# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

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

1 Import stock prices

symbols <- c( "AMZN", "SKT", "BBWI", "TSLA", "JBLU")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns (monthly)

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"))

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMZN" "BBWI" "JBLU" "SKT"  "TSLA"
#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 AMZN       0.25
## 2 BBWI       0.25
## 3 JBLU       0.2 
## 4 SKT        0.2 
## 5 TSLA       0.1

4 Build a 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  0.0407 
##  2 2013-02-28 -0.0129 
##  3 2013-03-28  0.0370 
##  4 2013-04-30  0.0596 
##  5 2013-05-31  0.0366 
##  6 2013-06-28  0.0113 
##  7 2013-07-31  0.0766 
##  8 2013-08-30 -0.00841
##  9 2013-09-30  0.0835 
## 10 2013-10-31  0.0517 
## # ℹ 50 more rows

5 Compute Sharpe Ratio

# 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                       0.358

6 Plot: Rolling Sharpe Ratio

# Create custom function
Calculate_rolling_SharpeRatio <- function(data) {
  
  rolling_SR <- SharpeRatio(R = data,
                            Rf = rfr, 
                            FUN = "StdDev")
  return(rolling_SR)
}

# Define Window
Window <- 24

#Transform data: calculate 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")

rolling_sr_tbl
## # A tibble: 60 × 3
##    date        returns rolling_sr
##    <date>        <dbl>      <dbl>
##  1 2013-01-31  0.0407          NA
##  2 2013-02-28 -0.0129          NA
##  3 2013-03-28  0.0370          NA
##  4 2013-04-30  0.0596          NA
##  5 2013-05-31  0.0366          NA
##  6 2013-06-28  0.0113          NA
##  7 2013-07-31  0.0766          NA
##  8 2013-08-30 -0.00841         NA
##  9 2013-09-30  0.0835          NA
## 10 2013-10-31  0.0517          NA
## # ℹ 50 more rows
rolling_sr_tbl %>% 
  ggplot(aes(x = date, y = rolling_sr)) +
  geom_line(color = "cornflowerblue") +

#Labeling
labs(x = NULL, y = "Rolling Sharpe Ratio")

How has your portfolio performed over time? # My portfolio performed okay over time, but there was a sharp decline at the end of the period. 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?
# It looks like there’s a break at the end of the period. This could be because of a change in the risk free rate, or because of various economic events.