GRU Model for Financial Predictions

This repository contains the initial implementation of a GRU (Gated Recurrent Unit) model designed for financial predictions across multiple asset classes. This model aims to provide accurate forecasts for various financial instruments, including indices, cryptocurrencies, commodities, and currency pairs. Below, you will find detailed performance metrics for each asset class and links to external resources that complement this project.

Performance Metrics

BTC-USD (Bitcoin)

Metric	Open	High	Low	Close
Mean Squared Error	0.0006704011	0.0007466893	0.0010460546	0.0010510000
Mean Absolute Error	0.0189357042	0.0207899107	0.0244501190	0.0251394672
R-squared	0.9698693476	0.9671408209	0.9520648807	0.9536147883
Median Absolute Error	0.0147797754	0.0157247866	0.0198321750	0.0206766406
Explained Variance Score	0.9714662622	0.9682738453	0.9575608982	0.9563927096

GC=F (Gold Futures)

Metric	Open	High	Low	Close
Mean Squared Error	0.0006704011	0.0007466893	0.0010460546	0.0010510000
Mean Absolute Error	0.0189357042	0.0207899107	0.0244501190	0.0251394672
R-squared	0.9698693476	0.9671408209	0.9520648807	0.9536147883
Median Absolute Error	0.0147797754	0.0157247866	0.0198321750	0.0206766406
Explained Variance Score	0.9714662622	0.9682738453	0.9575608982	0.9563927096

EURUSD (Euro/US Dollar)

Metric	Open	High	Low	Close
Mean Squared Error	0.0003879169	0.0004876292	0.0005445911	0.0003651724
Mean Absolute Error	0.0158024339	0.0179194147	0.0182679915	0.0155702525
R-squared	0.9101155429	0.8892027422	0.8765603505	0.9154974316
Median Absolute Error	0.0132580484	0.0159090454	0.0155098954	0.0136890470
Explained Variance Score	0.9107513364	0.8902119109	0.8874938781	0.9170630250

GSPC (S&P 500 Index)

Metric	Open	High	Low	Close
Mean Squared Error	0.0006503769	0.0006529812	0.0006666432	0.0009187557
Mean Absolute Error	0.0187974513	0.0194058691	0.0198970860	0.0233750671
R-squared	0.9516866515	0.9538492619	0.9504676043	0.9351719127
Median Absolute Error	0.0138990470	0.0166170954	0.0170245137	0.0193339874
Explained Variance Score	0.9526338713	0.9568179300	0.9515982439	0.9413512079

Related Websites

Predict Price

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Magical Prediction

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Magical Analysis

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About This Project

This GRU 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 GRU, 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!