Aggregates a group of returns by asset into portfolio returns
Source:R/tq_portfolio.R
tq_portfolio.RdAggregates 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
- data
A
tibble(tidy data frame) of returns in tidy format (i.e long format).- assets_col
The column with assets (securities)
- returns_col
The column with returns
- weights
Optional parameter for the asset weights, which can be passed as a numeric vector the length of the number of assets or a two column tibble with asset names in first column and weights in second column.
- col_rename
A string or character vector containing names that can be used to quickly rename columns.
- ...
Additional parameters passed to
PerformanceAnalytics::Return.portfolio- n
Number of times to repeat a data frame row-wise.
- index_col_name
A renaming function for the "index" column, used when repeating data frames.
Details
tq_portfolio is a wrapper for PerformanceAnalytics::Return.portfolio.
The main advantage is the results are returned as a tibble and the
function can be used with the tidyverse.
assets_col and returns_col are columns within data that are used
to compute returns for a portfolio. The columns should be in "long" format (or "tidy" format)
meaning there is only one column containing all of the assets and one column containing
all of the return values (i.e. not in "wide" format with returns spread by asset).
weights are the weights to be applied to the asset returns.
Weights can be input in one of three options:
Single Portfolio: A numeric vector of weights that is the same length as unique number of assets. The weights are applied in the order of the assets.
Single Portfolio: A two column tibble with assets in the first column and weights in the second column. The advantage to this method is the weights are mapped to the assets and any unlisted assets default to a weight of zero.
Multiple Portfolios: A three column tibble with portfolio index in the first column, assets in the second column, and weights in the third column. The tibble must be grouped by portfolio index.
tq_repeat_df is a simple function that repeats
a data frame n times row-wise (long-wise), and adds a new column for a portfolio index.
The function is used to assist in Multiple Portfolio analyses, and
is a useful precursor to tq_portfolio.
See also
tq_transmute()which can be used to get period returns.PerformanceAnalytics::Return.portfolio()which is the underlying function that specifies which parameters can be passed via...
Examples
# Load libraries
library(dplyr)
# Use FANG data set
# Get returns for individual stock components
monthly_returns_stocks <- FANG %>%
group_by(symbol) %>%
tq_transmute(adjusted, periodReturn, period = "monthly")
##### Portfolio Aggregation Methods #####
# Method 1: Use tq_portfolio with numeric vector of weights
weights <- c(0.50, 0.25, 0.25, 0)
tq_portfolio(data = monthly_returns_stocks,
assets_col = symbol,
returns_col = monthly.returns,
weights = weights,
col_rename = NULL,
wealth.index = FALSE)
#> # A tibble: 48 × 2
#> date portfolio.returns
#> <date> <dbl>
#> 1 2013-01-31 0.260
#> 2 2013-02-28 -0.00456
#> 3 2013-03-28 -0.0195
#> 4 2013-04-30 0.0810
#> 5 2013-05-31 -0.0139
#> 6 2013-06-28 -0.0179
#> 7 2013-07-31 0.254
#> 8 2013-08-30 0.104
#> 9 2013-09-30 0.145
#> 10 2013-10-31 0.0420
#> # ℹ 38 more rows
# 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)
#> # A tibble: 48 × 2
#> date portfolio.returns
#> <date> <dbl>
#> 1 2013-01-31 0.260
#> 2 2013-02-28 -0.00456
#> 3 2013-03-28 -0.0195
#> 4 2013-04-30 0.0810
#> 5 2013-05-31 -0.0139
#> 6 2013-06-28 -0.0179
#> 7 2013-07-31 0.254
#> 8 2013-08-30 0.104
#> 9 2013-09-30 0.145
#> 10 2013-10-31 0.0420
#> # ℹ 38 more rows
# Method 3: Working with multiple portfolios
# 3A: Duplicate monthly_returns_stocks multiple times
mult_monthly_returns_stocks <- tq_repeat_df(monthly_returns_stocks, n = 4)
#> Ungrouping data frame groups: symbol
# 3B: Create weights table grouped by portfolio id
weights <- c(0.50, 0.25, 0.25, 0.00,
0.00, 0.50, 0.25, 0.25,
0.25, 0.00, 0.50, 0.25,
0.25, 0.25, 0.00, 0.50)
stocks <- c("META", "AMZN", "NFLX", "GOOG")
weights_table <- tibble(stocks) %>%
tq_repeat_df(n = 4) %>%
bind_cols(tibble(weights)) %>%
group_by(portfolio)
# 3C: Scale to multiple portfolios
tq_portfolio(data = mult_monthly_returns_stocks,
assets_col = symbol,
returns_col = monthly.returns,
weights = weights_table,
col_rename = NULL,
wealth.index = FALSE)
#> # A tibble: 192 × 3
#> # Groups: portfolio [4]
#> portfolio date portfolio.returns
#> <int> <date> <dbl>
#> 1 1 2013-01-31 0.260
#> 2 1 2013-02-28 -0.00456
#> 3 1 2013-03-28 -0.0195
#> 4 1 2013-04-30 0.0810
#> 5 1 2013-05-31 -0.0139
#> 6 1 2013-06-28 -0.0179
#> 7 1 2013-07-31 0.254
#> 8 1 2013-08-30 0.104
#> 9 1 2013-09-30 0.145
#> 10 1 2013-10-31 0.0420
#> # ℹ 182 more rows