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Limex DataHub Integration and Strategy Examples

This repository contains a collection of Jupyter notebooks written in Python and R that demonstrate how to connect to and utilize data from Limex DataHub. These resources are intended to assist data analysts, developers, and traders who wish to implement various trading strategies such as momentum or mean reversion, utilizing quality financial data.

Introduction

Limex DataHub offers a broad spectrum of financial data critical for developing and testing trading strategies. To facilitate ease and flexibility, we provide code samples that can be readily adapted to your unique requirements.

Repository Contents

connect_limex_py.ipynb: A Python Jupyter notebook that illustrates how to establish a connection with Limex DataHub. connect_limex_r.ipynb: An R Jupyter notebook demonstrating the method of connecting to Limex DataHub. momentum_strategy_py.ipynb: An example in Python of implementing the momentum strategy using data from Limex DataHub. mean_reversion_strategy_r.ipynb: An example in R of implementing the mean reversion strategy using data from Limex DataHub. Getting Started

To begin using these notebooks, you will need:

An installed Python 3.x interpreter or R environment. The Jupyter package installed to run notebooks. Access to Limex DataHub. Detailed instructions for setting up and configuring your programming environment are contained within the respective notebooks.

How to Use These Notebooks

Clone the repository to your local machine. Install any dependencies described in each notebook. Open the notebooks via Jupyter and follow the contained instructions. Contributing

We are open to contributions! If you have suggestions for code improvement, or if you want to add examples of new strategies, please feel free to make a pull request or open an issue.

License

[Specify License Type], see the LICENSE file in this repository for details.

Contact

If you have any questions, please don't hesitate to reach out to us via [link to your support service or profile].

Adjust and expand upon this template as needed to suit your project and audience. Be sure to include accurate and up-to-date information that reflects the content of your repository and is understandable to your intended users.

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Quantitative research of stock market: Python and R notebooks

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