XGBoost Model for Financial Predictions

This repository contains an implementation of an XGBoost model, specifically designed for predicting the prices of financial instruments such as currencies, stocks, and cryptocurrencies. The XGBoost 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 XGBoost 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	0.0010794601	0.0009626428	0.0010511041	0.0010825764
Mean Absolute Error	0.0225182790	0.0205550581	0.0226770898	0.0226641239
R-squared	0.9551282071	0.9606687535	0.9553621361	0.9556489283
Median Absolute Error	0.0145998074	0.0133912742	0.0157449225	0.0139960765
Explained Variance Score	0.9566631208	0.9621831866	0.9580375663	0.9573230196

GC=F (Gold Futures)

Metric	Open	High	Low	Close
Mean Squared Error	0.0009140586	0.0008637735	0.0008719756	0.0009674982
Mean Absolute Error	0.0238410336	0.0228749418	0.0228566718	0.0246819105
R-squared	0.9537574445	0.9559063133	0.9560443828	0.9507698329
Median Absolute Error	0.0194905209	0.0160515960	0.0179657931	0.0190607390
Explained Variance Score	0.9596390659	0.9636102064	0.9624192446	0.9580219942

EURUSD (Euro/US Dollar)

Metric	Open	High	Low	Close
Mean Squared Error	0.0003323588	0.0002540997	0.0002617769	0.0003305155
Mean Absolute Error	0.0141802724	0.0125211860	0.0121102308	0.0141899579
R-squared	0.9292000159	0.9467354170	0.9450123323	0.9293981487
Median Absolute Error	0.0118845602	0.0108199617	0.0089974120	0.0118840140
Explained Variance Score	0.9292148194	0.9467440000	0.9452642656	0.9293982576

GSPC (S&P 500 Index)

Metric	Open	High	Low	Close
Mean Squared Error	0.0005145034	0.0004543764	0.0005155069	0.0006490753
Mean Absolute Error	0.0178120621	0.0168959445	0.0177888720	0.0202859200
R-squared	0.9622727325	0.9682476349	0.9618888358	0.9542881139
Median Absolute Error	0.0153078058	0.0138028655	0.0152938393	0.0167618105
Explained Variance Score	0.9691471274	0.9744320164	0.9675984899	0.9619588025

Related Websites

Predict Price

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About This Project

This XGBoost 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 XGBoost, 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!