tidyquant
, version 0.3.0, is a pretty sizable release that includes a little bit for everyone, including new financial charting and moving average geoms for use with ggplot2
, a new tq_get
get option called "key.stats"
for retrieving real-time stock information, and several nice integrations that improve the ease of scaling your analyses. If your not already familiar with tidyquant
, it integrates the best quantitative resources for collecting and analyzing quantitative data, xts
, zoo
, quantmod
and TTR
, with the tidyverse
allowing for seamless interaction between each. I’ll briefly touch on some of the updates by going through some neat examples. The package is open source, and you can view the code on the tidyquant github page.
Speed Up Your Code Part 2: Parallel Processing Financial Data with multidplyr + tidyquant
Since my initial post on parallel processing with multidplyr
, there have been some recent changes in the tidy
eco-system: namely the package tidyquant
, which brings financial analysis to the tidyverse
. The tidyquant
package drastically increase the amount of tidy financial data we have access to and reduces the amount of code needed to get financial data into the tidy format. The multidplyr
package adds parallel processing capability to improve the speed at which analysis can be scaled. I seriously think these two packages were made for each other. I’ll go through the same example used previously, updated with the new tidyquant
functionality.
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 tidyverse
allowing for seamless interaction between each. I’ll briefly touch on some of the updates. The package is open source, and you can view the code on the tidyquant github page.
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 interaction between each. While this post aims to introduce tidyquant
to the R community, it just scratches the surface of the features and benefits. We’ll go through a simple stock visualization using ggplot2
, which which shows off the integration. The package is open source, and you can view the code on the tidyquant github page.
Speed Up Your Code: Parallel Processing with multidplyr
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 can result in large gains. In this post, I’ll perform a quantitative stock analysis on the entire list of Russell 2000 stock components using the R programming language. Building on the methodology from my S&P Analysis Post, I develop screening and ranking metrics to identify the top stocks with amazing growth and most consistency. I use R for the analysis including the rvest
library for web scraping the list of Russell 2000 stocks, quantmod
to collect historical prices for all 2000+ stock components, purrr
to map modeling functions, and various other tidyverse
libraries such as ggplot2
, dplyr
, and tidyr
to visualize and manage the data workflow. Last, I use plotly
to create an interactive visualization used in the screening process. Whether you are familiar with quantitative stock analysis, just beginning, or just interested in the R programming language, you’ll gain both knowledge of data science in R and immediate insights into the best Russell 2000 stocks, quantitatively selected for future returns!
Quantitative Stock Analysis Tutorial: Screening the Returns for Every S&P500 Stock in Less than 5 Minutes
Develop quantitative trading strategies in R. Analyze every stock in the S&P 500 to screen risk versus reward.
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 Component Analysis (PCA) to visualize customer groups. The third and final post performs Network Visualization (Graph Drawing) using the igraph
and networkD3
libraries as a method to visualize the customer connections and relationship strengths.
Customer Segmentation Part 2: PCA for Segment Visualization
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 problem from a different angle than k-means, we can get different insights. We’ll compare both the k-means results with the PCA visualization. Let’s see what happens when we apply PCA.
Customer Segmentation Part 1: K Means Clustering
In this machine learning with R tutorial, use k means clustering to segment customers into distinct groups based on purchasing habits.
orderSimulatoR: Simulate Orders for Business Analytics
In this post, we will be discussing orderSimulatoR
, which enables fast and easy R
order simulation for customer and product learning. The basic premise is to simulate data that you’d retrieve from a SQL
query of an ERP system. The data can then be merged with products and customers tables to data mine. I’ll go through the basic steps to create an order data set that combines customers and products, and I’ll wrap up with some visualizations to show how you can use order data to expose trends. You can get the scripts and the Cannondale bikes data set
at the orderSimulatoR
GitHub repository. In case you are wondering what simulated orders look like, click here to scroll to the end result.
Marketing Strategy: Why MBAs Can Benefit from Learning Analytics
Just because you’re a business professional does not mean you can’t or you shouldn’t pursue furthering yourself in analytics. Businesses view strategic decision making as a competitive advantage. You should too! Learning the basics behind data science not only adds value to your organization, it increases your value and thus your demand too.