# 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("MCD", "WEN", "YUM", "DPZ", "SBUX")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2021-12-31")

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] "DPZ"  "MCD"  "SBUX" "WEN"  "YUM"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 DPZ         0.2
## 2 MCD         0.2
## 3 SBUX        0.2
## 4 WEN         0.2
## 5 YUM         0.2

4 Build a portfolio

# ?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: 108 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0524 
##  2 2013-02-28  0.0273 
##  3 2013-03-28  0.0496 
##  4 2013-04-30  0.0226 
##  5 2013-05-31  0.0218 
##  6 2013-06-28  0.00976
##  7 2013-07-31  0.0804 
##  8 2013-08-30 -0.00594
##  9 2013-09-30  0.0690 
## 10 2013-10-31  0.00341
## # … with 98 more rows

5 Compute Sharpe Ratio

# Define risk free rate
rfr <- 0.0003

portfolio_Sharpe_tbl <- portfolio_returns_tbl %>%
    tq_performance(Ra = returns, performance_fun = SharpeRatio,
                   Rf = rfr,
                   FUN = "StdDev")

portfolio_Sharpe_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.367

6 Plot: Rolling Sharpe 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 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: 85 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.537
##  2 2015-01-30      0.536
##  3 2015-02-27      0.562
##  4 2015-03-31      0.490
##  5 2015-04-30      0.493
##  6 2015-05-29      0.513
##  7 2015-06-30      0.519
##  8 2015-07-31      0.459
##  9 2015-08-31      0.326
## 10 2015-09-30      0.275
## # … with 75 more rows
rolling_sr_tbl %>%
    
    ggplot(aes(x = date, 
               y = rolling_sr)) +
    geom_line(color = "violet") +
    
    # Labeling
    labs(x = NULL, y = "Rolling Sharpe Ratio")

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 performed relatively stable over time. Sharpe ratio stayed within a range of low of ~0.3, to a high of ~0.55 between 2016 to the beginning of 2020. This is not a lot of fluctuation. In 2020, the ratio plummeted to under 0.05, likely as a result of the economic impacts of Covid-19. It seems to be slowly recovering since this period, hovering at ~0.2.

As for the Code along assignment, I am not exactly sure what would have caused a sharp decline in the Sharpe ratio in November 2016. The only thing that comes to mind is that Donald Trump was elected. I am not sure what effects that had on the economy at that time.