# 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("SPY", "EFA", "IJS", "EEM", "AGG")

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

2 Convert prices to returns

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
#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 AGG        0.25
## 2 EEM        0.25
## 3 EFA        0.2 
## 4 IJS        0.2 
## 5 SPY        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,
                replace_on = "months", 
                col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0204 
##  2 2013-02-28 -0.00220
##  3 2013-03-28  0.0127 
##  4 2013-04-30  0.0173 
##  5 2013-05-31 -0.0113 
##  6 2013-06-28 -0.0233 
##  7 2013-07-31  0.0342 
##  8 2013-08-30 -0.0231 
##  9 2013-09-30  0.0513 
## 10 2013-10-31  0.0305 
## # ℹ 50 more rows

5 Caluculate Kurtosis

portfolio_kurt_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
   
     tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    
    select(Kurtosis)

portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1    0.337
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <-
    mean(portfolio_returns_tbl$returns)

portfolio_kurt_tidyquant_builtin_percent
## # A tibble: 1 × 1
##   Kurtosis
##      <dbl>
## 1    0.337

##6 Compute Sharp Ratio

# Define risk free rate
rfr <- 0.003
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=0.3%,p=95%)`
##                           <dbl>
## 1                         0.135

#Rolling Sharp 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 value for 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    0.137  
##  2 2015-01-30    0.0777 
##  3 2015-02-27    0.144  
##  4 2015-03-31    0.115  
##  5 2015-04-30    0.113  
##  6 2015-05-29    0.123  
##  7 2015-06-30    0.136  
##  8 2015-07-31    0.0612 
##  9 2015-08-31    0.00696
## 10 2015-09-30   -0.122  
## # 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("2018-01-01"), y = 0.7,  label = "This portfolio has done quite well since 2016.",
         color = "red", size = 6)

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 gone through periods of both strong and weak performance over time. In January 2016, there was a structural shift, with the Sharpe ratio rising to 0.8 by 2018. However, in January 2022, another structural break occurred, leading to a steady decline in the Sharpe ratio, nearly reaching zero, possibly due to inflation and a drop in the value of the US dollar.