# 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.

“MSFT”, “AAPL”, “F”, “JPM”, “SBUX”

from 2012-12-31 to present

1 Import stock prices

symbols <- c("MSFT", "AAPL", "F", "JPM", "SBUX")

prices <- tq_get(x    = symbols,
                 from = "2012-12-31")

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] "AAPL" "F"    "JPM"  "MSFT" "SBUX"
# 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)

4 Build a portfolio

# ?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col = asset,
                 returns_col = returns,
                 weigts = w_tbl,
                 rebalance_on = "months", 
                 col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 142 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31 -0.000198
##  2 2013-02-28 -0.00229 
##  3 2013-03-28  0.0162  
##  4 2013-04-30  0.0584  
##  5 2013-05-31  0.0744  
##  6 2013-06-28 -0.0293  
##  7 2013-07-31  0.0580  
##  8 2013-08-30 -0.00248 
##  9 2013-09-30  0.0252  
## 10 2013-10-31  0.0460  
## # ℹ 132 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                       0.248

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: 119 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.514
##  2 2015-01-30      0.444
##  3 2015-02-27      0.512
##  4 2015-03-31      0.458
##  5 2015-04-30      0.455
##  6 2015-05-29      0.409
##  7 2015-06-30      0.440
##  8 2015-07-31      0.412
##  9 2015-08-31      0.312
## 10 2015-09-30      0.276
## # ℹ 109 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("2020-06-01"), y = 0.6, 
             label = "This portfolio has fluctuated since 2016", color = "red", size = 3.5)

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?

The portfolio’s performance, shown by the rolling Sharpe ratio, has had a lot of ups and downs since 2016, with a noticeable shift around November 2016. This change could be due to major events like the U.S. presidential election, which caused market volatility and impacted investor confidence. The drop in the Sharpe ratio suggests that big economic and political events can have a strong effect on returns, especially when looking at risk-adjusted performance.