# Load packages
# Core
library(tidyverse)
library(tidyquant)
Visualize and examine changes in the underlying trend in the performance of your portfolio in terms of Sharpe Ratio.
Choose your stocks.
from 2012-12-31 to present
# Choose stocks
symbols <- c("AAPL", "DIS", "NKE", "SBUX", "GE")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2012-12-31",
to = "2017-12-31")
asset_returns_tbl <- prices %>%
# Calculate monthly returns
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "log") %>%
slice(-1) %>%
ungroup() %>%
set_names(c("asset", "date", "returns"))
# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "DIS" "GE" "NKE" "SBUX"
# weight
weights <- c(0.25,
0.25,
0.20,
0.20,
0.10)
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 AAPL 0.25
## 2 DIS 0.25
## 3 GE 0.2
## 4 NKE 0.2
## 5 SBUX 0.1
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 0.00658
## 2 2013-02-28 0.00714
## 3 2013-03-28 0.0296
## 4 2013-04-30 0.0396
## 5 2013-05-31 0.0141
## 6 2013-06-28 -0.0206
## 7 2013-07-31 0.0549
## 8 2013-08-30 -0.00593
## 9 2013-09-30 0.0549
## 10 2013-10-31 0.0700
## # ℹ 50 more rows
# define risk free rate
rfr <- 0.0003
portfolio_SharpeRatio_tbl <- portfolio_returns_tbl %>%
tq_performance(Ra = returns,
performance_fun = SharpeRatio,
Rf = rfr,
FUN = "StdDev")
portfolio_SharpeRatio_tbl
## # A tibble: 1 × 1
## `StdDevSharpe(Rf=0%,p=95%)`
## <dbl>
## 1 0.315
# create a custom function to calculate rolling SR
Calculate_rolling_SharpeRatio <- function(data) {
Rolling_SR <- SharpeRatio(R = data,
Rf = rfr,
FUN = "StdDev")
return(Rolling_SR)
}
# define window
window <- 24
# transform data: calculate rolling sharpe ratio
Rolling_sr_tbl <- portfolio_returns_tbl %>%
tq_mutate(select = returns,
mutate_fun = rollapply,
width = window,
FUN = Calculate_rolling_SharpeRatio,
col_rename = "rolling_sr") %>%
select(-returns) %>%
na.omit()
Rolling_sr_tbl
## # A tibble: 37 × 2
## date rolling_sr
## <date> <dbl>
## 1 2014-12-31 0.590
## 2 2015-01-30 0.568
## 3 2015-02-27 0.619
## 4 2015-03-31 0.570
## 5 2015-04-30 0.562
## 6 2015-05-29 0.577
## 7 2015-06-30 0.627
## 8 2015-07-31 0.601
## 9 2015-08-31 0.452
## 10 2015-09-30 0.428
## # ℹ 27 more rows
Rolling_sr_tbl %>%
ggplot(aes(x = date, y = rolling_sr)) +
geom_line(color = "cornflowerblue") +
# labeling
labs(x = NULL, y = "rolling sharpe ratio") +
annotate(geom = "text",
x = as.Date("2016-06-01"), y = 0.5,
label = "this portfolio has done
quite poor since 2015",
color = "red", size = 5)
How has your portfolio performed over time?
The Portfolio has performed pretty poor from 2015 all the way to 2018.
Provide dates of the structural breaks, if any. The Code Along Assignment 9 had one structural break in November 2016. What do you think the reason is?
There were no structural breaks, it was a pretty consistent downward slope all the way to 2018. There is a possibility that right before 2015 was my structural break.