# 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", "GOOG", "VOO", "MSFT", "NFLX")
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
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "GOOG" "MSFT" "NFLX" "VOO"
# weights
weights <- c(.1, .45, .2, .15, .1)
weights
## [1] 0.10 0.45 0.20 0.15 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.1 
## 2 GOOG       0.45
## 3 MSFT       0.2 
## 4 NFLX       0.15
## 5 VOO        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,
                 rebalance_on = "months",
                 col_rename = "returns")
portfolio_returns_tbl
## # A tibble: 167 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.112  
##  2 2013-02-28  0.0487 
##  3 2013-03-28  0.00658
##  4 2013-04-30  0.0680 
##  5 2013-05-31  0.0482 
##  6 2013-06-28 -0.0219 
##  7 2013-07-31  0.0279 
##  8 2013-08-30  0.0171 
##  9 2013-09-30  0.0284 
## 10 2013-10-31  0.105  
## # ℹ 157 more rows

5 Compute Sharpe Ratio

# define risk free rate
rfr <- .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
##   `SharpeRatio(Rf=0%,p=95%)`
##                        <dbl>
## 1                      0.299

6 Plot: Rolling Sharpe Ratio

# create a custom function to calculate rolling sharpe ratio
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: 144 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.554
##  2 2015-01-30      0.517
##  3 2015-02-27      0.525
##  4 2015-03-31      0.441
##  5 2015-04-30      0.443
##  6 2015-05-29      0.412
##  7 2015-06-30      0.415
##  8 2015-07-31      0.453
##  9 2015-08-31      0.400
## 10 2015-09-30      0.348
## # ℹ 134 more rows
rolling_sr_tbl %>%
    ggplot(aes(x = date, y = rolling_sr)) +
    geom_line(color = "cornflowerblue") + 
    
    # labelling
    labs(x = NULL, y = "Rolling Sharpe Ratio")

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 portfolios rolling sharpe ratio ratio is positive for almost the entire timer period. It is a cyclical graph that normally sits between .2 and .6. It would be better if the ratio was above 1, but being above zero and under 1 means that there is probably too much risk or volatility for the amount of possible return. There is a structural break that happened in 2022. I did a quick search of the VOOs graph and it appears that the market did experience a decline in 2022 so the graph is showing that there isnt enough return in that time period for the amount of risk that my portfolio is subject to.