# 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("SOXX", "TKR", "VUG", "AMZN", "GOOG")
prices <- tq_get(x = symbols, 
                 from = "2012-12-31",
                 to = "2026-11-4")

2 Convert prices to returns

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMZN" "GOOG" "SOXX" "TKR"  "VUG"
# 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 GOOG       0.25
## 3 SOXX       0.2 
## 4 TKR        0.2 
## 5 VUG        0.1

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")

portfolio_returns_tbl
## # A tibble: 167 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.0725 
##  2 2013-02-28           0.0249 
##  3 2013-03-28           0.0163 
##  4 2013-04-30          -0.0124 
##  5 2013-05-31           0.0574 
##  6 2013-06-28           0.00642
##  7 2013-07-31           0.0398 
##  8 2013-08-30          -0.0465 
##  9 2013-09-30           0.0688 
## 10 2013-10-31           0.0626 
## # ℹ 157 more rows

5 Calculate Sharpe Ratio

# Define risk free rate
rfr <- 0.0003
portfolio_SharpeRatio_tbl <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = portfolio.returns,
                   performance_fun = SharpeRatio,
                   Rf = rfr, 
                   FUN = "StdDev")

portfolio_SharpeRatio_tbl
## # A tibble: 1 × 1
##   `SharpeRatio(Rf=0%,p=95%)`
##                        <dbl>
## 1                      0.253

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 = portfolio.returns, 
              mutate_fun = rollapply,
              width = window,
              FUN = Calculate_rolling_SharpeRatio,
              col_rename = "rolling_sr") %>%
    na.omit()

rolling_sr_tbl
## # A tibble: 144 × 3
##    date       portfolio.returns rolling_sr
##    <date>                 <dbl>      <dbl>
##  1 2014-12-31          -0.0301       0.395
##  2 2015-01-30           0.00241      0.335
##  3 2015-02-27           0.0762       0.372
##  4 2015-03-31          -0.0211       0.325
##  5 2015-04-30           0.0133       0.356
##  6 2015-05-29           0.0208       0.326
##  7 2015-06-30          -0.0358       0.269
##  8 2015-07-31           0.0736       0.293
##  9 2015-08-31          -0.0396       0.303
## 10 2015-09-30          -0.0389       0.194
## # ℹ 134 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 = 0.5,
             label = "There are massive fluctuations in RAR across time",
             color = "red", size = 4)

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?

Risk-Adjusted return has swung massively over time, due to the nature of the world economy and the volatility of my chosen stocks. The 2018 all-time peak of return was driven by tax cuts and strong corporate earnings. The 2020 dive was caused by the onset of COVID, and the recovery was fueled by the subsequent actions of the fed. The 2022-2023 depression was caused by fed’s actions, pushing returns all the way to the negative. The most recent 2024-2025 recovery was caused by central bank’s rate cuts and a huge rally in tech megacap stocks, which the portfolio is almost completely comprised of.

I believe the Code Along 9’s structural break was caused by the post-election market rally caused by the inauguration of President Trump.