tidyquant
tidyquant integrates the best quantitative resources for collecting and analyzing quantitative data, zoo, xts, quantmod and TTR, with the tidy data infrastructure of the tidyverse allowing for seamless interaction between each and working within the tidyverse.
Benefits
The tidyquant philosophy:
- A few core functions with a lot of power, that
- leverage the quantitative analysis power of
zoo,xts,quantmodandTTR, and are - designed to be used and scaled with the
tidyverse.
Installation
Development Version with Latest Features:
# install.packages("devtools")
devtools::install_github("mdancho84/tidyquant")CRAN Approved Version:
install.packages("tidyquant")Examples
Start by loading tidyquant.
# Loads tidyquant, tidyverse, lubridate, quantmod, TTR, and xts/zoo
library(tidyquant) Getting Data:
tq_get() is the one-stop shop for retrieving data. The full list of get options are:
tq_get_options()
#> [1] "stock.prices" "stock.index" "dividends" "splits"
#> [5] "financials" "key.ratios" "economic.data" "exchange.rates"
#> [9] "metal.prices"Stock Prices:
Set get = "stock.prices" to get stock prices. Notice the output is always a tibble.
aapl_prices <- tq_get("AAPL", get = "stock.prices")
aapl_prices
#> # A tibble: 2,522 × 7
#> date open high low close volume adjusted
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2007-01-03 86.29 86.58 81.90 83.80 309579900 10.90416
#> 2 2007-01-04 84.05 85.95 83.82 85.66 211815100 11.14619
#> 3 2007-01-05 85.77 86.20 84.40 85.05 208685400 11.06681
#> 4 2007-01-08 85.96 86.53 85.28 85.47 199276700 11.12147
#> 5 2007-01-09 86.45 92.98 85.15 92.57 837324600 12.04533
#> 6 2007-01-10 94.75 97.80 93.45 97.00 738220000 12.62176
#> 7 2007-01-11 95.94 96.78 95.10 95.80 360063200 12.46562
#> 8 2007-01-12 94.59 95.06 93.23 94.62 328172600 12.31207
#> 9 2007-01-16 95.68 97.25 95.45 97.10 311019100 12.63477
#> 10 2007-01-17 97.56 97.60 94.82 94.95 411565000 12.35501
#> # ... with 2,512 more rowsFinancial Statements:
Set get = "financials" to get financial statements. The statements are returned as nested tibbles, that can be unnested and analyzed together.
tq_get("AAPL", get = "financials")
#> # A tibble: 3 × 3
#> type annual quarter
#> * <chr> <list> <list>
#> 1 BS <tibble [168 × 4]> <tibble [210 × 4]>
#> 2 CF <tibble [76 × 4]> <tibble [76 × 4]>
#> 3 IS <tibble [196 × 4]> <tibble [245 × 4]>Key Ratios:
Set get = "key.ratios" to get 10 years of 89 different key ratios (e.g. P/E, P/S, EPS, ROA, ROE, current ratio, debt/equity, inventory turnover, and many more), separated into seven primary sections.
tq_get("AAPL", get = "key.ratios")
#> # A tibble: 7 × 2
#> section data
#> <chr> <list>
#> 1 Financials <tibble [150 × 5]>
#> 2 Profitability <tibble [170 × 5]>
#> 3 Growth <tibble [160 × 5]>
#> 4 Cash Flow <tibble [50 × 5]>
#> 5 Financial Health <tibble [240 × 5]>
#> 6 Efficiency Ratios <tibble [80 × 5]>
#> 7 Valuation Ratios <tibble [40 × 5]>Stock Indexes:
There are 18 indexes available to select from, which can be seen with a call to tq_get_stock_index_options().
tq_get_stock_index_options()
#> [1] "DOWJONES" "DJI" "DJT" "DJU" "SP100"
#> [6] "SP400" "SP500" "SP600" "RUSSELL1000" "RUSSELL2000"
#> [11] "RUSSELL3000" "AMEX" "AMEXGOLD" "AMEXOIL" "NASDAQ"
#> [16] "NASDAQ100" "NYSE" "SOX"Set x to one of the options and get = "stock.index" to get a full list of stocks within the specified index.
tq_get("SP500", get = "stock.index")
#> Getting data...
#> # A tibble: 501 × 2
#> symbol company
#> <chr> <chr>
#> 1 MMM 3M
#> 2 ABT ABBOTT LABORATORIES
#> 3 ABBV ABBVIE INC
#> 4 ACN ACCENTURE
#> 5 ATVI ACTIVISION BLIZZARD
#> 6 AYI ACUITY BRANDS
#> 7 ADBE ADOBE SYSTEMS
#> 8 AAP ADVANCE AUTO PARTS
#> 9 AET AETNA
#> 10 AMG AFFILIATED MANAGERS GROUP
#> # ... with 491 more rowsOther Options:
There are many other get options including dividends, splits, economic data from the FRED, and exchange rates and metal prices from Oanda.
Working in the tidyverse
You may already know and love tidyverse packages like ggplot2, dplyr, tidyr, purrr, readr, and tibble along with lubridate for working with date and datetime. tidyquant works solely in tibbles, so all of the tidyverse functionality is intact.
A simple example inspired by Kan Nishida's blog shows the dplyr and lubridate capability: Say we want the growth in the stock over the past year. We can do this with dplyr operations.
Getting the last year is simple with dplyr and lubridate. We first select the date and adjusted price (adjusted for stock splits). We then filter using lubridate date functions. We can use the mutate to add columns to the data frame: Add the baseline price using the first function, add the growth and growth percent versus baseline columns using standard mathematical operations. We tack on a final select statement to remove unnecessary columns. The final workflow looks like this:
aapl_prices %>%
select(date, adjusted) %>%
filter(date >= today() - years(1)) %>%
mutate(baseline = first(adjusted),
growth = adjusted - baseline,
growth_pct = growth / baseline * 100) %>%
select(-(baseline:growth))
#> # A tibble: 253 × 3
#> date adjusted growth_pct
#> <date> <dbl> <dbl>
#> 1 2016-01-07 94.35077 0.0000000
#> 2 2016-01-08 94.84967 0.5287736
#> 3 2016-01-11 96.38550 2.1565601
#> 4 2016-01-12 97.78438 3.6391934
#> 5 2016-01-13 95.27031 0.9746004
#> 6 2016-01-14 97.35395 3.1829958
#> 7 2016-01-15 95.01597 0.7050287
#> 8 2016-01-19 94.55621 0.2177364
#> 9 2016-01-20 94.68337 0.3525186
#> 10 2016-01-21 94.20404 -0.1555144
#> # ... with 243 more rowsTransforming & Mutating Data with zoo, xts, quantmod, and TTR Functions
You may already know and love zoo, xts, quantmod, and TTR, which is why the core functionality is fully integrated. The workhorse functions, tq_transform() and tq_mutate(), apply zoo, xts, quantmod, and TTR functions to tibbles. The full list of compatible functions are shown with a call to tq_transform_fun_options(). Remove the %>% str() to expand the list.
tq_transform_fun_options() %>% str()
#> List of 4
#> $ zoo : chr [1:14] "rollapply" "rollapplyr" "rollmax" "rollmax.default" ...
#> $ xts : chr [1:27] "apply.daily" "apply.monthly" "apply.quarterly" "apply.weekly" ...
#> $ quantmod: chr [1:25] "allReturns" "annualReturn" "ClCl" "dailyReturn" ...
#> $ TTR : chr [1:61] "adjRatios" "ADX" "ALMA" "aroon" ...tq_transform
tq_transform() returns a new data set that can either be in the same periodicity or a different periodicity as the original data set. Let's use tq_transform to transform the periodicity of the aapl_prices. The quantmod OHLC codes are used to select the open, high, low, close, and volume (OHLCV) columns, which are then sent to the transformation function, xts::to.period, for transformation to monthly periodicity. We now have a much smaller data set containing the monthly prices.
aapl_prices %>%
tq_transform(ohlc_fun = OHLCV, transform_fun = to.period, period = "months")
#> # A tibble: 121 × 6
#> date open high low close volume
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2007-01-31 84.86 86.00 84.35 85.73 214017300
#> 2 2007-02-28 83.00 85.60 83.00 84.61 229868800
#> 3 2007-03-30 94.28 94.68 92.75 92.91 150139500
#> 4 2007-04-30 100.09 101.00 99.67 99.80 154127400
#> 5 2007-05-31 120.07 122.17 119.54 121.19 324266600
#> 6 2007-06-29 121.97 124.00 121.09 122.04 284460400
#> 7 2007-07-31 142.97 143.48 131.52 131.76 440598200
#> 8 2007-08-31 139.49 139.65 137.41 138.48 219221800
#> 9 2007-09-28 153.44 154.60 152.75 153.47 153775300
#> 10 2007-10-31 187.63 190.12 184.95 189.95 208327700
#> # ... with 111 more rowstq_mutate
The cousin of tq_transform() is tq_mutate(). While tq_transform() produces a new, transformed data set, tq_mutate() modifies the existing data set. This is very useful for applying TTR functions like BBands, MACD, Moving Averages, etc. There is one caveat: the mutation must be in the same periodicity as the original data set (otherwise you can't add columns because the rows will not match up). Let's use tq_mutate() to add some Bollinger Bands and MACD using the closing prices (Cl in OHLC notation).
aapl_prices %>%
tq_mutate(ohlc_fun = Cl, mutate_fun = MACD) %>%
tq_mutate(ohlc_fun = HLC, mutate_fun = BBands)
#> # A tibble: 2,522 × 13
#> date open high low close volume adjusted macd signal
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2007-01-03 86.29 86.58 81.90 83.80 309579900 10.90416 NA NA
#> 2 2007-01-04 84.05 85.95 83.82 85.66 211815100 11.14619 NA NA
#> 3 2007-01-05 85.77 86.20 84.40 85.05 208685400 11.06681 NA NA
#> 4 2007-01-08 85.96 86.53 85.28 85.47 199276700 11.12147 NA NA
#> 5 2007-01-09 86.45 92.98 85.15 92.57 837324600 12.04533 NA NA
#> 6 2007-01-10 94.75 97.80 93.45 97.00 738220000 12.62176 NA NA
#> 7 2007-01-11 95.94 96.78 95.10 95.80 360063200 12.46562 NA NA
#> 8 2007-01-12 94.59 95.06 93.23 94.62 328172600 12.31207 NA NA
#> 9 2007-01-16 95.68 97.25 95.45 97.10 311019100 12.63477 NA NA
#> 10 2007-01-17 97.56 97.60 94.82 94.95 411565000 12.35501 NA NA
#> # ... with 2,512 more rows, and 4 more variables: dn <dbl>, mavg <dbl>,
#> # up <dbl>, pctb <dbl>tq_tranform_xy and tq_mutate_xy
The "xy" variants are useful in situations where (1) you have two inputs (hence x and y) that don't fit into the OHLC function, or (2) you are working with a single column of non-OHLC data.
Two inputs that don't fit OHLC mold:
aapl_prices %>%
tq_mutate_xy(x = close, y = volume, mutate_fun = EVWMA)
#> # A tibble: 2,522 × 8
#> date open high low close volume adjusted v1
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2007-01-03 86.29 86.58 81.90 83.80 309579900 10.90416 NA
#> 2 2007-01-04 84.05 85.95 83.82 85.66 211815100 11.14619 NA
#> 3 2007-01-05 85.77 86.20 84.40 85.05 208685400 11.06681 NA
#> 4 2007-01-08 85.96 86.53 85.28 85.47 199276700 11.12147 NA
#> 5 2007-01-09 86.45 92.98 85.15 92.57 837324600 12.04533 NA
#> 6 2007-01-10 94.75 97.80 93.45 97.00 738220000 12.62176 NA
#> 7 2007-01-11 95.94 96.78 95.10 95.80 360063200 12.46562 NA
#> 8 2007-01-12 94.59 95.06 93.23 94.62 328172600 12.31207 NA
#> 9 2007-01-16 95.68 97.25 95.45 97.10 311019100 12.63477 NA
#> 10 2007-01-17 97.56 97.60 94.82 94.95 411565000 12.35501 94.95
#> # ... with 2,512 more rowsWorking with a single column of non-OHLC data:
tq_get("GDPC1", get = "economic.data") %>%
tq_mutate_xy(x = price, mutate_fun = rollapply, width = 5, FUN = mean)
#> # A tibble: 39 × 3
#> date price rollapply
#> <date> <dbl> <dbl>
#> 1 2007-01-01 14726.0 NA
#> 2 2007-04-01 14838.7 NA
#> 3 2007-07-01 14938.5 NA
#> 4 2007-10-01 14991.8 NA
#> 5 2008-01-01 14889.5 14876.90
#> 6 2008-04-01 14963.4 14924.38
#> 7 2008-07-01 14891.6 14934.96
#> 8 2008-10-01 14577.0 14862.66
#> 9 2009-01-01 14375.0 14739.30
#> 10 2009-04-01 14355.6 14632.52
#> # ... with 29 more rowsScaling with the tidyverse
All functions return data sets as tibbles, which allows for interaction within the tidyverse. This means we can:
- Use
dplyrandtidyrto select, filter, nest/unnest, etc. - Use the pipe (
%>%) for chaining operations. - Seamlessly scale data retrieval and transformations/mutations using
purrrto map functions.
A very basic example is retrieving the stock prices for multiple stocks. We can do this by piping a tibble of stock symbols to a mutation that maps the tq_get(get = "stock.prices") function.
tibble(symbol = c("AAPL", "GOOG", "AMZN", "FB", "AVGO", "SWKS","NVDA")) %>%
mutate(stock.prices = map(.x = symbol, ~ tq_get(.x, get = "stock.prices")))
#> # A tibble: 7 × 2
#> symbol stock.prices
#> <chr> <list>
#> 1 AAPL <tibble [2,522 × 7]>
#> 2 GOOG <tibble [2,522 × 7]>
#> 3 AMZN <tibble [2,522 × 7]>
#> 4 FB <tibble [1,167 × 7]>
#> 5 AVGO <tibble [1,869 × 7]>
#> 6 SWKS <tibble [2,522 × 7]>
#> 7 NVDA <tibble [2,522 × 7]>Further Information
This just scratches the surface of the features. See the tidyquant vignette for further details on the package.