tidyquant, version 0.2.0, is now available on CRAN. If your not already familiar, tidyquant integrates the best quantitative resources for collecting and analyzing quantitative data, xts, zoo, quantmod and TTR, with the tidy data infrastructure of the ... [Read more...]

My new package, tidyquant, is now available on CRAN. tidyquant integrates the best quantitative resources for collecting and analyzing quantitative data, xts, quantmod and TTR, with the tidy data infrastructure of the tidyverse allowing for seamless in... [Read more...]

There’s nothing more frustrating than waiting for long-running R scripts to iteratively run. I’ve recently come across a new-ish package for parallel processing that plays nicely with the tidyverse: multidplyr. The package has saved me countless ho... [Read more...]

The Russell 2000 Small-Cap Index, ticker symbol: ^RUT, is the hottest index of 2016 with YTD gains of over 18%. The index components are interesting not only because of recent performance, but because the top performers either grow to become mid-cap stocks or are bought by large-cap companies at premium prices. This means ... [Read more...]

Quantitative trading strategies are easy to develop in R if you can manage the data workflow. In this post, I analyze every stock in the S&P500 to screen in terms of risk versus reward. I’ll show you how to use quantmod to collect daily stock pri... [Read more...]

This post is the third and final part in the customer segmentation analysis. The first post focused on K-Means Clustering to segment customers into distinct groups based on purchasing habits. The second post takes a different approach, using Pricipal C... [Read more...]

This post is the second part in the customer segmentation analysis. The first post focused on k-means clustering in R to segment customers into distinct groups based on purchasing habits. This post takes a different approach, using Pricipal Component Analysis (PCA) in R as a tool to view customer groups. ... [Read more...]

In this post, we’ll be using k-means clustering in R to segment customers into distinct groups based on purchasing habits. k-means clustering is an unsupervised learning technique, which means we don’t need to have a target for clustering. All we need is to format the data in a ... [Read more...]