How has your portfolio performed over time?

Load packages

# Core
library(tidyverse)
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Goal

Visualize and examine changes in the underlying trend in the performance of your portfolio in terms of Sharpe Ratio.

1. Import stock prices

symbols <- c("AMZN", "META", "NFLX", "NVDA", "GOOGL")

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

head(prices)
## # A tibble: 6 × 8
##   symbol date        open  high   low close   volume adjusted
##   <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
## 1 AMZN   2012-12-31  12.2  12.6  12.1  12.5 68380000     12.5
## 2 AMZN   2013-01-02  12.8  12.9  12.7  12.9 65420000     12.9
## 3 AMZN   2013-01-03  12.9  13.0  12.8  12.9 55018000     12.9
## 4 AMZN   2013-01-04  12.9  13.0  12.8  13.0 37484000     13.0
## 5 AMZN   2013-01-07  13.1  13.5  13.1  13.4 98200000     13.4
## 6 AMZN   2013-01-08  13.4  13.4  13.2  13.3 60214000     13.3

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"))

head(asset_returns_tbl)
## # A tibble: 6 × 3
##   asset date        returns
##   <chr> <date>        <dbl>
## 1 AMZN  2013-01-31  0.0567 
## 2 AMZN  2013-02-28 -0.00464
## 3 AMZN  2013-03-28  0.00837
## 4 AMZN  2013-04-30 -0.0488 
## 5 AMZN  2013-05-31  0.0589 
## 6 AMZN  2013-06-28  0.0311

3. Assign a weight to each asset

# 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")

head(portfolio_returns_tbl)
## # A tibble: 6 × 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

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                       0.339

6. Plot: Rolling Sharpe Ratio

# Custom function for rolling SR
Calculate_rolling_SharpeRatio <- function(data) {
    rolling_SR <- SharpeRatio(R   = data, 
                              Rf  = rfr,
                              FUN = "StdDev")
    return(rolling_SR)
}

# Define window
window <- 24

# Calculate rolling SR
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()

head(rolling_sr_tbl)
## # A tibble: 6 × 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

Rolling Sharpe Ratio Plot

rolling_sr_tbl %>%
    ggplot(aes(x = date, y = rolling_sr)) +
    geom_line(color = "cornflowerblue", linewidth = 1) +
    labs(x = NULL, 
         y = "Rolling Sharpe Ratio", 
         title = "Rolling 24-Month Sharpe Ratio") +
    theme_minimal() +
    theme(plot.title = element_text(hjust = 0.5))

Interpretation

You can identify structural breaks—major shifts in the portfolio’s trend—by visually observing the chart or performing a statistical breakpoint test (optional).
In the Code Along 9 example, there was one noticeable structural break in November 2016, likely due to macroeconomic events such as: - The 2016 U.S. presidential election, which caused market volatility. - Shifts in tech sector valuations and expectations for interest rates. - Increased investor risk appetite and capital inflows to growth stocks.

Overall, post-2016 periods reflect higher volatility but also greater returns in the tech-heavy portfolio.