# 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("NKE", "ADDYY", "SKX", "UAA")

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] "ADDYY" "NKE"   "SKX"   "UAA"
# weights
weights <- c(0.4, 0.2, 0.25, 0.15)
weights
## [1] 0.40 0.20 0.25 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 ADDYY      0.4 
## 2 NKE        0.2 
## 3 SKX        0.25
## 4 UAA        0.15

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  0.0385
##  2 2013-02-28  0.0150
##  3 2013-03-28  0.0760
##  4 2013-04-30  0.0284
##  5 2013-05-31  0.0519
##  6 2013-06-28  0.0120
##  7 2013-07-31  0.0604
##  8 2013-08-30  0.0204
##  9 2013-09-30  0.0542
## 10 2013-10-31  0.0157
## # 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                       0.353

6 Plot: 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      0.406
##  2 2015-01-30      0.393
##  3 2015-02-27      0.442
##  4 2015-03-31      0.417
##  5 2015-04-30      0.439
##  6 2015-05-29      0.429
##  7 2015-06-30      0.439
##  8 2015-07-31      0.459
##  9 2015-08-31      0.374
## 10 2015-09-30      0.364
## # 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 consistently increased since 2016.",
             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?

The portfolio’s performance improved significantly after a structural break in November 2016, as indicated by a sharp increase in the rolling Sharpe Ratio. This improvement was likely driven by macroeconomic shifts (e.g., post-election market rally), improved consumer spending, and strong individual stock performance. The portfolio was relatively flat before 2016 but showed strong, consistent growth in risk-adjusted returns afterward.