we have, now what's remarkable here is, what did I say? Take a look at this conversation, okay? This is the one that jumped out at me getting ready for the program. Where are yields right now? Back to where we were on Thursday, okay? Where are yields right now? Right back to where we were Thursday before CPI, okay? Where are yields? Right back to before we were Thursday CPI, where's the dollar index? The dollar is up a full point from where we are, so what you're seeing here is, okay, yes, higher yields are going to bring dollar strength, okay? But pay attention to the moves you're getting in the dollar, because what's happening is, is that man, that is going to have an impact regardless of where yields are, getting a little bit of a pop right now. Now, where does that come from? Does that sustain? We will find out, but nonetheless, we got dollar strength right now as we have yields rising, but we are well above the correlation in terms of where we were Thursday before CPI versus where we are right now. Nonetheless, you jump over the VIX volatility index this morning. We've got about 20 minutes to go until the opening bell. Pay attention to this one as well. Also, double as well. Yeah, because we had a lot of negative action in the market last week. You didn't see spikes to almost 14. So what's going on this week, right? What's going on this week that's separating those, as you do have a pretty respectable VIX at 14 that, yeah, nonetheless, we got a little bit of a spike in the VIX. You got a little bit of negative action right now, and we'll see where we go from there. All right, and where do we kick things off? Let's kick it off with Boeing, man. What were we talking about last week, 220, right? We had 220 on Friday, boom, 210 today. And yeah, it's probably not going to stop, man. In this area, you put this back on a weekly, okay? All you're doing is coming into this consolidation area that you were in for the beginning of 2023, and now you're at 220, 210, and you're probably going to the bottom of that, which is a nice round number of 200. Now that's just the first stop, okay? I'm staying current. Longer term, you may have some big issues, all right? We would just do 180 in October.  And here's the headline. Let's pull it up. Where are we? I just lost you. I had you. Where are you? There it is. Slow deliveries is what they're worrying about here. Falling after a long weekend of developments tied to that 737 MAX jet led analysts to raise concerns that the crisis could slow aircraft deliveries, yeah. So they're lower like we just talked about here. You had analysts at Wells Fargo's cut their rating on Boeing to equal weight from overweight, increased risk that growing scrutiny of the company's manufacturing quality will have an impact on the pace of production or deliveries. Well, if only you're interested in, the only thing you're interested in is profits, that might be a problem. But if they're pushing out plans that the bolts are tightened on, then they might have to slow down that pace of production and deliveries, and it would make sense, right? It is kind of a conundrum when you think about it that all the plans they have are already sold. Here's the incentive to make sure that they're doing their best. Now I know that there are incentives, of course. I know the FAA holds them accountable to a certain degree, but it is interesting when you think about the fact that they have planes sold for as far as they can deliver right now, and maybe they need to slow that down a bit. And that seems to be a pretty reasonable opinion when you put it in that context. The FAA has grounded most of those jets. They're investigating practices at Boeing in the wake of the accident, yeah. And it's going to increase monitoring of so-called in-service events on the Max 9 and plans to audit the production line, as well as those suppliers. So yeah, analysts waking up to the fact over the weekend, I guess, that it's sometimes man, you know, analysts can be dramatically wrong sometimes folks, but they can also really have a good insight in terms of that's their job, that's the industry they're paying attention to, and they're understanding that things are going to slow down. It's going to pause deliveries, that's going to impact the bottom line. And yeah, you've got Boeing down to 210, just like that. It's never just a one-day deal, that's what our man Kevin Hink said last week, not going to get his take. Now he is on vacation, so we're not going to be chatting with him, but keep that in mind, because I was thinking about that this morning as well, excuse me, that it's never a one-day deal on a Boeing deal like that, right? There's your blowout.  So tough deal on Boeing, man, as we await, but yeah, they're going to have some problems. And remember how that company has progressed in terms of the pullbacks that are possible. And even if you go to where we were in 2021, right, from 278 down to 113, you hear that folks, from 278 down to 113 in Boeing had before you just accelerated up to the highs of 267, so don't be surprised if you get some pullbacks here on Boeing shares as we kick off the session down $7, that's putting some weight on the Dow off 141 points right now. You get the S&Ps off about 410 percent, off by 20 points. Stay tuned, folks. We got a big week of, we got some heads up, we got some earnings, kicking things off, and we'll come back, we'll talk a little bit of golden as they're out with their numbers as well. Stay tuned, folks. Come back, we'll talk some more. If you're looking for potential trading setups in the stock market, then Rocket Equities and Options Report is a newsletter you should try. Tommy O'Brien 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. Trading in the universe is governed by the Fibonacci sequence. This mathematical principle is responsible for everything, from the most aesthetically pleasing artwork to patterns in the stock market. To stay on top of stock patterns you can take advantage of, sign up for the Fibonacci 24-7 newsletter at TFNN.com. When you subscribe, you'll get a weekly report from Veteran Day Trader Larry Pezzavento on stocks you need to pay attention to, and you can trust Larry's analysis. After all, he's got 45 years experience as a day trader. Larry will also provide daily charts, videos, and data on the key markets that he's tracking. Expect notifications from Larry on market movement you need to act on at any time. 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. Welcome to the Fibonacci 24-7 newsletter today, TFNN.com, educating investors. 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 Tigresses for just $1 for the year. There's no cash 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. Steve Rhodes started his trading career as a student almost 20 years ago, and the student has now become the master. Steve won the prestigious Timer of the Year award in 2018 and barely missed that mark again in 2019, finishing at number two for the year, an amazing accomplishment. Steve Rhodes is committed to sharing his techniques and knowledge with anyone who wants to learn, and he shares his vast amount of trading knowledge every day in his Mastering Probability Newsletter. Steve's award-winning newsletter, Mastering Probability, is delivered every trading day with updates throughout the afternoon. Sign up for Steve's Market Newsletter, Mastering Probability, and you'll receive access to seven of Steve's educational webinars absolutely free. At TFNN, all our newsletters come with a 30-day money-back guarantee, so you have absolutely nothing to worry about. Visit TFNN.com and try Mastering Probability, 30 days risk-free today, TFNN Educating Investors. Welcome back, folks. We have the S&Ps, negative by 21, NASDAQ 100, negative by about 80. As I mentioned, Kevin Hinks, he's on vacation this week. My dad back in the saddle today. He'll be back on Tuesday. We've got our man Basil Chapman here. Next everyone back in the saddle right now. We've got Basil coming up, of course, next with the Tiger Technician Tower. We've got our man Steve Rhodes coming up after that with the Trader's Edge. We have Fast Market that will be going live, even with Kevin out for the week, and then, of course, we've got our man Larry Pesvento and my dad back live from three to four. It's going to be an interesting Tuesday, man. You get a little bit of a reversal, a little bit of a rollover, and we jump to Goldman. So Goldman, we've got the big banks out on Friday. We've got the investment banks out, and we've got Goldman rolling over a bit. You do accelerate higher. That was saving the market a bit. We're rolling over a bit. Goldman was all the way up to 384, just like that. We're basically flat on their earnings. You jump over to Morgan Stanley. Negative we go. You were at about 90 as of the close yesterday. You're pushing about 87.40, and you jump over the headline for Goldman over here. Equity trading surge drives jump in profits. Asset management revenue climbs as fixed income disappoints. Equity trading revenue was triple what was supposed to be expected. How about that? Right? Those equity traders. A year of execution is how their CEO, David Solomon, called it. When we achieved in 2023, coupled with our clear and simplified strategy, we have a much stronger platform for 2024, as what they're talking up right there. Net income was just over $2 billion, not bad, or $5.48 a share on $11.3 billion of revenue. Asset and wealth management business, $4.39 billion, up 23% from a year earlier. Management fees contributed the biggest chunk, as the bank highlights what it hopes will be predictable growth in those fees, which previously were overshadowed by its principal bet. Now, remember what we talked about on Thursday for the CPI? What's one of the biggest things that's going up right now? Financial fees. The prices in finance right now are going up, and there's an example of it with Goldman going up 23% for asset and wealth management business. How is that even possible? We know it's possible. They have trimmed their historical principal bets to $16.3 billion, dropping close to the $15 billion target that they have outlined. Let's see. Fixed income traders brought in $2 billion, a drop driven by low revenue from rates and currencies, investment banking, $1.65, following short of the $1.68. They were looking for, and they got a charge in there, of course, with the Silicon Valley and Signature Bank. Nonetheless, decent numbers, and that's going to be a steady business, man. Let me get back down to it. The asset and wealth management business with fees going up 23% from a year earlier on revenue of $4.39 billion, sounds like a sweet business to put it lightly. All right, what other news we got out there? Let's talk about Apple. Apple gets supplanted as the biggest company in the world last week, at least for a short period of time. We'll see how this week shakes out, but yeah, there's a problem for you. Apple, pretty remarkable how it rolls over now. Let's check out Microsoft as we do this. Look at this, man. There's a new king in town, folks. AI is running the show, CHEP, GPT is running the show, $10 billion investment. That's all it takes, man, which is nothing for the impact that this is having right now. It's broader than that. We know it is, but check it out, man. As you've got Microsoft up $5 to kick off the session, you're pushing $393 right now in the same vein. You're going to have Apple down $5 to kick off the session. You're looking at Microsoft at $2.92 is the number right now, and Apple is pushing $2.81. Just like that, they have $100 billion lead in market cap. The story out there for Apple, they're going to pull the blood oxygen tool from watches to avoid the US ban if the appeal fails. Now, I don't think that this story is probably that big of a drop-off in terms of what's going on, but nonetheless, a little bit of a perfect storm brewing on Apple. Maybe somebody else can help me out in the den in terms of what's going on with Apple, because that's quite a pullback, man, $5. But maybe it is just that. But they are planning to remove the blood oxygen feature from its latest smartwatches, the Series 9 and Ultra II, to get around a US ban of the devices if an appeal of the decision fails. So that was disclosed on Monday by the company that's suing them, Massimo, and they are locked in that feud. It said that the Customs and Border Protection approved the move on January 12. The agency decided that Apple's redesign falls outside. The scope of an import ban signaling the adjustment will let Apple keep its watches on the market. It's just going to keep going. Pretty remarkable, something so crucial to what they're doing in terms of health, wearables, all that stuff. Pretty interesting how that goes, and yeah, so they developed a software workaround intended to sidestep the dispute and present to the solution last week to the Customs agency. They're in charge of enforcing the import bans. They explained that the redesign watches definitively do not contain the technology at issue. Massimo, they got a quote from them down here. They seem okay. Apple's claim that its redesign watch does not contain pulse oximetry is a positive step toward accountability. Pretty remarkable, right? Yeah, significant move as they call it. Removing the technology from the Apple Watch would be a significant move. The engineers have been working on a software update that changes the blood oxygen app and its algorithms in a way that might circumvent the issue without losing the feature, but eliminating the capability would likely be the quickest way to avoid seeing the ban reimpose, which could have occurred as early as this month. They don't want to have to stop selling those watches, man. Now what's interesting is, so they already have watches. Check it out. So they've already begun shipping modified Series 9 and Ultra II watches to retail locations in the US in the event that the appeal fails. The stores were told not to open or sell the tweak devices until they receive approval from Apple's corporate offices, and you've got to separately a federal appeals court is expected to hear an Apple motion as early as this week for a continued stake. We're going to battle it out. And you're talking about lower prices in China. Thanks, Bob. Yeah, it's got to be bigger than just the blood oxygen level on their watch. But you're going to face some heat, man, today. We had a little bit of rising rates. We got a stronger dollar. You got Apple giving it up, man. And that's going to, I mean, even if you get a new dog in town in terms of Microsoft, if you got Apple tank, and it's still a $3 trillion company as we come into, look at Microsoft, man, $393 we're going to hit. I guess you're going to touch $400, right? It would make sense. Man, you're almost at 100% bagger from where you were a year ago in Microsoft. Just remarkable, man. Let's jump over to Nvidia. Nvidia shares right now. You're up by about $2 to kick off the session. Not bad. $549. You're going to be pushing near all-time highs for Nvidia as well. We jump over to some of the other Magnificent 7. Google shares holding up relatively well right now. $143.49. We closed at $144.24 last week. You jump over to Meta shares, Meta off a bid at $374 from $374.49. Tesla, we're going to talk a little bit of Tesla, man. Tesla's having some issues. And Elon's going to the board asking for a huge payday. Yet again, interesting how that comes down. He needs some more shares, man. That's basically what he's saying. I need more shares to be interested. I sold my shares for Twitter. Can't make this stuff up, man. Tesla, you're down by another $3.80. Tough week for them. Last week and it just continues. We were at $240, $241 last week. You're going to open at $215. Just like that, 10% hit to Tesla shares. And yeah, you're rolling over, man. You've been bumping up against that channel line.  Where's the volume? The volume's all back here, man. It's all near those lows. Maybe we get that first acceleration, right? What are we coming into right now? $215 or so. You get a couple bars to finish at about $200. Back from January 23rd and January 30th. We'll talk a little bit of Tesla, as well as some other equities when we get back folks. We've still got to talk about all the earnings we got coming down the line this week. Don't go away. TFNN has just launched their new trading room, 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. And now they are expanding their reach with the Tiger's Den, available to all Tigers and Tigresses for just $1 for the year. There's no cash or added costs when you join our community of traders. In the Tiger's Den, you can look over the shoulders of Tom O'Brien and the other TFNN hosts while they analyze charts during their live Tiger TV programs and join an interactive trading community with hundreds of members exchanging ideas, interact with other Tigers and Tigresses as they share trading ideas, news analysis, and discuss the market action all trading day, even at night and on the weekends. The Tiger's Den at Discord is accessible on mobile or tablets as well. 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Head over to TFNN.com and subscribe to Market Insights today. Don't miss out on this opportunity to supercharge your trading results. Market Insights comes with a 30-day money-back guarantee for all new subscribers, so you have nothing to risk. Don't miss out on this opportunity to revolutionize your trading game. Head over to TFNN.com right now to join thousands of traders who have already experienced the power of Tom O'Brien's award-winning newsletter, Market Insights firsthand. TFNN, Educating Investors. Don't forget, you can listen to TFNN live on your mobile device 24 hours per day. Go to TFNN.com and hit Watch Tiger TV. Welcome back, folks. We got the S&P is sitting right at 4800. Nice round number. We're down by 17 points to kick off the trading week. That's about a loss of about 310% in the S&P's. NASDAQ 100, you're off by about 310% as well. Negative by 44.169.25.  Let's jump around. Some of those equities are pretty good. Let's jump around. Some of those equities, Boeing, off about 3.1%. Analysts waking up to the fact that, yes, this may actually materially impact Boeing in terms of how long it's gonna take to get deliveries out there. You got Apple, as we were talking about. They're off 1.9%. Appreciate it, Bob, with the heads up. Found that article. Saw this over the weekend. Didn't see it this morning as I was getting ready, but yeah, you talk about it. So they offer the rare iPhone 15 discount in China amid demand fares. So that was out this weekend. They got a promotion out there. It's basically $70, but they haven't had a sale in years. So yeah, pay attention, man. They're not doing sales if demand is on fire. They're doing sales if demand is weak. Demand is probably weak. They've got a sale going on for the first time in a while. It's a three-day sale. Make it a four-day sale, January 18th to the 21st. Ahead of their new lunar New Year shopping season. And it's equivalent to about 5% off its top-of-the-line gadgets. So not a substantial number, but any number for Apple, man. Apple is the king. You're not getting any type of crazy deals for Apple. Maybe once in a blue moon on Black Friday, right? Target or Walmart, they'll have some type of, whether it's some version of their tablet on sale or something like that. But yeah, pay attention to that one, man. Especially when they've been the big dog for so long and now you got some weakness across the board. They're having some problems with their watches, et cetera, to put it lightly. All right, how about this one? Let's talk a little bit of Elon. Well, first let's take a look at Tesla. Yeah, so Tesla opens down 2.5% right now. You jump over to the headline from Bloomberg they've got this morning. Elon Musk pressures Tesla's board for another massive payday. CEO suggests he'll build products elsewhere without a 25% stake. You can't make it up.  You can't make it up, man. I wonder if at this stage in the company, okay? I'm just asking a question for the fanboys out there. I wonder if at this stage in the company, he's best suited to lead that company, especially if he's arguing that he needs the pay that he's talking about just to be CEO. It's one thing having that type of share structure when you're in founder, maybe when you're early on. I mean, the last payday he got, I gotta pull it up, man. There's somebody helped me in the dent. It was a remarkable payday. And yes, he delivered on the share price. But when you look at the type of money that you're talking about in there as a CEO, it is pretty remarkable when you get those levels and now he wants more of it. And he's literally saying that unless he has roughly 25% voting control, he'd prefer to build artificial intelligence. He'd prefer to do stuff elsewhere. I mean, imagine if another shareholder CEO was doing that. And even founder. Yeah, 55 billion. That was the last one. In 2018, he got a 55 billion dollar CEO pay. Folks, CEO pay. I mean, we're like numb to this stuff going on. They call it an awkward time in this article, right? They've lost $100 billion in market value already this year. Elon seems distracted to say the least. You could make the case that you can make a lot of cases, right? Yeah, the market cap has risen more than 11 fold since the board announced that pay award in January of 2018. Yeah, on the other hand, its valuation peaked at more than 1.2 trillion before the Twitter deal. The Twitter deal's got to come into things, right? When you think about the CEO, and I know I'm digressing a bit, we'll get back to it. We're gonna jump to the earnings that are coming up right now. But to be a CEO of a trillion dollar publicly traded company, it usually encompasses all of your available work time to put it lightly. So it's gonna be interesting to see how that one shakes out, man. He just got 55 billion just for his pay in 2018. And the stock is up 12 times since then, okay? Let's see where the S&P was in 2018. Yeah, S&P's about doubled over that time. It's been a decent run to say the least, right? Especially for some of those tech companies. But yeah, you take a look at Tesla shares and you're talking about being two years removed from an all-time high and your company is still cut in half from where you were then. You jump over to the fundamentals. I mean, and this is where it gets murky, okay? His CEO pay represents almost one-tenth of the value of the entire company right now. This is where things get bonkers. So what's he want? Does he want another 55 billion? So before you know it, you have a public company that's trading at 600 billion and the CEO has gotten paid 100 billion. That's not what he started out with as an owner, folks. That's what they've given him as CEO, okay? That's bonkers to put it lightly. Now, it's risen 11-fold since he gave them that. When they gave him that, but you keep adding those numbers up, you're down 50% over the last 24 months. The fanboy acceleration is strong as in pretty remarkable. Not many others would be having that conversation right now. But nonetheless, that's the conversation and there we go. All right, what else have we got pulled up? All right, let's talk a little bit of economic data. Let's talk a little bit of the earnings this week. So what do we have going on? Decent weaker earnings. We also get retail sales. That number out tomorrow, okay, for December. Retail sales expected to be an increase of about 0.4%. The number previous for November was 0.3%. If you take out autos and gas, they're looking at 0.3% is what they're looking at. 0.6 was the previous. You're gonna get mortgage applications as well in there tomorrow, be interesting with what yields have done. And then we get into some of the earnings. So some of the earnings that jump out. For Wednesday, we got Charles Schwab, you get Alcoa. Okay, we get US Bank Corp. You continue with the banks. We get Citizens Financial as well in there. On Thursday, for economic data, we're gonna get initial jobless claims in there. What are we gonna get? You get some housing starts in there for earnings. Yeah, we keep going with some of the banks in there. Bank of Ozar, Key Corp. Northern Trust, M&T. Yeah, we get some consumer. University of Michigan Consumer Sentiment is what we get on Friday. We get existing home sales as well. And we get the University of Michigan one-year inflation expectation for January. 3.1% was the number before. And then on Friday in the earnings, we get travelers, we get 5th 3rd State Street regions, financial. So you get a lot of regional banks that are coming out throughout the rest of the week. We get Ally Financial in there Friday as well. Yeah, let's see how those banks are kicking off the trading session that came out with their numbers today. Goldman, flat. Chopping around on both sides, but right now we're literally within about 20 pennies to where you are on Friday. You jump over to Morgan Stanley, not the case, man. Morgan down 3.4% to kick it off. Equities that are moving. Boeing's not stopping, man. Yeah, we've seen this play out before, man. Investors skittish, rightfully so, Boeing. Down another 4.4% right now. And the big dog, no longer the big dog. Apple down 2% as you got Microsoft up a full percent. They give it back on the open though. Check that out. We'll be right back, folks. TFNN has just launched their new trading room, 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. And now they are expanding their reach with the Tiger's Den. Available to all Tigers and Tigresses for just $1 for the year. There's no cash or added costs when you join our community of traders. In the Tiger's Den, you can look over the shoulders of Tom O'Brien and the other TFNN hosts while they analyze charts during their live Tiger TV programs and join an interactive trading community with hundreds of members exchanging ideas, interact with other Tigers and Tigresses as they share trading ideas, news analysis and discuss the market action all trading day, even at night and on the weekends. The Tiger's Den at Discord is accessible on mobile or tablets as well. So it's always at your reach. To sign up today and become a part of this educational community of traders, just visit the front page of TFNN.com. 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. 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You're talking about about 60, 70 points from the run up we had on Monday. The Russell though, how about it man? Look at the divergence, right? That's why I jumped through. Just into relation of the week we've had, the Russell down 1.2% right now. Meanwhile, yeah, for what it's worth, right? NASDAQ been a strong man, to put it lightly. We jump over to Apple shares, they're off by 2%. Microsoft does give it up on the open man, still up by 6, 10th percent, they're the biggest company in the world right now. Jump over to Amazon, Amazon shares down about half a percent. We jump over to Tesla. Tesla down 2.3%. Now jump on over to some of the discourse on X, previously known as Twitter. And how do you get away with this man? This is the tweet, so January 15th, when was that? Yesterday, yeah, yesterday afternoon man, time flies. I'm uncomfortable growing Tesla to be a leader in AI and robotics without having 25% voting control enough to be influential, but not so much that I can't be overturned, 25% voting control. Unless that's the case, I would prefer to build products outside of Tesla. I mean, imagine if another CEO was saying this, right? You don't seem to understand that Tesla is not one startup, but it doesn't simply look at Delta between what Tesla does in GM. Well, many have made that case for sure. You talk about multiples. As for stock ownership itself, being enough motivation, fidelity, and other owned similar stakes to me, why don't they show up for work? This was the part I wanna talk about for a second, okay? Because if you're in the market, you know that Elon got a $55 billion CEO package, CEO package, okay, in 2018. That's not stock ownership. And it goes both ways, because when you have big companies like that, that take active stakes in publicly traded companies, you better believe that they try and make sure they have an influence of what's going on, okay? And I know he's saying, they have huge positions in here as well. Why are they running it? But he's running it for his CEO pay, folks. He's running it for his ownership as well. Anyway, it's a remarkable comparison. And then he gives some kudos to the board, right? I should know the Tesla board is great. You can't make it up. It's like a character stress. I don't know what's a stick. I don't know what's going on. And so what they are waiting on is that they got sued for that huge pay package in 2018. And the trial was held in 2022, I guess a verdict has yet to be made. So you can't blame them for not going forward with the next compensation package before they find out whether the way they structured the previous compensation package was actually legal, $55 billion. Remember that number that he just got in 2018 to be CEO? And he's coming back saying, what's the point of you? What's my motivation? He received $55 billion in shares. I don't think any other CEO would be able to do that. And $55 billion, I mean, where does that put you on the rich people list? Pretty freaking high, man. All right, enough on that. All right, let's see how some of those banks are trading on Friday. Yeah, so Morgan Stanley is trading low on their earnings today. Goldman is about flat. And you got JPMorgan, decent numbers on Friday, but they gave it up and they give it up today. Look at this, JPMorgan down by 2.5% right now, Bank of America off 2.7%. Wells Fargo off 2.1 and Citi off three. Delta, everything's just continued. Look at these numbers, man, Delta was off big on Friday. They're off 3.2% right now. Let's see how the other end lines. United's off 2.2 right now. Americans off 2%, we jump over to JetBlue. Yeah, this thing's the problem, problem Citi. JetBlue off 1.4% with a four handle at 483. You got Apple down 2.4%. They're posting the Tigers then. That they're not gonna take a look at that appeal in the antitrust suit challenging the app store. And when it rains, it pours, man. Microsoft holds onto the gains. We jump over to Google shares. I Google up about 1.10%. We jump over to MetaShares down about 1% so far. All right, what else we got pulled up? Yeah, I was reading both. I was reading the journal piece on this as well as the Bloomberg piece in terms of Elon. You know, the way that the journal puts it for whatever reference. Elon seeks greater control over Tesla ahead of AI advancements, right? They're two in the same with the multiple Tesla has folks. The reason why I'm clinging to the story and I can't get off it because it's pretty remarkable and pay attention to what's going on. They are two in the same. The valuation of that company is built off of AI, electronics, technology, okay? And energy, not just electric vehicles. And the energy part and AI are totally gonna be intertwined, just a matter of when, not if. And everything to put it in that context. And yeah, for whatever worth. We'll jump off the Elon story for right now, but that's not what I would wanna be hearing if I was a Tesla shareholder, folks. Let's put it that way. With everything else going on, right? That the CEO is basically saying, what's the point of me being CEO? So pay attention to that one, man. As they get some tough roads ahead, I believe. Let's check in on some commodities right now. Check in on the gold contract. Gold right now, down about $10 at 2041. Great time to try out the gold report, folks. Goldman Volatile, particularly lately. My dad, new issues on Mondays usually when the week starts, but that issue gonna be out on Tuesday. As Monday, we were closed yesterday. Gold though, down by 10 bucks. We start off the week at about 2041. You jump over to silver. Silver right now, down about 20 pennies. We're trading at 2312 right now. You jump over to natural gas. A lot of talk about natural gas in the tiger's den recently. Natural gas, pulling back. Talk about volatility, right? Natural gas right now, down about 12%. You're pulling back 40 pennies. We're at a two handle right now. Check out that run we just had, man. The last month, you just go from 220 up to 340. Looks like that run may be over those. We're pulling back a bit to 291 in the price of natural gas. As this market's just loosened some steam, what's gonna be the impetus to buy, right? What's gonna be the impetus to buy right now? Jump over to yields. The 10 year, down about 14 ticks right now. That's correlating to a 10 year yield of 4.02%. Yeah, a few different stories. We've got some acquisitions, right? You got, yeah. And then of course you got the politics, man. But Trump and Iowa shouldn't be surprising to anybody. But it is January 16th and we have a presidential election in November. The politics, actual numbers, right? Primaries, gonna start becoming a real deal. The general gonna start becoming a real deal. We'll see how that plays into the market, if at all. They started last night. Stay tuned, folks, one more segment. Come back. We'll have what else we have happening this week. We'll be right back. The Gold Report. As a precious metal, gold is still king. It continues to hold the most effective safe haven and hedging properties across the global major trading hubs of the London OTC market, the US futures market, and the Shanghai Gold Exchange. The Gold Report. Tom O'Brien publishes his weekly Gold Report every Monday morning for subscribers, consisting of coverage of the XAU, HUI, GDX, the Dollar, Bonds, the South African Rand, as well as 25 different mining equities with specific buy-sell recommendations. The Gold Report. New subscribers get a 30-day money-back guarantee so you have nothing to risk. Subscribe to Tom O'Brien's Gold Report newsletter now at TFNN.com. 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 Basil 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 in stock prices.  First-time subscribers also get a 30-day money-back guarantee. 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That's TFNN.com and hit Watch Tiger TV. Welcome back, folks. We got the S&Ps off and even 30 right now trading at 47.86. We get a little negative action to kick off the trading week. NASDAQ, you're off by 94. You get the Dow right now off by 268. I gotta jump back to Tesla for a second. So Elon's talking about owning potentially 25% of the company, right? This story out here from CNN, just talking about some of the quotes, et cetera. If I have 25%, it means I'm influential but can be overridden if twice as many shareholders vote against versus from me, okay? If 15% are lower, the four against ratio to override me makes a takeover more by dubious interest too easy, okay? He was above 20% before he dumped his shares for Twitter, which is the most remarkable part of it all. He's roughly at 13% right now and he was above 20. He held the stake above 20 before he sold all those shares to buy X. He's now at 13 and he's now making the case. Yeah, and as it goes in in here, of course that he's getting sued. And what is the definition of excessive? I don't know, that's where it gets tough in terms of legality-wise of that, right? Yeah, nonetheless, Tesla, they catch a little bit of a bid. You're five bucks off the lows right now for Tesla shares trading at 217.58.  You jump over to the most expensive company in the world, Microsoft, up by half a percent. Google shares up by half a percent as well. Jump over to NVIDIA, whoo, it's not stopping. All-time highs for NVIDIA, man. The AI train marches on. NVIDIA shares up by 1.7%, Goldman up by 2.10s, but Morgan Stanley, not so much, off by 3.3 and you got all the other banks trading lower. JP Morgan down by 2.1 and we finish it up with yields, folks. You got the 10-year right now, down about 14 ticks. You're looking at a 10-year yield exactly at 4.02. As I say that, it takes slightly lower. 4.018, we'll call it 4.02 to start things off. And how about the VIX? Let's jump over. Pay attention to that VIX, man. The VIX at an elevated level of 14, pretty decent from where we've been recently. Folks, thanks so much for starting your trading week right here. Stay tuned. Basil's off next. We got Steve Rhodes, Fast Market, Larry Pezzavento and my dad, Tom O'Brien, live from three till four. Have a great two.


This is one person who broke all the rules and went down without any connection. Never been a political person, but all parties in the country, every politician and every human being in the country respected him so much that he naturally elevated himself into that position. Just as his simplicity, ingenuity took him places. He's now, you know, any kind of party behind him has ever become a president of a nation. Just as pure sincerity and his enthusiasm, the way he came up in the scientific community and people, he just sort of naturally floated into that position of being a president. He's been such a unique person and so approachable as a president like never before. Anybody who's been head of a nation has been an approachable. This is the story of a boy from a very poor family born in a small town in southern India in Tamil Nadu with his simple aspirations and his longing and his desire to get somewhere in his life, educated himself, slowly growing to one of the most prominent aeronautical engineers, not only in the country, in the world, went on to become a great space scientist and then went on to become the president of this country. He's an incredible human being. He has no political affiliations, never been a political person, but all parties in the country, every politician and every human being in the country respected him so much that he naturally elevated himself into that position. Just to make you understand how simply he loves his life is, last time when I was, I think I was coming from Lebanon, I was coming from Beirut and I stopped over at Dubai airport for about a few hours, five hours or so and I was just reading the local newspapers. There was a report, a boxed report, the Indian president talked to the security man, something like that. I was interested, I was just reading about it. What happened was, in the hotel in which he was staying, some big hotel in Dubai, when he was walking in, he saw a security man and he recognized him. Hey, well, Tommy, what are you doing? This security man worked as a driver for him for a short period, about 30 years ago.  So this is an interview with a security person, this guy sharing with the journalist how the president came and spoke to him and invited him to his room, but he felt it's not appropriate for him to go there and, you know, that's the kind of person he is. Just a simple human being pushing his way through life without being a great competitor, without being a great, you know, fired up ambitious kind of person, simple desire to do something for people and just allowing himself to grow from a village boy to the president of the country.  Village students especially, who live in a certain sense of social advantages, the thinking beyond their villages like a huge adventure. It's something that people who come from outside the country will not understand. It's just even thinking beyond their village is a big adventure. Going out of a village is like a great adventure.  It's not something that happens in a place like this because generally unless a person has the necessary family, educational, other kinds of background, they don't praise. This is one person who broke all the rules and went out without any connections, without any political support, without any kind of family backgrounds, without even any kind of necessary cunning and ambition for a person to grow. Just as his simplicity in choosing a place.