CatBoostRegressor Model for Financial Predictions

This repository contains an implementation of an CatBoostRegressor model, specifically designed for predicting the prices of financial instruments such as currencies, stocks, and cryptocurrencies. The CatBoostRegressor 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 CatBoostRegressor 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	0.000986	0.000814	0.000899	0.000973
Mean Absolute Error	0.02191	0.01842	0.02084	0.02130
R-squared	0.9572	0.9653	0.9606	0.9590
Median Absolute Error	0.01520	0.01196	0.01370	0.01316
Explained Variance Score	0.9585	0.9663	0.9625	0.9603

GC=F (Gold Futures)

Metric	Open	High	Low	Close
Mean Squared Error	0.000902	0.000829	0.000838	0.000875
Mean Absolute Error	0.02353	0.02184	0.02249	0.02326
R-squared	0.9557	0.9588	0.9589	0.9565
Median Absolute Error	0.01780	0.01444	0.01784	0.01863
Explained Variance Score	0.9606	0.9640	0.9636	0.9615

EURUSD (Euro/US Dollar)

Metric	Open	High	Low	Close
Mean Squared Error	0.000308	0.000245	0.000254	0.000314
Mean Absolute Error	0.01344	0.01245	0.01202	0.01349
R-squared	0.9298	0.9453	0.9441	0.9294
Median Absolute Error	0.01194	0.01036	0.01027	0.01177
Explained Variance Score	0.9298	0.9453	0.9441	0.9295

GSPC (S&P 500 Index)

Metric	Open	High	Low	Close
Mean Squared Error	0.000474	0.000422	0.000470	0.000565
Mean Absolute Error	0.01702	0.01579	0.01702	0.01898
R-squared	0.9653	0.9705	0.9654	0.9604
Median Absolute Error	0.01409	0.01262	0.01491	0.01554
Explained Variance Score	0.9699	0.9746	0.9690	0.9661

Related Websites

Predict Price

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Magical Prediction

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Magical Analysis

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About This Project

This CatBoostRegressor 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 CatBoostRegressor, 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!