Gradient Boosting RegressorModel for Financial Predictions

This repository contains an implementation of an Gradient Boosting Regressormodel, specifically designed for predicting the prices of financial instruments such as currencies, stocks, and cryptocurrencies. The Gradient Boosting Regressoralgorithm 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 Gradient Boosting Regressormodel. 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	0.000797	0.000723	0.000725	0.000812
Mean Absolute Error	0.02081	0.01837	0.02003	0.02089
R-squared	0.96620	0.96989	0.96883	0.96646
Median Absolute Error	0.01419	0.01293	0.01677	0.01487
Explained Variance Score	0.96623	0.97027	0.96928	0.96649

GC=F (Gold Futures)

Metric	Open	High	Low	Close
Mean Squared Error	0.001308	0.000704	0.000780	0.000876
Mean Absolute Error	0.02866	0.02011	0.02144	0.02286
R-squared	0.93471	0.96448	0.96116	0.95602
Median Absolute Error	0.02487	0.01455	0.01742	0.01764
Explained Variance Score	0.95845	0.96589	0.96486	0.95955

EURUSD (Euro/US Dollar)

Metric	Open	High	Low	Close
Mean Squared Error	0.000416	0.000265	0.000228	0.000396
Mean Absolute Error	0.01574	0.01247	0.01126	0.01552
R-squared	0.90842	0.94280	0.95060	0.91206
Median Absolute Error	0.01178	0.00913	0.00968	0.01274
Explained Variance Score	0.90849	0.94311	0.95062	0.91206

GSPC (S&P 500 Index)

Metric	Open	High	Low	Close
Mean Squared Error	0.000497	0.000394	0.000479	0.000606
Mean Absolute Error	0.01670	0.01403	0.01650	0.01863
R-squared	0.96356	0.97249	0.96462	0.95739
Median Absolute Error	0.01234	0.01036	0.01296	0.01415
Explained Variance Score	0.96636	0.97444	0.96632	0.96067

Related Websites

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About This Project

This Gradient Boosting Regressormodel 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 SVR, 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!