Model Description
This is a supervised machine learning model trained to forecast short-term cryptocurrency trend bias (Bullish, Bearish, or Neutral). It is the core prediction engine for the CryptoRadar full-stack platform.
- Model Type: Scikit-Learn Classifier
- Primary Use Case: Predicting directional market momentum based on technical and sentiment data.
Input Features
The model evaluates a 10-dimensional feature vector:
volume, dxy_index, price_change_pct, rsi, volatility, dist_from_smasentiment_coin, sentiment_trend_coin, sentiment_btc, sentiment_trend_btc
Limitations
This model is for educational and portfolio purposes only. Cryptocurrency markets are highly volatile, and this model should not be used for actual financial trading.
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("DaProgammer/crypto_radar_brain", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html