tq_mutate() adds new variables to an existing tibble;
tq_transmute() returns only newly created columns and is typically
used when periodicity changes
tq_mutate(
data,
select = NULL,
mutate_fun,
col_rename = NULL,
ohlc_fun = NULL,
...
)
tq_mutate_(data, select = NULL, mutate_fun, col_rename = NULL, ...)
tq_mutate_xy(data, x, y = NULL, mutate_fun, col_rename = NULL, ...)
tq_mutate_xy_(data, x, y = NULL, mutate_fun, col_rename = NULL, ...)
tq_mutate_fun_options()
tq_transmute(
data,
select = NULL,
mutate_fun,
col_rename = NULL,
ohlc_fun = NULL,
...
)
tq_transmute_(data, select = NULL, mutate_fun, col_rename = NULL, ...)
tq_transmute_xy(data, x, y = NULL, mutate_fun, col_rename = NULL, ...)
tq_transmute_xy_(data, x, y = NULL, mutate_fun, col_rename = NULL, ...)
tq_transmute_fun_options()A tibble (tidy data frame) of data typically from tq_get().
The columns to send to the mutation function.
The mutation function from either the xts,
quantmod, or TTR package. Execute tq_mutate_fun_options()
to see the full list of options by package.
A string or character vector containing names that can be used to quickly rename columns.
Deprecated. Use select.
Additional parameters passed to the appropriate mutatation function.
Parameters used with _xy that consist of column names of variables
to be passed to the mutatation function (instead of OHLC functions).
Returns mutated data in the form of a tibble object.
tq_mutate and tq_transmute are very flexible wrappers for various xts,
quantmod and TTR functions. The main advantage is the
results are returned as a tibble and the
function can be used with the tidyverse. tq_mutate is used when additional
columns are added to the return data frame. tq_transmute works exactly like tq_mutate
except it only returns the newly created columns. This is helpful when
changing periodicity where the new columns would not have the same number of rows
as the original tibble.
select specifies the columns that get passed to the mutation function. Select works
as a more flexible version of the OHLC extractor functions from quantmod where
non-OHLC data works as well. When select is NULL, all columns are selected.
In Example 1 below, close returns the "close" price and sends this to the
mutate function, periodReturn.
mutate_fun is the function that performs the work. In Example 1, this
is periodReturn, which calculates the period returns. The ...
are additional arguments passed to the mutate_fun. Think of
the whole operation in Example 1 as the close price, obtained by select = close,
being sent to the periodReturn function along
with additional arguments defining how to perform the period return, which
includes period = "daily" and type = "log".
Example 4 shows how to apply a rolling regression.
tq_mutate_xy and tq_transmute_xy are designed to enable working with mutatation
functions that require two primary inputs (e.g. EVWMA, VWAP, etc).
Example 2 shows this benefit in action: using the EVWMA function that uses
volume to define the moving average period.
tq_mutate_, tq_mutate_xy_, tq_transmute_, and tq_transmute_xy_
are setup for Non-Standard
Evaluation (NSE). This enables programatically changing column names by modifying
the text representations. Example 5 shows the difference in implementation.
Note that character strings are being passed to the variables instead of
unquoted variable names. See vignette("nse") for more information.
tq_mutate_fun_options and tq_transmute_fun_options return a list of various
financial functions that are compatible with tq_mutate and tq_transmute,
respectively.
# Load libraries
library(tidyquant)
library(dplyr)
##### Basic Functionality
fb_stock_prices <- tq_get("FB",
get = "stock.prices",
from = "2016-01-01",
to = "2016-12-31")
# Example 1: Return logarithmic daily returns using periodReturn()
fb_stock_prices %>%
tq_mutate(select = close, mutate_fun = periodReturn,
period = "daily", type = "log")
#> # A tibble: 252 × 9
#> symbol date open high low close volume adjusted daily.returns
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 FB 2016-01-04 102. 102. 99.8 102. 37912400 102. 0
#> 2 FB 2016-01-05 103. 104. 102. 103. 23258200 103. 0.00498
#> 3 FB 2016-01-06 101. 104. 101. 103. 25096200 103. 0.00233
#> 4 FB 2016-01-07 100. 101. 97.3 97.9 45172900 97.9 -0.0503
#> 5 FB 2016-01-08 99.9 100. 97.0 97.3 35402300 97.3 -0.00604
#> 6 FB 2016-01-11 97.9 98.6 95.4 97.5 29932400 97.5 0.00185
#> 7 FB 2016-01-12 99 100. 97.6 99.4 28395400 99.4 0.0189
#> 8 FB 2016-01-13 101. 101. 95.2 95.4 33410600 95.4 -0.0404
#> 9 FB 2016-01-14 95.8 98.9 92.4 98.4 48658600 98.4 0.0302
#> 10 FB 2016-01-15 94.0 96.4 93.5 95.0 46132800 95.0 -0.0352
#> # … with 242 more rows
# Example 2: Use tq_mutate_xy to use functions with two columns required
fb_stock_prices %>%
tq_mutate_xy(x = close, y = volume, mutate_fun = EVWMA,
col_rename = "EVWMA")
#> # A tibble: 252 × 9
#> symbol date open high low close volume adjusted EVWMA
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 FB 2016-01-04 102. 102. 99.8 102. 37912400 102. NA
#> 2 FB 2016-01-05 103. 104. 102. 103. 23258200 103. NA
#> 3 FB 2016-01-06 101. 104. 101. 103. 25096200 103. NA
#> 4 FB 2016-01-07 100. 101. 97.3 97.9 45172900 97.9 NA
#> 5 FB 2016-01-08 99.9 100. 97.0 97.3 35402300 97.3 NA
#> 6 FB 2016-01-11 97.9 98.6 95.4 97.5 29932400 97.5 NA
#> 7 FB 2016-01-12 99 100. 97.6 99.4 28395400 99.4 NA
#> 8 FB 2016-01-13 101. 101. 95.2 95.4 33410600 95.4 NA
#> 9 FB 2016-01-14 95.8 98.9 92.4 98.4 48658600 98.4 NA
#> 10 FB 2016-01-15 94.0 96.4 93.5 95.0 46132800 95.0 95.0
#> # … with 242 more rows
# Example 3: Using tq_mutate to work with non-OHLC data
tq_get("DCOILWTICO", get = "economic.data") %>%
tq_mutate(select = price, mutate_fun = lag.xts, k = 1, na.pad = TRUE)
#> # A tibble: 2,866 × 4
#> symbol date price lag.xts
#> <chr> <date> <dbl> <dbl>
#> 1 DCOILWTICO 2011-01-03 91.6 NA
#> 2 DCOILWTICO 2011-01-04 89.4 91.6
#> 3 DCOILWTICO 2011-01-05 90.3 89.4
#> 4 DCOILWTICO 2011-01-06 88.4 90.3
#> 5 DCOILWTICO 2011-01-07 88.1 88.4
#> 6 DCOILWTICO 2011-01-10 89.2 88.1
#> 7 DCOILWTICO 2011-01-11 91.1 89.2
#> 8 DCOILWTICO 2011-01-12 91.8 91.1
#> 9 DCOILWTICO 2011-01-13 91.4 91.8
#> 10 DCOILWTICO 2011-01-14 91.5 91.4
#> # … with 2,856 more rows
# Example 4: Using tq_mutate to apply a rolling regression
fb_returns <- fb_stock_prices %>%
tq_transmute(adjusted, periodReturn, period = "monthly", col_rename = "fb.returns")
xlk_returns <- tq_get("XLK", from = "2016-01-01", to = "2016-12-31") %>%
tq_transmute(adjusted, periodReturn, period = "monthly", col_rename = "xlk.returns")
returns_combined <- left_join(fb_returns, xlk_returns, by = "date")
regr_fun <- function(data) {
coef(lm(fb.returns ~ xlk.returns, data = as_tibble(data)))
}
returns_combined %>%
tq_mutate(mutate_fun = rollapply,
width = 6,
FUN = regr_fun,
by.column = FALSE,
col_rename = c("coef.0", "coef.1"))
#> # A tibble: 12 × 5
#> date fb.returns xlk.returns coef.0 coef.1
#> <date> <dbl> <dbl> <dbl> <dbl>
#> 1 2016-01-29 0.0977 -0.0244 NA NA
#> 2 2016-02-29 -0.0471 -0.00655 NA NA
#> 3 2016-03-31 0.0672 0.0882 NA NA
#> 4 2016-04-29 0.0305 -0.0503 NA NA
#> 5 2016-05-31 0.0105 0.0489 NA NA
#> 6 2016-06-30 -0.0381 -0.0138 0.0188 0.190
#> 7 2016-07-29 0.0845 0.0710 0.00355 0.626
#> 8 2016-08-31 0.0176 0.0116 0.0154 0.511
#> 9 2016-09-30 0.0170 0.0210 0.0136 0.453
#> 10 2016-10-31 0.0212 -0.00753 -0.00143 0.924
#> 11 2016-11-30 -0.0960 0.00169 -0.0189 1.42
#> 12 2016-12-30 -0.0285 0.0226 -0.0254 1.40
# Example 5: Non-standard evaluation:
# Programming with tq_mutate_() and tq_mutate_xy_()
col_name <- "adjusted"
mutate <- c("MACD", "SMA")
tq_mutate_xy_(fb_stock_prices, x = col_name, mutate_fun = mutate[[1]])
#> # A tibble: 252 × 10
#> symbol date open high low close volume adjusted macd signal
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 FB 2016-01-04 102. 102. 99.8 102. 37912400 102. NA NA
#> 2 FB 2016-01-05 103. 104. 102. 103. 23258200 103. NA NA
#> 3 FB 2016-01-06 101. 104. 101. 103. 25096200 103. NA NA
#> 4 FB 2016-01-07 100. 101. 97.3 97.9 45172900 97.9 NA NA
#> 5 FB 2016-01-08 99.9 100. 97.0 97.3 35402300 97.3 NA NA
#> 6 FB 2016-01-11 97.9 98.6 95.4 97.5 29932400 97.5 NA NA
#> 7 FB 2016-01-12 99 100. 97.6 99.4 28395400 99.4 NA NA
#> 8 FB 2016-01-13 101. 101. 95.2 95.4 33410600 95.4 NA NA
#> 9 FB 2016-01-14 95.8 98.9 92.4 98.4 48658600 98.4 NA NA
#> 10 FB 2016-01-15 94.0 96.4 93.5 95.0 46132800 95.0 NA NA
#> # … with 242 more rows