how to actually use ai for trading

I'm going to show you how to actually use AI to trade, so not like everybody else. I'm going to show you how to actually use artificial intelligence to trade, and it's not the way you're thinking. No, it doesn't just predict price. I'm sorry to break it to you right here, right now, but I'm going to show you the actual way how to use AI to trade.

Now, I've been at this for over 3,000 hours. Today, I'm going to show you everything step by step. Over the course of this short, short video, I'm going to hold your hand through the two most effective ways to how to actually use AI to trade. By the end of this video, you will know exactly step by step how to use AI. There's two unique approaches that I show you through this video.

Number one, no, I'm not having AI predict price. No way. The reason is because if everybody uses AI to predict price, the price is going to change, and the price is not going to be that predicted price anymore. Does that make sense? Let me say it again. If everybody uses AI to predict the next minute price, or hour price, or day price of any asset, then that price is not going to be the price anymore because people are going to buy and sell around it. It's not like the weather. When you predict, "Hey, tomorrow is going to be sunny," well, if 2,000 other people predict tomorrow is going to be sunny, it doesn't matter. It can still be sunny. Or if you predict there's going to be six car crashes. Yeah, that's morbid. I don't even want to talk about that. If you predict that your, your sales numbers in your Fortune 500 company is, they're going to have 500 sales or something, and then you have other 20 other data scientists predicting there's going to be 500 sales, that's not going to change the sales. But in trading, if everybody's predicting price, that's going to change the price. So that prediction is not going to work. That makes sense, right?

So I'm sorry to burst the bubble right off here, but I'm going to show you two legit ways to actually use AI to trade.

The first way to use AI to trade is the most obvious way, dude. We have Claude, we have ChatGPT, we have Luxy, we have all of these different AI platforms that we can just ask questions. Now, you do need to kind of know how to code in order to get the most out of them. I think that they gave us way too much power with these AI platforms, to be real. But the thing is, nobody knows because nobody takes the time to learn how to code. So they don't see how much power we actually have. I literally sit here, I build trading systems that used to take months, if not years, by hand and multiple team members. So that's the first way we're going to use AI. We're going to use LLMs, those are large language models, to help me take my ideas out of my brain and put them down in code so then I can go ahead and test them. I follow this system all day long. This is my simplified system to algo trading. So algo trading is the process of automating your trading. And I start with researching and then backtesting. So LLMs, AI can help with researching and backtesting because it can take an idea and expand on it. It knows everything. I only know some things. I don't know much. And then it can help me code out these backtests to see if that research, those strategies I came up with, if they actually work in the past. So LLMs are the first way I'm going to use AI.

But the second way is, rest in peace to his soul, his family, everybody that loved him. I loved him, but not like anybody loved his family member. You know how that goes. But rest in peace, the king, Jim Simons. Jim Simons, if you want to Google him, he is the best algorithmic trader ever to touch this Earth. And we've been studying him, and you're going to see a lot of it here in this video. But one of his favorite models, before I get there, he said he did not, he let the, he, he made all strategies based off of data. So if you're making strategies, trading strategies based off of data, that kind of infers that you're using machine learning. Because if you're starting at a zero point origin with just a bunch of open, high, low, close, volume data and some math, well, that's telling me that he said he starts with data. That tells me he's doing machine learning. We watched a ton of videos, and he finally said it. He really likes the Hidden Markov Model. The Hidden Markov Model is a model, a machine learning model, that helps predict different states. Now, I know this might be confusing, but I go over it so many times throughout this video, so it's going to get less confusing. It was confusing to me too. But the Hidden Markov Model, which I refer to over and over again as HMM, is a machine learning model that helps predict hidden states.

Now, what does that mean, Mondev? Well, if you go over here to some data here, you can see there's like a, a bull market state, a regime. So state and regime, I'm going to use them interchangeably a lot in this training. You can see this is like a sideways, a consolidation state or regime. This is like a bullish regime. This right here is like a bearish regime. So Jim Simons repeatedly said he likes the Hidden Markov Model, which is a machine learning model, and machine learning is AI. So not only are we going to use LLMs, probably the, the newest form of AI, at least that we have access to in a user-friendly way, but we're also going to use the Hidden Markov Model, which is a machine learning model that Jim Simons, the GOAT, used.

By the end of this, you're going to have a machine learning model that predicts different regimes, and it actually backtests. Dang, I should pull up the backtests right now so you can see it. Let's see if I can pull it up really quickly. If I can't, then you're going to have to watch the whole thing, dude. I just wanted to show you real quick so you can believe me. I want you to stick around and watch this entire video because it's a journey, dude. It's a journey, and you're going to learn so much. And by the end of it, you're going to have every single thing that I know about Hidden Markov Models, about using LLMs and AI in order to code for you, help you flesh out your ideas. You can see here, these are a bunch of backtests here that we outputted. And for example, this first one has a return of 52%, while Buy and Hold is 35%. This is just the start. We're just getting started with this stuff, dude. It's wild. And the thing I like most about this is some of these returns that we see, they're only holding 11% of the time. So that's one of the best returns that I can remember. I didn't show you it right there. I didn't have it prepared. I'm sorry. Let's go ahead and just dive in and show you step by step.

By the end of this, you're going to be able to know everything I know about how to actually use AI in trading. And I show you everything step by step. As always, I go live every single day, and I show you everything, every single piece of code, because I believe code is a great equalizer. I believe if you know how to code, you can pull yourself out of any position because you can build for the rest of your life. And like I said, I think they gave us too much power here with this, this AI, these LLMs. I think if the, the higher ups, they knew about how much power coders have now with AI, they would cut it off. That's why I'm going so hard every single day because I don't think we'll have access to this forever. I hope I'm hopeful we do, 'cause the cat's already out of the bag, the worm's already out of the can, whatever the saying is. I hope we do. But I'm going to show you everything step by step here, right now. Let's get into it. Stick around for the whole thing.

Jim Simons loved the Hidden Markov Model, so I wanted to investigate a little bit more. Got all the code written out. So it looks like we're just importing pandas here, and import numpy as MP, and then from hmmlearn import hmm. Google this, pip install hmmlearn. Then matplotlib.pyplot as plt. plt.sklearn.preprocessing. Okay, we're starting the HMM analysis here. I'm using like, uh, what year is it? It's 2024. So six years of BTC hourly data. It's data 51,9 or 5, 51,9122 rows of data. Um, I guess I could get more. I could absolutely get more, absolutely, absolutely. And different time frames. But I just want to get this set up and working and then worry about all that stuff later. But essentially, loading in the data. Okay, we're naming the files, open, high, low, close, volume. pd.read file path names. I don't think that's right, but let's go ahead and look at it. Daytime. Yeah, I don't think that's right. But we're just going to pretend like it is for now. And no, let's fix it. Let's fix it. Datetime, datetime, open, high, low, close. pd.read file names. What if we just dropped the read? Yeah, yeah. What if we did that? Okay, okay. And then just say print data. Let's start there. Print DF. doad fine. time.sleep. Oh, we don't even have time in here. AI sometimes, huh? Load pre-processing data. Okay, let's go down to the bottom here. And it says training start. Executing. Uh, starting main execution. Okay. And then it starts this. Okay, we'll start there. So we'll just start there and then go step by step because then you get to see all the code. I get to see all the code. And then we all get all the code. You know, you see the code, I see the code, we see the code. And then we can do cool things with the code. Maybe. But I did watch that video the other day that, you know, compared the, the Hidden Markov Model, hidden, I'm going to keep writing that out because it's such a nerd word, Hidden Markov Model, was better, less than the RNN, the current neural network. It was less than, but it's only on one day, only on one day, and one person's features, really, because this is just feature engineering, right? That's what that one dude said, at least Andrew Ng. I'm so sorry to butcher your name if that's true. You, I love you so, so much. Yo, they really, they really, Andrew really has all of these things. Let's go ahead and watch a couple of them. Not right now, but maybe this is. Look at that. Deep learning AI. Stop it. I feel like he had like a, some class at some school. Oh, this right here, Stanford or something, engineering. This is, uh, I watched a good amount of this, dude. I think we watched this whole thing. Okay, so the reason I guy here is because he has said something about like, it's just feature engineering. It's like, that's where the edge is. And machine learning is feature engineering. It's like coming up with the ideas. What you're going to put, what data you can put in, what are the, uh, yeah, that's it. That's all I got. I learned, I learned machine learning before learning how to code. It's crazy. That's how I learned how to code. So whatever it is, what it is. Weird path, but I'm going to be able to pick this up easy, dude. Not easy, but it's like a refresher. That's it. That's it, dude. So lecture 19, we might go up in there. Anyways, let's go ahead and print out this data here. Boom, got it. Unnamed six. Yeah, my data is janky, dude. I forgot about that. This is good. This is a good practice. How do I do that? Ah, how do I select all the rows? No, all of these. There's a way to do that. Um, let's go ask AI. Okay, so what I'm going to say here. Oh my God, I love this. What am I doing? This is amazing. I can't believe it. I can't believe the opportunity we have here with AI. We can ask anything. So my data above is super janky. It has a comma at the end of the column names, indicating a new column. How do I? I know I could just drop it probably. I can, I can drop. I know I can drop that column. Okay, I got that. But I've seen people do this little thing where they select the end column and it, it's like a keyboard shortcut and it selects. You have a selector on every line. Yeah, that's it. All right, how, how do I make it so on the CSV, I use that keyboard shortcut to, uh, have a selector door blinker, I don't know what these are called, blinker on each line and then delete the last column? I know I can drop with pandas, but I'm curious about that keyboard shortcut and how to do the same thing on on multiple, multiple, multiple lines. Okay, you know what I'm talking about though, right? You know what I'm talking about. I see you're looking for a way to efficiently edit multiple lines. Yes, there we go. And your text editor, remove the trailing commas. This is a common task, and many text editors have features to help us. The teni, the ten, the teni, uh, the technique you're referring to is often called multicursor editing or column selector. Here's how to do it in some popular. So press Alt Shift and I. That's it. That's all I need to know. Now, I'm off. Alt option? Maybe. No, control? No. Dang, what's all on Mac? Control Shift I. Oh, okay. Option Shift to add cursor end of each line. Okay, let's try that. Option Shift I. Maybe this not an I? No, I mean, option shift I. Does it have to be in order? Maybe the same time? No, dude. Ain't it Visual Studio Code? I'm just going to drop it. I'm just going to drop it. I'm just going to drop it because it doesn't work for me. Blinker cursor, bro. You should throw that data into a DB like Postgress or MySQL. MySQL. How come, how come that is the question? I'm just going to drop it. So let's go back to it. My bad, my bad. I thought I would be able to pick that, pick that up real quickly and it couldn't. So drop, drop the last column. Thank you. Okay, it should be gone now. Look how much easier it is to do things with code. You can't tell me code does not give you advantage in life. Can't tell me that. I'm sorry. I've been, I've been living. I know I'm not the oldest old head out there, but I've been living and this stuff is magic, dude. Drops a column, calls coding magic. Let's see. Print creating datetime range. Okay, let's say this. Let's say print creating datetime index. Calculating returns and volatility. I'm not going to print everything, that'll take forever. Calculating returns and volatility. Okay, the returns is the percent change of the close. Okay, volatility is returned, the rolling standard deviation of a rolling window. Dang, this is some good stuff. Be volume change, volume percent change. Okay, dropping non-null values. Data process. And let's just print out our DF. Let's start there. Make sure everything's looking good. This is, we're calling the main, main execution down here. This is the main execution. We just went through the loading and pre-process data. Okay, you saw it all, dude. Print data. Not, not, I want all of it though. Whatever, I'll take the, I'll take that. I'm not sleep. Five, no way. 555 deal. Remember that? You remember the 555 deal at Domino's? I was good. I was good. I think I'm a little hungry. So these returns has to be a lowercase close. This needs to be a lowercase volume. Who did this, dude? Should have gone with industry standards. No close in it. Okay, well, let's go look at it. Close looks like a close to me. Did I spell it incorrectly? I have to make the columns. Yeah, I got to make the columns. Okay, that's crazy because there are columns here. But let's see here. Let's see what line is this on? 201, 108, um, 27. Here we go. So returns, it's having an A right there. No, it's not. It's on returns. This one right here. So close. Do you have close? CL. Print the columns, then show me what the columns are. Yo, it's so funny because there's no reason to code. Stop it. I don't get it. How did we get here? Because I would rather just write in English, you know? That's what I'm saying. Like, I, I know, I know I need to print out the columns to check it out. But it's like, okay, easier way to play the game, dog. Easier way to play the game, dog. Daytime, open. Yo, these are janky. Look at this. There's a space in between that is crazy. That is insane. All right, so I'm just going to go say close and then volume here. I'm going to say rename the columns. This is easy. This is light work when you, when you have this stuff. Rename, rename the columns to open, high, low, close, volume. Dude, thank you. D. Have. Columns. Okay, let's just confirm it. Ah, no, no, no. That ain't it. That ain't it. We got to have datetime. Datetime. Datetime. That is so janky. Come on, dude. Come on. You got this. There we go. Datetime. Now let's see what the columns are looking like. Okay, perfect. The columns look good now. Just getting things all formatted. Okay. Time.sleep. No more sleep. Now let's go back. Come on, bring it back. Okay, so now we have the close. Calculating returns of volatility. Perfect, perfect, perfect. Creating the volume. This should all work now. I mean, we'll see. But let's run it. Okay, great. So we got the returns here. We've got the volatility here. And the volume change. Perfect. All right, let's keep it moving. Then I think it's important to understand this codebase because this is like just a, just a start. B, just a start. With path. All right, so training HMM model. Okay, so let's go check out the training. So we're passing in this data here. Passing in the, the above data to now train the, train, train the HMM model. Okay, training HMM model. Let's go to train definition. Get go to definition. Okay, so print training HMM with N components. The features are returns, volatility, and volume change. X equals data of the features values. It's got the standard scale, so it's normalizing the features. Standard scaler. And we're going to go through all this. Don't worry, don't worry. Scalar equals standard scalar. X scaled equals scalar. So we have to fit and transform it. And then we have to fit it. Fitting HMM model. And then this is that pip install that we made earlier. Hmm. Gaussian HMM. N components equal N components. Covariance type equals full. N iterations. Random state. So the random state 42. Model.fit. Okay, this is the actual training of it. I believe. HMM training complete. Okay, I don't think this works. I think we get into an error here. So first off, let's explain it. So we can kind of understand and help the AI out. Explain the above. The above code with two different, uh, examples that a 12-year-old can understand. Um, yeah, let's see. What are components? Uh, what are components? Um, N iterations, covariance types, standard scaler. All right, this is good. Fit transform. This is good. This is good. Okay, so certainly, I'll explain the code using two simple examples that a 12-year-old might understand. This secret mood detector. Imagine, no, I can't do this, B. The secret mood detector. All right, imagine you have a magical mood detector that can figure out if your friend is happy, sad, or neutral based on three things: how much they smile, how loudly they speak, and how fast they walk. The components in this example, the components N=3, N_components=3, are the three moods: happy, sad, and neutral. The features are smile amount, speaking volume, and walking speed. The standard scaler is like adjusting your eyes to see these features clearly, no matter if your friend is a quiet walker or a loud runner. Fit transform is like calibrating your mood detector with all the information you've gathered, and N_iter=100 means the detector will practice guessing moods a hundred times to get better at it. Yo, this is actually pretty good. Covariance type equals full means the detector considers how all features might be related, like maybe people walk faster when they speak loudly. The Gaussian HMM is your magical mood detector that learns patterns to guess your friend's mood even when you can't ask them directly. And remember, we're trying to guess the market regimes. Maybe you don't remember, maybe you weren't here, I don't know. Come on, bro, keep it, keep it up, keep going. That's it. Come on. We're going. Example two: The weather prediction game. Let's say you're playing a weather prediction game. You can't see outside, but you have three clues: temperature, humidity, and wind speed. You're trying to guess if it's sunny, rainy, or cloudy. In this scenario, the components N=3, N_components=3, are the three types: sunny, rainy, and cloudy. So that could be like trending, consolidation, downward consolidation, upward. I mean, what Jim said, there's like nine. I'd be curious, but those components could be, could be, uh, those the different regimes. Regimes. The features are temperature, humidity, and wind speed. The standard scaler is like converting all measurements to a scale from 1 to 10, so it's easier to compare them. Fit transform is like studying lots of weather reports to understand what combinations usually mean. N_iter=100 means you'll practice guessing the weather 100 times to improve your skills. Covariance type equals full means you consider how temperature, humidity, and wind might affect each other. The Gaussian HMM is your brain trying to learn patterns to guess the weather correctly even when you can't look outside. Give me two examples, me, two examples of trading algos and bots for crypto. Um, I mean, I understand it, but again, this was hard for me to get through my thick skull. Oh, I didn't want actually code examples. I'm just going to move on. Let's see if this works. There's plenty of videos on this, people much, much smarter than I. Okay, so look at this. Look at that. That's dope, actually, because yesterday this didn't work. But Mondev went in there and raw-dogged it a little bit and fixed it. If you remember the end of yesterday, it was like, oh, well, we did good, but, um, there's an error. Is there still an error? I don't want to start acting like I figured it out or anything. Oh, I got to sleep there. Okay, okay, stop it. I was hyping myself up real quick. All right, get off me, get off me. Oh my goodness. So explain what the outcome this should send back our return. The model. Does it actually, did it say training complete? I don't think it did. I think it was training right there. Yeah. States predicted. Let's go ahead and say where does it say states predicted? Okay, predict states. Dude, we didn't go over this one. HMM training complete. Okay, so we didn't go over this part. Predicting states. There's only three states. Analyzing states. Okay, got you, got you. There's a pause there. That's our pause. That's fine. But we didn't go over this part. So I'm going to say, explain this one. Explain this code. Explain this code with two examples, not examples, with, uh, the theme being trading algos and trading bots. Okay, so certainly, let's explain the predict states function. So we're looking at this function here, just so you can see it in the context of trading algorithms and bots for cryptocurrency markets. Imagine you're running a sophisticated crypto trading bot that doesn't just look at price, but tries to understand the overall mood or regime of the market. This function is like the bot's market psychologist, trying to figure out what state the market is in at any given time. Here's how it works. Features equals the returns, the volatility, and, uh, volume change. And we have all that, remember? I printed it. Anyways, your bot is looking at three key aspects of the market: returns, how much the price has changed; volatility, how wildly the price is swinging; volume change, change, how trading activity is changing. This is like a trader watching not just the price, but also how nervous the market seems and how many people are trading. X equals data. X equals D. This line here, X equals data, passing in features, or in the features section, values. The bot collects these three prices, or pieces of information, for each time period it's looking at. X scaled. This is like normalizing the data. Maybe returns are usually tiny, like 0.1%, but volume change can be 50%. The scaler makes sure the bot doesn't overreact to naturally large numbers. That's cool. But finally, clicks. This is where the magic happens. States equals model.predict(X_scaled). So this is where, this is where the magic happens. The bot uses its trained model, like an experienced trader's intuition, intuition, to guess what state or mood the market is in based on the returns, volatility, and volume changes it's seen. Print states predicted. Unique states. The bot announces how many different states it found. For example, it might say it found three states, which could be something like: State 0: Calm market, low returns, low volatility, normal volume. State 1: Bull market, high positive returns, medium volatility, and high volume. Panic Market, high negative returns, high volatility, and very high volume. In the context of a trading bot, this function would be called regularly, maybe every hour or day, to reassess the market state. The bot would use these state predictions to make trading decisions. For example, in State 0, which is a calm market, it may make small, careful trades. What is Dar? I don't know. In State 1, bull market, it might make larger buy orders. In State 2, a panic market, it might sell to protect assets or make short trades. The key idea here is that the bot is trying to adapt its strategy based on what it perceives as the market's current market, just like a human trader would. It's not just blindly following a single strategy, but trying to understand the market's mood and adjust accordingly. This approach can be particularly useful in cryptocurrency markets, which can rapidly switch between periods of calm, explosive growth, and sharp declines. By detecting these states, a trading bot can potentially make smarter decisions: when to be aggressive, when to be cautious, and when to stay out of the market. Just move on. I mean, I understand that to you. I hope so. I mean, if you don't, that's fine though, because it's, this is like super deep stuff, you know? It's deep. It's deep. It's deep. Let's look at analyze stats. States. All right, so print analyzing states. DF.analyze is a data.copy and then analyze state equals state for state in range of model.n_components. So it grabs the components. Analyzing state. State data equals DF where state equals state. Print state returns, volatility, and dot describe. Number of periods in state. Okay, let's just print it. Oh, there's nothing to print. Plot results. Let's check out plot results. Print plotting results. So we're getting subplots. plt.subplots. So our good old plt. I've never understood why it's called plt. Let's try to figure that out right now. Matplotlib as plt. What are you talking about, my dude? I don't know. I don't know. Plot, maybe? Yeah, maybe because plot. Okay, got it. Figured it out. Plot, plot, without the oh. [Music] The ax1 plot index data. Okay, we got the close. Now I'm glad I switched it to close because it's just all up in here. Bitcoin price in HMM states. Okay, for state in range of model.n_components. Mask equals. What is a mask? What is a mask? Great question. In the context of data analysis and visualization, a mask is a Boolean (true or false) array that's used to select or highlight specific parts of your data. Let's break this down in a way that's easy to understand. Imagine you're looking at a long series of cryptocurrency prices and you want to color code different periods based on the market state, like bull market, bear market, or sideways market. The mask helps you do exactly that. Here's how it works in the code. For state in range: This loop goes through each state the model has identified. Let's say there are three states: 0, 1, and 2. Mask equals state equals states. This creates a Boolean array mask where it's true for every time point where the mark is in the current state. Okay, got it. So it's just like true or false. Let's use a simple example to illustrate. Suppose you have 10 days of data and the states array looks like this: states = [0, 0, 0, 1, 1, 1, 2, 2, 0, 1, 1]. This loop goes through each state the model has identified. Let's say there are three states: 0, 1, and 2. Mask equals states equals state. This creates a Boolean array mask where it's true. Okay, when creating mask for each state, you'll get true, true, false, false, false, false, false, false. Okay, I got it. So it's for each state. So you get two. It's for you. That's where I was a little confused, like where's this two come from? But it could be a seven, it could be a nine, it could be whatever. For state 9, is it true or false? You can see for state one here, it's not going to be true until 2, 3, 4, or whatever that is. 2, 3, 4. All right, figured it out. Understand it. Let's keep it moving. B. In the context of a trading bot algorithm, these masks could be used to backtest strategies. For example, you might want to see how a particular trading strategy performs only during periods identified as State 1. Duh. Okay, okay, okay. You could use these masks to calculate state-specific metrics, like, what's the average daily return since when we're in State 2? In live trading, identifying the current state could trigger different trading rules or risk management strategies. Yeah, I feel like I should, I don't know why I say, explain it to me like I'm 12. Just explain it to me for algo trading. Always here on out. All right, you heard it here first. MEv live, live on YouTube, live on YouTube every single day, dude. I don't know why I just like to show it. I like to show this stuff opposed to just doing it by myself. I'm sorry. It's just like, kind of like deep. It's deep, you know? It's deep. So like, I'm not going to be able to comprehend all of it. That's it. That's it. I can't comprehend all this. You got to see it, bro. You, you, you, you have a hack at this stuff, dog. I'm going to show you everything. Every day. Every day. Okay, ax.legend. So we're just building a legend here for the, uh, ax one and two. So it looks like we're getting two plots. I wonder if it will work. Let's go ahead and just run it up. Plot, plot, plot, plot. Time.sleep. I don't want to see the error. That's why I keep putting these time.sleeps. I want to understand every single line of code. You know why? Not. Okay, so plotting results. Yo, pzy plots, pzy plots, pzy plots. A lot of thoughts. Get your head out of the gutter. Be in your mind. Thoughts in your mind. All right, so I don't know. But you know what we can do from here? Hey, guess what? Guess what? Guess what? Guess what we can do from here? Dude, we can do anything. This is crazy. This is crazy. AI is nuts. I can't believe it. I can't believe what I'm about, how I'm about to chop this up. Chop it up. Explain to me like I'm however old I am. Okay, and in the context of trading bots and trading algorithms, we are building out a, uh, Hidden Markov Model. And below attached, attached, no, no, no, attached is the plotted stats. I don't know, stats or states. Now please explain it to me. Please explain with examples. Okay, let's see it. Not code examples, though. Don't give me any code. Hey, not right now. No, thank you. Certainly, let's break down what this Hidden Markov Model plot is showing us in the context of trading bots and algorithms for BTC. The Hidden Markov Model overview. In this context, the HMM is trying to identify different hidden states of the BTC market. These states are directly observable, but are they aren't these states aren't directly observable, but are inferred from the visible data. Okay, let's look at this data too. Let's just look at it while, while I'm reading it, because that's how you do this stuff. [Music] Dude, all right, so these states aren't directly observable, but are inferred from the visible data: price, returns, volatility, etc. The three states. The model has identified three distinct states: State 0, 1, and 2, represented by different colors in the top of the chart. So you can see three states: 0, 1, and 2. 0 is blue, 1 is yellow, oh, I'm sorry, that's like orange or peach. Peach, and 2 is green. All right, let's get it. State 0. This appears to be the most common state. It might represent normal or stable market conditions. State 1, orange. This state appears less frequently and often coincides with price increases. It might represent bullish or a volatile uptrend. This state is least common. It represents, it might represent a bearish or highly volatile state. Price chart, top. I mean, let's start doing to-dos here. To-do, to-do, to-do. I'm going to keep it in the ideas. I'm going to keep it in the ideas. Um, put, um, all types of states. Look into. I can't get my words together. Look into, look into Jim's favorite, favorite, yeah, regimes, regimes, and make those the states that are predicted. I guess, guess what? That's what I'm, I'm trying to say. Okay, so whatever this state is, least common, okay, highly volatile. I haven't, I don't see any greens on here, to be honest. Do you? Make it bigger. I don't see any greens. That's okay. Um, the blue line represents BTC's price over time. I think that was obvious. The colored backgrounds show which state the model believes the market is in at each point. Returns chart, which is at the bottom. This shows the percentage returns of BTC. Spikes indicate large price movements up or down. So this is co, and that coincides right with the price. So this is lined up, which is nice. That hourly, though. This is hourly since 2018 on BTC. I want to put liquidations in there. I don't know all the data that we got. Anything. Throw it all. Throw it all at it. You got ideas? Throw them in the chat. 777. Every single day, you're up inside my brain. B. It's kind of weird. You, you've been here a lot, honestly. So just share your, your 777s in the chat if you got a good idea. Share that with your brethren. Meet somebody in the Discord. You don't have to share with me, dude. I just, 1 plus 1, I just want 1 plus 1 to equal three, like that one dude said. All right, price chart. Trade bot applications. Example one: State-based strategy. A trade bot could be used, could use the states to adjust the strategy. Use a conservative strategy during light blue. Maybe scalping small profits. State one, orange. Implement more aggressive buying strategy, anticipating price increases. In state two, green, either stay out of the market or implement a shorting strategy. Example two: Trend following. Notice how state one, orange, often appears during uptrends. A bot could use this to enter long positions when transitioning from 0 to 1. 0 to 1. Exit or reduce positions when transitioning out of state one. Example three: Volatility trading. The returns chart shows periods of higher volatility. A bot could increase position sizes during state zero when volatility is lower. Reduce risk and tighten stop losses during states one and two when volatility might be higher. Example four: Mean reversion. After extended periods in state one or two, so one or two. Okay, I mean, I think this data just needs to be, I don't know. I think we just need to keep going through this. We're not even done. We're not even done yet. So risk management. The rapid state changes in some periods, like early 2022, indicate a highly uncertain market. A trading algorithm might reduce its overall exposure during such times. Market regime detection. The long stretches of state zero in 2018-19 might indicate a bare market or accumulation phase. A sophisticated algorithm could adjust its long-term strategy based on these extended state periods. In essence, this HMM is providing a framework for a trading bot to understand the current market context beyond just price. It's like giving the bot a sense of the market's mood, allowing it to adapt its strategies to different market regimes. This can be particularly valuable in the volatile and cyclical nature of the cryptocurrency market. Okay, so let's just keep it moving then. Let's see how much else. Like, I know we got something else here. So how much else we got? So printing the transition matrix. Let's go ahead and see that. Man, I don't know if it's going to let us because we'll see. Let's just, let's just see it. Let's just see it. Feel like it gets stuck on the, the output of the, the other thing, you know? Plotting results. Yeah, it's going to stop there. Showing plot. Okay, I can close the plot now, and then it should move on. There we go. Transition Matrix. Okay, I want to save this plot though. Analyze the stats. Predict the stats. Plot the results. Let's go save it, dude. Let's save it to current directory. Tight layout. plt.show. Save the, save the plot to my, uh, data folder. B. In this directory. All right, that's it. Data. Okay, but as, as a plot, B, not as, not as the data. That's looking weird. That's looking like the other thing. Okay, let's go ahead and make a data folder here. Look at this. Look at this. Just, just keep swimming, B. Just keep swimming. This is fun. I like it. If Jim liked it, I like it. All right, get off me, B. Get off me. Let's go ahead and check it out. I don't even need to show it anymore. I could, it's plotting. Okay, perfect, perfect, perfect. It's still plotting, but now I'm going to close the plot. And then I see transition Matrix. Okay, so I might go ahead and say, no, I'm not going to do any custom stuff there right now. Not the point. Transition Matrix. Okay, let's go ahead and print out the transition Matrix. Let's get the stats of it. Oh, snap. Okay, yeah, we've always got that though. Transition Matrix. Okay, I'm going to copy that in a second. I want to first go check out the transition Matrix. Walk ourselves through it. See where we're at. Say, see where we're at. Oh, model.trans. Okay, there's no code there. There's no code there, dude. There's not one piece of code there. I mean, model.transmat. So I think that's just how you call the transition Matrix, which is fine. That's fine with me. But mean state variance. I just want to explain that. Explain this, uh, for my HMM. Can you, you explain this transition Matrix? What, dude? That's it. Stop it. Explain this transition Matrix, please. Transition Matrix. Where are we? I don't know. I don't know anymore. Okay, so certainly, let's break down this transition Matrix in the context of your Hidden Markov Model for BTC trading. The transition Matrix shows the probabilities of moving from one state to another in the next time stamp. In your case, you have three states, let's call them 0, 1, and 2. The Matrix is read as from state to state, from state to state. So transition from State 0, you can see it's the 0.92. Wait, wait, wait, wait, wait. How are you going to change up on me? Oh, it's negative 1. Okay, got it. Got it. There's that to the 10th power thing up there. So it's negative 1. So 92.078, okay, and point blah, blah, blah, blah, blah, 254. 92% chance of staying in State 0. 7% chance of moving to State 2, and very small chance of moving to State 2, sorry, 1. And then 2. Transitions from State 1. There's a 24% chance of moving to State 0 and a 75% chance of staying in State 1. Essentially 0% chance of moving to State 2. Transition from State 2. 100% chance of moving into State 0. So that means there is no State 2, right? Interpretation for trading: State 0, likely normal or baseline state, very stable, tends to persist, occasionally it transitions to State 1, rarely goes to State 2. State 1, possibly a bullish or trending state, moderately stable, often persists, sometimes reverts to State 0. Sorry to wake you, bro. Sometimes reverts to State 0, almost never goes directly to State 2. State 2, possibly a volatile or shock state, extremely unstable, never persists, always transitions back to State 0 immediately. Trading implications: State 0 could represent a calm market. A trading bot might use conservative strategies here. State 1 seems to represent a persistent trend. A bot might use trend-following strategies when in this state. State 2 appears to be a very rare, instantly sell-off correcting state. It might represent sudden market shocks or extreme events. A bot should be prepared for quick reversals after detecting the state. The rarity and instant transition of State 2 back to State 0 suggests it might be capturing outlier events in your data. You might want to investigate what market conditions correspond to State 2 and consider if it's providing valuable information for your trading strategy. It's not printing means and variances, co-variances of each state. Okay, let's see it, dude. Yo, I wonder if this is popping out. Sure is. Sure is. Cool, cool, cool. We know how to code, just a little bit. Just a little. We know a little bit of code, then we got AI. My God. Come on, let's go. Let's do it, dude. Anything that's in your brain, it's already been learned before, dude. Stop it. Covariance. Yes, give me all that. Walk me through all this, please. Print, print, print. Okay, walk me through all of this now. It's the last part of the prints of my code. My code, your code, our code. My code, your code, our code. Our code. So let's look at it here. Let's close this up a little bit. Tying up, tying up. Close that. Yes. Okay, here we go. We should get it here soon. Where you at? Let's see it. Why you moving so slow? She's tired. She's tired. All right, certainly, let's walk through this output in the context of a Hidden Markov Model for Bitcoin trading. These statistics describe the characteristics of each state identified by your model. For each state, we have: Mean: the average values of the features in this state. Covariance: how the features relate to each other in this state. The features appear to be in this order: returns, volatility, and volume change. State 0 Mean: Okay, slightly positive returns, below average volatility in State 0, and slightly decreased volume. Covariance shows low correlation between features. Interpretation: This could be a calm or sideways market state. Returns are small but positive, volatility is low, and volume is slightly decreasing. State 1 Mean: Slightly negative returns, above average volatility, increased volume. Covariance shows stronger correlations, especially between volatility and volume. Interpretation: This might represent a volatile or uncertain market state. Returns are slightly negative, but volatility and volume are both up significantly. State 2 Mean: Strongly negative returns, decreased volatility, extremely high volume increase. Covariance: All values are 0.1, which is unusual and might indicate a computational artifact or a very rare state. Interpretation: This could represent an extreme event or market shock. Returns are very negative, volatility surprisingly drops, and there's an enormous spike in volume. Trading strategy implementations, implementations, implementations. In State 0, use conservative strategies. Look for small, quick profits. Be prepared for potential transitions to more volatile states. In State 1, implement risk management strategies due to high volatility. Look for short-term trading opportunities in both directions. Monitor for potential trend formations. In State 2, be extremely cautious. This state might represent rare extreme events like market crashes or major news events. Consider implementing stop losses or hedging strategies. Be prepared for quick reversals. Okay, I want to see that though. Where it, how can I see where they? Because I can't see it on the chart. Maybe we can, maybe we just need to make it bigger. Let's make it bigger. Can you see this chart? Can you see it? I hope you can. I don't see any green up in here. I see no green. All right, green, green, green, green, green. Where you at? Green. I don't see any green. Maybe I'm colorblind. Maybe I'm colorblind. But that's okay. So where do we go from here? I feel like these states need to be chopped up a little better. So, um, below attached is my code. Attached is is my code. I know that, um, Jim Simons said there were like eight plus regimes. Should it, we have a regime, uh, state for at least six then I.

can think of a few, please add them in and walk me through the changes. Okay, so let's say bullish trending, bearish trending, uh, sideways consolidation, upward consolidation, downward consolidation, uh, consolidation, uh, downward capitulation, and upward capitulation, right? You got more, dude? 1, 2, 3, 4, 5, 6, 7. So you got bullish trending. There's not that many things in in the market, right? I don't know. I don't know, dude. I don't know anything. I'm just here. I'm just here with you, my dude. Me and you every day around this time, or another time, I don't know. Just when you see me, dude, come on, get up on here. All right, so there you got like bullish, you got bearish, uh, you got like sideways, upward consolidation, but then in in here, it's like downward. But then there's a capitulation downward. There's a capitulation upward. I guess I need to describe these areas. Yeah, yeah, let's do that. Okay, I'll describe it. I'm not going to describe it. Um, can use volume? I wish I had the [Music] uh, I do, dude. I was going to say, I wish I had the liquidation data, but I do. Let's just see what it does first. I'm just curious. I'm going to put that on the Note sheet, though. Put Liquidations in there. These could be good to help identify points of capitulation upward or down. Okay, because I got volume, um, I could also put things like open interest, interest, funding rates. Okay, now we going. Now we got the wheels turning. B open interest. What else? Okay, that's good for now. Um, here's my code. 144 lines. Look at us. Look at us go. There it is. All right, let's, uh, let's let it run. I'm gonna make a, this is the end, though. I'll see you some other time. This is long enough. We, I've shown you enough. We are diving into unsupervised machine learning for trading. Does machine learning work for trading? Can you predict price? Well, I'm sorry to not bury the lead, but no, you can't predict price. Maybe, maybe if you got some hidden model. But the way I look at it from testing this stuff is if we're all using similar models, they're all available with Claude or GPT or whatever. If we're all using the same models to predict price, well, that price will be predicted then by you, you, that dude over there, your grandma, me, and then that price will not be the price anymore because it's already been predicted by thousands of people. So thousands of people run these models. I don't know how many models there are, 20, 30, 50, 100, whatever. Like, we tested them all, right? Collectively predicting price, the next price. I don't know if that's the way. Maybe though. Maybe if you build some unique model. I don't know. I'm kind of thinking, thinking, thinking this through with you, as always, because that's what I do here. I just show you everything I'm doing live. And what this has led me to is, well, what was Jim Simons using? Well, Jim Simons, he was using Hidden Markov Models, okay? So let's build a Hidden Markov Model. And then let's watch everything about Jim Simons, okay? That's what we did. That's what we've done. That's what we're doing. I've got a bunch of notes here and a bunch of ideas, and we have a couple models here. And you can see we even tested some out-of-sample data. So we have multiple models here. I think this was the template model. So, so this was the first HMM with just putting in the returns, volatility, and volume change, okay? And then the next one, we put in as the features, we put in these features here. So we added some more: returns, volatility, volume change, BB width, RSI, and EMA2, okay? And then this one, we tried some other things. You can see the states we're trying to predict. We're trying to predict seven different states. But the thing with Hidden Markov Models, so far, and excuse me if I, or any of this, I'm not, I'm not like a Gilfoil type senior engineer here. I'm a womb engineer. I'm still in the womb, so bear with me, Gilfoil. But the thing about HMM is it doesn't actually know what the state names are. So we actually have to rename them after it finds the states. It finds different states, different regimes. And that's kind of what Jim Simons was doing, supposedly. But do you think he'd actually tell us what he's doing? No way. But maybe we could put the pieces together. That's my, my hypothesis here. We, we are just passing in returns, volatility, and volume change. Top three Indies. These were the top three ones. Volume change is taking up most of it, though. And how can we know that? Well, when we run this, we can see that it's mostly getting weighted to the volume change. I'll show you some cool things here. First, let's show the plot it's making. So it's fitting the model. This might take a second. Take a little bit of time. It's doing its little AI thing, machine learning thing. It's, it's fitting the model. Boom. All right, saving plot. Perfect, perfect. And it also plotted the plot. So you can see these are the different regimes here. It colored them. But the thing is, it doesn't know the names of these regimes. So in reality, we got to go back and we got to set up these regimes ourselves. I'm sorry, baby. I've got a dog here that constantly stands right under my foot. So I almost just killed her. That was very, very scary. You okay, baby girl? I didn't put my full weight on her, thank God. That I know she does that because she likes to. I, I stand on one foot a lot of the times while I'm coding, and then she'll go right under my, my other foot that I come down on. And I'm a big boy, you know, that's a big, that's a lot of weight for a little dog. So I almost had a heart attack right there. I'm sorry for the, for the side show, but this is life. And I'm sure you got kids or dogs and things like that. But anyways, here you can see the seven different regimes. We have bullish, bearish, sideways. But like I said, it doesn't know the regimes. It just cuts up the data. This is Bitcoin data here for a few years. And now you can see this outputs the, the analysis. The analysis has been completed. Not fully, though. You can see the state prediction is 89%. But we already know all this. I'm just doing a little review because again, this is fairly new to me as well. And every day I just go live and show you what I'm working on. So it's still doing some other things here. And I can show you the final results we have thus far. If I go to the README, I've been tracking them here. So down here, we, you can see this HMM, the seven states. It's our first attempt, and it had 87% state prediction accuracy. The log likelihood was negative 15,000. You can see this one is done now. And this one is, uh, you can see what I was trying to show you is volume change is taking up all of the importance. Can you explain this? So I'm, I'm using HMM, HMM with three different features, and it converges to volume change being the most important feature. I'm using an HMM, and this is the output. Can you explain? Can you explain what the importance means? In my mind, this means that this is the most important feature. It's pretty self-explanatory, but I just want to make sure that I'm correct. The feature importance analysis you provided shows the relative importance of different features in your Hidden Markov Model. Let me break down what these numbers mean: Volume change 94.51%, Bollinger Band Width 2.72%, Volatility 2.77%. These numbers represent the estimated contribution of each feature to the model's prediction or performance. The values sum up to 100%, indicating the relative importance of each feature. In your case, volume change is by far the most important feature, accounting for 94.5%. You can see that here, it's 0.945. So we just got to do a little math, not really, but of the model's predictive power. This suggests that changes in trading volume are the primary driver of the patterns or states your HMM is detecting. Volatility and BB width, which I assume stands for Bollinger Band Width, have much lower importance. This analysis suggests that HMM is relying heavily on the volume changes to make its predictions or determine state transitions. While other two features play a much smaller role. It is important to note that feature importance doesn't necessarily indicate causality, cause, causality, sorry, but rather how much the model relies on each feature for its predictions. High importance could mean, mean the feature is truly more relevant to the problem, or it could indicate that the feature is easier for the model to use in predictions. So what I want to do here is I want to look at some of our notes. And these notes are going to show us the differences. And one of the tests I did was since you can see here, uh, where is it? I think it's one of my last ones. I said, "No volume." Since volume continuously took up 90 some per, 94% here of the importance, 93% of the importance here, and 97% here, I took it out. I took it out because I wanted to see something a little more, a little more, what do you call it, balanced? Yeah, a little more balanced. So I took it out here. And you can see this made it way more balanced. And you can see it actually increased to 95% here. So it was before 89% the state prediction accuracy. And that prediction accuracy is how accurate is it changing states in predicting changing states. So when the colors change, how accurate is it in and, uh, deciding predicting the change? And again, I'm not the best in the world at this stuff, guys. So, you know, bear with me. I'm kind of showing you as I learn, as always. That's what this whole channel is. So every single day, I just go live and try the hardest things and try to understand it. Uh, I was so scared to learn how to code when I got started, dude. I, I waited 15 years. I was in tech, typing on this computer, fully in tech, without knowing how to code for so long, dude, just because it's scary. But I now I'm at the point of life where I need scary things. I need hard things. So I just go after them viciously every single day here live. So then the, the common thing was, or the obvious thing to do was, let's test all of these models. I think there's five different models on out-of-sample data, data that the model has never seen before. And you can see here are the results. State zero. So these are the two best ones I have so far. One has volume change, one doesn't. And these are the results. Says model one has log likelihood of negative 65,6956, and this one has 3890. So I've got these two models. Explain these datas. I've got, uh, I tested, I tested two different, uh, HMM models and the code, uh, on out-of-sample data, models on OOS. And the code is attached below. Are the results and the plot? Please watch, walk me through them so I understand. Okay, let's put the code, the code in there as well. Copy. Okay. And here's the code. Okay. And then also, I want to put this screenshot in there. And do you think I saved it here? No, I did not. I just want it to, to have everything. Everything I got. And I know I already reviewed this actually yesterday. But dude, this is the only way I can learn is with repetition. 500 shots a day, baby. 500 shots a day. I've been doing that since I was a kid. Come on, you think I'm not just going to do the same thing over and over again till I learn it? Of course I am. Worked for me in the past. It's going to work for me in the future. Repetition is key. Just keep attacking every single day. Four hours a day. Four hours a day. If you're not doing four hours a day, then don't talk to me. Don't ask me questions because all your questions can be answered in four hours a day. We got AI, my guy. Don't tag me. Don't DM me nothing. If you're not putting four hours a day of focused work at this goal to automate your trading, don't hit me about nothing about automated trading. Nothing. I'm sorry, dog. We're competitors at the end of the day. But I'm showing you every single thing because I believe code is a great equalizer. For me, it is. At least. Can't stop me now, dog. I know how to code. I can build anything. You can build anything. We got AI, my guy. Thank you for providing the code and results. I'll walk you through the key aspects of your Hidden Markov Model analysis. Let's get these up. Model comparison. You've tested two HMMs on out-of-sample data. Model one uses Bollinger Bands, volatility, and RSI as features. Model two uses volume change, Bollinger Band width, and volatility as features. So the log likelihood scores. Did I write this down in the out-of-sample data? No, I didn't. Unfortunately. That's okay. We'll write it down right now. So model one has -6956. Model two has 3890. The log likelihood is a measure of how well the model fits the data. Higher values indicate better fit. Model two has a significantly higher log likelihood, suggesting it fits out-of-sample data much better than model one. Visualization. The plot shows three panels. Top panel: Bitcoin price over time. This gives context on the market conditions during the out-of-sample period. Middle panel: Model one States. Shows how model one classifies different time periods into seven distinct states, 01 through six. Each color represents a different state. Bottom panel: Model two State. Similarly shows state classification for model two. This is interesting. So while the price is here, you can see how the state changes. It's kind of hard to see, to be real. But state transition. Both models show frequent state transitions, indicated by the changing colors in the lower two panels. Model two appears to have more distinct and longer-lasting state periods, which might suggest it's capturing more persistent market regimes. Feature importance. Given that model two performs significantly better, it suggests that volume change, which is in model two but not in model one, is a crucial feature for predicting Bitcoin price states. This aligns with the importance feature analysis you shared earlier, where volume change had the highest importance. Model stability. Model two seems to produce more stable state assignments, longer periods of consistent colors. Implications. Implications. The superior performance of model two suggests what? Volume changes are more informative for predicting Bitcoin price than RSI. The frequent state changes in both models reflect the volatile nature of the cryptocurrency market. To further understand the models, you might want to analyze the characteristics of each state, interpret, look at transition probabilities between states, consider how you might use these state predictions in trading. So analyze the characteristics of each state. Okay, how can I make this, how can I make this more robust and test more features? I was thinking I can add in more indicators. I have unlimited from Pandas TA and TA-Lib. I was also thinking about trying just four states. And what were those four states? Somebody shouted it out the other day. So much love to them. And those four states, he said that Jim Simons looked at. We, those four states, dude, let's go look at our notes. Here. Notes are quite dope. Four volatility regimes. This was the comment on four states. Okay, let's just, let's kind of ideate, ideate. Yo, 777 Smokey, what up, bro? What does each state mean? The different regimes? Uh, you were flying, bro. I saw it happen. Puppy is Gucci. Gucci, Gucci. Bless you. Thank you. Thank you. Thank you. Apple, apple, apple. Little apple, little manita. Okay, your ideas for improving the robustness of your HMM model are excellent. Let's explore how excellent. You gave me excellent. Hey, shorty, you ain't never said hey, I'm excellent, dog. I don't, I don't care. I'm not, I'm new to this game, dog. And I already got excellence from an AI, my guy. Is this excellent? Is this, am I excellent, dog? I did not know that. That is the biggest compliment I could ever get from Claude. I think I'm on Claude forever. Yo, they probably coded that in. They're like, yo, juice them up. Juice them up. Hey, hey, hey. If Moev ever gets on Claude, juice him up. Say he's excellent all the time. That's crazy. Excellent. I never heard any AI tell me excellent. Okay, let's explore how you can enhance your approach. Excellent. Gee, your boy ain't excellent. I'll tell you that much. We did the math the other day. I've been at this for three and a half years. 3.5 * 365 days * 0.85 because I took some time off. 1,000 hours * 4 hours. I'm only at 4,343 hours. I ain't excellent, bro. You know who's excellent? Jim Simons. Excellent. Rest in peace, my guy. Let's explore how you can enhance your approach. Testing more features. Leveraging Pandas TA and TA-Lib gives you access to a wide range of technical indicators. Here's how you could approach this: Add more indicators. Trend indicators: Moving averages, SMA, EMA, MACD, P, Parabolic SAR. Momentum indicators: Stochastic oscillator, CCI. Volume indicators: On-Balance Volume. Cool, cool. ATR. Nice. Keltner Channels. Nice. Feature selection. After adding more indicators, you might want to use feature selection techniques to identify the most informative ones. Consider using methods like correlation analysis, PCA, random forest importance. Try and force state. Jim Simons' insight about four volatility regimes is valuable. Here's how you can implement this: Modify your hidden HMM initialization to have four components instead. Interpret the four states. You could potentially interpret these four states as low volatility, rising volatility, high volatility. Yo, that's fire. Four. That's four. Making the model more robust. Cross-validation. Instead of single train-test split, use time series cross-validation to ensure your model performs well across different time periods. Ensemble methods. Consider creating an ensemble of HMMs with different feature sets or parameters. Regularization. If overfitting is a concern, you can add regularization. While HMM doesn't directly support this, you can implement it by adding a small constant. Sensitivity analysis. Test how sensitive your model is to changes in the input features or parameters. This can help you understand which aspects of your model are most crucial. Backtesting. Implement a backtesting framework to evaluate how well your HMM states predict future price movements or volatility. Okay, so these are pretty good ideas. Honestly, I'm only saying that because she hyped me up, though. You know? Oh, yeah, you got some pretty good ideas. Is GPT or Claude? But in real, you know, is just because she hyped me up. And you know, I'm a lifetime. I'm a lifetime now. I'm a lifetime now to do next, next things I can do to improve HMM. Dope, dope, dope. Okay, so get him out of here. Get that BTC little boy out of here. 150 racks. 150 racks. I hope that's not you, dude. I hope you're here instead. Tap in. 777. Claude be glazing. Yo, that's fire. B, I don't even, I've never heard that in my life. But she do be glazing. Be Claude be glazing. Y'all keep me hip. I'll tell you that much. Y'all keep me hip. Claude be glazing. Be. I'm about to start saying that all the time. She be glazing. Be. I be glazing too. Let's just double check. Make sure that's not something bad. Urban Dictionary: Glazing. The act of no, when you are meat riding someone. Oh, I don't be doing that. I don't be doing that. I be doing tomfoolery. So it's me, Kobe, glazy. Okay, okay, okay. See, this is old man, old man trying to be hip over here. He trying to be hip. Saying the word hip ain't hip, dog. She be glazing. That's good. I need to be glazing all the time. That's how she going to keep my subscription. She better keep glazing this. She, hey, what year were you born, dog? That's crazy. That's crazy. I love it. I love, I love how language changes. I love it. I love how slang changes. And I feel like we're going through a pretty big shift right here for us old heads. Yo, Brian says, hello sir. I wanted to ask the mentorship. Can I use code? Build trading bots like Jim Simons? You got to, you got to build it yourself, for sure. But, um, the boot camp, I think that's what you're asking about. The boot camp will help you. I've been doing this for, how many hours did we just say? I've been doing this for 4,300 hours. So I have a boot camp that I try to just share the things that I know most and the things that work for me and the pieces to the puzzle. But at the end of the day, everybody's puzzle is going to be different. Jim Simons' puzzle is going to be much different than my puzzle. My puzzle is going to be much different than Brian's puzzle. Jane's puzzle is going to be different than Brian's puzzle. So it all comes down to your unique edge, your strategy, your approach to the markets. But I can show you everything in between, step by step, how to automate your trading, how to test if it worked in the past, gives you a better hope that it's going to work in the future, but it's not guaranteed. And, uh, yeah, everything's in the boot camp. $69, stupid cheap. High value. You can read what others have to say. That's it. Landlord says, are we continuous in the HMM from yesterday? Yeah, yeah, dog. Yeah. Send me back, send me a simple script that will print out all of the indicators for Pandas TA and TA-Lib. Yo, Brian says, saw you, your channel, and you're a king, sir. I appreciate you, dog. That is so much love. Much love to you, dude. Glad you're here. I'm so, so happy you're here. Let's say, uh, all indicators. All sh. No, all indicators, please. AI, my guy. AI, AI, dude. This is over. It's over for me and you. All of the indicators. Yo, all of the indicators. You see that, dude? You want that script? Come [ __ ] with me, dog. I'll show it every day, dog. I'll show you everything. Everything. Output them to a, output to a TXT in my data folder. Yo, I don't care who you are, dog. I don't care if you've been coding for 30 years, dog. I'm coming at your neck every single day. I got the power now. We got the AI, my guy. Stop it. Stop it. Do you see this? It's just one idea and boom, I get every single indicator just like that. I don't need some long tutorial. I don't need a C stack overflow. I don't need to go to school for 82 years like you did, dude. We're not the same. I go harder than you. You can't keep up. I'm sorry. Yo, print them out too, cousin. Whatever. That's cool. So these are all the indicators here for TA-Lib, Pandas TA indicators. So we have all these. I'm going to use ADX. Yeah, let's do that. Let's do that. So I'm going to open this to the side here. Split right. Show all indicators. Sorry, burped in your ear. My bad. That was super impolite. Yo, Mev, you need to start using multiprocessing and multithreading. Why? It's good comment though. It's good comment. But why? Especially when accessing large sets of data. Okay, okay. I seen somebody play with that the other day up in the Discord. Just so I can test faster. Backtest faster. Multiple times. Multiple, multiple processors, I guess. I like the idea. I appreciate the share. Much love to you for that. You can use Python and C++ e for. All right, so I'm going to go ahead and start writing some of these ideas now. So test two, test three Indies to try. So I want to try the ADX because homegirl talked about it a lot. Um, one from my first podcast I did. Mansion says, I use multiprocessing. It's much faster to train the model. Okay, there we go. That's a good why. That's a good why. That's on track. Use multiprocessing to train models and also to backtest faster. Appreciate you guys. This is like, you see that right there? Like it might have taken me two years to get there. And y'all just dropped a dime like that. You just dropped Mev a dime. You threw the alley-oop and I just dunked it. I haven't dunked it yet, but I'll dunk it soon. Landlord says, he wishes he had a supercomputer. Connor says, was using my uni one for the last couple years and I miss it dearly. Dang, dude. What's the supercomputer looking like? Connor says, plus one for the faster training. Thank you, landlord. Thank you. Appreciate all of your, your brilliant minds. I don't know a lot. I don't know most, honestly. I only have 4,334 hours in this game so far. And, um, Quantum computer. Dang. I got the, I got the Quantum MacBook. No, I'm just kidding. Let's see what other indicators we want to try. I'm just going to use ones that are, um, I guess I don't know. That's the hard part. Like, which ones do you use? ADX. Try that. I just, the ones that I guess I know. ATR. There's just so many. Donchian. I'm just based off memory of tests I've done in the past or ideas I've heard. I'm just going to use those, I guess. And if you got any ideas that you see here, holler at your boy. Just be like, yo, Moon, try that one. Linear Regression, MACD. Yo, how much like RAM and stuff does a supercomputer have? I'm just curious to see like, yeah, how far off I am. I know I don't have anything near a supercomputer, but like, what is, yeah, cores, two, I guess. Again, I'm not very, I'm not very technical. I'm just kind of a new, but appreciate y'all. Yeah, I want to cur, I'm curious how far off I am from supercomputer status. What is out there? And then can we use GCP, Google Cloud, or GC, whatever, to get supercomputers? Is it worth it? I'm curious about all that stuff. But this is fun. This is fun. True Range. I love this game, dog. This is just, this is my element here. Feature engineering. This is where I'm at. This is, this is my game right here. Come up with ideas. I love it. I love it. I love it. TA-Lib. They got all the math here. Cosine, Sine, RSI, Stoch, Stoch RSI. How about that? What's Kama? Kamala? CDL, CDL Engulfing. What are these? I don't even know what these are. D, CDL is chart something. Doji Hammer. Let's put it. Let's just try it. I don't know, dude. I'm just trying everything, dude. This is the game, right? I don't know. We'll see. See, I mean, we'll see if it's better or worse. Linear Standard DV, T Range, ATR. We got the ATR in there already. The VAR, the OBV. You down with OBV? Yeah, you know me. Candlesticks. CDLs got it. Thank you, bro. Um, so Connor says, it was mainly the GPU access. Training models was like day and night when looking at using a supercomputer versus not. Angga says, you can use TPUs, Tensor Processing Units, in Google Colab. Nice. Landlord says, you can run your scripts on AWS and GCP, but you pay for the increase in RAM. I think I'll stick with GCP for now. If a candlestick is 100, it's bullish. If it's negative 100, it's bearish. Good intel, bro. For you, good. I didn't kill you over there, did I? Okay, let's try this. Um, I'm going to say top three Indies. No, no, no. Do not there. Top three Indies. Is this? Use the volume change. Okay, let's say update this code. Update the attached code to not use the features or the, yeah, features it currently has, but use the ones below from Pandas TA. I think that's the first one, right? Let's try these three. Yo, is this, is this an approach? Is this how people approach this stuff? Or are there like smarter people out there? I found that three is better than seven. So Myra says, you come from a data science background. Munda, I mean, I don't know. Not really. Not at all, actually. So I learned how to code three and a half years ago, but I learned through watching like 10 machine learning courses. So I've seen everything. Not everything, but I've seen a lot of machine learning stuff. I was just super fascinated by it. But I also was learning how to code at the same time. So I was just typing all this machine learning code out in Python and kind of learning Python at the same time. I don't think it was the correct way to do it, but I've seen it all now. And now I'm three and a half years deeper. And I actually know how to code in Python. I can build any bots, anything like that. Build a bunch of algos that I run every single day. And now I'm diving back into machine learning. So I think it's going to click. It has been clicking a little easier. So if you want to say that's a background, then yes. But no, I didn't go to school for any of this stuff, dog. I did not go to school for coding, machine learning, nothing. I didn't read a lot of these boring books and try to absorb what I can. So all this, all everybody commenting here, you guys are lifesavers, bro, teaching me what you know. Because I know we got some Gilfoils in the audience. Connor says, for individual sessions, I could run, I could request up to 72 cores. Dang. Wow. So 72 times eight. I can't do that math, bro. All right, so I'm way off. I got 96 gigabytes of RAM on my computer, though. Altered says, I want to enter your community. I don't speak English. Is it possible to add French language or Italian? Dude, you literally just said that in English. You do speak English. Give yourself some credit. First off. But yeah, um, yeah, everything's got subtitles for you, bro. So you can pick French or Italian. That's cool. You know, French and Italian, that's amazing. But yeah, you can join the boot camp. Everything in the boot camp has subtitles to every language, pretty much. So that's dope. Uh, Myra says, that's cold. Straight to ML. Had to go for the top, man. Had to. You know, that's, that's who I got inspired by. It seems super hard, super fascinating too. And it was before AI was here, well before LLMs were usable from the desktop like they are. So I'm glad I learned it kind of by him before I just demand AI to do everything. All right, I just had to finish my little waffle there. What do we want to call this one? ADX, ATR, Donchian. ADX, ATR, Don. I like that. ADX, ATR, Donchian. Let's just pop it in. Let's pop the code in. Dump it too. Yeah, dude, dump it. I want that. Update this though. I want to say trained models here. Data. I want to make a folder. This make this a little neater. Trained models. And let's put all of these models here. Copy path. Update the code so it. I'm not going to do that. Just going to do it myself, dude. Did I put it in there already? Dang, that boy nice. That boy nice. Look at this. I can type code in the, in the thing, dude. All right, let's see what it does. Run it. Do fit in fitting. Somebody just got licked. We got licks. Always. Let's see what's going on today. Oh my God, people getting wrecked today. I'm looking at the 15-minute chart. That's why this taking a long time to fit or whatever. There it is. There we go. So be in the market in green and out of the market outside of the green. I like this. In the market, green. Out of the market, outside green. Yo, Call Trading, what up, bro? Oh, is JPY going crazy again? What's going on over there? Connor was training models to predict gravitational waveforms of binary black hole collisions on the supercomputer. Very resource-intensive stuff. Not as applicable as I'll go trading, though. Oh, man, that sounds intense, though. Okay, so this is interesting data. So let's go ahead and analyze it. We just got to do repetition, dog. I don't know where you're at in your journey, but for me, I just need reps. I need reps, reps, reps, reps, reps. 98% likelihood. Wow. I'm curious about the importance here. Count. Okay, that's fine. I was like, why are they all the same? It's still working here. Nice to see you here again, Call of Duty. What is happening? Is the expectation over the September meeting in FOMC 25 bips versus 50 bips? And inflation is running relatively high in Japan, which leads them to have to increase rates. They will do so slightly. But what matters most is future expectations over falling rates in the US. Interesting. Okay, look at this. I like this too because this, uh, see how it's pretty even? I don't know if that matters, but and compare it to our best model below. And what was our best model below is the question. Damn. Um, hey, it didn't update into our models. Uh oh. Possibly just overwrote it, huh? H. I put it in the data folder. Yeah, my bad. It's typically me. That's what I love about coding. When something goes wrong, it's typically me. It's like such great accountability. And then I want to compare it to, let's see where are our other HMM models here? No, all. Dang, I don't know how to tell which is the best one. I can look at the out-of-sample. So out-of-sample data here. And X2, X1. So X2, Model 2 is the best. Volume change, BB width, and volatility. Vol, BB, and VA. I love this game, dog. You'll never get me to stop. I'm sorry. You'll never get me to stop this game. I'm sorry. BB, volatility, and RSI. I'm sorry, dude. I'm not going anywhere for 60 years, at least. I'll be standing my black ass right here for 60 years, at least. BB width, volatility, and RSI. How do you evaluate Indie importance? I don't know, dude. It says right here. It's a good question, though. Um, and then compare it to our best model, which is Model 2. So I just don't, I don't know any of this yet. I'm, I'm getting there, though. I'm getting there. I'm starting to understand a little better. A little bit better every day. Every day, everywhere. Uh, it's Jan says, is each console log a unique trading bot? No, it's not. Right now, I'm working on the HMM, which is a Hidden Markov Model, which supposedly Jim Simons liked. But, you know, that boy's capping. Ain't nobody going to tell us what that was actually working. But we did figure out yesterday that he definitely starts with data first. And if you start with data first, that means that you're not looking at indicators and stuff like that. You're looking at the data. So we're going to let the data be do it. Thug fizzle. You know, we're going to start with data first. Data first. And that's what we're doing now. We're going to, we're sending into the, the AI, the machine learning. What's this JPY? You're talking about? I don't even know how to get there. Let's see if I can get there. JPY. I'm not a, I'm not a Forex guy. I see. Oh boy. Is this what's this mean? What does up mean? Is that bad or good? Because I know with these pairs and stuff, cuz the other day, didn't it like, okay, the JPY capitulated down here. Honestly, I do not know. I need to get a little better at currency understanding, I think. Because it looks like it's important. But see if this is done yet. Being, I feel like you need to understand currencies and how they move. USD JPY. Oh, so this was opposite. Oh, that's a different story. So was that just backwards? Did I just look at it backwards? This is what, yeah, okay, yeah. This crashed the other day. But that doesn't look like a backwards chart, does it? JPY. I don't know. JPY USD. Uh, that's not backwards, though. Is that a different thing? Sorry for my ignorance. Again, appreciate you guys' love, though. Okay, so it's the inverse. Okay, so this down bad today, huh? I should add this to the list here. What are some other good ones I should start looking at that like move the economy? Let me just add them to the list because, you know, I canceled this. You know, I canceled the, uh, TradingView. There's no reason for it. What are some good symbols I should watch in order to get more in tune with Forex? Because I know a lot of you want Forex over here. And, um, I've heard that Forex is more mean-reverting, so it could be interesting to look into. But let's go ahead and analyze this data. So we got two new models done, or one new model. And I want to keep just working through this until it just, so, so clear. Okay, yo, thank you for this. So we have, we've got some good suggestions as well. I know I'm always doing this, but always getting over here. So Euro USD. That makes sense. Just got to get them on the, uh, the thing before it cancels on me. So Euro USD. What else? GBP. GBP. The British Pound. GBP USD. And USD JPY. Those are the main three. Sweet. Yo, I appreciate you, dude. Call Trading and Landlord. I appreciate both of y'all. So these are the three movers. So Euro USD. GBP. So the British Pound. Who uses the British Pound? Britain. Oh, the UK. Okay. So Jane uses the British Pound. And then everybody else uses the Euro, right? So I see why this is important. Is there not like one for, uh, China? China's dollar and stuff, whatever that is. Is not important or something? Jane uses the British Pound. Everybody else in Europe uses the Euro. Japan uses the JPY. I feel like the two missing from an outsider standpoint are the Yen. Yeah, the Yen. And then also India and Russia. Those are all the big ones. I don't know if those are tradable, though. So it's interesting. Swiss Franc. Okay. Dang. So yeah, I'm not going to go down that, that rabbit hole because I guess there's a lot more. Not everybody uses the Euro. I was thinking everybody uses the Euro, but I guess not like everybody on over there. Over there where y'all at? Everybody over there uses EUR. Yen. Yen is Japan. Oh, Yen is you. W. You. W. Anyways, let's go ahead and analyze this data because we got a new model here. And this is what it looks like. It looks good to me. Did you see it? Look at this. Look at me. 98% prediction. The thing is, the whole system's all dollarized. So the majority of the flow is dependent on the US. The Swiss Franc is interesting because of how tied into international banking it is. Wonder what info could be inferred from analyzing that. Dang. UJ is going to fly higher in this coming week. What is UJ? Is that Euro or US Japan? DXY is a good indicator of USD strength. I think I got that right here. Yep. DXY. So dollar down, risk assets up. Dollar up, risk assets down. That's what I learned. At least I don't know if that's right. But dollar down today and risk assets down. So down BTC down. Interesting. This game is super fun because there's so many different levels to it and different arenas. Like y'all are over here in the, uh, Forex arena. And I haven't touched that. I read Baby Pips when I was in college, maybe, or whatever. And I, I was bored to tears. I was bored to tears, dude. I was bored to tears. That Baby Pips. There's so much information there. But I probably could, I probably could dive back in now that I'm a little more financial savvy, I guess. That's my interpretation too. Connor says, it is half right. It's half right until like a big down day like this. I feel like then it's like everything just hits the fan. That's what I've observed at least. Is that like on days that it's slightly down, risk assets up? If it's mad down, then everything down. DXY is a basket of currencies against the dollar. So if people in Europe want to invest in US securities, they need to raise the US dollar. Interesting. Well, let's stop. Let's stop talking about that stuff because I, I don't know where that's going to take me. So maybe in those days, there's a rotation from Euro to USD. Dang. It's awesome. There's so many games in this game. That's why like, I'm not worried about allocating time to algorithmic trading. Like, you allocate time to like new trends like Shopify or apps or, I don't know, social media. You don't know if it's going to be here in five years. I needed something that was so hard. And I know that's going to be here for the next 60 years. And I know for a fact there's going to be some sort of market here for the next 60 years. So every single minute I put into this stuff, I'm compounding. And I know in the future, it's going to be useful. I'm getting better and better every day. And thankful to you guys that like accelerate this stuff too. So if you're in the chat, you see everything I see. I wouldn't miss it. I wouldn't miss it if I were you. Thank you for providing the results of both models. I'll compare them based on the given metrics and features. State prediction accuracy: New model (ADX, ATR, Donchian) 98%. Model 2 (Volume change, BB width, volatility) 89%. The new model shows higher accuracy in predicting the next state. New model log likelihood: -93,000. Model 2: +14,000. Model 2 has a much higher log likelihood, indicating a better fit. Back to Model 2. Bayesian Information Criterion: Damn, 187,000 vs. -27,000. Lower BIC indicates a better model. Model 2 has a significantly lower negative BIC, suggesting it's a better fit. Cross-validation score: New model -47,47k vs. 20,000. Model 2 has a higher and positive cross-validation score, indicating better generalization to unseen data. Feature importance: New model ADX 27.85%, ATR 0.4%. I like how it's a little more spread out. But Model 2 is all about volume essentially. Visual inspection: The new model's chart shows more distinct and prolonged state periods, especially in the later half of the time series. This could indicate that it's capturing longer-term market regimes. Conclusion: While the new model (ADX, ATR, Donchian) shows higher state prediction accuracy and more balanced feature importance, Model 2 (Volume change, BB width, and volatility) outperforms it in terms of log likelihood, BIC, and cross-validation score. This suggests that Model 2 is likely the better model overall, as it fits the data and generalizes more effectively to unseen data. The heavy reliance on volume change in Model 2 appears to be capturing something fundamental about Bitcoin price movements that others don't. However, the new model's more balanced feature importance and distinct state visualization might offer different insights. Using Model 2 as a primary model, keeping the new model as a complimentary tool for additional insights. All right, this is dope, dude. I love this game because, you know what's next? A new idea. And then, you know what's next after that? A new idea. I don't care if I miss the shot, bro. I will just keep shooting. I will keep shooting. I know I'm a shooter. I'mma keep shooting. I'mma keep shooting, dude. I'm just going to keep shooting and keep shooting. And then I'mma shoot again. And then I'm going to shoot again. And I don't care how much time this stuff takes, dog, because I'm here for 60, 60 years. B, I don't care. I'm here. Present. Do you use volume as a percentage change? Yes, sir. Volume change. All right, let's try another one. Use these three new features instead of the old ones in the attached code. They are all from Pandas TA. Yo, I haven't watched Python for probably 20 years, though. So I don't know what I'm even referring to. Paid in full. When did this launch? Yeah, 2002, probably. Watched it when it came out or around that time. I was a kid. I need to watch this again as an adult because, you know, all that stuff goes over your head. All of it goes over your head. So.

Uh, I started watching this show I grew up on, like, uh, Fresh Prince of Bel-Air. I love that show, yo. They have a new show. Oh, because you said B, remind... oh, okay. They, maybe that's where I got it from. B, I don't know. But, um, I don't watch TV too much, but I started watching the new Fresh Prince. It's pretty, it's pretty good. A lot of, like, a lot of references to old black movies, which is dope. "Can do you care if you live or you die?" That threw me off. I was like, I know what he's about to say. That's exactly what Kane's grandpa said to him. And they just led this, they started the show, the new Fresh Prince show. Oh, it's called Bel-Air, sorry. It's called Bel-Air. Um, it started with, "Will, you care if you live or you die?" And that's like, you got to be, you got to be watching all the old movies in order to understand the show fully, which is dope. I love it. I love it. I love it. I love it. And they give, I love how they also, like, totally changed the show. Like, Carlton is somewhat cool. Um, Jazz, Jazz finally swoops his girl, whatever her name is. Uh, Jeffrey is like an assassin. L. Key, he'd be holding it down. It's crazy. I love how they just, like, switched it up a little bit. At first, I was thrown off, like, why is Uncle Phil some, like, skinny dude? Rest in peace, Uncle Phil. I like how they did that. At first, I was like, apprehensive towards it. Like, why is Uncle Phil, like, is he, who is this guy? But he's still a great father figure, takes care of Will. I'm only on, like, the seventh episode. So I was happy to see that there's three seasons to it. It's a good show, and I don't say that about many shows. I, I don't usually watch this. St a got time. B, yo, I need to watch this movie again though. Got Regina, young Regina Hall. We got Cameron in it. "Hey Ma." That used to be my head. I used to sing that all day long. Damn, there's some good movies. I'm an old head, dude. There's just some old head stuff to be saying. Back in my day, back in my day, we had some good movies. Cast is elite. Absolutely. Damn, I can't wait. I might watch this tonight. I mean, I'm sure it's on somewhere. Netflix, 8 more where to watch, YouTube. I got Amazon Prime. Thank you. You're an territory. I know it's, uh, it's crazy, dog. It's really crazy. I mean, what it is, what it is. But I am, I'm there. I'm there. Officially an uncle. You never think you're going to get here till you get here, dude. I'm literally an uncle. I got two, two little young bucks. And it's cool. It's cool, man. It's cool to see. Cool to see. Cool to see him jack my sch, SW, my swag is irrelevant, but they jack it anyways because I'm, you know, so somebody got to give it to him. All right, so I'm writing down Pon full real quick. All right, let's get back to it. I'm going to go ahead and test one more. So use these three new features instead of the old ones in the attached code. Lin regression, MACD, and true range. See, I like just switching it up entirely. So our best one is volume change, but we just tried the ADX and it, at first, it looked better because of the 98%. It looked better, but was it better? No way. Uh, send me back, send me back all the code.

All right, I love this game, dog. We literally are competing with the, the machine learning people over at, name your FANG. Name your FANG. You gave us too much power, dude. You gave us too much power. Claudia, you gave us too much power. Gigi, you gave us too much power. Luxie, you don't understand it. Okay, go ask AI. I say code's the great equalizer, but like, damn, AI is really the great equalizer. But you got, you got to know how to code. You got to know how to code to be able to get the best out of AI because what are you using it for otherwise, right? Like emails and stuff. Okay, I see through, I, I can see through that. You can't see through my code, you know what I'm saying? Like code is code. So like, you can kind of feel when AI is talking to you almost. I mean, not always, but like, how often are you writing papers? How often are you writing emails? I mean, how often are you writing Facebook posts? I mean, it's cool, whatever, like that's a cool use case for it. But coding is the killer app. Coding is the killer app for AI right now. And if you don't know how to code, you can't use it properly. I mean, you can learn through it at least, but you got to, you got to be able to learn it. So that sends me to, let's, let's, let's actually go through it because I know there are people here that weren't here at the start of this. So this one's Lin Rag, MACD, and TR. Lly reg Mac and tr.py. Paste it in. And then we're going to just do a little study session here because I need to see it, dude. Like I said, I, I grew up getting 500 shots a day, dog. And this is the same game. This is literally the same game. You get better over time, get wetter over time. Every single day. I don't care how tired I am. I don't care, don't care how little I slept last night. I don't care. That's when I really double down and I make sure to get my hours in. So import. These are a bunch of imports. Then we're starting the HMM analysis on BTC. We're loading in, pre-processing. We're dropping a column because my data is janky. We're creating the index. Okay, open, high, low, close. This is the linear, linear regression with the TA pandas TA library. Pandas TA library. So linear regression is a type of machine learning as well. So cool. We got MACD here, calculating the MACD. We're getting the true range here. Then we're dropping any NA, any non. I'm actually about to go get some mother naan right now. Be train HMM seven components. Lyre, MACD, true range. I swear there's a new NA place. I mean, it's Indian place, but you know, you know how, you know how that goes. Train HMM. We've got seven components. That's seven different regimes we are training on. But we have features, three features: linear regression, MACD, and true range. We normalize those features, meaning put them between zero and one, I believe. Then we run the HMM. The Gaussian HMM. Are there other HMMs that we can run? Put it on the read me. Put it on the read me. We are using the Gaussian HMM. Are there others that we can run? Where we at? Where we at? Ly red. Okay, so and now we, the Asian is completed. We predict states. So it's predicting states. Remember, this is the actual prediction here. We have seven different states. But the thing is, is it doesn't actually know the states. It's just ch them in the states. And then we're going to have to do that later. But that's okay. I just want to see a lot. I want to see a lot and then see what the best is of a lot. Since I'm starting at a zero point origin, where Moev knows nothing, I want to see a lot of different tests here. And then out of those tests, imagine you've never played a game before. I have no idea how to play this game. I'm going to play the game a hundred times and see my score over the course of a hundred times. And then I'm going to go with the method of the best score. That's all I'm doing. That's that's all I'm ever doing. That's what this whole, this whole world is about to me right now. That's what life is about, really. Just a test your way into figuring things out. Predict next state. Save changes. It's just saving the changes. St changes pending it to a folder. And we haven't even been looking at them. But okay. Model scaler. Where are those? I can delete these actually. Need to update them real quick. I'm going to update them in my thing. So you can see here that I updated the file path. So here at the bottom, I added a folder to all these. So I got to just update this real quick. Um, HMM model two. Here, every time I run this, it's going to try to save it again. So this is our best model so far. Model one here. Second best. Actually, I don't know if it's second best, but I don't really care about the second best. I just want the best. Be that's it. Just trying to be the best I can be. I don't know how good I can be, but been able to figure it out thus far. So I'm G keep going. Plot the results. Okay, that's just a bunch of plotting. I just want to make sure you see all this code so you can leave here with your hands full of ideas and code. Data saved to a plot. Calculate prediction accuracy. Calculate BIC. BIC is a evaluation technique. So something about Bayesian and something, something, something. Time series CV. Okay. Analyze feature importance. Okay. Starting main execution. Training the model. Predicting states. These are the names of the states, but remember, these names are just made up, really. The model, we have to place the states later. This is just running all the code. But I just want to make sure you see it all. Try to show you all the code. But sometimes I'll be forgetting, dog. That's my, my bad. Mal M bro. Lin reg 14 key error. Come on, cousin. I showed you all the wrong code. Then it's not done yet. Calculating lyre. Okay, I get this air, but I can see in the available, available indicators that, let's see what the available indicators look like. These are all the available indicators on Lin rag. I have these two options. I have, uh, indicators on Panda TA that I have these options here. Okay. Um, let's see. They gave us too much power. Llord said, I think they did. And they can't take it back now. Was the crazy thing? You can't go backwards with this. They did give us too much power. They gave us kids out here too much power now. Noobs, you can't tell that I'm a noob anymore. Like if you look at my code, if you look at my code, you can't tell that I'm, I'm just at the start of the game. I'm only at my 3,400 hour. And I have to get to 10,000 hours before I know anything. Before I'll ever. People ask, oh, Moev, get on my podcast, tell me this. And it's like, bro, I don't know nothing yet. I'm not even halfway there yet. Stop it. Maybe I know more than some, but 4,000 hours? No way. I need to get 10,000 hours. I need to get to 10,000 hours before I can confidently say I know anything. I'm sorry, dude. Sorry to do it to you. I'm sorry I'm not your guru. Sorry I don't know everything in the world. The only thing I do know is I will keep going. I will keep finding new secrets. Live every single day. There's aha moments every single day, dog. Every single day. Take care. Make sure you're here. I think, uh, man, man, Manuel, maybe I think learning, writing, and coding separately are very important. Like me, I'm learning the English language because of AI. Sick. That's fire, dog. You're born in New England, so you're learning English because of AI? That's fire. Okay, I could see that. I can see that. Learning just in general with AI is crazy. Like anything you want to learn, it knows mostly everything. At leasts a structure to everything. That's awesome. You're learn English and coding. This would be killer for resumes too. Absolutely, dude. The BIC indicator thing is a comparison of the log likelihoods. It's used to gauge whether you could choose one model or the other. Thank you, bro. So the BIC, it compares the log likelihoods, which we've seen a lot of, and we're about to see it again. Bayesian factor, Bayesian, Bayesian factor. Base factor. So that's cool, man. You show up to these lives, dude, you learn at exponential pace because I'm pushing us into a direction that I, I'm interested in every day. And then there's people way smarter than me up in the chat that are so gracious with their time and gracious with their thoughts. And they went to school for this stuff, dog. I ain't go to school for this. Let's try it again, doggy doggy style. Sad, sad. Did I not put the right code in? H. You nerd. I apologize for the confusion. You're right, and thank you for providing the information. The error occurs in the Lin R function in pandas TA with different column names. Okay, let's modify it. So we need to put the load and pre-processed data. We need to change that. Here are the other parts of the code that need to be updated. Okay, I'll do that. All right, do it. Load and pre-process data here. Beaters. Yo, what up, Nick? How are you? How are you, Nick? Have never seen your name before, so welcome, welcome, welcome, welcome in the predict states function. So I'm going to go to all the Lin Rags here. Train HMM. Here. What did it change? Do I don't think it changed anything. Anything. Think is plenty. Man, that looks guzy to me. Beef. All right, let's just run it. See what happens. Send back full code with any prints you need to debug. My dog does unknown says, I upgraded Twitter to get Grok but can't handle heavy coding challenges. Wonder why. PO is the best amount for free. Interesting. Nick says he a newbie here. Salute, salute to you, dude. Much love. 777. How pass. Love. Good vibes all the time. You'll see it all the time in the chat. Light the chat up with the 777s for our new friend, Nick. So much love. So much love. All right, let's go ahead here and try this new code out. See what you got. Litt Mama. Nothing. Linear regression calculating time series. Dang, we're stuck. This is good. This is good exercise though. Good exercise. Jane says, 777. Welcome to the fan fam fam. Wow, dollar. Huh? Look at that dollar. We look at the dollar now. BTC. This liquidation point still got me tripping here. 150 mil liquidated right here. Going downwards. Came all, all the way back up. Did a cherry kiss to it. And then reverted right back down. And then jump. Got liquidated right here. That's wild. That's wild. That's all I've got to say though, because I don't want to predict market movements or anything like that here. It's not what we do here, dude. We let the AI do the work. Yo, I like your pfp, bro. Nick. It's a good one. Yo, what happened to the live stream yesterday? I cut it off randomly, didn't I? I forgot. I had a hard stop at 3 and then it was 3:05. West Side. See what she found out here. I apologize for the confusion. It seems that TA Lin red function is returning a series instead of a data frame. Run it back. Then there we go. Shorty, we can do it all, dog. We can do it all. Where's the M D at though? Okay, there we go. Beautiful looking chart. I'll tell you that much. Beautiful. All right, let's analyze it. Do exactly what we did with the last one. Repetition is key, dude. For me, and this is my world. I'm sorry if you're way ahead of me and this is too easy for you, but for me, it's a challenge. I like, I like challenges. But I got to understand this stuff. Fly FY, because I got to be able to grab it by the mother balls and then just understand all. Okay, you know, if I understand it all, I'm a threat. If I don't understand it all, I'm not a threat, dude. I'm just following in the footsteps of Jim Simons. He, he did this till he died, dog. That's amazing. It's amazing. I'mma do it till I die too. I'm do it till I die. I'm just waiting on this stat, these stats to be done here. And I actually think I have my, um, best one here. Show all indicators. The, if I got to read me here, I wonder why it takes a little bit of time in between this. Okay, here we go. Let's see if she can just compare them to my model to, to the previous ones. Let's just not even give it to her. Let's see what she really about. Let's see what Claudia is really about. See if she can really memorize things. Nine more messages. Get off my D. You know I'm running it up every single day. Nine more messages. Why do I even pay you, fool? What you even pay for? Who even pays for this stuff, dog? I do. Dag. NAIT. Jiminy Christmas. Certainly, I'll compare the results of this new model using Lin Rag, MACD, and true range with the previous two models we discussed. Let's break it down by each metric. And let's get that thing back up here. Let's get the image, the image. Open revealing finder. Please plot it. Plot it like it's hot. Leg is hot, baby. My leg is hot. Yeah, you don't get much. I mean, you get a good amount, Jane, but you can see I only got eight more messages left for like three more hours. This crazy stuff. So I think I'm going pretty hard on it today, but, um, I feel like it's less than yesterday. I'll say that much. Feel like it's less than yesterday. I've been using Kote says, I've been using open AI API plus Pinecone DB to avoid the rate limit. Okay, okay. I feel you. What is Pinecone DB? Oh, the V vector, vector B. I remember this tripo. Yo, I got Luxie, so I'm Gucci. Like I got a backup. I got Luxie is, you know, unlimited. So she's my backup and she does great. It's using all GPT stuff. So I'm Gucci on that on the backup. So I was just complaining to complain, cuz, but I appreciate all the tips because this is how, how we find out about new stuff, you know? Okay, my monthly spends looking pretty solid here on GPT. I was trying to keep it down a little bit, but, you know, it's all good. We're going to use Luxie here when we get worn out of or we're done with the whatevers. So here's the model here that we just tested. And this model uses ADX, the ATR. No, sorry. New model is linear regression, MACD, and true range. 97%. And we're comparing it to our other model, or other two models. Now, previous model was 0.98, so that's better. There. Model two, though, which is our best model, has a lower prediction state prediction accuracy. The new model performs very well in terms of state prediction accuracy, almost matching the ADX ATR Daan model and outperforming the volume change model. New model is 30,000 minus 30,000, and the other one was minus 93. So it made an improvement. Model two is 14,000. The new model shows a significant improvement over the ADX ATR, but still doesn't reach the positive log likelihood of volume change. That's funny. Our first one of our first models, or second model. BIC, Bayesian Information Criterion. New model, 61k. Previous model was 187k. And then model two was minus 27k. The new model has a much lower, which is better, BIC than the ADX ATR Donan model, indicating a better fit while accounting for model complexity. However, the volume change model still has the best lowest BIC. Cross validation score. New model is -100k. Previous model is -50k. And I think this is the important part. The standard deviation. Model two is 2,000. Still the best. The new model shows worst cross validation performance compared to new previous model with a lower mean score. Feature importance. New model, Lin regression, um, 47. So this doesn't matter too much. It shows more balance, which is nice. Visual inspection. The new model's chart shows more frequent state changes compared to the previous ones, particularly in the earlier part of the time series. This could indicate that it's more sensitive to short-term price movements. Conclusion: The new model, Linreg, MACD, true range, shows improvements in some areas, particularly in state prediction accuracy and log likelihood compared to the ADX ATR Donan model. However, it still doesn't outperform the volume change. The new model's feature importance is more balanced than the volume change one, which might provide more diverse insights into market behavior. The frequent state changes in the visual representation suggest that this model might be more reactive to short-term price movements. Overall, while the new model shows some promising characteristics, especially in its balanced feature importance and high state prediction accuracy, it doesn't conclusively outperform the volume change model across all metrics. The volume change model still appears to be the most effective in capturing the underlying dynamics of Bitcoin price movements.

Okay, sick. Well, let's keep it moving then. Let's try it again. I'm going to take all this information, just plop it in there because why wouldn't I? Just plop her in. Plopped it in. Perfect, perfect. I'm getting comfortable with this. I can see Ly regres, MACD, true range. Like, sometimes this stuff's so intim, then you can't even see it, you know? You can't even look at it. Like, don't even look at me. Don't. Yo, you are so complicated. I don't even want you to look at me right now. CL. No, don't look at me. Stop looking at me, dude. Could you imagine if I was on camera? There's no way. The show would not go on if y'all fools were looking at me this whole time. I would stop it. Stop it. Stop it. Stop it. Stop it. Stop it. Y'all would fall in love with me. You already fall in love with my voice. Voice. 97% males in the audience, by the way. Weirdos. Oh my god. Let's go ahead and try some more. So what I'm going to do do here is I'mma paste this in. The things I do without actually thinking. I did is crazy. You see that over and over again. Like, Moev, you already did that. Look at this. Look at this professional looking file, dude. This is a good looking. I'm GNA push this to GitHub right now. This is just a good looking, non-Moev. Like, dang, really cool. Really cool. I remember back in the day when I saw folders, I would just, oh, pretty much just puke. I pretty much just puke because it's so intimidating looking at this code over here. And then folders of the code. How do you even? Oh, man. That's why it's hard to work on other people's code bases in my opinion. That's one of the reasons. What is this folder called? Hidden Markov Model. Rest in peace, Jim. So much love to you, dog. You don't understand how inspiring you are, dog. Appreciate every, every thought you put out. Every thought you put out in the universe. I know you're not going to show us everything. And now we'll never know. But I feel like you gave us enough. Enough pieces. I'm grateful. I'm grateful for him. Thank you, Jim. Rest in peace. 777 to you, your loved ones. I hope there's somewhere amazing that you had it after death. And other gy too. I love you like a pops to me. Much love. Eerie that you both died in the same year. Eerie, eerie stuff. When I say Jim, I'm really talking to both y'all. Much love, pops. Let's go ahead here and let's try these new ones. So instead of using the current indicators in the above code, use these three from TA. All right. And one of them is a candlestick. That's weird. I don't know if it'll work. But tripo. Metacla. GPT. Did you check Grok? NOP. Nope. So the vector database. I remember that from, uh, um, Auto GPT days. Jane says, Grok, many APIs. It looks like it's a drop-in code for OpenAI compatible. So not much changes. That's dope. Feel like I got my trippy kit for now, but keep it coming. Keep it coming. So I need to go to HMM models. What was the last one we did? We did Lin Rag. So I'm going to copy all this code over. And this new one is called Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um, strategy could be good ideas. But just curious if this is the right route, route to take when approaching ML for trading. Is it really just about testing all these different ideas, features, indicators, etc., trying to find a model that works best? What is best? I still, I'm not clear on that. My best right now is just, I'm trying to compare it against others, you know, a different other ones that I do. But, you know, Rick, RI, what's up, bro? Good to have you here, dude. Anga G says, you're just fine-tuning the parameters and putting the model through various tests you have designed to get it to reflect reality as close as possible. Essentially. So do you feel like this is a good path? I mean, I feel like I haven't found anything better than the volume one yet. And I'm going to keep going, obviously, keep going. But I just want to make sure, like, when I come back at it, that, um, it's the right path to take. But I, I like this so far. I also want to try some. Should I be trying? Should I be trying RNNs or other, um, models? Where can, can I find all the suitable models to use on this time series data? Do you have a GitHub? You says, Nah, I mean, I got a GitHub, but it's a private GitHub. Um, Kieran says, first time here and I don't understand a thing on the screen, LOL. That's fun, man or lady. Yo, B, I'm going to stick it to B. That's fun. I'm, I, I'm really proud to hear that, honestly, because I didn't understand any of this when I got started either. And it just takes like persistence day after day. Um, I believe code's a great equalizer. If you don't know how to code, then you got to learn, bro. You got to learn. That's the first step. Cuz then you can build anything. Riss says, I'm pursuing a data science degree at the moment and have beginner coding experience. What concepts do you recommend I study needed to make a trading bot? Damn, you're going to school for this. You should be recommending stuff to me, honestly. No, but in all seriousness, I've been coding here for 3 and a half years. So I don't know where you're at, but I show it all on my YouTube channel. I've been tra building trading bots pretty much specifically for three and a half years on my YouTube. So you can just start here and just kind of pick through however you want. I know you're in school, so you probably don't have a lot of cash, but the boot camp, I have a boot camp that walks you through all this stuff step by step. You can read about it here and probably reverse engineer it if you don't want to join. But $69, I know you're already spending a ton of money at school, so no worries if you can't afford it. Um, you can always just learn stuff at a little, little slower speed on my YouTube because everything that I've learned, I've done here live on YouTube or somewhat live. And, um, I just put the good stuff in the boot camp. So my YouTube has everything. And people always ask me to do like short, concise videos, but this helps motivate me to keep going when I'm, I'm kind of like showing you over my shoulders. It's like, you guys are watching me do this entire thing every single day for the last multiple years. And I can't stop. I can't stop because we're a squad now, you know? Yo, Kieran's from Singapore. And you is from Singapore. Yo, you, y'all are on it. Your whole country is on it. I never been to Malaysia, but, um, I know Singapore, like, y'all are high tech over there. Angan G says, I think you're on the right path. Industries like aerospace, weapons, and GPS have un or utilize HMM the most. But finding the position of something in a Newtonian space is way easier than markets. Ah, yeah, makes sense. But I still think it's worth looking at if you're stuck for examples. Ah, it's good idea. It's a good idea to look at that stuff over there in other spaces. Okay, so I was just, I wanted to ask AI this eventually, but for now, let's go ahead and compare the data. Or what were we doing? Oh, I was doing a new one. Stoke comma Hammer. Yo, am I doing this right? Like, that's my question. Is this, what is this, what machine learning is? It's just coming up with a bunch of ideas and testing them. Anybody in the audience know, like, you know, are you supposed to switch models more often? Am I doing this right? Yeah, I guess that's the question. I feel like I'm just going in a loop, which is good because I'm seeing new data. But again, I'm trying to get like 50 shots up just so I can see where to go. But I'm curious if there's another approach to this. I'm actually just GNA ask AI. I'm running out of, I'm running out of chat, so I'm not gonna, I'm not gonna ask her. Not right now, but I'll put it on the note sheet. Let's go to read me. Ask AI. Am I doing this right? Am I literally supposed to just just test all of these different indicators, indicators, and ideas with different models and features to predict different things? I know price isn't the best to predict, but test, uh, predicting market regimes and when to run each, um

I got to reset this or something. Um, Jane's got meetings, she's getting ready. Certainly, I'll compare this new model using Stoke, RSI, camo, and ADX with the previous models we've discussed. Yo, have a great day, Jane. Much love to you always. 777. Landlord said about 20 minutes. Okay, that's not bad. Zeus, I'm going to, I'm going to restart this here soon. I just want to compare these models.

So, new model is 41, previous 97, 98, 89. New model likelihood, minus 3us 37k. Previous model minus 30, and the best one was 142. The new model has a lower log likelihood, indicating poor fit. Okay, so this model sucks. This model sucks. That's okay. That's good. That's a shot. That's a shot we took. And, um, at the end of the day, and I know you can't see this, so it's kind of awkward, but at the end of the day, this Stoke comma, unfortunately, doesn't work well. You know, I'll let Vlad go ahead and say his seven steps, though. I'd love to hear it. I'd love to hear your seven steps because you've been dropping dimes today. Other than that, I'm out.

Alright, so I've tried seven different hmm, which are hidden Markov models, and the best one so far is model two. So, model two. Was there another good one? This Stoke did not do well. So we passed in some different indicators. Oh, that's a good idea right there. So, model two is the best. What are the main predictors here? It was Stoke. Here, let's see, let's see. No, it was, um, it was a volume change. So, this is the best thing for these inputs thus far. Best, I don't know how to define that, but it is what it is. Let's go ahead and see. Okay, this is 0.41, but that, that model didn't do well. What is our second best model? Yeah, from what you've seen, from what you've seen, which are our top two models? So, model two. Okay, this model consistently outperformed others with high state prediction accuracy, 89%, but then the lowest log, or the highest log, and the lowest BIC. The second one is the linear, linear regression MACD and True Range model. This model showed good performance, though not as strong as number one. So, let's go ahead and say, so far, the best one is this model two. Where can we put that? Let's just go ahead and put a date here. Date it. I don't know. The thing is, his time is irrelevant. So, let's go ahead and say 820b. And top model is model two, best obviously. And second is Lin reg Mac TR. Okay, let's go ahead and just say, hey, okay, well, let's test this on how to sample data for both of them. Um, update the above code in order to test model two verse the, um, and model two and the Linreg Mac one as well, because you said it's second best. This is OOS data. Where are we, dude? How do we get here? What am I even saying at this point? OOS. Wow, wow, wow, wow, wow, wow, wow, wow, wow. This is why I don't like coding. B, it's a whole different language. That's it. They speak in a different language. Y'all speak in a different language too. It's just, it's just like any industry, though. There's jargon. So, just got to get with it or get lost, essentially. And they had me lost for a long time. Okay, this is OOS data. LMA above, please. What is the, please, please send new code back that will test these models. Ah, man, I feel like I'm missing something there. She's going to need something else, but I'm kind of BL. Certainly, I'll update the code. Model LR. So, these aren't quite it, but okay. I see. I could have just done this myself, really. But it's okay. Why would I type? Why would I waste my? I only have so many keystrokes left in my life. I'mma save them. I'mma save them. O testing this one here is, oh, that's testing two. It's the same thing. Let's see how much it changes. 78, 79. Okay. What did I have up here? Nothing. Perfect. So, this isn't quite right, but that's okay. I just have to say something in front of it. Model two scaler two. I said trained models. Trained models. I was about to use some of my key extra keystrokes right there, but I'm just going to copy paste. I mean, that's a smart thing to do. O S. Okay. Slope. What is going on here, dude? Why do you have an error? Why would you ever have an error on me, dog? But you know, I got shooters. Be Claude. Claude. And then GBT. That's it, dude. Two messages left. You are kidding me, right? You are joking, right? You are a silly, silly B. Billy, right? That is some wild, wild stuff. I apologize for the oversight. The error is occurring because ta.linreg function is returning a series instead of a data frame. It's all good, shorty. Don't worry about it. I'm gon let you slide this time. Obviously, don't do it again. But you decide to handle it at that time. There we go, shorty. That's all you needed. All you needed. A little threat. Little threat. That's all you need. But Claude, Claude is Claude is nice. I like it so far. I haven't switched back to GPT. Have you? Have I? I mean, other than like, N. I like it. I have no, no complaints. I think it's is mobbing too. But you know, I can always jump back on the GPT when they need me. Um, when I need it, honestly. When I'm out, I'm actually about to jump on it. So I better stop pumping up Claude because I guess that's the complaint. That's the complaint I got. What do you mean you're cutting me off, dog? For an hour? Get out of here, dog. How you going to just cut me off? Maybe pay more. That's fine. You're just telling me that you're burning VC cash. Then you're exposing your hand, dog. That's all. That's all it tells me. If you have to cut me off at $20 and you won't let me, you won't let me, uh, run it up, then that tells me that these AI companies are, they're upside down, dog. The metrics ain't right. B, something's going on there. I'm sniffing it. I'm sniffing it. Why you kind of, why you charge and then not let me buy more? I guess that's the question. What's going on over there? Is it super expensive to run these models? No. Yeah, it could be. It's cuz you're buying chips like a bajillion years out. I don't know. I'm not educated enough to have this argument. I'm sorry. But this is the data. So let's just go look at it. Let's use the, use our last couple shots here. Claude, I love you so much. You got to understand that it's all from love. I love this. You, you got me through some hard, hard times with Rust. The GPT couldn't do. She couldn't do it. And you could. Dude, let's, uh, use one of our. We only have one message left. And this is going to be the end. So the end of me and Claude. I'm going to make it a good one. So I'm going to put a bunch of questions in it. Yeah, let's run it up, dude. Let's run it up. One, please follow the following. Follow the steps. Follow the following steps. One, help me analyze this. Return print out and the chart to understand which did better in the out of sample data. Why did it do better? I don't know. This is a lot of pressure to have a limit on. I can't just, I can't just be thinking like that. Come on. I'm closing this. I'm gonna put a bunch of good questions in here, though, because I got this question. Ask AI. Get that up in there. Paste it. Paste that in. What else? What are we using? Go. Yep. Yep. There's another good question. See, just stack the brain, dude. I, I mean, I would smash the me of the past, not just only because I know better Python, but just like we have AI now, my guy. They gave us way too much power. But only coders can use it. If this was widespread, if this, if everybody could have access to the power that we have access to with Python and AI, or coding and AI in general, they would shut that stuff down quick. They just don't know. They don't know, dude. We can launch a startup in an hour, like a SAS. We're literally sitting here and building Algos all day long. I know. I'm just getting started. Know. I don't know anything yet. I'm only 4,300 hours into this game. And I'm not saying anything till I'm at 10,000. I'm not saying anything. I don't have to say nothing. I'm just going to code. You can watch the code and feel the vibes. Send love 24/7. 777. Maybe not 24/7, but if you really want to watch me 24/7, you could think about that, bro. That's all love. It's all love. Alright, use multiprocessing to train models. Okay. I don't really want to take a hard pivot, but I might need this. I might need this. How would, dang. I was excited to, I was excited to get, get this going today. I'm going to use GBD4 Mini. Yeah. EP it. See, see how she can get down, you know? Let's see if she can move. Alright, we're going to GBT4 Mini. And I'm not even going to ask this question. Well, man, no, I am going to ask this question. Let's get one more good question then. So what else is there? Use multiprocessing. We are using Gazi and hmm. Good. Ask that predict price. Um, or send me back some code that predicts the state change and then uses backtesting.py like the below. The attached. The attached script does in order to combine processes or whatever the most efficient way of doing this is. The goal is to be able to to test the a strategy where it switches from where where when when price switches from not price, when the state switches from one state to another, buy or sell based on what you see in the image attached. Make things, uh, use the optimizer. Use optimizer like the attached code shows so that you can test for all types of state changes. Okay. And whether to buy, sell, or hold. I don't know, dog. I'm asking the world of her right now. Why wouldn't I? Why wouldn't I? Now we got to figure out where that code's at, because I was making big promises over there. I was making very, very big promises. And I got to figure out where the codes at. Do so, just get up in the little hyper liquid action here. Push a little code here and there. Can cancel because that's not going to open Visual Studio code. Okay, it's already open. The now I want to just see what type of back test. Any back test that looks good that has optimizer in it that runs good. Thing we do this a lot, right? That's a good thing we do this a lot. Okay, there we go. Okay, let's run it. Let's see what you got, dog. Oh, she can't even handle this. She couldn't handle that paste right there. Where you at? Oh, you're down there. Okay. I was going to say. Alright, so I threw a back test in there. Asked a bunch of questions from her. Ox Auto says, is this live? It's not. It's crazy pre-recorded, though. DUS Monta says, how do I find the moonshot coin before it explodes and a bot to take trade quickly? Any video you would suggest? None of them, bro. Nah. No moonshots around here, dude. Actually, the sniper, I guess, was kind of a moonshot, but, um, back in February, I was building the Solana sniper. If that's what you're referring to. 777 OE, what up, bro? Good to have you here. I'm asking the biggest. I'm asking the most out of, out of whatever her name is right now. And I just did because it's my last chat. It's the last chat. That's the finale. So, certainly, let's go through your questions step by step. One, analysis of the out of sample data results. The model volume, volume change, BB width, volatility performed significantly better on the out of sample data with a likelihood of 3859 compared to 27798 for the Lin regge model. A higher log likelihood indicates a better fit to the data. Looking at the chart, the volume model shows more varied and frequent state changes, which might be capturing more nuanced market behaviors. The Lin regge model seems to get stuck in one state, state six, for most of the later part of the data, which suggests it's not adapting well to changing market conditions. The volume model likely performed better because volume changes can be a strong indicator of market sentiment and potential price movements. The combination of volume, volatility, and price range, BB width, captures different aspects of the market behavior. It seems more responsive to changing markets, as evidenced by the frequent state changes. Approach to ML for trading. Okay, so this was the first part, and that was interesting. Um, you know, I think the winner out of this whole test so far is model two. That volume percent change is producing best results. Okay, there we go. I just got so much in that readme, dude. Approach to ML. You're on the right track. H, that feels good. Feels good. You're on the right track. The process of testing different indicators, features, and models is indeed a crucial part of developing machine learning strategies for trading. There are some key points. Feature selection and engineering is critical. Testing different combinations helps identify what's most predictive for your specific problem. When predicting price directly, while predicting price directly is challenging, predicting market regimes or optimal strategy selection can be very valuable. It's important to have a clear objective, maximizing returns, minimizing drawdowns, and to evaluate models based on these criteria, not just statistical measures. Consider using other models like random forest, gradient boosting, or neural networks, including RNNs and LSTMs for time series. I mean, yes, you are on the right track. Cool. Just want to make sure I'm on the right track, you know, on the race. I'm racing, but I want to make sure I'm racing the right way. Look into libraries like scikit-learn, TensorFlow, and PyTorch for a wide range of ML models suitable for time series data. Hmm. Variations. Okay, got some more alpha here. So, hmm variations. Dang, I can't see it. Okay, here we go. Discreet hmm. Higher AR kle hmm. Factorial factorial hmm. Input output hmm. W. You might also consider consider other state space models like Kalman filters or particle filters. Oh goodness. Sorry. Oh, the Inner Circle. This your shot. That was your chance. That was your chance. What's going on here? So, looks like a wild day. Everybody's getting liquidated, both sides. I want to feed in liquidation data, dog. Feed in liquidation data and clump it to be hourly, daily, 4-hour, etc. Dude, that's going to be far. You know that's going to be far. That's why you're here, dog. You're like, yeah, that's going to be far. That's going to be far. It's for sure going to be far. Let's start that now. Yo, 777 blessings to you, dude. And Dr. Or Mr. Tomur. Re, what's up, dog? Uh, can you teach me what you do? Um, yeah, I can, for sure. Hey, man, he said he said he is literally teaching in every video. Watch all the old videos. Yeah, dude, that's the way. That's the way. Just watch the old videos. Pick out what you want. Um, good to have you guys here. Much love. Jason, hey, dude, how are you? I'm doing, I'm doing well, thank you. Thank you very much. We, uh, working on these hidden Markov models right now. And, um, man, I love this stuff. I love this stuff, dude. Find some good ideas. Alright, um, we ran out of credits here, but this is going to be interesting. Here's sample code that combines hmm state predicting with backtesting. Alright, so let's see how this finale is. Go to hmm models. Dang, I knew something was eerie around here. I haven't had music on, bro. Wow. Spooky hours. Like, what is going on, dog? E. This was, this was, I'm going to listen to a little. I'm gonna switch it up a little bit. Oh, it's not on my computer. You nerdy nerd. But yeah, I'mma switch it up a little bit. Let's get a little something going, you know? Let's get a little vibe going. Alright, now I got it. There we go. Now we can hear it. Okay, um, feed in liquidation data and clump it into hourly, 4-hour. So, let's go ahead and see if we can get this back test. Uh, hmm bt.py. This is a new avenue here. We just took a left here. Not full left, but here's the sample code. Let's just see what it does. Does it work? We off bat, I got to put the data in. Let's not hold it to that high of a standard. No way this ain't going to work, but maybe. Hey, this is looking nice. Where's the data go, dude? Backtest cash. Hmm strategy. Cash. Why does it say it twice though? That's interesting. I don't know what's going to happen here if this is going to work, but let's just work our way through it. Final results is stats. They, okay, so yeah, yeah, let's just walk through it because I can't even ask. Oh, your data path. Here we go. I was like, what is going on, dude? Load data. But I want to see the, yeah, so I don't know if this is going to do it. Not quite. I, what I need. I didn't explain it well enough. It's always me. That's okay. Run backtest. Run optimization. Run final backtest with best parameters. Okay, let's just read through it. See what we can get out of this. Load and pre-process data. Okay, we got that. We know that. We load the data in. So I'mma put the data here. But the thing is, dude, is I want, I want different data. This is out of sample data here. I don't know. I got to figure out how to piece these together. But we, we got. Let's just move forward. Let's just move forward. I'mma put this data in there for now. Copy path. Paste in. Okay. And then let's, and get the first error. Whatever that first error is. Let's get it. No clothes. What are you talking about? No clothes. I don't have AI on my side anymore. Yes, I do. Above is the sample data of the CSV attached. Uh, CSV for the code attached. I got to say, I like the desktop app, big time. I like the app. I don't want a summary, dog. Maybe she's going to do a summary. And what did I put in there, dude? That was stupid of me. I'm sorry. I put the wrong thing in in there. So I'm sorry. AI GG. First minute back. I'm already dogging you. No way. It's me. It's always me. That's why I love this game so, so much. Oh, you lose money? That's you. Oh, you make money? That's you. It's crazy, dude. It's a good game. The error you're encountering in case the code is trying to access a column name "closes" in your data frame, but the data doesn't exist. Typically happens the CSV doesn't match. What are you talking about? We have it. I thought. Is it because I have the file? Maybe because I have the file? Let's see if it drops it. Let's see. One time. No, I think it's because the B CSV is a bit quirky and has an additional row. We need to drop B like it's hot. Alright, let's run it. Let's see what's Gucci. Wow, you're really thinking about it. That's, that's good. Yeah, but I got to have the code in there too, you dumb dummy. Nah, she's, know, she knows the code. Oh, man, it's not a good first showing here, but let's go ahead and, uh, make sure that we get everything that we have in here because remember we started with the, uh, clock and now we're over on GG. Send back full code. Send back fixed code with correct paths. Uh, ideally, ideally, we train on the data that is above and then we save the model. No, no, I have a model. I already have it trained, dude. Let's go look at it. Let's go get it. Trained model. Okay. Model two. Model two. Copy path. Copy path. GG, are you here? Are you here to stay? We'll see. I want to scratch this. No. And when we save the model, ideally we train on the data which we already did. And now we are just using using the OOS data below in order to test the model. I guess I don't understand why we need to train the model again when I have one already trained. I just now want to use it in a backtest and be able to change the variables of the backtest. This is a, the backtest. The backtest should be testing what happens when the bot buys at state two and then sells at state four with changeable variables. Making those, making those, making those variables easy to change. Okay. Okay, here's the other data. Um, scaler two. Copy path. What else do you need? OOS data. Copy path. Sample of OOS data. CSV. Okay, let's show it. Alright, let's see what you got. GG40 Mini, whatever your name is. Let's see what you got. We need you. Okay, to utilize your pre-trained HMM model and scaler of Al S day for backtesting. Here's how you would modify your script. Load the trained model and scaler from the provided paths. Process the OOS data to use the same features as used in the training phase. Process the OOS data to use the same features as used in the training phase. Perform the backtest. Use the loaded model to predict the states and evaluate the strategy. Try to prepare features. Predict stat using pre-trained model. Find the strategy for backtesting. Okay, she just needs a little help with the backtesting part. It's tricky. GG, don't notice. Uh, above is a backtester I use and it, it works. Please use it as a template in order to recode the above. Just recode it for me, please. Alright, hmm models. BT. Okay, this is where we're at. Attempting to backtest the open out of sample data. I missed the start. What's your main goal of this rewind? How did you train your model, or did you download pre-trained models? Um, I don't know, honestly. I don't know the answer to that. These are pre-trained. Pre-trained model. Loaded the hmm. Oh, no, I trained my own model. Yeah, now it's pre-trained. I think that's the answer. I'm sorry. Sorry, I can't answer. Yeah, but thanks for getting that in my mind. Okay, let's pass it in. Okay, so let's see here. How does this look now, dude? Okay, we're passing in the model path. Okay, this is the path. Model two is the best. Scaler two is the best. Okay. The model equals jb.load. I don't know what that does, dude, but it's okay. I don't need to know everything. This is the out of sample data. This is out of sample data. It reads it in. Okay. Okay. Process the data. Okay. But what about TA, dude? What TA are we using? That's why we look. It's why we look at it, dude. It doesn't matter though, cuz you know, you know how that goes. We got unlimited, unlimited uses over here at Open AI. I guess we're Open AI guys again. Look at that. We switch up quick. I don't care, dog. Just get the job done. Let's get it done, dude. Okay, print out the best optimization. I, I'm, I'm not, I'm not too confident in this, but it's okay. Cuz we'll just keep going. So define the strategy. Define by state. Define sell state. I like this, actually. How did we get here? I don't know. Last state. None. Okay. Yes, there we go. Buy and then sell. And you can see it sets it up correctly. But we don't have a stop loss. Why would we have a stop loss? I think that I can just remove all of this, be honest. But let's just let it. Let it do it. Thug fizzle. Let's add one more zero there because no. Why do I even have this, dude? I'm taking it out. I'm just going to take it out. Sorry. Look at that. Your boy's learning. Maybe I don't want any of this here. I'm just going to take all this out. No stop loss. Wait, wait. I think it's just buying. So all. Let's check it. No way, dude. That's wild. That's really wild. 989 minus 46%. Cool that we got that the first time though. Line 89. I mean, we kind of got it, you know. No attribute called take profit on line 89. I get it. Out of here. I don't want it. Then I don't want it. I don't want it. I don't want it. Get it out of here. Bye, bye-bye. Good night. Alright, dude, we done did it. We done did it. Look at this. Oh, how can we do things so fast? This AI is crazy, man. It's really crazy, dude. Really crazy. So the original or the best it converged on was one trade, pretty much buy and hold. Whoop, whoop, whoop. Fun, fun, fun. And it optimized from buy state to four, sell state to one. Are you running the code locally? Yeah, I am. I am. I am. I'm here locally. Oh, what you doing, boo? I didn't even know you were down there. I'm local. Yeah, I'm running on my computer. So this is an interesting thing here that I mean, what are we testing through? Let's look at that. What is and component? I don't know what that is. Optimizing, optimizing for N components. Yeah, but I want all that seven. That's how many states I have. Perfect, perfect. I was just double checking. Just double checking, you know. I reckon we all double check a little bit more. But insure buy and sell states are different. All right, well, need a better idea than this, clearly, because I don't see anything wrong with this. I said buy change from buy from changing one state to another. And with the hardcoded stuff, it's down 45, but that was like a total random guess. And then the optimizer just converges to buy and hold. But you know, you can always do that. That's one way to do this. But that's not really trading. And there's a lot of risk. Risk. Risiz says, hi bro, I'm a CS student. How can I start into trading? And what's your advice? My advice is to go watch my oldest YouTube video. And wherever you're at in this process, find the ones that vibe with you and what you learn from. And if you ever need extra help, I got the boot camp. But the boot camp is not required by any means. It's just like, it's like the fast track at Disneyland, bud. It's like the fast track. But it's all here. It's all on YouTube. So I want to make sure it's like an equal playing field, you know what I mean? It's unequal because people get the fast track, but it's unequal enough. And other than that, I've learned everything on YouTube, like while doing it on YouTube. Alright, so this is just not that good of an idea, but it's a good baseline. Peran. Okay, let's copy path here. This is a good baseline. Solid baseline for now. Chopping the market up into regimes and then testing only in one regime. The code attached is, um, I don't know. Is is predicting the next date and then just buying or selling. So that can be useful in order to code out something that is only buying or selling during specific periods. Blow blow up says, have I made an algo for stocks? One of my first algos was for stocks, but I just got distracted by crypto and it's just more interesting to me, essentially. So it's not really a distraction. I'm just more interested in crypto. And but yeah, one of my first, or was, uh, interactive stock bot, interactive brokers. So I got it in my bag. Let's just say that. I got it in the bag. And, um, maybe 2025 or something, I'll play around there. I don't know, though, to be honest. I've been saying that. Just more interested in crypto. And you got to do what you're interested in in order to do things a long time. I'm in the boot camp. Do you teach machine learning and how to get your data sets? Um, no, I teach you how to get data sets, though. So this is a boot camp you're asking about. And I do give you a data source in the boot camp. So if you need like open high low close volume data for minute, five minute, whatever you need, whatever you need, dude, I show you exactly how to get it. Robo, what's up, dog? He said, my dude, I started taking Harvard CS50 Python class and I've been dabbling with my own bots in Python and also Pine script. You're a big Ino. Yo, I'm so happy you're here, dude. Thank you. Thank you for introducing yourself. I keep showing up. If you keep showing up, dog, this is, uh, this is a game we can play forever. Get always, always find new edges. And as our boy Jim said, the market's constantly changing. The market's constantly changing. So there will always be new edges that appear. Anomalies. Anomalies. There we go. Let's look through that. I think this is a really good place. We got, got to, because now I got this tool that can number one, split any data into multiple regimes based on the hmm. I haven't done this yet. Not done, but maybe I'll do that right now. And then to get back testing done. Back testing equation. This now I have a great starting place for hmm bt.py, as it allows for buying selling in different regimes. Just update to only allow trading in certain regimes. Okay, but let's do this real quick. Let's see what GBT can do. Update the above hmm so that it only looks for four different, um, states. I want to see if the results change. Send back all code. So let's do a, uh, seven states and then, uh, four states. Okay. These are going to be four states. I'm going to say seven regimes forward. James, okay. So below is a continuation. Oh, snap. I'm using GG free right now. So we'll see how she does. Dang, Claude got me hooked, B. I might have to upgrade to, uh, chat GPT again. Are you running multiple Python programs? Robo asks. Um, yeah, like you can see all the ones like on the screen right now. What up programs? And they collect data. They'll do things like that. You just got GPT Plus and it's worth it. Nice. I used to have it, but then I, uh, switched to Claude, cuz that's what all cool kids are doing these days. Okay, let's go ahead and look through this code here. N components. Okay, this is good. This is good. Let's see if it's really good, though. This is four. It should be four components now. So the, the idea here was we have somebody that works, uh, at the quant firm. And they say that they use, uh, four regimes. Risk on, risk off. I should do two regimes too. Let's just pass it in there. Let's start there. Yeah, let's see what's going on down here, though. State names. Update the state names, B. What is going on? How many has got? How many, uh, components does it have here? Is that what it's called? I don't even know. Seven components, dog. What? So does she just not have memory? Like, risk on, risk off? What else they say? Do please, pretty please. Send back full code. I might have to upgrade right now. Levia, what's up? Where do I start learning if I want to get into data science? YouTube, dude. YouTube. YouTube has it all. Any channels? Moon Dev. Moon Dev. This a lot of code. Let's see what GBT 4 Mini's like here. Let's see if she can handle it. Can she do it? Oh, she looking a little soft, be honest. She's looking a little soft. I'm nervous for her. That's why I'm looking through to see where she done messed up. We got risk on, risk off, high volatility, low volatility. All. Let's run it, dog. GBT 40 Mini. She can handle it. She can do it too. Okay, that's nice. That's really nice. Here we go. We got four different colors now. Do I get access to the, uh, thing? I can't believe I'm over here. I'm over here without GPT 40. She's the one who started it for us. And I turn my back. I turn my back. That's so whack. That's so whack. But GBT 40 Minis, they're trying to take over the world. Give it to me for free, then. I'll take it for free. I'll take it for free. Yeah, sure, sure, sure. Hey, but now it's missing the, uh oh. Okay, but it worked, which is good to see. Risk on, risk off. That's all I want to know. Yo, use this. Use this as a template. You, you lost me, B. It looks like you lost a little code. Use the below as a template, but just with the four gem Simons RI features. Features or states. Ah, this is tough, dude. A lot of new stuff coming at me. Send back auto code. Send back full code. Nothing less, please. Pretty please. Got to risk it for the biscuit. Risk on, baby. Robo says, 777. Eric, good to see you. D Robo, we're about to Robo, we about to start working through GBT virtually. I'mma ask him stuff and he's going to put it into his GBT and then send it to me. Oh, get him, get him. I would run up your bill, though. Not your bill, but I'd be running up your credits, mad. You see, I already got logged out. Oh, it's about that time, though. 11 minutes. I can wait. I can wait for it. I might do a little meditation in between. Actually, that sounds quite nice. You've hit the free plan limit. Continue with other basic models or. Wow, wow, wow, wow, wow. I didn't know they had a. I did not know they had an upsell right there, dude. That got me. That one got me. They're going to, they're choking me out here, B. They're choking me out. I think I'm just going to meditate then, because I own my time. Come on, B. Let's go. Let me upgrade. Claude, is there a bigger plan? You got a bigger plan? Come on, B. I'll give you 10 more. Claude pricing. I'll give you a little bit more. I'll give you 30. I'll give you 30, bro. Okay, team per. Why you guys just always copy each other? Be creative. Come on. Throw this 69 on the price. Price. Build annually. Free. Learn about Claude team. I learn about it. Don't know if I'm going to be about it, but okay. Well, that's too bad. Um, I guess I could just get another membership. But if I'm going to get another membership, I'm, I'm going back to Open AI. Yeah, that's what I'm feeling like. That's how Doug said. Claude can have my babies. Shaking you down for that 20. Robo says, I run mine up every day, but I don't got experience like you. Robo said, well, thank you, man. That's, that's odd. That's odd to think, but I'm just a, I'm a womb engineer. I'm new to the game. 777 KW, what up, bro? Much love to you. Much love, much love. Okay, so Claude's got me. Claude's got me in a chokehold. So I think I'm just going to meditate and come back later. Use hmm to predict four V regimes and then determine risk on or risk off. Okay, let's just start there because I have these risk on, risk off. Here and these are the four regimes, right? This is what we created. Let's go ahead and see what's Gucci with it. So, okay, now it has four different regimes here. And it says risk on, risk off, high volatility, low volatility. But remember, remember, those don't really mean anything. They're just four different things here. People are getting licked right now. Um, did we already test this? Let's see here. Execution complete. I don't think we did the second part of this yet because we got cut off. We got cut off. Train the hmm. Analyze states. Save state changes. Let's go to our best one here. So this is the B best hmm so far. And the idea I wanted to tap into today was splitting up to four, four different states, maybe even less. No, I think four would be appropriate. Okay, so here's the, the best one here. I just want to see the data that I get back. I get this stuff back. So, hmm likelihood, state prediction, all that good stuff. So that's what I need back here. And if I go to the best here, best is here. And then I'm just going to say, um, state prediction. Okay, I need all this code, dog. So I'm going to copy all this into Claude and say, what's up? Hey there, I have attached some code of an hmm, which is about 250 lines of code. Use that as a template to update this. The other, the other script. It is missing a few prints and model saves. Send back full code. Right back to it. Claude cuts us off and it slows us down for a second. I think I'm just going to, I think I'm just going to buy Open AI as well. I'm just careful with that subscription addiction. Careful. But we'll see. Today, if it cuts us off, I might have to just light it back up. Just run it back up. Yo, what up, Nicholas? How are you? 777 Aqua, what's up, bro? He says, yeah, Aqua says, yeah, it's weird paying for something and still being cut off. AI products are interesting, bro. Drop your script. PL says, okay, so I've dropped it. It's dropped. I dropped it on the floor. I dropped it on every single YouTube video. But let's keep it going, B. Much love, much love. 777. Alright, so I understand you'd like me to update the second script from the past 2.txt using the first script from paste.txt as a template, adding some prints and model saves. I'll do that and provide the full updated code. Added more print statements, included additional analysis. Alright, let's check it out. Copy. There we go. That's looking better. It's looking a lot better. And it's got the save. Yes, there we go. Wow, they really gave us too much power here with this AI, dog. Too much power. That's why they had to cut us off. Yeah, duh. I'm just going to, I'm just going to get Open AI back and I'll just, I'll be running both. Cuz if it's going to cut me off every day, I wonder if it's based on times too, like times of day. But this is our data back. Or this is our chart back. You can see it splits into four different states. They call it, um, I often refer to as regimes, and risk on, risk off, high volatility, low volatility are the four regimes. But these names don't matter. Remember that. Remember that. Remember that. Okay, here we go. This is the stuff we really wanted to see here. It's going to take a second. It's going to take a second. There it is. Boom. Okay. And it saved the model as well. So that is our analysis here. And that's pretty good. I mean, the prediction at least. Let's go ahead and triple triple. No, no, no. Let's put it. I need to put it somewhere better. I need to put it somewhere better. Uh, readme. [Music] um, live ops.md. I love it, dog. Alright, so let's go ahead and say 821. Okay, this is the, this is for risk on, risk off. Copy relative path. Alright, so that's that. Now I have that. Let's just open live ops to the right and then go ahead and go back and get the best model here. So the best model, model two, best. And then I want to run this against that and just see what the diffys are. If there are diffys or not. If there's not, then we'll just, I don't know, move on. Keep, keep it moving, you know? Keep moving forward. That's it. Next idea. You see the list. Alright, this is fitting the model. So it takes a little bit of time. Time, time, time. So this one predicts seven different states. So the reason I went down to four was because I wanted to try four. And I've heard Jim Simons say, or I haven't heard him, but I've heard it. It's been said that he thinks there's nine states. But I've also heard somebody say that he thought there's four. So I did seven cuz I couldn't think of nine right away. Remember, these names don't matter. Alright, so I'm just testing both. Just testing both. Copy path. Copy relative path here. And say VV the heck. This is supposed to be our best model. Best model. I don't expect good, but you know, that's our best. And let's see. Let's look at this one. The BIC. I think this is, I mean, that looks better. Not better, but I can't remember. That's the thing is, I can't remember what the, the good one was. But you can see the BIC here on our best model. It's supposed to be negative. So it looks like this one's not very good either, unfortunately. Our best model is still the volume change. Okay. H, what does that tell me? I don't know. But that's my prediction. Let's see if I've learned. Because if I've learned, that's that's awesome. We've been here and, uh, you've been here, dog. So let's go ahead and compare these two models here. Claude, what's up? Above I have two, two hmm models. One with four states and the other with seven states. Um, please walk me through the results and compare them. Which is better? Okay. And that just had me think of something. What are the inputs we're putting into this one? That's the question. Because here you can see it, volume change. And that's taking up most of the, I don't know, I don't know what you call it, dog. The brain power of the hmm. The volume change. Is that weighted the most? It's the most important. It's the most important. So what am I using over here? Am I using the volume change? I am. Perfect. Perfect. Perfect. Are these the same? These should be the same. Let's go and look at it. Lynn. Okay. These should be the same. They should be the same. Let's see it. They're the same. Perfect. Perfect. So it's a fair match. It's a fair match. Four versus seven. This one has seven. This one has four regimes. Okay, let's go ahead and see what shorty said. Certainly, I'll walk you through the results of both models and compare them based on the metrics provided. Let's analyze each aspect. State prediction accuracy. For state model, 91. That was the new one. Seven state model, 89. The four state model has a slightly higher accuracy in predicting the next state. This suggests it might be more state in it. State transitions. The log likelihood of four state is -29k. Seven state is 14k. The seven state model has a much higher log likelihood, which typically indicates a better fit to the data. A positive log likelihood, as in the seven state model, is generally better than a negative one. [Music] Bayesian Information Criterion. BIC. Four state, 58k. And seven state, -27k. Lower BIC is better. Cross-validation. Negative has a higher positive cross-validation, indicating better generalization on unseen data. However, both have high standard deviations, suggesting some instability. Cross-folds. Feature importance. The four state volume change, 97%, but 94% on the seven state. Interesting. A little bit more, but did not make it better. Model fit comparison and conclusion. The seven state model shows a significantly better fit to the data, higher log likelihood, lower BIC. Generalization. The seven state model generalizes better to unseen data, higher cross-validation, despite having more states. The seven state model performs better according to BIC, suggesting the additional complexity is justified by improved fit. Prediction stability. The four state model is slightly more stable in predicting the next state, but the difference is small. Overall, the seven state model appears to be a better choice. It demonstrates superior fit to the data, better better generalization, and more nuanced state representation. The only metric where it is slightly behind is the state prediction. Okay, so the seven state still better. Seven state still better. Okay, well, I think we, uh, I'm going to call this multi-regimes. Regim. Reimy. Multi-regimes. Okay. Risk on, risk off. I'm going to say four here. Yo, what happened to it? Four regimes. Okay, I need to do this for all of them. Let's do it for two, three. Two, three. Turn this into just two regimes. Three regimes. Uh, risk on, risk off, and frenzy. Send back all code. Send back all code. Frenzy. That sounds fun. A frenzy. All right, so three regimes.py. All. I'm just going to start cooking up here. I'm going to say two regimes.py. You working for me today, baby girl? You working for me? Regimes.py. Six. Six regimes. Six. There's got to be a better way to do this, but oh my god, I'm just going to use all my credits here. I'm going to use all my credits. Let's run it. Three regimes. Okay. Okay. Risk on, risk off, frenzy. Risk on, risk off, and frenzy. That's good. 95% killer. Wow. Log likelihood though. Let's.

See it. Let's see our good log likelihood. Log likelihood. This is like a test. How do you, how do you remember this? How do you remember if it's supposed to be high or low? Just repetition. Be so, let's go ahead and say, uh, this one is that. And what is it though? Three regimes. Copy, copy, copy. Relative path. You thought I was going to say path. All right, send me, um, now do one with only two regimes. Excuse me. Yo, send me back all the code too. Don't play me. Don't play me. Not today. Don't play me, dude. I think this one's better. No, it's not. Cross validation. Not looking good. That should be positive. If you look here, see our best one here. Um, up here. Hey, where are you, dude? It was positive. All these others are negative. This one's not good either. But it's, uh, SS. Copy, paste it. Run it. Do this the same. Do the same, but with five regimes. Then do the same with six regimes. Then do the same with eight regimes. You name the states. Send back full code. One script at a time. Then please ask me if I would like to move to the next one.

Yo, you know what I was thinking just right now? Is Sonet 3.5. They got to change the name because 3.5, and they don't got to do anything, honestly. They can do whatever they want to do. But 3.5 like degrades it lowkey because, you know, OpenAI over here. 3.5 is not, you know, not popping like that. We're on four now. Dos, dude. All right, let's get this plot out of here. Actually, I want to look at it first. Risk on, risk off. Risk on, risk off. All right, so now you see it. Log likelihood. Okay, okay. So this one here. Then live Ops and paste it right there. And this was two regimes, right? Copy P. We got AI working over there, right? She working, right? She working, right? I hope so. Yes. There you go, shorty. That's how we're going to do it from here on out. Okay, so that was, I believe that was five. Let's see, let's see, let's see what their names are. One, two, three, four, five. Okay, because the names don't matter. Remember that. Just remember that. Dog names don't matter. We got to label them later, I guess. I guess that's how it goes. I don't know. To be honest, I'm just out here in the ocean. I swam out in the ocean. There's no boat. Actually, there is a boat. Cloud, but she left me yesterday. That was so whack. It was so, so whack. Okay, handle that. Claudia. Claudia. Claudia. All right, so this one's looking bullish, bearish, sideways, volatile, and accumulation. Looks suitable to me. 92%. Let's wait for this to finish up. And then I'm gonna have, oh yeah, you're going to keep going. Yes. Copy. You, you're going to keep going. Yes. Let's go ahead and put six up in there. Let's go check out the uniqueness of the names they came up with. Strong ball, weak bull, sideways, weak bear, strong bear, high volatility. Okay, we got this. This is a good flow right here. Nice, dude. Don't drop the ball. Don't drop the ball in this nice flow. Let's go. Let's keep going. What was that? Five. Copy relative path. Yes. And then that was the last one we wanted. Let's do a nine. To why not? Why not? Oh, because they cut you off. Yeah, that's why not. But that's it is what it is. I'm just about to run it up. I'm gonna get them both. My goal is to get cut off from both OpenAI and Claudia in one day. That would be dope. That means we're working. That means we're going hard. And I'm sorry. I'm sorry. I actually feel better. Bad. I feel bad. I feel bad. Like if I feel bad if somebody has any hard feelings about that's being able code so fast now. God. One, two, three, four, five, six. One, two, three, four, five, six, seven, eight. All right, let's do it. Let's run it. Don't drop the ball. I said, don't drop the ball. Keep it going. Did we get six on there? I hope so. Six. We didn't. Okay, so let's run six first. It's going to take a little bit longer. I believe. I don't know though. How's this looking? Whoa, whoa, whoa, whoa, whoa. You can print out 10 now. Get copy nine regimes.py. Okay. And then please, please print out 10. Please print out 10. Okay. And we got. I'm not going to show you the plot on this one. Sorry. I'm just closing it. So we're on six, I believe. So let me go ahead and grab the copy path here. Five. And then let's just wait for it. Let's make 10. 10 regimes.py. Now, just wait on six. So these look like they're getting longer every time. I guess that makes sense. I guess that makes sense. 95. Okay, look at this cross validation score. Take shots, dude. So six, six is dirty. But I don't know. I, I don't trust me on that. I have to ask AI because, you know, they know everything. So this is eight. And then we got to run eight. Let's run it. And then we got 10 in the oven. Yep, there it is. I'm not going over 10. I'm not going over 10. That's just [Music] egregious. There we go. Dang, my computer working right now. I can feel it. I can smell it. So this is eight. We're running right now. And then here we go. What? Yo, what's up, Wishy? Anthropic. Um, everything in Cosmos is up. Is Claude better than GPT-4? Uh, I don't know. I like it right now, but I don't. I'm indifferent. All right, this is taking a long time, huh? This is eight. So we'll see if nine comes through. I'm chilling. Wishy, how you doing, bro? How you doing? There we go. Got it. All right, let's do nine now. Copy path. Relative path here. Hey, hey, hey. Copy relative path. Okay, perfect, perfect, perfect. All right, now did we get eight in there? Yeah, we did. We did. Nine. Copy path. Okay, now I'm just waiting for the data. Just waiting for this fitting of the machine learning model. The Hidden Markov Model. Um, let's see. You got any questions? What do you always mean with 777? It's just a way to some love and some good vibes. Aqua says, 777 symbolizes spiritual perfection and divine completion, representing the ultimate alignment of mind, body, and spirit with cosmic forces. Wow, that's deep. That is deep. I love it. So this is the thing, but I'm just going to let it go through. Perfect, perfect. You can see this one's a little less here. 88. Uh, how much resources does this kind of AI use? Oh, that's a good question, dude. I don't know. Um, I don't know. I don't know about that. Everything in Cosmos is up. Cosmos. Everything in Cosmos is up. All right, since I got a little time, I'll go over there. See what categories. Maybe is that how you do it? Oh, clicked on an ad. They got me already. Monetize me already. Um, Adam. Yes. So this is awkward. I thought they had like a category or something. Maybe you just go to Adam or [Music] something similar. Coins. Marcus, how do you? Yeah, how do you observe? How do you observe? Everything's up in Cosmos. That's my question. So this is what number is this? That was nine. Nice. Now we're just going to do 10. Copy path. Copy relative path here. And then I'm going to run this. Yo, Derek, what's up? 77. Bro, 777. How are you? How are you? Daniel, 777 to you as well. Much love. Peace. All right, so this one is training with 10 components. 10 different components. 10 states. 10 regimes. Whatever you want to call them. Call them what you want to call them. All right, so here we go. And, um, these are the 10 states now. So what do you think about him? What are your thoughts? My thoughts are, close it. And let's get the data back. And then let's get some a professional thoughts. How about that? That's a good idea, right? I think so. We got all the data from our, our file here. Compare all of these 10 models. Maybe not. Maybe not. I'm going to do it one by one. Yo, Wishy. Yeah, I'm working on a Hidden Markov Model. Jim Simons tipped me off. So I just wanted to dive into it. Spent some time. It's been the last few days. Still noob as usual. Just a womb engineer over here trying to find his way in this deep, deep ocean without a boat. But I got a boat until the boat rugs me. And we'll see if Claudia rugs me today. If she does, then, you know, maybe it's time to go beg, beg for forgiveness from OpenAI and get back on ChatGPT. Because I might need both. How's Gemini in comparison to GPT-4 and Claude 3.5? Are be curious cuz it, you know, I just be curious. Come on, baby, show yourself. Show yourself. You're almost there. So it has 10 states. So it should be a little bit long, longer. Daniel says, thanks. I've been discovering and testing algo trading thanks to your channel. My goal for 2025 is to move from web dev to trading and bot. That's dope, dude. That's dope. Welcome to the world. All right, here we go. Glad you're here, my brother. Let's go ahead and throw this 10 in there. And compare all these. Just compare them all. Just compare them all. Except the first one, because the first one we know is doooo berries above. Is [Music] are a bunch of different hmm. Test Iran. Please walk me [Music] through them. What? Which did best? Why? I mean, that's, that's, you know, it's good enough. It's good enough. It's good enough prompt. You know, we're deep, deep, deep in this game now. And, um, you know, it's been a journey. This has been a journey. I love, I love these journeys though. I do. I do love these journeys. [Music] Bro, uh oh. Are we back on mic? Talk. Is my mic messing up again? Everything clear for everybody? [Music] Dang. I'm from the US West Coast. Where you? Where are you? I think, I think you've told me before, Daniel. The mic sounds good. Okay, cool. Oh, it's clear. That's why I'm asking. It's a, um, cool. That's cool to hear because yesterday we took a pivot. Some, uh, there was an audio engineer here or somebody that's sitting in a studio. And he had some problems with the bass. So he helped me with like the, I don't even know, honestly. It was like learning how to code again. Playing with the little meters and stuff. Anyways, it's a ATR or it's a audio tech. Audio tech. Sorry. It's upside down. Audio tech. Nika Cuban. Half Cuban, half Spanish. I'm from Spain. Dope, dude. Great place. It's a great place. I would love to go back. All right, let's go ahead and see what they said. So certainly, I'll walk you through the results of the different hmm models and analyze their performance. Let's break this down by looking at various metrics. State prediction accuracy. So two regimes had 97. Three regimes had 95. Five regimes 92. Six regimes. Okay, this kind of makes sense. I guess the models with fewer states tend to have higher prediction accuracy. This is expected as fewer states are easier to predict. But it doesn't necessarily mean they're better at capturing market dynamics. Log likelihood. Higher is better. 10 regimes was the best. Nine regimes was second best. Okay, so this just goes 10, nine, six, all the way down. Straight up. This is insightful because it seems like it's all in order. The models with more states have higher log likelihood, indicating better fit to the data. BIC. Bayesian Information Criterion. Lower is better. So 10 regimes is the lowest. So it's just the more regimes, the better. The BIC penalizes model complexity, yet we see the more complex models, more states, perform better. Cross validation score. Higher is better. So the more states, the better. I mean, that's what it's saying. I ain't say it. I mean, we'll see. We'll see. We're not even done reading. Come on, Moon. Read the whole thing again. Models with more states perform better in the cross validation. Feature importance. Most models heavily rely on volume change, except for the two regime model, which gives more weight to the BB W width and volatility. Based on these metrics, the models that perform best are 10 regime, nine regime, and eight regime. And we had, we weren't even going to test those, dude. Give me something with 50 regimes. B. Let's see. See how that does. The 10 regime model shows the best performance across most metrics. Highest log likelihood, lowest BIC, highest cross validation score. Why did, why did these models perform best? Complexity matching market dynamics. The cryptocurrency market, especially Bitcoin, is known for its complex and volatile behavior. Models with more states can capture more nuanced market conditions, balancing fit and generalization. While the 10 regime model has the highest complexity, it still outperforms simpler models in cross validation, suggesting it's not overfitting, capturing diverse market states. With 10 regimes, the model can distinguish between subtle market conditions, easy, neutral, bullish, and neutral versus neutral, bearish, which might be crucial for trading strategies. Feature utilization. As the number of regimes increases, the models start to make slightly more balanced use of BB width and volatility features, potentially capturing more information from the data. However, it's worth noting the difference in performance between 8, 9, and 10 seems relatively small. The 8 regime model might be a good balance between performance and complexity. Get them out of here. LDO. Whatever that is. Somebody just got Chopped off. Rest in peace. Don't trade by hand. Somebody else just got Chopped off. N. They just got shot. Dang. Both ways. What's going on over there? That's always interesting when I see liquidations on both sides. How did this guy get licked right there on BTC? What is you doing, dude? Right here. What are you [Music] doing? What are you doing? How do you get licked right there? How much levy did you use? Maybe that's a mistake too. I don't know. LDO. Okay, let's check it out. Let's see what LDO is. Sorry for the switch up here, but dang, I don't get that. Maybe this data comes in wrong sometimes. Um, I don't know, but that would not be good. Somebody just got wrecked for a mill on Uniswap. A hand trader, of course. It's so sad to see. May he rest in peace. On Uniswap, you were shorting Uniswap and got licked. Let's see his decision. Okay, so I mean, shorting by hand is just a bad idea in general, in my opinion. All right, so let's go ahead and say, however, it's worth noting the difference in performance between 8, 9, and 10. The two regime model stands out for its unique feature importance distribution, giving weight to BB width. While it performs poorly on the other metrics. 400,000 Chopped out of here. Every time you hear that chopper, that means somebody just lost all that cash. B. They're on tilt. They'll probably lose some more. Boy, don't go back there, dog. Don't go back there. As the number of regimes increases, the state prediction accuracy decreases. This trade-off might be important depending on your specific case. In conclusion, the 10 regime performs best overall, but eight or nine may be the. Give me, please, please send me the full code for a 15 regime one. Yo, Civil, what's up, dog? Good to see you. Yo, Rybear, why are so many people hyped over Tron and their memes? Are they that good? I don't know, dog. I don't know. I don't know. But Jane hit me up about it. You hit me up about it right there. Anybody else hearing things about Tronny Tron? I've had bad outlook on Tron since, you know, for a long time. So I instantly faded. But then I think it through and it's like, okay, if I instantly fade something, I got to at least look at it. I'm that biased against a whole chain. I got to at least peek. But I feel like they always have this thing every year or so. They've got some sort of bubble of something that goes on. Um, I'd be curious if other people are hearing about Tron and all that stuff. Um, I've never really been a fan. But, you know, that makes me want to look into it. I get bad vibes from Tron. Derek says, I agree. Always have. Probably always will. Unfortunately. So I don't know enough. Just though, I don't want to dog it or anything. But 10 regimes. Now we got the 15 regime Mac Daddy. Let's see how this one does. Let's see it dance. I know you're going to take a while. It's okay. I'm here. I'm patient. I'll be here for 60 years until I can't stand no more. Let's read out these names. Extreme bull. Let's see. One, two, strong bull. Three, four, five, six, seven, eight, nine, 10, 11, 12, 13, 14, 15. Indeed. So extreme bear, strong bear, moderate bear, weak bear, bearish consolidation, neutral bearish, slightly bearish, neutral, slightly bullish, neutral bullish, bullish consolidation, weak bull, moderately bull, strong bull, extreme bull. I can see how this does well now that they've explained it to me. Thank you, AI. Here we go. It's plotting. We're done with half of it. You want to see it? I should. You do. Let me just copy this over real quick. Seven state is still better. Not 8, 9, 10 is best. All right. Past it. Yo, Civil, bro, you are the, you, you are the man. Thank you, dude. Got to hop in a meeting. Just wanted to say, hope you all a blessed day. Dang, so much love to you, dude. 777 from me and everybody in this chat. That is so much love, dog. You didn't ever have to do that. So kind of you. All right, so let's see this. This here 15. I see how this could be good. How many days are in the year? 365. So what would this equate to? 365 divided by 15. 24 different. 24 day regimes. Yeah, 24 day regimes, essentially. Let's, let's not get ahead of ourselves. Looks cool. Colorful. Yeah, yeah. Let's check it out though. So, okay, okay, okay. So 10 regimes. What's the highest right here? Log likelihood. We need high. 47,000 is the highest. Oh, snap. So what if Jim was capping? Not capping, but, you know, like, you got to keep his stuff a secret. And I don't know where I heard him say nine regimes, but somebody else said four regimes. What if it's really like 18 regimes? What if it's 20 regimes? What if it's 30 regimes? I don't know. I'm going to keep testing this though. Yeah, why wouldn't I? Why wouldn't I? Why wouldn't I, dude? So much love to you, dog. Civil. He said, be for real. You putting [ __ ] on game for free 99 every day. Got to respect that. You're a good dude, man. You're a good dude. You got me over here shooting jumpers. He just threw five at your boy for no reason. I'm over here shooting jump air jumpers. Probably a couple air balls. But, you know, appreciate you, man. That's love. That's love. I just know the pro, the process of pulling out a credit card to to send a super like that is just like, you're spending your time, which is so kind. So kind. So kind. Good to see you, my dude. Good to see you. So this takes longer every time, which is, it makes sense, right? It makes sense. Explain why. Explain why it takes longer to run the, um, man, this stuff. This part of run the code with more, um, more states. I just want to get an AI's perspective on this because, you know me, I know nothing. B, I'm just here vibing. All right, so here we go. This is good. The increase in runtime for models with more states is due to several factors related to the computation, computational complexity of Hidden Markov Models. Hmm. Let me explain the main reasons. Parameter estimation in an HMM. The N states. We need to estimate N * N transition probabilities, the transition matrix, and initial state probabilities, probabilities, and sets of emissions probabilities in this case, means and covariances for each state. As N increases, the number of parameters to estimate grows rapidly, requiring more computations. Expectation Maximization EM algorithm. The Baum-Welch algorithm, a type of EM, is typically used to train HMMs. Each iteration of this algorithm involves forward-backward procedure complexity, where T is the length of the sequence. Reestimation. Maybe I asked to, I asked the wrong question. Reestimation of parameters also grows within more states. Mean more iterations are often needed for convergence. State sequence prediction. The Viterbi algorithm has a time complexity of N squared S, as N increases, the time to predict states grows quadratically. Matrix operations. Operations like matrix multiplication have a complexity of O to the N cubed for N * N matrices. Larger state spaces. Okay, so convergence time. The likelihood landscape becomes more complex with more parameters. This can lead to more iterations of the EM. Memory usage. More states require more memory to store parameters and intermediate calculations. If memory usage exceeds available RAM, it can lead to disk swapping, dramatically slowing down computations. Cross validation and model evaluation. Okay, so it just takes longer. Sweet. I'm going to get one. Send me one. Send me one. One with, uh, I want two. Two per month. 24 states. Two states per month. Because what if that's the case? You know, so this is the new one. And it looks like this was 15 regime. So more. I don't know how to make my notes look good. But 15 state. Cuz that's what she said. No, it's not what she said. But I'm just going to have it there. So BIC. How's that looking? So it's obviously not as good. 86, 83. So it just goes straight down. That makes sense. I don't know. Maybe I'm going overboard now. But we'll see. You know, uh, HMM. It means Hidden Markov Model. Rybear says, I've always stayed away from Tron. He did a fork of pump fun on Tron. The volume looks interesting. But Tron, Tron. Yo, what's the link? It's called Sun pump or something. That's just so sketch. This is it? Okay. Um, I'll look into this a little bit later though. Let me, uh, get this last one done here. And then I think this is a pretty good tippity tip tip, dude. I think we learned something big here, dude. We learned something big today already, dude. All right, so this is the 24 regimes. Let's see if it's truth. I think it is. Let's run it, dude. All right, so this is 24 regimes. If you got questions, let me know. Sun pump. Are there any other resources for Sun pump? While I'm waiting on this. Hmm. This is so funny. We're using machine learning models to predict market regimes, but also looking at some pump. Oh my God. Okay, so I don't see much movement like the other one. But what's Sun swap? Is that there? That thing. Um, one minute. The thing is, I don't know how to use pump fun. I never, I never used it. And I can't build a bot for it. I'm not that interested. Is this just because it just launched? Like if there's a ton of volume? I don't know. What is this? Where are you guys getting this volume from? Like, uh, not volume, where are you getting this, uh, this information from? Is like Twitter? Everybody's talking about it on Twitter. Are there anybody? Yeah, like I'm curious. I'm curious. Anyways, here's 24. This, this is for Kobe. So it should be the best. Yeah, it is the best log likelihood. So far. I'm going to go ahead and say, hey, please tell me one or two things about my data. Please go ahead here and tell me this. Here's the 15. Here's 15 regimes. Attached is 15 regimes and 24 regimes. Compare them to my 7, 8, 9, 10. Which is best? Okay, so that's going to be done here in a few. Everybody's talking about this over here. It seems super sketch already. I mean, I think pump fun seems sketch. But so if you get to put it on Tron, then it's like, okay, a little sketcher. But so sketch on sketch equals super sketch in my opinion. A few seconds ago. Here we go. I'm a pass for now. I'm a pass on any, uh, any of this. I'mma fade it. I'mma fade it. I think I'm gonna fade. Is this a bad idea? I don't think so. I think it's, I think it's a good idea. Anything Tron, I instantly fade. I'm sorry. It's sketch. I don't know why, but I got no proof. I've got no proof. But see, they got API. He's over here like, they got API though. Yeah, I'm good. I'll keep an eye on it. Thank you for like, like it for me. But, you know, pump fun seems cool. Like I just never got into it. I can't use right API. Whatever. I build snipers. I build snipers. So I just kind of faded pump fun. Um, probably for the better. It seems like I'll keep an eye on it. So Tron double fade. I'mma stick get. I'mma stay right here. I'mma stay right here. I'mma stay right here. Let's check out the, see what people are saying though. Maybe, maybe it will convince me. While we're waiting. Tron dropped an R. All right, nothing yet. Nothing yet. Dang, this is taking a long time. But that's good. Let's see what my computer activity is looking like. I haven't looked at that in a long time. That's a luxury right there, dude. Remember back in the day when I could barely keep the stream up? We've come so far. I could barely keep this stream going. Now we got every order coming in. Every order on Binance flying through our screen all the time. Add a Apple. Good job. Good job. Much love. So much love. So much love. Here we go. This might be the best one. So I mean, what does that tell me? I don't know. 24 state. Let's just, let's just go ahead and see what she says. Oh, no, no, no, no, no. Did that? Oh my God. Copy pass. No, no, no, no. Paste it in. Paste it in. 24 states. Copy P. Why do I just switch up the whole thing from here? I don't get that. Okay, okay, now we're good. We're good. Now that's the path. This is the path. Every day, dog. Every day. Every day, dude. Every day. Just keep going. Just keep going. Peace by piece. Makes a little more sense. Every day. Every day, dude. Copy path. Flag it. That's a flag, right? That's what they call it. I'm growing up in front of your eyes. It's amazing. Thank you for all your help. Could not have done it without you. You teach me things I don't even know I need to know. Attached is 15 regimes and 24 regimes. Compare them to the 8, 9, and 10. And we just keep on going. Come on, dog. I'm still here. Where you at? The screen's gone. Is what you're telling me. I was just talking about this. I was just giving you props. Apple. The screen's gone again. So that means I'm gone again. That's crazy, dude. Essentially, it takes the stream down. All right, this makes sense though. I'm might have to cut some of these orders off. I was just giving daps to Apple. Thank you for providing the results for the 15 regime and the 24 regime models. Let's compare these with your previous 8, 9, and 10 regime models to determine which performs best. We analyze each metric. State prediction accuracy. Eight regimes not provided. Nine regimes 88. 10 regimes 86. 81. Okay, the accuracy decreases as the number of regimes increases, which is expected. The more states to make predictions more challenging. Log likelihood. Higher is better. The log likelihood consistently increases. 15,000 versus 68,000 log likelihood. It consistently increases with more regimes, indicating better fit to the data. BIC. Lower is better. The BIC continues to decrease with more regimes, suggesting that the increased complexity is justified to improve to the improved fit. Cross validation score. Higher is better. Dude, so this is just telling me that more regimes, more better. More regimes, more better. Let's do 56 regimes. No, no, no, no. Come on now. You're going crazy, dog. 24 is fine. What do you think I should do? 56. Throw a 56. And if I should do 56, it's going to take a minute. I might have to shut some of this stuff down. Shut some of these order flows down. Should I do 56? That's how many years are months are in the year, right? Weeks in a year? 52. 56. It's 52, isn't it? It's 52, isn't it? How many weeks in a year? How many weeks in a year? Time is irrelevant to me. I don't live by time. I time. I live. I time my living. 4 hours a day, dog. That's it. I locked in. I didn't know how to code. I locked in for 4 hours a day. And it was so hard. It was so hard, dog. It was so hard. Boohoo me. Oh, I have to tap my fingers for 4 hours a day on this little code. And now we got AI. This is so, so hard. 4 hours a day. You kidding me, dog? I used to game for 8 hours a day, dude. I used to game for 8 hours a day. 4 hours is easy. Feature importance. Interesting. I like this. On the 24 regimes, it goes down a little bit. It, it passes it over to volatility analysis. Model fit. The 24 regimes chose to fit best fit the data with the highest log likelihood and lowest BIC. The 24 regime. This is for Kobe. This is for Kobe. We got to use the 24. Yeah, I'm, I'm sticking with it though. Maybe the, maybe it's better. Maybe there's others that are better. But I got to, I got to kick it with the 24. I mean, 50 is pretty, pretty N. I got to do 50. Give me the code for a 50 state. That's probably gonna be too many. But, but let's just get it anyways. Let's just get it anyways. Might take a while. That's okay. I'll just run it on a terminal, dog. That we can keep it coding. Duh. Come on, Moon. You got to be thinking sharp all the time. Come on, B. Tap in. Based on the metrics, the 24 model appears to be the best performing model overall. It shows the best fit to the data, the best generalization, and the the most balanced features. This is fun, dog. This is fun. All right, I'm going to do 150. And we're just going to let this run for a while over somewhere else. I'm not going to worry about it. But it's free. It's free, dog. This is free. A free beat, dog. If it's working, 24 or 50. Those are the two most important numbers to me. So I would love that to be part of, part of a system. Because then I get to smile every time I think about it. Cond activate T flow, bro. Cond activate T flow, bro. Python. Run it. What? Cussing? What is going on? Copy P. Python. Python. Python. Python. Python. Python. Python. Whoa, dude. What's going on here? Cond activate T FL. Okay, I'm there. Copy path. LS. CD. Oh, maybe I got to do that. No, I don't have to CD in anything. [Music] Python. What's going on, dog? What is this right here? Is that a thing? It's always been there. Look at it. I've never seen that. Yeah, I guess so. And does it run from here? Oh, okay. I'm not quite sure. All right, big guy. Love you, bro. I don't know if the 50 is going to work out for us, but let's try it. Let's give it, let's give it a try. Give it a try. Let's give it a try. 24 seems acceptable though. That's what, um, every 10 days or something. 4 days. 14 days. 17. N. 365 divided by 24. 365 divided by 24. So 15 days. Every 15 days. Main execution. This is what we're changing, dude. This is what we're changing. Okay, let's check it out then. Main execution. No, we want all of that out of here. Peace. All right, let's run it. All right, so here we go. We got 50 for the big guy. This is going to take a long time. Let's see if I can run it from my other thing here. Copy path. Okay. Um, Cond. All right, we're not doing 50. We're not doing 50. I'm deleting in it. Much love. 24 is the best. So if there's 24 regimes for Kobe, that means that. Okay, I got this now. If there's 24 for Kobe, I was pre-shock. That was pre-shock for sure. Post-Shack trained. I want to get one of these back tests going. BT. Okay, the first paste. Let started. New, new little chesy. The first paste of code above is a template. Template for you to follow for the HMM plus backtest. I want to test the trading idea of. I mean, what idea do we want to do? Is the question, dude. Hmm. Models. 24 regimes, dog. This is what my. I want to make a script here to explore this data. No, dude. I want to make a script to explore the above data with Matplotlib. Um, I'd like to print out pie of it. All right, so I'm plotting it out now. Matplotlib. All right, so, so from all this, I found the conclusion is 24 regimes. Volume change heavy. There you go. There's the alpha, dude. Much love. I don't know if it's actually alpha, but we'll see. Strong ball. Okay, this is sketch AF, dog. I don't like it already. I'm sorry. So this is, I'm glad we're looking at this because this is, this shows me, okay, this is looking at the past, right? Let's ask AI. Let me try to get this thought out here. A sample of the state changes for the 24 regime is above. My worry is that each row has a different regime, but these are all decided after they happen. So how do we know that the accuracy is 80%? 80% or whatever is shown below? I do that. Why am I like this? It's not whatever below. It's literally 83% dog. Dang, that's funny. I didn't even do it. I didn't even get it. I think it's up here though. Post Haw classification. You're correct that these regimes or states are typically classified after the fact. This is common in financial market analysis where patterns and trends are often identified retrospectively. Accuracy measurement. When someone claims an accuracy at 80% or any percentage for such a classification system, they're referring to how well the system performs in predicting future market behavior based on these identified states. Here's how to think this might work. Historical analysis. The system is first developed and tested on historical data where states are identified post HW. Forward testing. The system is then applied to new unseen data, either through out-of-sample testing or actual real-time application to see how well it predicts the future market behavior. Accuracy calculation. The accuracy percentage would typically be calculated based on how often the market behaves as expected given a particular state classification. Verification process. To truly verify the claimed 80%, you would need a clear definition of what constitutes correct prediction for each state, a set of test data that wasn't used in developing the classification system. That's what we need to do. Thank you, AI. Alis Le. Okay, I want to, I want to go back here. Send me back the code that. Yo, what up, 777? Bailey, uh, that, um, okay, so model regimes. Hmm. Models trained models. 24. Copy path. Where's the other 24? Here somewhere. Copy P. Okay. And then what was the best one before? It was seven. Seven. Seven. Six. 10. Three. Where is seven? We on a seven here? All right, we're going to do eight. Then we're going to do eight, cuz eight was good too. Copy path. Oh, sorry. Seven is two, duh. Seven is two. Model two. Copy pass. Duh. Copy path. Okay, so yeah, I want to do that now. So now let's go up here. Now back to or not now. Doing an OOS test on model 2, 7 states versus model 24 states. Stats. Okay, so that's going to be good, dude. It's going to be really good. I like the idea. How about you? How about you, dude? Do you like it? I like it a lot. OOS. OOS. Okay. OOS. Two. No. Seven verse. Seven v24.py. Hi, my name is Mev. Let's run it. Cousin. All right, there we go. What's that looking like? I don't know. You don't know. I don't know. I need shots. I need to get shots up. I need to get shots up till I can say anything. I don't know anything in confidence. I'm sorry. Um, thank you. But I also want to get my, uh, BIC. 15. 24. Here's my 24 that I need because I didn't write it down. And it's the best. It's the best in the ass. All right, 24. Past it in there. Yes. Let's do a little two-hand action here. BIC. Cross Valley. Feature importance. Ball change. BB width. Uh oh. I'm lost. To be there. Okay, there we go. Now what? This. I'm going to actually run this again because I'm going to get on over here and say, hey, I actually, no, this ain't it. Hmm. Did we get this script though? This ain't it. This ain't it. Oh, no. I lost it. Okay, that's fine. Fine. We're just going to run it from here then. OSS. OOS. S. S. OOS. Let's get it. Okay, okay. [Music] Okay, to throw extra couple K's on there because I was sketch boy sketch. Seven State versus 24 State. Please break down the, uh, OOS test and the results. Which is better? So I did a OOS test here. Copy, copy, copy. Relative path. Okay. Live Ops here. Let's go ahead and check it out. Certainly, let's break it down. Out of sample. Uh, test for the seventh state and the 24 hidden state applied to BTC price. Log likelihood. Seven State 3890. 24933. The log likelihood is a measure of how well the model fits the data. A higher log likelihood indicates a better fit. In this case, the 24 model has significantly higher, suggesting it fits the out-of-sample data better than the seven State model. Visual interpretation. The seven State model shows distinct broad state changes over time. The 24 State model exhibits more frequent and granular state transitions. The 24 State model appears to capture more nuanced market conditions, potentially identifying s sulo subtler shifts in the Bitcoin price dynamics. State utilization. These seven State model seems to use all of its states regularly. The 24 State model also appears to utilize most, if not all, the states, indicating that the additional complexity is being put to use in describing the data. Responsiveness to price movements. The 24 State model shows more frequent state changes, which could indicate higher responsiveness to short-term price movements and market conditions. The seven State model captures broader, longer-term trends in the market. Complexity versus interpretability. Interpretability. Being able to interpret it. The seven State model is simpler and potentially easier to interpret, with each state possibly representing a more distinct market condition. The 24 State model offers more granularity but may be more challenging to interpret. The differences between some states could be subtle. Which is better? Based on the information, the 24 State model appears to perform better in terms of fitting out-of-sample data, as evidenced by a higher log likelihood score. It captures more nuanced market behavior and potentially offers a more detailed representation. However, better depends on your specific goals. If you're aiming for the most accurate representation of market states and don't mind dealing with increased complexity, the 24 State model is superior. If you prefer a simpler model that's easier to interpret and still captures major market trends, the state seven State model might be more appropriate. For trading purposes, the 24 State model might provide more frequent signals, which could be beneficial for short-term trading strategies. The seven State model might be more suitable to for longer-term trend following. The 24 State model's better fit suggests it might be more predictive, but this should be verified with additional metrics like prediction accuracy on future data. What other metrics could we print out to see which is better? Jane, what up, 777? Good to see you. Good to see you. Jane says, check your DMs. I'll check them. I'll check them. I'll check them. I appreciate you so, so [Music] much. Lots of licks happening here. Excellent question. To get more comprehensive comparison between the 7th State and 24th State models, we can add several additional metrics. Here are some important metrics we could calculate and print out. The Bayesian. The BIC. The Bayesian Information Criterion. This metric balances model fit with model complexity. Lower BIC is better. AIC. Akaike Information Criterion. Similar to BIC, but penalizes complexity less. Lower AIC is better. State prediction accuracy. How well the model predicts the next state. Higher accuracy is better. Transition Matrix analysis. Entropy of the transition matrix to to measure state stability. Feature importance. To see which features are most influential in each model. Perplexity. A measure of how well the model predicts a sample. Send back full code. I'm getting the most out of you. You're typing every letter. I'm sorry. I'm sorry. I'm not sorry. I'm going to run you up every single day until you cut me off. And then I'm going to go get somebody else. I'm sorry. Every single day, dude. Claude, you know, we're hitting, we're hitting the limits. Every single day. I'm not leaving till we hit the limits. That's my new thing. I'm not leaving till I hit the limits on both. No, let's start with Claude. Let's start. No, no, but I get, I be get the limits too early. No way. Keep going. Keep going. Every single second for IBY contents. 10 messages. Look, I'm already there. That's funny. Bunny. Some funny bunny stuff. Think I'm just going to sign right back up. I think I'm just going to sign right back up. So this is going to give us a little bit more detail, supposedly. Let's check it out though. If that's truth or not. Oh, man. Here we go. We got some things though. There we go. That's what I like to see, dude. All code back. If they're like, limiting on messages, number of messages, then that's different. Have you experimented with any high frequency models for voting agents? I'm new. Your streams introduced from Twitter. Don't know which AI to use, if any, to execute logic, or if it's all Quant. What are HF models? Not sure what that is. Like high frequency models as voting agents? Nah, man. You're ahead of me. You're ahead of me, bro. Let's go ahead and pop this in here. Thanks for stopping by though. Appreciate you. Much love. Let's go ahead and say, run it up. There we go. Okay, this is more info. Here's more info. Oh, and there's. Okay, okay, cool, cool. Co above is the print out. Please walk me through it and how if and if it changes your outlook or things I should know about it. I don't know, dude. I don't know, dude. I just be asking anything. This is why I run out of chat so fast. They want me to be more thoughtful. And I'm not going to be. I'm sorry. Certainly, let's walk through the results for both models and analyze what they tell us about their performance on the out-of-sample data. Seven State Model results. Log likelihood of 3890. This is lower than the 24 State, indicating a less precise fit. BIC minus 7,000 2. And the AIC is minus 7, 601. This is on the 7th State. Lower values indicate better models. These are higher, less negative than the 24 State, suggesting the 24 State model provides a better balance of fit. Let's go see it. I mean, okay, so here I can see a little bigger here, just so we can kind of understand. 24 State. You can see the accuracy is much lower though. Transition entropy. What the heck does that mean, dude? What does that mean? I don't know. It's okay though, cuz it's just a word. Feature importance. Perplexity. I'm perplexed. You would say perplexity on the print out. Average State duration. 3.59. 6.3. Average State [Music] duration. Okay, we looked at them. Well, let's compare them now. So State prediction is 84% on the 7 versus 72. Transition entropy. 3.96 versus 2.41. And then that stuff doesn't matter. I mean, it does matter, but State prediction is 84 on the 24, indicating a better short term. Is that cap? No, no, this is higher than the 24. All right, transition entropy. 2.41. Lower than 24 State model, suggesting stable state transitions. Feature importance. The volume change of 94%. Perplexity is 0.0000. Very low perplexity and indicates high confidence in predictions. Nice. Average State duration is 6.3. States persist no longer for longer periods compared to the 24 State model. State usage. Zero, two, four, and six are most frequently used, while state three is never used. All right, this is actually useful. This is awful right here. I mean, maybe we'll see. Let's go check it out. Let's go check it out. See what can do with this thing, dude. All right, so State usage for the 24. I want to see that for seven too. Why? Because then I can, I can do some thg dizzles over here and try some things. All right, for seven. Cool, cool, cool. Um, now let's says model fit. The 24 State model provides a better fit to the data. Higher log. Okay, the seventh state has a a higher short-term prediction accuracy. Stability. The seventh state shows more stable state transitions. Feature utilization. The 24 State model makes more balanced use of all features. Unused states. Both models have unused states. 1.

And 7 and 2 and 24, suggesting some redundancy in the space. State distribution. The 24th state has a more even State. Chopper him out of here, get him out of here. 400k off. Damn, dude, that hurts. That hurts my soul. That hurts my soul. That hurts my soul. Rest in peace, young cat.

Changes in Outlook. The 24-state model shows stronger statistical performance, better fit, lower BIC, AIC than initially thought, despite its complexity. The seven-state model's higher prediction accuracy and stability could make it more practical for certain applications, especially short-term predictions.

Things to consider. The trade-off between model complexity and interpretability is more pronounced than initially apparent. The unused states in both models suggest that further optimization of the number of states might be beneficial. The high reliance on volume change in both models indicates this feature is crucial for Bitcoin price dynamics. The very low perplexity in both models suggests high confidence, but this should be verified with additional out-of-sample testing to ensure it's not overfitting.

In conclusion, while the 24-state model shows better statistical fit, the seven-state model's higher prediction accuracy and stability make it a stronger contender, especially for practical applications where interpretability and short-term predictions are important. The choice between them would depend on the specific use case and whether the additional complexity of the 24-state model provides actionable insights for your Bitcoin analysis or trading strategy.

All right, so that's interesting. So, RIP 24, much love, my brother. We miss you. But I think we got to go with seven. We got to hit the Jaylen Brown on them. I think we got to hit the Jaylen Brown on them. All right, so that's all this is saying. I mean, not entirely, but okay. The top two, the top two are 24 and seven, but it may be beneficial to use just the, um, just the seven so we can read states easier. All right, so that's that.

People are getting licked over here. What's going on? Let's check it out. Oh, yeah, we had that huge Uniswap liquidation of 1 million. That was earlier today. Looks like BTC is up to this liquidation here. I'm sure we have it. What day was this? August 20th at 14:400. Probably this right here. Yeah, that's what I would guess. It's my best guess. But not much liquidity, huh? Not much liquidation going on for this move right here, to be real with you. Because what is this? Two hours of time. So I'm looking for like a group of two hours of liquidation. Seven to 11, that's four hours. Dude, this is it right here. 9:30 to 11. So that only that move right here. What does that tell you, dog? I don't know. I don't know. But that move right there, 3% move, was this much in liquidations. It's interesting. What does it tell you, though? I don't know.

Let's go look at the seven regimes. It's funny that we are back on model number two after we tested 82 models. Not 82, but I want to go ahead and I want to go get this backtest going again. I know I just asked AI for some sort of backtest, but I don't think I went through with it. So, multi-regimes here, and then BTC. I want to go see what this backtest is looking like, dude. Copy. Why did I copy it? I don't know. Save. Close. Save. Close. Close. Close. Close. Close. Close. Close.

All right, we closed them all. And now I'm going to look at the backtest because I want to see what the backtest was doing. Was this the seven? 34 weeks, train scaler, model two. Okay, okay, okay, okay. So I want to say, I don't want to say anything yet, but I want to run this model two again. And then try to identify some of the, yeah, that's what we're about to do right now. I want to identify some of the, um, the things, identify the seven regimes and label them. Um, okay, that's still a lot though, but maybe I can like zoom in or something. This gets a little bit of everything, right? Still be, oh, I'll do that. I'll do that. I got these seven regimes right here. Dagn, where did I put those suckers? Right here. We got them right here. So this is, um, 24, seven. Do you see seven in here? I don't see it. No.

Volume. Yeah, it's just that one. Okay, so it's not what I wanted, though. Sheesh. I think it's this right here. Let's double-check it. You can see here that it says bullish trending, bearish trending, so sideways consolidation. I see it. Bearish trending. I see it. Sideways consolidation. I see it. Bearish trending. I see it. Sideways consolidation. I see it. Bearish trending. I see it. Sideways consolidation. I see it. Downward consolidation. I see it. What's the objective here? I wish they stayed longer, but I guess that's what the, like, three states does. Let's go look at the three states then, dude. How are we back to three states? Open, reveal, and finder. Okay. Why is this like this, though? That's weird because it says green here. Huh. Let's check out three states here with, um, less frenzy. Risk on. Frenzy. Risk on. Frenzy. Risk on. Frenzy. Risk on.

Huh. This only says risk on. I'm not sure about this. How do it only show once this entire data from 2018 and just keep switching? This is a good exercise, though. Strong neutral bullish. It's just looping through these, dude. Strong bear. Strong bear. Neutral bullish. But these again, these aren't, these aren't real. The names aren't real. So it keeps tricking me in my mind. They're not real.

All right, I'm going to say build a backtest here. What are the, um, the state uses zero, two, four, and six? Okay, so I have these states. Use the below as a template backtest in order to send me back a full backtest code that tests all possible buy and sell combos. Like buy at zero, sell at next zero, then test, then test sell at two, then four, then six. All different iterations of this backtest. And I think this is different what I did before. It's tricky to for my brand to get around it, but we'll see. Um, I optimized it before, but I was trying. Let me just get this out. We'll see. Um, all different iterations on this backtest. So after, so test buying on two, selling on zero, then two, then four, etc. Below is the model we will use. Get them all right. So this, we like deep up in here. We're so deep. Copy path. Okay. And then model two here. Scaler two. Okay. By the way, what is the model two job lib versus the scaler two? Okay. Let's paste in the BT code. The BT code. Okay. Uh, BT, the back has code somewhere. This is so intimidating, bro. I tell you that much. So I'm sorry. I'm really sorry. Send back pull code. Okay. There we go. And, um, let's watch her work. Trying to cut me off. Crazy, crazy stuff.

Hey man, can I make a suggestion for this rate limit issue? Please, please, please do. Please, please, please. Josiah says, oh, I already wrote that one. Go for it. Subp Jane, hope you ate on time today. Not at all, brother. Thank you for all you do. My pleasure, bro. My pleasure. Agnan G says, I've been trailing this VC fork code fork cursor AI that implements its own C-pilot. You get 500 CLA uses per month and cursor expands the context to your entire codebase. It's like $20 a month. And if you have an Anthropic API key, different than the base cloud plan, you can use Claude at cost instead. I've been trailing this VC code fork. What do you mean? No way. So you're just watching? Okay, you saw somebody get funded and you've been watching their code? That's dope. Smart, smart man. That implements its own co-pilot. You get 500 Claude uses per month and cursor expands the context to your entire codebase. It's like a $20 a month. Or if you have an Anthropic API, different than base, you can, um, use Claude instead. Dang, I'm going to write that down. Appreciate you sharing that. How did you get started? I just started on YouTube and just started learning. It's all there. Um, Josiah Valentine says, don't want to distract you from the session. Here's a link that runs through how to leverage multiple models to complete tasks. You never have to shut up, bro. Stop it. How do you get started? How's it going? I'm doing good, man. Thank you. Was thanks. Do Uber scalping in one minute candle? Something about scalping the one minute. I'm not sure. What do you think about scalping in one minute? It's harder down there. Zeus, what's up, bro? 777, much love. Hope you're doing great. He said thank you. 777, ditto. Jane, yo, ditto. Even ditto. Ditto. Zeus, you, you on Discord? Discord question. Mar, we have a Discord. Zeus, please. One sec. Finally, I've joined the Discord. Zo says, by the way, I'm working on a project for a SAS course at my university. I am extremely impressed with the Claude Dev plugin for VS Code. It's like Claude on steroids. That's the second time I've heard that. That's the second time I've heard it. Claude Dev. Eest, appreciate your shares, fellas. Okay, so thank you. Thank you both for that plugin by sa rzan. There are a few YouTube videos on it. If you search something like Claude Dev, that might help at first to get a hang of it. I gotta go. Peace. See you. Jane, 777, to be honest, they got you for this $20. This is super valuable. Nice. I'll just check out the YouTube videos or something. Why YouTube video? Okay, there we go. It's called cursor AI. Essentially, essentially, actually for real. So test states here, zero, two, four, and six. Buy, stay. Okay, let's see how this do. Do, dude. Seven regimes. Multi-regimes. Ooh, ooh. Testing. Let's call these, uh, testing. Where that backtest at? You come in here. The testing, dude. Testing. Okay, let's add it. Um, what do we do here? We say this is, uh, BT seven states. I don't know, dog. I don't know. I know nothing except I'm going to keep going. I'm going to keep going and going. And then I'm going to keep going. I'm going to get RX'd. I'm going to keep going. I'm going to be stuck. I'm going to keep going. Every day, dude. Every day.

All right, look at that. Uh-oh. What did I just show you? What did I just show you, dude? Win rate 60%. 1.1 Sharpe. Return 23%. Okay, I mean, exposure times 11%. That's what I just showed you. Is that worth it to you? I like it. I would give up a few percentage points for that. Okay, let's go ahead and copy this error here. Man, I love this game, dog. Love this game so much. It's so hard. I love it so much. It's so hard. Boohoo. Let's get it. Every day, dog. Come on. Come on. That's just a warning, cousin. That's great to see. Let's see what it said, though. Those warnings are related to the Bokeh library used for plotting backtesting package. They don't affect the functionality of your backtest, but let's address them to clean up the output. Clean up the output for me, please. Clean up the output for me, please. Cuz you know we're just getting started. Oh, I thought we've been going a while now. No way. No way, dude. Why, why would I slow down? I'm sorry. I'm sorry. Every time you see me here, I'm working on something new. My bad. My bad. I'm going to keep going. I'll show you everything, though. I'll show you everything, bro. Every day. What I find, you find. That's the game. Replace the original plotting call with this. Okay, let's go to the plot. Plot. Plot. Plot. BT plot. Oh, is that the one? Nah. Yeah, it is. All right, all right. Let's go ahead and run it now. Warning free. Beautiful. Beautiful. Beautiful. Okay, dude, what else can I do? There's a lot I can do. I like this a lot. All right, so I'm just going to save this then, dog. Stop playing. Stop playing, dog. That's out of sample data too. I like this. Dude, 23% return is not better than buy and hold, but you're only holding 12% of the time, dog. Eight months, nine months, eight months. Stop it. Stop playing with your boy. I don't know how many hours this has been, dog, but it's been a lot of hours. Been here a long time. Feel like this is just a start, though.

Max trade time two days. Expectancy. How long is your boy? Nate, 2.3. Okay, this doesn't mean anything, though. All it means is we're just getting started. Seven states. Let's do 24 states, cousin. I got mad. I got mad things I need to do now. Okay, live ops. Copy path. Copy path. Copy path. Copy relative path. Read me. Live ops. Where you at, dog? There you are. Okay, so I'm just going to keep vibing out here until I'm done. I don't know when I'm done. I'm never going to be done, dog. I'm never going to be done. There is no done in this game. That's why I love it. Hey, mm. This is a great-looking backtest with 11% exposure and 1.1 Sharpe. It's not the highest, but you know, it's a good start. Now I want to test with, uh, ETH hourly data. Let's grab that ETH data right now. Let's just grab it right now. ETH data. How many weeks we want is the question. Do I already got it? No, I'm going to get it. I'm going to get it from here. How many weeks we want is the question? Let's do 100 weeks. Let's do 104 weeks. Let's do 104 weeks. That's two years, dog. Maybe I should go more, though. 10 weeks. What do I got in here already? I got 34 weeks. I got a thousand weeks of BTC data. Okay, I should try it on that for sure. Copy path. This we probably just break it, but it's okay. There's a thousand weeks of data. And I'm going to put this janky little comma there because no, wait, wait, wait, wait. Needs to be in the data folder. One. No janky comma. No janky comma. No comma. No comma. I'm just going to get all the data I can. Yeah, I'm just going to do like 500 weeks of ETH data. I can't imagine it's going to disrupt things, but what's going to happen is it's going to not be as good. I mean, but it wasn't as good as buy and hold anyways. That's fine. How many weeks? Let's do 200 weeks. So that's like four years. Just I got to be conscious of the time that takes to download all the data. But now it's downloading.

Um, one of the many things I show you in the boot camp. The boot camp shows you step by step how to automate your trading. 100% money-back guarantee. Always, always, always. If, if you got questions about any of this stuff, it's probably not because you can't do it, it's just you haven't started yet. So this will help you out a lot. I wish I had this when I got started. Step by step how to automate your trading, how to backtest, how to build your own edge. And, um, yeah, I'm flying, bro. I'm flying. I'm out of here. I'm going to keep going. All right, so see what happens. Can I just pop this in? Let's see if I can just pop it in. Yeah, this is out of sample data. Copy relative path here. And backtest seven states. This is for that. Okay. And now we'll try it with this thousand. I don't expect it to be better than buy and hold because this, this is all Bitcoin data. And there's, that's a pretty good strategy in retrospect to just buy and hold from the start of Bitcoin. But that's not what we're looking at. All right, so copy path. Okay. I'm just going to plop in that data now. Let's just say, uh, let's mark this one out. And then maybe my AI can just do it for me. Yes. Thanks, shorty. Look at that. I don't even have to code. I just move things around. Playing Legos. All right, let's see if it can do it with a thousand. Then it looks like it's working. So I bet we have our information here in a little bit of time. There it is, dude. Okay, how's that look to you? I don't know. It's 10 years of data. 2015. Wowers, bro. Cool stuff. Cool stuff. All right, so the profit factor is 1.6. The expectancy is 1.83. SQN is 1.47. 280 trades. Sharpe ratio kind of low. The return, 56% annualized. But like I said, it's the buy and hold is like obviously goat here. Exposure time, 68%. So over 3,000 days, it's not the goat. But you can't go back 3,000 days in Bitcoin. That's the, that's the tricky part about backtesting against a lot of Bitcoin data. So I like to look at other things like the expectancy. That's nice. The profit factor is nice. Win rate is solid. Drawdown is not solid, but it's, you know, that return.

Um, oh, cool. I got the, uh, ETH data. I'm going to do the same thing for, uh, SOL. But all of these are just up over the years, which, which makes it tricky, tricky, tricky. I'm going to do it for SOL as well. So let's go ahead here and try it out. ETH, one hour. Okay, this is the one-hour data for 200 weeks, it says. And I haven't tried a different, um, Chopper him out of here. Get, get them ghost. It's time to go home, buddy. Quit the game. If you're going to play the game the same way you always play, quit the game. You're going to get liquidated over and over and over and over again till you can't even play the game no more. So quit the game now. Put the game down. Put the game down, dude. Come on. Stop playing. Stop playing. Stop playing like that. So you can see it's a 380% return. I like this one, though. Exposure is only 33%. So to get that close of return versus buy and hold, that's like a lot of capital free. That's a lot less risk. Sharpe's not great. 48, 228. This is cool, though. This is a cool new, um, new backtest here. So yeah, that's pretty neat. It's pretty neat. It's got me a lot. It got me thinking a lot like, um, where'd it go from here with this? Cuz this backtest is really just like a filter. It's a regime creator. It's a regime creator. So I'm going to grab this one as well. I'll just leave this here for now. 23% buy and hold, 35. This is a, I mean, as a filter. And now layer strategies in could be interesting. We'll see. We shall see. Copy this down here. I already did that, dummy. Come on, baby boy. Come on. Copy relative path. Okay, so that's that. And this is not as good return as buy and hold if you would have bought on 10/23 of this date to this date, four years. But a lot of free capital. And this is just a filter. So I'm actually super interested in this now. Man, I need to write some stuff down. So ETH was like four, 380% return versus 500 buy and hold with 30% whole time. But that's pretty biased. Like I just wrote that down. There's another one in there. Um, what I look forward to, what I look forward to here is that these are simply regimes or filters. So being able to find a profitable in the past, not guaranteed the profit in the future, strat off regime changes is dope. But also the market just goes straight up. So I'd be interested to see how this looks on other data. Yo, Rigar, what's up, dude? Samir says, is the strategy finding the hidden states on the entire data set once, or is HMM predicting future states based on a window of past data?

Um, I, uh, it's, it's past data. Past data. It's trained on past data and predicts with that model. Jamie says, hey man, I see your streams appear on my YouTube. What is it that you're doing? Hey, dude, welcome. Much love to you. I am, uh, I'm coding. I'm coding a, uh, machine learning model. It's a hidden Markov model. And we've been playing around with it for a while. And we got the backtesting it today. It's a pretty big, uh, pretty big moment, actually. Pretty big moment. But I still think it's just a start. I still think it's just the start, cuz there's so many ways we can slice and dice this now. So live ops. I wanted to write that. Okay. I want to do the same thing. I want to do the same concept here for the 24, um, state model. I want to do the same type of backtest where we test all variables again, uh, but for this, these many more states listed above, which appear most in the 24 model. Okay. And then we got to get that code, because I don't know if she remembers. I don't even remember. So, you know, how's she going to remember if I can't even remember? Cuz she's smarter than you. Moon. She can definitely remember that. She can definitely. I didn't know it was the last one. It's just been a while. We've been over here executing. What a great teammate she is, huh? It's crazy. It's crazy stuff here, dude. Trained model. Let's go to 24. Copy path. She probably doesn't even need this to be honest. She's so smart. Send back full code. For for what? Up for for.

All right, so this is the 24 backtest. Just curious, you know. 24. Rest in peace, King. All right. Run it. All right, so looks, uh, oh, this is on the that data too. Wait, what data is on? Worst trade. Good expectancy. Good profit factor. Beats the buy and hold. Uh-oh. What does that tell me, dog? That tells me good. Dang, dude. Another one. Huh. So 240 days. All right, so that's that. And what data was that on, dude? 31 weeks. 34 weeks. Okay, let's try on some other data then. This one's almost impossible to beat, but might as well try it. The, uh, data from BTC, 10 years. 10-year BTC data. Let's see it. Let's see it, dude. Let's see you dance. All right, it's going to take a little bit of time. There it is. Expanses nuts. 8,000% versus, um, 23,000. So no, didn't beat it. 53, 88. Win rate is good, though. 54%. Expectancy is good. Drawdown. How's that drawdown looking? 74%. So not so hot, but I think that's pretty standard for this 70% exposure time in a volatile asset. I don't know what to conclude. I have no conclusion except low S S. Lo S.

All right, let's go ahead. Paste it here. Beat it up here. Okay, so it's cool. It's really cool. I like this idea. I like this idea of cutting things up into regimes and having AI mess with it. So here I'm going to say this is the out of sample. Out of sample data here. This is the first one. Okay. And now this one here is the BTC one hour. All right, sick. So let's try that ETH here. Let's say copy path. Um, I don't know why I'm up in there, but copy path. And say data path. Let's go ahead and delete this one. Be out, dog. Data path. Okay, that's cool with me. Hey, guess what else? We got some more data too. So, oh, you tickling me. Oh, you hungry again? You hungry? I'm hungry too. We both hungry. Shorty. We both hungry. I'm going to feed you right now. You grow and grow and grow. Um, you stay on track just for a few more seconds here. And then I'm going to get you something. I want to get this SOL one hour in here. All right, I got it in there. Um, this here is the detailed results for that strat. 24 states. This one was for the ETH. Okay. It looks solid. 2.54 Sharpe. A little better. Seven beats the buy and hold. It's cool to see. Exposure time is two-thirds of the time. Okay. So what was that? That was the ETH one hour, I believe. Let's try something new, dude. Let's try something new. Let's say data path equals data path. Data path. Data path. Data path. Data path. Data path. Data path. Data path. Copy path. Copy path. Paste it in. Okay. Now we're going to say up here. Okay. Copy. Run it. All right, that one's going to take a minute. But this is it. This is that. Oh, snap. What is this though? Okay, so this was ETH. Copy relative path here. ETH states. I just want to make sure that it's here so I know what I'm doing. Data. Okay. What did the, what did that chart look kind of? So this is the drawdown. August 2022. Interesting. Profit factor is 10. Only six trades. Interesting. And it beat buy and hold. But not very many trades. So, you know. So quick look. Six trades. This one had 150 trades. A little more statistically significant. Better than buy and hold. But then the BTC one from 10 years is not. What about BTC for? Like, I don't have it. I could get it pretty easily, though. I could do other time frames, but, you know, I think I don't want to overtest this. Just it is what it is. It's good information to have. What you going to do with that information, though? That's a cute dude. That's the queue. So this is a good place. This is a good place to now look at this and see how do we layer it? How do we take it from here? I'm always constantly just following this. If you're ever wondering what I'm doing, it's this. Every day. Every day for the next 60 years, bro. RBI system for AO trading. It's just my simplified version of this. The process of automating your trading comes down to researching trading strategies, watching videos, listening to podcasts, reading books. I didn't love reading growing up, but now you're just searching, searching for ideas. One idea can change the world. Then backtest those ideas, strategies to see if they actually work in the past. So the idea today was the HMM, a Markov model to predict the different states in the market. And then have a backtest that trades based off of the changes of those states. See if it works in past data. And then if that's profitable in the past, it might work in the future. So I always start with tiny size. But most of the work is in the R and the B. And everybody wants to build a bot, including myself. Just take that lesson from my experience. Seriously. And don't just go build a bot. Don't buy a bot on the internet. None of that. That dude, please. If everybody's running the same algo, it's going to converge to zero profits over time. Pretty much guaranteed. It's just math. And I think we went over this a few times in this, this, uh, this stretch, this journey, this marathon. This journey. That's what it's been. It's been a journey. That's why machine learning seems to be hard for trading because if everybody can predict the price, then the price is not going to be the same anymore because everybody's going to trade off that price. It's not like if everybody knows the prediction of the weather, it's going to change the weather. No, it's different. It's a fun journey. I like this journey a lot.