# 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", "META", "NFLX", "NVDA", "GOOGL")
prices <- tq_get(x    = symbols,
                 get  = "stock.prices", 
                 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] "AMZN"  "GOOGL" "META"  "NFLX"  "NVDA"
# 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 AMZN        0.2
## 2 GOOGL       0.2
## 3 META        0.2
## 4 NFLX        0.2
## 5 NVDA        0.2

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: 154 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.171  
##  2 2013-02-28  0.0186 
##  3 2013-03-28 -0.00879
##  4 2013-04-30  0.0548 
##  5 2013-05-31  0.0166 
##  6 2013-06-28 -0.00730
##  7 2013-07-31  0.131  
##  8 2013-08-30  0.0349 
##  9 2013-09-30  0.0951 
## 10 2013-10-31  0.0665 
## # ℹ 144 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                       0.341

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: 131 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.475
##  2 2015-01-30      0.458
##  3 2015-02-27      0.493
##  4 2015-03-31      0.464
##  5 2015-04-30      0.477
##  6 2015-05-29      0.485
##  7 2015-06-30      0.498
##  8 2015-07-31      0.498
##  9 2015-08-31      0.474
## 10 2015-09-30      0.412
## # ℹ 121 more rows
rolling_sr_tbl %>%
    
    ggplot(aes(x = date, y = rolling_sr)) +
    geom_line(color = "cornflowerblue") +
    
    # Labeling
    labs(x = NULL, y = "Rolling Sharpe Ratio", title = "Rolling 24-Month Sharpe Ratio") +
    theme(plot.title = element_text(hjust = 0.5))

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?

Answer: Since 2012-12-31, my portfolio has a calculated sharpe ratio of 0.341; any metric below 1 is indicative of sub-par risk-adjusted performance. Concerning structural breaks in my chart, there were quite a few; these breaks occurred in 2016, 2018, 2020, 2021, 2023, and 2025. Overall, the chart demonstrates a lot of volatility from 2012 until now. Regarding the structural break which occurred in November 2016, the most obvious answer is that the markets rallied after the election of President Donald Trump in his first term.