File size: 2,126 Bytes
c99c263 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
language: en
tags:
- cryptocurrency
- stellar
- price-prediction
- machine-learning
- time-series
license: mit
---
# Stellar (XLM) Price Prediction Models
Trained ML models for predicting Stellar (XLM) cryptocurrency prices.
## 📊 Model Performance
| Model | RMSE | MAE |
|-------|------|-----|
| Random Forest | 0.0217 | 0.0175 |
| Gradient Boosting | 0.0221 | 0.0183 |
| Linear Regression | 0.0019 | 0.0015 |
| LSTM | 0.0136 | 0.0105 |
## 🎯 Training Details
- **Trained on**: 2025-10-24 07:49:07
- **Data Source**: CoinGecko API
- **Historical Days**: 365
- **Features**: 23 technical indicators
- **GPU**: Accelerated with TensorFlow
## 📦 Files Included
- `stellar_sklearn_models.pkl`: Scikit-learn models (RF, GB, LR)
- `stellar_scaler.pkl`: Feature scaler
- `stellar_lstm_model.h5`: LSTM neural network
- `stellar_metadata.json`: Training metadata
## 🚀 Usage
```python
from huggingface_hub import hf_hub_download
import joblib
from tensorflow.keras.models import load_model
# Download models
sklearn_path = hf_hub_download(
repo_id="YOUR_USERNAME/YOUR_REPO",
filename="stellar_sklearn_models.pkl"
)
scaler_path = hf_hub_download(
repo_id="YOUR_USERNAME/YOUR_REPO",
filename="stellar_scaler.pkl"
)
lstm_path = hf_hub_download(
repo_id="YOUR_USERNAME/YOUR_REPO",
filename="stellar_lstm_model.h5"
)
# Load models
models = joblib.load(sklearn_path)
scaler = joblib.load(scaler_path)
lstm = load_model(lstm_path)
# Make predictions
# (prepare your features first)
predictions = models['RandomForest'].predict(scaled_features)
```
## 📈 Features
The models use 23 technical indicators including:
- Moving Averages (SMA 7, 25, 99)
- Exponential Moving Averages (EMA 12, 26)
- RSI (Relative Strength Index)
- MACD & Signal Line
- Bollinger Bands
- Stochastic Oscillator
- Volatility measures
- Lag features
## ⚠️ Disclaimer
These models are for educational and research purposes only. Cryptocurrency markets are highly volatile and unpredictable. Do not use these predictions for actual trading decisions without proper risk management.
## 📄 License
MIT License
|