import joblib, json, numpy as np
from huggingface_hub import hf_hub_download

# Download model files
lgb_model = joblib.load(hf_hub_download("lvizcaya/forex-eurusd-direction", "lgb_model.joblib"))
xgb_model = joblib.load(hf_hub_download("lvizcaya/forex-eurusd-direction", "xgb_model.joblib"))
scaler = joblib.load(hf_hub_download("lvizcaya/forex-eurusd-direction", "scaler.joblib"))
feature_cols = json.load(open(hf_hub_download("lvizcaya/forex-eurusd-direction", "feature_columns.json")))

# Prepare your features (see predict.py for full pipeline)
# X = your_features[feature_cols].values
# X_scaled = scaler.transform(X)
# prob_up = (lgb_model.predict_proba(X_scaled)[:,1] + xgb_model.predict_proba(X_scaled)[:,1]) / 2
# direction = "UP" if prob_up >= 0.5 else "DOWN"

See predict.py for a complete inference example.

Methodology

Based on published financial ML literature:

arxiv:2511.18578 — GBM ensemble with walk-forward validation (most robust approach)
arxiv:2405.08045 — Technical indicator engineering for FOREX
arxiv:2511.15960 — Proper baselines (ZeroR) to avoid overfitting claims

Walk-Forward Validation

Minimum 3 years training data
Retrain monthly (21 trading days)
Expanding window (no data discarded)
No random splits (prevents temporal leakage)

⚠️ Disclaimer

This model is for research and educational purposes only. It is NOT financial advice. Forex trading involves significant risk. Past performance does not guarantee future results. Realistic accuracy for daily direction prediction is 52-56% (literature consensus).

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Try ML Intern: https://smolagents-ml-intern.hf.space
Source code: https://github.com/huggingface/ml-intern