# 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("AAPL", "NFLX", "GOOG", "TSLA", "NVDA")

prices <- tq_get(x = symbols, 
                 get = "stock.prices",
                 from = "2012-12-31",
                 to   = "2017-12-31")

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" "GOOG" "NFLX" "NVDA" "TSLA"
#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 AAPL       0.25
## 2 GOOG       0.25
## 3 NFLX       0.2 
## 4 NVDA       0.2 
## 5 TSLA       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", 
                col_rename   = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.104 
##  2 2013-02-28  0.0343
##  3 2013-03-28  0.0109
##  4 2013-04-30  0.0855
##  5 2013-05-31  0.0988
##  6 2013-06-28 -0.0394
##  7 2013-07-31  0.0925
##  8 2013-08-30  0.0665
##  9 2013-09-30  0.0442
## 10 2013-10-31  0.0483
## # ℹ 50 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                       0.553

6 Plot: Rolling Sharpe Ratio

portfolio_returns_tbl %>%
    
    ggplot(aes(x = returns)) +
    geom_histogram(binwidth = 0.01, fill = "cornflowerblue", alpha = 0.5) +
    
    geom_vline(xintercept = rfr, color = "green", size = 1) +
    
    annotate(geom = "text", 
             x = rfr + 0.002, y = 13, 
             label = "risk free rate",
             angle = 90) +
    
    labs(y = "count")

Scatterplot of Returns around Risk Free Rate

portfolio_returns_tbl %>%
    
    # Add a new variable
    mutate(excess_returns = if_else(returns > rfr,
                                    "rfr_above",
                                    "rfr_below")) %>%
    
    # Plot
    ggplot(aes(x = date, y = returns)) +
    geom_point(aes(color = excess_returns)) + 
    geom_hline(yintercept = rfr, color = "cornflowerblue",
               linetype = 3, size = 1) +
    geom_vline(xintercept = as.Date("2016-11-01"),
               color = "cornflowerblue", size = 1, alpha = 0.5) +
    
    theme(legend.position = "none") +

annotate(geom = "text",
         x = as.Date("2016-12-01"), y = -0.04,
         label = "Election", size = 5, angle = 90) +
    
    annotate(geom = "text",
             x = as.Date("2017-05-01"), y = -0.01,
             label = str_glue("No returns below RFR 
                              after the 2016 election."),
             color = "green") +
    
    labs(y = "monthly returns", x = NULL)

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: 37 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.589
##  2 2015-01-30      0.571
##  3 2015-02-27      0.596
##  4 2015-03-31      0.513
##  5 2015-04-30      0.513
##  6 2015-05-29      0.487
##  7 2015-06-30      0.515
##  8 2015-07-31      0.509
##  9 2015-08-31      0.458
## 10 2015-09-30      0.409
## # ℹ 27 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("2016-06-01"), y = 0.5,
             label = "This portfolio has done quite well since 2016.",
             color = "red", size = 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 showed a clear structural break in November 2016, when performance and the Sharpe Ratio improved significantly. This change was driven by the 2016 U.S. presidential election, which boosted investor confidence and led to a strong market rally—especially in technology and automotive stocks like AAPL, TSLA, and NVDA. As a result, returns stayed above the risk-free rate, improving overall portfolio performance.