OK, we're going to get started. This is an introduction level talk. If you want a really, really big deep dive on Kubeflow or something advanced, this isn't it. So you can leave. Also, normally this talk, it's me and a co-presenter, and we're telling jokes the whole time. But jokes don't really go over the language barrier well. So if you are expecting a lot of jokes, this is actually going to be real contents. And I'm sorry. So you can also leave now, and I won't be offended. But if you like jokes, I recommend looking up another version of this talk, because it's really just me and Holden just cracking jokes for 30, 45 minutes about stuff with pretty much same slides. A little about me. First of all, things that aren't on here. And also a little bit about this presentation. Normally when I give talks, I maybe like the day before, that's when I really start practicing. I go through it a lot. I correct all the errors I find on the slides. For this, I submitted the slides a week ago, so they were all done great. I came here, and I wasn't able to get into my Google account, so I couldn't edit any of the slides. So there's lots of errors and mistakes, and I'll try to point those out. So I apologize about that in advance. Also, I changed some content, so the slides don't exactly match up with what I'm talking about, but that's OK, too. I also talk really fast. I am working very hard to talk as slow as I can. But if I am talking too fast, please just raise your hands and let me know that I need to slow down. And that's OK. We'll do questions at the end as well. Also, there's some idioms. Same, if I say something and it sounds weird and doesn't make any sense, just raise your hand and I'll stop and I'll explain what I mean by that. And I'll also be watching my live translators in the back. If I'm going too fast, just let me know, and I'll slow it down. So about me, my name is Trevor Grant. I'm from Chicago. I work at IBM doing stuff. I've worked a lot in the Apache communities. That's a lot of my open source history has been. Kubeflow is my first adventure into the Kubernetes ecosystem. I'm putting together an IoT track at Apache Con North America, if y'all want to come, you should. You can find me pretty much all over the internet with this handle of rock and treble. Got a website and blogs and GitHub and you name it, rock and treble, it's probably me. So at this point in the talk, I normally start with some jokes about how many buzzwords Kubeflow has. The point of this is, though, that there is a proliferation of frameworks, machine learning, libraries, serving things. It's been a problem that people have been talking about in AI and ML space for a while now, that there's is there ever going to be a tool, a single tool that everyone rallies on? And I think the answer is no. And so then the question is, how do we start dealing with this rampant proliferation of frameworks? Because it seems to be growing too. There's more and more frameworks popping up to solve more and more new problems. Another example is, there's three talks on just frameworks on Kubernetes going on right now. I appreciate you sticking around and coming to mind, but that, again, underlines how much it underlines how much there is the problem of this proliferation. Best case scenario right now, you're using some of these tools we're going to talk about, these components, and a little glue code. But that's maybe you are, maybe you aren't. There's more jokes here, but the gist of this is an algorithm. Data scientists like to think they're very important. We have built them up to think they're very, very important. And everything they do is so important. The reality is, the model or the algorithm that data science makes is really one small and trivial part of creating productionalized machine learning and artificial intelligence. There's data prep. There is machine learning library version control. There is model version control. There is validation of the model and doing all these things in real time. And while the data scientist model, having a good one is good, a very elegant model that you can't productionalize is worth less than a simple linear regression that you can put in production, in my opinion. Oh, another little asterisk on this. I work at IBM, but all of the things I say are my opinions and do not necessarily represent the opinions of IBM. So there's that. Yeah, going back into just the model serving and model training, being able to serve a model and being able to scale gracefully on different types of hardware is an important thing for your machine learning, your artificial intelligence pipelines. This joke still carries. So as you, your data scientist, you can usually tell, you can get a feel for how experienced they are by how much they're demanding that they need so much and many more GPUs. Cloud providers were always happy to sell you GPUs in time on our GPU machines. What is Kubernetes? As everyone at a Kubernetes conference, I assume that you know, and if you don't, it's magic and containers. So the other bummer about me not having my slides working the way I thought they were going to is there's a lot of good animations. And this animation, the cat is going, so you can imagine that. And this is our data scientist who thinks that they're very important and that they're like the critical piece of the organization. In 2019, again, Trevor, not IBM. In 2019, anyone who is self-identifying as a data scientist is not a technical user. So in addition to all these problems that I was just talking about, we also need to create something that data scientists can use, which has a nice user interface, is easy to configure. Otherwise, they're just not going to use it, and they're going to write some glue code. So yeah, another fun animation that you can't see. Thank you. So what is Kubeflow? The problems we're discussing are common. The solutions to these problems, which they're common across all industries, all sectors, lots of businesses. The solutions that people come up with traditionally are ad hoc, glue code, hack scripts. And by that, those are all idioms. And by that, I mean it's individual teams creating these little one-off solutions that will solve this particular problem that they have on their team or on their product for this one case. And the problem with that is you get lots of duplicated effort when you do that. You're writing a, it's the same thing over and over, but a lot of times maybe it's copy and paste and then just change a couple of things to fix it for this next problem. And the even bigger problem with that is you're getting a lot of technical debt. So if you have one person who's in charge of productionalizing all of these things, well, when that person leaves your company or gets hit by a bus, then no one knows how anything's in production. And so you just don't touch it because it works. And that's fine. And someday you'll fix it. Someday you'll fix it the right way, but for now it's good.  No one, really? OK, there's one, and that's a problem though. And so we try to avoid that. So if you think Kubeflow is Kubernetes plus TensorFlow, that's OK. Lots of people think that. It's not the case. Kubeflow is more appropriately thought about as a buffet. And a buffet has lots of choices and options. Here are some of them. It's not all of them. You can add your own. But in general, the pieces of this buffet you need to create a machine learning meal, for lack of a better word, is you're going to want to have machine learning libraries, which are version-controlled. You need data prep. You're going to need the models, data prep, model management. So when you have a model and then you have the next version of the model, maybe you need to roll back. And then you're going to want to have serving. So how does this all fit into the ecosystem? So you might be saying right now, hey, I don't need to do all that stuff. I just do research. My management comes and they say, we need to know something, and then we do research, and then we give them the answer in a report. That's great. You've got a great job. Keep it. Because those jobs are going away in favor of, we want answers in real time. We want to know. We don't want to have to do a post-mortem analysis on why sales were down last quarter. We want to know in the middle of the quarter why sales are down, why they're down, and how to fix them. And that's where the world is going. But for everyone else, that's the problem that Kubeflow is trying to solve, trying to help. And also to scale out those resources. The data scientists on the laptop, a big problem with laptops is that they're not great for training machine learning models a lot of times. They don't have a lot of power. That's not what they were built to do. They were built so you could code stuff in a coffee shop. Everyone can see you. You look cool. But they're not great for actually training models. So you can scale out your training resources. You can deploy to production. And so why would you want to use this? My notes are out of order. So here's how you set up Kubeflow. And this is also there. OK, so there's a lot of errors on this slide. Go look at the docs. It's not that hard. It's even easier than this. There's not control scripts anymore. There's another version coming out next week that's going to make it even easier. I'm just going to short it out with that. The chefs recommended pairing, though. If you use those out-of-the-box scripts, you're going to get Jupyter Hub. You're going to get TensorFlow Serving, TensorFlow Job Hub, PyTorch, Catib, which is hyperparameter tuning, a list of things, and Pipelines, which is Argo and a little bit of Magic. And you might be saying to yourself, what are those Pipelines? What does that mean? There used to be a joke about this cat, and it made sense with Pipelines, but I've forgotten it. So you're probably thinking, OK, cool. I've got some scripts that let me deploy a lot of services and components on Kubernetes, but it still needs to be easy for my non-technical data scientists to use because they're not going to go learn Kubernetes. They're going to keep doing things their old way and writing their glue code, and we're never going to get anywhere with this. And also, that's not an impressive. You wrote an install script that just installs a lot of things, which I guess with Pipelines is saying is that's not what Kubeflow is. Kubeflow is a way for these non-technical data scientists to keep track, to set up experiments, to run these different experiments with different hyperparameters, configuring in and out different, let's say, input data streams, also to have reproducible results. Are there any data scientists in here? And if so, I'm sorry, I'm making fun of you so bad. And no one wants to raise their hand now, of course, because I've been teasing data scientists. Well, I think data scientists sometimes do with their Jupyter notebooks is they will make a model. And they say, you know, I'm going to change it. But I want to keep this old version so I'm going to make. I'm going to copy the notebook, and I'm going to name this new one dash v2. And then the next one is like dash v2.1. And then dash v2.1-final dash v2.1-final dash v2. And it goes on like that. And that's really hard when you're trying to roll back and find the old models. So you want to be able to keep track of those experiments. And then it's even worse if somebody else wants to also know they're maybe working on something similar. OK, I'm just going to zip up all these notebooks and send them over. Good luck. It's not a great system. So you want to have this easy to use UI. You can build experiments, run experiments, where you can take the same pipeline of data prep and data validation and all of those things that are important and then move it from training to production. And nothing changes. And that's a really important thing, too. Because if you train a model and you prep your data one way, but then in production, the data prep does something different and you wouldn't think that would happen. But changes in machine learning libraries will quietly introduce changes in the way a data prep step is done or the way a model is computed. And you train with sklearn 23.0 when you are training, but the production server is running 18.1 or 0.181. And now your model is totally wrong and no one knows why. You can find those things, but it's better if you don't have to work with that in the first place. So pipelines are going to show a good one. This is actually a screenshot from Kubeflow in the pipeline UI. It's not doing a great job of showing how to set it up. It's done with the Ammo files and Python. You can do with Python as well if you like. But it has some important parts to it where you've got your validating your data. And you can, again, doing that the same in production, where you've got streaming data coming in and making sure it looks the same. And also, not only schema is the same, but also that your sensors aren't broken. So there's two ways your data can fail, if you will. You can have a schema change. And maybe something that used to be true false is now coming in as a 1 and a 0, and it's being treated as an integer instead of a Boolean. That's a problem. But you could also have busted sensors. So if we've got a sensor that's supposed to be between negative 10 and positive 20 degrees, those are reasonable values. If that thing starts reporting like 500, it could be that the thing's running at 500, but it also could be that your sensor's broken. And so you want to be able to know that fast. You don't want the thing to just start making predictions based on the machine's running at 500 degrees, activate the fired suppression system. Well, no, you need to fix the sensor. And you want to know that quick. So another big important part about this is treating errors in your data like they're bugs in software. That kind of goes down a rabbit hole. It's just something to think about. And keep in mind when you are deploying machine learning jobs to production. So also you've got your data transformation. That's the pre-processing. I've already kind of touched on why that doesn't always work out as well in production as it does in your training. Or things can change quietly. And another problem that you have when you move from training to production is your data scientists will say, oh, we found this really good predictor variable. And it does. It accounts for 98% of the variation in the model. Well, that's because it gets calculated after later. It isn't available at runtime. You want to find that out sooner rather than later. So I think a lot of people are maybe here for Spark and Kuflo. Holden is a PMC on Apache Spark and really, really knows this topic very well. I can give you her contact information. And I can also point you to some other videos where she goes into depth on this. I am going to go through it at a pretty high level. That's just pictures of stuff. PySpark is the interface later, basically, here. She's talking about the serialization. There's a lot of serialization going on. And she says you should use Arrow. This is another aside. Don't ever trust vendor benchmarks. Vendor benchmarks are usually garbage because the vendor sets the test up to make whatever they want to say look great. I had some notes because, again, I couldn't get to my G drive. I can't see the exact numbers. Using Arrow still does give you a really good speed up with Spark, but not this good. This is a lie. Not a lie. They fake news. This is how things were before Arrow. And again, it's just kind of going through some code of the changes you have to make. This is it after Arrow. But again, me trying to talk through this when she has everything's like an ish or whatever. I can't really do that very well. So CrossCloud. This is something I can't talk about. So one of the great things about Kubernetes is we are theoretically moving toward a world where you can create these jobs and you can deploy them on various cloud providers. IBM, Amazon, Alibaba, Huawei, maybe has a cloud, you're of any rate, various cloud providers. You don't have to get locked into one cloud provider. In theory, the same Kubernetes job should work wherever you deploy it. It's not there yet, but in theory. That's good because that creates a commoditization of the cloud. You just go and you do your training wherever it's cheapest today. You serve your model wherever it's cheapest today. That being said, how do you do that with Kubeflow? Well, the good news is if you get Kubeflow up and running on your cloud provider, which is not exactly trivial on all cloud providers at the moment, I will be the first to admit. However, we're working on it. The pipelines make it very easy to transfer your training to different jobs and create consistent results on different clouds. There, it's a series of steps. It's Argo really again with a thin wrapper over it. But that's good because you can run an example and then you say, well, I wanna try it like this. But then your cloud provider is more expensive and this one's cheaper. Cool, I'm gonna shift the job. Data gravity is a new buzzword that you can learn today. That is, where is your data stored? If your data is all in cloud provider one, then you're gonna have to upload it to another cloud provider. That will be a cost. But if you're seeing significant variation in the price of their cloud time, probably worth it. Because again, you wanna train near your data. So, I don't know. The other way you can do this is, let's say you've trained a model on cloud provider one, but over time they think, oh, we've really, really got you locked in. We're gonna keep turning up those Kubernetes costs on you. You take the pipeline, you redeploy to cluster number two or cloud provider number two. That's what happens is you are able to basically tell cloud provider number one, all right, it's been fun but we're leaving now. And run on a cheaper cloud provider. Again, all this is about preventing vendor lock-in. Which again, is all about me saying I'm not representing IBM and I hope they never watch my videos. So, yeah. There are some other videos. Oh man, I'm guessing you guys can't get to most of these links, sorry about that. I just didn't realize until the day, just now actually. Why you shouldn't use this? It's zero five now, it's zero six. Lots of API breaking changes are being introduced all the time. It's starting to settle out, but it's still a thing. I personally would not say this is production grade software yet. I don't think many people would. But it's interesting and it's exciting and it's something to start thinking about for your six to 12 month architectural plans. Like what are we gonna move all our data scientists on to? Compared to doing it locally, this is a lot of overhead. You got a whole Kubernetes cluster, you got 15 different services running. You need like, your laptop's gonna start like just buckling under the weight of all the stuff you're running just to get up and do simple like machine learning. It's hard to do a hello world example. There's also workarounds for that right now, but another issue. All of these components mean that three different talks on Kubeflow could give you three totally different component sets and not even look like it's the same project, though it is. And then also, and again, this is me saying this, but Kubeflow's not in a foundation and I'm not saying this is a shot against Kubeflow, but you should always be aware of software that's not open source software that's owned by a company not a foundation. I think Kubeflow does have a very strong community and they seem to be working really well with the community and I don't think that's gonna change, but it's just, anytime you're looking at some open source software and it's owned by a company not a foundation, be careful. That being said, there are some workshops, a book that you can, we're writing, you can sign up and get updates. There we go, I'm giving you some time to take the pictures of that. I promised that I practiced this like four or five times and I thought I had it really, really dialed in to being about four minutes longer than it was, so we're gonna have a little bit more time than expected for questions. I hope you please ask some. I also have, I believe, a Kubeflow conmitter in the audience. Yeah, there he is, okay. So if I don't know, I'm just gonna pull it off to him but he can answer anything you want to know if I can make up something that sounds legitimate. Book again, all right, and these are just screenshots at this point. So yeah, with that, do we have any questions answered? All right, there we go. The question was, okay, go ahead. Yeah, is the book to be published soon? We're hoping to get the book out before sometime near January 1st. So I wouldn't say soon, soon. Part of the problem and the reason for that is Kubeflow just recently refactored away from case on it which means a lot of the code changed and we're like, let's not write anything because we're gonna have to change everything anyway in a month, so we're basically just getting started at the moment, even though we've been hyping it for six months already. Yeah, software, forward selling. How do you manage the dataset associative with the training jobs or the prediction? There are a few ways. You have, you can use network file stores, what they're called, persistent volumes, S3, versioning your data. I don't believe there is anything, I'm kind of looking for a head nod or a shake, but I don't believe there's anything associated with Kubeflow at the moment for versioning your data, correct? There's no, okay, that's wrong. So there is something at the moment. So now it's only about managing. Okay, correct, then I was correct. There is not anything at the moment. Go ahead, Zain. Yeah, so now it's only about managing the version model and the code and your computation jobs not about the data. Yes, that is correct. That's also very important. I would agree, that is very important. For managing your data at the moment, Trevor, not the Kubeflow project, IBM or anybody else would recommend zip data v1-final, that zip. But that's a good point, definitely. So you mentioned the data gravity and the difficulty of moving to different cloud providers. Did you have experience within Kubeflow using TensorFlow or any other tools federated learning that means actually moving your models towards the data ease? So I think that would be the idea with the pipelines. That you would basically just set up your Kubeflow cluster next to the data. So if I've got my data, let's say I saved two copies of my data or two slices of my data, one on AWS and one on IBM, just because that's what I work for and I should plug them at least once. So the idea there would be that you would have, you'd run the job on AWS and you'd run around Kubeflow as far as merging things back together, like if you had two slices and you want to merge it together, that I don't believe again that there's an, I don't have experience. I can thought exercise how I would go about it, but I'd be literally making it up on the fly. Yeah. But yes, moving the compute to the data. Because I think that that actually can mitigate that problem with data distribution and also with eventually keeping data confidential at the whoever owns the data and through some kind of encryption, homomorphic encryptions and so on, let you get just the insights. And I know that at least I saw that new TensorFlow has this federated learning options. I don't know for other tools. And I was wondering, you know, how difficult would be in Kubeflow to set up these pipelines and to define this federated learning model actually. Richard, how hard it would be to set up federated learning in Kubeflow? Yeah. Our TensorFlow apparently has it as a new feature. So this is kind of off the record, but... One of the GCP's major directions in the future is to work on hybrid cloud and like including multi-cloud environments. And there's a broad projects called Anthos and this was announced at GCP Next this year. So training that's like on premise and across multiple cloud. That's one of the general direction for Anthos. So the AI component for your situation will be also available. But I cannot give an estimate on that at this point. And to that point too, that we've been talking about like these public clouds, but private cloud is also, I think, reality for a lot of companies because there is fear about for various reasons putting data up in a public cloud. And so you also wanna be able to, for example, thank you. You also wanna be able to let's say, and maybe a more realistic use case is to train on your private cloud where the data is and then serve scalably on a public cloud. And I always forget to mention that, but that is literally the use case of the moving the pipeline and the cross-cloud training. But I always just come up with arbitrary clouds and that's wrong. So thank you for that as well. Yeah, yeah. So you would, yeah, you train on your private cloud and then serve on the public cloud. And that can be done without federated learning because all your data is private cloud. Oh, wait. Oh, no, go ahead. I saw your hand up first, so that's fine. Oh, sure. Also fair. It sounds like a lot of the driving force behind Kubeflow is in order to enable data science and machine learning at large scale. My question actually relates to small scale and the applicability of Kubeflow there. I'm working with a research team at a university and right now it's a machine learning project, but our pipeline is a series of Jupyter notebooks scattered across five GitHub repositories. You have to look in the 2017 repository for some data. It's really, really bad. And I see a framework like this, which is almost like a full stack kind of data pipeline machine learning framework, comes with a little bit of overhead given that you need to have Kubernetes involved. But my question is from your experience in the project so far, do you think that the job management and the model management and the versioning that you get from Kubeflow is applicable to smaller scale projects or are there simpler frameworks that I should be looking at? As someone who's talking about and writing a book on Kubeflow, I 1,000% think Kubeflow is the perfect solution to your problem. Do you agree? Yeah. Yeah. I don't want to over slam the, whatever, you need a server and that can be public cloud too. Or you can, as a university I'm guessing you've got old blades laying around here and there and everywhere. You really just need somewhere that can hold, that'll just stay on and holding data. Like if you've got that, you're probably gonna be okay. Yeah, that's the, but I think, yes, this is definitely something to watch and keep an eye on. We got 30 seconds so, as best. Yeah, so for my company, we have many local servers in our house. And so a lot of work is done without the cloud or even on your local computer because you do experiments locally. So we use a lot of conda containers. So it's not Docker or real cloud containers. So I think in the future we really need something that can also work off the cloud and Kubernetes but also can integrate and go production very quickly. And I also use MLflow from Databricks and they have a machine learning packaging with conda container. So it's more easy to work for small scale. I think we are out of time. I'm gonna answer that. And just after we're gonna shut it down. Thank you everyone again for coming to our talk. Thanks for coming to KubeCon. Thanks for being great people and maybe clap for me. Thanks for clapping. Yeah, there we are. All right, awesome. And we'll be around after to answer questions. So please stop on by.


السلام عليكم ورحمة الله وبركاته وبركاتكم بوضع حكام SOS الذي يتحدث عن الموضوع الإسلام أن يجمع أن يستخدمه الإدار بشكل يدار والدار الأميري الذي يقولboth لقد أعطنا他 سيدي أنا your host Moss Insha ومتذيب بالعصوات مع شيخ علي مواصل السلام عليكم ورحمة الله في آخر اشهد لوفاك نتحدث عن الإميز الأداء ونبحث عن أشياء عادية ونبحث عن حديث عندما يأتي إلى أجهزة أجهزة على أجهزة أجهزة أجهزة أجهزة تصدرنا أعوذ بالله السميع العليم من الشيطان الرجيم بسم الله الرحمن الرحيم الحمد لله رب العالمين وصلى الله عليه وسلم وعليه الطيبين الطاهرين الله صلى الله عليه وسلم هناك 7 أجهزة في which the commas becomes a budget as a payment of course we will not discuss all the seven we just discuss the ones in which we are facing today in our life but I mentioned them quickly and then we will go through to the main ones the first one is profits and gains from earnings and trade that's the first category or an item in which the commas must be paid إذا كان شخص يوجد بيزنس و يجعل جميعه مستخدمه ما يجعله مستخدمه باستخدامه في نهاية العام يجب أن يأخذ 1.5 من ذلك المستخدمه و يجعله مستخدمه و بالنسبة لذلك هؤلاء الذين يعملون أيضاً as employees عندما يقومون باستخدام المستخدمه يجب أن يقومون باستخدام المستخدمه في نهاية العام الثالثة is the Halal wealth which is mixed with the Haram wealth and that happens in some situations where you have somebody selling Haram and Halal products and now they want to start paying for example this is the situation in which they can purify their wealth by paying the hummus in this case number 3 is the minerals which are acquired number 4 is the gemstones obtained through diving in the sea whatever you gain you have to pay the hummus of that amount gained number 5 is the treasure the trophies which are found whatever you find in the deep ground for example that is actually required for the one to pay the hummus for it so you can't own it because you just found the treasure and you want to take it for yourself number 6 is the land that them me i.e. the Christian who lives under the government of the Muslim country when he purchases from a Muslim the land in which purchased from a Muslim this individual who is not a Muslim when he purchases land from a Muslim he must pay hummus for it and that of course as i've said some of them we need to discuss more which we face everyday in life and the last one number 7 the spoils of the war as i mentioned in the last episode that in wartime whatever we get مع غنمتم as the holy verse says فأن لله this is specifically for the war situation and case whatever you gain then you pay hummus from what you have you gain from the aftermath of the war event i.e. the first category you were discussing is profit and earnings could you go through that in terms of hummus i.e. we have to pay hummus on profit and earnings indeed it becomes obligatory and wajib for the individual who is making some money either through trade and business or through the income that he is let's say an employee in a company or a teacher or even a student who is receiving grants from the government to study university or college whatever income is gained and owned by that individual then that becomes wajib for that individual to pay after the expenses at the end of the year any surplus and any addition and left over from that income to pay one fifth or twenty percent of that money for the cause of Allah and what supports the origin of Allah to the مرجع to the فقي in which they follow and I will come into more details about what share is paid to whom and how it works so شخص if I am earning some amount of money how do I calculate how much homes do I have to pay do I just look at okay this is how much I've saved over the year and I just take what one fifth twenty percent of that and that's it that's how simple it is initially for those who trade they have business those who are making some money incomes coming in whoever has some kind of an amount of money is coming to his or her account they have to initially set a day in the year let's say the first of محرم or first of the الحجة or tenth of month of رمضان for example or I don't know whatever dates of course the hijri dates the Islamic dates to set that date so when that individual is meets that date that specific date in the year they have to sit down and calculate whatever they have gained in terms of income and to calculate and to take out twenty percent or homes or one fifth of that remaining money which is in the account or cash in hand for example or could be an asset or an item that haven't been used for example as a gift gifted to somebody or for example I haven't used these things for example I bought them I never use them for example clothing all the assets and belongings to the ones individual to see if they haven't used it and they have to pay for example so I set a date and I try to calculate whatever I have left over with at the end of the day from the excess money left as I've mentioned in other words whatever you spend during the year it's all fine it's yours but by the end of the year that first day of let's say Muharram or month of Ramadan or tenth of Rajab for example whatever date you want to set you have to make sure that whatever is left over money or assets they are all calculated and then you pay twenty percent of that amount to the Hakam to the Taqi شكرا do I pay annually or do I pay monthly can one do his Choms monthly as in he can look at his expenses how much he's earning look at his profit working out and then every month pay Choms or is it done once a year the payment of Choms is immediate so if it's today it's due then you have to pay today you have to work out how much is left the surplus and you pay it straight away you cannot delay it unless you speak to your Marja to the Hakam الشرا then they can allow you and permit you to delay it whatever is the reason or to make installment payments to break it down into monthly for example so you have to speak to your Marja and Alem or the reps of the Maraja and to see if you can be able to spread the cost for example in installments or delay the payment for example what the reason is for example and that should be fine شخص what if an individual has more than one source of income let's say an individual has a main job a career he does Monday to Friday let's say on the weekend he has his own small business that he does market stall or something and on top of that he's got an inherited property and he's got rent coming in as well this individual does he have to pay homes on each source of income or just on one source of income and that's you know exempt him from the others at the end of the day those individuals who have income no matter what the source is is it a business is it a working let's say night shifts at a manufacturer for example company is it from somebody who is for example they're servicing for example whatever this source of income is is it rent business being an employee as I've said student getting grant from the government to study whatever is left over as surplus to this individual's animal expenses whatever is left over so he collects all these source of income the surplus so let's say he's got 1000 pounds left over from his business 5000 from the rent 2000 from a service he made for example provided and so forth a grant he received from the government for example for his projects whatever is left over weren't spent during the year then it becomes obligatory for that individual to pay 20% 5% of that left over profits or income and try to calculate each one separately because you have now different sources so it's difficult to actually be able to recognize which of which so try to make sure that accurately calculates whatever what is left over and then take out 25% of that left over income شيخنا what about those who spend on the business so from the business gains and losses from the business profits they spend on traveling for the business food and drink clothes furniture office wear sometimes you need are we obliged to pay on those items or are those items seen as expenses these are known as the expenses of during the year in other words after you paid your previous last year's for example and now you have into the new year now you can spend by pay and so forth receive income and spend it during that year you have all that 12 months to spend and enjoy your life and business but when it reaches that date the due date of paying so whatever is left from those unspent money and wealth then it is there that you have to pay otherwise during the year whatever you spend it's fine you spend you go to holidays and so forth whatever you have done that's fine because previously you have paid you've cleansed yourself from the payment of hummus and purified your soul and your wealth by paying the hummus last year so during the year that year whatever you spend it's fine it's حلال but when the due date comes and you have amount of money then you have to make sure that you calculate and you pay the hummus of that left over money on the due date that's right I think this misconception sometimes when you go to hummus that it's on what you haven't spent not on what you have spent on it's to do with the surplus the extra the savings rather than I spent money here and there do I own hummus on it it doesn't really work like like it's more on what you haven't spent on and what's there the surplus money I said شخنا now there's a controversial question so be careful how you answer this one the محر the dowry for a wife is she obliged to pay hummus on that or not when you go to the dowry or the محر there's no hummus on paying the محر there are actually three exemptions that you don't have to pay hummus for one of the exemptions are the محر so when the wife receives the محر or the dowry from the husband she doesn't have to pay hummus at all unless the husband hasn't paid the hummus for and she knows that he hasn't paid it he doesn't pay the hummus it becomes واجب on the husband to pay the hummus of this dowry so if she discovers that he doesn't pay the hummus at all and she receives that amount and she knows that this amount has not been paid hummus for she has to go back to the husband and ask him to pay the hummus for this dowry if the husband insisted not to pay and sometimes happens he doesn't believe in hummus although he's a Muslim and a follower of the Al-Bait in this case it becomes the duty of the wife to take out 20% of the hummus of this dowry otherwise in general the main rule is that there's no hummus on dowry received by the wife from the husband what about inheritance if my my father leaves me X amount of money do I have to pay hummus on the inheritance this is the second exemption for not paying the hummus on inheritance the inheritance received from the father the deceased father whatever the the sons and the daughters and the children receive from the deceased father land property buildings money whatever they receive there's no hummus to be paid on inheritance and that makes things even a lot easier for the children and the inheritors that they can spend more money now I mean compared to today's western world for example in the world that there's inheritance tax over 30% tax so imagine how Islam made it easier that you don't have to pay even hummus which is a wajib so remove the hummus from the inheritance so that people can enjoy more in their life so the children they can enjoy you know spend the remaining money left from and the wealth left from their deceased father on their own life to pay their debts for example to buy a house for example car and so forth and it doesn't matter which family member has passed away it's not just the father both the mother passes or the grandfather whatever inheritance one receives there's no hummus on that exactly the inheritance that's received who is obliged the one who has the right to receive the hummus nobody else that becomes for them حلال and there's no hummus for that what about if one has paid hummus has got some money and has purchased some property now this person has passed away now so this person paid hummus the following year he purchased the property he's passed away that property now is inherited by his son now the and that son decides to sell the property there's a son has to pay hummus on the money so we have hummus money paid the money's now حلال and everything's okay purchased the property the person who purchased the property passed away the son inherited the property couple of years of gone property value has gone up he's decided to sell the property is hummus obligatory upon that sale you see the initial inheritance which is received by the sons or the children of that deceased father for example that inheritance there's no hummus on it but when they buy with that money land or property and the value goes up every year they have to pay hummus for that extra and excess value of the property or land when it goes up let's say it went up this year 20% from this 20% whatever is the amount they have to pay hummus out of this 20% which has gone up or 30% has gone up the initial money is حلال there's no requirements to pay the hummus for but when it's spent and bought something and the value goes up and they haven't actually used that property of that item so they have to pay hummus for that value which went up then it goes exactly ما شاء الله شكرا thank you very much for that discussion and thank you to all our viewers for joining us إن شاء الله we'll have more discussions on the films next time on نحكام SOS سلام عليكم ورحمة الله وبركاته


A PET scan creates 3D images of the body. It does this by using radioactive tracers, which are usually administered to a patient through intravenous injection. The tracers are made up of carrier molecules that are tightly bonded to a radioactive atom called an isotope. The carrier molecule can interact with or bind to specific proteins or sugars in the body. The carrier molecule that will be used depends on what the doctor is looking for. If she suspects cancer or is monitoring a known cancer's growth, she may use FDG, a modified form of glucose, which gets absorbed by tissues. When tissues absorb a lot of glucose, it may indicate a cancerous tumor. The radiation from the tracers poses little danger to the patient since they quickly pass out of the body. The isotope produces small particles called positrons, which interact with surrounding electrons. This interaction results in the complete annihilation of both particles, releasing two photons that speed off in opposite directions. The detectors in the PET scanner measure these photons and use this information to create an image of the distribution of FDG in the body.


Our first lesson deals with performance as something very different than activity. It is not doing things that fires achievement, but knowing what you must accomplish. Let me introduce you to Doug the Caveman. He lives in a hole in the side of a hill, and he lives by hunting and foraging. He lives in the presence of dangerous animals, and many of these are quite willing to hunt him for food, and so he goes armed with a sharp flint spear and another flint that he has sharpened into a cutting tool. Now we give Doug a modern hammer. He sees immediately that this is much stronger than his spear and a far better weapon than his flint knife. The tongs won't break when he applies them to a pig's tough skull. So why doesn't Doug use it to build himself other tools or to build protection from the weather so that he doesn't have to cower in a hole? Doug has never seen such things, and he has no nails, and the idea of building structures is far beyond anything in his experience. And so it is with the tools you will encounter in the study of performance. These are tools that you have to see in use before you learn a real appreciation for what they can do. And what you can do with them tomorrow is just the beginning of what you will be able to do in the future. And this brings us to our first technical performance tool, the engineering black box. I was in an engineering laboratory for a circuit design course when I first encountered this lesson. We were presented with a black wooden box fixed with a hasp and lock so that it could not be open to see what was inside. Each end of the box had two electrical terminals. Our given task was first to apply electrical current and power to one end and to measure the result on the other, then based on what we found we were to design an electrical circuit that would give the same results. We had basic knowledge to do this and completed the exercise without too much difficulty. But what the lessons we were to learn was very different. When we turned in our papers the boxes were placed still locked back on the shelf. We had already learned how to apply various power sources and take measurements. We had already learned how to design circuits. These were not the lessons being taught. The lesson was the box itself, that we did not have to know what was in the box. The lesson was that what was in the box didn't matter because we had just designed circuits that would replace it. It was a performance lesson. It was one of the most important engineering lessons of all. If you know what went into a performance effort and you know what came out of the performance effort you can always design a process to replace whatever was being done before. This has amazing versatility for the engineer. When it comes to solving performance problems you need to know the desired results and the resources that are available and the rest is distraction. You can take all the details, the unknowns and all those distractions and you can lock them out of sight in a black box. You can concentrate on what you have to do to achieve the result instead of worrying over all the details. On his daughter's fifth birthday John, a former football player, takes her to her first live football game. She is clearly challenged by the noise and excitement of the game and he explains the purpose as moving the football to the far end of the field. She learns to cheer for her dad's team and gets caught up in the excitement of the game. Having one fourteen to six John takes Susie down on the field where he reunites with his old coach, sharing both the win and their history. Susie, finding daddy engaged and paying no attention to her, wanders over to the bench and picks up a football. He has to use both her little arms to carry it but she does so. She is almost at the far end of the field when John realizes that she has wandered off and spies her carrying the ball cradled in her arms. Glancing at the sound of his voice she completes the walk to the far end zone before turning and coming back carrying the ball. When she is approaching the sidelines again he asks, What are you doing baby? He finishes her walk before answering and setting the ball carefully down tells him, I knew it couldn't be that hard. If the purpose was to move the ball as her father told her then Susie was able to move the ball. The lesson is that we are daily taught to concentrate on processes, on how we do things rather than to look to the desired end result. Being and maintaining our own focus on a valued end result will require discipline. The desired result for players was to win the game moving the ball to the end of the field was part of the process they used to win. The crowds in the stands did not share purpose with those who were on the field. They were there taking part in the crowd and cheering for their champions. Their purpose was entertainment, enjoying the drama and appreciating the skills and efforts for those who took part in winning. For us the lesson is that performance only has meaning when we have something to accomplish.


Hey guys, Ryan here for Movie Nerds.  Crash Bang! There you go, that's how you know it's the 90s. Very cool. Truth be told, I was ready to hang it up till I met you today. Yeah boy. So, you're not from around here. It's hard to explain. Young Samuel. Oh yeah. I keep having these memories. I see flashes. I had a life here, but I can't tell if it's real. Where's my super suit? The white threads are out there. Ooh, sprawls baby. You can't do this alone. Yeah boy. Comes out on my birthday, can't get wrong with that. I'm going to stop that before 10 crazy rules that makes Marvel Studios fail plays. That looks awesome. Cannot wait for that to come out. That cannot come anytime soon, March 2019.  Because we need to watch that as soon as possible. Listen to what you guys thought in the comments below because I think that looks unreal. Can't wait for it to come out. Until next time, I'll catch you nerds at the movies.


Every year we take a vacation to an exotic place, which we have a travel ritual, holiday ritual, family get-together ritual. You know, you can see it's all rituals of form, repetition of things that we do to keep our relationship spiced up. Patterns. It's always patterns. And this what we're talking about is holding on to a purpose and really wanting to communicate. You know, really wanting to say, well, the form isn't so important. Darling, I may not be spending you with a Valentine's Day card this year, but let's sit down and have a heart-to-heart talk for three hours or something. That would be kind of an expression or a symbol of moving towards wanting to really connect with the mind and and that could even be in terms of Valentine's Day card. A lot of people do try to really put a lot of thought in selecting something that, you know, really says something meaningful. Well, any of that, you know, whether it's sending a Valentine card or whether it's cutting fresh flowers for the table, it's all a matter of what's the intention from which I'm doing it. If it's a symbol of my caring and my, you know, extending, then that's one thing, you know, and if it's a way to get something, or done out of obligation or guilt or thinking that it's causative in some way, that it will add to in some way to maybe the relationship or, yeah, then that's that's something else. And it's in the intention, and I would say that again, you're moving to a place of seeing that the really the only thing you can share and the only thing you want to share is ideas. In other words, Jesus was not a big philanthropist. He did not go around when the beggars were begging and just go here are some gold for you and, you know, this and that. I mean, he he shared his ideas. He shared about his father in heaven. He shared about the kingdom of heaven and how it comes to the kingdom of heaven. He wasn't big on gifts, you know, there's not a lot in the Bible about him. They talked about the three wise men bringing gold and her and frankincense and this and that, but there's not a lot of mention of Jesus going around like Santa Claus. Santa Claus is like the ego symbol that it's covered over Christmas with because what better symbol of the reversal of Jesus's idea of just sharing these beautiful ideas of going around and turning into a materialistic holiday, you know, that's Santa Claus in many respects. If you really look at it is it could be a symbol of the ego of trying to cover over this unconditional love that Jesus represents with forms and rituals. So it's really important as you move along more and more you can see that initially when you believe in stuff and it may feel real comfortable like Beverly saying to spill as a symbol to give cards or flowers to people or do this and that and as your mind gets clearer and clearer and you're called on to be a teacher of God then even those things I found in my life start to you know, dwindle away.  It's gotten to the point where they are looking for the other stuff because it's not there. The presence and the ideas is the gift and that's truly how you keep them in your mind is by giving these ideas away. It's not like I'm trailing around the country trying to enlighten a bunch of other bodies, but really it's just I do it because it's my joy. That's how I keep them in my mind is let them come extend It makes sense. That's how you keep you keep something in your mind is by giving it away


Welcome to Access a Trader, the number one community for those who are committed to taking control of their trading in order to achieve success, profitability, and longevity. Thank you for joining us. Here's Dan Shapiro to help you find your edge, master your process, and own your future. Hey guys, good evening everybody. Welcome to a Tuesday edition of the Access a Trader dot com nightly wrap up show. Hope everybody is doing well. You know, here we are again, right? Here we are again. We are on the bottom of the range in the NASDAQ 100, right? NASDAQ composite. We're here, right? We're here again. A couple of takeaways, a couple of takeaways from the day and kind of where we are possibly going forward. Number one, if you guys remember yesterday's session, okay, everything was in the middle of the ranges. Okay, there was very, very aggressive areas where the market got pulled. There were very, very aggressive area where speculation money still came in and the market spiked. So we had kind of everything here, right? We literally had kind of everything here. And going into today's session when the market was up, you know, 300 points. Again, the big footnote of the day was supposedly Russia found the cure for COVID. Again, if you've ever dealt with, and I'm Russian, right? I grew up around Russians. If you ever dealt everything that was going on in the universe, you kind of know that if the Russians really did find the cure for anything, okay? They were going to definitely hold the world hostage for any type of aggressive vaccine, obviously. So the market gapped up 300 points. The market started coming back in and we started seeing a lot of similarities that we saw yesterday. And that similarity was that a lot of these names just couldn't get above supply. So the names that had big run-ups, Amazon, okay, Amazon couldn't rally, Roku couldn't rally, right? All these stocks couldn't rally. You had Beyond couldn't rally. Even the stronger names that attempted to have rallies, such as an Apple of the world, such as in the videos of the world, again, they kind of came to a stop. And today was kind of one of those days that you saw the potential aggressive nature of what could happen. And what we saw today was a lot of big violence to the downside. Even the names that were strong early, and there were some strong names. You had Facebook that was very, very strong at some point. You had Roku that was strong, Boeing was very, very strong. You had all these names, right? All these names that potentially could have led us higher. And the question that we had kind of yesterday that we asked, I go, well, is everything else in the market going to kind of pull everything up, right? All the beta names or the technology names, or is technology going to pull everything down? Again, remember, we had this really, really big run into the market. And now the point is what happens next. And today was a perfect example of, again, that technical analysis does matter. It's very, very important to have an opinion. I think that is crucial. And we talk about this every single day. But the most important part is to understand that you have to be flexible. Let's say, for example, for tomorrow's session, I'm pretty sell bias. But again, that's my opinion. One thing that we've seen over and over again in this kind of George Costanza market, every single time the bears look like they're about to seize control, here comes the bulls, right? They put the magic wand and everything rallies again. But we're here. We're here, again, on rising support. So I think going into tomorrow, of course, I'm sell bias. I'm definitely sell bias. The question is, are we going to get that confirmation channels to confirm our sell bias? And that is, again, the big question. And again, if you look back all the way, right, literally all the way back to the March lows, you'll see a lot of the same similarities. They come back to the bottom of the range and they rally. They come back to the bottom of the range. They rally so forth and so on, so forth and so on, so forth and so on, so forth and so on. The only day that they actually confirmed to the downside was on July the 23rd and the queues went from 256 all the way down to 251. And then a day later, we reclaimed the rising support again and started going back higher. So this is kind of like Groundhog's Day into tomorrow. And the most important part for me for tomorrow is, again, I'm not going to anticipate every move, okay? I'm going to watch the action tomorrow. If we get a gap opening and the names that I am definitely watching to the downside today, okay?  And the most important part of any day that you are looking for a macro move, it's very, very important to kind of sit there and wait for it to play out. The last thing I want to do tomorrow is wake up in the morning. I have my strong opinion. I have my strong bias. I'm watching the technical analysis. I'm watching the channels to the bottom and start predicting or forecasting what's going to happen next. It's very crucial that, again, being wrong is okay. If I'm wrong tomorrow and it's an update, fine. Right? Fine.  But the most important part of tomorrow's session at least for me is wait, sit patiently, wait for these channels to confirm. Understand that, again, this George Costanza market does have ways to kind of throwing a curve ball and going right back up. But the most important part is being flexible, staying flexible, waiting for confirmation and letting the market dictate where to go. Again, opinion to the south side. Now let's see everything confirmed. I thought today's action reflected a lot of what we saw yesterday, okay? Aggressive polls, very, very aggressive polls. As I'm sitting here recording, I'm seeing Tesla run up 70, 80 points. I haven't read the news. I kind of know what the news is. That kind of obviously defeats the trade for tomorrow, which kind of sucks as I'm recording this. But again, I'm assuming if it's gapping up like this is I'm assuming it's the S&P that we talked about. But again, it's something that I'm assuming I have to look at the news. But everything else that we are looking at from yesterday into today's session, again, saw the aggressive nature of what happens when the market does confirm downside channels. And if you look at just from where you are from the technical areas, again, outside of the queues, you're really not going to see what potentially could happen. Again, you had this really, really big breakout on the IWM. It's resting. Really, really big breakout on the spies. Again, you got a back test. It did close below the five-day moving average. That is a little bit of a red flag. Again, it's very, very important to kind of watch this 332 area, 332, 331 area on the spies. Again, if the bear starts season control again, nobody's talking about a swan dive into 300. All we're talking about is an orderly back test right back to the 10-day moving average initially. Again, we just kind of want to take baby steps little by little, little by little. If you look at the diamonds, again, very, very strong. They broke out. They rested today. Again, granted, they gave up 300 points, 350 points to the upside. So that's a little bit of a red flag, maybe a reversal bar. But again, the most important part is, again, taking a day by day, having an opinion, and see what happens. So tomorrow, I think outside of Tesla, again, I haven't read the news. I kind of have an idea what it is because I'm watching this thing move up aggressively. But if you look at all the other beta names today, you'll see a lot of similarities. You see Amazon trading to the bottom of the range. Again, is this going to confirm tomorrow? That's the plan, right? That's the plan. Any gap up tomorrow, that is sold, that confirms today's channel will go lower. We'll see. Look at Netflix. Netflix first closed below the 50-day moving average. Obviously, that's negative. We're going to watch that for tomorrow. Very, very clean, right? Very, very clean action there. Roku, again, another name that's attempting to break down. The only reason it held up today was this 143 Rising Bollinger Band. So again, we're going to look for confirmation for tomorrow's cycle as well. So it's a pretty clean day for tomorrow as far as setups. I think it's pretty clear. All we need to do is kind of wait for the setups, wait for the confirmations, second entries, and again, strike with extreme prejudice and confidence knowing that technical analysis is usually going to provide a pretty good guide of what's going to happen next. If you look at the pivots today, again, you'll see a lot of really strong aggression. Very, very strong aggression today. And again, it was predominantly, it was definitely predominantly to the downside. There were some long plays today, like Boeing, for example, like Facebook. Not many, right? Not many, but you could see the aggressive nature of the selling and you'll see a little bit of muted action to the upside. So let's talk about this. So BYND 12680, 12650. If it builds below, it can flush. Here was BYND, right? So here is the 2680, right? So here was the 2680, right here, right? 2680, 2650. Again, went all the way down to 25. Very, very strong move. I put in the wrong prize, guys, so I apologize. It was 3100, 3100 on Amazon. It wasn't 3,000, so I apologize. Closed at the lows of the day. Closed at the lows of the day. Again, we're going to be traded all the way down to the 30s, 70s. We're definitely watching tomorrow's downside channel for more extended selling. But again, you kind of get the idea here. Overstock got destroyed. Not only did overstock get destroyed, overstock, I think they did an offering after the close. So here it is, 90 and a half, 90 if it builds below can flush. Here was overstock, right? Here was overstock. Here was the 90, the pre-market and traded all the way down to 85. And after the close, again, it's kind of moving lower on that offering. Shop got hit, 986 if it builds below can flush. Here was shop, right? So here's the 986, right? Here's the 986 and went all the way down to 964. So you could see very, very strong aggression, especially at the open. V-ray didn't do anything. I think it traded to like 360, 361 and kind of fell apart. NIO didn't even get close to the 1590, 16 area. Boeing was very strong today. Boeing 18520 needs to build. Here was Boeing. Here's the 18520, right? Here's the 18520 and traded as high as almost to 190. Big move there on Boeing. Shop, yeah, Roku got hit as well, 14850, 14750. The two areas of support if it builds below can flush more, right? So here was Roku, right? So here was Roku. Here's the whole 14850, 14750 was yesterday's low and went all the way down to the 143 level. Really got hit very, very hard. So again, this is kind of the effect of what we're looking for for tomorrow's session, especially if all these stocks start to confirm macro. Group pot was actually a pretty nice little move. 2680, 27 needs to build. Again, not a huge move because again, the market is weak. You can see the market's weak. So here's the 2680, 27 level, traded up to 2750. Again, not a big move. Again, everything got pulled later, obviously. So that was that. FSLY, I caught a pretty nice move here. 75 for builds below can flush. I covered, I'll always cover it was in the 73s. So here was first FSLY. I still like it for tomorrow. So here was the FSLY. It took out this whole 75 range. Traded down to actually 7250s. I definitely, definitely still like it for tomorrow. FSLY, take on the way down. Nice flush, new highs. I said 2770s potential traded to 2750s. Facebook, again, not a big move. Again, it really did show what happens. It really does show what happens when there's a buyer strike. Even strong stocks get pulled. So 265 is supply, needs to build. A buyer is coming in for the 250 calls. Again, only one up, less than a buck. Again, that's the whole point. So here is the 65, right? Here is the 65 traded, you know, just traded about 266 and then got really, really pulled with everything else. So very, very important to understand where the at least sentiment meter is. And the same thing Roku, Roku 152 needs to build. Only ran up a quarter, right? Literally only ran up a quarter and then faded pretty back. Hard pretty, pretty aggressively. Amazon obviously never got back to the upside here as well. Tesla, again, you know, again, I'm pretty confident I already know the news before I read it. A 1385 line in the sand, you know, went down about 15 points. This would have been the number, right? This would have been the number. But again, I have a suspicion I already know what the news is. SPAQ never got up there as well. So again, you know, I'm definitely setting up sell bias for tomorrow. It's going to be very, very interesting to see if the bulls can muster. Again, it's very, you know, we've been here before. It's kind of Groundhog's Day.  It's exactly the same view. So if you have a confirmation both on the 60 and the daily charts, you could have some aggressive potential swan dyes. But again, we wait. We're not forecasting. We're not, you know, we're not trying to will these stocks lower. We want to see confirmation. We want to make sure that we are entering these trades properly. Again, if I'm wrong, it's not going to cost me any money. Again, I say this all the time. You could be wrong a thousand percent. Literally, just don't be wrong financially. Don't be stubborn. Wait for these plays to play out. Guys, have a great night, everybody. I'm going to go look at the Tesla news. Have a great night. Oh, God's help. I'll see you all tomorrow. Take care. Congratulations for putting in the time to take control of your trading. You're one step closer to owning your future and achieving the success you desire. Want daily trade ideas directly from Dan? Straight off his personal watch list? Unlock our free PS60 vault where you'll get nightly updates on pivot opportunities we're watching for the next day's session. Click the link in the description to get started today.


What's up guys, this is Alex from Xtrades and today we're going to be going over a crash course on supply and demand zones. So I've actually made a video on these prior probably about two years ago, I had a little bit less sophisticated equipment, I have a better microphone now, I have better editing software and overall I feel like I just have a better speaking voice and speaking ability than I did two years ago. So I figured I'd remake this, make it more educational, also kind of go more into depth on these and also kind of just make it a cool little quick crash course that's easy and not too difficult. So today we're going to be going over supply and demand zones, we're going to go over how to draw, identify and trade them. And you can see in this opening example here, supply and demand zones are actually created from base candles and you can see I have these highlighted here, your base candle is where it's circled right here. So this is your base candle for supply where the zone originated, then you have a base candle right here for demand where the zone originated as well. And you can see price reacting very cleanly to them, you got a bounce, you got a bounce and a rejection and a rejection. So this is kind of like your buy zone and your sell zone as highlighted in this example. So this is actually a real time chart of spies, maybe like a day old, and now actually spies back within the supply zone. So this is just like one day old, but either way still shows a good example of what supply and demand zones are and these are still pretty valid now as we're speaking. So for our first example, we're going over a rally based rally zone and it's exactly how it sounds. So the sequence opens up with the rally, it creates a base slash consolidation area and then it makes a higher high and a breakout.  So you can see that this base candle makes up a whole zone and we're not just marking the low of the candle like you would with a regular support resistance.  So the sequence opens up with a rally creates the base slash consolidation area and then rallies to the upside making higher highs. So it's actually pretty much like a higher low, higher high kind of sequence and your base candle is always your zone origin and you draw these after the sequence is finished. So after you get your big bullish candle right here, putting in a higher high, then you come in and draw the zone and your zone is the opening price to wick low from the base slash higher low of the sequence. So you see this is your base candle. We have it marked at the opening price down to wick low and some people they do just mark the whole base candle from wick high to low. I personally focus on the open to the wick low. I feel like the opening of the candle had such a large imbalance and just a great volume from market on open orders from institutions in Wall Street that the open is just a good area to focus on. It's a good area to mark and I feel like it just gives the best reactions in terms of price reacting and rallying off the zone. And you can see on spy here, you got to enter here. We actually traded off this before. So I actually set sent out an alert on this trade specifically and then I actually came back down again and bounced off that again. And this is pretty much spy in real time right now. Well today as we speak, it's actually back within resistance and supply. But either way, really nice run off demand here. So rather than like trading the breakout, right, you're not trading right when it gets over your base candle, you're waiting for it to come back for a first test. And then you're entering. So you're not entering over here right after the sequence gets made. You're not entering after a higher high. You're waiting for price to rally. Let it do its thing. You wait for it to return and then you enter and you can see just by waiting had just as good of a reaction here as it did right here. And arguably this actually went up faster than if you were to enter right here and wait for this little kind of chop up area slash melt up. So this is just a straight bounce and this was also just a straight bounce and arguably it's because we tapped this, this rally based rally area. So when it comes to supply and demand zones, usually they're better when they're fresh and untouched. So you can see this is actually, let's say that this hasn't happened yet. This is the first test of the demand zone. And initially it gives you the best first reaction. It just, it's a straight rally, crazy buying balance to the upside. And the second you can see it kind of, it bounced just as good almost, but it didn't get up as high until it found a consolidation. So the first reaction is usually the best in my experience, but it can have multiple tests. You just don't want to hang it around down here for too long. You want to see those quick reactions. You want to see nice wick reactions like this. You want to see a nice wick reaction like that, or just overall, just a nice buy in balance following through after it tests the zone. So that's for rally based rally. This is just the first sequence. There's actually four different kinds. So we're going to go over the other three right now. And for our next one, we're going into a drop based rally demand zone, also known as DBR. So DBR is a reversal slash counter trend zone. And you can kind of see that down here pretty much opens up with strong selling. It creates the base and then rallies to the upside as seen in this bullet point right here. And then similar to an RBR demand zones, DBRs are actually made up of bearish base candles as well. So if you saw the base candle that we marked in the last example for rally based rally, you saw that it was a bearish red base candle. Likewise for this, this is also a bearish red base candle that we're marking for our demand zone. And just like the other ones, you go from the opening price to the wake low. And also drawing your zone comes after the rally is strong and prices showed a cleared reversal. So that would be pretty much after it puts in this like nice bullish candle or high or high above this little structure or above your base candle structure. That's your confirmed rally for the sequence. So you got a drop, heavy selling, you got a base consolidation and then rally follow through to the upside. And that means your sequence is finished and you can see after it finishes really nice run to the upside, but you're not trading this. You're waiting for it to return. And you can see once it returned down here, a great entry, really nice rally to the upside. And this, just like the last example, the first test is always the best. So this is your first test. Great reaction. And you don't see any other tests on this zone. So this only needed one test to go higher. And that kind of just ties into my theory of fresh and untouched zones being the best.  And then just like rally based rally demand, your zone is equal to the opening price to week low. So that's the opening price down to week low. And the reason why I marked the open is because volume is so significant at the open. There's usually a strong imbalance around the open for any daily candle or any trading session. The volume is the highest at the open. And I really feel like that plays into why the opening price of the zone is the most important. And likewise, I mean, you could go for the whole candle. Like you see, I'm marking right here. You can mark the high if you want. I personally don't do it. I see the best reactions when you go from open to week low of your base candle. And that's just my experience. And like I said earlier, not trading a sequence once it happens instantly, you're waiting for price to return for a first test. And then you enter. And next, we're going to be going into supply. And our first one is called a drop based drop zone. So this is literally the opposite of rally based rally demand. Your sequence opens up with strong selling, which is the drop. It creates a base. Your consolidation or kind of like a bounce area lower high and then follows through with strong flush to the downside and a lower low. And you can see once it broke this structure that puts in a lower low that most of supply zones will actually be made up a bullish base candle. So literally the opposite of demand where we're marking bearish base candles, we're actually marking bullish base candles of the base and similar to demand where we're focusing on the higher low and bounce area that led to a rally. Instead, we're focusing on the lower high and bounce area that led to strong selling. So this is your lower high area or bounce area that led to strong selling and you draw your zone after the sequence is finished, just like all the others and confirms that they lower low with the closed candles. This is a closed candle that's your lower low. You can see it had nice follow through, but then once it comes back up for the first test, look at this downside.  And this is actually a pretty recent supply zone on spy. So I just want to make sure I'm using recent examples of the spy. I don't know this exact date, but this is still within like a pretty close price of where we're trading now. So I wanted you to see that this is still valid. This is still a good strategy and it's really good for the higher time frames, especially if you're swing trading. You need to focus on the one day chart. If you're day trading, I mean, this even makes a good zone to day trade off as well. If you're like scalping or getting those quick entries and exits, but for supply and demand, mostly I use these for swing trading just because they work so well and you really find the best areas to either go short or go long. And also it gives you a good area to take profit once it gets up to supply and it gives you a good area to take profit on puts once it gets back down to demand. And then instead of the opening price to wick low, like you would mark and demand, you're actually marking the opening price to wick high. So you do the open up to the wick high and you can see that's how it's marked right here. And you're going to do that with your other supply zone as well, which we'll go into next. And just like the others, you're waiting for price to return, so you're not entering after it puts in this low, you're not entering down here, you're waiting for it to get up, test the zone, and then you can enter if you get the right confirmation, the right rejection candle, the right reaction. In this instance, you have a nice strong upper shadow wick. You have a nice reversal candle signalling that it could go lower as well as these two buy emails candles that were likely to fill back up to the downside anyways. So you want us to look for something like that, and that's going to give you an optimal entry. And next we're going to be going into rally-based drop. So this is actually opposite of the drop-based rally demand. They both share a common theme, which is they are both countertrend and also like reversal supply and demand. So the sequence is actually created by rallying to the upside, it creates the base, and then it drops to the downside with strong selling. And you can see that's exactly how it is here. Drawing your zone comes after the selling is strong and price is showing a clear reversal. So after you got the rally-based drop, I would say after this little candle, candle number three, after your base candle, that's confirming strong selling. You can see once price came back up, rejected pretty hard a couple of times, and then even now to this day, this supply still stayed pretty valid. It rejected here, also rejected here pretty strongly. And just like the last example, your zone equals opening price to WIC high. So you got opening price right here up to WIC high, and that's focusing on that opening candle. So something happened in this candle to lead to this selling balance. And that's kind of what you're focusing on, and it stays relevant. And maybe even new liquidity gets opened up here, more liquidity right here, and it just results in more selling to the downside and more of a sell-in balance. That's probably why price just re-reacts to it, even though it's already had the test one and test two here. Test three and four still stays valid right here. So it doesn't always have to be the first test being the best, but it's just a rule of thumb that I kind of keep. I like to look for those first test ones because they do work really well. But this kind of does show that you don't have to only trade off the first or second test because test three and test four right here work pretty well. And then price provides an entry after it returns to the zone. So you're not entering after it reverses up here. You're waiting for it. You're letting it do its thing. You maybe could have traded off it right here, but you're waiting for it to get all the way back to it. You can go short. You can take profit on your calls, do whatever you need to do. That's what the zone is there for. It's there for you to be able to make an educated decision on where price is gonna go next. If it gets up to supply, we're gonna assume that it's probably gonna go back down short-term at least. So for supply and demand zones, key points to remember here. Zones are always better, fresh and untouched. Just like I was showing you, except for the last example, the last example, I mean, it was like the third and fourth test and it was still working pretty good, but the best tests and the fastest tests are usually from the first one. You'll have price reacting the fastest to those. If price breaks all the way through, the zone is most likely void. So if it closed under over a supply and demand zone with a closing candle, closing prices over it, it's likely probably not working as good anymore and it might go void. You could use it as a future reference point for support or resistance, but I usually just go ahead and remove them. For demand zones, your zone is the opening price to wick low for your base candle. Supply zone is opening price to wick high for your base candle. Always wait for price to return for trade entries. That's important one to remember. You're not trading right after the sequence is made, you're waiting for price to return afterward.  This one's important also, look for strong reactions when price taps the zones. Example, you wanna see strong wicks showing buy or sell pressure. So if you're trying to see a nice reaction of demand, you're gonna look for nice lower bottom wick shadows pushing up to the upside and likewise for supply, you'll be looking for upper shadow wicks or reversal candle showing strong selling pressure. Now for stop losses, this one's also important. You wanna keep it slightly above wick low for demand zones. So you wanna keep it slightly under the zone because if it breaks under, it's likely gonna go void. Stop loss for supply can be slightly above wick high. So you wanna keep it just a little bit above there because if it closes over with a one day candle, it's likely gonna go void. So that's all I got for you guys today. I hope you guys enjoyed. Make sure you like, comment, and subscribe to our Xtrade's YouTube channel. I'll probably start making a little bit more educational videos if I can. I do make a weekly watch list video every week and I get kinda tired of doing those and I wanted to make something new for the users and I just love teaching and I just really wanted to reiterate supply and demand zones because I made a video on this two years ago. I just feel like it wasn't as quality. I have a better microphone now, better speaking abilities and just overall I have more of a will to teach. So hope you guys enjoyed. I love you guys. Make sure you like, comment, and subscribe to our Xtrade's YouTube channel and I'm out.


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