# 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("NVDA", "WMT", "AMZN")

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

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  0.0270 
##  2 2013-02-28  0.0148 
##  3 2013-03-28  0.0313 
##  4 2013-04-30  0.0217 
##  5 2013-05-31  0.0213 
##  6 2013-06-28 -0.00161
##  7 2013-07-31  0.0509 
##  8 2013-08-30 -0.0369 
##  9 2013-09-30  0.0534 
## 10 2013-10-31  0.0532 
## # ℹ 50 more rows

5 Compute Sharpe Ratio

# Risk free rate
rfr <- 0.0003

portfolio_sharpe_tbl <- portfolio_returns_tbl %>%

    tq_performance(Ra = returns,
                   Rf = rfr,
                   performance_fun = SharpeRatio,
                   FUN = "StdDev") 

portfolio_sharpe_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.561

6 Plot: Rolling Sharpe Ratio

# Custom function
# necessary because we would not be able to specify FUN = "StdDev" otherwise

calculate_rolling_sharpeRatio <- function(df) {

    SharpeRatio(df,
                Rf = rfr,
                FUN = "StdDev")

}

# dump(list = "calculate_rolling_sharpeRatio",
#      file = "00_scripts/calculate_rolling_sharpeRatio.R")

# Set the length of periods for rolling calculation
window <- 24

# Calculate rolling sharpe ratios
rolling_sharpe_tbl <- portfolio_returns_tbl %>%

    tq_mutate(select = returns,
              mutate_fun = rollapply,
              width = window,
              align = "right",
              FUN = calculate_rolling_sharpeRatio,
              col_rename = "sharpeRatio") %>%
    na.omit()

rolling_sharpe_tbl
## # A tibble: 37 × 3
##    date       returns sharpeRatio
##    <date>       <dbl>       <dbl>
##  1 2014-12-31 -0.0451       0.336
##  2 2015-01-30  0.0227       0.332
##  3 2015-02-27  0.0588       0.368
##  4 2015-03-31 -0.0279       0.303
##  5 2015-04-30  0.0343       0.314
##  6 2015-05-29 -0.0116       0.280
##  7 2015-06-30 -0.0437       0.231
##  8 2015-07-31  0.0668       0.242
##  9 2015-08-31 -0.0160       0.266
## 10 2015-09-30  0.0277       0.246
## # ℹ 27 more rows
# Figure 7.5 Rolling Sharpe ggplot ----

rolling_sharpe_tbl %>%

    ggplot(aes(date, sharpeRatio)) +
    geom_line(color = "cornflowerblue") +

    labs(title = paste0("Rolling ", window, "-Month Sharpe Ratio"),
         y = "rolling Sharpe Ratio",
         x = NULL) +
    theme(plot.title = element_text(hjust = 0.5)) +

    annotate(geom = "text",
             x = as.Date("2016-06-01"), y = 0.5,
             label = "This portfolio HAS FALLEN SINCE 2016.",
             size = 5, color = "red")

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 had an increasing sharpe ratio since 2016. Structural breaks can be seen around 2017 and the begining of 2018. These structural breaks could be due to the economy and global politics at this time.