veronicahurtado / fintech-module10-challenge

Crypto Clustering

Case study

A Financial Advisory firm is looking for a new way of constructing investment portfolios based on cryptocurrencies.

This is a prototype to trial an approach that considers not only returns and volatility, but also other factors that might impact the crypto market. The prototype is presented as a Jupyter Lab notebook.

Data sources

• Price change data of some cryptocurrencies in different periods: crypto_market_data.csv

Tasks to support the Analysis of the data

• Importing and preparing the data.
• Find the best value for k by using the original data.
• Clustering of cryptocurrencies with K-means by using the original data.
• Optimising the clusters with principal component analysis.
• Finding the best value for k by using the PCA data.
• Clustering the cryptocurrencies with K-means by using the PCA data.
• Plotting the results to compare the performance of the clusters visually.

Technical Environment

This tool utilises the following technologies:

• Pandas DataFrame: Documentation
• hvplot Bar chart, Line plot, Scatter: Documentation
• sklearn Cluster, decomposition, preprocessing: Documentation

Disclaimer

Please be aware this is an Academic Case Study. The conclusions from this work should not be considered as financial advice.

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Report

Impact of using fewer features to cluster the data using K-Means

Since we can only plot 2 dimensions of the data (PCA 1 and PCA 2), we are not able to get a lot of meaningful information. However, the Cryptocurrency segmentation is clearer when using PCA.