| Title: | Tidy Quantitative Financial Analysis |
|---|---|
| Description: | Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format. The main advantage is being able to use quantitative functions with the 'tidyverse' functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See the 'tidyquant' website for more information, documentation and examples. |
| Authors: | Matt Dancho [aut, cre], Davis Vaughan [aut] |
| Maintainer: | Matt Dancho <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.9 |
| Built: | 2024-10-02 05:30:42 UTC |
| Source: | https://github.com/business-science/tidyquant |
The main advantage of tidyquant is to
bridge the gap between the best quantitative resources for collecting and
manipulating quantitative data, xts, quantmod and TTR,
and the data modeling workflow and infrastructure of the tidyverse.
In this package, tidyquant functions and supporting data sets are
provided to seamlessly combine tidy tools with existing quantitative
analytics packages. The main advantage is being able to use tidy
functions with purrr for mapping and tidyr for nesting to extend modeling to
many stocks. See the tidyquant website for more information, documentation
and examples.
Users will probably be interested in the following:
Getting Data from the Web: tq_get()
Manipulating Data: tq_transmute() and tq_mutate()
Performance Analysis and Portfolio Aggregation:
tq_performance() and tq_portfolio()
To learn more about tidyquant, start with the vignettes:
browseVignettes(package = "tidyquant")
Maintainer: Matt Dancho [email protected]
Authors:
Davis Vaughan [email protected]
Useful links:
Report bugs at https://github.com/business-science/tidyquant/issues
Requires the alphavantager packager to use.
av_api_key(api_key)av_api_key(api_key)
api_key |
Optionally passed parameter to set Alpha Vantage |
A wrapper for alphavantager::av_api_key()
Returns invisibly the currently set api_key
tq_get() get = "alphavantager"
## Not run: if (rlang::is_installed("alphavantager")) { av_api_key(api_key = "foobar") } ## End(Not run)## Not run: if (rlang::is_installed("alphavantager")) { av_api_key(api_key = "foobar") } ## End(Not run)
Zoom in on plot regions using date ranges or date-time ranges
coord_x_date(xlim = NULL, ylim = NULL, expand = TRUE) coord_x_datetime(xlim = NULL, ylim = NULL, expand = TRUE)coord_x_date(xlim = NULL, ylim = NULL, expand = TRUE) coord_x_datetime(xlim = NULL, ylim = NULL, expand = TRUE)
xlim |
Limits for the x axis, entered as character dates in "YYYY-MM-DD" format for date or "YYYY-MM-DD HH:MM:SS" for date-time. |
ylim |
Limits for the y axis, entered as values |
expand |
If |
The coord_ functions prevent loss of data during zooming, which is
necessary when zooming in on plots that calculate stats using data
outside of the zoom range (e.g. when plotting moving averages
with geom_ma()). Setting limits using scale_x_date
changes the underlying data which causes moving averages to fail.
coord_x_date is a wrapper for coord_cartesian
that enables quickly zooming in on plot regions using a date range.
coord_x_datetime is a wrapper for coord_cartesian
that enables quickly zooming in on plot regions using a date-time range.
# Load libraries library(dplyr) library(ggplot2) # coord_x_date AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31") AAPL %>% ggplot(aes(x = date, y = adjusted)) + geom_line() + # Plot stock price geom_ma(n = 50) + # Plot 50-day Moving Average geom_ma(n = 200, color = "red") + # Plot 200-day Moving Average # Zoom in coord_x_date(xlim = c("2016-01-01", "2016-12-31")) # coord_x_datetime time_index <- seq(from = as.POSIXct("2012-05-15 07:00"), to = as.POSIXct("2012-05-17 18:00"), by = "hour") set.seed(1) value <- rnorm(n = length(time_index)) hourly_data <- tibble(time.index = time_index, value = value) hourly_data %>% ggplot(aes(x = time.index, y = value)) + geom_point() + coord_x_datetime(xlim = c("2012-05-15 07:00:00", "2012-05-15 16:00:00"))# Load libraries library(dplyr) library(ggplot2) # coord_x_date AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31") AAPL %>% ggplot(aes(x = date, y = adjusted)) + geom_line() + # Plot stock price geom_ma(n = 50) + # Plot 50-day Moving Average geom_ma(n = 200, color = "red") + # Plot 200-day Moving Average # Zoom in coord_x_date(xlim = c("2016-01-01", "2016-12-31")) # coord_x_datetime time_index <- seq(from = as.POSIXct("2012-05-15 07:00"), to = as.POSIXct("2012-05-17 18:00"), by = "hour") set.seed(1) value <- rnorm(n = length(time_index)) hourly_data <- tibble(time.index = time_index, value = value) hourly_data %>% ggplot(aes(x = time.index, y = value)) + geom_point() + coord_x_datetime(xlim = c("2012-05-15 07:00:00", "2012-05-15 16:00:00"))
A record of functions that have been deprecated.
tq_transform(data, ohlc_fun = OHLCV, mutate_fun, col_rename = NULL, ...) tq_transform_xy(data, x, y = NULL, mutate_fun, col_rename = NULL, ...)tq_transform(data, ohlc_fun = OHLCV, mutate_fun, col_rename = NULL, ...) tq_transform_xy(data, x, y = NULL, mutate_fun, col_rename = NULL, ...)
data |
A |
ohlc_fun |
Deprecated. Use |
mutate_fun |
The mutation function from either the |
col_rename |
A string or character vector containing names that can be used to quickly rename columns. |
... |
Additional parameters passed to the appropriate mutatation function. |
x, y
|
Parameters used with |
tq_transform() - use tq_transmute()
tq_transform_xy() - use tq_transmute_xy()
as_xts() - use timetk::tk_xts()
as_tibble() - use timetk::tk_tbl()
summarise_by_time() - Moved to timetk package. Use timetk::summarise_by_time()
50+ date and time functions familiar to users coming from an Excel Background. The main benefits are:
Integration of the amazing lubridate package for handling dates and times
Integration of Holidays from timeDate and Business Calendars
New Date Math and Date Sequence Functions that factor in Business Calendars (e.g. EOMONTH(), NET_WORKDAYS())
These functions are designed to help users coming from an Excel background. Most functions replicate the behavior of Excel:
Names in most cases match Excel function names
Functionality replicates Excel
By default, missing values are ignored (same as in Excel)
AS_DATE(x, ...) AS_DATETIME(x, ...) DATE(year, month, day) DATEVALUE(x, ...) YMD(x, ...) MDY(x, ...) DMY(x, ...) YMD_HMS(x, ...) MDY_HMS(x, ...) DMY_HMS(x, ...) YMD_HM(x, ...) MDY_HM(x, ...) DMY_HM(x, ...) YMD_H(x, ...) MDY_H(x, ...) DMY_H(x, ...) WEEKDAY(x, ..., label = FALSE, abbr = TRUE) WDAY(x, ..., label = FALSE, abbr = TRUE) DOW(x, ..., label = FALSE, abbr = TRUE) MONTHDAY(x, ...) MDAY(x, ...) DOM(x, ...) QUARTERDAY(x, ...) QDAY(x, ...) DAY(x, ...) WEEKNUM(x, ...) WEEK(x, ...) WEEKNUM_ISO(x, ...) MONTH(x, ..., label = FALSE, abbr = TRUE) QUARTER(x, ..., include_year = FALSE, fiscal_start = 1) YEAR(x, ...) YEAR_ISO(x, ...) DATE_TO_NUMERIC(x, ...) DATE_TO_DECIMAL(x, ...) SECOND(x, ...) MINUTE(x, ...) HOUR(x, ...) NOW(...) TODAY(...) EOMONTH(start_date, months = 0) EDATE(start_date, months = 0) NET_WORKDAYS(start_date, end_date, remove_weekends = TRUE, holidays = NULL) COUNT_DAYS(start_date, end_date) YEARFRAC(start_date, end_date) DATE_SEQUENCE(start_date, end_date, by = "day") WORKDAY_SEQUENCE(start_date, end_date, remove_weekends = TRUE, holidays = NULL) HOLIDAY_SEQUENCE( start_date, end_date, calendar = c("NYSE", "LONDON", "NERC", "TSX", "ZURICH") ) HOLIDAY_TABLE(years, pattern = ".") FLOOR_DATE(x, ..., by = "day") FLOOR_DAY(x, ...) FLOOR_WEEK(x, ...) FLOOR_MONTH(x, ...) FLOOR_QUARTER(x, ...) FLOOR_YEAR(x, ...) CEILING_DATE(x, ..., by = "day") CEILING_DAY(x, ...) CEILING_WEEK(x, ...) CEILING_MONTH(x, ...) CEILING_QUARTER(x, ...) CEILING_YEAR(x, ...) ROUND_DATE(x, ..., by = "day") ROUND_DAY(x, ...) ROUND_WEEK(x, ...) ROUND_MONTH(x, ...) ROUND_QUARTER(x, ...) ROUND_YEAR(x, ...)AS_DATE(x, ...) AS_DATETIME(x, ...) DATE(year, month, day) DATEVALUE(x, ...) YMD(x, ...) MDY(x, ...) DMY(x, ...) YMD_HMS(x, ...) MDY_HMS(x, ...) DMY_HMS(x, ...) YMD_HM(x, ...) MDY_HM(x, ...) DMY_HM(x, ...) YMD_H(x, ...) MDY_H(x, ...) DMY_H(x, ...) WEEKDAY(x, ..., label = FALSE, abbr = TRUE) WDAY(x, ..., label = FALSE, abbr = TRUE) DOW(x, ..., label = FALSE, abbr = TRUE) MONTHDAY(x, ...) MDAY(x, ...) DOM(x, ...) QUARTERDAY(x, ...) QDAY(x, ...) DAY(x, ...) WEEKNUM(x, ...) WEEK(x, ...) WEEKNUM_ISO(x, ...) MONTH(x, ..., label = FALSE, abbr = TRUE) QUARTER(x, ..., include_year = FALSE, fiscal_start = 1) YEAR(x, ...) YEAR_ISO(x, ...) DATE_TO_NUMERIC(x, ...) DATE_TO_DECIMAL(x, ...) SECOND(x, ...) MINUTE(x, ...) HOUR(x, ...) NOW(...) TODAY(...) EOMONTH(start_date, months = 0) EDATE(start_date, months = 0) NET_WORKDAYS(start_date, end_date, remove_weekends = TRUE, holidays = NULL) COUNT_DAYS(start_date, end_date) YEARFRAC(start_date, end_date) DATE_SEQUENCE(start_date, end_date, by = "day") WORKDAY_SEQUENCE(start_date, end_date, remove_weekends = TRUE, holidays = NULL) HOLIDAY_SEQUENCE( start_date, end_date, calendar = c("NYSE", "LONDON", "NERC", "TSX", "ZURICH") ) HOLIDAY_TABLE(years, pattern = ".") FLOOR_DATE(x, ..., by = "day") FLOOR_DAY(x, ...) FLOOR_WEEK(x, ...) FLOOR_MONTH(x, ...) FLOOR_QUARTER(x, ...) FLOOR_YEAR(x, ...) CEILING_DATE(x, ..., by = "day") CEILING_DAY(x, ...) CEILING_WEEK(x, ...) CEILING_MONTH(x, ...) CEILING_QUARTER(x, ...) CEILING_YEAR(x, ...) ROUND_DATE(x, ..., by = "day") ROUND_DAY(x, ...) ROUND_WEEK(x, ...) ROUND_MONTH(x, ...) ROUND_QUARTER(x, ...) ROUND_YEAR(x, ...)
x |
A vector of date or date-time objects |
... |
Parameters passed to underlying |
year |
Used in |
month |
Used in |
day |
Used in |
label |
A logical used for |
abbr |
A logical used for |
include_year |
A logical value used in |
fiscal_start |
A numeric value used in |
start_date |
Used in Date Math and Date Sequence operations. The starting date in the calculation. |
months |
Used to offset months in |
end_date |
Used in Date Math and Date Sequence operations. The ending date in the calculation. |
remove_weekends |
A logical value used in Date Sequence and Date Math calculations. Indicates whether or not weekends should be removed from the calculation. |
holidays |
A vector of dates corresponding to holidays that should be removed from the calculation. |
by |
Used to determine the gap in Date Sequence calculations and value to round to in Date Collapsing operations.
Acceptable values are: A character string, containing one of |
calendar |
The calendar to be used in Date Sequence calculations for Holidays from the |
years |
A numeric vector of years to return Holidays for in |
pattern |
Used to filter Holidays (e.g. |
Converters - Make date and date-time from text (character data)
General String-to-Date Conversion: AS_DATE(), AS_DATETIME()
Format-Specific String-to-Date Conversion: YMD() (YYYY-MM-DD), MDY() (MM-DD-YYYY), DMY() (DD-MM-YYYY)
Hour-Minute-Second Conversion: YMD_HMS(), YMD_HM(), and friends.
Extractors - Returns information from a time-stamp.
Current Time - Returns the current date/date-time based on your locale.
Date Math - Perform popular Excel date calculations
EOMONTH() - End of Month
NET_WORKDAYS(), COUNT_DAYS() - Return number of days between 2 dates factoring in working days and holidays
YEARFRAC() - Return the fractional period of the year that has been completed between 2 dates.
Date Sequences - Return a vector of dates or a Holiday Table (tibble).
DATE_SEQUENCE(), WORKDAY_SEQUENCE(), HOLIDAY_SEQUENCE - Return a sequence of dates between 2 dates that
factor in workdays and timeDate holiday calendars for popular business calendars including NYSE and London stock exchange.
Date Collapsers - Collapse a date sequence (useful in dplyr::group_by() and pivot_table())
FLOOR_DATE(), FLOOR_DAY(), FLOOR_WEEK(), FLOOR_MONTH(), FLOOR_QUARTER(), FLOOR_YEAR()
Similar functions exist for CEILING and ROUND. These are wrappers for lubridate functions.
Converters - Date or date-time object the length of x
Extractors - Returns information from a time-stamp.
Current Time - Returns the current date/date-time based on your locale.
Date Math - Numeric values or Date Values depending on the calculation.
Date Sequences - Return a vector of dates or a Holiday Table (tibble).
Date Collapsers - Date or date-time object the length of x
# Libraries library(lubridate) # --- Basic Usage ---- # Converters --- AS_DATE("2011 Jan-01") # General YMD("2011 Jan-01") # Year, Month-Day Format MDY("01-02-20") # Month-Day, Year Format (January 2nd, 2020) DMY("01-02-20") # Day-Month, Year Format (February 1st, 2020) # Extractors --- WEEKDAY("2020-01-01") # Labelled Day WEEKDAY("2020-01-01", label = FALSE) # Numeric Day WEEKDAY("2020-01-01", label = FALSE, week_start = 1) # Start at 1 (Monday) vs 7 (Sunday) MONTH("2020-01-01") QUARTER("2020-01-01") YEAR("2020-01-01") # Current Date-Time --- NOW() TODAY() # Date Math --- EOMONTH("2020-01-01") EOMONTH("2020-01-01", months = 1) NET_WORKDAYS("2020-01-01", "2020-07-01") # 131 Skipping Weekends NET_WORKDAYS("2020-01-01", "2020-07-01", holidays = HOLIDAY_SEQUENCE("2020-01-01", "2020-07-01", calendar = "NYSE")) # 126 Skipping 5 NYSE Holidays # Date Sequences --- DATE_SEQUENCE("2020-01-01", "2020-07-01") WORKDAY_SEQUENCE("2020-01-01", "2020-07-01") HOLIDAY_SEQUENCE("2020-01-01", "2020-07-01", calendar = "NYSE") WORKDAY_SEQUENCE("2020-01-01", "2020-07-01", holidays = HOLIDAY_SEQUENCE("2020-01-01", "2020-07-01", calendar = "NYSE")) # Date Collapsers --- FLOOR_DATE(AS_DATE("2020-01-15"), by = "month") CEILING_DATE(AS_DATE("2020-01-15"), by = "month") CEILING_DATE(AS_DATE("2020-01-15"), by = "month") - ddays(1) # EOMONTH using lubridate # --- Usage with tidyverse --- # Calculate returns by symbol/year/quarter FANG %>% pivot_table( .rows = c(symbol, ~ QUARTER(date)), .columns = ~ YEAR(date), .values = ~ PCT_CHANGE_FIRSTLAST(adjusted) )# Libraries library(lubridate) # --- Basic Usage ---- # Converters --- AS_DATE("2011 Jan-01") # General YMD("2011 Jan-01") # Year, Month-Day Format MDY("01-02-20") # Month-Day, Year Format (January 2nd, 2020) DMY("01-02-20") # Day-Month, Year Format (February 1st, 2020) # Extractors --- WEEKDAY("2020-01-01") # Labelled Day WEEKDAY("2020-01-01", label = FALSE) # Numeric Day WEEKDAY("2020-01-01", label = FALSE, week_start = 1) # Start at 1 (Monday) vs 7 (Sunday) MONTH("2020-01-01") QUARTER("2020-01-01") YEAR("2020-01-01") # Current Date-Time --- NOW() TODAY() # Date Math --- EOMONTH("2020-01-01") EOMONTH("2020-01-01", months = 1) NET_WORKDAYS("2020-01-01", "2020-07-01") # 131 Skipping Weekends NET_WORKDAYS("2020-01-01", "2020-07-01", holidays = HOLIDAY_SEQUENCE("2020-01-01", "2020-07-01", calendar = "NYSE")) # 126 Skipping 5 NYSE Holidays # Date Sequences --- DATE_SEQUENCE("2020-01-01", "2020-07-01") WORKDAY_SEQUENCE("2020-01-01", "2020-07-01") HOLIDAY_SEQUENCE("2020-01-01", "2020-07-01", calendar = "NYSE") WORKDAY_SEQUENCE("2020-01-01", "2020-07-01", holidays = HOLIDAY_SEQUENCE("2020-01-01", "2020-07-01", calendar = "NYSE")) # Date Collapsers --- FLOOR_DATE(AS_DATE("2020-01-15"), by = "month") CEILING_DATE(AS_DATE("2020-01-15"), by = "month") CEILING_DATE(AS_DATE("2020-01-15"), by = "month") - ddays(1) # EOMONTH using lubridate # --- Usage with tidyverse --- # Calculate returns by symbol/year/quarter FANG %>% pivot_table( .rows = c(symbol, ~ QUARTER(date)), .columns = ~ YEAR(date), .values = ~ PCT_CHANGE_FIRSTLAST(adjusted) )
Excel financial math functions are designed to easily calculate Net Present Value (NPV()),
Future Value of cashflow (FV()), Present Value of future cashflow (PV()), and more.
These functions are designed to help users coming from an Excel background. Most functions replicate the behavior of Excel:
Names are similar to Excel function names
By default, missing values are ignored (same as in Excel)
NPV(cashflow, rate, nper = NULL) IRR(cashflow) FV(rate, nper, pv = 0, pmt = 0, type = 0) PV(rate, nper, fv = 0, pmt = 0, type = 0) PMT(rate, nper, pv, fv = 0, type = 0) RATE(nper, pmt, pv, fv = 0, type = 0)NPV(cashflow, rate, nper = NULL) IRR(cashflow) FV(rate, nper, pv = 0, pmt = 0, type = 0) PV(rate, nper, fv = 0, pmt = 0, type = 0) PMT(rate, nper, pv, fv = 0, type = 0) RATE(nper, pmt, pv, fv = 0, type = 0)
cashflow |
Cash flow values. When one value is provided, it's assumed constant cash flow. |
rate |
One or more rate. When one rate is provided it's assumed constant rate. |
nper |
Number of periods. When 'nper“ is provided, the cashflow values and rate are assumed constant. |
pv |
Present value. Initial investments (cash inflows) are typically a negative value. |
pmt |
Number of payments per period. |
type |
Should payments ( |
fv |
Future value. Cash outflows are typically a positive value. |
Net Present Value (NPV) Net present value (NPV) is the difference between the present value of cash inflows and the present value of cash outflows over a period of time. NPV is used in capital budgeting and investment planning to analyze the profitability of a projected investment or project. For more information, see Investopedia NPV.
Internal Rate of Return (IRR) The internal rate of return (IRR) is a metric used in capital budgeting to estimate the profitability of potential investments. The internal rate of return is a discount rate that makes the net present value (NPV) of all cash flows from a particular project equal to zero. IRR calculations rely on the same formula as NPV does. For more information, see Investopedia IRR.
Future Value (FV) Future value (FV) is the value of a current asset at a future date based on an assumed rate of growth. The future value (FV) is important to investors and financial planners as they use it to estimate how much an investment made today will be worth in the future. Knowing the future value enables investors to make sound investment decisions based on their anticipated needs. However, external economic factors, such as inflation, can adversely affect the future value of the asset by eroding its value. For more information, see Investopedia FV.
Present Value (PV) Present value (PV) is the current value of a future sum of money or stream of cash flows given a specified rate of return. Future cash flows are discounted at the discount rate, and the higher the discount rate, the lower the present value of the future cash flows. Determining the appropriate discount rate is the key to properly valuing future cash flows, whether they be earnings or obligations. For more information, see Investopedia PV.
Payment (PMT)
The Payment PMT() function calculates the payment for a loan based on constant payments and a constant interest rate.
Rate (RATE) Returns the interest rate per period of a loan or an investment. For example, use 6%/4 for quarterly payments at 6% APR.
Summary functions return a single value
NPV(c(-1000, 250, 350, 450, 450), rate = 0.05) IRR(c(-1000, 250, 350, 450, 450)) FV(rate = 0.05, nper = 5, pv = -100, pmt = 0, type = 0) PV(rate = 0.05, nper = 5, fv = -100, pmt = 0, type = 0) PMT(nper = 20, rate = 0.05, pv = -100, fv = 0, type = 0) RATE(nper = 20, pmt = 8, pv = -100, fv = 0, type = 0)NPV(c(-1000, 250, 350, 450, 450), rate = 0.05) IRR(c(-1000, 250, 350, 450, 450)) FV(rate = 0.05, nper = 5, pv = -100, pmt = 0, type = 0) PV(rate = 0.05, nper = 5, fv = -100, pmt = 0, type = 0) PMT(nper = 20, rate = 0.05, pv = -100, fv = 0, type = 0) RATE(nper = 20, pmt = 8, pv = -100, fv = 0, type = 0)
"IFS" functions are filtering versions of their summarization counterparts.
Simply add "cases" that filter if a condition is true.
Multiple cases are evaluated as "AND" filtering operations.
A single case with | ("OR") bars can be created to accomplish an "OR".
See details below.
These functions are designed to help users coming from an Excel background. Most functions replicate the behavior of Excel:
Names are similar to Excel function names
By default, missing values are ignored (same as in Excel)
SUM_IFS(x, ...) COUNT_IFS(x, ...) AVERAGE_IFS(x, ...) MEDIAN_IFS(x, ...) MIN_IFS(x, ...) MAX_IFS(x, ...) CREATE_IFS(.f, ...)SUM_IFS(x, ...) COUNT_IFS(x, ...) AVERAGE_IFS(x, ...) MEDIAN_IFS(x, ...) MIN_IFS(x, ...) MAX_IFS(x, ...) CREATE_IFS(.f, ...)
x |
A vector. Most functions are designed for numeric data.
Some functions like |
... |
Add cases to evaluate. See Details. |
.f |
A function to convert to an "IFS" function.
Use |
"AND" Filtering: Multiple cases are evaluated as "AND" filtering operations.
"OR" Filtering:
Compound single cases with | ("OR") bars can be created to accomplish an "OR".
Simply use a statement like x > 10 | x < -10 to perform an "OR" if-statement.
Creating New "Summarizing IFS" Functions:
Users can create new "IFS" functions using the CREATE_IFS() function factory.
The only requirement is that the output of your function (.f) must be a single
value (scalar). See examples below.
Summary functions return a single value
Summary Functions - Return a single value from a vector
Sum: SUM_IFS()
Center: AVERAGE_IFS(), MEDIAN_IFS()
Count: COUNT_IFS()
Create your own summary "IFS" function
CREATE_IFS(): This is a function factory that generates summary "_IFS" functions.
library(dplyr) library(timetk, exclude = "FANG") library(stringr) library(lubridate) # --- Basic Usage --- SUM_IFS(x = 1:10, x > 5) COUNT_IFS(x = letters, str_detect(x, "a|b|c")) SUM_IFS(-10:10, x > 8 | x < -5) # Create your own IFS function (Mind blowingly simple)! Q75_IFS <- CREATE_IFS(.f = quantile, probs = 0.75, na.rm = TRUE) Q75_IFS(1:10, x > 5) # --- Usage with tidyverse --- # Using multiple cases IFS cases to count the frequency of days with # high trade volume in a given year FANG %>% group_by(symbol) %>% summarise( high_volume_in_2015 = COUNT_IFS(volume, year(date) == 2015, volume > quantile(volume, 0.75)) ) # Count negative returns by month FANG %>% mutate(symbol = forcats::as_factor(symbol)) %>% group_by(symbol) %>% # Collapse from daily to FIRST value by month summarise_by_time( .date_var = date, .by = "month", adjusted = FIRST(adjusted) ) %>% # Calculate monthly returns group_by(symbol) %>% mutate( returns = PCT_CHANGE(adjusted, fill_na = 0) ) %>% # Find returns less than zero and count the frequency summarise( negative_monthly_returns = COUNT_IFS(returns, returns < 0) )library(dplyr) library(timetk, exclude = "FANG") library(stringr) library(lubridate) # --- Basic Usage --- SUM_IFS(x = 1:10, x > 5) COUNT_IFS(x = letters, str_detect(x, "a|b|c")) SUM_IFS(-10:10, x > 8 | x < -5) # Create your own IFS function (Mind blowingly simple)! Q75_IFS <- CREATE_IFS(.f = quantile, probs = 0.75, na.rm = TRUE) Q75_IFS(1:10, x > 5) # --- Usage with tidyverse --- # Using multiple cases IFS cases to count the frequency of days with # high trade volume in a given year FANG %>% group_by(symbol) %>% summarise( high_volume_in_2015 = COUNT_IFS(volume, year(date) == 2015, volume > quantile(volume, 0.75)) ) # Count negative returns by month FANG %>% mutate(symbol = forcats::as_factor(symbol)) %>% group_by(symbol) %>% # Collapse from daily to FIRST value by month summarise_by_time( .date_var = date, .by = "month", adjusted = FIRST(adjusted) ) %>% # Calculate monthly returns group_by(symbol) %>% mutate( returns = PCT_CHANGE(adjusted, fill_na = 0) ) %>% # Find returns less than zero and count the frequency summarise( negative_monthly_returns = COUNT_IFS(returns, returns < 0) )
The Pivot Table is one of Excel's most powerful features, and now it's available in R!
A pivot table is a table of statistics that summarizes the data of a more extensive table
(such as from a database, spreadsheet, or business intelligence program).
These functions are designed to help users coming from an Excel background. Most functions replicate the behavior of Excel:
Names are similar to Excel function names
Functionality replicates Excel
pivot_table( .data, .rows, .columns, .values, .filters = NULL, .sort = NULL, fill_na = NA )pivot_table( .data, .rows, .columns, .values, .filters = NULL, .sort = NULL, fill_na = NA )
.data |
A |
.rows |
Enter one or more groups to assess as expressions (e.g. |
.columns |
Enter one or more groups to assess expressions (e.g. |
.values |
Numeric only. Enter one or more summarization expression(s) (e.g. |
.filters |
This argument is not yet in use |
.sort |
This argument is not yet in use |
fill_na |
A value to replace missing values with. Default is |
This summary might include sums, averages, or other statistics, which the pivot table groups together in a meaningful way.
The key parameters are:
.rows - These are groups that will appear as row-wise headings for the summarization, You can modify these groups by applying collapsing functions (e.g. (YEAR()).
.columns - These are groups that will appear as column headings for the summarization. You can modify these groups by applying collapsing functions (e.g. (YEAR()).
.values - These are numeric data that are summarized using a summary function
(e.g. SUM(), AVERAGE(), COUNT(), FIRST(), LAST(), SUM_IFS(), AVERAGE_IFS(), COUNT_IFS())
R implementation details.
The pivot_table() function is powered by the tidyverse, an ecosystem of packages designed to manipulate data.
All of the key parameters can be expressed using a functional form:
Rows and Column Groupings can be collapsed. Example: .columns = ~ YEAR(order_date)
Values can be summarized provided a single value is returned. Example: .values = ~ SUM_IFS(order_volume >= quantile(order_volume, probs = 0.75))
Summarizations and Row/Column Groupings can be stacked (combined) with c(). Example: .rows = c(~ YEAR(order_date), company)
Bare columns (e.g. company) don not need to be prefixed with the ~.
All grouping and summarizing functions MUST BE prefixed with ~. Example: .rows = ~ YEAR(order_date)
Returns a tibble that has been pivoted to summarize information by column and row groupings
# PIVOT TABLE ---- # Calculate returns by year/quarter FANG %>% pivot_table( .rows = c(symbol, ~ QUARTER(date)), .columns = ~ YEAR(date), .values = ~ PCT_CHANGE_FIRSTLAST(adjusted) )# PIVOT TABLE ---- # Calculate returns by year/quarter FANG %>% pivot_table( .rows = c(symbol, ~ QUARTER(date)), .columns = ~ YEAR(date), .values = ~ PCT_CHANGE_FIRSTLAST(adjusted) )
Excel reference functions are used to efficiently lookup values from a data source. The most popular lookup function is "VLOOKUP", which has been implemented in R.
These functions are designed to help users coming from an Excel background. Most functions replicate the behavior of Excel:
Names are similar to Excel function names
Functionality replicates Excel
VLOOKUP(.lookup_values, .data, .lookup_column, .return_column)VLOOKUP(.lookup_values, .data, .lookup_column, .return_column)
.lookup_values |
One or more lookup values. |
.data |
A |
.lookup_column |
The column in |
.return_column |
The column in |
VLOOKUP() Details
Performs exact matching only. Fuzzy matching is not implemented.
Can only return values from one column only. Use dplyr::left_join() to perform table joining.
Returns a vector the length of the input lookup values
library(dplyr) lookup_table <- tibble( stock = c("META", "AMZN", "NFLX", "GOOG"), company = c("Facebook", "Amazon", "Netflix", "Google") ) # --- Basic Usage --- VLOOKUP("NFLX", .data = lookup_table, .lookup_column = stock, .return_column = company) # --- Usage with tidyverse --- # Add company names to the stock data FANG %>% mutate(company = VLOOKUP(symbol, lookup_table, stock, company))library(dplyr) lookup_table <- tibble( stock = c("META", "AMZN", "NFLX", "GOOG"), company = c("Facebook", "Amazon", "Netflix", "Google") ) # --- Basic Usage --- VLOOKUP("NFLX", .data = lookup_table, .lookup_column = stock, .return_column = company) # --- Usage with tidyverse --- # Add company names to the stock data FANG %>% mutate(company = VLOOKUP(symbol, lookup_table, stock, company))
15+ common statistical functions familiar to users of Excel (e.g. ABS(), SQRT())
that modify / transform a series of values
(i.e. a vector of the same length of the input is returned).
These functions are designed to help users coming from an Excel background. Most functions replicate the behavior of Excel:
Names in most cases match Excel function names
Functionality replicates Excel
By default, missing values are ignored (same as in Excel)
ABS(x) SQRT(x) LOG(x) EXP(x) RETURN(x, n = 1, fill_na = NA) PCT_CHANGE(x, n = 1, fill_na = NA) CHANGE(x, n = 1, fill_na = NA) LAG(x, n = 1, fill_na = NA) LEAD(x, n = 1, fill_na = NA) CUMULATIVE_SUM(x) CUMULATIVE_PRODUCT(x) CUMULATIVE_MAX(x) CUMULATIVE_MIN(x) CUMULATIVE_MEAN(x) CUMULATIVE_MEDIAN(x)ABS(x) SQRT(x) LOG(x) EXP(x) RETURN(x, n = 1, fill_na = NA) PCT_CHANGE(x, n = 1, fill_na = NA) CHANGE(x, n = 1, fill_na = NA) LAG(x, n = 1, fill_na = NA) LEAD(x, n = 1, fill_na = NA) CUMULATIVE_SUM(x) CUMULATIVE_PRODUCT(x) CUMULATIVE_MAX(x) CUMULATIVE_MIN(x) CUMULATIVE_MEAN(x) CUMULATIVE_MEDIAN(x)
x |
A vector. Most functions are designed for numeric data. |
n |
Values to offset. Used in functions like |
fill_na |
Fill missing ( |
Mutation functions return a mutated / transformed version of the vector
Mutation Functions - Transforms a vector
Lags & Change (Offsetting Functions): CHANGE(), PCT_CHANGE(), LAG(), LEAD()
Cumulative Totals: CUMULATIVE_SUM(), CUMULATIVE_PRODUCT()
# Libraries library(timetk, exclude = "FANG") library(dplyr) # --- Basic Usage ---- CUMULATIVE_SUM(1:10) PCT_CHANGE(c(21, 24, 22, 25), fill_na = 0) # --- Usage with tidyverse --- # Go from daily to monthly periodicity, # then calculate returns and growth of $1 USD FANG %>% mutate(symbol = forcats::as_factor(symbol)) %>% group_by(symbol) %>% # Summarization - Collapse from daily to FIRST value by month summarise_by_time( .date_var = date, .by = "month", adjusted = FIRST(adjusted) ) %>% # Mutation - Calculate monthly returns and cumulative growth of $1 USD group_by(symbol) %>% mutate( returns = PCT_CHANGE(adjusted, fill_na = 0), growth = CUMULATIVE_SUM(returns) + 1 )# Libraries library(timetk, exclude = "FANG") library(dplyr) # --- Basic Usage ---- CUMULATIVE_SUM(1:10) PCT_CHANGE(c(21, 24, 22, 25), fill_na = 0) # --- Usage with tidyverse --- # Go from daily to monthly periodicity, # then calculate returns and growth of $1 USD FANG %>% mutate(symbol = forcats::as_factor(symbol)) %>% group_by(symbol) %>% # Summarization - Collapse from daily to FIRST value by month summarise_by_time( .date_var = date, .by = "month", adjusted = FIRST(adjusted) ) %>% # Mutation - Calculate monthly returns and cumulative growth of $1 USD group_by(symbol) %>% mutate( returns = PCT_CHANGE(adjusted, fill_na = 0), growth = CUMULATIVE_SUM(returns) + 1 )
15+ common statistical functions familiar to users of Excel (e.g. SUM(), AVERAGE()).
These functions return a single value (i.e. a vector of length 1).
These functions are designed to help users coming from an Excel background. Most functions replicate the behavior of Excel:
Names in most cases match Excel function names
Functionality replicates Excel
By default, missing values are ignored (same as in Excel)
SUM(x) AVERAGE(x) MEDIAN(x) MIN(x) MAX(x) COUNT(x) COUNT_UNIQUE(x) STDEV(x) VAR(x) COR(x, y) COV(x, y) FIRST(x) LAST(x) NTH(x, n = 1) CHANGE_FIRSTLAST(x) PCT_CHANGE_FIRSTLAST(x)SUM(x) AVERAGE(x) MEDIAN(x) MIN(x) MAX(x) COUNT(x) COUNT_UNIQUE(x) STDEV(x) VAR(x) COR(x, y) COV(x, y) FIRST(x) LAST(x) NTH(x, n = 1) CHANGE_FIRSTLAST(x) PCT_CHANGE_FIRSTLAST(x)
x |
A vector. Most functions are designed for numeric data.
Some functions like |
y |
A vector. Used in functions requiring 2 inputs. |
n |
A single value used in |
Summary Functions
All functions remove missing values (NA). This is the same behavior as in Excel and most commonly what is desired.
Summary functions return a single value
Summary Functions - Return a single value from a vector
Sum: SUM()
Count: COUNT(), COUNT_UNIQUE()
Change (Summary): CHANGE_FIRSTLAST(), PCT_CHANGE_FIRSTLAST()
# Libraries library(timetk, exclude = "FANG") library(forcats) library(dplyr) # --- Basic Usage ---- SUM(1:10) PCT_CHANGE_FIRSTLAST(c(21, 24, 22, 25)) # --- Usage with tidyverse --- # Go from daily to monthly periodicity, # then calculate returns and growth of $1 USD FANG %>% mutate(symbol = forcats::as_factor(symbol)) %>% group_by(symbol) %>% # Summarization - Collapse from daily to FIRST value by month summarise_by_time( .date_var = date, .by = "month", adjusted = FIRST(adjusted) )# Libraries library(timetk, exclude = "FANG") library(forcats) library(dplyr) # --- Basic Usage ---- SUM(1:10) PCT_CHANGE_FIRSTLAST(c(21, 24, 22, 25)) # --- Usage with tidyverse --- # Go from daily to monthly periodicity, # then calculate returns and growth of $1 USD FANG %>% mutate(symbol = forcats::as_factor(symbol)) %>% group_by(symbol) %>% # Summarization - Collapse from daily to FIRST value by month summarise_by_time( .date_var = date, .by = "month", adjusted = FIRST(adjusted) )
A dataset containing the daily historical stock prices for the "FANG" tech stocks, "META", "AMZN", "NFLX", and "GOOG", spanning from the beginning of 2013 through the end of 2016.
FANGFANG
A "tibble" ("tidy" data frame) with 4,032 rows and 8 variables:
stock ticker symbol
trade date
stock price at the open of trading, in USD
stock price at the highest point during trading, in USD
stock price at the lowest point during trading, in USD
stock price at the close of trading, in USD
number of shares traded
stock price at the close of trading adjusted for stock splits, in USD
https://www.investopedia.com/terms/f/fang-stocks-fb-amzn.asp
Bollinger Bands plot a range around a moving average typically two standard deviations up and down.
The geom_bbands() function enables plotting Bollinger Bands quickly using various moving average functions.
The moving average functions used are specified in TTR::SMA()
from the TTR package. Use coord_x_date() to zoom into specific plot regions.
The following moving averages are available:
Simple moving averages (SMA):
Rolling mean over a period defined by n.
Exponential moving averages (EMA): Includes
exponentially-weighted mean that gives more weight to recent observations.
Uses wilder and ratio args.
Weighted moving averages (WMA):
Uses a set of weights, wts, to weight observations in the moving average.
Double exponential moving averages (DEMA):
Uses v volume factor, wilder and ratio args.
Zero-lag exponential moving averages (ZLEMA):
Uses wilder and ratio args.
Volume-weighted moving averages (VWMA):
Requires volume aesthetic.
Elastic, volume-weighted moving averages (EVWMA):
Requires volume aesthetic.
geom_bbands( mapping = NULL, data = NULL, position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, ma_fun = SMA, n = 20, sd = 2, wilder = FALSE, ratio = NULL, v = 1, wts = 1:n, color_ma = "darkblue", color_bands = "red", alpha = 0.15, fill = "grey20", ... ) geom_bbands_( mapping = NULL, data = NULL, position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, ma_fun = "SMA", n = 10, sd = 2, wilder = FALSE, ratio = NULL, v = 1, wts = 1:n, color_ma = "darkblue", color_bands = "red", alpha = 0.15, fill = "grey20", ... )geom_bbands( mapping = NULL, data = NULL, position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, ma_fun = SMA, n = 20, sd = 2, wilder = FALSE, ratio = NULL, v = 1, wts = 1:n, color_ma = "darkblue", color_bands = "red", alpha = 0.15, fill = "grey20", ... ) geom_bbands_( mapping = NULL, data = NULL, position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, ma_fun = "SMA", n = 10, sd = 2, wilder = FALSE, ratio = NULL, v = 1, wts = 1:n, color_ma = "darkblue", color_bands = "red", alpha = 0.15, fill = "grey20", ... )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
position |
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The
|
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
ma_fun |
The function used to calculate the moving average. Seven options are
available including: SMA, EMA, WMA, DEMA, ZLEMA, VWMA, and EVWMA. The default is
|
n |
Number of periods to average over. Must be between 1 and
|
sd |
The number of standard deviations to use. |
wilder |
logical; if |
ratio |
A smoothing/decay ratio. |
v |
The 'volume factor' (a number in [0,1]). See Notes. |
wts |
Vector of weights. Length of |
color_ma, color_bands
|
Select the line color to be applied for the moving average line and the Bollinger band line. |
alpha |
Used to adjust the alpha transparency for the BBand ribbon. |
fill |
Used to adjust the fill color for the BBand ribbon. |
... |
Other arguments passed on to |
The following aesthetics are understood (required are in bold):
x, Typically a date
high, Required to be the high price
low, Required to be the low price
close, Required to be the close price
volume, Required for VWMA and EVWMA
colour, Affects line colors
fill, Affects ribbon fill color
alpha, Affects ribbon alpha value
group
linetype
size
See individual modeling functions for underlying parameters:
TTR::SMA() for simple moving averages
TTR::EMA() for exponential moving averages
TTR::WMA() for weighted moving averages
TTR::DEMA() for double exponential moving averages
TTR::ZLEMA() for zero-lag exponential moving averages
TTR::VWMA() for volume-weighted moving averages
TTR::EVWMA() for elastic, volume-weighted moving averages
coord_x_date() for zooming into specific regions of a plot
library(dplyr) library(ggplot2) library(lubridate) AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31") # SMA AAPL %>% ggplot(aes(x = date, y = close)) + geom_line() + # Plot stock price geom_bbands(aes(high = high, low = low, close = close), ma_fun = SMA, n = 50) + coord_x_date(xlim = c(as_date("2016-12-31") - dyears(1), as_date("2016-12-31")), ylim = c(20, 35)) # EMA AAPL %>% ggplot(aes(x = date, y = close)) + geom_line() + # Plot stock price geom_bbands(aes(high = high, low = low, close = close), ma_fun = EMA, wilder = TRUE, ratio = NULL, n = 50) + coord_x_date(xlim = c(as_date("2016-12-31") - dyears(1), as_date("2016-12-31")), ylim = c(20, 35)) # VWMA AAPL %>% ggplot(aes(x = date, y = close)) + geom_line() + # Plot stock price geom_bbands(aes(high = high, low = low, close = close, volume = volume), ma_fun = VWMA, n = 50) + coord_x_date(xlim = c(as_date("2016-12-31") - dyears(1), as_date("2016-12-31")), ylim = c(20, 35))library(dplyr) library(ggplot2) library(lubridate) AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31") # SMA AAPL %>% ggplot(aes(x = date, y = close)) + geom_line() + # Plot stock price geom_bbands(aes(high = high, low = low, close = close), ma_fun = SMA, n = 50) + coord_x_date(xlim = c(as_date("2016-12-31") - dyears(1), as_date("2016-12-31")), ylim = c(20, 35)) # EMA AAPL %>% ggplot(aes(x = date, y = close)) + geom_line() + # Plot stock price geom_bbands(aes(high = high, low = low, close = close), ma_fun = EMA, wilder = TRUE, ratio = NULL, n = 50) + coord_x_date(xlim = c(as_date("2016-12-31") - dyears(1), as_date("2016-12-31")), ylim = c(20, 35)) # VWMA AAPL %>% ggplot(aes(x = date, y = close)) + geom_line() + # Plot stock price geom_bbands(aes(high = high, low = low, close = close, volume = volume), ma_fun = VWMA, n = 50) + coord_x_date(xlim = c(as_date("2016-12-31") - dyears(1), as_date("2016-12-31")), ylim = c(20, 35))
Financial charts provide visual cues to open, high, low, and close prices.
Use coord_x_date() to zoom into specific plot regions.
The following financial chart geoms are available:
geom_barchart( mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, colour_up = "darkblue", colour_down = "red", fill_up = "darkblue", fill_down = "red", ... ) geom_candlestick( mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, colour_up = "darkblue", colour_down = "red", fill_up = "darkblue", fill_down = "red", ... )geom_barchart( mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, colour_up = "darkblue", colour_down = "red", fill_up = "darkblue", fill_down = "red", ... ) geom_candlestick( mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, colour_up = "darkblue", colour_down = "red", fill_up = "darkblue", fill_down = "red", ... )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The statistical transformation to use on the data for this layer.
When using a
|
position |
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The
|
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
colour_up, colour_down
|
Select colors to be applied based on price movement
from open to close. If |
fill_up, fill_down
|
Select fills to be applied based on price movement
from open to close. If close >= open, |
... |
Other arguments passed on to |
The following aesthetics are understood (required are in bold):
x, Typically a date
open, Required to be the open price
high, Required to be the high price
low, Required to be the low price
close, Required to be the close price
alpha
group
linetype
size
See individual modeling functions for underlying parameters:
geom_ma() for adding moving averages to ggplots
geom_bbands() for adding Bollinger Bands to ggplots
coord_x_date() for zooming into specific regions of a plot
library(dplyr) library(ggplot2) library(lubridate) AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31") # Bar Chart AAPL %>% ggplot(aes(x = date, y = close)) + geom_barchart(aes(open = open, high = high, low = low, close = close)) + geom_ma(color = "darkgreen") + coord_x_date(xlim = c("2016-01-01", "2016-12-31"), ylim = c(20, 30)) # Candlestick Chart AAPL %>% ggplot(aes(x = date, y = close)) + geom_candlestick(aes(open = open, high = high, low = low, close = close)) + geom_ma(color = "darkgreen") + coord_x_date(xlim = c("2016-01-01", "2016-12-31"), ylim = c(20, 30))library(dplyr) library(ggplot2) library(lubridate) AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31") # Bar Chart AAPL %>% ggplot(aes(x = date, y = close)) + geom_barchart(aes(open = open, high = high, low = low, close = close)) + geom_ma(color = "darkgreen") + coord_x_date(xlim = c("2016-01-01", "2016-12-31"), ylim = c(20, 30)) # Candlestick Chart AAPL %>% ggplot(aes(x = date, y = close)) + geom_candlestick(aes(open = open, high = high, low = low, close = close)) + geom_ma(color = "darkgreen") + coord_x_date(xlim = c("2016-01-01", "2016-12-31"), ylim = c(20, 30))
The underlying moving average functions used are specified in TTR::SMA()
from the TTR package. Use coord_x_date() to zoom into specific plot regions.
The following moving averages are available:
Simple moving averages (SMA):
Rolling mean over a period defined by n.
Exponential moving averages (EMA): Includes
exponentially-weighted mean that gives more weight to recent observations.
Uses wilder and ratio args.
Weighted moving averages (WMA):
Uses a set of weights, wts, to weight observations in the moving average.
Double exponential moving averages (DEMA):
Uses v volume factor, wilder and ratio args.
Zero-lag exponential moving averages (ZLEMA):
Uses wilder and ratio args.
Volume-weighted moving averages (VWMA):
Requires volume aesthetic.
Elastic, volume-weighted moving averages (EVWMA):
Requires volume aesthetic.
geom_ma( mapping = NULL, data = NULL, position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, ma_fun = SMA, n = 20, wilder = FALSE, ratio = NULL, v = 1, wts = 1:n, ... ) geom_ma_( mapping = NULL, data = NULL, position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, ma_fun = "SMA", n = 20, wilder = FALSE, ratio = NULL, v = 1, wts = 1:n, ... )geom_ma( mapping = NULL, data = NULL, position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, ma_fun = SMA, n = 20, wilder = FALSE, ratio = NULL, v = 1, wts = 1:n, ... ) geom_ma_( mapping = NULL, data = NULL, position = "identity", na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, ma_fun = "SMA", n = 20, wilder = FALSE, ratio = NULL, v = 1, wts = 1:n, ... )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
position |
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The
|
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
ma_fun |
The function used to calculate the moving average. Seven options are
available including: SMA, EMA, WMA, DEMA, ZLEMA, VWMA, and EVWMA. The default is
|
n |
Number of periods to average over. Must be between 1 and
|
wilder |
logical; if |
ratio |
A smoothing/decay ratio. |
v |
The 'volume factor' (a number in [0,1]). See Notes. |
wts |
Vector of weights. Length of |
... |
Other arguments passed on to |
The following aesthetics are understood (required are in bold):
x
y
volume, Required for VWMA and EVWMA
alpha
colour
group
linetype
linewidth
size
See individual modeling functions for underlying parameters:
TTR::SMA() for simple moving averages
TTR::EMA() for exponential moving averages
TTR::WMA() for weighted moving averages
TTR::DEMA() for double exponential moving averages
TTR::ZLEMA() for zero-lag exponential moving averages
TTR::VWMA() for volume-weighted moving averages
TTR::EVWMA() for elastic, volume-weighted moving averages
coord_x_date() for zooming into specific regions of a plot
library(dplyr) library(ggplot2) AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31") # SMA AAPL %>% ggplot(aes(x = date, y = adjusted)) + geom_line() + # Plot stock price geom_ma(ma_fun = SMA, n = 50) + # Plot 50-day SMA geom_ma(ma_fun = SMA, n = 200, color = "red") + # Plot 200-day SMA coord_x_date(xlim = c("2016-01-01", "2016-12-31"), ylim = c(20, 30)) # Zoom in # EVWMA AAPL %>% ggplot(aes(x = date, y = adjusted)) + geom_line() + # Plot stock price geom_ma(aes(volume = volume), ma_fun = EVWMA, n = 50) + # Plot 50-day EVWMA coord_x_date(xlim = c("2016-01-01", "2016-12-31"), ylim = c(20, 30)) # Zoom inlibrary(dplyr) library(ggplot2) AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31") # SMA AAPL %>% ggplot(aes(x = date, y = adjusted)) + geom_line() + # Plot stock price geom_ma(ma_fun = SMA, n = 50) + # Plot 50-day SMA geom_ma(ma_fun = SMA, n = 200, color = "red") + # Plot 200-day SMA coord_x_date(xlim = c("2016-01-01", "2016-12-31"), ylim = c(20, 30)) # Zoom in # EVWMA AAPL %>% ggplot(aes(x = date, y = adjusted)) + geom_line() + # Plot stock price geom_ma(aes(volume = volume), ma_fun = EVWMA, n = 50) + # Plot 50-day EVWMA coord_x_date(xlim = c("2016-01-01", "2016-12-31"), ylim = c(20, 30)) # Zoom in
These palettes are mainly called internally by tidyquant scale_*_tq() functions.
palette_light() palette_dark() palette_green()palette_light() palette_dark() palette_green()
library(scales) scales::show_col(palette_light())library(scales) scales::show_col(palette_light())
Query or set Quandl API Key
quandl_api_key(api_key)quandl_api_key(api_key)
api_key |
Optionally passed parameter to set Quandl |
A wrapper for Quandl::Quandl.api_key()
Returns invisibly the currently set api_key
tq_get() get = "quandl"
## Not run: if (rlang::is_installed("Quandl")) { quandl_api_key(api_key = "foobar") } ## End(Not run)## Not run: if (rlang::is_installed("Quandl")) { quandl_api_key(api_key = "foobar") } ## End(Not run)
Search the Quandl database
quandl_search(query, silent = FALSE, per_page = 10, ...)quandl_search(query, silent = FALSE, per_page = 10, ...)
query |
Search terms |
silent |
Prints the results when FALSE. |
per_page |
Number of results returned per page. |
... |
Additional named values that are interpretted as Quandl API parameters. |
A wrapper for Quandl::Quandl.search()
Returns a tibble with search results.
tq_get() get = "quandl"
## Not run: quandl_search(query = "oil") ## End(Not run)## Not run: quandl_search(query = "oil") ## End(Not run)
The tidyquant scales add colors that work nicely with theme_tq().
scale_color_tq(..., theme = "light") scale_colour_tq(..., theme = "light") scale_fill_tq(..., theme = "light")scale_color_tq(..., theme = "light") scale_colour_tq(..., theme = "light") scale_fill_tq(..., theme = "light")
... |
common parameters for |
theme |
one of "light", "dark", or "green". This should match the |
scale_color_tqFor use when color is specified as an aes() in a ggplot.
scale_fill_tqFor use when fill is specified as an aes() in a ggplot.
# Load libraries library(dplyr) library(ggplot2) # Get stock prices stocks <- c("AAPL", "META", "NFLX") %>% tq_get(from = "2013-01-01", to = "2017-01-01") # Plot for stocks g <- stocks %>% ggplot(aes(date, adjusted, color = symbol)) + geom_line() + labs(title = "Multi stock example", xlab = "Date", ylab = "Adjusted Close") # Plot with tidyquant theme and colors g + theme_tq() + scale_color_tq()# Load libraries library(dplyr) library(ggplot2) # Get stock prices stocks <- c("AAPL", "META", "NFLX") %>% tq_get(from = "2013-01-01", to = "2017-01-01") # Plot for stocks g <- stocks %>% ggplot(aes(date, adjusted, color = symbol)) + geom_line() + labs(title = "Multi stock example", xlab = "Date", ylab = "Adjusted Close") # Plot with tidyquant theme and colors g + theme_tq() + scale_color_tq()
The theme_tq() function creates a custom theme using tidyquant colors.
theme_tq(base_size = 11, base_family = "") theme_tq_dark(base_size = 11, base_family = "") theme_tq_green(base_size = 11, base_family = "")theme_tq(base_size = 11, base_family = "") theme_tq_dark(base_size = 11, base_family = "") theme_tq_green(base_size = 11, base_family = "")
base_size |
base font size, given in pts. |
base_family |
base font family |
# Load libraries library(dplyr) library(ggplot2) # Get stock prices AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31") # Plot using ggplot with theme_tq AAPL %>% ggplot(aes(x = date, y = close)) + geom_line() + geom_bbands(aes(high = high, low = low, close = close), ma_fun = EMA, wilder = TRUE, ratio = NULL, n = 50) + coord_x_date(xlim = c("2016-01-01", "2016-12-31"), ylim = c(20, 35)) + labs(title = "Apple BBands", x = "Date", y = "Price") + theme_tq()# Load libraries library(dplyr) library(ggplot2) # Get stock prices AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31") # Plot using ggplot with theme_tq AAPL %>% ggplot(aes(x = date, y = close)) + geom_line() + geom_bbands(aes(high = high, low = low, close = close), ma_fun = EMA, wilder = TRUE, ratio = NULL, n = 50) + coord_x_date(xlim = c("2016-01-01", "2016-12-31"), ylim = c(20, 35)) + labs(title = "Apple BBands", x = "Date", y = "Price") + theme_tq()
This function lists all the conflicts between packages in the tidyverse and other packages that you have loaded.
tidyquant_conflicts(only = NULL)tidyquant_conflicts(only = NULL)
only |
Set this to a character vector to restrict to conflicts only with these packages. |
There are four conflicts that are deliberately ignored: intersect,
union, setequal, and setdiff from dplyr. These functions
make the base equivalents generic, so shouldn't negatively affect any
existing code.
tidyquant_conflicts()tidyquant_conflicts()
Requires the riingo package to be installed.
tiingo_api_key(api_key)tiingo_api_key(api_key)
api_key |
Optionally passed parameter to set Tiingo |
A wrapper for riingo::ringo_set_token()
Returns invisibly the currently set api_key
tq_get() get = "tiingo"
## Not run: tiingo_api_key(api_key = "foobar") ## End(Not run)## Not run: tiingo_api_key(api_key = "foobar") ## End(Not run)
tibble formatGet quantitative data in tibble format
tq_get(x, get = "stock.prices", complete_cases = TRUE, ...) tq_get_options()tq_get(x, get = "stock.prices", complete_cases = TRUE, ...) tq_get_options()
x |
A single character string, a character vector or tibble representing a single (or multiple) stock symbol, metal symbol, currency combination, FRED code, etc. |
get |
A character string representing the type of data to get
for
|
complete_cases |
Removes symbols that return an NA value due to an error with the get
call such as sending an incorrect symbol "XYZ" to get = "stock.prices". This is useful in
scaling so user does not need to
add an extra step to remove these rows. |
... |
Additional parameters passed to the "wrapped" function. Investigate underlying functions to see full list of arguments. Common optional parameters include:
|
tq_get() is a consolidated function that gets data from various
web sources. The function is a wrapper for several quantmod
functions, Quandl functions, and also gets data from websources unavailable
in other packages.
The results are always returned as a tibble. The advantages
are (1) only one function is needed for all data sources and (2) the function
can be seamlessly used with the tidyverse: purrr, tidyr, and
dplyr verbs.
tq_get_options() returns a list of valid get options you can
choose from.
tq_get_stock_index_options() Is deprecated and will be removed in the
next version. Please use tq_index_options() instead.
Returns data in the form of a tibble object.
tq_index() to get a ful list of stocks in an index.
tq_exchange() to get a ful list of stocks in an exchange.
quandl_api_key() to set the api key for collecting data via the "quandl"
get option.
tiingo_api_key() to set the api key for collecting data via the "tiingo"
get option.
av_api_key() to set the api key for collecting data via the "alphavantage"
get option.
# Load libraries # Get the list of `get` options tq_get_options() # Get stock prices for a stock from Yahoo aapl_stock_prices <- tq_get("AAPL") # Get stock prices for multiple stocks mult_stocks <- tq_get(c("META", "AMZN"), get = "stock.prices", from = "2016-01-01", to = "2017-01-01") ## Not run: # --- Quandl --- if (rlang::is_installed("quandl")) { quandl_api_key('<your_api_key>') tq_get("EIA/PET_MTTIMUS1_M", get = "quandl", from = "2010-01-01") } # Energy data from EIA # --- Tiingo --- if (rlang::is_installed("riingo")) { tiingo_api_key('<your_api_key>') # Tiingo Prices (Free alternative to Yahoo Finance!) tq_get(c("AAPL", "GOOG"), get = "tiingo", from = "2010-01-01") # Sub-daily prices from IEX ---- tq_get(c("AAPL", "GOOG"), get = "tiingo.iex", from = "2020-01-01", to = "2020-01-15", resample_frequency = "5min") # Tiingo Bitcoin Prices ---- tq_get(c("btcusd", "btceur"), get = "tiingo.crypto", from = "2020-01-01", to = "2020-01-15", resample_frequency = "5min") } # --- Alpha Vantage --- if (rlang::is_installed("alphavantager")) { av_api_key('<your_api_key>') # Daily Time Series tq_get("AAPL", get = "alphavantager", av_fun = "TIME_SERIES_DAILY_ADJUSTED", outputsize = "full") # Intraday 15 Min Interval tq_get("AAPL", get = "alphavantage", av_fun = "TIME_SERIES_INTRADAY", interval = "15min", outputsize = "full") # FX DAILY tq_get("USD/EUR", get = "alphavantage", av_fun = "FX_DAILY", outputsize = "full") # FX REAL-TIME QUOTE tq_get("USD/EUR", get = "alphavantage", av_fun = "CURRENCY_EXCHANGE_RATE") } ## End(Not run)# Load libraries # Get the list of `get` options tq_get_options() # Get stock prices for a stock from Yahoo aapl_stock_prices <- tq_get("AAPL") # Get stock prices for multiple stocks mult_stocks <- tq_get(c("META", "AMZN"), get = "stock.prices", from = "2016-01-01", to = "2017-01-01") ## Not run: # --- Quandl --- if (rlang::is_installed("quandl")) { quandl_api_key('<your_api_key>') tq_get("EIA/PET_MTTIMUS1_M", get = "quandl", from = "2010-01-01") } # Energy data from EIA # --- Tiingo --- if (rlang::is_installed("riingo")) { tiingo_api_key('<your_api_key>') # Tiingo Prices (Free alternative to Yahoo Finance!) tq_get(c("AAPL", "GOOG"), get = "tiingo", from = "2010-01-01") # Sub-daily prices from IEX ---- tq_get(c("AAPL", "GOOG"), get = "tiingo.iex", from = "2020-01-01", to = "2020-01-15", resample_frequency = "5min") # Tiingo Bitcoin Prices ---- tq_get(c("btcusd", "btceur"), get = "tiingo.crypto", from = "2020-01-01", to = "2020-01-15", resample_frequency = "5min") } # --- Alpha Vantage --- if (rlang::is_installed("alphavantager")) { av_api_key('<your_api_key>') # Daily Time Series tq_get("AAPL", get = "alphavantager", av_fun = "TIME_SERIES_DAILY_ADJUSTED", outputsize = "full") # Intraday 15 Min Interval tq_get("AAPL", get = "alphavantage", av_fun = "TIME_SERIES_INTRADAY", interval = "15min", outputsize = "full") # FX DAILY tq_get("USD/EUR", get = "alphavantage", av_fun = "FX_DAILY", outputsize = "full") # FX REAL-TIME QUOTE tq_get("USD/EUR", get = "alphavantage", av_fun = "CURRENCY_EXCHANGE_RATE") } ## End(Not run)
tibble formatGet all stocks in a stock index or stock exchange in tibble format
tq_index(x, use_fallback = FALSE) tq_index_options() tq_exchange(x) tq_exchange_options() tq_fund_holdings(x, source = "SSGA") tq_fund_source_options()tq_index(x, use_fallback = FALSE) tq_index_options() tq_exchange(x) tq_exchange_options() tq_fund_holdings(x, source = "SSGA") tq_fund_source_options()
x |
A single character string, a character vector or tibble representing a single stock index or multiple stock indexes. |
use_fallback |
A boolean that can be used to return a fallback data set
last downloaded when the package was updated. Useful if the website is down.
Set to |
source |
The API source to use. |
tq_index() returns the stock symbol, company name, weight, and sector of every stock
in an index.
tq_index_options() returns a list of stock indexes you can
choose from.
tq_exchange() returns the stock symbol, company, last sale price,
market capitalization, sector and industry of every stock
in an exchange. Three stock exchanges are available (AMEX, NASDAQ, and NYSE).
tq_exchange_options() returns a list of stock exchanges you can
choose from. The options are AMEX, NASDAQ and NYSE.
tq_fund_holdings() returns the the stock symbol, company name, weight, and sector of every stock
in an fund. The source parameter specifies which investment management company to use.
Example: source = "SSGA" connects to State Street Global Advisors (SSGA).
If x = "SPY", then SPDR SPY ETF holdings will be returned.
tq_fund_source_options(): returns the options that can be used for the source API for tq_fund_holdings().
Returns data in the form of a tibble object.
tq_get() to get stock prices, financials, key stats, etc using the stock symbols.
# Stock Indexes: # Get the list of stock index options tq_index_options() # Get all stock symbols in a stock index ## Not run: tq_index("DOW") ## End(Not run) # Stock Exchanges: # Get the list of stock exchange options tq_exchange_options() # Get all stocks in a stock exchange ## Not run: tq_exchange("NYSE") ## End(Not run) # Mutual Funds and ETFs: # Get the list of stock exchange options tq_fund_source_options() # Get all stocks in a fund ## Not run: tq_fund_holdings("SPY", source = "SSGA") ## End(Not run)# Stock Indexes: # Get the list of stock index options tq_index_options() # Get all stock symbols in a stock index ## Not run: tq_index("DOW") ## End(Not run) # Stock Exchanges: # Get the list of stock exchange options tq_exchange_options() # Get all stocks in a stock exchange ## Not run: tq_exchange("NYSE") ## End(Not run) # Mutual Funds and ETFs: # Get the list of stock exchange options tq_fund_source_options() # Get all stocks in a fund ## Not run: tq_fund_holdings("SPY", source = "SSGA") ## End(Not run)
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()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()
data |
A |
select |
The columns to send to the mutation function. |
mutate_fun |
The mutation function from either the |
col_rename |
A string or character vector containing names that can be used to quickly rename columns. |
ohlc_fun |
Deprecated. Use |
... |
Additional parameters passed to the appropriate mutatation function. |
x, y
|
Parameters used with |
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.
Returns mutated data in the form of a tibble object.
# Load libraries library(dplyr) ##### Basic Functionality fb_stock_prices <- tidyquant::FANG %>% filter(symbol == "META") %>% filter( date >= "2016-01-01", date <= "2016-12-31" ) goog_stock_prices <- FANG %>% filter(symbol == "GOOG") %>% filter( date >= "2016-01-01", date <= "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") # 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") # 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) # 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") goog_returns <- goog_stock_prices %>% tq_transmute(adjusted, periodReturn, period = "monthly", col_rename = "goog.returns") returns_combined <- left_join(fb_returns, goog_returns, by = "date") regr_fun <- function(data) { coef(lm(fb.returns ~ goog.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")) # 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]])# Load libraries library(dplyr) ##### Basic Functionality fb_stock_prices <- tidyquant::FANG %>% filter(symbol == "META") %>% filter( date >= "2016-01-01", date <= "2016-12-31" ) goog_stock_prices <- FANG %>% filter(symbol == "GOOG") %>% filter( date >= "2016-01-01", date <= "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") # 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") # 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) # 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") goog_returns <- goog_stock_prices %>% tq_transmute(adjusted, periodReturn, period = "monthly", col_rename = "goog.returns") returns_combined <- left_join(fb_returns, goog_returns, by = "date") regr_fun <- function(data) { coef(lm(fb.returns ~ goog.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")) # 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]])
Asset and portfolio performance analysis is a deep field with a wide range of theories and
methods for analyzing risk versus reward. The PerformanceAnalytics package
consolidates many of the most widely used performance metrics as functions that can
be applied to stock or portfolio returns. tq_performance
implements these performance analysis functions in a tidy way, enabling scaling
analysis using the split, apply, combine framework.
tq_performance(data, Ra, Rb = NULL, performance_fun, ...) tq_performance_(data, Ra, Rb = NULL, performance_fun, ...) tq_performance_fun_options()tq_performance(data, Ra, Rb = NULL, performance_fun, ...) tq_performance_(data, Ra, Rb = NULL, performance_fun, ...) tq_performance_fun_options()
data |
A |
Ra |
The column of asset returns |
Rb |
The column of baseline returns (for functions that require comparison to a baseline) |
performance_fun |
The performance function from |
... |
Additional parameters passed to the |
Important concept: Performance is based on the statistical properties of returns, and as a result this function uses stock or portfolio returns as opposed to stock prices.
tq_performance is a wrapper for various PerformanceAnalytics functions
that return portfolio statistics.
The main advantage is the ability to scale with the tidyverse.
Ra and Rb are the columns containing asset and baseline returns, respectively.
These columns are mapped to the PerformanceAnalytics functions. Note that Rb
is not always required, and in these instances the argument defaults to Rb = NULL.
The user can tell if Rb is required by researching the underlying performance function.
... are additional arguments that are passed to the PerformanceAnalytics
function. Search the underlying function to see what arguments can be passed through.
tq_performance_fun_options returns a list of compatible PerformanceAnalytics functions
that can be supplied to the performance_fun argument.
Returns data in the form of a tibble object.
tq_transmute() which can be used to calculate period returns from a
set of stock prices. Use mutate_fun = periodReturn with the appropriate periodicity
such as period = "monthly".
tq_portfolio() which can be used to aggregate period returns from
multiple stocks to period returns for a portfolio.
The PerformanceAnalytics package, which contains the underlying functions
for the performance_fun argument. Additional parameters can be passed via ....
# Load libraries library(dplyr) # Use FANG data set # Get returns for individual stock components grouped by symbol Ra <- FANG %>% group_by(symbol) %>% tq_transmute(adjusted, periodReturn, period = "monthly", col_rename = "Ra") # Get returns for SP500 as baseline Rb <- "^GSPC" %>% tq_get(get = "stock.prices", from = "2010-01-01", to = "2015-12-31") %>% tq_transmute(adjusted, periodReturn, period = "monthly", col_rename = "Rb") # Merge stock returns with baseline RaRb <- left_join(Ra, Rb, by = c("date" = "date")) ##### Performance Metrics ##### # View options tq_performance_fun_options() # Get performance metrics RaRb %>% tq_performance(Ra = Ra, performance_fun = SharpeRatio, p = 0.95) RaRb %>% tq_performance(Ra = Ra, Rb = Rb, performance_fun = table.CAPM)# Load libraries library(dplyr) # Use FANG data set # Get returns for individual stock components grouped by symbol Ra <- FANG %>% group_by(symbol) %>% tq_transmute(adjusted, periodReturn, period = "monthly", col_rename = "Ra") # Get returns for SP500 as baseline Rb <- "^GSPC" %>% tq_get(get = "stock.prices", from = "2010-01-01", to = "2015-12-31") %>% tq_transmute(adjusted, periodReturn, period = "monthly", col_rename = "Rb") # Merge stock returns with baseline RaRb <- left_join(Ra, Rb, by = c("date" = "date")) ##### Performance Metrics ##### # View options tq_performance_fun_options() # Get performance metrics RaRb %>% tq_performance(Ra = Ra, performance_fun = SharpeRatio, p = 0.95) RaRb %>% tq_performance(Ra = Ra, Rb = Rb, performance_fun = table.CAPM)
Aggregates a group of returns by asset into portfolio returns
tq_portfolio( data, assets_col, returns_col, weights = NULL, col_rename = NULL, ... ) tq_portfolio_( data, assets_col, returns_col, weights = NULL, col_rename = NULL, ... ) tq_repeat_df(data, n, index_col_name = "portfolio")tq_portfolio( data, assets_col, returns_col, weights = NULL, col_rename = NULL, ... ) tq_portfolio_( data, assets_col, returns_col, weights = NULL, col_rename = NULL, ... ) tq_repeat_df(data, n, index_col_name = "portfolio")
data |
A |
assets_col |
The column with assets (securities) |
returns_col |
The column with returns |
weights |
Optional parameter for the asset weights, which can be passed as a numeric vector the length of the number of assets or a two column tibble with asset names in first column and weights in second column. |
col_rename |
A string or character vector containing names that can be used to quickly rename columns. |
... |
Additional parameters passed to |
n |
Number of times to repeat a data frame row-wise. |
index_col_name |
A renaming function for the "index" column, used when repeating data frames. |
tq_portfolio is a wrapper for PerformanceAnalytics::Return.portfolio.
The main advantage is the results are returned as a tibble and the
function can be used with the tidyverse.
assets_col and returns_col are columns within data that are used
to compute returns for a portfolio. The columns should be in "long" format (or "tidy" format)
meaning there is only one column containing all of the assets and one column containing
all of the return values (i.e. not in "wide" format with returns spread by asset).
weights are the weights to be applied to the asset returns.
Weights can be input in one of three options:
Single Portfolio: A numeric vector of weights that is the same length as unique number of assets. The weights are applied in the order of the assets.
Single Portfolio: A two column tibble with assets in the first column and weights in the second column. The advantage to this method is the weights are mapped to the assets and any unlisted assets default to a weight of zero.
Multiple Portfolios: A three column tibble with portfolio index in the first column, assets in the second column, and weights in the third column. The tibble must be grouped by portfolio index.
tq_repeat_df is a simple function that repeats
a data frame n times row-wise (long-wise), and adds a new column for a portfolio index.
The function is used to assist in Multiple Portfolio analyses, and
is a useful precursor to tq_portfolio.
Returns data in the form of a tibble object.
tq_transmute() which can be used to get period returns.
PerformanceAnalytics::Return.portfolio() which is the underlying function
that specifies which parameters can be passed via ...
# Load libraries library(dplyr) # Use FANG data set # Get returns for individual stock components monthly_returns_stocks <- FANG %>% group_by(symbol) %>% tq_transmute(adjusted, periodReturn, period = "monthly") ##### Portfolio Aggregation Methods ##### # Method 1: Use tq_portfolio with numeric vector of weights weights <- c(0.50, 0.25, 0.25, 0) tq_portfolio(data = monthly_returns_stocks, assets_col = symbol, returns_col = monthly.returns, weights = weights, col_rename = NULL, wealth.index = FALSE) # Method 2: Use tq_portfolio with two column tibble and map weights # Note that GOOG's weighting is zero in Method 1. In Method 2, # GOOG is not added and same result is achieved. weights_df <- tibble(symbol = c("META", "AMZN", "NFLX"), weights = c(0.50, 0.25, 0.25)) tq_portfolio(data = monthly_returns_stocks, assets_col = symbol, returns_col = monthly.returns, weights = weights_df, col_rename = NULL, wealth.index = FALSE) # Method 3: Working with multiple portfolios # 3A: Duplicate monthly_returns_stocks multiple times mult_monthly_returns_stocks <- tq_repeat_df(monthly_returns_stocks, n = 4) # 3B: Create weights table grouped by portfolio id weights <- c(0.50, 0.25, 0.25, 0.00, 0.00, 0.50, 0.25, 0.25, 0.25, 0.00, 0.50, 0.25, 0.25, 0.25, 0.00, 0.50) stocks <- c("META", "AMZN", "NFLX", "GOOG") weights_table <- tibble(stocks) %>% tq_repeat_df(n = 4) %>% bind_cols(tibble(weights)) %>% group_by(portfolio) # 3C: Scale to multiple portfolios tq_portfolio(data = mult_monthly_returns_stocks, assets_col = symbol, returns_col = monthly.returns, weights = weights_table, col_rename = NULL, wealth.index = FALSE)# Load libraries library(dplyr) # Use FANG data set # Get returns for individual stock components monthly_returns_stocks <- FANG %>% group_by(symbol) %>% tq_transmute(adjusted, periodReturn, period = "monthly") ##### Portfolio Aggregation Methods ##### # Method 1: Use tq_portfolio with numeric vector of weights weights <- c(0.50, 0.25, 0.25, 0) tq_portfolio(data = monthly_returns_stocks, assets_col = symbol, returns_col = monthly.returns, weights = weights, col_rename = NULL, wealth.index = FALSE) # Method 2: Use tq_portfolio with two column tibble and map weights # Note that GOOG's weighting is zero in Method 1. In Method 2, # GOOG is not added and same result is achieved. weights_df <- tibble(symbol = c("META", "AMZN", "NFLX"), weights = c(0.50, 0.25, 0.25)) tq_portfolio(data = monthly_returns_stocks, assets_col = symbol, returns_col = monthly.returns, weights = weights_df, col_rename = NULL, wealth.index = FALSE) # Method 3: Working with multiple portfolios # 3A: Duplicate monthly_returns_stocks multiple times mult_monthly_returns_stocks <- tq_repeat_df(monthly_returns_stocks, n = 4) # 3B: Create weights table grouped by portfolio id weights <- c(0.50, 0.25, 0.25, 0.00, 0.00, 0.50, 0.25, 0.25, 0.25, 0.00, 0.50, 0.25, 0.25, 0.25, 0.00, 0.50) stocks <- c("META", "AMZN", "NFLX", "GOOG") weights_table <- tibble(stocks) %>% tq_repeat_df(n = 4) %>% bind_cols(tibble(weights)) %>% group_by(portfolio) # 3C: Scale to multiple portfolios tq_portfolio(data = mult_monthly_returns_stocks, assets_col = symbol, returns_col = monthly.returns, weights = weights_table, col_rename = NULL, wealth.index = FALSE)