The CNN Time-Series + Mean Reversion Strategy Stock Trading Model is a state-of-the-art deep learning solution tailored for predicting stock price movements. By leveraging the intricacies of convolutional neural networks (CNN) for time-series analysis in conjunction with the mean reversion trading strategy, this model offers a unique perspective for those keen on enhancing their stock trading tactics.

• Features
• Prerequisites
• Installation
• Usage
• Contributing
• Licence
• Acknowledgements

• Deploy CNN for time-series prediction on stock price datasets.
• Implement the mean reversion strategy for informed stock trading decisions.
• Analyse stock price trends and historical data for improved predictions.
• Experiment with various CNN architectures for optimal time-series forecasting.
• Benefit from real-time stock trading signals based on the combined strength of CNN and mean reversion.

• Python 3.7 or higher.
• TensorFlow library.
• PyTorch library.
• A foundational understanding of stock trading, time-series analysis, and convolutional neural networks.

1. Clone this repository:

   git clone https://github.com/amidstdebug/Deep-Learning-Trading-Model.git

2. Navigate to the project directory:

   cd "Deep Learning Trading Model"

1. Ensure you have access to relevant stock price datasets, formatted suitably for time-series analysis.

2. Refer to instructions from readme.txt

1. Fork the project.
2. Create your feature branch (git checkout -b feature/UniqueFeature).
3. Commit your changes (git commit -m 'Add some UniqueFeature').
4. Push to the branch (git push origin feature/UniqueFeature).
5. Open a pull request.

For major changes, please open an issue first to discuss what you would like to change.

This project is licensed under the CC BY-NC 4.0 License - consult the LICENCE file for further details.

• TensorFlow for its powerful deep learning tools.
• Financial analysts and researchers who have paved the way for innovative stock trading strategies.
• All contributors and enthusiasts who continuously bring in insights to enhance the field of financial modelling with AI.

For any queries or issues, kindly reach out to the project maintainers or raise an issue. Your feedback and contributions are invaluable!

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