This is a project submission for my machine learning nano degree. The project centred around using clustering (K-Means to classify into N classes) and cluster trained tabular predictors (N predictors trained one on each of the classes from the K-Means predictor) to predict the price of gold using historical technical information about the price of gold and the S&P 500 index. Ultimately I have determined that none of the configurations resulted in a system capable of predicting the future value of gold with any significant degree of accuracy and so this does not challenge the efficient market hypothesis. That said, the novel approach to predicting, along with some possibly useful results means that I feel this avenue might have some merit both in the field of price and market prediction and in other fields.
This project is intended to be run from withing AWS.
- Start a SageMaker Studio Notebook
- Pull in this repository via the GIT interface in the Notebook.
- Open the
Capstone Training.ipynbnotebook. - Read the notebook and run it. You may wish to change the number of clusters, or other aspects of the notebook.
- If you have any feedback, please make a fork of this project and work on it. I would love to hear if it works for you and if you found anything interesting.
- Read the doc/Report.pdf file for a report detailing the completed project.
- Read the doc/Proposal.pdf file for details of the proposal for creating this project.
- The datasets are stored in the datasets directory.