# Load packages
# Core
library(tidyverse)
library(tidyquant)
Collect individual returns into a portfolio by assigning a weight to each stock
five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”
from 2012-12-31 to 2017-12-31
symbol <- c("SPY", "EFA", "IJS", "EEM", "AGG")
prices <- tq_get(x = symbol,
get = "stock.prices",
from = "2012-12-31",
to = "2017-12-31")
asset_returns_tbl <- prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "log") %>%
slice(-1) %>%
ungroup() %>%
set_names(c("asset", "date", "returns"))
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
## symbols weights
## <chr> <dbl>
## 1 AGG 0.25
## 2 EEM 0.25
## 3 EFA 0.2
## 4 IJS 0.2
## 5 SPY 0.1
# ?tq_portfolio
portfolio_returns_tbl <- asset_returns_tbl %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w_tbl,
rebalance_on = "months",
col_rename = "returns")
portfolio_returns_tbl
## # A tibble: 60 × 2
## date returns
## <date> <dbl>
## 1 2013-01-31 0.0204
## 2 2013-02-28 -0.00239
## 3 2013-03-28 0.0121
## 4 2013-04-30 0.0174
## 5 2013-05-31 -0.0128
## 6 2013-06-28 -0.0247
## 7 2013-07-31 0.0321
## 8 2013-08-30 -0.0224
## 9 2013-09-30 0.0511
## 10 2013-10-31 0.0301
## # ℹ 50 more rows
rfr <- 0.0003
portfolio_SharpRatio <- portfolio_returns_tbl %>%
tq_performance(Ra = returns,
performance_fun = SharpeRatio,
Rf = rfr,
FUN = "StdDev")
portfolio_SharpRatio
## # A tibble: 1 × 1
## `StdDevSharpe(Rf=0%,p=95%)`
## <dbl>
## 1 0.239
###Histogram of returns with risk free rate
portfolio_returns_tbl %>%
ggplot(aes(x = returns)) +
geom_histogram(binwidth = 0.01,
fill = "cornflowerblue",
alpha = 0.5) +
geom_vline(xintercept = rfr,
color = "green",
size = 1) +
annotate(geom = "text",
x = rfr + 0.002,
y = 13,
label = "risk free rate",
angle = 90) +
labs(y = "count")
portfolio_returns_tbl %>%
# Add a new variable
mutate(excess_returns = if_else(returns > rfr, "rfr_above",
"rfr_below")) %>%
# Plot
ggplot(aes(x = date,
y = returns)) +
geom_point(aes(color = excess_returns)) +
geom_hline(yintercept = rfr,
color = "cornflowerblue",
linetype = 3,
size = 1) +
geom_vline(xintercept = as.Date("2016-11-01"),
color = "cornflowerblue",
size = 1,
alpha = 0.5) +
theme(legend.position = "none") +
annotate(geom = "text",
x = as.Date("2016-12-01"),
y = -0.04,
label = "Election",
size = 5,
angle = 90) +
annotate(geom = "text",
x = as.Date("2017-05-01"),
y = -0.01,
label = str_glue
("no returns below RFR after the 2016 election"),
color = "green") +
labs(y = "monthly returns",
x = NULL)
Calculate_rolling_SharpRatio <- function(data) {
rolling_SR <- SharpeRatio(R = data,
Rf = rfr,
FUN = "StdDev")
return(rolling_SR)
}
# Define Window
window <- 24
# Transform Data: calculate Rolling Sharp Ratio
rolling_sr_tbl <- portfolio_returns_tbl %>%
tq_mutate(select = returns,
mutate_fun = rollapply,
width = window,
FUN = Calculate_rolling_SharpRatio,
col_rename = "rolling_sr") %>%
select(-returns) %>%
na.omit()
rolling_sr_tbl %>%
ggplot(aes(x = date,
y = rolling_sr)) +
geom_line(color = "cornflowerblue") +
# Labeling
labs(x = NULL, y = "Rolling Sharp Ratio") +
annotate(geom = "text",
x = as.Date("2016-06-01"),
y = 0.5,
label = "This portfolio has done quite well since 2016",
color = "red",
size = 5)