Our short introduction to
tidyquant on YouTube.
Check out our entire Software Intro Series on YouTube!
tidyversetools in R for Data Science
ggplot2functionality for beautiful and meaningful financial visualizations
Minimizing the number of functions reduces the learning curve. What we’ve done is group the core functions into four categories:
Get a Stock Index,
tq_index(), or a Stock
tq_exchange(): Returns the stock symbols
and various attributes for every stock in an index or exchange. Eighteen
indexes and three exchanges are available.
Get Quantitative Data,
one-stop shop to get data from various web-sources.
tq_transmute(), and Mutate,
tq_mutate(), Quantitative Data: Perform and scale
financial calculations completely within the
These workhorse functions integrate the
PerformanceAnalytics integration enables analyzing
performance of assets and portfolios. Refer to Performance
Analysis with tidyquant.
For more information, refer to the first topic-specific vignette, Core Functions in tidyquant.
There’s a wide range of useful quantitative analysis functions (QAF)
that work with time-series objects. The problem is that many of these
wonderful functions don’t work with data frames or the
tidyverse workflow. That is until now. The
tidyquant package integrates the most useful functions from
enabling seamless usage within the
Refer below for information on the performance analysis and portfolio
attribution with the
For more information, refer to the second topic-specific vignette, R Quantitative Analysis Package Integrations in tidyquant.
The greatest benefit to
tidyquant is the ability to
easily model and scale your financial analysis. Scaling is the process
of creating an analysis for one security and then extending it to
multiple groups. This idea of scaling is incredibly useful to financial
analysts because typically one wants to compare many securities to make
informed decisions. Fortunately, the
integrates with the
tidyverse making scaling super
tidyquant functions return data in the
tibble (tidy data frame) format, which allows for
interaction within the
tidyverse. This means we can:
%>%) for chaining operations
purrr: mapping functions with
For more information, refer to the third topic-specific vignette, Scaling and Modeling with tidyquant.
tidyquant package includes charting tools to assist
users in developing quick visualizations in
the grammar of graphics format and workflow.
For more information, refer to the fourth topic-specific vignette, Charting with tidyquant.
Asset and portfolio performance analysis is a deep field with a wide
range of theories and methods for analyzing risk versus reward. The
PerformanceAnalytics package consolidates many of the most
widely used performance metrics as functions that can be applied to
stock or portfolio returns.
tidquant implements the
functionality with two primary functions:
tq_performanceimplements the performance analysis functions in a tidy way, enabling scaling analysis using the split, apply, combine framework.
tq_portfolioprovides a useful toolset for aggregating a group of individual asset returns into one or many portfolios.
Performance is based on the statistical properties of returns, and as a result both functions use returns as opposed to stock prices.
For more information, refer to the fifth topic-specific vignette, Performance Analysis with tidyquant.