Bringing financial analysis to the tidyverse
tidyquant integrates the best resources for collecting and analyzing financial data,
PerformanceAnalytics, with the tidy data infrastructure of the
tidyverse allowing for seamless interaction between each. You can now perform complete financial analyses in the
- A few core functions with a lot of power
- Integrates the quantitative analysis functionality of
TTR, and now
- Designed for modeling and scaling analyses using the the
tidyversetools in R for Data Science
ggplot2functionality for beautiful and meaningful financial visualizations
- User-friendly documentation to get you up to speed quickly!
One-Stop Shop for Serious Financial Analysis
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_mutate(). Integration for many financial functions from
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.
Coercing Data To and From xts and tibble:
as_xts(). There are a ton of Stack Overflow articles on converting data frames to and from xts. These two functions can be used to answer 99% of these questions.
Performance Analysis and Portfolio Analysis:
tq_portfolio(). The newest additions to the
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...
Evaluating Stock Performance
What about stock performance? Quickly visualize how a $10,000 investment in various stocks would perform.
Evaluating Portfolio Performance
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:
tidyquant package includes several vignettes to help users get up to speed quickly:
- TQ00 - Introduction to
- TQ01 - Core Functions in
- TQ02 - R Quantitative Analysis Package Integrations in
- TQ03 - Scaling and Modeling with
- TQ04 - Charting with
- TQ05 - Performance Analysis with
tidyquant vignettes for further details on the package.