Help for package stocks

Type:

Package

Title:

Stock Market Analysis

Version:

1.1.4

License:

GPL-3

Date:

2018-08-30

Author:

Dane R. Van Domelen

Maintainer:

Dane R. Van Domelen <vandomed@gmail.com>

Description:

Functions for analyzing stocks or other investments. Main features are loading and aligning historical data for ticker symbols, calculating performance metrics for individual funds or portfolios (e.g. annualized growth, maximum drawdown, Sharpe/Sortino ratio), and creating graphs. C++ code is used to improve processing speed where possible.

Depends:

rbenchmark, quantmod

Imports:

dvmisc, graphics, grDevices, Hmisc, lubridate, methods, RColorBrewer, Rcpp (≥ 0.12.15), stats, TTR, zoo

Suggests:

knitr, rmarkdown, pander, printr

LinkingTo:

Rcpp

RoxygenNote:

6.0.1

NeedsCompilation:

yes

Packaged:

2018-08-30 14:21:46 UTC; Dane

Repository:

CRAN

Date/Publication:

2018-08-31 04:30:03 UTC

Stock Market Analysis

Description

Details

Package:	stocks
Type:	Package
Version:	1.1.4
Date:	2018-08-30
License:	GPL-3

See CRAN documentation for full list of functions and the GitHub page for an overview of the package with some examples.

Author(s)

Dane R. Van Domelen
vandomed@gmail.com

References

Eddelbuettel, D. and Francois, R. (2011) Rcpp: Seamless R and C++ Integration. Journal of Statistical Software, 40(8), 1-18. http://www.jstatsoft.org/v40/i08/.

Eddelbuettel, D. (2013) Seamless R and C++ Integration with Rcpp. Springer, New York. ISBN 978-1-4614-6867-7.

Eddelbuettel, D. and Balamuta, J.J. (2017). Extending R with C++: A Brief Introduction to Rcpp. PeerJ Preprints 5:e3188v1. https://doi.org/10.7287/peerj.preprints.3188v1.

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Acknowledgment: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-0940903.

Beta for Last 50 Daily Gains

Description

Calculates beta for a ticker symbol based on the previous 50 daily gains.

Usage

beta_trailing50(ticker, bench = "SPY", ...)

Arguments

ticker

Character string with ticker symbols that Yahoo! Finance recognizes.

bench

Character string with ticker symbol for benchmark.

...

Arguments to pass to load_gains.

Value

Numeric value.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Calculate TLT's beta based on the previous 50 daily gains
beta_trailing50("TLT")

## End(Not run)

Backtest a Hedged Contango-Based Volatility Trading Strategy

Description

Implements the following strategy: Each day, hold XIV/SPXU (weighted for zero beta) if contango > xiv.spxu.cutpoint, hold VXX/UPRO (weighted for zero beta) if contango < vxx.upro.cutpoint, and hold cash otherwise. Perhaps not very useful since XIV closed on Feb. 20, 2018.

Usage

contango_hedged(contango, xiv.spxu.gains = NULL, vxx.upro.gains = NULL,
  xiv.spxu.cutpoint = 6.36, vxx.upro.cutpoint = 5.45,
  xiv.allocation = 0.46, vxx.allocation = 0.46, xiv.beta = NULL,
  vxx.beta = NULL, initial = 10000)

Arguments

contango

Numeric vector of contango values at the end of each trading day.

xiv.spxu.gains

2-column numeric matrix with gains for XIV and SPXU. Should have the same number of rows as contango and be date-shifted one value to the right. For example, the first row should have the XIV and SPXU gains for the day AFTER the first contango value.

vxx.upro.gains

2-column numeric matrix with gains for VXX and UPRO. Should have the same number of rows as contango and be date-shifted one value to the right. For example, the first row should have the VXX and UPRO gains for the day AFTER the first contango value.

xiv.spxu.cutpoint

Numeric value giving the contango cutpoint for XIV/SPXU position. For example, if xiv.spxu.cutpoint = 5, XIV/SPXU will be held whenever contango is greater than 5%.

vxx.upro.cutpoint

Numeric value giving the contango cutpoint for VXX/UPRO position. For example, if vxx.upro.cutpoint = -5, VXX/UPRO will be held whenever contango is less than -5%.

xiv.allocation

Numeric value specifying XIV allocation for XIV/SPXU position. For example, if set to 0.46, 46% is allocated to XIV and 54% to SPXU when contango > xiv.spxu.cutpoint.

vxx.allocation

Numeric value specifying VXX allocation for VXX/UPRO position. For example, if set to 0.46, 46% is allocated to VXX and 54% to UPRO when contango < vxx.upro.cutpoint.

xiv.beta

Numeric value specifying XIV's beta. If specified, the function figures out what xiv.allocation needs to be for zero-beta XIV/SPXU positions. For example, if set to 3.5, then 46.2% XIV/53.8% SPXU achieves zero beta.

vxx.beta

Numeric value indicating VXX's beta. If specified, the function figures out what vxx.allocation needs to be for zero-beta VXX/UPRO positions. For example, if set to -3.5, then 46.2% VXX/53.8% UPRO achieves zero beta.

initial

Numeric value giving the initial value of the portfolio.

Details

You can find historical contango values from The Intelligent Investor Blog. You can click the first link at http://investing.kuchita.com/2012/06/28/xiv-data-and-pricing-model-since-vix-futures-available-2004/ to download a zip file containing an Excel spreadsheet. Then, you will need to calculate whatever version of "contango" you prefer. I typically define contango as what percent higher the second-month VIX futures are acompared to the first-month futures, i.e. dividing the "2nd mth" column by the "1st mth" column, subtracting 1, and then multiplying by 100.

To load daily gains for XIV, SPXU, VXX, and UPRO, you can use load_gains, which uses the quantmod package to load data from Yahoo! Finance. You will have to specify the from and to inputs to match the date range for your contango values.

Value

List containing:

Character vector named holdings indicating what fund was held each day (XIV/SPXU, VXX/UPRO, or cash).
Numeric vector named port.gains giving the portfolio gain for each day, which will be 0 for days that cash was held and the weighted XIV/SPXU or VXX/UPRO gain for days that one of those positions was held.
Numeric vector named port.balances giving the portfolio balance each day.
Numeric value named trades giving the total number of trades executed.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Backtest a Simple Contango-Based Volatility Trading Strategy

Description

Simple strategy: Each day, hold XIV if contango > xiv.cutpoint, hold VXX if contango < vxx.cutpoint, and hold cash otherwise. Perhaps not very useful since XIV closed on Feb. 20, 2018.

Usage

contango_simple(contango, xiv.gains = NULL, vxx.gains = NULL,
  xiv.cutpoint = 0, vxx.cutpoint = -Inf, initial = 10000)

Arguments

contango

Numeric vector of contango values at the end of each trading day.

xiv.gains

Numeric vector of gains for XIV. Should be same length as contango and date-shifted one value to the right. For example, the first value of xiv.gains should be the XIV gain for the day AFTER the first contango value.

vxx.gains

Numeric vector of gains for VXX. Should be same length as contango and date-shifted one value to the right. For example, the first value of vxx.gains should be the VXX gain for the day AFTER the first contango value.

xiv.cutpoint

Numeric value giving the contango cutpoint for XIV, in percent.

vxx.cutpoint

Numeric value giving the contango cutpoint for VXX, in percent.

initial

Numeric value giving the initial value of the portfolio.

Details

I think the most common approach for contango-based volatility strategies is holding XIV (inverse volatility) when contango is above some value (e.g. 0%, 5%, or 10%), and holding cash otherwise. You can do that with this function by leaving vxx.cutpoint as -Inf. However, you may also want to hold VXX (volatility) when contango is below some value (e.g. 0%, -5%, -10%), also known as "backwardation". You can implement an XIV-only, VXX-only, or XIV and VXX strategy with this function.

To load daily gains for XIV and/or VXX, you can use load_gains, which uses the quantmod package [1] to load data from Yahoo! Finance. You will have to specify the from and to inputs to match the date range for your contango values.

Value

List containing:

Character vector named holdings indicating what fund was held each day (XIV, VXX, or cash).
Numeric vector named port.gains giving the portfolio gain for each day, which will be 0 for days that cash was held and the XIV or VXX gain for days that XIV or VXX was held.
Numeric vector named port.balances giving the portfolio balance each day.
Numeric value named trades giving the total number of trades executed.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Convert Gain from One Time Interval to Another

Description

For example, you can use this function to figure out that an 8 trading days is 31.9

Usage

convert_gain(gain, units.in = 1, units.out = 1)

Arguments

gain

Numeric value specifying a gain, e.g. 0.005 for 0.5 a vector of gains.

units.in

Numeric value gving the time period over which the gain was achieved.

units.out

Numeric value giving the time period you want to convert to.

Value

Numeric value or vector.

Examples

# Calculate annualized gain for an 8% gain over a 70-day period
convert_gain(gain = 0.08, units.in = 70, units.out = 252)

# Calculate the annual growth rate of a fund that gains 0.02% per day
convert_gain(gain = 0.0002, units.in = 1, units.out = 252)

# Calculate the annual growth rate of a fund that gains 1% per week
convert_gain(gain = 0.01, units.in = 1, units.out = 52)

# You invest in AAPL and gain 0.5% in 17 business days. Express as a 5-year
# growth rate.
convert_gain(gain = 0.005, units.in = 17, units.out = 252 * 5)

# Your portfolio has tripled in a 13-year period. Calculate your average 
# annual gain.
convert_gain(gain = 2, units.in = 13, units.out = 1)

Convert Daily Gain to X-year Gain

Description

For example, you can use this function to calculate that an investment that gains 0.1 days).

Usage

daily_yearly(gain, years = 1)

Arguments

gain

Numeric value specifying a gain, e.g. 0.005 for 0.5 a vector of gains.

years

Numeric value.

Value

Numeric value or vector.

Examples

# Calculate annual gain for an investment that gains 0.1% per day
daily_yearly(gain = 0.001)

# Calculate 5-year gains corresponding to various daily gains
daily_yearly(gain = seq(0, 0.001, 0.0001), years = 5)

Lagged Differences (Alternate Implementation)

Description

Calculates differences between subsequent (or lagged) elements of a vector. Very similar to diff, but written in C++.

Usage

diffs(x, lag = 1L)

Arguments

x

Numeric vector.

lag

Numeric value (e.g. 2 for differences between 1st and 3rd element, 2nd and 4th, ...).

Value

Numeric vector.

Examples

# Generate 1 million values from Poisson(3) distribution
x <- rpois(100000, 3)

# Calculate vector of differences between subsequent values
y <- diffs(x)

# Could get same result from base R function diff
z <- diff(x)
all.equal(y, z)

# But diffs is faster
benchmark(diffs(x), diff(x), replications = 100)

Scatterplot of Investment Gains

Description

Useful for visualizing relationship between one (or several) investments and a benchmark. First fund in tickers, gains, or prices is used as the benchmark.

Usage

gains_graph(tickers = NULL, ..., gains = NULL, prices = NULL,
  orders = 1, add.plot = FALSE, include.legend = TRUE, colors = NULL,
  lty = NULL, plot.list = NULL, points.list = NULL, legend.list = NULL,
  pdf.list = NULL, bmp.list = NULL, jpeg.list = NULL, png.list = NULL,
  tiff.list = NULL)

Arguments

tickers

Character vector of ticker symbols that Yahoo! Finance recognizes, if you want to download data on the fly.

...

Arguments to pass along with tickers to load_gains.

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

orders

Numeric vector specifying the orders of linear regression models for each y-axis investment. Set to 1 for simple linear regression, 2 for linear regression with first- and second-order terms, and so on.

add.plot

Logical value for whether to add plot data to current plot frame rather than open a new one.

include.legend

Logical value.

colors

Character vector of colors for each curve.

lty

Numeric vector specifying line types for each curve.

plot.list

List of arguments to pass to plot.

points.list

List of arguments to pass to points.

legend.list

List of arguments to pass to legend.

pdf.list

List of arguments to pass to pdf.

bmp.list

List of arguments to pass to bmp.

jpeg.list

List of arguments to pass to jpeg.

png.list

List of arguments to pass to png.

tiff.list

List of arguments to pass to tiff.

Value

In addition to the graph, a list containing fitted linear regression models returned by lm for each investment vs. the benchmark.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Plot daily gains for SSO and UPRO vs. VFINX
fig <- gains_graph(c("VFINX", "SSO", "UPRO"))

## End(Not run)

Convert Gains to Prices

Description

Calculates prices based on initial balance and vector of gains.

Usage

gains_prices(gains, initial = 10000)

Arguments

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

initial

Numeric value.

Value

Numeric value if gains is a vector, numeric matrix if gains is a matrix.

Examples

# Simulate daily gains over a 5-year period
set.seed(123)
gains <- rnorm(n = 252 * 5, mean = 0.001, sd = 0.02)

# Plot balance over time if initial balance is $10,000
prices <- gains_prices(gains)
plot(prices)

Calculate Growth Rate From a Vector of Gains

Description

The formula is simply: prod(gains + 1) - 1. If units.rate is specified, then it converts to x-unit growth rate.

Usage

gains_rate(gains, units.rate = NULL)

Arguments

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

units.rate

Numeric value specifying the number of units for growth rate calculation, if you want something other than total growth. For annualized growth rate, set to 252 if gains has daily gains, 12 if gains has monthly gains, etc.

Value

Numeric value if gains is a vector, numeric matrix if gains is a matrix.

Examples

# Create vector of daily gains for a hypothetical stock
daily.gains <- c(-0.02, -0.01, 0.01, 0.02, 0.01)

# Overall growth is 0.95%
gains_rate(daily.gains)

# Average daily growth is 0.19%
gains_rate(daily.gains, 1)

# Corresponds to 61.0% annual growth
gains_rate(daily.gains, 252)

Graph Investment Growth

Description

Useful for comparing performance of investments over time.

Usage

growth_graph(tickers = NULL, ..., gains = NULL, prices = NULL,
  initial = "10k", add.plot = FALSE, colors = NULL, lty = NULL,
  plot.list = NULL, points.list = NULL, grid.list = NULL,
  legend.list = NULL, pdf.list = NULL, bmp.list = NULL,
  jpeg.list = NULL, png.list = NULL, tiff.list = NULL)

Arguments

tickers

Character vector of ticker symbols that Yahoo! Finance recognizes, if you want to download data on the fly.

...

Arguments to pass along with tickers to load_gains.

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

initial

Numeric value specifying what value to scale initial prices to. Can also be character string ending in "k", e.g. "10k" to graph growth of $10k without all the 0's.

add.plot

Logical value for whether to add plot data to current plot frame rather than open a new one.

colors

Character vector of colors for each curve.

lty

Numeric vector specifying line types for each curve.

plot.list

List of arguments to pass to plot.

points.list

List of arguments to pass to points.

grid.list

List of arguments to pass to grid.

legend.list

List of arguments to pass to legend.

pdf.list

List of arguments to pass to pdf.

bmp.list

List of arguments to pass to bmp.

jpeg.list

List of arguments to pass to jpeg.

png.list

List of arguments to pass to png.

tiff.list

List of arguments to pass to tiff.

Value

In addition to the graph, a list containing:

Numeric matrix named prices with prices for each investment.
Numeric vector named means with mean of gains for each investment.
Numeric matrix named corr.matrix with correlation matrix for gains for each investment.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Plot growth of $10k in VFINX and BRK-B
fig <- growth_graph(c("VFINX", "BRK-B"))

## End(Not run)

High-Yield ETFs from ETFdb.com and Inception Dates

Description

High-Yield ETFs from ETFdb.com and Inception Dates

Source

http://etfdb.com/etfdb-category/high-yield-bonds/#etfs&sort_name=assets_under_management&sort_order=desc&page=2

Largest 100 Market Cap ETFs (as of 3/2/18) and Inception Dates

Description

Largest 100 Market Cap ETFs (as of 3/2/18) and Inception Dates

Source

http://etfdb.com/compare/market-cap/

Download and Align Gains for a Set of Tickers

Description

Downloads and aligns historical investment gains for specified tickers from Yahoo! Finance, using the quantmod package.

Usage

load_gains(tickers, intercepts = NULL, slopes = NULL, from = "1950-01-01",
  to = Sys.Date(), time.scale = "daily", preto.days = NULL,
  prefrom.days = NULL, earliest = FALSE, latest = FALSE)

Arguments

tickers

Character vector with ticker symbols that Yahoo! Finance recognizes.

intercepts

Numeric vector of values to add to daily gains for each ticker.

slopes

Numeric vector of values to multiply daily gains for each ticker by. Slopes are multiplied prior to adding intercepts.

from

Date or character string (e.g. "2015-01-15".

to

Date or character string (e.g. "2016-01-30").

time.scale

Character string controlling time frame for gains. Choices are "daily", "monthly", and "yearly".

preto.days

Numeric value. If specified, function returns gains for preto.days trading days prior to to. To illustrate, to load the most recent 50 daily gains, you would leave to and time.scale as the defaults and set preto.days = 50.

prefrom.days

Numeric value. If specified, function returns gains for prefrom.days trading days prior to from. Useful when you want to test a trading strategy starting on a particular date, but the strategy requires data leading up to that date (e.g. trailing beta).

earliest

Logical value for whether to retain only the subset of tickers with data going the furthest back. Set to FALSE if you want all tickers retained and gains over their mutual lifetimes.

latest

Logical value for whether to retain only the subset of tickers with data going the furthest forward, e.g. dropping funds that were discontinued at some point.

Details

In aligning historical prices, dates on which not all funds have data are simply dropped. Messages are printed indicating which dates are dropped for which tickers.

Value

Numeric matrix.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Load gains for Netflix and Amazon over their mutual lifetimes
gains <- load_gains(c("NFLX", "AMZN"))

## End(Not run)

Download and Align Historical Prices for a Set of Tickers

Description

Downloads and aligns historical prices for specified tickers from Yahoo! Finance, using the quantmod package.

Usage

load_prices(tickers, intercepts = NULL, slopes = NULL,
  from = "1950-01-01", to = Sys.Date(), time.scale = "daily",
  preto.days = NULL, prefrom.days = NULL, initial = NULL,
  earliest = FALSE, latest = FALSE)

Arguments

tickers

Character vector with ticker symbols that Yahoo! Finance recognizes.

intercepts

Numeric vector of values to add to daily gains for each ticker.

slopes

Numeric vector of values to multiply daily gains for each ticker by. Slopes are multiplied prior to adding intercepts.

from

Date or character string (e.g. "2015-01-15".

to

Date or character string (e.g. "2016-01-30").

time.scale

Character string controlling time frame for gains. Choices are "daily", "monthly", and "yearly".

preto.days

prefrom.days

initial

Numeric value specifying what value to scale initial prices to.

earliest

Logical value for whether to retain only the subset of tickers with data going the furthest back. Set to FALSE if you want all tickers retained and gains over their mutual lifetimes.

latest

Logical value for whether to retain only the subset of tickers with data going the furthest forward, e.g. dropping funds that were discontinued at some point.

Details

In aligning historical prices, dates on which not all funds have data are simply dropped. Messages are printed indicating which dates are dropped for which tickers.

Value

Numeric matrix.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Load prices for Netflix and Amazon over their mutual lifetimes
prices <- load_prices(c("NFLX", "AMZN"))

## End(Not run)

Maximum Drawdown

Description

Calculates maximum drawdown from vector of closing prices, highs and lows, or gains.

Usage

mdd(prices = NULL, highs = NULL, lows = NULL, gains = NULL,
  indices = FALSE)

Arguments

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

highs

Numeric vector of daily high prices.

lows

Numeric vector of daily low prices.

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

indices

Logical value for whether to include indices for when the maximum drawdown occurred.

Value

Numeric value, vector, or matrix depending on indices and whether there is 1 fund or several.

Examples

## Not run: 
# Simulate minute-to-minute stock gains over a 2-year period
set.seed(123)
stock.gains <- rnorm(6.5 * 60 * 252 * 2, 0.000005, 0.001)

# Convert to stock prices assuming an initial price of $9.50 per share
stock.prices <- gains_prices(gains = stock.gains, initial = 9.50)

# Plot minute-to-minute stock prices (200k data point, may be slow)
plot(stock.prices)

# Maximum drawdown based on stock prices
mdd(prices = stock.prices)

# Same answer using gains rather than prices
mdd(gains = stock.gains)

## End(Not run)

Calculate Various Performance Metrics

Description

Useful for comparing metrics for several investments. The first investment is used as the benchmark if requested metrics require one.

Usage

metrics(tickers = NULL, ..., gains = NULL, prices = NULL,
  perf.metrics = c("mean", "sd", "growth", "cagr", "mdd", "sharpe", "sortino",
  "alpha", "beta", "r.squared", "pearson", "spearman", "auto.pearson",
  "auto.spearman"))

Arguments

tickers

Character vector of ticker symbols that Yahoo! Finance recognizes, if you want to download data on the fly.

...

Arguments to pass along with tickers to load_gains.

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

perf.metrics

Character vector specifying metrics to calculate.

Value

List containing:

Numeric matrix named perf.metrics with performance metrics.
Numeric matrix named cor.mat with correlation matrix for gains for the various investments.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Calculate performance metrics for SSO and UPRO, using SPY as benchmark 
# for alpha and beta
metrics1 <- metrics(tickers = c("SPY", "SSO", "UPRO"))

## End(Not run)

Graph Performance Metric for Various Investments

Description

Useful for visualizing the performance of a group of investments. The first investment is used as the benchmark if the requested metric requires one.

Usage

onemetric_graph(tickers = NULL, ..., gains = NULL, prices = NULL,
  y.metric = "cagr", add.plot = FALSE, sort.tickers = TRUE,
  plot.list = NULL, points.list = NULL, axis.list = NULL,
  pdf.list = NULL, bmp.list = NULL, jpeg.list = NULL, png.list = NULL,
  tiff.list = NULL)

Arguments

tickers

Character vector of ticker symbols that Yahoo! Finance recognizes, if you want to download data on the fly.

...

Arguments to pass along with tickers to load_gains.

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

y.metric

Character string specifying y-axis performance metric. Choices are:

"mean" or "sd" for mean or standard deviation of gains.

"growth" or "cagr" for total or annualized growth.

"mdd" for maximum drawdown.

"sharpe" or "sortino" for Sharpe or Sortino ratio.

"alpha", "beta", or "r.squared" for those metrics from a fitted linear regression on benchmark fund.

"pearson" or "spearman" for Pearson or Spearman correlation with benchmark fund.

"auto.pearson" or "auto.spearman" for Pearson or Spearman autocorrelation, defined as the correlation between subsequent gains.

add.plot

Logical value for whether to add plot data to current plot frame rather than open a new one.

sort.tickers

Logical value for whether to sort investments in decreasing order of the performance metric.

plot.list

List of arguments to pass to plot.

points.list

List of arguments to pass to points.

axis.list

List of arguments to pass to axis.

pdf.list

List of arguments to pass to pdf.

bmp.list

List of arguments to pass to bmp.

jpeg.list

List of arguments to pass to jpeg.

png.list

List of arguments to pass to png.

tiff.list

List of arguments to pass to tiff.

Value

In addition to the graph, a data frame containing the performance metric for each investment.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Compare annualized growth for VFINX, SSO, and UPRO
fig <- onemetric_graph(tickers = c("VFINX", "SSO", "UPRO"), 
                       plot.list = list(ylim = c(0, 50)))

## End(Not run)

Graph Performance Metric Over Time for Various Investments

Description

Useful for visualizing the performance of a group of investments over time. The first investment is used as the benchmark if the requested metric requires one.

Usage

onemetric_overtime_graph(tickers = NULL, ..., gains = NULL, prices = NULL,
  y.metric = "cagr", window.units = 50, add.plot = FALSE, colors = NULL,
  lty = NULL, plot.list = NULL, points.list = NULL, legend.list = NULL,
  pdf.list = NULL, bmp.list = NULL, jpeg.list = NULL, png.list = NULL,
  tiff.list = NULL)

Arguments

tickers

Character vector of ticker symbols that Yahoo! Finance recognizes, if you want to download data on the fly.

...

Arguments to pass along with tickers to load_gains.

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

y.metric

Character string specifying y-axis performance metric. Choices are:

"mean" or "sd" for mean or standard deviation of gains.

"growth" or "cagr" for total or annualized growth.

"mdd" for maximum drawdown.

"sharpe" or "sortino" for Sharpe or Sortino ratio.

"alpha", "beta", or "r.squared" for those metrics from a fitted linear regression on benchmark fund.

"pearson" or "spearman" for Pearson or Spearman correlation with benchmark fund.

"auto.pearson" or "auto.spearman" for Pearson or Spearman autocorrelation, defined as the correlation between subsequent gains.

window.units

Numeric value specifying the width of the moving window.

add.plot

Logical value for whether to add plot data to current plot frame rather than open a new one.

colors

Character vector of colors for each curve.

lty

Numeric vector specifying line types for each curve.

plot.list

List of arguments to pass to plot.

points.list

List of arguments to pass to points.

legend.list

List of arguments to pass to legend.

pdf.list

List of arguments to pass to pdf.

bmp.list

List of arguments to pass to bmp.

jpeg.list

List of arguments to pass to jpeg.

png.list

List of arguments to pass to png.

tiff.list

List of arguments to pass to tiff.

Value

In addition to the graph, a numeric matrix containing the performance metric over time for each investment.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Plot BRK-B's 50-day alpha over time since the start of 2016
fig <- onemetric_overtime_graph(tickers = c("VFINX", "BRK-B"), 
                                y.metric = "alpha", 
                                from = "2016-01-01")

## End(Not run)

Lagged Proportion Changes

Description

Calculates proportion changes between subsequent (or lagged) elements of a vector.

Usage

pchanges(x, lag = 1L)

Arguments

x

Numeric vector.

lag

Numeric value (e.g. 2 for differences between 1st and 3rd element, 2nd and 4th, ...).

Value

Numeric vector.

Examples

# Generate 10 values from N(0, 1)
x <- rnorm(10)

# Calculate vector of proportion changes between subsequent values
(y <- pchanges(x))

# Equivalent base R computation
len <- length(x)
p1 <- x[2: len] 
p2 <- x[1: (len - 1)] 
y2 <- p1 / p2 - 1
all.equal(y, y2)

Lagged Proportion Differences

Description

Calculates proportion differences between subsequent (or lagged) elements of a vector.

Usage

pdiffs(x, lag = 1L)

Arguments

x

Numeric vector.

lag

Numeric value (e.g. 2 for differences between 1st and 3rd element, 2nd and 4th, ...).

Value

Numeric vector.

Examples

# Generate 10 values from N(0, 1)
x <- rnorm(10)

# Calculate vector of proportion differences between subsequent values
(y <- pdiffs(x))

# Equivalent base R computation
len <- length(x) 
p1 <- x[2: len]
p2 <- x[1: (len - 1)]
y2 <- (p1 - p2) / (0.5 * (p1 + p2))
all.equal(y, y2)

Convert Prices to Gains

Description

Calculates gains based on vector or matrix of prices.

Usage

prices_gains(prices)

Arguments

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

Value

Numeric vector or matrix.

Examples

## Not run: 
# Load 2017 prices for Netflix and Amazon, and calculate growth of $10k
prices <- load_prices(c("NFLX", "AMZN"), initial = 1000)

# Calculate gains
gains <- prices_gains(prices)

## End(Not run)

Calculate Growth Rate From a Vector of Prices

Description

The formula is simply: prices[length(prices)] / prices[1] - 1. If units.rate is specified, then it converts to x-unit growth rate.

Usage

prices_rate(prices, units.rate = NULL)

Arguments

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

units.rate

Numeric value specifying the number of units for growth rate calculation, if you want something other than total growth. For annualized growth rate, set to 252 if prices has daily prices, 12 if prices has monthly prices, etc.

Value

Numeric value if prices is a vector, numeric matrix if prices is a matrix.

Examples

# Create vector of daily closing prices for a hypothetical stock
prices <- c(100.4, 98.7, 101.3, 101.0, 100.9)

# Overall growth is 0.50%
prices_rate(prices)

# Average daily growth is 0.12%
prices_rate(prices, 1)

# Corresponds to 36.7% annualized growth
prices_rate(prices, 252)

Ratios of Subsequent Elements in a Vector

Description

Calculates vector of ratios of a vector, i.e. ratio of x[2] to x[1], ratio of x[3] to x[2], and so forth.

Usage

ratios(x)

Arguments

x

Numeric vector.

Value

Numeric vector.

Examples

# Generate 10 values from N(0, 1)
x <- rnorm(10)

# Calculate vector of ratios
(y <- ratios(x))

# Slower base R computation
len <- length(x)
y2 <- x[2: len] / x[1: (len - 1)]
all.equal(y, y2)

Risk-Return Ratio

Description

Calculates risk-return ratio, defined as growth rate divided by maximum drawdown.

Usage

rrr(prices = NULL, gains = NULL)

Arguments

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

Value

Numeric value or vector.

Examples

# Simulate daily gains over a 5-year period
set.seed(123)
stock.gains <- rnorm(252 * 5, 0.0005, 0.01)

# Convert to daily balances assuming an initial balance of $10,000
daily.balances <- gains_prices(stock.gains + 1)

# Total return is about 1.23
daily.balances[length(daily.balances)] / daily.balances[1] - 1

# Maximum drawdown is about 0.19
mdd(prices = daily.balances)

# Ratio of these two is about 6.48
(daily.balances[length(daily.balances)] / daily.balances[1] - 1) / 
mdd(daily.balances)

# Easier to calculate using rrr function
rrr(daily.balances)

Sector SPDR ETFs and Inception Dates

Description

Sector SPDR ETFs and Inception Dates

Source

http://www.sectorspdr.com/sectorspdr/sectors/performance

Sharpe Ratio

Description

Calculates Sharpe ratio from vector of gains or prices. The formula is: (mean(gains) - rf) / sd(gains), where rf is some risk-free rate of return.

Usage

sharpe(gains = NULL, prices = NULL, rf = 0)

Arguments

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

rf

Numeric value.

Value

Numeric value.

Examples

# Simulate daily gains over a 5-year period
set.seed(123)
stock.gains <- rnorm(252 * 5, 0.0005, 0.01)

# Calculate Sharpe ratio using risk-free return of 0
sharpe(stock.gains)

Sortino Ratio

Description

Calculates Sortino ratio from vector of gains or prices. The formula is: (mean(gains) - rf) / sd(gains[gains < 0]), where rf is some risk-free rate of return.

Usage

sortino(gains = NULL, prices = NULL, rf = 0)

Arguments

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

rf

Numeric value.

Value

Numeric value or vector.

Examples

# Simulate daily gains over a 5-year period
set.seed(123)
stock.gains <- rnorm(252 * 5, 0.0005, 0.01)

# Calculate Sortino ratio using risk-free return of 0
sortino(stock.gains)

Backtest a Fixed-Allocation Trading Strategy

Description

Implements a trading strategy aimed at maintaining a fixed allocation to each of several funds, rebalancing when the effective allocations deviate too far from the targets.

Usage

targetall(tickers = NULL, intercepts = NULL, slopes = NULL, ...,
  tickers.gains = NULL, target.alls = NULL, tol = 0.05,
  rebalance.cost = 0, initial = 10000)

Arguments

tickers

Character vector of ticker symbols that Yahoo! Finance recognizes, if you want to download data on the fly.

intercepts

Numeric vector of values to add to daily gains for each ticker.

slopes

Numeric vector of values to multiply daily gains for each ticker by. Slopes are multiplied prior to adding intercepts.

...

Arguments to pass along with tickers to load_gains.

tickers.gains

Numeric matrix of gains, where each column has gains for a particular fund.

target.alls

Numeric vector specifying target allocations to each fund. If unspecified, equal allocations are used (e.g. 1/3, 1/3, 1/3 if there are 3 funds).

tol

Numeric value indicating how far the effective allocations can drift away from the targets before rebalancing.

rebalance.cost

Numeric value specifying total cost of each rebalancing trade.

initial

Numeric value specifying what value to scale initial prices to.

Value

List containing:

Numeric matrix named fund.balances giving fund balances over time.
Numeric value named rebalance.count giving the number of rebalancing trades executed.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Backtest equal-allocation UPRO/VBLTX/VWEHX strategy
port <- targetall(tickers = c("UPRO", "VBLTX", "VWEHX"))
plot(port$fund.balances[, "Portfolio"])

## End(Not run)

Backtest a Two-Fund Strategy that Targets a Certain Beta

Description

Implements a two-fund strategy where allocations to each fund are adjusted to maintain some user-specified portfolio beta. For example, you could back-test a zero-beta (i.e. market neutral) UPRO/VBLTX strategy using this function.

Usage

targetbeta_twofunds(tickers = NULL, intercepts = NULL, slopes = NULL, ...,
  benchmark.ticker = NULL, reference.tickers = NULL, tickers.gains = NULL,
  benchmark.gains = NULL, reference.gains = NULL, target.beta = 0,
  tol = 0.15, window.units = 50, failure.method = "closer",
  maxall.tol = tol - 0.05, initial = 10000)

Arguments

tickers

Character vector specifying 2 ticker symbols that Yahoo! Finance recognizes, if you want to download data on the fly.

intercepts

Numeric vector of values to add to daily gains for each ticker.

slopes

Numeric vector of values to multiply daily gains for each ticker by. Slopes are multiplied prior to adding intercepts.

...

Arguments to pass along with tickers to load_gains.

benchmark.ticker

Character string specifying ticker symbol for benchmark index for calculating beta. If unspecified, the first fund in tickers is used as the benchmark.

reference.tickers

Character vector of ticker symbols to include on graph as data points for comparative purposes.

tickers.gains

Numeric matrix of gains, where each column has gains for a particular fund.

benchmark.gains

Numeric vector of gains for the benchmark index for calculating beta. If unspecified, the first fund in tickers.gains is used as the benchmark.

reference.gains

Numeric vector or matrix of gains for funds to include on graph as data points for comparative purposes.

target.beta

Numeric value.

tol

Numeric value specifying how far the effective portfolio beta has to deviate from target.beta to trigger a rebalancing trade.

window.units

Numeric value specifying the width of the trailing moving window used to estimate each fund's beta.

failure.method

Character string or vector specifying method(s) to use when fund betas are such that the target portfolio beta cannot be achieved. Choices are "cash", "fund1", "fund2", "fund1.maxall", "fund2.maxall", "inverse1", "inverse2", and "closer". See Details.

maxall.tol

Numeric value specifying tolerance to use when implementing the "fund1.maxall" or "fund2.maxall" failure method. To illustrate, if target.beta = 0, fund 1 has a current beta of 1, fund 2 has a current beta of 0.25, failure.method = "fund2.maxall", and maxall.tol = 0.1, a trade will be triggered that results in 40% fund 2 and 60% cash. The portfolio beta is 0.4 * 0.25 = 0.1. The reason you might want maxall.tol to be less than tol is to avoid frequently triggering another trade on the very next day, as fund 2's beta changes a little and moves the portfolio beta outside of [target.beta - tol, target.beta + tol].

initial

Numeric value specifying what value to scale initial prices to.

Details

The general implementation is as follows. Beta for each of the two funds is estimated based on the first window.units gains. Initial allocations are selected to achieve portfolio beta of target.beta. If that is not possible - for example, if target.beta = 0 and both funds have positive beta - then the action taken depends on what method is selected through the failure.method input (details below).

Assuming the target beta is attainable, the function moves over 1 day, and applies each fund's gains for that day. It then re-calculates each fund's beta based on the window.units-width interval, and determines the effective portfolio beta based on fund allocations and betas. If the effective beta is outside of [target.beta - tol, target.beta + tol], a rebalancing trade is triggered. As before, if the target beta cannot be achieved, certain actions are taken depending on the selected method.

When outside of a trade because the target beta could not be achieved, the function attempts to rebalance each time it shifts over to a new day, regardless of the effective portfolio beta.

When failure.method = "cash", the entire portfolio balance is allocated to cash when the target beta cannot be achieved.

When failure.method = "fund1" (or "fund2"), the entire portfolio balance is allocated to the first (or second) fund when the target beta cannot be achieved.

When failure.method = "fund1.maxall" (or "fund2.maxall"), when the target beta cannot be achieved, fund 1 (or fund 2) is combined with cash, with the fund 1 (fund 2) allocation as high as possible while staying within maxall.tol of target.beta.

When failure.method = "inverse1" (or "inverse2"), an inverse version of the first (or second) fund is used when the target beta cannot be achieved. In many cases where the target beta cannot be achieved with the two funds, it can be achieved with an inverse version of one and the other. If the target beta still cannot be achieved, the entire portfolio balance is allocated to cash.

When failure.method = "closer", the entire portfolio balance is allocated to whichever fund has a beta closer to target.beta.

Value

For each method, a 4-element list containing:

Numeric matrix named fund.balances giving fund balances over time.
Numeric matrix named fund.betas giving fund betas over time.
Numeric vector named effective.betas giving effective portfolio beta over time.
Numeric value named trades giving the total number of trades executed.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Backtest zero-beta UPRO/VBLTX strategy
beta0 <- targetbeta_twofunds(tickers = c("UPRO", "VBLTX"), target.beta = 0)
plot(beta0$fund.balances[, "Portfolio"])

## End(Not run)

Graph One Performance Metric vs. Another for Three-Fund Portfolio as Allocation Varies

Description

Useful for visualizing performance of three-fund portfolios, typically by plotting a measure of growth vs. a measure of volatility. Only works for one three-fund set at a time.

Usage

threefunds_graph(tickers = NULL, intercepts = NULL, slopes = NULL, ...,
  benchmark.tickers = NULL, reference.tickers = NULL,
  tickers.gains = NULL, benchmark.gains = NULL, reference.gains = NULL,
  step.data = 0.0025, step.points = 0.1, step.curves = 0.2,
  x.metric = "sd", y.metric = "mean", tickerlabel.offsets = NULL,
  reflabel.offsets = NULL, add.plot = FALSE, colors = NULL, lty = NULL,
  plot.list = NULL, points.list = NULL, text.list = NULL,
  pdf.list = NULL, bmp.list = NULL, jpeg.list = NULL, png.list = NULL,
  tiff.list = NULL)

Arguments

tickers

Character vector of ticker symbols that Yahoo! Finance recognizes, if you want to download data on the fly.

intercepts

Numeric vector of values to add to daily gains for each ticker.

slopes

Numeric vector of values to multiply daily gains for each ticker by. Slopes are multiplied prior to adding intercepts.

...

Arguments to pass along with tickers to load_gains.

benchmark.tickers

Character vector of length 1 or 2 indicating ticker symbols for benchmark indexes. Only used if x.metric and/or y.metric require benchmark indexes to calculate. For example, to plot correlation with SPY on the x-axis and correlation with TLT on the y-axis, set x.metric = "pearson", y.metric = "pearson2" (i.e. Pearson correlation with 2nd benchmark), and benchmark.tickers = c("SPY", "TLT").

reference.tickers

Character vector of ticker symbols to include on graph as data points for comparative purposes.

tickers.gains

Numeric matrix of gains, where each column has gains for a particular fund.

benchmark.gains

Numeric vector or matrix of gains for 1 or 2 benchmark indexes. Only used if x.metric and/or y.metric require benchmark indexes to calculate. For example, to plot correlation with SPY on the x-axis and correlation with TLT on the y-axis, set x.metric = "pearson" and y.metric = "pearson2", and input benchmark.gains as a 2-column matrix of gains for SPY and TLT.

reference.gains

Numeric vector or matrix of gains for funds to include on graph as data points for comparative purposes.

step.data

Numeric value specifying allocation increments for plotting curves.

step.points

Numeric value specifying allocation increments for adding data points on top of curves. Set to NULL to suppress data points.

step.curves

Numeric value specifying allocation increments for first fund in each set.

x.metric

Character string specifying x-axis performance metric. Choices are:

"mean" or "sd" for mean or standard deviation of gains

"growth" or "cagr" for total or annualized growth

"mdd" for maximum drawdown

"sharpe" or "sortino" for Sharpe or Sortino ratio

"alpha", "beta", or "r.squared" for those metrics from a fitted linear regression on benchmark fund

"pearson" or "spearman" for Pearson or Spearman correlation with benchmark fund

"alpha2", "beta2", "r.squared2", "pearson2", or "spearman2" for same as previously described, but using the second benchmark index

"auto.pearson" or "auto.spearman" for Pearson or Spearman autocorrelation, defined as the correlation between subsequent gains

"allocation" for allocation to first fund in each pair.

y.metric

Same as x.metric, but for the y-axis

tickerlabel.offsets

Either a numeric vector of length 2 giving the x- and y-axis offsets for all ticker labels, or a 2-column matrix where each row gives the x- and y-axis offsets for a ticker.

reflabel.offsets

Either a numeric vector of length 2 giving the x- and y-axis offsets for all reference ticker labels, or a 2-column matrix where each row gives the x- and y-axis offsets for a reference ticker.

add.plot

Logical value for whether to add plot data to current plot frame rather than open a new one.

colors

Character vector of colors for each curve.

lty

Numeric vector specifying line types for each curve.

plot.list

List of arguments to pass to plot.

points.list

List of arguments to pass to points.

text.list

List of arguments to pass to text.

pdf.list

List of arguments to pass to pdf.

bmp.list

List of arguments to pass to bmp.

jpeg.list

List of arguments to pass to jpeg.

png.list

List of arguments to pass to png.

tiff.list

List of arguments to pass to tiff.

Value

In addition to the graph, a list containing:

List named portfolio.xy where each element is a two-column matrix of x- and y-axis values for a curve.
Numeric vector named means with mean gains for each fund.
Numeric matrix named corr.matrix with a correlation matrix for gains for each fund.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Plot mean vs. SD for UPRO/VBLTX/VWEHX portfolio, and compare to VFINX and 
# BRK-B
fig <- threefunds_graph(tickers = c("VWEHX", "VBLTX", "UPRO"), 
                        reference.tickers = c("VFINX", "BRK-B"))

## End(Not run)

Get Yahoo! Finance Start/End Dates for Tickers

Description

Typically useful for determining a time period over which to compare several funds.

Usage

ticker_dates(tickers, from = "1950-01-01", to = Sys.Date())

Arguments

tickers

Character vector with ticker symbols that Yahoo! Finance recognizes.

from

Date or character string (e.g. "2015-01-15".

to

Date or character string (e.g. "2016-01-30").

Value

Data frame with ticker symbol, start date, end date, and number of trading days for each ticker.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# See what dates are available for Apple and Amazon
ticker_dates(c("AAPL", "AMZN"))

## End(Not run)

Graph One Performance Metric vs. Another for Two-Fund Portfolios as Allocation Varies

Description

Useful for visualizing performance of two-fund portfolios, typically by plotting a measure of growth vs. a measure of volatility. First two investments are used as the first two-fund pair, next two as the second two-fund pair, and so on.

Usage

twofunds_graph(tickers = NULL, intercepts = NULL, slopes = NULL, ...,
  benchmark.tickers = NULL, reference.tickers = NULL,
  tickers.gains = NULL, benchmark.gains = NULL, reference.gains = NULL,
  step.data = 0.0025, step.points = 0.1, x.metric = "sd",
  y.metric = "mean", tickerlabel.offsets = NULL, reflabel.offsets = NULL,
  add.plot = FALSE, colors = NULL, lty = NULL, plot.list = NULL,
  points.list = NULL, text.list = NULL, pdf.list = NULL,
  bmp.list = NULL, jpeg.list = NULL, png.list = NULL, tiff.list = NULL)

Arguments

tickers

Character vector of ticker symbols that Yahoo! Finance recognizes, if you want to download data on the fly.

intercepts

Numeric vector of values to add to daily gains for each ticker.

slopes

Numeric vector of values to multiply daily gains for each ticker by. Slopes are multiplied prior to adding intercepts.

...

Arguments to pass along with tickers to load_gains.

benchmark.tickers

reference.tickers

Character vector of ticker symbols to include on graph as data points for comparative purposes.

tickers.gains

Numeric matrix of gains, where each column has gains for a particular fund.

benchmark.gains

reference.gains

Numeric vector or matrix of gains for funds to include on graph as data points for comparative purposes.

step.data

Numeric value specifying allocation increments for plotting curves.

step.points

Numeric value specifying allocation increments for adding data points on top of curves. Set to NULL to suppress data points.

x.metric

Character string specifying x-axis performance metric. Choices are:

"mean" or "sd" for mean or standard deviation of gains

"growth" or "cagr" for total or annualized growth

"mdd" for maximum drawdown

"sharpe" or "sortino" for Sharpe or Sortino ratio

"alpha", "beta", or "r.squared" for those metrics from a fitted linear regression on benchmark fund

"pearson" or "spearman" for Pearson or Spearman correlation with benchmark fund

"alpha2", "beta2", "r.squared2", "pearson2", or "spearman2" for same as previously described, but using the second benchmark index

"auto.pearson" or "auto.spearman" for Pearson or Spearman autocorrelation, defined as the correlation between subsequent gains

"allocation" for allocation to first fund in each pair.

y.metric

Same as x.metric, but for the y-axis

tickerlabel.offsets

Either a numeric vector of length 2 giving the x- and y-axis offsets for all ticker labels, or a 2-column matrix where each row gives the x- and y-axis offsets for a ticker.

reflabel.offsets

Either a numeric vector of length 2 giving the x- and y-axis offsets for all reference ticker labels, or a 2-column matrix where each row gives the x- and y-axis offsets for a reference ticker.

add.plot

Logical value for whether to add plot data to current plot frame rather than open a new one.

colors

Character vector of colors for each curve.

lty

Numeric vector specifying line types for each curve.

plot.list

List of arguments to pass to plot.

points.list

List of arguments to pass to points.

text.list

List of arguments to pass to text.

pdf.list

List of arguments to pass to pdf.

bmp.list

List of arguments to pass to bmp.

jpeg.list

List of arguments to pass to jpeg.

png.list

List of arguments to pass to png.

tiff.list

List of arguments to pass to tiff.

Value

In addition to the graph, a list containing:

List named portfolio.xy where each element is a two-column matrix of x- and y-axis values for a fund pair.
Numeric vector named means with mean gains for each fund.
Numeric matrix named corr.matrix with a correlation matrix for gains for each fund.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Plot mean vs. SD for UPRO/VBLTX portfolio, and compare to VFINX and BRK-B
fig1 <- twofunds_graph(tickers = c("UPRO", "VBLTX"), 
                       reference.tickers = c("VFINX", "BRK-B"))
                     
# Same funds, but annualized growth vs. maximum drawdown
fig2 <- twofunds_graph(tickers = c("UPRO", "VBLTX"), 
                       reference.tickers = c("VFINX", "BRK-B"),
                       x.metric = "mdd", y.metric = "cagr")

## End(Not run)

Graph One Performance Metric vs. Another for Various Investments

Description

Useful for visualizing the performance of a group of investments. The first investment is used as the benchmark if x.metric or y.metric require one benchmark, and the first two investments are used as benchmarks if x.metric and y.metric require different benchmarks.

Usage

twometrics_graph(tickers = NULL, ..., gains = NULL, prices = NULL,
  x.metric = "mdd", y.metric = "cagr", tickerlabel.offsets = NULL,
  add.plot = FALSE, colors = NULL, plot.list = NULL, points.list = NULL,
  text.list = NULL, pdf.list = NULL, bmp.list = NULL, jpeg.list = NULL,
  png.list = NULL, tiff.list = NULL)

Arguments

tickers

Character vector of ticker symbols that Yahoo! Finance recognizes, if you want to download data on the fly.

...

Arguments to pass along with tickers to load_gains.

gains

Numeric matrix with 1 column of gains for each investment (can be a vector if there is only one).

prices

Numeric matrix with 1 column of prices for each investment (can be a vector if there is only one).

x.metric

Character string specifying x-axis performance metric. Choices are:

"mean" or "sd" for mean or standard deviation of gains.

"growth" or "cagr" for total or annualized growth.

"mdd" for maximum drawdown.

"sharpe" or "sortino" for Sharpe or Sortino ratio.

"alpha", "beta", or "r.squared" for those metrics from a fitted linear regression on benchmark fund.

"pearson" or "spearman" for Pearson or Spearman correlation with benchmark fund.

"alpha2", "beta2", "r.squared2", "pearson2", or "spearman2" for same as previously described, but using the second benchmark index.

"auto.pearson" or "auto.spearman" for Pearson or Spearman autocorrelation, defined as the correlation between subsequent gains.

y.metric

Same as x.metric, but for the y-axis.

tickerlabel.offsets

Either a numeric vector of length 2 giving the x- and y-axis offsets for all ticker labels, or a 2-column matrix where each row gives the x- and y-axis offsets for a ticker.

add.plot

Logical value for whether to add plot data to current plot frame rather than open a new one.

colors

Character vector of colors for each curve.

plot.list

List of arguments to pass to plot.

points.list

List of arguments to pass to points.

text.list

List of arguments to pass to text.

pdf.list

List of arguments to pass to pdf.

bmp.list

List of arguments to pass to bmp.

jpeg.list

List of arguments to pass to jpeg.

png.list

List of arguments to pass to png.

tiff.list

List of arguments to pass to tiff.

Value

In addition to the graph, a data frame containing the performance metrics for each investment.

References

Ryan, J.A. and Ulrich, J.M. (2017) quantmod: Quantitative Financial Modelling Framework. R package version 0.4-12, https://CRAN.R-project.org/package=quantmod.

Examples

## Not run: 
# Plot annualized growth vs. maximum drawdown for VFINX, SSO, and UPRO
fig <- twometrics_graph(tickers = c("VFINX", "SSO", "UPRO"))

## End(Not run)