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README

Bringing financial and business analysis to the tidyverse

Our short introduction to tidyquant on YouTube.

tidyquant integrates the best resources for collecting and analyzing financial data, zoo, xts, quantmod, TTR, and PerformanceAnalytics, with the tidy data infrastructure of the tidyverse allowing for seamless interaction between each. You can now perform complete financial analyses in the tidyverse.

• A few core functions with a lot of power
• Integrates the quantitative analysis functionality of zoo, xts, quantmod, TTR, and now PerformanceAnalytics
• Designed for modeling and scaling analyses using the tidyverse tools in R for Data Science
• Implements ggplot2 functionality for beautiful and meaningful financial visualizations
• User-friendly documentation to get you up to speed quickly!

• Excel in R - Pivot Tables, VLOOKUPs, and more: Details on the Excel integrations are covered in the blog article.

With tidyquant all the benefits add up to one thing: a one-stop shop for serious financial analysis!

• Getting Financial Data from the web: tq_get(). This is a one-stop shop for getting web-based financial data in a “tidy” data frame format. Get data for daily stock prices (historical), key statistics (real-time), key ratios (historical), financial statements, dividends, splits, economic data from the FRED, FOREX rates from Oanda.

• Manipulating Financial Data: tq_transmute() and tq_mutate(). Integration for many financial functions from xts, zoo, quantmod,TTR and PerformanceAnalytics packages. tq_mutate() is used to add a column to the data frame, and tq_transmute() is used to return a new data frame which is necessary for periodicity changes.

• Performance Analysis and Portfolio Analysis: tq_performance() and tq_portfolio(). The newest additions to the tidyquant family integrate PerformanceAnalytics functions. tq_performance() converts investment returns into performance metrics. tq_portfolio() aggregates a group (or multiple groups) of asset returns into one or more portfolios.

Visualizing the stock price volatility of four stocks side-by-side is quick and easy…

What about stock performance? Quickly visualize how a $10,000 investment in various stocks would perform.

Ok, stocks are too easy. What about portfolios? With the PerformanceAnalytics integration, visualizing blended portfolios are easy too!

• Portfolio 1: 50% FB, 25% AMZN, 25% NFLX, 0% GOOG
• Portfolio 2: 0% FB, 50% AMZN, 25% NFLX, 25% GOOG
• Portfolio 3: 25% FB, 0% AMZN, 50% NFLX, 25% GOOG
• Portfolio 4: 25% FB, 25% AMZN, 0% NFLX, 50% GOOG

This just scratches the surface of tidyquant. Here’s how to install to get started.

Development Version with Latest Features:

# install.packages("devtools")
devtools::install_github("business-science/tidyquant")

CRAN Approved Version:

install.packages("tidyquant")

The tidyquant package includes several vignettes to help users get up to speed quickly:

• TQ00 - Introduction to tidyquant
• TQ01 - Core Functions in tidyquant
• TQ02 - R Quantitative Analysis Package Integrations in tidyquant
• TQ03 - Scaling and Modeling with tidyquant
• TQ04 - Charting with tidyquant
• TQ05 - Performance Analysis with tidyquant
• Blog Article: Excel in R - Pivot Tables, VLOOKUPs, and more!

• Learning Lab #9:

  • Performance Analysis & Portfolio Optimization with tidyquant - A 1-hour course on tidyquant in Learning Labs PRO

• Learning Lab #10:

  • Building an API with plumber - Build a stock optimization API with plumber and tidyquant

• Learning Lab #16:

  • Stock Portfolio Optimization and Nonlinear Programming - Use the ROI package with tidyquant to calculate optimal minimum variance portfolios and develop an efficient frontier.

• Learning Lab #30:

  • Shiny Financial Analysis with Tidyquant API & Excel Pivot Tables - Learn how to use the new Excel Functionality to make Pivot Tables, VLOOKUPs, Sum-If’s, and more!

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