# 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", "MSFT", "GOOG")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()

w <- c(0.35,
       0.35,
       0.30)

w_tbl <- tibble(symbols, w)

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset,
                 returns_col  = returns,
                 weights      = w_tbl,
                 col_rename   = "returns",
                 rebalance_on = "months")

portfolio_returns_tbl
## # A tibble: 142 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.0231 
##  2 2013-02-28  0.0178 
##  3 2013-03-28  0.00654
##  4 2013-04-30  0.0570 
##  5 2013-05-31  0.0450 
##  6 2013-06-28 -0.0435 
##  7 2013-07-31  0.0247 
##  8 2013-08-30  0.0281 
##  9 2013-09-30  0.00311
## 10 2013-10-31  0.108  
## # ℹ 132 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                       0.328

6 Plot: Rolling Sharpe Ratio

calculate_rolling_sharpeRatio <- function(df) {

    SharpeRatio(df,
                Rf = rfr,
                FUN = "StdDev")}
window <- 24

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: 119 × 3
##    date        returns sharpeRatio
##    <date>        <dbl>       <dbl>
##  1 2014-12-31 -0.0449        0.569
##  2 2015-01-30 -0.0156        0.585
##  3 2015-02-27  0.0756        0.622
##  4 2015-03-31 -0.0404        0.534
##  5 2015-04-30  0.0499        0.530
##  6 2015-05-29  0.00285       0.486
##  7 2015-06-30 -0.0389        0.496
##  8 2015-07-31  0.0695        0.526
##  9 2015-08-31 -0.0473        0.417
## 10 2015-09-30 -0.00827       0.403
## # ℹ 109 more rows
rolling_sharpe_tbl %>%

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

    labs(title = paste0("Rolling ", " Sharpe Ratio"),
         y = "rolling Sharpe Ratio",
         x = NULL) +
    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? My portfolio has had a lot of ups and downs overtime. One of the breaks falls around the same time at Code Along 9 in November of 2016, I am going to assume this is for the same reason, the election. There is then another break surrounding 2020, I would attribute this to the pandemic. Lastly there is most recently a break starting in 2023. I am unsure of the cause of this break, but it is finally starting to turn around because all of 2024 has only shown growth.