OK, we're going to get started. Thanks, everyone. We don't have a very large cloud. Yeah, thanks for coming to the talk. And so I'll just quickly introduce ourselves. So I'm Wenbo. He's my colleague right now. And we're software engineers working at Google Cloud. We work on the open source project, GRPC, but we're also supporting the Google's internal infrastructure that powers the GCP services. Sometimes we call these the cloud APIs. And also the non-cloud Google's external facing services like Gmail or Search. So today, we're going to talk about a specific case study. We'd like to share with you some experience about how to harden in the data path over the hybrid cloud. Yeah, so OK. Sorry about this. So first, we're going to talk about what I really mean by hybrid cloud. I do not have exact definition of a hybrid cloud. Specifically, the use case we're talking about here is when some large scale online applications, they try to move their database layer, the storage layers into the public cloud. And when the majority of the application will still stay on the on-premise data center. Now, this is a shifting paradigm. If you look at the picture on the left side, it's a traditional on-premise deployment when you have applications talking to a database, for example, MySQL. And the network links is usually reliable. And more importantly, they share the same faith. When something goes wrong, typically the database itself goes wrong first or together with the network. But once you move everything, move the storage layer, the database layer to the public cloud, now the entire database is different. Now, part of the reasons they're moving the database to public cloud is that they want to leverage the scalability, reliability of the so-called sub-service. But then the data path suddenly becomes different. It becomes longer. The failure mode is also different, which we'll talk about. Now, that doesn't mean the hyper-cloud is harder. It's impossible to do it wrong. Or it's inherent and unreliable. I think the key problems that we like to address is here that when the application layer, they're not adapted to this new sort of data path, the longer RTT, the different failure modes. And that's basically the use case we're looking at today. So we look at the whole problem. Then we start to realize that the underlying problem, the technical problem, is pretty generic. It's not really anything application-specific. It's sort of the touch of the core fundamentals of any distributed computing and that kind of things. So instead of talking about the specific applications we've been working with, we thought, let's make this more useful, try to generalize it. At the same time, we also think we wanted to find some more concrete, like example, concrete workload, not too generalized. So here's what we come up with. So there are a few properties for this kind of the use case. First, in this case, in the hyper-cloud case, the client from the on-premise side to the public cloud, that is a long trip time, is larger than the service itself. And if you look at the diagram, so you're looking at about either a single-digit millisecond or 10th of a millisecond's network launch of time. When the actual service, in this case, it's actually Google Cloud Spanner, you're looking at a single-digit millisecond. And also between the client and the actual service, there is also a layer 7 load balancer. So this is sort of the RTT, sort of the paradigm. Then the second one is that for this particular case study, we're looking at there are two types of sort of RPCs. One is transactional. Here, we just simply refer to this as rights. It doesn't mean they're all just rights. They can also be read. And then there's a non-transactional apps, which we just simply refer to as reads. And then when the read timeout and the underlying software can't read try, but eventually there is a data line. When the data exceeded, some kind of errors will actually go back to the user-facing layers. Then the end user may actually see it. In some cases, that actually can cause business impacts. For example, the user may say the application is now responsive. And then same thing for the rights. If the transaction, if the rights failed, the whole transaction may get aborted. Imagine you're doing online ordering, and then the order may be canceled, and then you have to redo it. And the user may actually just walk away. Then the last thing is that there's a connectivity here, affinity here based on connections. So all the traffic that's generated, all the requests generated by the client over the single TCP connections, they will be sent to the same server process, because that's where some level of session state is managed, or caching, especially like metadata caching. So yeah, so there's affinity. You can imagine that all the connections will send all the, you have all these different sessions, and they will all be mapped to different connections. And then all the requests within a single session will all be routed to the same server process. Now, we do think this will present a lot of workloads. Similarly, this is pretty a generalized version of this. So now I'm going to talk about the exact sort of the things that are specific to the Layer 3 network in case of the hybrid cloud deployment. Now, so first thing is that in the hybrid cloud, there's this concept of like a flow. So flow is, you know, this is a very different term, right? You can imagine it's just some kind of IP routes. And what that means is that each flow is fixed. It goes through a fixed set of software hardware components, all links, like physical links, like fiber links. And then TCP connections, based on the source IP and port, especially with IPv6, they get mapped to a single flow.  Typically, they only affect sort of one-way traffic, not necessarily bi-directional. And at the same time, we notice that most of the failures seems to be like a black hole. You know, the whole flow is basically gets broke down as opposed to on the internet, what you're seeing is those partial packet loss.  But periodically, they do, you know, because all the different components involved, you do see these kind of failure patterns. Now, on top of that, on the server side, the proxy, the actual service process, they also get overloaded, and that's where all the so-called tail latency comes from. And typically, they are not correlated to the L3, in most cases. Now, one thing I didn't mention on the slide is that, yeah, this is mostly a latency sensitive workload, not so much about throughput as such. You know, we don't necessarily talk about the congestion control flow control here. So when we have all this, in that particular use case, what the department we're facing is, then the applications, you know, the customer basically, our public cloud. The first question is from the networking team is always like this. You know, when there's a network outage, I'm seeing the packet loss is 1%. Why the RPC error rate is so much higher, you know, different, so that's sort of the first round question. Then the second one is for the application developers, they are launching the application and try to access the source databases. That question typically is like, why do I need to know all this? Can you just recover everything for me so that, you know, the applications that are used as my middleware, you know, middle layer, they do not have to see any errors because that's business impact, it's bad. So when we heard all of those questions, you know, my immediate answer is, for the first question is, we don't know, right? They don't suppose to be related. The packet layer, packet loss, how it maps to RPC, obviously they should be different. But then the next question is, yeah, you know, to what extent, can you quantify that? Then for the second question is, we kind of think, yeah, obviously, you know, we can do something, you know, if we know there's errors, we can try to recover. But then you think deeply, you start to realize that it's pretty challenging problem. If you try to react to failures too aggressively, then you may introduce cost in a long time when there's no failures, like you have those false alarms. And also in the worst case, you very easily can potentially cause cascading failures. But if you do not react, and by the time, you know, all the failures are gone because they're short-lived, it's already too late, right? The RPC errors are already being through to the users and they already have impacts. So when I was looking at that, the immediate picture I have, you know, for this talk is, it is just like a cage fight. The margin of errors is so small, you have all kind of constraints on the trade-offs you have to deal with. Now I'm going off topic a little bit. So I thought this could be kind of actually to put a nice picture here. So I was thinking about this cage fight. Everyone's talking about six months ago. That never happened. So I did a Google search, there's all this nice picture. But then before I posted it, I thought, you know, I just let you, I don't really know the copyright or that. I just let you guys to picture it. And maybe you want to do a Google image search. Yes, sorry about this off topic. Who doesn't know the cage fight I'm talking about? Okay, so anyway, so this is so really just sort of mentally I want you to picture that, right? The problem is like what it's like. So then what we did is that we look into the problem, we think that the most useful thing is actually try to create some kind of benchmark framework to quantify and provide answers or some kind of insights to both the problems. So I'm going to talk about sort of the setup for this particular benchmarking. And then my colleague will sort of show you the results we're getting from there. So in this particular benchmark, the current is running on the, you know, GCE Google Compute Engine VMs. And the failures are injected. We didn't use any physical, you know, the physical hybrid car link because for the most part of the software components are the same between VM, between on-premise VM and GCE VM all the way to the, you know, the so-called layer seven proxy, which is a boundary between the user facing part of the infrastructure. And for the read, which are simply using gets, you understand that is the response is large but the request is very tiny. And then for writes we use post, which is other way around. And we use 80% of our reads and combined with 20%. Now all the requests are generated using a so-called Poisson stream. Now what that means is requests are generated asynchronously and then are waiting for response. And the interval between every two requests that they're using the exponential distribution. Right, so this is used for that if you spread the so-called Poisson stream, if you spread the traffic across multiple clients or multiple TCP connections, as long as a total request per second rate is the same. On the server side, the requests will arrive in the same pattern, the server wouldn't know the difference. Now this is very useful way to do the benchmarking if you're curious, right? So this allows you to generate enough concurrency on the network and also on the server without necessarily overloading the network or server at the same time. So that's sort of how the workload is generated. And then, like I said, so the packet loss is injected on a single connection in the wrong time, repeated it and that's how we get the data. So now I'm gonna pass to Vinod to talk about the results we're getting, especially sort of to answer the first question, like how do you quantify between the packet loss rate and the RPC timeouts? Yeah, hello? So Wenbo asked me to collect a lot of data and I would like to present some of that to you which I thought was interesting and maybe there are some obvious and not so obvious conclusions. So the first slide I just put in there to show what kind of network we are dealing with. This is a probability distribution of the round trip time measured in the kernel. We use DBPF to measure the network latency between two acts. So it's pretty stable at around 1.2, 1.4 milliseconds and the tail is also not that long at 1.9 milliseconds. But when you go to see the RPC latency, this is the end-to-end RPC latency at different percent of injected loss, we can see that the tail start increasing rapidly and since this is a very latency-sensitive application, we can see that at 5% packet loss, the P99 latency is already over five times more than the loss at 0%. So for our particular application, which is database latency-sensitive, things go bad at around before even 10% partial packet loss. And these graphs also show what Wenbo mentioned earlier that the network tails are not correlated with the RPC latency trails. So I want to explain how we came up with the percentage RPC timeout. So we ran a lot of simulations and we ran them at different QPSs and different durations. So we had to normalize it across all these parameters. So the percentage RPC timeouts is a ratio of all the RPCs that failed over the entire run divided by the RPCs that we start during the induced failure periods. And similar to the previous slide, we can see that the network for our purposes stopped getting useful or get very bad around 10% and there is very little difference between 20% packet loss and a complete black hole. Now I want you to take a mental picture of this image and I will compare it to the next slide where we change the transport from using HTTP2 to HTTP3 over quick. And we see a huge difference at the different packet loss percentages. This is because of the head of line blocking that TCP forces an order for all the packets to be received, which means that one RPC will block the next RPC and you will see this effect. So if you're using HTTP3 over quick, you can see that even in lossy, high RTT networks, you can still get a decent performance. That's why quick should be used in such connections like over the internet or very long RTT payloads. And to drive this point even further home, we are, this is a graph of where we varied the requests per second for HTTP2. And you can see specifically in this 10% loss case that once the request per second goes about 20 requests per seconds, that's where you see actual, actually see two RPCs contending for each other on the same connection. And you see a huge spike at after 20 requests per second. And this is the head of line blocking. And if you do that for quick, it is much, much better. The, just to make sure that it was a fair comparison, the black hole in case where 100% packet loss was induced is pretty much same across all the scenarios. So yeah, I would like to ask Wenbo to talk more about the failure detection and recovery. Thanks Wenbo. Yeah, I just want to add a quick note about, why we look at a quick in this case. So we still think that TCP-HP2 is the transport and the GRPC for the input, which is based on HTTP2. It's a transport for data-send communication even for hybrid cloud. And using quick for data-send communication, I think it's probably still in a pretty early phase. We do have some initial sort of experiments, but broadly speaking, quick will introduce overhead. A lot of the infrastructure today is really just built to support TCP. So we don't want to sort of have to take away, say, hey, if I have this kind of workload, I should use quick because there will be packet loss. Really two things, right? First, most of the packet loss, we actually experienced the total packet loss black hole. In that case, as you say, quick doesn't help. Second is that you do want a connection pool, not just because, you know, head of line blocking and other things. The reason you want a connection pool is really for low distribution or low balancing, right? It's not necessarily try to make the traffic, you know, the connection more tolerant to this kind of failures, which only happened once every several months or several weeks. Okay, so just want to clarify that. And but we thought that the results would be very useful because you can immediately see the role of the multiplexing place in the TCP case when there's a packet loss, a partial packet loss, and how the RPC will get affected. So then, yeah, so the next part is, I'm just going to talk about, you know, how we plan to address the second question, right? Is this like what you can do with this kind of failures is possible at the latest seven, you can actually recover from it completely, okay? Now, so this is just for benchmarking. We come up with this kind of, you know, what I would consider pretty intuitive recovery scheme, although it does involve multiple parameters, so I'll take a little bit of time to explain. So really there are two parameters you can tune for to detect a failures on the current side, right? We are knowing all the details on the server side or infrastructure side. One is that how many acts you're waiting and which gets time out? By act, we simply mean anything that's the response data that is time bound, that's, you know, associated with the request. It can be headers, can be some part of the response bytes, or can be the end of the response. There are anything that's time bound. So generally we just refer to this some form of acts. Now, as the current send multiple traffic asynchronously, there are acts, you know, those requests will be in flight. You're waiting for some form of response coming back. And the total number of those pending acts is the first parameter. In this case, we call this the B, right? And then you have the time out, how long do I wait for each individual act before I decided this act is time out? So the particular algorithm we come up with is taking into account that when sometimes the connection may be seeing a lot more traffic and sometimes it's mostly idle but still have enough traffic. Now, if it's total idle, you don't care, right? You just let it run. So both parameters, we make it so that it's dynamic. It can be one act that's got time out or it can be two or it can be three. And then the total time out is adjusted based on the number of acts. The more number of acts that's in flight that's got time out at the same time, sort of the more confidence you have that, you know, there are probably a real failure is happening that I need to react. So in this case, you know, if you do a little math, right, we're saying if it's a three act, a four act, I'll wait for four RTTs. Otherwise I'll go all the way to 10 RTTs, right? If there's only one act that's pending. But also as you can see, because all the requests gets send generated asynchronously on the server side, it's multi-threaded. They also get a process asynchronously. The TCP does have the five for ordering, right? So which means that there can be out of order, you know, the request that's appeared on the TCP on the connections, they may not match the order on the request and the order on the act, they don't match each other. So now we're saying, hey, now let's say with this recovery algorithm, what is the matrix we can evaluate the effectiveness of this in the long time? So we come up with two metrics. So first one is called mean time to failure detection. I don't know if that's a standard term, so we sort of made it up. So this is saying, hey, when the failure is actually injected, how long does it take for the algorithm to actually detect that it's a real failure, right? And so that's, and the shorter, the better. And then the second matrix is basically more important. It's a false positive. If I run this algorithm on a connection without any failure injected, we're at a forcibly detect there's a failure. And for the second matrix, we choose to normalize it. So if it's 1.0, it would mean that for the whole duration of the benchmark run, which is hours or 20 hours can be even longer. And the algorithm never detect any failures, which is great, right, which is what you wanted. And if it's 0.2 mean through the whole span of the benchmark, there's five, four, five times that there's a false positive cases. Okay, so we didn't run this directly in the long time. So we use a simulation to do that because once you have all the samples, you can just run, you can just use a simulation. So we did use the basically similar to descriptive event simulation. So we can get the results. We can tune the different primaries and get results in just a few seconds. Otherwise, the total samples we've been using feed it to the algorithm is hours of samples. And remember, our RTT is very small. So we're talking about like a potential millions of samples. So okay, so on the left side, we're basically saying here, we're gonna fix, we're gonna fix the number of X. It has to be three or three more X that's got time out at the same time before we declare the connection has failed. And then we start to increase the total time out because our RTT is so low. So we start to put an effect, one, two, three effect of two. So the first bar, you can see that it's possible to reach 1.0 if you fix the number of X to the X to three and then gradually increase the time out. At that point, you can have the idea situation that there's no false positive and also the delay to detect the packet loss is pretty reasonable. It's looking like a few hundred millisecond, right? But if you try to reduce the number of X, especially to deal with a case when there's not enough active traffic and then you have some kind of false positive, but at the same time, this does allow you to detect the real failures more quickly. And then on the right side, we did it the other way around, right? So instead, we'll sort of fix the total time out and then we start to decrease the number of X. You know, number two, you can have two X or only one X and then you say, hey, if this particular X time out, I'll just declare that this connection has failed. And you can see that it's pretty sensitive to that, right? So apparently, if you reduce the number of X to two, then there's a significant false positive rate. And then if it's one, you have a different result. So the idea is that once you have the samples, you have this kind of workload, you have these whole samples and you can run the simulation start to apply different algorithm and to evaluate how safe it is if I wanna go aggressive or how effective it is in terms of being actually detected the failures. So the next is sort of, yeah. So once a connection is failed, like what you can do, right? Then this is sort of an interesting part. There are a lot of things you can do. In this particular case, given the workload we have, this sort of strategy we come up with, we did implement sort of a more conservative version of this. Also, I skip a lot of details, right?  But overall, this is, we basically decided, we just let the requests run to completion even on failed connections. And then we fail over all the new requests to separate a connection. And then the most important thing is that we decided that we should decouple the failure detection timeout and the actual read or write timeout, which are application-specific, right? Which is like requirements, like it's a deadline from the upper layers. And that's important because a lot of those sort of similar solutions, they typically track the actual failures and then to sort of do the fail. But in this case, we just thought that's decoupled that so we can understand in how far, where the limits are for this whole problem. And lastly, I will say, if you really, if all the parameters works nicely, potentially the errors can be more or less completely masked. If, for example, the read timeout is long enough, and also the write timeout is longer than the actual failure windows. And we find that typically the failures only last a few seconds or 10 seconds or things. And that really potentially can give you a zero failure case as far as the application is concerned. Now, the first positive is what you will always have, right, in the real production environment, it seems to be even less stable. So the cost in our case is, you may have duplicate reads, which are fine, but there's actually overhead. And if the writes get misaligned because it gets sent to a different connection, and then it will have to be forwarded on a server side. So with all that, we kind of just wanna share sort of what we think in this case, like what is really the key takeaway from here, right? So I basically say, this is a sort of example of the end-to-end argument, right? Is that try to leverage the service level semantics, try to handle things gracefully, try to be able to adapt to the runtime conditions and do it at a high level, as opposed to try to solve the problem at the lower transport layer, which typically it doesn't have all the context. You cannot really do it for this kind of extreme case, very low latency, you know, you have to manage all the trade-offs very carefully in order to sort of reach the minimum error rates. Now, everything's relative, right? So on one side, we also think that later three recovery, if it can be done very gracefully and quickly and transparent, it's also still a good thing because as you can imagine, this involves a lot of user space code when if you can do it in the later three will be nice. Google does publish a paper in the sitcom this year, it's called preemptive rerolling, right? So in this case, when a particular flow is broken, then we will reroll all the TCP connection package to a different flow. And in that case, everything is just work, transparent to the applications. So this is a good example, right? It's not low-level recovery, it's still desired if it can be done optimally. So I want to sort of conclude with this part.  You have all the data. What happens to production environment? Can I really just use this as sort of a reference implementation or solution for your actual production workload? So my answer is probably not, right? Probably not. If I were asked the same question again after this, my answer would still be no, because really this whole thing is all about data driven, right? You need to know the data. You need to know the work code. You can really just sort of take this kind of benchmarking data conclusion and apply it as is. On the other hand, we do think that, you know, this kind of quantification analysis is useful in the sense that I give you the insights understanding what different parameters, how they play together. Okay, so that's our goal here. So then this goes back to another area we're looking into that, right? It's like, okay, so we all understand the value of data, then where you get the data from. So we look at, in our case, the kind of SAS data path we're looking at. We find that the big gap is in these two boxes are highlighted here. It's basically the transport layer in two ways. One is that it's usually very difficult to get the data in that layer, especially with a very low overhead lightweight monitoring. The second one is the correlation between this layer, the user space, the guest, with the layer three layers, because you can imagine on one side, you're talking about surveys, RPCs, on the other hand, you're talking about a packet, right? A packet may contain, you know, bytes from different RPCs and bytes first. So this is the sort of the monitoring gaps. Yeah, we think that that's really important. If you look at a production environment and monitoring is really the critical part. So on that note, we also like sort of to publish this project, which we have been working on for a while. We just recently posted this in Google. Yeah, you can take a picture. The site is available, there's a link there. So this is sort of our first attempt to provide a solution which is lightweight, stand alone to give you insights in this so-called transport layer that we think it's very important, especially for debugging, for dealing with failures of things. We are still, you know, we'll continue working on this project. Hopefully that will become more useful for you, for everyone. And specifically in terms of language support, currently only support our goal, and we're adding Java and other language support. And we think that not only try to get the data, but how to process, explore the data in a very lightweight is also critical, because the amount of data is going to be overwhelming. So we're looking at that problem as well. And we do think that critical HTTP-3 is also important, and so we're looking into that as well. So yeah, so this is, so thanks to everyone. We like your feedback and also even better contributions in the three areas. Like one, take a look. If you think of the EBPF monitoring project, it's interesting. It has all the information on the GitHub. The benchmarking framework, we will over time try to open-source it. We put some there on the client, you know, all the client-side stuff. We didn't quite put the simulation part there as well yet, but you know, take a look and if you wanna have any feedback, you can reach out to us. And the last thing, I also think that if you actually have a particular use case in mind, you're kind of dealing with the same problem.  So just understand if there's a way to create a more general purpose of solution for these kind of problems. The other thing is we, in dealing with this problem, we also don't know like how widespread this problem is, right? This whole hybrid cloud of problems. Maybe it's just one particular case we happen to run into this. Otherwise, everyone seems to be doing fine. All this is actually a pretty profound problem that everyone is dealing with. It's just, we don't know that. So yeah, without, we really wanna hear from you. Okay, so thanks everyone. We'll start to take questions. I have a quick question. So would you mind just describe the formula like the algorithm that introduced before? Like how do your team come up with that formula? Yeah, this one. Yeah, the question is about how do we come up with this so-called formula? You know, we just, we kind of know, right? There are really only two fundamental signals, how many pending acts and how long you wanna wait for. And then we try to make it more dynamic. Instead, you wanna have a static say, hey, wait for 100 milliseconds, three acts. And then we just time out everything. Instead, I would try to be a little bit more adaptive, especially considering that sometimes the connection may say a lot of concurrent requests. Sometimes they may not have that much, right? So yeah, so that's the algorithm we come up with. And also because we're doing this as a benchmarking and simulation so we can easily change the parameters. Now in the actual implementation, it may not go as that far because this does introduce a lot of sort of variables, right? You want? Yeah, okay, thank you. Yeah, hi, it's very common in microservices to look at configuration specific to microservices like Java threads, et cetera, and people research on that. I'm wondering if the network, if these knobs are also something if exposed would benefit the microservice performance. No one talks about the network today, right? We just assume GRPC works, TCP work. But these are knobs that can possibly be tuned for microservice, to make the microservice performance better, especially in multi-cloud kind of deployment. Yeah, thanks. So the question is, yeah, can we sort of generalize this, expose this as sort of more formal configuration knobs for microservice, for the API to workload? Yeah, we definitely look into that. We, I guess let's sort of go back to the last question I have. We don't know how, you know, why this problem really is, right? Because like I said, those things, they do solve the problems when they are actual failures, but it does introduce, you know, false positives. There's a cost to with that as well. Yeah, so, but we're definitely looking into that. Thanks. Yeah, the other thing I guess I'll correct if I just follow your questions. Part of the reason we're doing this is, this is like a passive monitoring. You're doing this as a data plan. But most of the workload, you don't really need to react in such a short window. Right, you can rely on centralized control plan, health tracking, those things. Usually that just work. And this is more like a very low latency, very long RTT compared to the latency requirements. Like you try to really squeeze the last bit of the sort of the performance that reliability. Okay, thanks everyone. Hopefully enjoy the conference and the lunch.


Thank you. Thank you. So first of all, welcome. Good morning. Buenos dias. It's a great pleasure to be here today I've been waiting for this day for a long time, so I'm super excited to be here with all of you Now my name is Juan and I work at Kassau for engineering the trust and safety team at beat Lee However today, I won't talk about trust nor safety nor beat Lee instead I want to talk about some tiny and cute experiment that I did which among other things going to explain why Vacuum veteran Sunday makes me happy. So I've been using a mood tracking app for over a thousand days That's a little bit more than three years and he started as you may know to go to the app store You see some new exciting app you click on it and you start to play around with it However, this one became more than that it became a little bit more special Because those five to ten seconds that I spend every day to enter my mood became some sort of my daily meditation And you know what I'm talking about that time when you close your eyes you look at the ceiling and you ask yourself How am I feeling today? And this is the app. I'm using it's called a Leo and no I do not work for them and every night at 7 and 9 p.m. Local time It asked me he sent me a notification asking how are you feeling today when I click on it? It shows me this nice green that we see here I think we have five faces ranging from extremely happy or rad to awfully sad Of course you enter your mood click and then it's gonna ask you what have you been up to what have you been doing today? And by default it has around 20 to 15 activities, but of course you're able to add your own Activities, and I'm gonna show you some of my later Well, not later. Let's wait now. Yeah So this experiment covered a thousand and seventeen days. I started on January 4th 2019 New Year's resolution, right? And I am this experiment here is top on October 19, 2021 Why did I stop to reason first? I had to gather the data to analyze the data, of course And also around this time I was having some sort of existential existential crisis And I thought of leaving Germany what I was leaving before I'm moving to Austin Texas what I'm leaving now So that I wanted to somehow have some sort of before Germany Analysis so I could later do an after Germany analysis and maybe I'll present it next year who knows so in this time I have locked my mood a thousand and thirty one times and At first I wanted to do it actually twice a day, but I realized like no one come on that This is way too much. So I just stop after the first week And these are the mood frequency and the first chart that I'd be showing I heard we have all the moods starting from good Which is the fourth best rat meh and bad and we can see that mood is the top one with sixty one percent Followed by rat meaning that I have felt quite good in 80 percent of my days that we have made with 19 And bad with one however awful is missing here and that's because fortunately so far so far I have never had to resolve to that awful mood So I think I'm quite a happy guy. I would say This is the login time as I mentioned earlier the app notifies me at 7 and 9 p.m. And the median is around 9 p.m. With some small exception that I'm going to cover later now in this time I have reported 44 unique Activities and these are the frequency count. You don't have to focus on all of them I'm going to explain some of the most important one and there's one with a bad word, but I mean we are humans, right? So the first one is friends and I want to focus on friends because usually when I enter my activity What I'm actually entering that night is the highlight of my day And if I'm with friends chances are that this is going to be the highlight of my day Then we have Ariel and no, that's not the little mermaid. That's actually my ex We spent around four years together. So of course her name is going to show To pop out many times here during the conference doing the presentation. Sorry Jesus. So then yeah Video then we have other cool activities like riding exploding coding got me on Netflix Lockdown COVID-19 Pokemon with refers to Pokemon related activities I'm a huge fan of Pokemon as you can see my Pikachu tattooed and Others like hiking formula one which refers to either watch a formula one race or just being their life Something that I've been doing for the last two years. It's quite cool. I'm quite expensive Now this is the average mood Score when the activity is present starting from the worst one to the best one And I want to focus on the bad one for now So we have locked down COVID-19 and to give you some tiny context when COVID happened it started I was back back in through Asia, New Zealand, which sounds cool But of course at some point I was stuck in New Zealand for like four months then Ariel's you went back to Germany I'm no German so I couldn't go with her so that was quite sad and angry, you know Because I was just sitting there on employees waiting for some miracle which came maybe after four months Then we have napping and it's not that napping makes me sad It's just that I usually napped when I'm feeling sick and sick makes me sad So there's some kind of correlation there then we have others like flying Because I don't like flying.   What happened? So that happened when this computer decided to stop working two weeks before I had to deliver the draft on my book So that was very angry and it was in New Zealand So I have to go to to go to Auckland and they told me we don't have the piece We need to send the computer with to Australia to see me and I was like Jesus Lord What about my book? You know, but anyway everything worked and then we have others like friends Of course, I is quite high here the average mood because friends miss me happy also baking before point zero not for the standard deviation and the one of course we have the standard deviation of the Mood when an activity is present and we have a baking is the first one with a very low standard deviation But we know that baking is four point zero. So, you know, it makes me happy So we can see that baking indeed makes me always happy. It's not rad. It's just happy just good So then we have others very cool. I friends the standard deviation is quite Hi, I would say what usually friends is Here so I would say that is either good or rad And then we have aerial and this is like a very interesting one I'm maybe the highlight of this one and I mean, you know, when you're in a relationship You have your normal days, you know, where you just watch Netflix or just cook something Then you had your rad days where you are doing something very cool You're traveling you're fulfilling a dream and then of course you have your me days where maybe unfortunately you have some sort of Disagreement or discussion and that's why there's a whole range of emotions right here and we can see you with the large standard deviation now here I'm just like cherry picking some of the Activities that are like the most and how they influence the average mood when the activity is present So we have that lockdown it kind of decreased one whole mood from zero to minus one point zero a game napping and sick Which I mentioned earlier then we have bleeping Mac and then we have others that make me happy like writing gaming F1 friends Pokemon and Exploring but now let's move on and let's talk about our combinations and correlation So I didn't enter any activity in 31% of the days Why maybe there wasn't any highlight on that day or I just wasn't feeling like it Then in 31% I enter one and in 38% I enter more than one and these are the top First we have good meal and friends because I believe there's nothing better than having a good meal with your friends Maybe we'll do that tonight. Then we have writing reading friends drinking traveling exploding friends and Aerial, but then I want to look at this. This is the correlation Sorry a correlation pyramid and don't focus on them. Don't focus on this tiny color Let's focus on this these are the top or the highlights from here And we have that the highest correlation is in photography and exploring with exploring I mean going to a new city new country new forest and just walking around and usually when I'm doing that I have my camera with me like I did yesterday like I have today my camera in my backpack So for me, there's one of the best things going to a new place with my camera and doing those things and That's why the correlation is zero point 46 the highest one of all of all the activities Now the lowest is friends and writing because if I'm with friends Maybe I'm not writing and if I'm writing it's because I want to ignore my friends on that day And that's where the correlation is negative point 16 Not for my family the ones we have F1 and bouldering so when I was in Germany for some reason I used to enjoy doing bouldering on Sundays Maybe because it's empty because I don't know Sunday was the day But of course if you are a formula one fan, then you know that the races or the Grand Prix day take place on Sunday So there's some quite a very nice correlation here But there's something else that I like that I like to do after bouldering and while watching formula one and that's baking So I can have some fresh bread for the week And since we already know that baking makes me happy and the formula one race take place on Sunday Then we can somehow assume that baking better Sunday makes me happy and that's the point Not for the next one. Let me Time series right and here what I want to show is the how my mood has evolved over time And also I want to highlight the daily weekly and yearly Seasonality and this is how my data set looks like it's only had like two columns And the first one is the timestamp of when I enter my mood and then the Y column is the the mood itself That which is a number from one to five and this is the trend We can see that it started quite high Christmas season. I was in Puerto Rico, which I love being there 4.2 very nice Then sort of went down a tiny bit, but I mean it's still okay 4.0 is still good. But then what happened here Covid again my computer stopped working. I was in New Zealand. I was alone No idea what to do one employee Just sadness was running through my heart, you know, and that was literally the lowest one of my life I'm willing to say I'm being so dramatic. I know but that's fine But then of course thing went better. I was able to enter Germany after a long time waiting and then it went back to normality and Yeah, it's all good now. I'm happy today. So Now for the weekly seasonality It's quite okay. There's nothing that great here besides that well Sundays once again It's the highest point of the week. You already know why and maybe Thursday evening Because as a good Puerto Rican, we love going on a Thursday.  What is it Thursday?  You listen to your bad money You call your friends the best day ever Thursday Then there's your decisionality. It's quite okay. There's just I mean the difference is not that big It's that maybe here. I love the fall season. I mean fall in the north and hemisphere and Because I don't know star boss from pumpkin spice latte. That's we start to get yellow and orange my birthday is in October So a lot of good things happen here. I don't really know why I need to go out travel new Pokemon games They come out on November. So best thing of the year, you know I'm also I'm getting ready to go to Puerto Rico, which I usually do for Christmas season No for the daily and this is a cool one. We can see that for some reason the mood is higher in the mornings Why is that? Well? suppose We're going out tonight as we are do right. So we'll have been a good time We'll be talking chatting and sharing stories and whatnot. However at some point my app is gonna tell me yo It's time your mood, but I'll be like no, I'll do it tomorrow morning. That's the catch, right? So when I click the notification which I'll do tomorrow morning the date is still going to be Yesterday's day because the notification was from Thursday However, the time the app shows is the current time of course you're able to change it But I never do because it's in that cheating. So in other words, I'm looking Thursday's mood Using today's time and that's why it's usually highest in the morning because again if I didn't use my phone the last night because I Will have doing something very very fun No for the moods. Let's go back to it And here we have like an I stable with all the counts on everything But I went to include the longest trick here and in rad we have 14 days I believe that was doing Puerto Rico Good 24 days and I was like wow 24 days quite a lot one. So that went back to my map Pictures, you know notes. I'm always logging a lot of data random stuff. Yeah, my friends tell me I'm crazy And I realized that on this day at least in Germany McDonald's I to relieve the special edition Pokemon cars and I was like, huh, I won all of them You know when apparently to me 24 days to collect all of them So I ate a lot of McDonald's on those days to be honest one day I got like seven happy meals, you know, I call my friends and I've just come here Why McDonald's party what I seven happy meals? I don't like fries that I have fries and They also give you now apples in the happy meals like apples and some kind of ice cream So that was just like I just come over so I did it for 24 days and I got all of them by the way. Yes Then we have meh longest week seven days during COVID lockdown and then bad just 11 and the longest week lasted in two days and not for the weekdays we can see here that Usually I'm feeling rad on Saturday, so apparently some very good day and These are the activity that are more common in these kind of days in rad days and of course the percentage So we have a friends friends up here more in the rad days with about 35 percent Followed by aerial exploring good meal and photography, but we have others like Pokemon conference So today I'm gonna feel rad. I'm feeling very rad right now and then we have a formula one Which is freaking cool, right? Now the same thing, but I was good here the distribution is quite normal.  So people like to complain about Mondays for me. They're just fine Another day. I'm it's the beginning of a new week new things new events, right? Like traveling to Argentina The activities these activities are more These are the activities that are more common in good days and there's a huge interesting fact here is that these activities they are more like Activities I do on my own like gaming writing coding watching Netflix or reading while The one that are rad are more social thing like friends exploring good meal photography So I feel like that was quite nice highlight then What else this is meh and we can see that may it does not happen Saturday and Sunday because there's nothing bad about Sunday Besides formula one and baking and these are the activities from the med day sick fucking Mac and travel I feel good to say that yeah, and then we have bad. Of course, there's nothing bad about Thursday This is bad Bonnie Puerto Rican day and then Sunday And Percenture of bad day that have this activity. Well here you have them Final words. I'm a happy human. I love my friends Pokemon watching formula one Exploding on photography is a perfect combination. I don't like flying or napping then of course baking or Sunday makes me happy and Looking bad at this data make me all fuzzy, you know and sentimental because it's nice You know to go back and look at all those moments and see the good things the bad things So thanks to the data and of course, thanks to all of you See it. Yeah, 25 Pokemon cars. Yeah, I Believe we have like one minute for question that took way longer, but yeah a few yes here This was the last one by the way So so thank you for being very vulnerable with data and showing I wouldn't have shown people that My mood and how much like in my pooping and stuff But I appreciate that This is a bit of a nerdy question I'm wondering if you try to impute which days that you didn't log data using any of the data around it So you said that there is 319 missing data points So can you have used some kind of an imputation method to say and it is also added to my second a second question Maybe you did had awful days, but maybe there's some bias and not logging those Good one. Yeah, it's to be honest. I did the first awful Three weeks three months ago Because something very weird happened and I was like, God, I'm actually what is this feeling and get us the awful feeling as for this first one Honestly, no, I didn't I didn't work forward to I didn't investigate more of those days What I didn't look any sort of activities because For me, it's not that important. It means just that there wasn't any highlight on that day However, there's always I'm always always logging the mood. That's something that I always do when I'm still doing it today It's been already like maybe like five years and my plan is to keep doing it until I don't know the app stuff working. Yeah Yes, are you worried anyway about being trapped? So you add an app and Been track. No, I'm not worried.  You are crazy.  I'm a day runner. It's awful. It's a weird obsession.  So I Don't know like I don't really care.  That's not her name, you know, and and something just to make You know, just to put an extra effort but like right now. I don't really I mean Google is rocking me. So I guess I just use that data for something else that is better, right? I mean, they're making money. So at least I can make you guys laugh. So, yeah, thank you so much We're now at time. Let's switch it out


This is stock number 7T340A, a 2014 Ram 1500 quad cab, short box, express with the 5.7 liter V8 Hemi. From this video, you'll be able to tell that this truck is extremely clean all the way around. We shoot our videos in HD, so if you do have HD capabilities, turn them on right now on your computer or smart device, and you can see just how clean this truck is. It's like you're actually here looking at the truck in person. This one has Goodyear Wrangler 20-inch, 275-60R 20-inch tires, has 20-inch factory alloy rims with the chrome covers, and you can see that these tires have about half the tread left. Blue streak metallic is the color. You can really see how nice that metal flake looks on that paint. Front grille and bumper, absolutely perfect. So dense dings or cracks. Passenger side rim is an absolutely perfect condition. This is a one owner clean auto check vehicle. This vehicle was bought brand new from us and traded in on a brand new truck. Down this side, you can see just how clean the body is, how reflective the paint is. We take these videos so if you are far away, or even if you're close by and you just can't make the trip down, but you're still interested in purchasing the vehicle, you can see the truck here, the truck, and have confidence in what you're looking at before you even get here. This back rim is an excellent condition as well, and you can see that the back tires have just as much tread as the front tires. Has the nice mud guards, keeps your underbody and frame looking good, keeping the value on your truck up. Underneath the lower rockers and frame looks very clean. Looks like that all the way underneath. Didn't see one single dent or ding down this side of the truck. See it does have the bed rail covers. The express package gives you the painted rear bumper. The hemi gives you the dual exhaust. No dents or dings on that rear bumper and the tailgate is in excellent shape as well. Does have a locking tailgate. The bed is in really nice condition. A couple light duty scratches, but for the most part very, very clean. No major dents or dings.  So when you get here, there's absolutely no surprises, and you'll know exactly what you're getting, which will make your process a lot faster. Heated mirrors, inside the express package gives you the gray cloth interior. There are no rips or tears on these seats. Absolutely perfect. This truck has never been smoked in. Carpentine has factory floor mats, and the carpentine is very clean underneath those. Auto headlamps, power windows, power locks, and power mirrors. As you can see, this truck has 22,451 miles. Has cruise control, and then the controls for the instrument cluster up there. You can see it has an outside temperature display, also a mileage display. You get the AM-FM serious radio capabilities, so you can get serious radio on it.  Passenger seat, very clean as well. No rips or tears on that. Side current airbags, headliner is absolutely perfect, and you do get three cup holders on the center console here, and it is a 40-20, 40-split bench seating. Take a quick look at the back seats, and then we'll check out under the hood. Back seats are very clean as well. No rips or tears back here. It does have the latch-child safety system. These seats do fold up for extra storage, and you can see this truck is very clean throughout. You have factory floor mats back here, and the carpentry is absolutely perfect. Under the hood, we have the 5.7-liter V8 Hemi motor. Engine bay is very clean, runs very smooth. This truck has been fully safety-ed, and inspected by our service shop, has a fresh oil and filter change. All the fluids have been checked and topped off. Truck has been gone through mechanically 100%, and this truck is 100% ready to go. To see more pictures of this one or one of our other 400 new and used cars, trucks, SUVs, minivans, Wranglers, half-tons, three-quarter-tons, one-tons, you name it, we got it, go to our website, www.SummitAuto.com. Full pictures and descriptions of every single vehicle on our lot. Videos of every single used vehicle, all at SummitAuto.com. Thank you so much for checking out my video. If you want to make this truck yours, give us a call right now,<PHONE_NUMBER>. Ask for one of our sales associates to make this truck yours today. Once again, that number is<PHONE_NUMBER>. Thanks again. And if you like the video, you can check out more at youtube.com. In fact, here's a link right on your left to our YouTube page to subscribe. A link on the right to more half-ton trucks and inventory on your right. And then on the bottom, if you're not on our website already, click the bottom link right there, and you'll go right to this truck on our website and check it out. This is the Ford Alpina with the Super Clean 2014 Ram 1500 Quad Cab Short Box. Thanks again.


Hello, hello Welcome everybody. Please could you give me a short feedback if you can see and hear me? Not that I have the wrong microphone Sometimes this happens But life is life as I say. Yeah, let's wait one second When we continue Hello Sandra nice to meet you. Good morning. Yeah, well, I am in Germany right now and it's after noon time here We have a 3 p.m. Where are you from Sandra? Hi Paris Always nice to know where you're from where you're based. Are you based in the United States or? Somewhere probably in Europe or Asia Anyway, then let's get started. Welcome to order flow with me. My name is Robert Rosa. I Started trading at the age of 13 and With the age of 17 I started my co-founder to my first investment company in Germany with two partners They actually educated me of how to trade what is private equity? What is what I hedge funds So they gave me the ground structure like we say the tools and the mindset How to operate an investment business? I sold my shares in that company a couple years later and in 2004 I moved to China where established myself Second investment company with the Chinese partner together. I managed over 100 million US dollars One of my best trading days was also 2008 during the financial crisis the subprime crisis I made 800,000 dollars with trading Currencies during that time actually it wasn't one day was kind of amazing But if you know a little bit of in of China, you know, if you're very if you're very successful in my investment company I had 30 people to be low me and yeah, we had a very good niche business. I Was translating Chinese stock market information to English at that time. It was very successful But if you're really successful the Chinese government decided to detain me in May 2011 And they charged me for outsmarting the Chinese financial system and for building an underground bank and Yeah, the result was that I spent innocently eight years or seven years and seven months in a Chinese prison I was released in December 2018 and of course I am back into training in the first year after my prison time I wrote the best-selling book in Germany. It's called the Dragon years Unfortunately, it's not yet translated to English. I hope this will be Translated one day Currently I have a trading community for German speaking clients and traders But I will expenses to English. So if you like to join my community You can drop me a message on YouTube or see on the discord channel I will also give you my link later for my English newsletter Also, I'm planning To do a community event in New York City So if you're interested to join to meet me in person and you are also some people from bookmap We talked about this. Let's get together to have a great meeting in In New York City Well, some information about me. My name is Roy Drosser. You can find me on YouTube Twitter and also common social media First of all, all bookman to limited materials information and presentations are for educational purposes only and should not be considered specific investment advice No recommendations risk disclosure trading futures equities and digital currencies involved substantial risk of loss and is not suitable for all investors Past performance is not necessarily indicative for future results. I received Letter from an email from Bruce from bookmap and he said that bookmap has very new features coming up For example a trading journal and trading statistics. They will be included for free in bookmap I think that's an amazing new tool for people To identify to learn more about themselves about the trading style and of course statistics For example, I know for myself. There are special times during the day where I should not trade I cannot tell you why but most of my losses are during a special period of time and statistics helped me to simply Solve this problem and do not trade at that time, you know It's sometimes really crazy what information you get and I'm training competitions will probably start with bookmap I think that's a great way to learn Trading without substantial risk Yes, Sandra. I will if you are on bookmap. I will send to their Here a link for my English speaking newsletter I Also share it on Twitter. Let me check one second. I think I cannot post Links here in YouTube So probably just go on my X account You can look here on X robot Rosa English Trader newsletter. You can click on it and register. I will also put it here into discord For people who are interested USA, okay, all from the States. That's good. I mean, let's dive into the markets. We had Financial news this morning or afternoon our time if you look at the calendar, I Like the website trading economics. It's simple We say no BS on that website. I get the information that I need So today there were Thursday at 2 30 p.m. Our time CET Co-op price index personal income and personal spending Personal spending only 0.2 percent personal income increased a lot Co-op price index raise a little bit. I have to tell you I'm not an expert in these numbers And I really cannot give you an idea if this is no good or bad for the future of the economy That's not my business. My business is simply to know that we have these data at this time And I'm looking if there are Yeah surprises or not so and if we check this on the SMP 500 I Like to trade sometimes these news. I can also show you in Second, how am I going to do that? We clear all drawings So I like this Pictures, you know just before the news release we can see The liquidity in book map is basically disappearing But on the other hand we could see that we have a major liquidity at 5060 and below at 5045 On the other hand we have this huge liquidity wall around 5100 So if we have surprises in the news basically I simply like to take Put a Sell limit order into the liquidity Sometimes the market just makes the jumps Absorbs liquidity and will go back to sender. I call it back to the breakout price the same thing on the Level below if the news are not good the market will crash shortly. Yeah Will empty the order book Fill in my order and the market returns back to sender. These are We are risky trades, you know, you should not do that with a prop trading account because sometimes The market can become very volatile and you have a huge slippage of Couple points or even the market runs very fast against you. But then basically it will turn back to the Entrance entry price and then I can catch like 20 points. Yeah 80 ticks Or more sometimes in a very short time we can also Compare this these news on other markets for example the euro US dollar You can also trade this market. We can see Every time when the news are Coming The order book is empty. I mean I I love this tool. I love I like to see this here in bookmark It's so obvious to see okay liquidity is gone. So what's going to happen? And these are the times when you really must be careful Because simply the split or the slippage between bid and ask price can be extreme and the market can simply jump many levels without Getting any trade, yeah So it's always a huge volatility And can sometimes really crash accounts if the news are really that bad or that good Um, you can make a fortune or well lose a fortune I have a story about that. I think it was second or third January 2001 Ellen Greenspan was still the chairman And somehow it was the first or second trading day of the year and he unexpectedly Cut the interest rates and that made the s&p jump like 50 points into my direction And I made like 25 grand In a couple of seconds and well it was it's the beginning of my trading career. I was Shocked I mean I was also totally overwhelmed because of that profit. It was amazing But on the other side could also go against you and um, yeah, you will Crash your call your account and get your margin call In virginia. Oh, hey nice. Well last year I went to us to Miami, savannah Smoky mountains this area nuxville at lenta and yeah, this year I will Go again probably to les vegas and new york city, of course Would be great to meet some of you. I'm very flexible what my travel journeys are But I like to get to people know personally. I also do that with my german speaking community I offer them free phone calls there are no attachments and In 15 minutes we can discuss your trading problems and I try to find a solution for you Sometimes it's just very simple. Yeah to get to the To the bottom of the things What I'm doing this now for 30 years. I educated many traders who managed my own Fund Money, so I have to be very Careful of what they're doing so now the As nest egg does anyone or has everyone from you the trading settings configures on the trainer map pro It's a very great tool. I will show it to get a clear picture of the nest egg chart Because you see all this um bracket orders this algos that are actually destroying the liquidity view on the nest egg It's very annoying. So this is why we have this add on liquidity tracker pro Yeah, I turn it on Not liquidity tracker. So it's trader map pro here And then I created different algos for I will just create a new one now for nq We select the market nq and I just call it 111 the name doesn't matter add Now we can add certain filters. I add two filters for myself. That is the order change filter And show only the orders that were Not changed by price And another order that another algos that I put on top of it is free. Oh, sorry not here um add Filter order duration filter only orders that are longer than I'd say 10 seconds in the market I want to see because I don't like to have this Yeah, break and see as they destroy the view and then you can see when we start this that you will get A new chart window. It's called 111. Yeah Now look at this picture That's very annoying for me And in a few seconds we should be able to get a clear view on the nest egg So if you have any questions, they are feel free to ask me either on discord Or here on youtube And now you can see okay still Yeah, we can see we are filtering out to get a better picture. We click here. Let's say on five minute to compare To the other one where we see these brackets here. They are Disappearing Whereas the market opens that in 15 minutes and we have a better picture, but here like this liquidity levels at 18 050 52 are clear 18040 And bottom line 1800 Well 18 000 here so let's have Look at the bigger picture The es actually which markets do you trade? Do you prefer any markets That you want to have a look at I personally trade es cl The euro us dollar Important for me is just to check the regular trading hours. I like to use the Volume profile Encard we web that's an amazing tool and we have it now for bookmap as well. It's a Yeah, my favorite trading Thing And for now we can see that we still have this on the regular trading hours. It's a huge gap Rome 50 52 down to 5000 for 998 if we really get I mean, we don't have any volume here. So if the market breaks down through this level of 5053 we can see or we can expect expect a rapid movement to make a gap close get close is always one of the Very secure trading strategies as we can see here Gap up at the all-time high The market the first thing the market did was closing the gap and actually at the gap close If you see in conjunction with bookmap liquidity, you basically can Trade a very nice reversal trade at that area and then of course the next target would be the end card we web also here Yesterday we opened gap down and the market closed the gap and really basically it's on the point You can get great trading opportunities by just focusing on gaps Now we still have this huge gap today because Of economy economic data were quite good. So to say the market is Gap up and we can look what's at this at the session Extended trading hours I always like to draw these sort of Support and resistance zones. Yeah They are like high areas high lows high volume nodes low volume nodes point of control And if I see a level like this and also the end card we web will come close to this level. These are very great supported resistance zones, which I double check on Bookmap and in bookmap. I place my orders for I know if the market goes into this area It's worse for me to watch out to have a trade Today like during news time you have to be careful now Yeah, we will trade With a gap up open with a gap up a couple of minutes And then we see what's going on. We could see that in bookmap the es has a huge liquidity At around 5100 5101 if we grab this liquidity at the beginning maybe up to five to 105 you can Check this here in five one five four five. I will Put the lines to the liquidity zones. So this area here above here this high In combination with the liquidity at 105 107 would point for me a nice Yeah, a short opportunity. I assume the market will have doomed the first 15 minutes of the opening um run to this liquidity zone grasp the equity or absorb the liquidity and then we have a pullback basically to Yeah To the encod web or the session view web, which will start at 9 30 or 3 30 p.m central european time To catch a trade on the other side. I definitely um looking out for a gap close If we um have this 15 points if we have to get close Definitely looking for a long entrance. This is the strategy that I have for myself for the first half of the day Then I definitely consider the initial balance The initial balance has many definitions. My definition is quite simple. What is the High of the first 30 trading minutes of the day and what is the low? So these two zones I will point into book map And then I know okay if you have a huge initial balance Then probably most of the business is already done. Let's say the data from the high minus low is 30 points not ticks points or 120 ticks We have a larger initial balance If we have a small one like only 10 points or 40 ticks Then we have a narrow range and this Means to me that we would have in the second half of the day Yeah, probably a trend if we have a huge initial balance at the beginning I'm looking more like to a site Waste market that means the high in the low the highs I would short the lows I would look for Long opportunities That's the definition for me Now we are we are simply going to wait what the market will do For the opening like I said to catch this liquidity up to 105 Is interesting And for everyone who's on youtube if you yeah press a like button, I would appreciate it. It's always good for the for the book map channel and of course For me and I don't know any one of you trading um cryptocurrencies either through book map On the on bios any crypto exchange or the crypto futures we We really almost hits the all-time highs that we are not far away from it and if we watch on the liquidity We can see Yeah, 64 k 65 k 69 70 k I think it's only a matter of a few days till we hit a new all-time highs in the crypto market, especially bitcoin Thanks for sharing about strategy. I was hoping to learn why we have the big red zone at the top and the bottom You mean in the es this zone this red zone. This means liquidity if we look here We Move a little bit into this market at 5100 And each tick we have 200 300 200 200 plus 300 contracts These are sell limit orders that are in the market. Why they are exactly at that point I really um, we don't know what the intention is of these traders If we try to compare it to a chart, we look at 5100 Do we have a special level here? These whole highs and lows 5100 It's not really a huge price action or chart pattern that we can see We probably if we want to understand it, we should look at the option prices. Yeah Or simply someone just want to drop his um His uh His future contracts. Yeah, we really don't know why exactly it is. We don't know who plays this order We cannot know that we can only make assumptions and Putting our guess out that somebody wants to either cover his long positions Or cover his option trades Or want to hedge himself for his Stock positions. Yeah, basically you can take the future to secure your larger investments in S&P 500 stocks So if the stock market is falling But you don't want to sell your stocks because you have an interest in that company You hedge yourself on the futures market I'm not trading why this always. Okay. I will Do it again. We had the same problem yesterday I started the screen and I was live And I know I go again life and then it simply turns off now. I should you should see me again Same problem yesterday. I was sure I started it and today. I also started it But somehow it I didn't press anything in discord and it just Turned off. Thanks for the reminder. Yeah. No, it's perfect. So I was wondering why nobody is Watching And yeah, same problem yesterday in the german stream. And then if you focus on the charts You always forgot to watch all the other channels But thanks for the reminder So patricia, I hope I could answer your question a little bit. Like I said, we cannot know exactly what's going on here but also Anyone who was trading last night or the closing actually once it's nighttime 10 p.m We had the same orders In the market, but they were they didn't find any Yeah Parties to buy them any other part So now the orders are back in the game. So somebody really wants to Sell future contracts And we don't know if they cover their profits if they hedge themselves if they protect themselves against options We don't know A spelling mistake patricia. That's absolutely no problem. I apologize for my english. It's not my native tongue, but um, I have to say I dream in english. I write in english. I only watch english movies somehow after 15 years in china um, yeah You start to sink in english and I lived one year in australia in sydney I was young at that time. It was 2004 So if you understand the australians, they told me you can understand everyone who can who speaks english And that's true But sometimes I was too lazy to work on my pronunciation But basically most of the people can understand me So now in two minutes the market is going to open So let's see if we hit the liquidity zone at 101 and 105 would be interesting to see and same for nest egg the 050 18 050 here And now we can see the picture is clear with the trader map pro add-on anyone trading um Crude crude oil cl. I like to trade this one, especially with the encard weweb creating a lot of trades Skyping techniques. I'm I like to develop a new strategies new ideas that I have and that we have in our community And then I'm getting proper accounts And I simply start to use these proper accounts to test my strategies before I use my personal account So if you crash one account, it doesn't matter only if your bucks are gone and you know the strategy doesn't work Then you start to use new strategies new accounts and you can get experience and see the weaknesses of trading strategies in a real environment without risking your own capital and then when you're sure and you Figured out the weakness and the strengths of different trading techniques you can Trade your own money if you want I wish I had this environment Like 25 years ago when I started trading, but there was nothing like these sort of prop trading firms That was very different And yeah at that time now it's easy open a prop account and you can start to experiment with trading and Without losing a fortune Okay, we hit 5100 now. Let's see what's going to happen here if we can absorb the liquidity and for me important setting the web at at the opening these create new line session starting at and the bands First standard deviation Now it's going to load The session we've up Okay. Oh, well they took the liquidity out of the market. They pulled it out. Interesting. So then it was basically I wouldn't say fake. I cannot say that but So actually there was no transaction. You see you can see very clearly liquidity was there. So they pulled it out here and the market has now three to go up to the 105 We look at the chart the next resistance zones is yeah here exactly um 107 07 Oh 10 this range we have the highs from monday and from last week last friday I definitely resisting resistant resistance Zones where we could expect Turn around So now what I definitely do is We have the vweb. We start we think if we can absorb this liquidity Then we could look for reversal signs like a market sweep which goes up the market Takes all the liquidity and then um, yeah has an exhaustion and will not go further then I would put an anchored vweb at that point and That to Yeah, try and then trade a retest to this vweb So we took the liquidity one hit no big deal Our still more liquidity is coming And we see more buyers coming into the market. Yeah as I view on the spy the etf do we see any liquidity surprises the spy is the etf on the s and p index And sometimes we see huge liquidity Chate differences between the futures market and the spy We have lots of liquidity on the upside here and the future doesn't have this probably The spy will go further and the future will continue to rise also So we compare this then on the right side. We have option data The so called zero dte options. These are the options that expire today they have already huge trading volume and we created an that tells us simply about the Sort of volume profile for the option data But this data only makes sense to use after 4 p.m. And around about 30 minutes Then we can see if we have yeah Overreaction of the prices Like we see here this green levels are three price levels, which we call As a fair value if the price is inside this area, it's fair If the price moves outside of this area and the option volume will not increase at this level We basically talk about an overpriced market. It doesn't mean that we immediately go short Or choose the opposite direction, but it's an indication that probably this trend is at an end But right now, like I said, now the data is still calculating in the Important values we will receive Yeah, around 4 p.m. So, yeah the market took the liquidity And now we have the pullback to you know, probably the v-web Now i'm still looking for price structures that we could draw But right now we still have this uptrend. We don't have any Yeah Serious price structures at the moment. We could say probably this high here and this I will So until I try to put this in From this level To the opening level as arranged. We should find support here But it's still weak. Yeah, let's wait a little bit what the market is Doing And yeah, like I said, we exactly hit the 105 If you're skyper 10 15 ticks in the s perfect trade and very clear to see this trade in bookmap We saw the liquidity the first liquidity at o2 was pulled out of the market And then it hit the real liquidity at 105 and now we have the drop If you only trade the chart you cannot have this confirmation Now probably a 50 minute chart we can talk about Yeah, probably be probably about reversal. Let's see how the Market will end if we continue to be strong above let's say 5100 We could talk about continue rising prices to the 110 and above level But if we have a drop like below if you close below 5100 Maybe we have a reversal back to the opening Hi, robert. I only trade ces, but I want to start trading cl2 since the volatility on the s can sometimes be pretty dull Any advice on things I watch out for the training cl compared to is um Yeah, definitely. I love to trade the cs cscl I mean and I totally agree with you on that point that the s and p is sometimes very dull or boring Yeah for the basically These are two different markets. Yeah first of all, you have to consider the news for oil inventories for example to have this on your focus For me, I like to trade really short term trades. I'm testing right now different things Like the av web The cl works very well with anchored web apps. We can look on the chart and this in any time frame much better than Than the es in my point of view and you can trade this really on a one minute chart If you look out for lows, you know, now I have a lot of Vwebs in here probably sometimes confusing this view But I have my own system in it And you can see many times points where the market really drops just here Here on certain vwebs. Yeah here well even a pullback you can trade here here These are trades with a low risk I I can tell you I don't use I just use a stop of Eight ticks and the cl with the profit target of 12 to 15 ticks I'm really only scalping this market, but you can also easily use longer trends For me, I'm considering this with the second Contract that I am buying so that I will enter the market with two contracts. The first one I will Take out when I reach 12 ticks profit and the second one I will Let it run until it hits either my profit target or my My stop loss. So for example, if you have moves like this here in the es, it's now a nice example. I just We went to the liquidity. We have this market sweep. We could say above I would simply take with the new bookmark tool the anchored revamp on the top here Create anchored revamp Take some time to load And then we have the reverse pattern. What I want to see is That we create some sort of Vacuum or for me black zone. I put the vweb on the high The market turns around and we have here this empty zone Now the market makes a retest to the vweb and that would be my short scenario eight ticks stop Profit 12 ticks again market comes here to the vweb And then basically after a while if the market makes The move we have this sell off. Yeah, and again, then you I already take my profit. I have my 12 My 12 or 13 or 14 ticks profit where I continue Sometimes expanding the tp. It's a very Time-consuming strategy. We can also put the anchored revamp here You must be focused Yeah, sit in front of the computer always put on the revamp and start trading revamp on the low Market makes a new A pullback and then we have this empty zone. Yeah, I call it really a vacuum black zone There's no liquidity It means for me the market is moving away from the vweb And then the market will always come back to the revamp So I would place my order exactly One tick above the vweb Get into the market and we see we have the other move again 12 ticks. No problem. You can take it So you have a risk to reward ratio from One to 1.5 basically my Hit rate or my winning rate is around 50 to 60 percent. So that means at the end of the day you still make profit Yeah, that's pure mathematics, but you have to sit in front of it I'm trying to improve the strategy with different tools like market piles or Iceberg stops and iceberg orders I haven't found the final solution yet. Yeah, it was an idea out of my german speaking trading community So I started to implement this. I registered some prop trading accounts of trading this now day in and day out during the day It's a morning Morning time european time. Yeah, it's night time us or Simply is the main trading hours. I think the main trading hours are Better for me But it's an interesting market you can always for scalping your 10 12 20 ticks are easily made And somehow this market really reacts very very Exact to anchored vwebs Since we have that tool in Uh here in bookmap, it's your game changer, but I have to say it's still a better version And I always get reminded of that it's not for production use yet because it creates some trouble with the core bookmap code I have to say that But right now for me it works quite well Yeah, very simple ac.  Yeah You have the longer term vwebs. You sign them in you draw them in And you get turn around points. Yeah, or I like here I the major zones on a 15 minute chart Here above Then you even have the chance to take bigger trades if you We know we have a huge resistance here. Let's say if you have a four hour chart We have a high high third high So the market will simply run through this area of 79 won't happen So if I draw if I see this intraday that the market will go into this direction Then I know I have a potential turnaround and I can take much bigger trades I would wait the market that the market is there Draw my anchored revamp at the highs Then simply right on the sell-off. Yeah And really it works great on a one minute chart But the big advantage in bookmaps is you get a much better Entrance.  Yeah Then you have a little bit patience and we wait for this level Got it Just try to experimentate with it or we have this very nice this low the sell-off Yeah, this is what I want to see takes the liquidity. I will add the anchored revamp here And probably we see this Empty zone and I could start to look for an entrance here at 40 Say 78 40 market only goes down to 78 36. So for ticks. It's all in my Comfort zone Yeah, and then just let it rock.  Uh, yeah, it's a real example from today I was following the revamp from this low.  So if you Nothing else to do during the day So, oh cool very fast today And well similar here what I also really enjoy trading. Um in the es is simply the The revamp itself is already somehow a little bit messed up. Yeah You need really, um special movements in the es to trade it but the Extension bands, yeah the standard deviations like we can see They actually work quite well as um, either trend confirmation zones Or as um, yeah resistant point So what is the es? Saying exactly we hit this level five one or five perfect trade and now we have to see We see what did we say on the 15 minute chart? Yeah, we have um Exhausted top I would call it Um For me right now, it's a little bit. It doesn't look like we would hit soon a new high Maybe a correction to the opening price and then we see if we can actually trade A gap close, yeah Perfect or huge Entrance points for me. Definitely. Yeah, I get close around 82082 in the es Where I would take a trade today really I My first idea trading idea was here. Yeah Before market opening we saw the liquidity and I said if the market will go to this movement We probably will have an exhaustion Exactly this is happening. That's a good entrance for at least 15 to 20 ticks Now the second idea That I have or that I still have is simply the gap close. Yeah, you know below here If the market would close this gap, I would look for a long entrance here and probably maybe take the run if he Breaks the low that we probably um, I can take a trade on the way down to the gap close To look for long since this area I'm careful because we have too many resistances in this area too many highs up to 112 113 I don't like to buy these sort of breakouts. It's not my trading style. I have to say that Again the volatility index down three percent. It's really here There's no fee in the market that probably would show us that we still can take new all-time highs again, the market is moving back to Back to the standard deviation. Now you can see If the market really has a breakout sign, yeah These standard deviations they had to spike up and this one to spike down, but now we don't have this Volatility here a liquidity or this power What we can see is now the first market structure Yeah, simply this I would This structure here and we see the market try to test it. We make always newer We are lower highs so to say And the major structure Is simply here. Well, probably if we cannot pass these Standard deviation we will fall back to the lower standard deviation at around 1500 But these are only about eight ten twelve ticks. It's very narrow range at the moment Yeah, if you have further questions, just ask, you know About anything And don't forget to put some likes here on Book map Still have to put in some drinks always must drink a lot of My tours really does of water Yeah, and again the market is rotating back into this price structure and we are trading between these Two standard deviations of the view app. So the short look at um bitcoin. Yeah 64 k Already touched again Like I said, it's a matter of time that we hit the all-time highs Luckily, I can say that I started to buy bitcoin again at around 17 18 k. So I'm and um isserium. So I'm quite a lucky guy I did not expect this move so fast I really have to say One of my other favorite tokens is render rnd out. They are offering trading computer resources For artificial intelligence and for the metaverse actually it started with the metaverse that they If you buy this token you can get Yeah Processing power to render your graphic design and what's and so on Great idea apple everyone invested and yeah this year it rose from Like 50 cent to seven eight dollars. We are new all-time high eight dollars Was cracked a couple days ago and if we look at book map. Yeah, well liquidity set ten dollars so Buy and hold and get lucky Though the spy still no information here That's the gap 509 to 507 also here's this simply the range for the moment NQ similar. That's the wrong NQ. This is a correct NQ liquidity shifting a little bit upward from 70 to 72.5 why I Yeah, why they shifted from here to there. I cannot tell you that either Probably it's exactly above the high Neuro us dollar So quiet. I did a trade in euro us dollar today, but I don't trade the The Futures most most year trader bureau us dollar spot very nice market to trade to just Short the highs buy the lows um Don't be too greedy take your 10 pips this is like from 85 to something like 86 Like from 60 to 50 these sort of ranges. Yeah, they are very Nice to take. This is basically the movements at the es or the Euro us dollar is doing that are very How to say reliable income sources And again any new Out should It's problem is sometimes there are too many revaps. Yeah, I can hide them take them out That's the official one We could delete them would also be good, but yeah, that is one of the promising tools So now we have another break out if we let's say These are the movements that I like to take in this strategy, you know, if you take an anchor revamp here Yeah, same thing Okay, I'm moving down probably here. I wouldn't have gotten an entrance, but At this area, but the market you are made the expected move And now probably you can also understand why I only Looking for 12 13 ticks in this market because that's the normal Algorithmic reaction from the computer programs I would say when they start to initiate buying it's the beginning Yeah, and then okay retest to the vweb and then suck The market will start the second buying process and we will move into my direction and this is what I want and again The es is still in that range between the two standard deviations Now I can still um, yeah draw the Initial balance high and low We take the low that is at the opening And we have the initial balance high and we have simply a difference from, yeah, let's say two and a half points and again another two and a half points five points from 97 to 05 50 25 So that is a very very narrow initial balance basically five five seven Three and five eight below 10 points. Yeah, like I said before initial balance below 10 points Can point to a breakout the second half of the day We don't know if the breakout will go to a new all-time highs or to make a gap close I will prefer to get close because then I have a nice entrance point at Yeah, what we said Here at around 81 it would be a nice That would be a nice trade Now the initial balance high and low you can follow this up during the day This will always be important resistant and support zones Our new liquidity is coming up below at five By well 110 And we can see just above the standard deviations selling orders coming into the market. So limit orders This market is being put into this range. Yeah Around the V web Now we just have to wait for the breakout Yeah, no, we already passed One hour so who wants to Be part of my English newsletter can do this You find me on x. Yeah with robert rosa Simply search for my name subscribe me and well click on this link and you will be Part of your my newsletter I will start in English speaking community soon And you can get insights of me of 30 years almost 30 years of trading the stocks Run a hedge fund with 100 million dollars under management and yeah, also Can tell a lot about prison life in china. I spent there seven years and seven months and also if you want to get updates about Community trev a community meeting in a new york city Together and most likely also together with book map. We raised this topic I would like to get to know all of you to yeah It's a huge difference here to get somebody personally to know To exchange ideas. I do this with my trading community in germany At least once a year. We are going to meet in november on an island And they're always 40 to 50 traders and they're coming now every year Will this year will be the fourth event. It's always a huge huge Pleasure. We're just being together eating drinking having a good time and exchanging ideas About trading investment and yeah all things about life Yeah, I would be happy to see you on my newsletter and then I would um End this session today Like I said, nothing is happening here if you have further questions about my um, yeah, cl strategy or any other market You can ask me anytime on discord And yeah, I would happy to Be of your assistance So Have a good day and let me know about your results a c if you try to implement this strategy and cl Let me know how it works. I always like feedback because feedback also can improve my personal training I'm always into observing checking and Experimentating this is how We survive I always um Oh, no, no, we have to get set off. Okay. I always will remind you the time of the floor traders When they all started to die out. There is this movie on youtube called floor I think everybody should be careful about the time with artificial intelligence. So be ready to adapt and um Yeah, always be open-minded I still know the time when I placed my orders through the phone to the cme And I know there's also another trader here from bookmap scott pochini I think he was on the floor He can also I believe tell many stories about that and also about the change from not being a floor trader anymore to being an online trader And soon we will face this to be uh, yeah AI trader, I really don't know yet, but just keep that in mind So then I would say have a great weekend Best greetings to you all and we see each other next Thursday For another session So bye


Formulas in Google Sheets can be used not only to multiply numbers or to select text or to combine things like that. They can also be used to do some basic formatting. And what's really nice about this is you can use these conditional formats, which are formula-based formats, to highlight data, bring your attention, for instance, to things that are performing very well, very poorly, issues that need to be resolved. I use them frequently to help me find open cases. And let me just show you quickly how this works. Start by selecting the text that you want to format. So, for instance, I'm going to come here to Teacher. Now, this is something I could do easily on my own, but if you had a very long list, you wouldn't want to do this manually. Let's highlight a particular teacher. And so what I do is I select this text, and then I come over to Format and down to Conditional Formatting. That's going to open up a little window here on the side, and it's going to apply to this range C3 through C9. That's the range I have selected here. And we're going to tell it, well, right now it says Cell is not empty. Well, they all have stuff in them. I'm going to say, instead, that the text contains, and then I'm going to put the name of one of the teacher's Emanuel. We have Kofi, Alex, and Emanuel. And then I'm just going to leave this default. I can change the color if I want by clicking on one of these. I don't need to. I'm going to hit Done. And now, if Emanuel's name pops up as the teacher, then it automatically gets highlighted. And if I were to change one of the other cells to say Emanuel, it would automatically inherit that. In fact, let's do that right here. Let's change this one to say, I got to spell it correctly, Emanuel. You see, it inherits the conditional formatting. Next is what if I want to highlight not just that person's name, but the entire row that they're in. This is actually what I use. I use a spreadsheet as a large database for assigning teachers to classes. And when a class isn't assigned, it says staff, but I want the whole row to highlight. And so I'm going to select this entire rectangle of data. So not just the teacher, but also the attendance and the dates. I'm going to get the whole bit of information. Then I'm going to come over to conditional formatting. I'm going to add a new rule. And the rule I'm going to add is a slightly funny one. You may not guess this one. I actually have to come down here to custom formula. And what I want to tell it to do is if the teacher is Emanuel, then highlight the entire row. And it goes counterintuitively like this, I write equals, and then the dollar sign sees they always look at column C. And then 13 is there because that's the first row in the block. And then if that cell is equal to Emanuel, and because it's text, I have to put it in quotes, then highlight it. And so that's what we've got right here. I hit done. And you see how it's highlighted. And again, if we were to change another name to Emanuel, you see how the entire row inherits that highlighting. And again, this is really useful for identifying important clients, it's for identifying unassigned cases, it's useful for identifying any number of situations that you can describe with a formula. And then finally, another good one is what's called a color scale. I'm going to come over here, where I have the information again, I'm going to select just the attendance information. And I'm going to select a new rule here. Now not a single color, which is what I've been doing so far by the color scale. And what this does is it lets me choose a range of colors that go from the minimum value through the midpoint up to the maximum. Now I don't want the grain to be the low one, I'm going to switch it the other way around. I've got some choices here. I'm going to pick this one. So it goes from white to green. And it highlights the higher values are going to hit done. And what you can see now is that the darkest green is on the day that we have 31 people, the white is for the lowest value, which in this set is 12. And so it can give you an impression of the range of the data. And again, maybe things you need to focus on, maybe you need to figure out what's going on the low days, maybe you need to figure out what you can do to increase the high days, but conditional formatting in any of these situations with the color scale, with highlighting a single sale with highlighting an entire row is a way of using formulas again, not just to manipulate numbers or to manipulate text, but to manipulate the highlighting and drawing attention through additional visual cues. And it makes it a lot easier to work with your data and find the things that you need to make sense.


we have to learn how to release judgment on the things that we did that we perceived to be wrong or not in alignment with that person that we want to be. So how do we do that?  Think of it as like a sims, like you're like a sims character, okay?