---
license: apache-2.0
tags:
- tabular-classification
- finance
- forex
- time-series
- lightgbm
- xgboost
- ensemble
- ml-intern
language:
- en
metrics:
- accuracy
- f1
- roc_auc
---

# EUR/USD Direction Prediction Model

Binary classification model that predicts whether EUR/USD will close **higher (UP)** or **lower (DOWN)** the next trading day.

## Model Details

- **Task**: Binary classification (Up/Down)
- **Pair**: EUR/USD
- **Horizon**: Next trading day
- **Models**: LightGBM + XGBoost ensemble (average probability)
- **Features**: 53 technical indicators and price features
- **Training**: Walk-forward expanding window (no data leakage)

## Performance (Out-of-Sample, Walk-Forward)

| Metric | Value |
|--------|-------|
| **Accuracy** | 0.6611 |
| **F1 (macro)** | 0.6610 |
| **F1 (binary)** | 0.6544 |
| **ROC AUC** | 0.7245 |
| **ZeroR Baseline** | 0.5041 |
| **Improvement** | +0.1570 |

## Features

The model uses 53 features including:

- **Log returns**: Multiple lookback windows (5, 10, 21, 63, 126, 252 days)
- **Momentum**: Price momentum at various horizons
- **Volatility**: Rolling standard deviation of returns
- **RSI**: Relative Strength Index (7, 14, 21 periods)
- **MACD**: Moving Average Convergence Divergence
- **Bollinger Bands**: %B and bandwidth
- **ATR**: Average True Range
- **Stochastic Oscillator**: %K and %D
- **ADX**: Average Directional Index
- **Williams %R, CCI**: Additional momentum indicators
- **Channel Position**: Price position within rolling high/low channels
- **Calendar**: Day of week, month, quarter

## Usage

```python
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).

<!-- ml-intern-provenance -->
## Generated by ML Intern

This model repository was generated by [ML Intern](https://github.com/huggingface/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