tidyquant

The mission of tidyquant is to bring financial and business analysis to the tidyverse in R. It achieves this by integrating the quantitative analysis functionality of several specialized R packages with the tidy data infrastructure.

Key integrations include:

• zoo and xts: For time-series data handling.
• quantmod: For quantitative financial modeling.
• TTR: For technical trading rules.
• PerformanceAnalytics: For advanced performance and portfolio analysis.

This integration allows users to perform scalable financial modeling and visualization using ggplot2 and other tidyverse tools while maintaining the performance of the xts system.

You can install the stable version of tidyquant from CRAN or the development version with the latest features from GitHub.

To install the CRAN version:

install.packages("tidyquant")

To install the development version using devtools:

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

For in-depth learning, tidyquant provides several specialized vignettes:

• 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

Additionally, there are advanced learning labs available through Business Science covering topics like Portfolio Optimization with ROI, building APIs with plumber, and using Excel-like functionality (Pivot Tables, VLOOKUPs) within R.

tidyquant integrates quantitative analysis functions from xts, zoo, quantmod, TTR, and PerformanceAnalytics into the tidyverse workflow using two primary functions:

• tq_mutate(): Used to add a new column (such as a technical indicator) to an existing data frame.
• tq_transmute(): Used to return a new data frame, which is necessary when performing operations that involve changes in periodicity.

The tq_get() function is a central tool for retrieving web-based financial data in a "tidy" data frame format. It supports various data types including:

• Daily stock prices: Historical data.
• Key statistics: Real-time data.
• Key ratios: Historical data.
• Financial statements
• Dividends and splits
• Economic data: From the FRED (Federal Reserve Economic Data).
• FOREX rates: From Oanda.

These functions integrate PerformanceAnalytics capabilities into the tidyverse:

• tq_performance(): Converts investment returns into various performance metrics.
• tq_portfolio(): Aggregates a group (or multiple groups) of asset returns into one or more portfolios.