# 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("F", "RIVN", "LCID", "TM", "HMC")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",    
                 from = "2012-12-31",
                 to   = "2025-10-25")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "F"    "HMC"  "LCID" "RIVN" "TM"
# 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 F          0.25
## 2 HMC        0.25
## 3 LCID       0.2 
## 4 RIVN       0.2 
## 5 TM         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: 154 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.00911
##  2 2013-02-28 -0.00101
##  3 2013-03-28  0.0183 
##  4 2013-04-30  0.0339 
##  5 2013-05-31  0.0209 
##  6 2013-06-28 -0.00155
##  7 2013-07-31  0.0236 
##  8 2013-08-30 -0.0196 
##  9 2013-09-30  0.0330 
## 10 2013-10-31  0.0177 
## # ℹ 144 more rows

5 Compute Sharpe Ratio

rfr <- 0.0003

portfolio_sharpe_tbl <- portfolio_returns_tbl %>%

    tq_performance(Ra = returns,
                   Rf = rfr,
                   performance_fun = SharpeRatio,
                   FUN = "StdDev") 

portfolio_sharpe_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                     -0.0494

6 Plot: Rolling Sharpe Ratio

calculate_rolling_sharpeRatio <- function(df) {

    SharpeRatio(df,
                Rf = rfr,
                FUN = "StdDev")

}

# dump(list = "calculate_rolling_sharpeRatio",
#      file = "00_scripts/calculate_rolling_sharpeRatio.R")

# Set the length of periods for rolling calculation
window <- 24

# Calculate rolling sharpe ratios
rolling_sharpe_tbl <- portfolio_returns_tbl %>%

    tq_mutate(select = returns,
              mutate_fun = rollapply,
              width = window,
              align = "right",
              FUN = calculate_rolling_sharpeRatio,
              col_rename = "sharpeRatio") %>%
    na.omit()

rolling_sharpe_tbl
## # A tibble: 131 × 3
##    date          returns sharpeRatio
##    <date>          <dbl>       <dbl>
##  1 2014-12-31 -0.00716        0.0802
##  2 2015-01-30 -0.00203        0.0576
##  3 2015-02-27  0.0543         0.151 
##  4 2015-03-31 -0.0000793      0.119 
##  5 2015-04-30  0.00221        0.0635
##  6 2015-05-29 -0.00590        0.0127
##  7 2015-06-30 -0.0194        -0.0214
##  8 2015-07-31  0.0111        -0.0460
##  9 2015-08-31 -0.0477        -0.0929
## 10 2015-09-30 -0.0166        -0.190 
## # ℹ 121 more rows
# Figure 7.5 Rolling Sharpe ggplot ----

rolling_sharpe_tbl %>%

    ggplot(aes(date, sharpeRatio)) +
    geom_line(color = "cornflowerblue") +

    labs(title = paste0("Rolling ", window, "-Month Sharpe Ratio"),
         y = "rolling Sharpe Ratio",
         x = NULL) +
    theme(plot.title = element_text(hjust = 0.5)) +

    annotate(geom = "text",
             x = as.Date("2016-06-01"), y = 0.5,
             label = "This portfolio has done quite well since 2016.",
             size = 5, color = "red")

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 has performed well sense November 2016, the largest break looking as if it was in 2024. The reasoning for these breaks I belive to be is increases in volatility.