AI Model for Predicting ETH/USDT Prices
Updated
Model Overview
This AI model is designed to predict the price of the cryptocurrency ETH/USDT based on historical data from multiple exchanges. The model utilizes machine learning technology, specifically recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) layers, which are particularly well-suited for time series and sequence-based predictions.
2. Data Sources
The model draws data from two main exchanges:
Binance
Bybit
For each exchange, historical data is retrieved, including price (open, high, low, close) and trading volume. These basic data points are supplemented with technical indicators such as RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and Bollinger Bands, which help better understand market behavior.
3. Data Processing
Data is first normalized to have a consistent range, which facilitates the training of the neural network. Datasets and sequences are then created for training the model.
4. Model Structure
The model is composed of several layers:
Input Layer: Accepts sequences of data of 60 steps in length.
LSTM Layers: Three layers of LSTM neurons, each with 50 units, with dropout regularization to prevent overfitting.
Dense Layers: Two dense layers for final signal processing and generating predictions.
5. Training and Validation
The model is trained using cross-validation (k-fold cross-validation), which allows for more efficient training and validation of the model. Each fold splits the data into training and testing sets, improving the model’s accuracy and robustness.
6. Model Saving
After successful training, the model is saved for future use, significantly speeding up the prediction process.
7. Generating Predictions
The model generates predictions for the next 48 hours (2 days). Predictions are visualized in a graph that also includes support and resistance levels, calculated based on pivot points.
Results of the Latest ETH/USDT Prediction
1. Visualization and Metrics
The latest ETH/USDT prediction is shown in the graph. The model achieved the following metrics:
MSE (Mean Squared Error): 0.0022867241799262895
RMSE (Root Mean Squared Error): 0.04675314113269089
These metrics indicate that the model has a relatively low prediction error, meaning it is quite accurate.
2. Analysis of the Latest Prediction
Based on the latest graph, it can be seen that the model predicts a significant increase in the ETH/USDT price after a period of stabilization. This prediction was generated using historical data from Binance and Bybit exchanges, ensuring robustness and reliability.
3. Comparison with Real-World Development
The latest graph also includes the real-world price development following the predicted period, allowing for a comparison of the model’s accuracy. In previous predictions, the model successfully captured major trends and price movements.
This AI model is designed to predict the price of the cryptocurrency ETH/USDT based on historical data from multiple exchanges. The model utilizes machine learning technology, specifically recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) layers, which are particularly well-suited for time series and sequence-based predictions.
2. Data Sources
The model draws data from two main exchanges:
Binance
Bybit
For each exchange, historical data is retrieved, including price (open, high, low, close) and trading volume. These basic data points are supplemented with technical indicators such as RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and Bollinger Bands, which help better understand market behavior.
3. Data Processing
Data is first normalized to have a consistent range, which facilitates the training of the neural network. Datasets and sequences are then created for training the model.
4. Model Structure
The model is composed of several layers:
Input Layer: Accepts sequences of data of 60 steps in length.
LSTM Layers: Three layers of LSTM neurons, each with 50 units, with dropout regularization to prevent overfitting.
Dense Layers: Two dense layers for final signal processing and generating predictions.
5. Training and Validation
The model is trained using cross-validation (k-fold cross-validation), which allows for more efficient training and validation of the model. Each fold splits the data into training and testing sets, improving the model’s accuracy and robustness.
6. Model Saving
After successful training, the model is saved for future use, significantly speeding up the prediction process.
7. Generating Predictions
The model generates predictions for the next 48 hours (2 days). Predictions are visualized in a graph that also includes support and resistance levels, calculated based on pivot points.
Results of the Latest ETH/USDT Prediction
1. Visualization and Metrics
The latest ETH/USDT prediction is shown in the graph. The model achieved the following metrics:
MSE (Mean Squared Error): 0.0022867241799262895
RMSE (Root Mean Squared Error): 0.04675314113269089
These metrics indicate that the model has a relatively low prediction error, meaning it is quite accurate.
2. Analysis of the Latest Prediction
Based on the latest graph, it can be seen that the model predicts a significant increase in the ETH/USDT price after a period of stabilization. This prediction was generated using historical data from Binance and Bybit exchanges, ensuring robustness and reliability.
3. Comparison with Real-World Development
The latest graph also includes the real-world price development following the predicted period, allowing for a comparison of the model’s accuracy. In previous predictions, the model successfully captured major trends and price movements.
Note
1 prediction: 
Note
2 prediction: Note
since I published a private post later on the first prediction, here is exactly the time when the model predicted the rise: Trade active
1st part of prediction correctNote
3 prediction: 
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