Welcome to the repository! This repository serves as a collaborative space to share and exchange ideas related to the ADIA Lab Market Prediction Competition. Whether you're a seasoned data scientist or a curious enthusiast, you'll find a wealth of insightful notebooks and code to help you tackle this exciting challenge.
The ADIA Lab Market Prediction Competition is an engaging and competitive event that challenges participants to develop robust market prediction models. The Abu Dhabi Investment Authority Lab (ADIA Lab, an independent research center supported by ADIA) in partnership with CrunchDAO host this competition to foster innovation in the field of financial machine learning and market prediction, attracting participants from diverse backgrounds with a passion for data science, machine learning, and finance.
Feel free to explore the notebooks in this repository to gain insights into different methodologies for market prediction. Each notebook is a self-contained project that you can run locally on your machine or experiment with using cloud-based Jupyter services.
To run a notebook locally, you'll need to set up the required dependencies and environment. Typically, the notebooks will require popular Python libraries for data manipulation (e.g., Pandas), machine learning (e.g., Scikit-learn), and data visualization (e.g., Matplotlib or Seaborn).
We welcome your participation in this collaborative space. If you have any questions, ideas, or feedback related to the ADIA Lab market prediction competition or this repository, feel free to open an issue or start a discussion. Together, we can advance our knowledge and create cutting-edge solutions for market prediction.
This repository hosts a collection of Jupyter notebooks contributed by various participants. Each notebook showcases a unique approach to tackling the ADIA Lab market prediction competition. The notebooks may include exploratory data analysis (EDA), feature engineering techniques, model building, evaluation metrics, and more.
Participants are encouraged to contribute their own notebooks to this repository. Whether you have a groundbreaking strategy or a clever data preprocessing technique, sharing your findings and code can be immensely valuable to the community. To contribute, simply fork this repository, add your notebook, and create a pull request.