# 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
symbols <- c("MTN", "AAPL", "NFLX", "DIS", "GE")
prices <- tq_get(x= symbols,
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
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "DIS" "GE" "MTN" "NFLX"
#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 AAPL 0.25
## 2 DIS 0.25
## 3 GE 0.2
## 4 MTN 0.2
## 5 NFLX 0.1
# ?tq_portfolio
portfolio_returns_tbl <- asset_returns_tbl %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w_tbl,
replace_on = "months",
col_rename = "returns")
portfolio_returns_tbl
## # A tibble: 60 × 2
## date returns
## <date> <dbl>
## 1 2013-01-31 0.0461
## 2 2013-02-28 0.0360
## 3 2013-03-28 0.0340
## 4 2013-04-30 0.0329
## 5 2013-05-31 0.0330
## 6 2013-06-28 -0.0411
## 7 2013-07-31 0.0795
## 8 2013-08-30 0.0183
## 9 2013-09-30 0.0387
## 10 2013-10-31 0.0591
## # ℹ 50 more rows
portfolio_kurt_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
tq_performance(Ra = returns,
performance_fun = table.Stats) %>%
select(Kurtosis)
portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
## Kurtosis
## <dbl>
## 1 0.0125
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <-
mean(portfolio_returns_tbl$returns)
portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
## Kurtosis
## <dbl>
## 1 0.0125
##6 Compute Sharp Ratio
# Define risk free rate
rfr <- 0.003
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=0.3%,p=95%)`
## <dbl>
## 1 0.375
#Rolling Sharp Ratio
# 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 value for 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 0.508
## 2 2015-01-30 0.494
## 3 2015-02-27 0.519
## 4 2015-03-31 0.471
## 5 2015-04-30 0.493
## 6 2015-05-29 0.504
## 7 2015-06-30 0.598
## 8 2015-07-31 0.587
## 9 2015-08-31 0.454
## 10 2015-09-30 0.368
## # 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("2018-01-01"), y = 0.7, label =
"This portfolio has done quite well
since 2016.",
color = "red", size = 6)
How has your portfolio performed over time? 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?
My portfolio has gone through both strong and weak periods over time. In early 2016, the Sharpe ratio dropped from about 0.6 and stayed below 0.4 until 2018, showing weaker performance.This shows that my portfolio has faced ups and downs due to changing market conditions, making it important to regularly check and adjust your investments.