LightGBM Model for Financial Predictions

This repository contains an implementation of an LightGBM model, specifically designed for predicting the prices of financial instruments such as currencies, stocks, and cryptocurrencies. The LightGBM algorithm leverages gradient boosting techniques, enabling it to capture intricate patterns in price movements and handle various dataset characteristics effectively. This approach enhances the accuracy and robustness of price forecasts across various datasets.

This is the original code sample for the LightGBM model. Explore my GitHub repository for additional models and implementations that cater to different financial prediction needs.

Performance Metrics

BTC-USD (Bitcoin)

Metric	Open	High	Low	Close
Mean Squared Error	0.003117	0.003003	0.003135	0.003120
Mean Absolute Error	0.032707	0.030720	0.032707	0.032671
R-squared	0.8679	0.8750	0.8653	0.8711
Median Absolute Error	0.015775	0.013368	0.014859	0.014888
Explained Variance Score	0.8866	0.8940	0.8853	0.8895

GC=F (Gold Futures)

Metric	Open	High	Low	Close
Mean Squared Error	0.002952	0.002961	0.002799	0.002867
Mean Absolute Error	0.042541	0.041599	0.041437	0.042491
R-squared	0.8526	0.8507	0.8605	0.8558
Median Absolute Error	0.035425	0.033177	0.034010	0.033128
Explained Variance Score	0.9053	0.9021	0.9126	0.9058

EURUSD (Euro/US Dollar)

Metric	Open	High	Low	Close
Mean Squared Error	0.000283	0.000226	0.000237	0.000277
Mean Absolute Error	0.012506	0.011682	0.011515	0.012449
R-squared	0.9377	0.9511	0.9486	0.9388
Median Absolute Error	0.010058	0.008580	0.010088	0.009810
Explained Variance Score	0.9379	0.9513	0.9487	0.9390

GSPC (S&P 500 Index)

Metric	Open	High	Low	Close
Mean Squared Error	0.001274	0.001255	0.001242	0.001345
Mean Absolute Error	0.030481	0.030138	0.029797	0.031667
R-squared	0.9066	0.9123	0.9082	0.9054
Median Absolute Error	0.030608	0.028235	0.028595	0.030568
Explained Variance Score	0.9525	0.9575	0.9510	0.9537

Related Websites

Predict Price

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About This Project

This LightGBM model is an initial implementation, released for public use. The project demonstrates the potential of deep learning models for financial predictions. While this repository focuses on LightGBM, I have also utilized other models, the code for which is available on my GitHub[https://github.com/taleblou/].

How to Use

1. Clone this repository.
2. Install the required libraries: pip install -r requirements.txt
3. Prepare your dataset and follow the instructions in the notebook or script.
4. Run the model and evaluate its performance using the provided metrics.

License

This project is open-source and available for public use under the MIT License. Contributions and feedback are welcome!