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tidyquant: Integrating quantitative financial analysis tools with the tidyverse

Description

The main advantage oftidyquant is tobridge the gap between the best quantitative resources for collecting andmanipulating quantitative data,xts,quantmod andTTR,and the data modeling workflow and infrastructure of thetidyverse.

Details

In this package,tidyquant functions and supporting data sets areprovided to seamlessly combine tidy tools with existing quantitativeanalytics packages. The main advantage is being able to use tidyfunctions with purrr for mapping and tidyr for nesting to extend modeling tomany stocks. See the tidyquant website for more information, documentationand examples.

Users will probably be interested in the following:

• Getting Data from the Web:tq_get()

• Manipulating Data:tq_transmute() andtq_mutate()

• Performance Analysis and Portfolio Aggregation:tq_performance() andtq_portfolio()

To learn more about tidyquant, start with the vignettes:browseVignettes(package = "tidyquant")

Author(s)

Maintainer: Matt Danchomdancho@business-science.io

Authors:

• Davis Vaughandvaughan@business-science.io

See Also

Useful links:

• https://business-science.github.io/tidyquant/

• https://github.com/business-science/tidyquant

• Report bugs athttps://github.com/business-science/tidyquant/issues

Pipe operator

Description

Seemagrittr::%>% for details.

Usage

lhs %>% rhs

Stock prices for the "FANG" stocks.

Description

A dataset containing the daily historical stock prices for the "FANG" tech stocks,"META", "AMZN", "NFLX", and "GOOG", spanning from the beginning of2013 through the end of 2016.

Usage

FANG

Format

A "tibble" ("tidy" data frame) with 4,032 rows and 8 variables:

symbol

stock ticker symbol

date

trade date

open

stock price at the open of trading, in USD

high

stock price at the highest point during trading, in USD

low

stock price at the lowest point during trading, in USD

close

stock price at the close of trading, in USD

volume

number of shares traded

adjusted

stock price at the close of trading adjusted for stock splits, in USD

Source

https://www.investopedia.com/terms/f/fang-stocks-fb-amzn.asp

Set Alpha Vantage API Key

Description

Requires the alphavantager packager to use.

Usage

av_api_key(api_key)

Arguments

Details

A wrapper foralphavantager::av_api_key()

Value

Returns invisibly the currently setapi_key

See Also

tq_get()get = "alphavantager"

Examples

## 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

Description

Zoom in on plot regions using date ranges or date-time ranges

Usage

coord_x_date(xlim = NULL, ylim = NULL, expand = TRUE)coord_x_datetime(xlim = NULL, ylim = NULL, expand = TRUE)

Arguments

Details

Thecoord_ functions prevent loss of data during zooming, which isnecessary when zooming in on plots that calculatestats using dataoutside of the zoom range (e.g. when plotting moving averageswithgeom_ma()). Setting limits usingscale_x_datechanges the underlying data which causes moving averages to fail.

coord_x_date is a wrapper forcoord_cartesianthat enables quickly zooming in on plot regions using a date range.

coord_x_datetime is a wrapper forcoord_cartesianthat enables quickly zooming in on plot regions using a date-time range.

See Also

ggplot2::coord_cartesian()

Examples

# Load librarieslibrary(dplyr)library(ggplot2)# coord_x_dateAAPL <- 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_datetimetime_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"))

Deprecated functions

Description

A record of functions that have been deprecated.

Usage

tq_transform(data, ohlc_fun = OHLCV, mutate_fun, col_rename = NULL, ...)tq_transform_xy(data, x, y = NULL, mutate_fun, col_rename = NULL, ...)

Arguments

Details

• tq_transform() - usetq_transmute()

• tq_transform_xy() - usetq_transmute_xy()

• as_xts() - usetimetk::tk_xts()

• as_tibble() - usetimetk::tk_tbl()

• summarise_by_time() - Moved totimetk package. Usetimetk::summarise_by_time()

Excel Date and Time Functions

Description

50+ date and time functions familiar to users coming from anExcel Background.The main benefits are:

1. Integration of the amazinglubridate package for handling dates and times

2. Integration of Holidays fromtimeDate and Business Calendars

3. 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 anExcel 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)

Usage

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, ...)

Arguments

Details

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.

• Extractors:SECOND(),MINUTE(),HOUR(),DAY(),WEEK(),MONTH(),QUARTER(),YEAR()

Current Time - Returns the current date/date-time based on your locale.

• NOW(),TODAY()

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 thatfactor in workdays andtimeDate holiday calendars for popular business calendars including NYSE and London stock exchange.

Date Collapsers - Collapse a date sequence (useful indplyr::group_by() andpivot_table())

• FLOOR_DATE(),FLOOR_DAY(),FLOOR_WEEK(),FLOOR_MONTH(),FLOOR_QUARTER(),FLOOR_YEAR()

• Similar functions exist for CEILING and ROUND. These are wrappers forlubridate functions.

Value

• 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

Examples

# Librarieslibrary(lubridate)# --- Basic Usage ----# Converters ---AS_DATE("2011 Jan-01") # GeneralYMD("2011 Jan-01")     # Year, Month-Day FormatMDY("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 DayWEEKDAY("2020-01-01", label = FALSE)                   # Numeric DayWEEKDAY("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 WeekendsNET_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/quarterFANG %>%    pivot_table(        .rows       = c(symbol, ~ QUARTER(date)),        .columns    = ~ YEAR(date),        .values     = ~ PCT_CHANGE_FIRSTLAST(adjusted)    )

Excel Financial Math Functions

Description

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 anExcel 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)

Usage

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)

Arguments

Details

Net Present Value (NPV)Net present value (NPV) is the difference between the present value of cash inflows andthe present value of cash outflows over a period of time. NPV is used in capital budgetingand investment planning to analyze the profitability of a projected investment or project.For more information, seeInvestopedia NPV.

Internal Rate of Return (IRR)The internal rate of return (IRR) is a metric used in capital budgeting to estimate theprofitability of potential investments. The internal rate of return is a discount ratethat makes the net present value (NPV) of all cash flows from a particular project equalto zero. IRR calculations rely on the same formula as NPV does.For more information, seeInvestopedia IRR.

Future Value (FV)Future value (FV) is the value of a current asset at a future date based on an assumedrate of growth. The future value (FV) is important to investors and financial plannersas 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 ontheir anticipated needs. However, external economic factors, such as inflation, can adverselyaffect the future value of the asset by eroding its value.For more information, seeInvestopedia FV.

Present Value (PV)Present value (PV) is the current value of a future sum of money or stream of cash flows given aspecified rate of return. Future cash flows are discounted at the discount rate, and the higherthe discount rate, the lower the present value of the future cash flows. Determining theappropriate discount rate is the key to properly valuing future cash flows, whether they be earningsor obligations. For more information, seeInvestopedia PV.

Payment (PMT)The PaymentPMT() 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.

Value

• Summary functions return a single value

Examples

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)

Excel Summarising "If" Functions

Description

"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 anExcel 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)

Usage

SUM_IFS(x, ...)COUNT_IFS(x, ...)AVERAGE_IFS(x, ...)MEDIAN_IFS(x, ...)MIN_IFS(x, ...)MAX_IFS(x, ...)CREATE_IFS(.f, ...)

Arguments

Details

"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 likex > 10 | x < -10 to perform an "OR" if-statement.

Creating New "Summarizing IFS" Functions:Users can create new "IFS" functions using theCREATE_IFS() function factory.The only requirement is that the output of your function (.f) must be a singlevalue (scalar). See examples below.

Value

• Summary functions return a single value

Useful Functions

Summary Functions - Return a single value from a vector

• Sum:SUM_IFS()

• Center:AVERAGE_IFS(),MEDIAN_IFS()

• Count:COUNT_IFS()

• Range:MIN_IFS(),MAX_IFS()

Create your own summary "IFS" function

• CREATE_IFS(): This is a function factory that generates summary "_IFS" functions.

Examples

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 yearFANG %>%    group_by(symbol) %>%    summarise(        high_volume_in_2015 = COUNT_IFS(volume,                                        year(date) == 2015,                                        volume > quantile(volume, 0.75))    )# Count negative returns by monthFANG %>%    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)    )

Excel Pivot Table

Description

The Pivot Table is one of Excel's most powerful features, and now it's available inR!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 anExcel background.Most functions replicate the behavior of Excel:

• Names are similar to Excel function names

• Functionality replicates Excel

Usage

pivot_table(  .data,  .rows,  .columns,  .values,  .filters = NULL,  .sort = NULL,  fill_na = NA)

Arguments

Details

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.

• Thepivot_table() function is powered by thetidyverse, 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) withc(). 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)

Value

Returns a tibble that has been pivoted to summarize information by column and row groupings

Examples

# PIVOT TABLE ----# Calculate returns by year/quarterFANG %>%    pivot_table(        .rows       = c(symbol, ~ QUARTER(date)),        .columns    = ~ YEAR(date),        .values     = ~ PCT_CHANGE_FIRSTLAST(adjusted)    )

Excel Reference Functions

Description

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 anExcel background.Most functions replicate the behavior of Excel:

• Names are similar to Excel function names

• Functionality replicates Excel

Usage

VLOOKUP(.lookup_values, .data, .lookup_column, .return_column)

Arguments

Details

VLOOKUP() Details

• Performs exact matching only. Fuzzy matching is not implemented.

• Can only return values from one column only. Usedplyr::left_join() to perform table joining.

Value

Returns a vector the length of the input lookup values

Examples

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 dataFANG %>%    mutate(company = VLOOKUP(symbol, lookup_table, stock, company))

Excel Statistical Mutation Functions

Description

15+ common statistical functions familiar to users of Excel (e.g.ABS(),SQRT())thatmodify / 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 anExcel 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)

Usage

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)

Arguments

Value

• Mutation functions return a mutated / transformed version of the vector

Useful functions

Mutation Functions - Transforms a vector

• Transformation:ABS(),SQRT(),LOG(),EXP()

• Lags & Change (Offsetting Functions):CHANGE(),PCT_CHANGE(),LAG(),LEAD()

• Cumulative Totals:CUMULATIVE_SUM(),CUMULATIVE_PRODUCT()

Examples

# Librarieslibrary(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 USDFANG %>%    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    )

Excel Statistical Summary Functions

Description

15+ common statistical functions familiar to users of Excel (e.g.SUM(),AVERAGE()).These functions return asingle value (i.e. a vector of length 1).

These functions are designed to help users coming from anExcel 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)

Usage

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)

Arguments

Details

Summary Functions

• All functions remove missing values (NA). This is the same behavior as in Excel and most commonly what is desired.

Value

• Summary functions return a single value

Useful functions

Summary Functions - Return a single value from a vector

• Sum:SUM()

• Center:AVERAGE(),MEDIAN()

• Spread:STDEV(),VAR()

• Range:MIN(),MAX()

• Count:COUNT(),COUNT_UNIQUE()

• Position:FIRST(),LAST(),NTH()

• Change (Summary):CHANGE_FIRSTLAST(),PCT_CHANGE_FIRSTLAST()

• Correlation:COR(),COV()

Examples

# Librarieslibrary(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 USDFANG %>%    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)    )

Plot Bollinger Bands using Moving Averages

Description

Bollinger Bands plot a range around a moving average typically two standard deviations up and down.Thegeom_bbands() function enables plotting Bollinger Bands quickly using various moving average functions.The moving average functions used are specified inTTR::SMA()from the TTR package. Usecoord_x_date() to zoom into specific plot regions.The following moving averages are available:

• Simple moving averages (SMA):Rolling mean over a period defined byn.

• Exponential moving averages (EMA): Includesexponentially-weighted mean that gives more weight to recent observations.Useswilder andratio args.

• Weighted moving averages (WMA):Uses a set of weights,wts, to weight observations in the moving average.

• Double exponential moving averages (DEMA):Usesv volume factor,wilder andratio args.

• Zero-lag exponential moving averages (ZLEMA):Useswilder andratio args.

• Volume-weighted moving averages (VWMA):Requiresvolume aesthetic.

• Elastic, volume-weighted moving averages (EVWMA):Requiresvolume aesthetic.

Usage

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",  ...)

Arguments

Aesthetics

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 Also

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

Examples

library(dplyr)library(ggplot2)library(lubridate)AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31")# SMAAAPL %>%    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))# EMAAAPL %>%   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))# VWMAAAPL %>%    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))

Plot Financial Charts in ggplot2

Description

Financial charts provide visual cues to open, high, low, and close prices.Usecoord_x_date() to zoom into specific plot regions.The following financial chart geoms are available:

• Bar Chart

• Candlestick Chart

Usage

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",  ...)

Arguments

Aesthetics

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 Also

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

Examples

library(dplyr)library(ggplot2)library(lubridate)AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31")# Bar ChartAAPL %>%    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 ChartAAPL %>%    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))

Plot moving averages

Description

The underlying moving average functions used are specified inTTR::SMA()from the TTR package. Usecoord_x_date() to zoom into specific plot regions.The following moving averages are available:

• Simple moving averages (SMA):Rolling mean over a period defined byn.

• Exponential moving averages (EMA): Includesexponentially-weighted mean that gives more weight to recent observations.Useswilder andratio args.

• Weighted moving averages (WMA):Uses a set of weights,wts, to weight observations in the moving average.

• Double exponential moving averages (DEMA):Usesv volume factor,wilder andratio args.

• Zero-lag exponential moving averages (ZLEMA):Useswilder andratio args.

• Volume-weighted moving averages (VWMA):Requiresvolume aesthetic.

• Elastic, volume-weighted moving averages (EVWMA):Requiresvolume aesthetic.

Usage

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,  ...)

Arguments

Aesthetics

The following aesthetics are understood (required are in bold):

• x

• y

• volume, Required for VWMA and EVWMA

• alpha

• colour

• group

• linetype

• linewidth

• size

See Also

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

Examples

library(dplyr)library(ggplot2)AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31")# SMAAAPL %>%    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# EVWMAAAPL %>%    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

tidyquant palettes for use with scales

Description

These palettes are mainly called internally by tidyquant⁠scale_*_tq()⁠ functions.

Usage

palette_light()palette_dark()palette_green()

Examples

library(scales)scales::show_col(palette_light())

Query or set Quandl API Key

Description

Query or set Quandl API Key

Usage

quandl_api_key(api_key)

Arguments

Details

A wrapper forQuandl::Quandl.api_key()

Value

Returns invisibly the currently setapi_key

See Also

tq_get()get = "quandl"

Examples

## Not run: if (rlang::is_installed("Quandl")) {quandl_api_key(api_key = "foobar")}## End(Not run)

Search the Quandl database

Description

Search the Quandl database

Usage

quandl_search(query, silent = FALSE, per_page = 10, ...)

Arguments

Details

A wrapper forQuandl::Quandl.search()

Value

Returns a tibble with search results.

See Also

tq_get()get = "quandl"

Examples

## Not run: quandl_search(query = "oil")## End(Not run)

tidyquant colors and fills for ggplot2.

Description

The tidyquant scales add colors that work nicely withtheme_tq().

Usage

scale_color_tq(..., theme = "light")scale_colour_tq(..., theme = "light")scale_fill_tq(..., theme = "light")

Arguments

Details

scale_color_tq

For use whencolor is specified as anaes() in a ggplot.

scale_fill_tq

For use whenfill is specified as anaes() in a ggplot.

See Also

theme_tq()

Examples

# Load librarieslibrary(dplyr)library(ggplot2)# Get stock pricesstocks <- c("AAPL", "META", "NFLX") %>%    tq_get(from = "2013-01-01",           to   = "2017-01-01")# Plot for stocksg <- stocks %>%    ggplot(aes(date, adjusted, color = symbol)) +    geom_line() +    labs(title = "Multi stock example",         xlab = "Date",         ylab = "Adjusted Close")# Plot with tidyquant theme and colorsg +    theme_tq() +    scale_color_tq()

tidyquant themes for ggplot2.

Description

Thetheme_tq() function creates a custom theme using tidyquant colors.

Usage

theme_tq(base_size = 11, base_family = "")theme_tq_dark(base_size = 11, base_family = "")theme_tq_green(base_size = 11, base_family = "")

Arguments

See Also

scale_manual()

Examples

# Load librarieslibrary(dplyr)library(ggplot2)# Get stock pricesAAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31")# Plot using ggplot with theme_tqAAPL %>% 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()

Conflicts between the tidyquant and other packages

Description

This function lists all the conflicts between packages in the tidyverseand other packages that you have loaded.

Usage

tidyquant_conflicts(only = NULL)

Arguments

Details

There are four conflicts that are deliberately ignored:intersect,union,setequal, andsetdiff from dplyr. These functionsmake the base equivalents generic, so shouldn't negatively affect anyexisting code.

Examples

tidyquant_conflicts()

Set Tiingo API Key

Description

Requires the riingo package to be installed.

Usage

tiingo_api_key(api_key)

Arguments

Details

A wrapper forriingo::ringo_set_token()

Value

Returns invisibly the currently setapi_key

See Also

tq_get()get = "tiingo"

Examples

## Not run:   tiingo_api_key(api_key = "foobar")## End(Not run)

Get quantitative data intibble format

Description

Get quantitative data intibble format

Usage

tq_get(x, get = "stock.prices", complete_cases = TRUE, ...)tq_get_options()

Arguments

Details

tq_get() is a consolidated function that gets data from variousweb sources. The function is a wrapper for severalquantmodfunctions,Quandl functions, and also gets data from websources unavailablein other packages.The results are always returned as atibble. The advantagesare (1) only one function is needed for all data sources and (2) the functioncan be seamlessly used with the tidyverse:purrr,tidyr, anddplyr verbs.

tq_get_options() returns a list of validget options you canchoose from.

tq_get_stock_index_options() Is deprecated and will be removed in thenext version. Please usetq_index_options() instead.

Value

Returns data in the form of atibble object.

See Also

• 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.

Examples

# Load libraries# Get the list of `get` optionstq_get_options()# Get stock prices for a stock from Yahooaapl_stock_prices <- tq_get("AAPL")# Get stock prices for multiple stocksmult_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 Seriestq_get("AAPL",       get        = "alphavantager",       av_fun     = "TIME_SERIES_DAILY_ADJUSTED",       outputsize = "full")# Intraday 15 Min Intervaltq_get("AAPL",       get        = "alphavantage",       av_fun     = "TIME_SERIES_INTRADAY",       interval   = "15min",       outputsize = "full")# FX DAILYtq_get("USD/EUR", get = "alphavantage", av_fun = "FX_DAILY", outputsize = "full")# FX REAL-TIME QUOTEtq_get("USD/EUR", get = "alphavantage", av_fun = "CURRENCY_EXCHANGE_RATE")}## End(Not run)

Get all stocks in a stock index or stock exchange intibble format

Description

Get all stocks in a stock index or stock exchange intibble format

Usage

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()

Arguments

Details

tq_index() returns the stock symbol, company name, weight, and sector of every stockin an index.

tq_index_options() returns a list of stock indexes you canchoose from.

tq_exchange() returns the stock symbol, company, last sale price,market capitalization, sector and industry of every stockin an exchange. Three stock exchanges are available (AMEX, NASDAQ, and NYSE).

tq_exchange_options() returns a list of stock exchanges you canchoose from. The options are AMEX, NASDAQ and NYSE.

tq_fund_holdings() returns the the stock symbol, company name, weight, and sector of every stockin an fund. Thesource parameter specifies which investment management company to use.Example:source = "SSGA" connects to State Street Global Advisors (SSGA).Ifx = "SPY", then SPDR SPY ETF holdings will be returned.

tq_fund_source_options(): returns the options that can be used for thesource API fortq_fund_holdings().

Value

Returns data in the form of atibble object.

See Also

tq_get() to get stock prices, financials, key stats, etc using the stock symbols.

Examples

# Stock Indexes:# Get the list of stock index optionstq_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 optionstq_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 optionstq_fund_source_options()# Get all stocks in a fund## Not run: tq_fund_holdings("SPY", source = "SSGA")## End(Not run)

Mutates quantitative data

Description

tq_mutate() adds new variables to an existing tibble;tq_transmute() returns only newly created columns and is typicallyused when periodicity changes

Usage

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()

Arguments

Details

tq_mutate andtq_transmute are very flexible wrappers for variousxts,quantmod andTTR functions. The main advantage is theresults are returned as atibble and thefunction can be used with thetidyverse.tq_mutate is used when additionalcolumns are added to the return data frame.tq_transmute works exactly liketq_mutateexcept it only returns the newly created columns. This is helpful whenchanging periodicity where the new columns would not have the same number of rowsas the original tibble.

select specifies the columns that get passed to the mutation function. Select worksas a more flexible version of the OHLC extractor functions fromquantmod wherenon-OHLC data works as well. Whenselect isNULL, all columns are selected.In Example 1 below,close returns the "close" price and sends this to themutate function,periodReturn.

mutate_fun is the function that performs the work. In Example 1, thisisperiodReturn, which calculates the period returns. The...are additional arguments passed to themutate_fun. Think ofthe whole operation in Example 1 as the close price, obtained byselect = close,being sent to theperiodReturn function alongwith additional arguments defining how to perform the period return, whichincludesperiod = "daily" andtype = "log".Example 4 shows how to apply a rolling regression.

tq_mutate_xy andtq_transmute_xy are designed to enable working with mutatationfunctions that require two primary inputs (e.g. EVWMA, VWAP, etc).Example 2 shows this benefit in action: using the EVWMA function that usesvolume to define the moving average period.

tq_mutate_,tq_mutate_xy_,tq_transmute_, andtq_transmute_xy_are setup for Non-StandardEvaluation (NSE). This enables programatically changing column names by modifyingthe text representations. Example 5 shows the difference in implementation.Note that character strings are being passed to the variables instead ofunquoted variable names. Seevignette("nse") for more information.

tq_mutate_fun_options andtq_transmute_fun_options return a list of variousfinancial functions that are compatible withtq_mutate andtq_transmute,respectively.

Value

Returns mutated data in the form of atibble object.

See Also

tq_get()

Examples

# Load librarieslibrary(dplyr)##### Basic Functionalityfb_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 requiredfb_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 datatq_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 regressionfb_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]])

Computes a wide variety of summary performance metrics from stock or portfolio returns

Description

Asset and portfolio performance analysis is a deep field with a wide range of theories andmethods for analyzing risk versus reward. ThePerformanceAnalytics packageconsolidates many of the most widely used performance metrics as functions that canbe applied to stock or portfolio returns.tq_performanceimplements these performance analysis functions in a tidy way, enabling scalinganalysis using the split, apply, combine framework.

Usage

tq_performance(data, Ra, Rb = NULL, performance_fun, ...)tq_performance_(data, Ra, Rb = NULL, performance_fun, ...)tq_performance_fun_options()

Arguments

Details

Important concept: Performance is based on the statistical properties of returns,and as a result this function uses stock or portfolio returns as opposedto stock prices.

tq_performance is a wrapper for variousPerformanceAnalytics functionsthat return portfolio statistics.The main advantage is the ability to scale with thetidyverse.

Ra andRb are the columns containing asset and baseline returns, respectively.These columns are mapped to thePerformanceAnalytics functions. Note thatRbis not always required, and in these instances the argument defaults toRb = NULL.The user can tell ifRb is required by researching the underlying performance function.

... are additional arguments that are passed to thePerformanceAnalyticsfunction. Search the underlying function to see what arguments can be passed through.

tq_performance_fun_options returns a list of compatiblePerformanceAnalytics functionsthat can be supplied to theperformance_fun argument.

Value

Returns data in the form of atibble object.

See Also

• tq_transmute() which can be used to calculate period returns from aset of stock prices. Usemutate_fun = periodReturn with the appropriate periodicitysuch asperiod = "monthly".

• tq_portfolio() which can be used to aggregate period returns frommultiple stocks to period returns for a portfolio.

• ThePerformanceAnalytics package, which contains the underlying functionsfor theperformance_fun argument. Additional parameters can be passed via....

Examples

# Load librarieslibrary(dplyr)# Use FANG data set# Get returns for individual stock components grouped by symbolRa <- FANG %>%    group_by(symbol) %>%    tq_transmute(adjusted, periodReturn, period = "monthly", col_rename = "Ra")# Get returns for SP500 as baselineRb <- "^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 baselineRaRb <- left_join(Ra, Rb, by = c("date" = "date"))##### Performance Metrics ###### View optionstq_performance_fun_options()# Get performance metricsRaRb %>%    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

Description

Aggregates a group of returns by asset into portfolio returns

Usage

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")

Arguments

Details

tq_portfolio is a wrapper forPerformanceAnalytics::Return.portfolio.The main advantage is the results are returned as atibble and thefunction can be used with thetidyverse.

assets_col andreturns_col are columns withindata that are usedto 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 containingall 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 unlistedassets default to a weight of zero.

• Multiple Portfolios: A three column tibble with portfolio index in the firstcolumn, assets in the second column, and weights in the third column. The tibblemust be grouped by portfolio index.

tq_repeat_df is a simple function that repeatsa data framen times row-wise (long-wise), and adds a new column for a portfolio index.The function is used to assist in Multiple Portfolio analyses, andis a useful precursor totq_portfolio.

Value

Returns data in the form of atibble object.

See Also

• tq_transmute() which can be used to get period returns.

• PerformanceAnalytics::Return.portfolio() which is the underlying functionthat specifies which parameters can be passed via...

Examples

# Load librarieslibrary(dplyr)# Use FANG data set# Get returns for individual stock componentsmonthly_returns_stocks <- FANG %>%    group_by(symbol) %>%    tq_transmute(adjusted, periodReturn, period = "monthly")##### Portfolio Aggregation Methods ###### Method 1: Use tq_portfolio with numeric vector of weightsweights <- 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 timesmult_monthly_returns_stocks <- tq_repeat_df(monthly_returns_stocks, n = 4)# 3B: Create weights table grouped by portfolio idweights <- 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 portfoliostq_portfolio(data = mult_monthly_returns_stocks,             assets_col = symbol,             returns_col = monthly.returns,             weights = weights_table,             col_rename = NULL,             wealth.index = FALSE)