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="avalanche-2_sklearn_models.pkl"
)
scaler_path = hf_hub_download(
    repo_id="YOUR_USERNAME/YOUR_REPO",
    filename="avalanche-2_scaler.pkl"
)
lstm_path = hf_hub_download(
    repo_id="YOUR_USERNAME/YOUR_REPO",
    filename="avalanche-2_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

phamluan/crypto-avalanche-2-predictor

Avalanche (AVAX) Price Prediction Models

📊 Model Performance

🎯 Training Details

📦 Files Included

🚀 Usage

📈 Features

⚠️ Disclaimer

📄 License

phamluan/
crypto-avalanche-2-predictor