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 actually use AI to trade. By the end of this video, you will know exactly, step by step, how to use AI.

There are 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 sales numbers in your Fortune 500 company are going to have 500 sales or something, and then you have 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 made all strategies based off of data. So, if you're making 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, Mundev? Well, if you go over here to some data, you can see there's like 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 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 AI, these LLMs. I think if the higher-ups 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 np`, and then `from hmmlearn import hmm`. Google this: `pip install hmmlearn`. Then `matplotlib.pyplot as plt`. `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: 51,922 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. Date time, date time, 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.head`. Fine. `time.sleep`. Oh, we don't even have time in here. AI sometimes, huh? Load preprocessing 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 Hidden Markov Model. Hidden—I'm going to keep writing that out because it's such a nerd word—Hidden Markov Model, uh, was the—was better less than the RNN, the recurrent 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 got 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'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, um, 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 know 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 multi-cursor editing or column selector. Here's how to do it in some popular—so, press Alt+Shift+I. That's it. That's all I need to know. Now, I'm, I'm off. Alt, Option, maybe? No, Control? No, dang. What's Alt 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? Nuh-uh, 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 Postgres or MySQL. My skill. 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 date time range. Okay, let's say this. Let's say print creating date time in 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-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 preprocess 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 have 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. `df.columns`. Okay, let's just confirm it. Ah, no, no, no, that ain't it. That ain't it. We got to have date time. Date time, date time. That is so janky. Come on, dude. Come on. You got, got this. There we go. Daytime. 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 = 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. `scaler = StandardScaler`. `X_scaled = scaler`. 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.GaussianHMM`. `n_components = n_components`. `covariance_type = 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, 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, um, 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_component=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 `it=` means the detector will practice guessing moods a hundred times to get better at it. Yo, this is actually pretty good. `covariance_type=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_it=100` means you'll practice guessing the weather 100 times to improve your skills. `covariance_type=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, give 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 Mundev went up 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 `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 (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 = data`. This line here, `x = 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 = 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, 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 zero (calm market, low returns, low volatility, normal volume); state one (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 zero, which is a calm market, it might make small, careful trades. What is Dar? I don't know. In state one, bull market, it might make larger buy orders. In state two, 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 mood, 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_analysis = data.copy()`. And then `analyze_state = state` for `state in range(model.n_components)`. So, it grabs the components. Analyzing state. State data equals `df` where `State == 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 O. 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(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, 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 = State == States`: this creates a Boolean array, `mask`, where it's true for every time point where the market 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 = States == State`: this creates a Boolean array, `mask`, where it's true. Okay, when creating mask for each state, you'd 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 nine, is it true or false? You can see for state one here, it's not going to be true until two, three, four, or whatever that is. Two, three, four. 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 one. 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 two? 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, Mundev, 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, `ax1` and `ax2`. 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 this, these `time.sleeps`. I want to understand every single line of code, you know? Why not? Okay, so plotting results. Yo, Pizzy plots, Pizzy plots, Pizzy plots. A lot of thoughts. Get your head out 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 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 then that's how you do this stuff. 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 zero, one, and two, represented by different colors in the top of the chart. So, you can see three states: zero, one, and two. Zero is blue, one is yellow. Oh, I'm sorry, that's like orange or peach, peach. And two is green. All right, let's get it. State zero: this appears to be the most common state. It might represent normal or stable market conditions. State one (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 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, G.Y. 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 it in the chat. 777. Every single day you're up inside my brain, B. It's kind of weird. You, you up in here a lot, honestly. So, just share 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+1. I just want 1+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 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 zero to one, zero to one. 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, 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 bear 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, 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 dot. 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, nah, 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.transmat`. 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 transation, 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 timestamp. In your case, you have three states. Let's call them 0, 1, and 2. The matrix is read, read as, from state to state, from state to state. So, transition from state zero: you can see it's the 0.92. Wait, wait, wait, wait, wait, wait. How are you going to change up on me? Oh, it's negative one. Okay, got it, got it. There's that to the 10th power thing up there. So, it's negative one. So, 0.92078. Okay, and point blah, blah, blah, blah, blah, 254. 92% chance of staying in state zero, 7% chance of moving to state two, and very small chance of moving to state two. Sorry, one, and then two. Transitions from state one: there's a 24% chance of moving to state zero and a 75% chance of staying in state one. Essentially, 0% chance of moving to state two. Transition from state two: 100% chance of moving into state zero. So, that means there is no state two, right?

Interpretation for trading: State zero, likely normal or baseline state. Very stable, tends to process. Occasionally, it transitions to state one. Rarely goes to state two. State one, possibly a bullish or trending state. Moderately stable, often persists. Sometimes reverts to state zero. Sorry to wake you, bro. Sometimes reverts to state zero. Almost never goes directly to state two. State two, possibly a volatile or shock state. Extremely unstable, never persists. Always transitions back to state zero immediately. Trading implications: State zero could represent a calm market. A trading bot might use conservative strategies here. State one seems to represent a persistent trend. A bot might use trend-following strategies when in this state. State two 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 this state. The rarity and instant transition of State two back to state zero suggests it might be capturing outlier events in your data. You might want to investigate what market conditions correspond to state two and consider if it's providing valuable information for your trading strategy. It's not printing means and variances, covariances 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 are 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 feature 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 zero mean: okay, slightly positive returns, below average volatility in state zero, 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 one: the 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 two: strongly negative returns, decreased VA, 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 zero, use conservative strategies. Look for small, quick profits. Be prepared for potential transitions to more volatile states. In state one, implement risk management strategies due to high volatility. Look for short-term trading opportunities in both directions. Monitor for potential trend formations. In state two, 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?

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, 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 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 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 to. 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, funding rates. Okay, now we're going, now we got the wheels turning. 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 going to 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, 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 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, but rather how much the model relies on each feature for its predictions. High importance could 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 percent, 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 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 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

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 me. 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. Uh, 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 jam. 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 them jack my swag. 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 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_macd_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. Stored 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_2. Here. Every time I run this, it's going to try to save it again. So this is our best model so far. Model_1 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 going to 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 an 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 error, 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 Pandas TA that I have these options here. Okay. Um, let's see. They gave us too much power. Lord 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 hours. 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 least a structure to everything. That's awesome. You're learning 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 an 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? I 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 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's 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 305. 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 MACD 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 do 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 OpenAI API plus Pinecone DB to avoid the rate limit. Okay, okay. I feel you. What is Pinecone DB? Oh, the vector, vector DB. 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, our other two models. Now, previous model was 0.98, so that's better. There. Model 2, 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 2 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 2 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 2 is 2000. 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 Lin regres, MACD, true range. Like, sometimes this stuff's so intimate, 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. Cla. 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 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 going to 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. Metaclass GPT. Did you check Grok? Nope. Nope. So the vector database. I remember that from, 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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 three 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 a 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 going to ask AI. I'm running out of, I'm running out of chat, so I'm not going to, I'm not going to 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

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 3.37k. 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.

All right, 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 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 8/20b. 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. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. 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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 3.37k. 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.

All right, 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 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 8/20b. 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. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. Wow. 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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. N. 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 40? 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 8. 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 them? 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 GPD 40 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 webdev 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, 9, 6, 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 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, is 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, Ry Bear, 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 out a 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 / 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. Ry Bear 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. 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. This keeps decreasing. 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 eight hours a day, dude. I used to game for eight hours a day. Four 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. It's 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 / 24. 365 / 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 back test. 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 a 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. Alice 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. S. 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 with. 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. O O S 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 7, but it may be beneficial, 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. Um, 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, 'cause what is this? Two hours of time. So I'm looking for like a group of two hours of liquidation. 7 to 11, that's 4 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 BT, I want to go see what this backtest 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 2. 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 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 2. joblib versus the scaler 2? 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 gonna 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 gonna keep going. I'm gonna keep going and going. And then I'm gonna keep going. I'm gonna get RX'd. I'm gonna keep going. I'm gonna be stuck. I'm gonna 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. 'Cause 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 gonna 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 gonna 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. 8 months, 9 months, 8 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 gonna keep vibing out here until I'm done. I don't know when I'm done. I'm never gonna be done, dog. I'm never gonna 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 sharp. 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 gonna get it. I'm gonna 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 gonna 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 gonna get all the data I can. Yeah, I'm just gonna do like 500 weeks of ETH data. I can't imagine it's gonna disrupt things, but what's gonna happen is it's gonna 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 gonna 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, uh, 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 gonna 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 1 hour. Okay, this is the 1-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? 'Cause this backtest is really just like a filter. It's a regime creator. It's a regime creator. So I'm gonna 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 3% hold 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 dataset 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 'cause 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 gonna remember if I can't even remember? 'Cause 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 gonna 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 1 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 gonna 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 gonna 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 sharp. A little better. 7 beats the buy and hold. It's cool to see. Exposure time is 2/3 of the time. Okay. So what was that? That was the ETH 1 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 gonna say up here. Okay. Copy. Run it. All right, that one's gonna 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 gonna 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, 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.