# 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

# Choose stocks


symbols <- c("MELI", "AFL", "NVDA", "TTD", "GOOG")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2022-12-31")

2 Convert prices to returns (monthly)

asset_returns_tbl <- prices %>%
    # Calculate monthly returns
    group_by(symbol) %>%
    tq_transmute(select = adjusted,
                 mutate_fun = periodReturn,
                 period = "monthly",
                 type = "log") %>%
    slice(-1) %>%
    ungroup() %>%
    # remane
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,
       0.15,
       0.20,
       0.15,
       0.15)
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: 120 × 2
##    date          returns
##    <date>          <dbl>
##  1 2013-01-31  0.0331   
##  2 2013-02-28 -0.0106   
##  3 2013-03-28  0.0393   
##  4 2013-04-30  0.0403   
##  5 2013-05-31  0.0527   
##  6 2013-06-28 -0.0000843
##  7 2013-07-31  0.0435   
##  8 2013-08-30 -0.0218   
##  9 2013-09-30  0.0635   
## 10 2013-10-31  0.0369   
## # … with 110 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                       0.270

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 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: 97 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31     0.345 
##  2 2015-01-30     0.260 
##  3 2015-02-27     0.337 
##  4 2015-03-31     0.279 
##  5 2015-04-30     0.271 
##  6 2015-05-29     0.220 
##  7 2015-06-30     0.193 
##  8 2015-07-31     0.170 
##  9 2015-08-31     0.138 
## 10 2015-09-30     0.0395
## # … with 87 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?
Over time my portfolio has performed well for periods of time and not so well at other times. There was a structural break in January 2016 when trump was in Office increasing to 0.8 by 2018. However, in January 2022 there was another structural break with a steady decrease in Sharpe ratio almost all the way back down to 0 because of inflation and drop in the value of the US dollar.