Tag: Bitcoin

Model Training on AMD 16-core CPU with 8GB RAM running in a virtual machine for Bitcoin Price Prediction – Part 2 – Updated

Continuing with Over 500,000+ Data Points for Bitcoin (BTC) Price Prediction

Using the Python program, the first method I tried was SVR (Support Vector Regression) for prediction. However… how many steps should I use for prediction? 🤔

Previously, I used a Raspberry Pi 4B (4GB RAM) for prediction, and… OH… 😩
I don’t even want to count the time again. Just imagine training a new model on a Raspberry Pi!

So, I switched to an AMD 16-core CPU with 8GB RAM running in a virtual machine to perform the prediction.

  • 60 steps calculation: Took 7 hours 😵
  • 120 steps: …Man… still running after 20 hours! 😫 Finally !!! 33 Hours

Do I need an M4 machine for this? 💻⚡

ChatGPT provided another approach.
OK, let’s test it… I’ll let you know how it goes! 🚀

🧪 Quick Example of More Time Steps Effect

Time Step (X Length)Predicted AccuracyNotes
30⭐⭐⭐Quick but less accurate for long-term trends.
60⭐⭐⭐⭐Balanced context and performance.
120⭐⭐⭐⭐½Better for long-term trends but slower.
240⭐⭐Risk of overfitting and slower training.

#SVR #Prediction #Computing #AI #Step #ChatGPT #Python #Bitcoin #crypto #Cryptocurrency #trading #price #virtualmachine #vm #raspberrypi #ram #CPU #CUDB #AMD #Nvidia

Model Training Using TensorFlow on Raspberry Pi 4B (4GB RAM) for Bitcoin Price Prediction

The development of a CRYPTO gaming system https://www.cryptogeemu.com/ has been ongoing for around two years. What does it actually do? Well… just for fun!

The system captures data from several major crypto market sites to fetch the latest price list every minute. It then calculates the average values to determine the price. Users can create a new account and are given a default balance of $10,000 USD to buy and sell crypto—but there’s no actual real-market trading.

The Thought Process

Suddenly, I started wondering:
How can I use this kind of historical data? Can I make a prediction?

So, I simply asked ChatGPT about my idea. I shared the data structure and inquired about how to perform predictions.

ChatGPT first suggested using Linear Regression for calculations. However, the predicted values had a large difference compared to the next actual data point.

Next, it introduced me to the Long Short-Term Memory (LSTM) method for training under the TensorFlow library.

I fed 514,709 lines of BTC price data into the training program on a Raspberry Pi 4B (4GB RAM).
The first run took 7 hours to complete the model !!!!!!!!!!!!!!!!!

But the result… um… 😐

I’m currently running the second round of training. I’ll update you all soon!

Sample Data:

YYYY/MM/DD-hh:mm:ss  Price  
2025/02/17-20:06:09  95567.20707189501  
2025/02/17-20:07:07  95582.896334665

P.S.: I’m not great at math. 😅

#BTC #Bitcoin #TensorFlow #AI #CryptoGeemu #RaspberryPi #Training #Crypto #ChatGPT #LinearRegression #LSTM #LongShortTermMemory