Blog Archives

tidyquant 0.2.0: Added Functionality for Financial Engineers and Business Analysts

January 7, 2017
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tidyquant 0.2.0: Added Functionality for Financial Engineers and Business Analysts

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 ...

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tidyquant: Bringing Quantitative Financial Analysis to the tidyverse

December 31, 2016
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tidyquant: Bringing Quantitative Financial Analysis to the tidyverse

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...

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Speed Up Your Code: Parallel Processing with multidplyr

December 17, 2016
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Speed Up Your Code: Parallel Processing with multidplyr

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...

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Russell 2000 Quantitative Stock Analysis in R: Six Stocks with Amazing, Consistent Growth

November 29, 2016
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Russell 2000 Quantitative Stock Analysis in R: Six Stocks with Amazing, Consistent Growth

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 selecting the best components...

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Quantitative Stock Analysis Tutorial: Screening the Returns for Every S&P500 Stock in Less than 5 Minutes

October 22, 2016
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Quantitative Stock Analysis Tutorial: Screening the Returns for Every S&P500 Stock in Less than 5 Minutes

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...

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Customer Segmentation Part 3: Network Visualization

September 30, 2016
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Customer Segmentation Part 3: Network Visualization

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...

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Customer Segmentation Part 2: PCA for Segment Visualization

September 3, 2016
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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. Because PCA attacks the...

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Customer Segmentation Part 1: K-Means Clustering

August 6, 2016
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Customer Segmentation Part 1: K-Means Clustering

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 way the algorithm can process, and we’ll let it...

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