# 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("MSFT", "NVDA", "JPM")

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()
symbols
## [1] "JPM"  "MSFT" "NVDA"
# weights
weights <- c(0.4, 0.3, 0.3)
weights
## [1] 0.4 0.3 0.3
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 JPM         0.4
## 2 MSFT        0.3
## 3 NVDA        0.3

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")

portfolio_returns_tbl
## # A tibble: 132 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31  0.0380  
##  2 2013-02-28  0.0333  
##  3 2013-03-28  0.000498
##  4 2013-04-30  0.0803  
##  5 2013-05-31  0.0775  
##  6 2013-06-28 -0.0256  
##  7 2013-07-31  0.00859 
##  8 2013-08-30 -0.0148  
##  9 2013-09-30  0.0240  
## 10 2013-10-31  0.0132  
## # ℹ 122 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                       0.348

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: 109 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.636
##  2 2015-01-30      0.351
##  3 2015-02-27      0.389
##  4 2015-03-31      0.339
##  5 2015-04-30      0.344
##  6 2015-05-29      0.294
##  7 2015-06-30      0.284
##  8 2015-07-31      0.295
##  9 2015-08-31      0.301
## 10 2015-09-30      0.291
## # ℹ 99 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("2018-06-01"), y = 1,
              label = "this portfolio has fallen 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?

My portfolio has been very up and down over time. It appears to skoyrocket every two years, and then crash two years after that. The highest point was in 2018, and the lowest point was this past year.