Linear Regression Model for Financial Predictions

This repository contains an implementation of an Linear Regression model, specifically designed for predicting the prices of financial instruments such as currencies, stocks, and cryptocurrencies. The Linear Regression 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 Linear Regression 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	0.000824	0.000706	0.000892	0.000830
Mean Absolute Error	0.0213	0.0190	0.0226	0.0215
R-squared	0.9642	0.9699	0.9609	0.9650
Median Absolute Error	0.0160	0.0141	0.0181	0.0164
Explained Variance Score	0.9648	0.9704	0.9615	0.9656

GC=F (Gold Futures)

Metric	Open	High	Low	Close
Mean Squared Error	0.000840	0.000801	0.000778	0.000842
Mean Absolute Error	0.0233	0.0224	0.0220	0.0231
R-squared	0.9587	0.9602	0.9619	0.9583
Median Absolute Error	0.0183	0.0195	0.0182	0.0204
Explained Variance Score	0.9595	0.9608	0.9625	0.9590

EURUSD (Euro/US Dollar)

Metric	Open	High	Low	Close
Mean Squared Error	0.000428	0.000405	0.000388	0.000428
Mean Absolute Error	0.0163	0.0155	0.0155	0.0163
R-squared	0.9023	0.9097	0.9132	0.9024
Median Absolute Error	0.0135	0.0129	0.0120	0.0135
Explained Variance Score	0.9028	0.9102	0.9136	0.9029

GSPC (S&P 500 Index)

Metric	Open	High	Low	Close
Mean Squared Error	0.000586	0.000480	0.000578	0.000604
Mean Absolute Error	0.0174	0.0158	0.0177	0.0183
R-squared	0.9568	0.9663	0.9573	0.9576
Median Absolute Error	0.0142	0.0121	0.0136	0.0141
Explained Variance Score	0.9578	0.9671	0.9583	0.9588

Related Websites

Predict Price

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About This Project

This Linear Regression 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 Linear Regression, 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!