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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE, message = FALSE, warning=F}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
dpi = 300,
message = F,
warning = F
)
```
# tidyquant <img src="man/figures/logo.png" width="147" height="170" align="right" />
<!-- badges: start -->
[](https://github.com/business-science/tidyquant/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/business-science/tidyquant)
[](https://cran.r-project.org/package=tidyquant)
<!-- badges: end -->
> Bringing financial and business analysis to the `tidyverse` in R.
Mission: Our number 1 goal is to make financial analysis easier in R. We've designed `tidyquant` to give you the flexibility of the tidyverse with the performance of the R `xts` system. The result: easier, faster, and more scalable financial analysis.
## Start: 2-Minutes To tidyquant
Our short introduction to `tidyquant` on
[YouTube](https://www.youtube.com/embed/woxJZTL2hok).
<a href="https://www.youtube.com/embed/woxJZTL2hok" target="_blank"><img src="http://img.youtube.com/vi/woxJZTL2hok/0.jpg" alt="Anomalize" width="100%" height="350"/></a>
# Features of tidyquant
`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_](https://r4ds.hadley.nz/)__
* __Implements `ggplot2` functionality for beautiful and meaningful financial visualizations__
* __User-friendly documentation to get you up to speed quickly!__
### New Excel Functionality in tidyquant
- [__Excel in R - Pivot Tables, VLOOKUPs, and more__](https://www.business-science.io/finance/2020/02/26/r-for-excel-users.html): Details on the __Excel integrations__ are covered in the blog article.
## 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...
```{r echo=FALSE, out.width='100%'}
knitr::include_graphics("man/figures/sample_img_1_volatility.png")
```
### Evaluating Stock Performance
What about stock performance? Quickly visualize how a $10,000 investment in various stocks would perform.
```{r echo=FALSE, out.width='100%'}
knitr::include_graphics("man/figures/sample_img_2_stock_returns.png")
```
### 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
```{r echo=FALSE, out.width='100%'}
knitr::include_graphics("man/figures/sample_img_3_portfolio_returns.png")
```
This just scratches the surface of `tidyquant`. Here's how to install to get started.
## Installation
Development Version with Latest Features:
``` {r, eval = FALSE}
# install.packages("devtools")
devtools::install_github("business-science/tidyquant")
```
CRAN Approved Version:
```{r, eval = FALSE}
install.packages("tidyquant")
```
## Further Information
The `tidyquant` package includes several vignettes to help users get up to speed quickly:
* [TQ00 - Introduction to `tidyquant`](https://business-science.github.io/tidyquant/articles/TQ00-introduction-to-tidyquant.html)
* [TQ01 - Core Functions in `tidyquant`](https://business-science.github.io/tidyquant/articles/TQ01-core-functions-in-tidyquant.html)
* [TQ02 - R Quantitative Analysis Package Integrations in `tidyquant`](https://business-science.github.io/tidyquant/articles/TQ02-quant-integrations-in-tidyquant.html)
* [TQ03 - Scaling and Modeling with `tidyquant`](https://business-science.github.io/tidyquant/articles/TQ03-scaling-and-modeling-with-tidyquant.html)
* [TQ04 - Charting with `tidyquant`](https://business-science.github.io/tidyquant/articles/TQ04-charting-with-tidyquant.html)
* [TQ05 - Performance Analysis with `tidyquant`](https://business-science.github.io/tidyquant/articles/TQ05-performance-analysis-with-tidyquant.html)
* [Blog Article: Excel in R - Pivot Tables, VLOOKUPs, and more!](https://www.business-science.io/finance/2020/02/26/r-for-excel-users.html)
# Want to Learn tidyquant?
- [Learning Lab #9:](https://university.business-science.io/p/learning-labs-pro)
- __Performance Analysis & Portfolio Optimization with `tidyquant`__ - A 1-hour course on `tidyquant` in Learning Labs PRO
- [Learning Lab #10:](https://university.business-science.io/p/learning-labs-pro)
- __Building an API with `plumber`__ - Build a stock optimization API with `plumber` and `tidyquant`
- [Learning Lab #16:](https://university.business-science.io/p/learning-labs-pro)
- __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:](https://university.business-science.io/courses/learning-labs-pro/lectures/14630075)
- __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!