You can deploy this model and start earning money today!

theonegareth/
GoldPricePredictor

sklearn

joblib

gold-price-prediction

Views

No views yet

Gold Price Direction Predictor

This model predicts the next-day direction of gold prices (up or down) based on historical Antam gold price data and technical indicators.

Model Description

Model Type: Binary Classification (Gradient Boosting / XGBoost / LightGBM)
Task: Predict whether gold price will go up or down the next day
Input: Feature vector with technical indicators (returns, lags, RSI, MACD, Bollinger Bands, etc.)
Output: Probability of price going up (0-1), thresholded at optimized value for prediction

Intended Uses & Limitations

Intended Uses

Financial analysis and decision support
Educational purposes for machine learning in finance
Research on gold price prediction

Limitations

Trained on historical Antam gold prices only
May not generalize to other markets or time periods
Prediction accuracy is around 55-60% (better than random but not perfect)
Requires up-to-date feature computation for real-time use

How to Use

Loading the Model

python

from huggingface_hub import hf_hub_download
from joblib import load

# Download model
model_path = hf_hub_download("theonegareth/GoldPricePredictor", "gold_direction_model.joblib")
model = load(model_path)

Making Predictions

The model expects a pandas DataFrame with the same feature columns used in training.

python

import pandas as pd

# Example feature vector (you need to compute these from your data)
features = pd.DataFrame({
    'ret': [0.01],
    'log_ret': [0.00995],
    'ret_lag_1': [0.005],
    # ... all required features
})

# Predict probability of going up
proba_up = model.predict_proba(features)[:, 1]
prediction = (proba_up >= 0.52).astype(int)  # Using optimized threshold

Feature Engineering

To use this model, you need to compute the same features from your gold price data:

Daily returns and log returns
Lagged returns (1-5 days)
Rolling means and stds (3,5,10,20 days)
RSI (14-day)
MACD and signal
Bollinger Bands
Day of week and month

See the training notebooks for the complete add_features_adaptive function.

Training Data

Source: Antam historical gold prices (Indonesian market)
Period: [Insert date range from your data]
Features: 25+ technical indicators
Target: Next-day price direction (up=1, down=0)

Performance

Based on holdout testing:

Accuracy: ~55%
F1 Score: ~56%
ROC AUC: ~58%

See the confusion matrix, ROC curve, and feature importance plots in the repository.

Training Procedure

Data preprocessing and feature engineering
Time-series split for cross-validation
Hyperparameter tuning with RandomizedSearchCV
Model selection based on F1 score
Threshold optimization for final predictions

Models compared: Gradient Boosting, XGBoost, LightGBM

Contact

For questions or issues, please open an issue on this repository.

License

MIT License