tidyquant: Bringing Quantitative Financial Analysis to the tidyverse
Written on January 1, 2017
My new package, tidyquant
, is now available on CRAN. tidyquant
integrates the best quantitative resources for collecting and analyzing quantitative data, xts
, quantmod
and TTR
, with the tidy data infrastructure of the tidyverse
allowing for seamless interaction between each. While this post aims to introduce tidyquant
to the R community, it just scratches the surface of the features and benefits. We’ll go through a simple stock visualization using ggplot2
, which which shows off the integration. The package is open source, and you can view the code on the tidyquant github page.
Table of Contents
 Updates
 Why tidyquant?
 Benefits
 Example: Visualizing Moving Averages
 Conclusion
 Recap
 Further Reading
Updates
 20170117: I updated the post to use the 0.2.0
tq_mutate
andtq_transform
function argumentsohlc_fun
,x
, andy
(replacesx_fun
,.x
and.y
, respectively). These will be deprecated in 0.3.0 so please make the switch! :)
Why tidyquant?
One of the reasons why I began my journey into R programming is because it’s the best opensource option for stock analysis. With quantitative financial analysis (QFA) packages like quantmod
, xts
and TTR
, stock data can quickly be retrieved, sliced and diced, transformed and mutated, and visualized so I can make investment decisions. It’s really a beautiful thing.
Over time, the R programming landscape has evolved. A major step forward was the tidyverse
, a collection of R packages that work in harmony, are built for scaleability, and are well documented in R for Data Science. However, a problem has surfaced: the QFA packages are not easy to use with the tidyverse
. The tidyverse
works with data frames while the QFA packages work with extensible timeseries (xts
) objects. Both are great, but they don’t easily work together.
As you can imagine, my workflow was longer than I’d like. I’d work in xts
to use various functions to calculate moving averages, moving average convergence divergence (MACD), Bollinger Bands, etc, and then convert to tibbles (tidy dataframes) for mapping functions with purrr
to scale to many stocks, for mutating dataframes with dplyr
to add new columns, and for visualizing my analysis using ggplot2
. This got very long and repetitive…
Enter tidyquant
. The package started off as a collection of scripts aimed at increasing my efficiency and performance of my stock analyses:
 I would start by getting data with
tq_get()
, which returns data, such as stock prices or financial statements, as atibble
object.  I’d use
tq_transform()
to use the variousquantmod
andxts
functions that can change periodicities, such as period returns and conversion from daily to monthly periodicity.  I’d use
tq_mutate()
to seamlessly apply the variousTTR
functions, such as moving averages, MACD’s, Bollinger Bands, etc.  And, I’d do all of this without ever leaving the
tidyverse
, which allowed me to mutate, pipe (%>%
), and scale my analyses at ease.
In this evolution and in the spirit of open source, I have released the tidyquant
package to the R community with the hope that others can benefit from the integration between the QFA packages (quantmod
, xts
, and TTR
) and the tidyverse
. I believe this is the right way to go, and I’m looking forward to hearing your feedback.
Benefits
The tidyquant
philosophy:
 A few core functions with a lot of power, that
 leverage the quantitative analysis power of
xts
,quantmod
andTTR
, and are  designed to be used and scaled with the
tidyverse
.
Example: Visualizing Moving Averages
I’ll go through an example of visualizing the 15day and 50day moving averages of the stock symbol, AAPL, which is for Apple Inc. Moving averages are a popular trading tool that stock analysts use to determine buying and selling signals. According to Investopedia, the moving average is…
A widely used indicator in technical analysis that helps smooth out price action by filtering out the “noise” from random price fluctuations. A moving average (MA) is a trendfollowing or lagging indicator because it is based on past prices. The two basic and commonly used MAs are the simple moving average (SMA), which is the simple average of a security over a defined number of time periods, and the exponential moving average (EMA), which gives bigger weight to more recent prices. The most common applications of MAs are to identify the trend direction and to determine support and resistance levels. While MAs are useful enough on their own, they also form the basis for other indicators such as the Moving Average Convergence Divergence (MACD).
Of particular interest is the crossover, the point at which a trend begins to emerge, which can be used as a buy or sell signal.
Source: Investopedia: Moving Averages
Let’s go through an example to visualize the 15day and 50day moving averages for AAPL.
Step 1: Prerequisites
The tidyquant
package can be downloaded from CRAN:
For those following along in R, you’ll need to load the following package:
I also recommend the opensource RStudio IDE, which makes R Programming easy and efficient.
Step 2: Use tq_get to get stock prices
We’ll start by getting the last year of stock prices. We use the tidyquant tq_get()
function for all data retrieval. Set the parameter get = "stock.prices"
to tell tidyquant we want the historical stock prices. We can use the from
argument to pass a date as the start of the collection, which accepts character string in the form of “YYYYMMDD”. We can use lubridate
functions today()
and years()
to get the date from one year ago.
We now have 251 days of stock prices as a tibble
object. This is exactly the format we want for working in the tidyverse
.
Step 3: Use tq_mutate to add moving averages
We need to get the 15day and 50day moving averages. We want to use the SMA()
function from the TTR
package. To use any of these functions in the tidyverse
, we have a few options with pros and cons:

dplyr::mutate()
: Used to add a single column to a data set. Only able to add a single column to a tibble. ForSMA()
, this works because a single column is generated. For other functions such asBBands()
andMACD
, multiple columns are generated that fail onmutate()
. 
tidyquant::tq_mutate()
: Used to add single or multiple columns to a data set. Usesquantmod
OHLC notation (more on this in a minute). The output generated must be the number of rows as the input dataframe (otherwise the data can’t be joined). Because multiple columns can be returned, works withBBands()
andMACD()
. 
tidyquant::tq_transform()
: Used to return a new data set with output only (does not return the input dataframe). Uses OHLC notation. Most flexible option. 
tidyquant::tq_mutate_xy()
: Same astq_mutate()
but works using up to two column inputs instead of OHLC notation. 
tidyquant::tq_transform_xy()
: Same astq_transform()
but works using up to two column inputs instead of OHLC notation.
For this tutorial, we will use tq_mutate()
to expose you to OHLC notation along with the tidyquant
function workflow. We’ll also show tq_mutate_xy()
so you can see the difference in arguments.
tq_mutate() function
tq_mutate()
has two primary arguments: ohlc_fun
and mutate_fun
:

ohlc_fun
: Takesquantmod::OHLC
functions, which areOp
,Cl
,Hi
,Lo
,Vo
,Ad
,HLC
,OHLC
, andOHLCV
. The OHLC notation is the basis of allquantmod
,xts
, andTTR
functions. These functions collect a subset of the dataframe columns matching open, high, low, close, volume, and/or adjusted. Think of the OHLC notation akin to thedplyr::select()
function, which selects columns.Op
selects the column named “open”, andHLC
selects “high”, “low” and “close” columns. 
mutate_fun
: Takes anyquantmod
,xts
, orTTR
function listed intq_mutate_fun_options()
(see below for compatible functions). Themutation_fun
performs the work. Any additional parameters of the passed via...
in thetq_mutate()
function go to themutation_fun
.
An example with SMA()
from the TTR
package helps solidify how it works. Reviewing the documentation for SMA
, we see that the function, SMA(x, n = 10, ...)
, accepts x
a price or volume and n
a number of periods to average over. For the 15day simple moving average, we would pass a set of prices, either “close” or “adjusted”, and n = 15
for 15 days. In OHLC notation ohlc_fun = Cl
for “close” or ohlc_fun = Ad
for adjusted. The mutate_fun = SMA
, and we pass n = 15
as an additional argument. Shown below, we pipe (%>%
) our tibble of AAPL stock prices to tq_mutate(ohlc_fun = Cl, mutate_fun = SMA, n = 15)
, which creates an additional column with the simple moving average of the close prices.
We need both the 15day and the 50day moving average, which is two steps with the pipe. I rename
in between steps so the column names are more descriptive.
tq_mutate_xy() function
Not all quantmod
, xts
, and TTR
functions work with OHLC notation. A few of these functions take two primary inputs. An example of this is the Delt
function from the quantmod
package. The function form is Delt(x1, x2 = NULL, k = 0, type = c("arithmetic", "log"))
, which has x1
and x2
arguments. In these situations you will need to use the XY variant, tq_mutate_xy()
, which accepts x
(required) and y
(optional). For the Delt
function, x = x1
and y = x2
.
For the SMA()
function, we don’t need the y
argument, but we can use the XY variant to accomplish the same task as the OHLC variant. The operation is the same except instead of ohlc_fun = Cl
we replace with x = close
(the name of the column being passed to the mutation function).
Back to the example
Returning back to our need, we get the simple moving averages using one of the the code options mentioned previously.
Step 4: Visualize the Simple Moving Averages
We have our 15day and 50day simple moving averages. Now all we need to do is visualize using ggplot2
. The format of the data will need to be tidy, which requires us to use gather()
from the tidyr
package to shift the close, SMA.15, and SMA.50 columns into a long form with type and price. The code and final data form is shown below.
Now, we can use ggplot2
to plot the tidy data. We use the same select and gather statements above and pipe to ggplot
. I add a custom palette to match the black, blue and red colors from the Investopedia graphic. The final code chunk for the visualization is as follows:
Conclusion
The tidyquant
package integrates the three primary QFA packages, quantmod
, xts
, and TTR
, with the tidyverse
.
Recap
The purpose of this post was twofold:
 Introduce you to the
tidyquant
package  Show an example of the integration between the QFA packages and the
tidyverse
.
We discussed why there is a need for tidyquant
, which is to help minimize the back and forth between xts
and tibble
(tidy dataframes). We also went through an example of getting simple moving averages, which previously required jumping back and forth between xts
and tibble
objects. The tidyquant
package made this much easier.
This example just scratches the surface of the power of tidyquant
. See the vignette for a detailed discussion on each of the tidyquant
features.
Further Reading

tidyquant Vignette: This tutorial just scratches the surface of
tidyquant
. The vignette explains much, much more! 
R for Data Science: A free book that thoroughly covers the
tidyverse
packages. 
Quantmod Website: Covers many of the
quantmod
functions. Also, see the quantmod vignette. 
Extensible TimeSeries Website: Covers many of the
xts
functions. Also, see the xts vignette. 
TTR Vignette: Covers each of the
TTR
functions.