Decision Trees Model for Financial Predictions

This repository contains an implementation of an Decision Trees model, specifically designed for predicting the prices of financial instruments such as currencies, stocks, and cryptocurrencies. The Decision Trees 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 Decision Trees 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	0.001102	0.001031	0.001055	0.001088
Mean Absolute Error	0.023607	0.021404	0.023555	0.023209
R-squared	0.952137	0.956042	0.953705	0.954044
Median Absolute Error	0.015861	0.013975	0.018705	0.015254
Explained Variance Score	0.953459	0.957307	0.955147	0.955362

GC=F (Gold Futures)

Metric	Open	High	Low	Close
Mean Squared Error	0.000906	0.000738	0.000861	0.001051
Mean Absolute Error	0.023316	0.021045	0.022679	0.025864
R-squared	0.955481	0.963340	0.957730	0.947757
Median Absolute Error	0.017781	0.016785	0.017012	0.021284
Explained Variance Score	0.959784	0.965641	0.961819	0.952374

EURUSD (Euro/US Dollar)

Metric	Open	High	Low	Close
Mean Squared Error	0.000381	0.000378	0.000348	0.000390
Mean Absolute Error	0.015032	0.014754	0.013481	0.015250
R-squared	0.912997	0.915765	0.923491	0.912639
Median Absolute Error	0.011845	0.011374	0.009529	0.012956
Explained Variance Score	0.912998	0.916441	0.923583	0.912707

GSPC (S&P 500 Index)

Metric	Open	High	Low	Close
Mean Squared Error	0.000558	0.000468	0.000531	0.000647
Mean Absolute Error	0.018496	0.016614	0.018006	0.019531
R-squared	0.959112	0.967311	0.960878	0.954661
Median Absolute Error	0.016778	0.014017	0.013337	0.015573
Explained Variance Score	0.963553	0.971674	0.963332	0.958335

Related Websites

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About This Project

This Decision Trees 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 Decision Trees, 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!