Bringing financial and business analysis to the tidyverse
2-Minutes To tidyquant
Our short introduction to tidyquant on
YouTube.
Features of tidyquant
tidyquant integrates the best resources for collecting and analyzing
financial data using zoo, xts, quantmod, TTR, and
PerformanceAnalytics, with the tidy data infrastructure of the
tidyverse allows 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!
New Excel Functionality in tidyquant
One-Stop Shop for Serious Financial Analysis
With tidyquant, all the benefits add up to one thing: a one-stop shop
for serious financial analysis!
Core Functions
-
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.
Comparing Stock Prices
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 is
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.
Installation
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:
Want to Learn tidyquant?
-
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, SUMIFs, and more!
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