Clustering Stocks

This project is a demonstration of how to cluster stocks values using a Self-Organizing Map (SOM) algorithm, a K-Means algorithm. Moreover, we will see how to solve the lack of dimensionality problem by using Principal Component Analysis (PCA) and Neural Network Encoder.

To do this, we will use real stock prices from the S&P 600 companies over the last 5 years.

We will also see if there is a correlation between the clusters and the sectors of the companies.

The project is inspired by this Kaggle notebook, which provides an introduction to time series clustering with a SOM algorithm. We've adapted the notebook to work with historical stock prices and added additional preprocessing and cleaning steps to ensure accurate clustering.

Requirements

To run the notebook, you'll need to install the following Python packages:

• yfinance
• pandas
• requests
• bs4
• matplotlib
• scikit-learn
• minisom
• numpy
• torch
• tslearn

You can install these packages using pip by running the following command in your terminal (using requirements.txt):

pip install -r requirements.txt

Getting Started

To get started with the project, first clone the repository to your local machine:

git clone https://github.com/MaximeSzymanski/StocksClustering.git

Then, open the notebook in Jupyter Notebook:

jupyter notebook 

This should open a browser window displaying the project files. Click on clustering_stocks.ipynb to launch the notebook.