Random Forest Regressor Model for Financial Predictions

This repository contains an implementation of an Random Forest Regressor model, specifically designed for predicting the prices of financial instruments such as currencies, stocks, and cryptocurrencies. The Random Forest Regressor 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 Random Forest Regressor 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	0.000867	0.000734	0.000784	0.000845
Mean Absolute Error	0.0209	0.0183	0.0202	0.0212
R-squared	0.9631	0.9693	0.9662	0.9649
Median Absolute Error	0.0138	0.0124	0.0152	0.0147
Explained Variance Score	0.9635	0.9696	0.9671	0.9651

GC=F (Gold Futures)

Metric	Open	High	Low	Close
Mean Squared Error	0.000920	0.000708	0.000838	0.001020
Mean Absolute Error	0.0239	0.0203	0.0226	0.0250
R-squared	0.9534	0.9638	0.9578	0.9481
Median Absolute Error	0.0188	0.0143	0.0179	0.0218
Explained Variance Score	0.9604	0.9659	0.9629	0.9555

EURUSD (Euro/US Dollar)

Metric	Open	High	Low	Close
Mean Squared Error	0.000402	0.000342	0.000288	0.000378
Mean Absolute Error	0.0159	0.0144	0.0127	0.0155
R-squared	0.9143	0.9285	0.9393	0.9195
Median Absolute Error	0.0140	0.0113	0.0097	0.0134
Explained Variance Score	0.9144	0.9289	0.9394	0.9196

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GSPC (S&P 500 Index)

Metric	Open	High	Low	Close
Mean Squared Error	0.000527	0.000431	0.000462	0.000626
Mean Absolute Error	0.0176	0.0157	0.0163	0.0198
R-squared	0.9614	0.9699	0.9658	0.9559
Median Absolute Error	0.0136	0.0126	0.0139	0.0156
Explained Variance Score	0.9644	0.9738	0.9680	0.9618

 

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About This Project

This Random Forest Regressor 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 Random Forest Regressor, 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!